Investigate a Dataset - FBI GUN DATA .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate a Dataset - FBI GUN DATA\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "### Dataset Description \n",
    "The data comes from the FBI's National Instant Criminal Background Check System(NICS). The NICS is used by to determine whether a prospective buyer is eligible to buy firearms or explosives. Gun shops call into this system to ensure that each customer does not have a criminal record or isn’t otherwise ineligible to make a purchase. \n",
    "\n",
    "\n",
    "Based on documentation gotten from an FBI publication, each column represents the type of transaction submitted to the National Instant Criminal Background Check System (NICS). In addition, each type of transaction is broken down by the type of firearm—handgun, long gun, and other. The types of firearms are defined by the Bureau of Alcohol, Tobacco, Firearms and Explosives as follows:\n",
    "\n",
    "* Month - This is the month the check was made\n",
    "\n",
    "* State - This column indicates the State the check was initiated.\n",
    "\n",
    "* permit - Number of permits issued.\n",
    "\n",
    "* permit_recheck - Number of rechecks made on permits.\n",
    "\n",
    "* Handgun - Number of firearms categorized as handguns\n",
    "\n",
    "* Long Gun - Number of firearms categorized as Long guns(Rifles)\n",
    "\n",
    "* Other - Number of other types of firearms\n",
    "\n",
    "* Multiple - denotes a background check where more than one type of firearm is associated to a single background check\n",
    "\n",
    "* Admin - denotes the administrative checks that are for other authorized uses of the NICS.\n",
    "\n",
    "* Pre-Pawn—background - indicates checks requested by an officially-licensed FFL on prospective firearm transferees seeking to pledge or pawn a firearm as security for the payment or repayment of money, prior to actually pledging or pawning the firearm.\n",
    "\n",
    "* Redemption—background checks requested by an officially-licensed FFL on prospective firearm transferees attempting to regain possession of a firearm after pledging or pawning a firearm as security at a pawn shop.\n",
    "\n",
    "* Returned/Disposition—background checks requested by criminal justice/law enforcement agencies prior to returning a firearm in its possession to the respective transferee, to ensure the individual is not prohibited.\n",
    "\n",
    "* Rentals—background checks requested by an officially-licensed FFL on prospective firearm transferees attempting to possess a firearm when the firearm is loaned or rented for use off the premises of the business.\n",
    "\n",
    "* Private Sale—background checks requested by an officially-licensed FFL on prospective firearm transferees attempting to possess a firearm from a private party seller who is not an officially-licensed FFL.\n",
    "\n",
    "* Return to Seller-Private Sale—background checks requested by an officially-licensed FFL on prospective firearm transferees attempting to possess a firearm from a private party seller who is not an officially-licensed FFL.\n",
    "\n",
    "* Totals - Total number of firearms for each month per state.\n",
    "\n",
    "Note: In general there are 3 classes of firearms recognized in this dataset namely; handguns, longuns, and others, hence, columns like \"private_sale_long_gun\" simply refer to a combination of the description for \"private_sale\" and \"long_gun\".\n",
    "\n",
    "### Question(s) for Analysis\n",
    "1. Which states have the most and least background checks for different firearm categories?\n",
    "2. Which states have the most and least private sales for the different firearm categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set-up complete\n"
     ]
    }
   ],
   "source": [
    "# Use this cell to set up import statements for all of the packages that you\n",
    "#   plan to use.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#import plotly.express as px\n",
    "import warnings\n",
    "%matplotlib inline\n",
    "#pd.options.display.max_rows = 9999\n",
    "# Remember to include a 'magic word' so that your visualizations are plotted\n",
    "#   inline with the notebook. See this page for more:\n",
    "#   http://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
    "sns.set_style(\"darkgrid\")\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "print(\"Set-up complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas==0.25.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/9a/7eb9952f4b4d73fbd75ad1d5d6112f407e695957444cb695cbb3cdab918a/pandas-0.25.0-cp36-cp36m-manylinux1_x86_64.whl (10.5MB)\n",
      "\u001b[K    100% |████████████████████████████████| 10.5MB 4.3MB/s eta 0:00:01   47% |███████████████                 | 4.9MB 33.9MB/s eta 0:00:01    94% |██████████████████████████████▍ | 9.9MB 33.8MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: pytz>=2017.2 in /opt/conda/lib/python3.6/site-packages (from pandas==0.25.0) (2017.3)\n",
      "Collecting numpy>=1.13.3 (from pandas==0.25.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/45/b2/6c7545bb7a38754d63048c7696804a0d947328125d81bf12beaa692c3ae3/numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl (13.4MB)\n",
      "\u001b[K    100% |████████████████████████████████| 13.4MB 3.1MB/s eta 0:00:01   22% |███████▎                        | 3.1MB 33.9MB/s eta 0:00:01    58% |██████████████████▋             | 7.8MB 30.7MB/s eta 0:00:01    69% |██████████████████████▎         | 9.3MB 28.5MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /opt/conda/lib/python3.6/site-packages (from pandas==0.25.0) (2.6.1)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5 in /opt/conda/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas==0.25.0) (1.11.0)\n",
      "\u001b[31mtensorflow 1.3.0 requires tensorflow-tensorboard<0.2.0,>=0.1.0, which is not installed.\u001b[0m\n",
      "Installing collected packages: numpy, pandas\n",
      "  Found existing installation: numpy 1.12.1\n",
      "    Uninstalling numpy-1.12.1:\n",
      "      Successfully uninstalled numpy-1.12.1\n",
      "  Found existing installation: pandas 0.23.3\n",
      "    Uninstalling pandas-0.23.3:\n",
      "      Successfully uninstalled pandas-0.23.3\n",
      "Successfully installed numpy-1.19.5 pandas-0.25.0\n"
     ]
    }
   ],
   "source": [
    "# Upgrade pandas to use dataframe.explode() function. \n",
    "!pip install --upgrade pandas==0.25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "\n",
    "### General Properties\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5734.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>11063.0</td>\n",
       "      <td>7946.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>4347.0</td>\n",
       "      <td>6063.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>366</td>\n",
       "      <td>51.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37165.0</td>\n",
       "      <td>24581.0</td>\n",
       "      <td>2984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     month       state   permit  permit_recheck  handgun  long_gun   other  \\\n",
       "0  2017-09     Alabama  16717.0             0.0   5734.0    6320.0   221.0   \n",
       "1  2017-09      Alaska    209.0             2.0   2320.0    2930.0   219.0   \n",
       "2  2017-09     Arizona   5069.0           382.0  11063.0    7946.0   920.0   \n",
       "3  2017-09    Arkansas   2935.0           632.0   4347.0    6063.0   165.0   \n",
       "4  2017-09  California  57839.0             0.0  37165.0   24581.0  2984.0   \n",
       "\n",
       "   multiple  admin  prepawn_handgun   ...    returned_other  rentals_handgun  \\\n",
       "0       317    0.0             15.0   ...               0.0              0.0   \n",
       "1       160    0.0              5.0   ...               0.0              0.0   \n",
       "2       631    0.0             13.0   ...               0.0              0.0   \n",
       "3       366   51.0             12.0   ...               0.0              0.0   \n",
       "4         0    0.0              0.0   ...               0.0              0.0   \n",
       "\n",
       "   rentals_long_gun  private_sale_handgun  private_sale_long_gun  \\\n",
       "0               0.0                   9.0                   16.0   \n",
       "1               0.0                  17.0                   24.0   \n",
       "2               0.0                  38.0                   12.0   \n",
       "3               0.0                  13.0                   23.0   \n",
       "4               0.0                   0.0                    0.0   \n",
       "\n",
       "   private_sale_other  return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "0                 3.0                       0.0                        0.0   \n",
       "1                 1.0                       0.0                        0.0   \n",
       "2                 2.0                       0.0                        0.0   \n",
       "3                 0.0                       0.0                        2.0   \n",
       "4                 0.0                       0.0                        0.0   \n",
       "\n",
       "   return_to_seller_other  totals  \n",
       "0                     3.0   32019  \n",
       "1                     0.0    6303  \n",
       "2                     0.0   28394  \n",
       "3                     1.0   17747  \n",
       "4                     0.0  123506  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load your data and print out a few lines. Perform operations to inspect data\n",
    "#   types and look for instances of missing or possibly errant data.\n",
    "df = pd.read_excel(\"Database_Ncis_and_Census_data/gun_data.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12485, 27)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12485 entries, 0 to 12484\n",
      "Data columns (total 27 columns):\n",
      "month                        12485 non-null object\n",
      "state                        12485 non-null object\n",
      "permit                       12461 non-null float64\n",
      "permit_recheck               1100 non-null float64\n",
      "handgun                      12465 non-null float64\n",
      "long_gun                     12466 non-null float64\n",
      "other                        5500 non-null float64\n",
      "multiple                     12485 non-null int64\n",
      "admin                        12462 non-null float64\n",
      "prepawn_handgun              10542 non-null float64\n",
      "prepawn_long_gun             10540 non-null float64\n",
      "prepawn_other                5115 non-null float64\n",
      "redemption_handgun           10545 non-null float64\n",
      "redemption_long_gun          10544 non-null float64\n",
      "redemption_other             5115 non-null float64\n",
      "returned_handgun             2200 non-null float64\n",
      "returned_long_gun            2145 non-null float64\n",
      "returned_other               1815 non-null float64\n",
      "rentals_handgun              990 non-null float64\n",
      "rentals_long_gun             825 non-null float64\n",
      "private_sale_handgun         2750 non-null float64\n",
      "private_sale_long_gun        2750 non-null float64\n",
      "private_sale_other           2750 non-null float64\n",
      "return_to_seller_handgun     2475 non-null float64\n",
      "return_to_seller_long_gun    2750 non-null float64\n",
      "return_to_seller_other       2255 non-null float64\n",
      "totals                       12485 non-null int64\n",
      "dtypes: float64(23), int64(2), object(2)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset as seen from the operations above contain 12485 entries with 27 columns, it's quite large as was to be expected since it was collected by aggregations by month and states. However, there are slight sssues with the structure of the data as some values are missing, also others might as well be dropped due to redundancies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, let's ensure all columns are having the right data types,(checks 3 cells up), fine. Next it's important to get rid of duplicates on entries and columns. So to check..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month                        False\n",
       "state                        False\n",
       "permit                       False\n",
       "permit_recheck               False\n",
       "handgun                      False\n",
       "long_gun                     False\n",
       "other                        False\n",
       "multiple                     False\n",
       "admin                        False\n",
       "prepawn_handgun              False\n",
       "prepawn_long_gun             False\n",
       "prepawn_other                False\n",
       "redemption_handgun           False\n",
       "redemption_long_gun          False\n",
       "redemption_other             False\n",
       "returned_handgun             False\n",
       "returned_long_gun            False\n",
       "returned_other               False\n",
       "rentals_handgun              False\n",
       "rentals_long_gun             False\n",
       "private_sale_handgun         False\n",
       "private_sale_long_gun        False\n",
       "private_sale_other           False\n",
       "return_to_seller_handgun     False\n",
       "return_to_seller_long_gun    False\n",
       "return_to_seller_other       False\n",
       "totals                       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose to check for duplicated columns.\n",
    "df.T.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that came good meaning no duplicates either way, next we focus on nulls/missing entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month                            0\n",
       "state                            0\n",
       "permit                          24\n",
       "permit_recheck               11385\n",
       "handgun                         20\n",
       "long_gun                        19\n",
       "other                         6985\n",
       "multiple                         0\n",
       "admin                           23\n",
       "prepawn_handgun               1943\n",
       "prepawn_long_gun              1945\n",
       "prepawn_other                 7370\n",
       "redemption_handgun            1940\n",
       "redemption_long_gun           1941\n",
       "redemption_other              7370\n",
       "returned_handgun             10285\n",
       "returned_long_gun            10340\n",
       "returned_other               10670\n",
       "rentals_handgun              11495\n",
       "rentals_long_gun             11660\n",
       "private_sale_handgun          9735\n",
       "private_sale_long_gun         9735\n",
       "private_sale_other            9735\n",
       "return_to_seller_handgun     10010\n",
       "return_to_seller_long_gun     9735\n",
       "return_to_seller_other       10230\n",
       "totals                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for nulls.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, that's an impressive amount of misising values in some of those columns, lets check the proportion of missing data by column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month                        0.00\n",
       "state                        0.00\n",
       "permit                       0.00\n",
       "permit_recheck               0.91\n",
       "handgun                      0.00\n",
       "long_gun                     0.00\n",
       "other                        0.56\n",
       "multiple                     0.00\n",
       "admin                        0.00\n",
       "prepawn_handgun              0.16\n",
       "prepawn_long_gun             0.16\n",
       "prepawn_other                0.59\n",
       "redemption_handgun           0.16\n",
       "redemption_long_gun          0.16\n",
       "redemption_other             0.59\n",
       "returned_handgun             0.82\n",
       "returned_long_gun            0.83\n",
       "returned_other               0.85\n",
       "rentals_handgun              0.92\n",
       "rentals_long_gun             0.93\n",
       "private_sale_handgun         0.78\n",
       "private_sale_long_gun        0.78\n",
       "private_sale_other           0.78\n",
       "return_to_seller_handgun     0.80\n",
       "return_to_seller_long_gun    0.78\n",
       "return_to_seller_other       0.82\n",
       "totals                       0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for nulls proportion in columns.\n",
    "null_sum = (df.isnull().sum())\n",
    "round(null_sum/df.shape[0], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown above, there is a need to drop some columns as they do not have sufficient data, so let's set a threshhold, say a column should hold at least 70% of data to be accepted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['month',\n",
       " 'state',\n",
       " 'permit',\n",
       " 'handgun',\n",
       " 'long_gun',\n",
       " 'other',\n",
       " 'multiple',\n",
       " 'admin',\n",
       " 'prepawn_handgun',\n",
       " 'prepawn_long_gun',\n",
       " 'prepawn_other',\n",
       " 'redemption_handgun',\n",
       " 'redemption_long_gun',\n",
       " 'redemption_other',\n",
       " 'totals']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting useable columns.\n",
    "useable_cols = []\n",
    "for col in df.columns:\n",
    "    if df[col].isnull().sum()/df.shape[0] < 0.70:\n",
    "        useable_cols.append(col)\n",
    "print(len(useable_cols))\n",
    "useable_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From what we've seen above, only 15 of the original 27 had records with actual entries over or equal to 70%. One important facet though is to check the reason for the missing data in the other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>returned_handgun</th>\n",
       "      <th>returned_long_gun</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12480</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12482</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12483</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12484</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         month          state  permit_recheck  returned_handgun  \\\n",
       "12480  1998-11       Virginia             NaN               NaN   \n",
       "12481  1998-11     Washington             NaN               NaN   \n",
       "12482  1998-11  West Virginia             NaN               NaN   \n",
       "12483  1998-11      Wisconsin             NaN               NaN   \n",
       "12484  1998-11        Wyoming             NaN               NaN   \n",
       "\n",
       "       returned_long_gun  returned_other  rentals_handgun  rentals_long_gun  \\\n",
       "12480                NaN             NaN              NaN               NaN   \n",
       "12481                NaN             NaN              NaN               NaN   \n",
       "12482                NaN             NaN              NaN               NaN   \n",
       "12483                NaN             NaN              NaN               NaN   \n",
       "12484                NaN             NaN              NaN               NaN   \n",
       "\n",
       "       private_sale_handgun  private_sale_long_gun  private_sale_other  \\\n",
       "12480                   NaN                    NaN                 NaN   \n",
       "12481                   NaN                    NaN                 NaN   \n",
       "12482                   NaN                    NaN                 NaN   \n",
       "12483                   NaN                    NaN                 NaN   \n",
       "12484                   NaN                    NaN                 NaN   \n",
       "\n",
       "       return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "12480                       NaN                        NaN   \n",
       "12481                       NaN                        NaN   \n",
       "12482                       NaN                        NaN   \n",
       "12483                       NaN                        NaN   \n",
       "12484                       NaN                        NaN   \n",
       "\n",
       "       return_to_seller_other  \n",
       "12480                     NaN  \n",
       "12481                     NaN  \n",
       "12482                     NaN  \n",
       "12483                     NaN  \n",
       "12484                     NaN  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check tail of entries with majority nulls.\n",
    "df.drop(columns=useable_cols[2:]).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>returned_handgun</th>\n",
       "      <th>returned_long_gun</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>382.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>632.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>California</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month       state  permit_recheck  returned_handgun  returned_long_gun  \\\n",
       "0  2017-09     Alabama             0.0               0.0                0.0   \n",
       "1  2017-09      Alaska             2.0              28.0               30.0   \n",
       "2  2017-09     Arizona           382.0              82.0                5.0   \n",
       "3  2017-09    Arkansas           632.0               0.0                0.0   \n",
       "4  2017-09  California             0.0               0.0                0.0   \n",
       "\n",
       "   returned_other  rentals_handgun  rentals_long_gun  private_sale_handgun  \\\n",
       "0             0.0              0.0               0.0                   9.0   \n",
       "1             0.0              0.0               0.0                  17.0   \n",
       "2             0.0              0.0               0.0                  38.0   \n",
       "3             0.0              0.0               0.0                  13.0   \n",
       "4             0.0              0.0               0.0                   0.0   \n",
       "\n",
       "   private_sale_long_gun  private_sale_other  return_to_seller_handgun  \\\n",
       "0                   16.0                 3.0                       0.0   \n",
       "1                   24.0                 1.0                       0.0   \n",
       "2                   12.0                 2.0                       0.0   \n",
       "3                   23.0                 0.0                       0.0   \n",
       "4                    0.0                 0.0                       0.0   \n",
       "\n",
       "   return_to_seller_long_gun  return_to_seller_other  \n",
       "0                        0.0                     3.0  \n",
       "1                        0.0                     0.0  \n",
       "2                        0.0                     0.0  \n",
       "3                        2.0                     1.0  \n",
       "4                        0.0                     0.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check top of entries with majority nulls.\n",
    "df.drop(columns=useable_cols[2:]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tentatively, at least, it would seem as though we're dealing with two datasets here from different time periods, let's investigate further and see if we can split them appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>27242.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25445.0</td>\n",
       "      <td>14785.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>884</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>192.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2859.0</td>\n",
       "      <td>2245.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>3898.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15745.0</td>\n",
       "      <td>8512.0</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>3670.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7954.0</td>\n",
       "      <td>5982.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>California</td>\n",
       "      <td>127715.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52139.0</td>\n",
       "      <td>32970.0</td>\n",
       "      <td>7054.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>220912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>8431.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23518.0</td>\n",
       "      <td>13327.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>1236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>14005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13096.0</td>\n",
       "      <td>4366.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>395.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2548.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Florida</td>\n",
       "      <td>28458.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62085.0</td>\n",
       "      <td>26356.0</td>\n",
       "      <td>3480.0</td>\n",
       "      <td>2036</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>30513.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18236.0</td>\n",
       "      <td>9696.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Guam</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>4025.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4221.0</td>\n",
       "      <td>3820.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>143606.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32112.0</td>\n",
       "      <td>12869.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>183520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29653.0</td>\n",
       "      <td>14356.0</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>230892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>23600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>9</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>1787.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9196.0</td>\n",
       "      <td>6580.0</td>\n",
       "      <td>586.0</td>\n",
       "      <td>381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>277415.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13847.0</td>\n",
       "      <td>7713.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15485.0</td>\n",
       "      <td>10374.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Maine</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4625.0</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Mariana Islands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>3458.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>5541.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>8547.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7211.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>201</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>27885.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17641.0</td>\n",
       "      <td>9995.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>27969.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12426.0</td>\n",
       "      <td>8624.0</td>\n",
       "      <td>875.0</td>\n",
       "      <td>414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10257.0</td>\n",
       "      <td>6713.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>365</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>11441.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28980.0</td>\n",
       "      <td>15693.0</td>\n",
       "      <td>1942.0</td>\n",
       "      <td>955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Montana</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3354.0</td>\n",
       "      <td>3574.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>205</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>8560.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2256.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12455</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12456</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12457</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12458</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Montana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12459</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12460</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12461</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12462</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12463</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12464</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12465</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>524.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12466</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12467</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>502.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12468</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12469</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12470</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12471</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12472</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12473</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12474</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12475</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12476</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1384.0</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12477</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Utah</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12478</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12479</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12480</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12482</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12483</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12484</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11385 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         month                 state    permit  permit_recheck  handgun  \\\n",
       "1100   2016-01               Alabama   27242.0             NaN  25445.0   \n",
       "1101   2016-01                Alaska     192.0             NaN   2859.0   \n",
       "1102   2016-01               Arizona    3898.0             NaN  15745.0   \n",
       "1103   2016-01              Arkansas    3670.0             NaN   7954.0   \n",
       "1104   2016-01            California  127715.0             NaN  52139.0   \n",
       "1105   2016-01              Colorado    8431.0             NaN  23518.0   \n",
       "1106   2016-01           Connecticut   14005.0             NaN  13096.0   \n",
       "1107   2016-01              Delaware     395.0             NaN   2548.0   \n",
       "1108   2016-01  District of Columbia      27.0             NaN     52.0   \n",
       "1109   2016-01               Florida   28458.0             NaN  62085.0   \n",
       "1110   2016-01               Georgia   30513.0             NaN  18236.0   \n",
       "1111   2016-01                  Guam       0.0             NaN    102.0   \n",
       "1112   2016-01                Hawaii    1379.0             NaN      0.0   \n",
       "1113   2016-01                 Idaho    4025.0             NaN   4221.0   \n",
       "1114   2016-01              Illinois  143606.0             NaN  32112.0   \n",
       "1115   2016-01               Indiana  183520.0             NaN  29653.0   \n",
       "1116   2016-01                  Iowa   23600.0             NaN    292.0   \n",
       "1117   2016-01                Kansas    1787.0             NaN   9196.0   \n",
       "1118   2016-01              Kentucky  277415.0             NaN  13847.0   \n",
       "1119   2016-01             Louisiana    2823.0             NaN  15485.0   \n",
       "1120   2016-01                 Maine     500.0             NaN   4625.0   \n",
       "1121   2016-01       Mariana Islands       0.0             NaN      0.0   \n",
       "1122   2016-01              Maryland    3458.0             NaN   3981.0   \n",
       "1123   2016-01         Massachusetts    8547.0             NaN   7211.0   \n",
       "1124   2016-01              Michigan   27885.0             NaN  17641.0   \n",
       "1125   2016-01             Minnesota   27969.0             NaN  12426.0   \n",
       "1126   2016-01           Mississippi    1442.0             NaN  10257.0   \n",
       "1127   2016-01              Missouri   11441.0             NaN  28980.0   \n",
       "1128   2016-01               Montana    2240.0             NaN   3354.0   \n",
       "1129   2016-01              Nebraska    8560.0             NaN    179.0   \n",
       "...        ...                   ...       ...             ...      ...   \n",
       "12455  1998-11             Minnesota       9.0             NaN     27.0   \n",
       "12456  1998-11           Mississippi       0.0             NaN    286.0   \n",
       "12457  1998-11              Missouri       0.0             NaN    116.0   \n",
       "12458  1998-11               Montana       0.0             NaN    101.0   \n",
       "12459  1998-11              Nebraska      88.0             NaN      1.0   \n",
       "12460  1998-11                Nevada       0.0             NaN     75.0   \n",
       "12461  1998-11         New Hampshire       0.0             NaN      8.0   \n",
       "12462  1998-11            New Jersey       0.0             NaN     20.0   \n",
       "12463  1998-11            New Mexico       0.0             NaN     86.0   \n",
       "12464  1998-11              New York       0.0             NaN     40.0   \n",
       "12465  1998-11        North Carolina     524.0             NaN     87.0   \n",
       "12466  1998-11          North Dakota       0.0             NaN     20.0   \n",
       "12467  1998-11                  Ohio       0.0             NaN    502.0   \n",
       "12468  1998-11              Oklahoma       0.0             NaN    259.0   \n",
       "12469  1998-11                Oregon       0.0             NaN    153.0   \n",
       "12470  1998-11          Pennsylvania       0.0             NaN      5.0   \n",
       "12471  1998-11           Puerto Rico       0.0             NaN      1.0   \n",
       "12472  1998-11          Rhode Island       0.0             NaN     13.0   \n",
       "12473  1998-11        South Carolina       0.0             NaN      0.0   \n",
       "12474  1998-11          South Dakota       0.0             NaN      8.0   \n",
       "12475  1998-11             Tennessee       0.0             NaN     19.0   \n",
       "12476  1998-11                 Texas       0.0             NaN   1384.0   \n",
       "12477  1998-11                  Utah       0.0             NaN     98.0   \n",
       "12478  1998-11               Vermont       0.0             NaN     23.0   \n",
       "12479  1998-11        Virgin Islands       0.0             NaN      0.0   \n",
       "12480  1998-11              Virginia       0.0             NaN     14.0   \n",
       "12481  1998-11            Washington       1.0             NaN     65.0   \n",
       "12482  1998-11         West Virginia       3.0             NaN    149.0   \n",
       "12483  1998-11             Wisconsin       0.0             NaN     25.0   \n",
       "12484  1998-11               Wyoming       8.0             NaN     45.0   \n",
       "\n",
       "       long_gun   other  multiple  admin  prepawn_handgun   ...    \\\n",
       "1100    14785.0  1266.0       884    0.0              8.0   ...     \n",
       "1101     2245.0   300.0       164    0.0              5.0   ...     \n",
       "1102     8512.0  1107.0       575    0.0              7.0   ...     \n",
       "1103     5982.0   305.0       340    0.0             11.0   ...     \n",
       "1104    32970.0  7054.0         0    0.0              0.0   ...     \n",
       "1105    13327.0  1474.0      1236    0.0              0.0   ...     \n",
       "1106     4366.0   236.0         0    9.0              0.0   ...     \n",
       "1107     1608.0   110.0        73    0.0              5.0   ...     \n",
       "1108        3.0     0.0         0    0.0              0.0   ...     \n",
       "1109    26356.0  3480.0      2036   36.0             13.0   ...     \n",
       "1110     9696.0   654.0       564    0.0             14.0   ...     \n",
       "1111       57.0    11.0         6    0.0              0.0   ...     \n",
       "1112        0.0     0.0         0    0.0              0.0   ...     \n",
       "1113     3820.0   266.0       156    0.0              2.0   ...     \n",
       "1114    12869.0     0.0       813    0.0              0.0   ...     \n",
       "1115    14356.0  1871.0       939    0.0              4.0   ...     \n",
       "1116     2600.0    44.0         9  109.0              0.0   ...     \n",
       "1117     6580.0   586.0       381    1.0              0.0   ...     \n",
       "1118     7713.0   403.0       416    0.0             11.0   ...     \n",
       "1119    10374.0  1054.0       642    0.0              0.0   ...     \n",
       "1120     3054.0   294.0       152    0.0              1.0   ...     \n",
       "1121        3.0     0.0         0    0.0              0.0   ...     \n",
       "1122     5541.0   116.0        16    0.0              0.0   ...     \n",
       "1123     3500.0   667.0       201    3.0              0.0   ...     \n",
       "1124     9995.0   670.0       215    0.0              1.0   ...     \n",
       "1125     8624.0   875.0       414    0.0              1.0   ...     \n",
       "1126     6713.0   422.0       365   20.0             23.0   ...     \n",
       "1127    15693.0  1942.0       955    0.0              9.0   ...     \n",
       "1128     3574.0   191.0       205   20.0              4.0   ...     \n",
       "1129     2256.0    37.0        13    1.0              0.0   ...     \n",
       "...         ...     ...       ...    ...              ...   ...     \n",
       "12455     280.0     NaN         3    0.0              NaN   ...     \n",
       "12456     491.0     NaN         7    0.0              NaN   ...     \n",
       "12457     458.0     NaN         4    0.0              NaN   ...     \n",
       "12458      98.0     NaN         2    0.0              NaN   ...     \n",
       "12459      96.0     NaN         1    0.0              NaN   ...     \n",
       "12460      76.0     NaN         8    0.0              NaN   ...     \n",
       "12461      46.0     NaN         1    0.0              NaN   ...     \n",
       "12462      53.0     NaN         2    2.0              NaN   ...     \n",
       "12463     121.0     NaN         4    0.0              NaN   ...     \n",
       "12464     279.0     NaN         0    0.0              NaN   ...     \n",
       "12465     695.0     NaN         4    0.0              NaN   ...     \n",
       "12466      38.0     NaN         0    0.0              NaN   ...     \n",
       "12467     434.0     NaN        16    0.0              NaN   ...     \n",
       "12468     361.0     NaN        13    0.0              NaN   ...     \n",
       "12469     186.0     NaN         5    0.0              NaN   ...     \n",
       "12470       8.0     NaN         4    0.0              NaN   ...     \n",
       "12471       1.0     NaN         0    0.0              NaN   ...     \n",
       "12472      23.0     NaN         2    0.0              NaN   ...     \n",
       "12473       6.0     NaN         0    0.0              NaN   ...     \n",
       "12474      66.0     NaN         0    0.0              NaN   ...     \n",
       "12475      85.0     NaN         3    0.0              NaN   ...     \n",
       "12476    1349.0     NaN        60    1.0              NaN   ...     \n",
       "12477     169.0     NaN         0    0.0              NaN   ...     \n",
       "12478      35.0     NaN         0    1.0              NaN   ...     \n",
       "12479       0.0     NaN         0    0.0              NaN   ...     \n",
       "12480       2.0     NaN         8    0.0              NaN   ...     \n",
       "12481     286.0     NaN         8    1.0              NaN   ...     \n",
       "12482     251.0     NaN         5    0.0              NaN   ...     \n",
       "12483     214.0     NaN         2    0.0              NaN   ...     \n",
       "12484      49.0     NaN         5    0.0              NaN   ...     \n",
       "\n",
       "       returned_other  rentals_handgun  rentals_long_gun  \\\n",
       "1100              0.0              NaN               NaN   \n",
       "1101              0.0              NaN               NaN   \n",
       "1102              0.0              NaN               NaN   \n",
       "1103              0.0              NaN               NaN   \n",
       "1104              0.0              NaN               NaN   \n",
       "1105              2.0              NaN               NaN   \n",
       "1106              0.0              NaN               NaN   \n",
       "1107              0.0              NaN               NaN   \n",
       "1108              0.0              NaN               NaN   \n",
       "1109              0.0              NaN               NaN   \n",
       "1110              0.0              NaN               NaN   \n",
       "1111              0.0              NaN               NaN   \n",
       "1112              0.0              NaN               NaN   \n",
       "1113              0.0              NaN               NaN   \n",
       "1114              0.0              NaN               NaN   \n",
       "1115              1.0              NaN               NaN   \n",
       "1116              0.0              NaN               NaN   \n",
       "1117              0.0              NaN               NaN   \n",
       "1118              0.0              NaN               NaN   \n",
       "1119              0.0              NaN               NaN   \n",
       "1120              0.0              NaN               NaN   \n",
       "1121              0.0              NaN               NaN   \n",
       "1122              0.0              NaN               NaN   \n",
       "1123              0.0              NaN               NaN   \n",
       "1124              0.0              NaN               NaN   \n",
       "1125              0.0              NaN               NaN   \n",
       "1126              0.0              NaN               NaN   \n",
       "1127              2.0              NaN               NaN   \n",
       "1128              3.0              NaN               NaN   \n",
       "1129              0.0              NaN               NaN   \n",
       "...               ...              ...               ...   \n",
       "12455             NaN              NaN               NaN   \n",
       "12456             NaN              NaN               NaN   \n",
       "12457             NaN              NaN               NaN   \n",
       "12458             NaN              NaN               NaN   \n",
       "12459             NaN              NaN               NaN   \n",
       "12460             NaN              NaN               NaN   \n",
       "12461             NaN              NaN               NaN   \n",
       "12462             NaN              NaN               NaN   \n",
       "12463             NaN              NaN               NaN   \n",
       "12464             NaN              NaN               NaN   \n",
       "12465             NaN              NaN               NaN   \n",
       "12466             NaN              NaN               NaN   \n",
       "12467             NaN              NaN               NaN   \n",
       "12468             NaN              NaN               NaN   \n",
       "12469             NaN              NaN               NaN   \n",
       "12470             NaN              NaN               NaN   \n",
       "12471             NaN              NaN               NaN   \n",
       "12472             NaN              NaN               NaN   \n",
       "12473             NaN              NaN               NaN   \n",
       "12474             NaN              NaN               NaN   \n",
       "12475             NaN              NaN               NaN   \n",
       "12476             NaN              NaN               NaN   \n",
       "12477             NaN              NaN               NaN   \n",
       "12478             NaN              NaN               NaN   \n",
       "12479             NaN              NaN               NaN   \n",
       "12480             NaN              NaN               NaN   \n",
       "12481             NaN              NaN               NaN   \n",
       "12482             NaN              NaN               NaN   \n",
       "12483             NaN              NaN               NaN   \n",
       "12484             NaN              NaN               NaN   \n",
       "\n",
       "       private_sale_handgun  private_sale_long_gun  private_sale_other  \\\n",
       "1100                   16.0                   15.0                 2.0   \n",
       "1101                    2.0                    0.0                 0.0   \n",
       "1102                   10.0                    9.0                 2.0   \n",
       "1103                    3.0                    6.0                 1.0   \n",
       "1104                    0.0                    0.0                 0.0   \n",
       "1105                    0.0                    0.0                 0.0   \n",
       "1106                    0.0                    0.0                 0.0   \n",
       "1107                   89.0                   23.0                 5.0   \n",
       "1108                    0.0                    0.0                 0.0   \n",
       "1109                   42.0                   15.0                 0.0   \n",
       "1110                    6.0                    4.0                 1.0   \n",
       "1111                    0.0                    0.0                 0.0   \n",
       "1112                    0.0                    0.0                 0.0   \n",
       "1113                    1.0                    3.0                 0.0   \n",
       "1114                    0.0                    0.0                 0.0   \n",
       "1115                   33.0                   21.0                 6.0   \n",
       "1116                    0.0                    2.0                 0.0   \n",
       "1117                   10.0                    7.0                 2.0   \n",
       "1118                   13.0                   13.0                 1.0   \n",
       "1119                    0.0                    7.0                 0.0   \n",
       "1120                    0.0                    0.0                 0.0   \n",
       "1121                    0.0                    0.0                 0.0   \n",
       "1122                    1.0                    7.0                 0.0   \n",
       "1123                   41.0                   20.0                 6.0   \n",
       "1124                    6.0                    0.0                 0.0   \n",
       "1125                   14.0                   10.0                 1.0   \n",
       "1126                    6.0                    2.0                 0.0   \n",
       "1127                   73.0                   36.0                 4.0   \n",
       "1128                    3.0                    1.0                 0.0   \n",
       "1129                    0.0                    2.0                 0.0   \n",
       "...                     ...                    ...                 ...   \n",
       "12455                   NaN                    NaN                 NaN   \n",
       "12456                   NaN                    NaN                 NaN   \n",
       "12457                   NaN                    NaN                 NaN   \n",
       "12458                   NaN                    NaN                 NaN   \n",
       "12459                   NaN                    NaN                 NaN   \n",
       "12460                   NaN                    NaN                 NaN   \n",
       "12461                   NaN                    NaN                 NaN   \n",
       "12462                   NaN                    NaN                 NaN   \n",
       "12463                   NaN                    NaN                 NaN   \n",
       "12464                   NaN                    NaN                 NaN   \n",
       "12465                   NaN                    NaN                 NaN   \n",
       "12466                   NaN                    NaN                 NaN   \n",
       "12467                   NaN                    NaN                 NaN   \n",
       "12468                   NaN                    NaN                 NaN   \n",
       "12469                   NaN                    NaN                 NaN   \n",
       "12470                   NaN                    NaN                 NaN   \n",
       "12471                   NaN                    NaN                 NaN   \n",
       "12472                   NaN                    NaN                 NaN   \n",
       "12473                   NaN                    NaN                 NaN   \n",
       "12474                   NaN                    NaN                 NaN   \n",
       "12475                   NaN                    NaN                 NaN   \n",
       "12476                   NaN                    NaN                 NaN   \n",
       "12477                   NaN                    NaN                 NaN   \n",
       "12478                   NaN                    NaN                 NaN   \n",
       "12479                   NaN                    NaN                 NaN   \n",
       "12480                   NaN                    NaN                 NaN   \n",
       "12481                   NaN                    NaN                 NaN   \n",
       "12482                   NaN                    NaN                 NaN   \n",
       "12483                   NaN                    NaN                 NaN   \n",
       "12484                   NaN                    NaN                 NaN   \n",
       "\n",
       "       return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "1100                        0.0                        1.0   \n",
       "1101                        0.0                        1.0   \n",
       "1102                        4.0                        2.0   \n",
       "1103                        1.0                        0.0   \n",
       "1104                        0.0                        0.0   \n",
       "1105                        0.0                        0.0   \n",
       "1106                        0.0                        0.0   \n",
       "1107                        3.0                        1.0   \n",
       "1108                        0.0                        0.0   \n",
       "1109                        0.0                        0.0   \n",
       "1110                        0.0                        0.0   \n",
       "1111                        0.0                        0.0   \n",
       "1112                        0.0                        0.0   \n",
       "1113                        0.0                        1.0   \n",
       "1114                        0.0                        0.0   \n",
       "1115                        3.0                        3.0   \n",
       "1116                        0.0                        0.0   \n",
       "1117                        2.0                        2.0   \n",
       "1118                        0.0                        2.0   \n",
       "1119                        0.0                        0.0   \n",
       "1120                        0.0                        0.0   \n",
       "1121                        0.0                        0.0   \n",
       "1122                        0.0                        1.0   \n",
       "1123                        3.0                        1.0   \n",
       "1124                        0.0                        0.0   \n",
       "1125                        1.0                        0.0   \n",
       "1126                        1.0                        2.0   \n",
       "1127                        0.0                        2.0   \n",
       "1128                        1.0                        3.0   \n",
       "1129                        0.0                        0.0   \n",
       "...                         ...                        ...   \n",
       "12455                       NaN                        NaN   \n",
       "12456                       NaN                        NaN   \n",
       "12457                       NaN                        NaN   \n",
       "12458                       NaN                        NaN   \n",
       "12459                       NaN                        NaN   \n",
       "12460                       NaN                        NaN   \n",
       "12461                       NaN                        NaN   \n",
       "12462                       NaN                        NaN   \n",
       "12463                       NaN                        NaN   \n",
       "12464                       NaN                        NaN   \n",
       "12465                       NaN                        NaN   \n",
       "12466                       NaN                        NaN   \n",
       "12467                       NaN                        NaN   \n",
       "12468                       NaN                        NaN   \n",
       "12469                       NaN                        NaN   \n",
       "12470                       NaN                        NaN   \n",
       "12471                       NaN                        NaN   \n",
       "12472                       NaN                        NaN   \n",
       "12473                       NaN                        NaN   \n",
       "12474                       NaN                        NaN   \n",
       "12475                       NaN                        NaN   \n",
       "12476                       NaN                        NaN   \n",
       "12477                       NaN                        NaN   \n",
       "12478                       NaN                        NaN   \n",
       "12479                       NaN                        NaN   \n",
       "12480                       NaN                        NaN   \n",
       "12481                       NaN                        NaN   \n",
       "12482                       NaN                        NaN   \n",
       "12483                       NaN                        NaN   \n",
       "12484                       NaN                        NaN   \n",
       "\n",
       "       return_to_seller_other  totals  \n",
       "1100                      0.0   73218  \n",
       "1101                      0.0    6085  \n",
       "1102                      0.0   31910  \n",
       "1103                      0.0   20437  \n",
       "1104                      0.0  220912  \n",
       "1105                      0.0   48214  \n",
       "1106                      0.0   31712  \n",
       "1107                      0.0    4886  \n",
       "1108                      0.0      82  \n",
       "1109                      0.0  125935  \n",
       "1110                      0.0   62707  \n",
       "1111                      0.0     176  \n",
       "1112                      0.0    1383  \n",
       "1113                      0.0   13197  \n",
       "1114                      0.0  189400  \n",
       "1115                      0.0  230892  \n",
       "1116                      0.0   26714  \n",
       "1117                      0.0   19443  \n",
       "1118                      0.0  302383  \n",
       "1119                      0.0   32192  \n",
       "1120                      0.0    8844  \n",
       "1121                      0.0       3  \n",
       "1122                      1.0   13321  \n",
       "1123                      0.0   20208  \n",
       "1124                      0.0   56723  \n",
       "1125                      0.0   50842  \n",
       "1126                      0.0   21330  \n",
       "1127                      0.0   61257  \n",
       "1128                      0.0   10722  \n",
       "1129                      0.0   11116  \n",
       "...                       ...     ...  \n",
       "12455                     NaN     319  \n",
       "12456                     NaN     784  \n",
       "12457                     NaN     578  \n",
       "12458                     NaN     201  \n",
       "12459                     NaN     186  \n",
       "12460                     NaN     159  \n",
       "12461                     NaN      55  \n",
       "12462                     NaN      77  \n",
       "12463                     NaN     211  \n",
       "12464                     NaN     319  \n",
       "12465                     NaN    1310  \n",
       "12466                     NaN      58  \n",
       "12467                     NaN     952  \n",
       "12468                     NaN     633  \n",
       "12469                     NaN     344  \n",
       "12470                     NaN      17  \n",
       "12471                     NaN       2  \n",
       "12472                     NaN      38  \n",
       "12473                     NaN       6  \n",
       "12474                     NaN      74  \n",
       "12475                     NaN     107  \n",
       "12476                     NaN    2794  \n",
       "12477                     NaN     267  \n",
       "12478                     NaN      59  \n",
       "12479                     NaN       0  \n",
       "12480                     NaN      24  \n",
       "12481                     NaN     361  \n",
       "12482                     NaN     408  \n",
       "12483                     NaN     241  \n",
       "12484                     NaN     107  \n",
       "\n",
       "[11385 rows x 27 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"permit_recheck\"].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, the cases of missing data are more prominent in earlier years. As a result of this, it would probably be a good idea to group by states then months for further comparisons.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>prepawn_long_gun</th>\n",
       "      <th>prepawn_other</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998-11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-02</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-03</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-04</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-05</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-06</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-07</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-08</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-09</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-06</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-07</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-08</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-04</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         permit  permit_recheck  handgun  long_gun  other  multiple  admin  \\\n",
       "month                                                                        \n",
       "1998-11       1               0        1         1      0         1      1   \n",
       "1998-12       1               0        1         1      0         1      1   \n",
       "1999-01       1               0        1         1      0         1      1   \n",
       "1999-02       1               0        1         1      0         1      1   \n",
       "1999-03       1               0        1         1      0         1      1   \n",
       "1999-04       1               0        1         1      0         1      1   \n",
       "1999-05       1               0        1         1      0         1      1   \n",
       "1999-06       1               0        1         1      0         1      1   \n",
       "1999-07       1               0        1         1      0         1      1   \n",
       "1999-08       1               0        1         1      0         1      1   \n",
       "1999-09       1               0        1         1      0         1      1   \n",
       "1999-10       1               0        1         1      0         1      1   \n",
       "1999-11       1               0        1         1      0         1      1   \n",
       "1999-12       1               0        1         1      0         1      1   \n",
       "2000-01       1               0        1         1      0         1      1   \n",
       "2000-02       1               0        1         1      0         1      1   \n",
       "2000-03       1               0        1         1      0         1      1   \n",
       "2000-04       1               0        1         1      0         1      1   \n",
       "2000-05       1               0        1         1      0         1      1   \n",
       "2000-06       1               0        1         1      0         1      1   \n",
       "2000-07       1               0        1         1      0         1      1   \n",
       "2000-08       1               0        1         1      0         1      1   \n",
       "2000-09       1               0        1         1      0         1      1   \n",
       "2000-10       1               0        1         1      0         1      1   \n",
       "2000-11       1               0        1         1      0         1      1   \n",
       "2000-12       1               0        1         1      0         1      1   \n",
       "2001-01       1               0        1         1      0         1      1   \n",
       "2001-02       1               0        1         1      0         1      1   \n",
       "2001-03       1               0        1         1      0         1      1   \n",
       "2001-04       1               0        1         1      0         1      1   \n",
       "...         ...             ...      ...       ...    ...       ...    ...   \n",
       "2015-04       1               0        1         1      1         1      1   \n",
       "2015-05       1               0        1         1      1         1      1   \n",
       "2015-06       1               0        1         1      1         1      1   \n",
       "2015-07       1               0        1         1      1         1      1   \n",
       "2015-08       1               0        1         1      1         1      1   \n",
       "2015-09       1               0        1         1      1         1      1   \n",
       "2015-10       1               0        1         1      1         1      1   \n",
       "2015-11       1               0        1         1      1         1      1   \n",
       "2015-12       1               0        1         1      1         1      1   \n",
       "2016-01       1               0        1         1      1         1      1   \n",
       "2016-02       1               1        1         1      1         1      1   \n",
       "2016-03       1               1        1         1      1         1      1   \n",
       "2016-04       1               1        1         1      1         1      1   \n",
       "2016-05       1               1        1         1      1         1      1   \n",
       "2016-06       1               1        1         1      1         1      1   \n",
       "2016-07       1               1        1         1      1         1      1   \n",
       "2016-08       1               1        1         1      1         1      1   \n",
       "2016-09       1               1        1         1      1         1      1   \n",
       "2016-10       1               1        1         1      1         1      1   \n",
       "2016-11       1               1        1         1      1         1      1   \n",
       "2016-12       1               1        1         1      1         1      1   \n",
       "2017-01       1               1        1         1      1         1      1   \n",
       "2017-02       1               1        1         1      1         1      1   \n",
       "2017-03       1               1        1         1      1         1      1   \n",
       "2017-04       1               1        1         1      1         1      1   \n",
       "2017-05       1               1        1         1      1         1      1   \n",
       "2017-06       1               1        1         1      1         1      1   \n",
       "2017-07       1               1        1         1      1         1      1   \n",
       "2017-08       1               1        1         1      1         1      1   \n",
       "2017-09       1               1        1         1      1         1      1   \n",
       "\n",
       "         prepawn_handgun  prepawn_long_gun  prepawn_other   ...    \\\n",
       "month                                                       ...     \n",
       "1998-11                0                 0              0   ...     \n",
       "1998-12                0                 0              0   ...     \n",
       "1999-01                0                 0              0   ...     \n",
       "1999-02                0                 0              0   ...     \n",
       "1999-03                0                 0              0   ...     \n",
       "1999-04                0                 0              0   ...     \n",
       "1999-05                0                 0              0   ...     \n",
       "1999-06                0                 0              0   ...     \n",
       "1999-07                0                 0              0   ...     \n",
       "1999-08                0                 0              0   ...     \n",
       "1999-09                0                 0              0   ...     \n",
       "1999-10                0                 0              0   ...     \n",
       "1999-11                0                 0              0   ...     \n",
       "1999-12                0                 0              0   ...     \n",
       "2000-01                0                 0              0   ...     \n",
       "2000-02                0                 0              0   ...     \n",
       "2000-03                0                 0              0   ...     \n",
       "2000-04                0                 0              0   ...     \n",
       "2000-05                0                 0              0   ...     \n",
       "2000-06                0                 0              0   ...     \n",
       "2000-07                0                 0              0   ...     \n",
       "2000-08                0                 0              0   ...     \n",
       "2000-09                0                 0              0   ...     \n",
       "2000-10                0                 0              0   ...     \n",
       "2000-11                0                 0              0   ...     \n",
       "2000-12                0                 0              0   ...     \n",
       "2001-01                0                 0              0   ...     \n",
       "2001-02                0                 0              0   ...     \n",
       "2001-03                0                 0              0   ...     \n",
       "2001-04                0                 0              0   ...     \n",
       "...                  ...               ...            ...   ...     \n",
       "2015-04                1                 1              1   ...     \n",
       "2015-05                1                 1              1   ...     \n",
       "2015-06                1                 1              1   ...     \n",
       "2015-07                1                 1              1   ...     \n",
       "2015-08                1                 1              1   ...     \n",
       "2015-09                1                 1              1   ...     \n",
       "2015-10                1                 1              1   ...     \n",
       "2015-11                1                 1              1   ...     \n",
       "2015-12                1                 1              1   ...     \n",
       "2016-01                1                 1              1   ...     \n",
       "2016-02                1                 1              1   ...     \n",
       "2016-03                1                 1              1   ...     \n",
       "2016-04                1                 1              1   ...     \n",
       "2016-05                1                 1              1   ...     \n",
       "2016-06                1                 1              1   ...     \n",
       "2016-07                1                 1              1   ...     \n",
       "2016-08                1                 1              1   ...     \n",
       "2016-09                1                 1              1   ...     \n",
       "2016-10                1                 1              1   ...     \n",
       "2016-11                1                 1              1   ...     \n",
       "2016-12                1                 1              1   ...     \n",
       "2017-01                1                 1              1   ...     \n",
       "2017-02                1                 1              1   ...     \n",
       "2017-03                1                 1              1   ...     \n",
       "2017-04                1                 1              1   ...     \n",
       "2017-05                1                 1              1   ...     \n",
       "2017-06                1                 1              1   ...     \n",
       "2017-07                1                 1              1   ...     \n",
       "2017-08                1                 1              1   ...     \n",
       "2017-09                1                 1              1   ...     \n",
       "\n",
       "         returned_other  rentals_handgun  rentals_long_gun  \\\n",
       "month                                                        \n",
       "1998-11               0                0                 0   \n",
       "1998-12               0                0                 0   \n",
       "1999-01               0                0                 0   \n",
       "1999-02               0                0                 0   \n",
       "1999-03               0                0                 0   \n",
       "1999-04               0                0                 0   \n",
       "1999-05               0                0                 0   \n",
       "1999-06               0                0                 0   \n",
       "1999-07               0                0                 0   \n",
       "1999-08               0                0                 0   \n",
       "1999-09               0                0                 0   \n",
       "1999-10               0                0                 0   \n",
       "1999-11               0                0                 0   \n",
       "1999-12               0                0                 0   \n",
       "2000-01               0                0                 0   \n",
       "2000-02               0                0                 0   \n",
       "2000-03               0                0                 0   \n",
       "2000-04               0                0                 0   \n",
       "2000-05               0                0                 0   \n",
       "2000-06               0                0                 0   \n",
       "2000-07               0                0                 0   \n",
       "2000-08               0                0                 0   \n",
       "2000-09               0                0                 0   \n",
       "2000-10               0                0                 0   \n",
       "2000-11               0                0                 0   \n",
       "2000-12               0                0                 0   \n",
       "2001-01               0                0                 0   \n",
       "2001-02               0                0                 0   \n",
       "2001-03               0                0                 0   \n",
       "2001-04               0                0                 0   \n",
       "...                 ...              ...               ...   \n",
       "2015-04               1                0                 0   \n",
       "2015-05               1                0                 0   \n",
       "2015-06               1                0                 0   \n",
       "2015-07               1                0                 0   \n",
       "2015-08               1                0                 0   \n",
       "2015-09               1                0                 0   \n",
       "2015-10               1                0                 0   \n",
       "2015-11               1                0                 0   \n",
       "2015-12               1                0                 0   \n",
       "2016-01               1                0                 0   \n",
       "2016-02               1                0                 0   \n",
       "2016-03               1                0                 0   \n",
       "2016-04               1                0                 0   \n",
       "2016-05               1                0                 0   \n",
       "2016-06               1                0                 0   \n",
       "2016-07               1                0                 0   \n",
       "2016-08               1                1                 1   \n",
       "2016-09               1                1                 1   \n",
       "2016-10               1                1                 1   \n",
       "2016-11               1                1                 1   \n",
       "2016-12               1                1                 1   \n",
       "2017-01               1                1                 1   \n",
       "2017-02               1                1                 1   \n",
       "2017-03               1                1                 1   \n",
       "2017-04               1                1                 1   \n",
       "2017-05               1                1                 1   \n",
       "2017-06               1                1                 1   \n",
       "2017-07               1                1                 1   \n",
       "2017-08               1                1                 1   \n",
       "2017-09               1                1                 1   \n",
       "\n",
       "         private_sale_handgun  private_sale_long_gun  private_sale_other  \\\n",
       "month                                                                      \n",
       "1998-11                     0                      0                   0   \n",
       "1998-12                     0                      0                   0   \n",
       "1999-01                     0                      0                   0   \n",
       "1999-02                     0                      0                   0   \n",
       "1999-03                     0                      0                   0   \n",
       "1999-04                     0                      0                   0   \n",
       "1999-05                     0                      0                   0   \n",
       "1999-06                     0                      0                   0   \n",
       "1999-07                     0                      0                   0   \n",
       "1999-08                     0                      0                   0   \n",
       "1999-09                     0                      0                   0   \n",
       "1999-10                     0                      0                   0   \n",
       "1999-11                     0                      0                   0   \n",
       "1999-12                     0                      0                   0   \n",
       "2000-01                     0                      0                   0   \n",
       "2000-02                     0                      0                   0   \n",
       "2000-03                     0                      0                   0   \n",
       "2000-04                     0                      0                   0   \n",
       "2000-05                     0                      0                   0   \n",
       "2000-06                     0                      0                   0   \n",
       "2000-07                     0                      0                   0   \n",
       "2000-08                     0                      0                   0   \n",
       "2000-09                     0                      0                   0   \n",
       "2000-10                     0                      0                   0   \n",
       "2000-11                     0                      0                   0   \n",
       "2000-12                     0                      0                   0   \n",
       "2001-01                     0                      0                   0   \n",
       "2001-02                     0                      0                   0   \n",
       "2001-03                     0                      0                   0   \n",
       "2001-04                     0                      0                   0   \n",
       "...                       ...                    ...                 ...   \n",
       "2015-04                     1                      1                   1   \n",
       "2015-05                     1                      1                   1   \n",
       "2015-06                     1                      1                   1   \n",
       "2015-07                     1                      1                   1   \n",
       "2015-08                     1                      1                   1   \n",
       "2015-09                     1                      1                   1   \n",
       "2015-10                     1                      1                   1   \n",
       "2015-11                     1                      1                   1   \n",
       "2015-12                     1                      1                   1   \n",
       "2016-01                     1                      1                   1   \n",
       "2016-02                     1                      1                   1   \n",
       "2016-03                     1                      1                   1   \n",
       "2016-04                     1                      1                   1   \n",
       "2016-05                     1                      1                   1   \n",
       "2016-06                     1                      1                   1   \n",
       "2016-07                     1                      1                   1   \n",
       "2016-08                     1                      1                   1   \n",
       "2016-09                     1                      1                   1   \n",
       "2016-10                     1                      1                   1   \n",
       "2016-11                     1                      1                   1   \n",
       "2016-12                     1                      1                   1   \n",
       "2017-01                     1                      1                   1   \n",
       "2017-02                     1                      1                   1   \n",
       "2017-03                     1                      1                   1   \n",
       "2017-04                     1                      1                   1   \n",
       "2017-05                     1                      1                   1   \n",
       "2017-06                     1                      1                   1   \n",
       "2017-07                     1                      1                   1   \n",
       "2017-08                     1                      1                   1   \n",
       "2017-09                     1                      1                   1   \n",
       "\n",
       "         return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "month                                                          \n",
       "1998-11                         0                          0   \n",
       "1998-12                         0                          0   \n",
       "1999-01                         0                          0   \n",
       "1999-02                         0                          0   \n",
       "1999-03                         0                          0   \n",
       "1999-04                         0                          0   \n",
       "1999-05                         0                          0   \n",
       "1999-06                         0                          0   \n",
       "1999-07                         0                          0   \n",
       "1999-08                         0                          0   \n",
       "1999-09                         0                          0   \n",
       "1999-10                         0                          0   \n",
       "1999-11                         0                          0   \n",
       "1999-12                         0                          0   \n",
       "2000-01                         0                          0   \n",
       "2000-02                         0                          0   \n",
       "2000-03                         0                          0   \n",
       "2000-04                         0                          0   \n",
       "2000-05                         0                          0   \n",
       "2000-06                         0                          0   \n",
       "2000-07                         0                          0   \n",
       "2000-08                         0                          0   \n",
       "2000-09                         0                          0   \n",
       "2000-10                         0                          0   \n",
       "2000-11                         0                          0   \n",
       "2000-12                         0                          0   \n",
       "2001-01                         0                          0   \n",
       "2001-02                         0                          0   \n",
       "2001-03                         0                          0   \n",
       "2001-04                         0                          0   \n",
       "...                           ...                        ...   \n",
       "2015-04                         1                          1   \n",
       "2015-05                         1                          1   \n",
       "2015-06                         1                          1   \n",
       "2015-07                         1                          1   \n",
       "2015-08                         1                          1   \n",
       "2015-09                         1                          1   \n",
       "2015-10                         1                          1   \n",
       "2015-11                         1                          1   \n",
       "2015-12                         1                          1   \n",
       "2016-01                         1                          1   \n",
       "2016-02                         1                          1   \n",
       "2016-03                         1                          1   \n",
       "2016-04                         1                          1   \n",
       "2016-05                         1                          1   \n",
       "2016-06                         1                          1   \n",
       "2016-07                         1                          1   \n",
       "2016-08                         1                          1   \n",
       "2016-09                         1                          1   \n",
       "2016-10                         1                          1   \n",
       "2016-11                         1                          1   \n",
       "2016-12                         1                          1   \n",
       "2017-01                         1                          1   \n",
       "2017-02                         1                          1   \n",
       "2017-03                         1                          1   \n",
       "2017-04                         1                          1   \n",
       "2017-05                         1                          1   \n",
       "2017-06                         1                          1   \n",
       "2017-07                         1                          1   \n",
       "2017-08                         1                          1   \n",
       "2017-09                         1                          1   \n",
       "\n",
       "         return_to_seller_other  totals  \n",
       "month                                    \n",
       "1998-11                       0       1  \n",
       "1998-12                       0       1  \n",
       "1999-01                       0       1  \n",
       "1999-02                       0       1  \n",
       "1999-03                       0       1  \n",
       "1999-04                       0       1  \n",
       "1999-05                       0       1  \n",
       "1999-06                       0       1  \n",
       "1999-07                       0       1  \n",
       "1999-08                       0       1  \n",
       "1999-09                       0       1  \n",
       "1999-10                       0       1  \n",
       "1999-11                       0       1  \n",
       "1999-12                       0       1  \n",
       "2000-01                       0       1  \n",
       "2000-02                       0       1  \n",
       "2000-03                       0       1  \n",
       "2000-04                       0       1  \n",
       "2000-05                       0       1  \n",
       "2000-06                       0       1  \n",
       "2000-07                       0       1  \n",
       "2000-08                       0       1  \n",
       "2000-09                       0       1  \n",
       "2000-10                       0       1  \n",
       "2000-11                       0       1  \n",
       "2000-12                       0       1  \n",
       "2001-01                       0       1  \n",
       "2001-02                       0       1  \n",
       "2001-03                       0       1  \n",
       "2001-04                       0       1  \n",
       "...                         ...     ...  \n",
       "2015-04                       0       1  \n",
       "2015-05                       1       1  \n",
       "2015-06                       1       1  \n",
       "2015-07                       1       1  \n",
       "2015-08                       1       1  \n",
       "2015-09                       1       1  \n",
       "2015-10                       1       1  \n",
       "2015-11                       1       1  \n",
       "2015-12                       1       1  \n",
       "2016-01                       1       1  \n",
       "2016-02                       1       1  \n",
       "2016-03                       1       1  \n",
       "2016-04                       1       1  \n",
       "2016-05                       1       1  \n",
       "2016-06                       1       1  \n",
       "2016-07                       1       1  \n",
       "2016-08                       1       1  \n",
       "2016-09                       1       1  \n",
       "2016-10                       1       1  \n",
       "2016-11                       1       1  \n",
       "2016-12                       1       1  \n",
       "2017-01                       1       1  \n",
       "2017-02                       1       1  \n",
       "2017-03                       1       1  \n",
       "2017-04                       1       1  \n",
       "2017-05                       1       1  \n",
       "2017-06                       1       1  \n",
       "2017-07                       1       1  \n",
       "2017-08                       1       1  \n",
       "2017-09                       1       1  \n",
       "\n",
       "[227 rows x 25 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"state\", \"month\"]).count().loc[\"Wyoming\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, it would seem as though the earlier assumptions of data getting larger over time were accurate. Let's split, then compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110,\n",
       "            1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121,\n",
       "            1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132,\n",
       "            1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143,\n",
       "            1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get index to use for slicing\n",
    "df[df[\"month\"] == \"2016-01\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_older = df.iloc[1100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_newer = df.iloc[:1100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30085.0</td>\n",
       "      <td>15948.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Washington</td>\n",
       "      <td>15736.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20583.0</td>\n",
       "      <td>11991.0</td>\n",
       "      <td>1832.0</td>\n",
       "      <td>863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>3527.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10746.0</td>\n",
       "      <td>7436.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>757</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>9420.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19465.0</td>\n",
       "      <td>12431.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>551.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2287.0</td>\n",
       "      <td>2036.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        month          state   permit  permit_recheck  handgun  long_gun  \\\n",
       "1095  2016-02       Virginia    784.0             0.0  30085.0   15948.0   \n",
       "1096  2016-02     Washington  15736.0             0.0  20583.0   11991.0   \n",
       "1097  2016-02  West Virginia   3527.0             0.0  10746.0    7436.0   \n",
       "1098  2016-02      Wisconsin   9420.0             0.0  19465.0   12431.0   \n",
       "1099  2016-02        Wyoming    551.0             0.0   2287.0    2036.0   \n",
       "\n",
       "       other  multiple  admin  prepawn_handgun   ...    returned_other  \\\n",
       "1095  1133.0         0    0.0              0.0   ...               0.0   \n",
       "1096  1832.0       863    1.0              3.0   ...               3.0   \n",
       "1097   357.0       757    5.0              6.0   ...               3.0   \n",
       "1098   821.0        62    0.0              0.0   ...               2.0   \n",
       "1099   139.0       150    0.0              3.0   ...               0.0   \n",
       "\n",
       "      rentals_handgun  rentals_long_gun  private_sale_handgun  \\\n",
       "1095              NaN               NaN                   0.0   \n",
       "1096              NaN               NaN                 578.0   \n",
       "1097              NaN               NaN                  11.0   \n",
       "1098              NaN               NaN                   5.0   \n",
       "1099              NaN               NaN                   0.0   \n",
       "\n",
       "      private_sale_long_gun  private_sale_other  return_to_seller_handgun  \\\n",
       "1095                    0.0                 0.0                       0.0   \n",
       "1096                  422.0                30.0                       5.0   \n",
       "1097                    5.0                 1.0                       3.0   \n",
       "1098                   15.0                 0.0                       0.0   \n",
       "1099                    4.0                 0.0                       1.0   \n",
       "\n",
       "      return_to_seller_long_gun  return_to_seller_other  totals  \n",
       "1095                        0.0                     0.0   47955  \n",
       "1096                       15.0                     0.0   56043  \n",
       "1097                        2.0                     0.0   27216  \n",
       "1098                        0.0                     0.0   42855  \n",
       "1099                        1.0                     0.0    5703  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_newer.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has been split into two tentatively, however, there seems to still be more work to be done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month                        0.0\n",
       "state                        0.0\n",
       "permit                       0.0\n",
       "permit_recheck               0.0\n",
       "handgun                      0.0\n",
       "long_gun                     0.0\n",
       "other                        0.0\n",
       "multiple                     0.0\n",
       "admin                        0.0\n",
       "prepawn_handgun              0.0\n",
       "prepawn_long_gun             0.0\n",
       "prepawn_other                0.0\n",
       "redemption_handgun           0.0\n",
       "redemption_long_gun          0.0\n",
       "redemption_other             0.0\n",
       "returned_handgun             0.0\n",
       "returned_long_gun            0.0\n",
       "returned_other               0.0\n",
       "rentals_handgun              0.3\n",
       "rentals_long_gun             0.3\n",
       "private_sale_handgun         0.0\n",
       "private_sale_long_gun        0.0\n",
       "private_sale_other           0.0\n",
       "return_to_seller_handgun     0.0\n",
       "return_to_seller_long_gun    0.0\n",
       "return_to_seller_other       0.0\n",
       "totals                       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_newer.isnull().sum()/df_newer.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! the df_newer dataset is definitely clean, and the inconsistencies negligible as set by our threshold of 70%. Let's fill the missing values with 0(since it's security related assumptions have dire consequencies) and move unto df_older:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>27765.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10132.0</td>\n",
       "      <td>7392.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>215.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2898.0</td>\n",
       "      <td>2816.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>7863.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>13693.0</td>\n",
       "      <td>8643.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>4116.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>6323.0</td>\n",
       "      <td>5053.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>367</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>California</td>\n",
       "      <td>77962.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46361.0</td>\n",
       "      <td>47594.0</td>\n",
       "      <td>17297.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18765.0</td>\n",
       "      <td>15176.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>1054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>14156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11328.0</td>\n",
       "      <td>4015.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>755.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1859.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>28106.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57902.0</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>4504.0</td>\n",
       "      <td>2324</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>20555.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15342.0</td>\n",
       "      <td>8597.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Guam</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>3606.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>125075.0</td>\n",
       "      <td>8969.0</td>\n",
       "      <td>21421.0</td>\n",
       "      <td>11881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>53844.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19508.0</td>\n",
       "      <td>12191.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>644</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>9391.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>2227.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6075.0</td>\n",
       "      <td>4942.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>329</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>342858.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9378.0</td>\n",
       "      <td>6615.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>363085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>2595.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21637.0</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>1031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Maine</td>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3426.0</td>\n",
       "      <td>3026.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Mariana Islands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>2383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3973.0</td>\n",
       "      <td>5505.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>7091.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6257.0</td>\n",
       "      <td>5108.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>439</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>15869.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>11308.0</td>\n",
       "      <td>8095.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>22422.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9743.0</td>\n",
       "      <td>9207.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10425.0</td>\n",
       "      <td>6125.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>459</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>6903.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20791.0</td>\n",
       "      <td>13575.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Montana</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2894.0</td>\n",
       "      <td>3882.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>194</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>3629.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>25678.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12754.0</td>\n",
       "      <td>9635.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>1171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15296.0</td>\n",
       "      <td>7203.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>13121.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30802.0</td>\n",
       "      <td>16707.0</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>1455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Montana</td>\n",
       "      <td>2279.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3779.0</td>\n",
       "      <td>4151.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>325</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>6096.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6643.0</td>\n",
       "      <td>3115.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4044.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6798.0</td>\n",
       "      <td>3464.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6007.0</td>\n",
       "      <td>3613.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>1576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7455.0</td>\n",
       "      <td>5049.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>New York</td>\n",
       "      <td>4971.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11963.0</td>\n",
       "      <td>15324.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>33753.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>14935.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>761.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>2419.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>16201.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45459.0</td>\n",
       "      <td>20745.0</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>1873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18984.0</td>\n",
       "      <td>10754.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>1290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17116.0</td>\n",
       "      <td>11665.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>29651.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61933.0</td>\n",
       "      <td>11791.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1288.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>12817.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15746.0</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4170.0</td>\n",
       "      <td>4009.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>12348.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36439.0</td>\n",
       "      <td>18121.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1546</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Texas</td>\n",
       "      <td>56900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72584.0</td>\n",
       "      <td>37980.0</td>\n",
       "      <td>3156.0</td>\n",
       "      <td>3891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>192417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Utah</td>\n",
       "      <td>12927.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2273.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30085.0</td>\n",
       "      <td>15948.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Washington</td>\n",
       "      <td>15736.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20583.0</td>\n",
       "      <td>11991.0</td>\n",
       "      <td>1832.0</td>\n",
       "      <td>863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>3527.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10746.0</td>\n",
       "      <td>7436.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>757</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>9420.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19465.0</td>\n",
       "      <td>12431.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>551.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2287.0</td>\n",
       "      <td>2036.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        month                 state    permit  permit_recheck  handgun  \\\n",
       "770   2016-07               Alabama   27765.0             0.0  10132.0   \n",
       "771   2016-07                Alaska     215.0             0.0   2898.0   \n",
       "772   2016-07               Arizona    7863.0           308.0  13693.0   \n",
       "773   2016-07              Arkansas    4116.0           677.0   6323.0   \n",
       "774   2016-07            California   77962.0             0.0  46361.0   \n",
       "775   2016-07              Colorado    6372.0             0.0  18765.0   \n",
       "776   2016-07           Connecticut   14156.0             0.0  11328.0   \n",
       "777   2016-07              Delaware     755.0             0.0   1859.0   \n",
       "778   2016-07  District of Columbia       0.0             0.0     57.0   \n",
       "779   2016-07               Florida   28106.0             0.0  57902.0   \n",
       "780   2016-07               Georgia   20555.0             0.0  15342.0   \n",
       "781   2016-07                  Guam       0.0             0.0     94.0   \n",
       "782   2016-07                Hawaii    1563.0             0.0      0.0   \n",
       "783   2016-07                 Idaho    3606.0             6.0   3592.0   \n",
       "784   2016-07              Illinois  125075.0          8969.0  21421.0   \n",
       "785   2016-07               Indiana   53844.0             0.0  19508.0   \n",
       "786   2016-07                  Iowa    9391.0            11.0    186.0   \n",
       "787   2016-07                Kansas    1320.0             3.0   6075.0   \n",
       "788   2016-07              Kentucky  342858.0             0.0   9378.0   \n",
       "789   2016-07             Louisiana    2595.0             0.0  21637.0   \n",
       "790   2016-07                 Maine     540.0             0.0   3426.0   \n",
       "791   2016-07       Mariana Islands       0.0             0.0      0.0   \n",
       "792   2016-07              Maryland    2383.0             0.0   3973.0   \n",
       "793   2016-07         Massachusetts    7091.0             5.0   6257.0   \n",
       "794   2016-07              Michigan   15869.0          3753.0  11308.0   \n",
       "795   2016-07             Minnesota   22422.0             0.0   9743.0   \n",
       "796   2016-07           Mississippi    1913.0             0.0  10425.0   \n",
       "797   2016-07              Missouri    6903.0             0.0  20791.0   \n",
       "798   2016-07               Montana    1356.0             0.0   2894.0   \n",
       "799   2016-07              Nebraska    3629.0            65.0     93.0   \n",
       "...       ...                   ...       ...             ...      ...   \n",
       "1070  2016-02             Minnesota   25678.0             0.0  12754.0   \n",
       "1071  2016-02           Mississippi    1171.0             0.0  15296.0   \n",
       "1072  2016-02              Missouri   13121.0             0.0  30802.0   \n",
       "1073  2016-02               Montana    2279.0             0.0   3779.0   \n",
       "1074  2016-02              Nebraska    6096.0            69.0    154.0   \n",
       "1075  2016-02                Nevada    3288.0             0.0   6643.0   \n",
       "1076  2016-02         New Hampshire    4044.0             0.0   6798.0   \n",
       "1077  2016-02            New Jersey       0.0             0.0   6007.0   \n",
       "1078  2016-02            New Mexico    1576.0             0.0   7455.0   \n",
       "1079  2016-02              New York    4971.0             0.0  11963.0   \n",
       "1080  2016-02        North Carolina   33753.0             0.0   1543.0   \n",
       "1081  2016-02          North Dakota     761.0             0.0   2100.0   \n",
       "1082  2016-02                  Ohio   16201.0             0.0  45459.0   \n",
       "1083  2016-02              Oklahoma       0.0             0.0  18984.0   \n",
       "1084  2016-02                Oregon      88.0             0.0  17116.0   \n",
       "1085  2016-02          Pennsylvania   29651.0             0.0  61933.0   \n",
       "1086  2016-02           Puerto Rico       0.0             0.0    885.0   \n",
       "1087  2016-02          Rhode Island       0.0             0.0   1288.0   \n",
       "1088  2016-02        South Carolina   12817.0             0.0  15746.0   \n",
       "1089  2016-02          South Dakota    1627.0             0.0   4170.0   \n",
       "1090  2016-02             Tennessee   12348.0             0.0  36439.0   \n",
       "1091  2016-02                 Texas   56900.0             0.0  72584.0   \n",
       "1092  2016-02                  Utah   12927.0            93.0   4413.0   \n",
       "1093  2016-02               Vermont       0.0             0.0   2273.0   \n",
       "1094  2016-02        Virgin Islands      86.0             0.0     25.0   \n",
       "1095  2016-02              Virginia     784.0             0.0  30085.0   \n",
       "1096  2016-02            Washington   15736.0             0.0  20583.0   \n",
       "1097  2016-02         West Virginia    3527.0             0.0  10746.0   \n",
       "1098  2016-02             Wisconsin    9420.0             0.0  19465.0   \n",
       "1099  2016-02               Wyoming     551.0             0.0   2287.0   \n",
       "\n",
       "      long_gun    other  multiple  admin  prepawn_handgun   ...    \\\n",
       "770     7392.0    538.0       465    0.0              5.0   ...     \n",
       "771     2816.0    239.0       165    0.0              7.0   ...     \n",
       "772     8643.0   1283.0       642    0.0              5.0   ...     \n",
       "773     5053.0    325.0       367    2.0              6.0   ...     \n",
       "774    47594.0  17297.0         0    0.0              0.0   ...     \n",
       "775    15176.0   1505.0      1054    0.0              0.0   ...     \n",
       "776     4015.0    252.0         0    4.0              0.0   ...     \n",
       "777     1569.0    140.0        74    0.0              0.0   ...     \n",
       "778        1.0      0.0         2    0.0              0.0   ...     \n",
       "779    28000.0   4504.0      2324   12.0              8.0   ...     \n",
       "780     8597.0    635.0       632    0.0             12.0   ...     \n",
       "781       56.0     16.0         7    0.0              0.0   ...     \n",
       "782        0.0      0.0         0    0.0              0.0   ...     \n",
       "783     3576.0    247.0       203    0.0              3.0   ...     \n",
       "784    11881.0      0.0       881    0.0              0.0   ...     \n",
       "785    12191.0   1600.0       644    8.0              1.0   ...     \n",
       "786     2227.0     27.0         1    1.0              0.0   ...     \n",
       "787     4942.0    462.0       329    2.0              6.0   ...     \n",
       "788     6615.0    321.0       534    0.0              7.0   ...     \n",
       "789    12252.0   1453.0      1031    0.0              8.0   ...     \n",
       "790     3026.0    266.0       166    0.0              1.0   ...     \n",
       "791        1.0      0.0         0    0.0              0.0   ...     \n",
       "792     5505.0    105.0        35    0.0              1.0   ...     \n",
       "793     5108.0   1498.0       439    2.0              0.0   ...     \n",
       "794     8095.0    510.0       239    0.0              0.0   ...     \n",
       "795     9207.0    913.0       373    0.0              0.0   ...     \n",
       "796     6125.0    436.0       459    3.0             22.0   ...     \n",
       "797    13575.0   1698.0       981    0.0             12.0   ...     \n",
       "798     3882.0    218.0       194   25.0              5.0   ...     \n",
       "799     1529.0     27.0         5    0.0              0.0   ...     \n",
       "...        ...      ...       ...    ...              ...   ...     \n",
       "1070    9635.0    793.0       400    0.0              2.0   ...     \n",
       "1071    7203.0    308.0       603    0.0             17.0   ...     \n",
       "1072   16707.0   1404.0      1455    0.0             10.0   ...     \n",
       "1073    4151.0    154.0       325   12.0              2.0   ...     \n",
       "1074    2078.0     27.0        11    0.0              0.0   ...     \n",
       "1075    3115.0    244.0       333    0.0              0.0   ...     \n",
       "1076    3464.0    156.0         5    7.0              0.0   ...     \n",
       "1077    3613.0    191.0         0    0.0              0.0   ...     \n",
       "1078    5049.0    393.0       506    0.0              6.0   ...     \n",
       "1079   15324.0   1130.0       203    0.0              0.0   ...     \n",
       "1080   14935.0    835.0       432    0.0             32.0   ...     \n",
       "1081    2419.0     99.0        85    0.0              0.0   ...     \n",
       "1082   20745.0   2196.0      1873    0.0             21.0   ...     \n",
       "1083   10754.0   1184.0      1290    0.0              9.0   ...     \n",
       "1084   11665.0      0.0         0    0.0              0.0   ...     \n",
       "1085   11791.0      0.0         0   63.0              0.0   ...     \n",
       "1086     152.0     24.0        22    0.0              0.0   ...     \n",
       "1087     848.0     64.0       152    0.0              0.0   ...     \n",
       "1088    6847.0    522.0       538    0.0              7.0   ...     \n",
       "1089    4009.0    169.0       252    0.0              1.0   ...     \n",
       "1090   18121.0      0.0      1546    2.0              0.0   ...     \n",
       "1091   37980.0   3156.0      3891    0.0             53.0   ...     \n",
       "1092    4000.0    259.0       220    0.0              0.0   ...     \n",
       "1093    1681.0    128.0        86    0.0              0.0   ...     \n",
       "1094       4.0      0.0         0    0.0              0.0   ...     \n",
       "1095   15948.0   1133.0         0    0.0              0.0   ...     \n",
       "1096   11991.0   1832.0       863    1.0              3.0   ...     \n",
       "1097    7436.0    357.0       757    5.0              6.0   ...     \n",
       "1098   12431.0    821.0        62    0.0              0.0   ...     \n",
       "1099    2036.0    139.0       150    0.0              3.0   ...     \n",
       "\n",
       "      returned_other  rentals_handgun  rentals_long_gun  private_sale_handgun  \\\n",
       "770              0.0              NaN               NaN                   9.0   \n",
       "771              0.0              NaN               NaN                   7.0   \n",
       "772              0.0              NaN               NaN                  13.0   \n",
       "773              0.0              NaN               NaN                   7.0   \n",
       "774              0.0              NaN               NaN                   0.0   \n",
       "775              0.0              NaN               NaN                   0.0   \n",
       "776              0.0              NaN               NaN                   0.0   \n",
       "777              0.0              NaN               NaN                  45.0   \n",
       "778              0.0              NaN               NaN                   0.0   \n",
       "779              1.0              NaN               NaN                  39.0   \n",
       "780              0.0              NaN               NaN                   3.0   \n",
       "781              0.0              NaN               NaN                   0.0   \n",
       "782              0.0              NaN               NaN                   0.0   \n",
       "783              0.0              NaN               NaN                   2.0   \n",
       "784              0.0              NaN               NaN                   0.0   \n",
       "785              1.0              NaN               NaN                  23.0   \n",
       "786              0.0              NaN               NaN                   0.0   \n",
       "787              1.0              NaN               NaN                   3.0   \n",
       "788              0.0              NaN               NaN                  13.0   \n",
       "789              0.0              NaN               NaN                   7.0   \n",
       "790              1.0              NaN               NaN                   0.0   \n",
       "791              0.0              NaN               NaN                   0.0   \n",
       "792              0.0              NaN               NaN                   2.0   \n",
       "793              0.0              NaN               NaN                  28.0   \n",
       "794              1.0              NaN               NaN                   2.0   \n",
       "795              0.0              NaN               NaN                   7.0   \n",
       "796              0.0              NaN               NaN                   7.0   \n",
       "797              1.0              NaN               NaN                  33.0   \n",
       "798              1.0              NaN               NaN                   1.0   \n",
       "799              0.0              NaN               NaN                   1.0   \n",
       "...              ...              ...               ...                   ...   \n",
       "1070             1.0              NaN               NaN                  10.0   \n",
       "1071             0.0              NaN               NaN                  11.0   \n",
       "1072             4.0              NaN               NaN                  62.0   \n",
       "1073             0.0              NaN               NaN                   5.0   \n",
       "1074             0.0              NaN               NaN                   0.0   \n",
       "1075             0.0              NaN               NaN                   0.0   \n",
       "1076             1.0              NaN               NaN                  11.0   \n",
       "1077             0.0              NaN               NaN                   0.0   \n",
       "1078             0.0              NaN               NaN                   1.0   \n",
       "1079             0.0              NaN               NaN                 333.0   \n",
       "1080             0.0              NaN               NaN                   4.0   \n",
       "1081             0.0              NaN               NaN                   9.0   \n",
       "1082             0.0              NaN               NaN                  25.0   \n",
       "1083             0.0              NaN               NaN                  25.0   \n",
       "1084             0.0              NaN               NaN                   0.0   \n",
       "1085             1.0              NaN               NaN                   0.0   \n",
       "1086             0.0              NaN               NaN                   0.0   \n",
       "1087             0.0              NaN               NaN                  17.0   \n",
       "1088             0.0              NaN               NaN                  10.0   \n",
       "1089             0.0              NaN               NaN                   3.0   \n",
       "1090            10.0              NaN               NaN                   0.0   \n",
       "1091             3.0              NaN               NaN                  47.0   \n",
       "1092            29.0              NaN               NaN                   0.0   \n",
       "1093             0.0              NaN               NaN                   2.0   \n",
       "1094             0.0              NaN               NaN                   0.0   \n",
       "1095             0.0              NaN               NaN                   0.0   \n",
       "1096             3.0              NaN               NaN                 578.0   \n",
       "1097             3.0              NaN               NaN                  11.0   \n",
       "1098             2.0              NaN               NaN                   5.0   \n",
       "1099             0.0              NaN               NaN                   0.0   \n",
       "\n",
       "      private_sale_long_gun  private_sale_other  return_to_seller_handgun  \\\n",
       "770                     5.0                 0.0                       1.0   \n",
       "771                    17.0                 0.0                       1.0   \n",
       "772                     9.0                 0.0                       0.0   \n",
       "773                     5.0                 1.0                       0.0   \n",
       "774                     0.0                 0.0                       0.0   \n",
       "775                     0.0                 0.0                       0.0   \n",
       "776                     0.0                 0.0                       0.0   \n",
       "777                    22.0                 5.0                       1.0   \n",
       "778                     0.0                 0.0                       0.0   \n",
       "779                    12.0                 1.0                       0.0   \n",
       "780                     3.0                 1.0                       0.0   \n",
       "781                     0.0                 0.0                       0.0   \n",
       "782                     0.0                 0.0                       0.0   \n",
       "783                     4.0                 0.0                       0.0   \n",
       "784                     0.0                 0.0                       0.0   \n",
       "785                     9.0                 1.0                       3.0   \n",
       "786                     4.0                 0.0                       0.0   \n",
       "787                     5.0                 0.0                       1.0   \n",
       "788                     8.0                 2.0                       0.0   \n",
       "789                     2.0                 0.0                       1.0   \n",
       "790                     2.0                 0.0                       0.0   \n",
       "791                     0.0                 0.0                       0.0   \n",
       "792                     6.0                 0.0                       0.0   \n",
       "793                    32.0                10.0                       0.0   \n",
       "794                     1.0                 1.0                       0.0   \n",
       "795                     6.0                 1.0                       0.0   \n",
       "796                     1.0                 0.0                       0.0   \n",
       "797                    31.0                 7.0                       1.0   \n",
       "798                     3.0                 1.0                       0.0   \n",
       "799                     1.0                 0.0                       0.0   \n",
       "...                     ...                 ...                       ...   \n",
       "1070                    7.0                 0.0                       1.0   \n",
       "1071                   11.0                 0.0                       0.0   \n",
       "1072                   31.0                 2.0                       4.0   \n",
       "1073                    5.0                 0.0                       0.0   \n",
       "1074                    0.0                 0.0                       0.0   \n",
       "1075                    0.0                 0.0                       0.0   \n",
       "1076                    1.0                 1.0                       0.0   \n",
       "1077                    0.0                 0.0                       0.0   \n",
       "1078                    2.0                 0.0                       1.0   \n",
       "1079                  211.0                12.0                       3.0   \n",
       "1080                    8.0                 0.0                       0.0   \n",
       "1081                   48.0                 0.0                       0.0   \n",
       "1082                    8.0                 1.0                       1.0   \n",
       "1083                   21.0                 6.0                       0.0   \n",
       "1084                    0.0                 0.0                       0.0   \n",
       "1085                    0.0                 0.0                       0.0   \n",
       "1086                    0.0                 0.0                       0.0   \n",
       "1087                    7.0                 3.0                       0.0   \n",
       "1088                    8.0                 0.0                       2.0   \n",
       "1089                    2.0                 0.0                       0.0   \n",
       "1090                    0.0                 0.0                       0.0   \n",
       "1091                   44.0                 6.0                       4.0   \n",
       "1092                    0.0                 0.0                       0.0   \n",
       "1093                    1.0                 0.0                       0.0   \n",
       "1094                    0.0                 0.0                       0.0   \n",
       "1095                    0.0                 0.0                       0.0   \n",
       "1096                  422.0                30.0                       5.0   \n",
       "1097                    5.0                 1.0                       3.0   \n",
       "1098                   15.0                 0.0                       0.0   \n",
       "1099                    4.0                 0.0                       1.0   \n",
       "\n",
       "      return_to_seller_long_gun  return_to_seller_other  totals  \n",
       "770                         1.0                     0.0   48927  \n",
       "771                         0.0                     0.0    6793  \n",
       "772                         1.0                     0.0   34496  \n",
       "773                         1.0                     0.0   19378  \n",
       "774                         0.0                     0.0  190218  \n",
       "775                         0.0                     0.0   43094  \n",
       "776                         0.0                     0.0   29755  \n",
       "777                         0.0                     0.0    4494  \n",
       "778                         0.0                     0.0      60  \n",
       "779                         0.0                     0.0  125208  \n",
       "780                         0.0                     0.0   49183  \n",
       "781                         0.0                     0.0     173  \n",
       "782                         0.0                     0.0    1565  \n",
       "783                         1.0                     0.0   12154  \n",
       "784                         0.0                     0.0  168227  \n",
       "785                         0.0                     0.0   88340  \n",
       "786                         0.0                     0.0   11937  \n",
       "787                         2.0                     0.0   14140  \n",
       "788                         0.0                     0.0  363085  \n",
       "789                         0.0                     1.0   41063  \n",
       "790                         1.0                     0.0    7702  \n",
       "791                         0.0                     0.0       1  \n",
       "792                         0.0                     0.0   12228  \n",
       "793                         3.0                     0.0   20480  \n",
       "794                         0.0                     0.0   40142  \n",
       "795                         0.0                     0.0   43368  \n",
       "796                         1.0                     0.0   21907  \n",
       "797                         3.0                     0.0   46637  \n",
       "798                         3.0                     0.0    9869  \n",
       "799                         0.0                     0.0    5429  \n",
       "...                         ...                     ...     ...  \n",
       "1070                        0.0                     0.0   50144  \n",
       "1071                        1.0                     0.0   28656  \n",
       "1072                        5.0                     0.0   67682  \n",
       "1073                        0.0                     0.0   12644  \n",
       "1074                        0.0                     0.0    8576  \n",
       "1075                        0.0                     0.0   14342  \n",
       "1076                        1.0                     1.0   14577  \n",
       "1077                        0.0                     0.0    9811  \n",
       "1078                        0.0                     0.0   16662  \n",
       "1079                        5.0                     0.0   34195  \n",
       "1080                        0.0                     0.0   58096  \n",
       "1081                        0.0                     0.0    5799  \n",
       "1082                        0.0                     0.0   90222  \n",
       "1083                        3.0                     0.0   37589  \n",
       "1084                        0.0                     0.0   28869  \n",
       "1085                        0.0                     0.0  104116  \n",
       "1086                        0.0                     0.0    1088  \n",
       "1087                        0.0                     0.0    2392  \n",
       "1088                        1.0                     0.0   39376  \n",
       "1089                        0.0                     0.0   10732  \n",
       "1090                        0.0                     0.0   68503  \n",
       "1091                        1.0                     3.0  192417  \n",
       "1092                        0.0                     0.0   22999  \n",
       "1093                        0.0                     0.0    4173  \n",
       "1094                        0.0                     0.0     115  \n",
       "1095                        0.0                     0.0   47955  \n",
       "1096                       15.0                     0.0   56043  \n",
       "1097                        2.0                     0.0   27216  \n",
       "1098                        0.0                     0.0   42855  \n",
       "1099                        1.0                     0.0    5703  \n",
       "\n",
       "[330 rows x 27 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_newer[df_newer[\"rentals_handgun\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_newer.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month                        0\n",
       "state                        0\n",
       "permit                       0\n",
       "permit_recheck               0\n",
       "handgun                      0\n",
       "long_gun                     0\n",
       "other                        0\n",
       "multiple                     0\n",
       "admin                        0\n",
       "prepawn_handgun              0\n",
       "prepawn_long_gun             0\n",
       "prepawn_other                0\n",
       "redemption_handgun           0\n",
       "redemption_long_gun          0\n",
       "redemption_other             0\n",
       "returned_handgun             0\n",
       "returned_long_gun            0\n",
       "returned_other               0\n",
       "rentals_handgun              0\n",
       "rentals_long_gun             0\n",
       "private_sale_handgun         0\n",
       "private_sale_long_gun        0\n",
       "private_sale_other           0\n",
       "return_to_seller_handgun     0\n",
       "return_to_seller_long_gun    0\n",
       "return_to_seller_other       0\n",
       "totals                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_newer.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'other'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get list of columns with at least 30% nulls\n",
    "dropped_cols = []\n",
    "for col in df_older.columns:\n",
    "    if (df_older[col].isnull().sum()/df_older.shape[0]) > 0.30:\n",
    "        dropped_cols.append(col)\n",
    "# Keep \"other\" category of firearms\n",
    "dropped_cols.pop(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month                  0.000000\n",
       "state                  0.000000\n",
       "permit                 0.002108\n",
       "handgun                0.001757\n",
       "long_gun               0.001669\n",
       "other                  0.613527\n",
       "multiple               0.000000\n",
       "admin                  0.002020\n",
       "prepawn_handgun        0.170663\n",
       "prepawn_long_gun       0.170839\n",
       "redemption_handgun     0.170400\n",
       "redemption_long_gun    0.170487\n",
       "totals                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_older.drop(columns=dropped_cols).isnull().sum()/df_older.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_older.drop(columns=dropped_cols, inplace=True)\n",
    "df_older.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The older dataset thus seems manageable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month                  0\n",
       "state                  0\n",
       "permit                 0\n",
       "handgun                0\n",
       "long_gun               0\n",
       "other                  0\n",
       "multiple               0\n",
       "admin                  0\n",
       "prepawn_handgun        0\n",
       "prepawn_long_gun       0\n",
       "redemption_handgun     0\n",
       "redemption_long_gun    0\n",
       "totals                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_older.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's check the consistency of our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_newer.shape[0] + df_older.shape[0] == df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To recap, the original dataset was found to contain too many null values, hence, it was seperated by a specific timeline and cleaned to get two better(in my opinion) subsets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "\n",
    "###  Which states have the most and least background checks for different firearm categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "firearm_cat = [\"handgun\", \"long_gun\", \"other\", \"totals\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupby states and aggregate by totals.\n",
    "state_firearms_old = df_older.groupby(\"state\").sum()\n",
    "state_firearms_new = df_newer.groupby(\"state\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 11)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_firearms_old.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 4)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the two datasets based on fire arm categories and totals.\n",
    "state_firearms = state_firearms_new.copy()\n",
    "for col in state_firearms[firearm_cat]:\n",
    "    state_firearms[col] += state_firearms_old[col]\n",
    "state_firearms[firearm_cat].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 55 entries, Alabama to Wyoming\n",
      "Data columns (total 25 columns):\n",
      "permit                       55 non-null float64\n",
      "permit_recheck               55 non-null float64\n",
      "handgun                      55 non-null float64\n",
      "long_gun                     55 non-null float64\n",
      "other                        55 non-null float64\n",
      "multiple                     55 non-null int64\n",
      "admin                        55 non-null float64\n",
      "prepawn_handgun              55 non-null float64\n",
      "prepawn_long_gun             55 non-null float64\n",
      "prepawn_other                55 non-null float64\n",
      "redemption_handgun           55 non-null float64\n",
      "redemption_long_gun          55 non-null float64\n",
      "redemption_other             55 non-null float64\n",
      "returned_handgun             55 non-null float64\n",
      "returned_long_gun            55 non-null float64\n",
      "returned_other               55 non-null float64\n",
      "rentals_handgun              55 non-null float64\n",
      "rentals_long_gun             55 non-null float64\n",
      "private_sale_handgun         55 non-null float64\n",
      "private_sale_long_gun        55 non-null float64\n",
      "private_sale_other           55 non-null float64\n",
      "return_to_seller_handgun     55 non-null float64\n",
      "return_to_seller_long_gun    55 non-null float64\n",
      "return_to_seller_other       55 non-null float64\n",
      "totals                       55 non-null int64\n",
      "dtypes: float64(23), int64(2)\n",
      "memory usage: 11.2+ KB\n"
     ]
    }
   ],
   "source": [
    "state_firearms.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Mariana Islands            462\n",
       "District of Columbia      5601\n",
       "Guam                     14997\n",
       "Virgin Islands           15108\n",
       "Puerto Rico             194464\n",
       "Name: totals, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total firearm checks.\n",
    "total_arms = state_firearms[\"totals\"].sort_values()\n",
    "total_arms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state\n",
      "Mariana Islands            462\n",
      "District of Columbia      5601\n",
      "Guam                     14997\n",
      "Virgin Islands           15108\n",
      "Puerto Rico             194464\n",
      "Hawaii                  197580\n",
      "Name: totals, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "state\n",
       "Florida         11695777\n",
       "Pennsylvania    13133876\n",
       "Illinois        15471534\n",
       "California      19014063\n",
       "Texas           19435186\n",
       "Kentucky        29762434\n",
       "Name: totals, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get top 5 lowest and highest totals.\n",
    "lowest_arms = total_arms.head(6)\n",
    "print(lowest_arms)\n",
    "highest_arms = total_arms.tail(6)\n",
    "highest_arms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hawai and Puerto Rico being so close means there is a need to extend to top 6 rather than 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function for a comparative visualizaton.\n",
    "def bar_chart(df, y_scale=1e03, y_scale_two=1e06):\n",
    "    df_one = df.sort_values().head(6)\n",
    "    df_two = df.sort_values().tail(6)\n",
    "    # Create a figure for 2 subplots (2 rows, 1 column)\n",
    "    fig, ax = plt.subplots(2, 1, figsize = (15,12))\n",
    "    \n",
    "    # Plot the bar plot ;).\n",
    "    # axis 0\n",
    "    sns.barplot(x=df_one.index, y=df_one/y_scale, ax=ax[0])\n",
    "    ax[0].set_xlabel(\"States\", fontsize=15)\n",
    "    ax[0].set_ylabel(\"Total Firearms (thousands)\", fontsize=15)\n",
    "    ax[0].set_title(\"lowest\", fontsize=15)\n",
    "    #ax[0].bar_label(df_one)\n",
    "    # axis 1\n",
    "    sns.barplot(x=df_two.index, y=df_two/y_scale_two, ax=ax[1])\n",
    "    ax[1].set_xlabel(\"States\", fontsize=15)\n",
    "    ax[1].set_ylabel(\"Total Firearms (millions)\", fontsize=15)\n",
    "    ax[1].set_title(\"Highest\", fontsize=15);\n",
    "\n",
    "    # Add a title to the Figure}\n",
    "    fig.suptitle(f\"Top {len(df_one)} States {df_one.name} Firearms checks.\", fontsize=25);\n",
    "\n",
    "    # Show the figure\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAAMRCAYAAABYvN+YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xl4THf///FXEgmJRBIhqAqxTLRqD7e9dqra6pKr3OVuVEspqrrRRZVb6UJrq52GqlobSu13bLWUqNpVEUvIHkuErOf3h1/OV2QxicQyno/r6tXMWd9n5syY15zP+XzsDMMwBAAAAACwSfb3ugAAAAAAQOEh9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANI/QBAGyev7+//Pz8tGHDhntdSq7WrFkjPz8//etf/7rXpaAA9enTR35+fvryyy/vdSlWGzFihPz8/DRw4MB7XQqAAlDkXhcAPOz8/Pzyve7o0aP1wgsvFGA1hW/r1q0KDg7Wn3/+qejoaDk7O8vb21u1a9fWM888o0aNGuVru6dPn9aCBQu0a9cunT17VtevX5eHh4e8vLzk6+srf39/NWrUSFWrVs203r59+7R161aVKlVK3bp1K4hDzOJu7KMwJCUlafr06ZKkrl27qnTp0g/kPu6Ffv36aePGjbddrnr16lq+fPldqAgA8DAj9AH3WKlSpbKdnpiYqMTExFyXKVasWKHVVdCuXbum999/X+vXrzenubm5KTExUcePH9fx48eVlpaWr9C3YMECjRo1SikpKZm2ffXqVUVHR+vo0aNavXp1tl+w9+3bp0mTJql69eqFGvoKex+FISkpSZMmTZIktW3bttBCX2Hv414qUqSIPDw8cpzv6emZ6bGrq6t8fX3l7u5e2KUBAB4ihD7gHvv999+znT5x4kTzy3BOyzwo0tLS1KdPH+3atUseHh4aOHCgOnXqJE9PTxmGoaioKG3fvj1TaLPWjh07NHz4cElSrVq11K9fPzVq1EjOzs6SpOjoaO3du1fr1q3T2bNnC/KwgNuqWrVqnq7kNWvWTGvWrCnEigAADyNCH4BCN2vWLO3atUsuLi6aP39+piaWdnZ2KlOmjJ5//vl8bXvOnDmSpEcffVTz5s3LcvWzdOnS6tChgzp06KDr16/n/yAAAAAeUIQ+wIZcuHBBs2bN0rZt23ThwgXZ29urQoUKatWqlXr27JltM7MjR46oS5cukqTdu3frwoULmj59unbt2qWLFy/K29tbrVu3Vr9+/VSyZMk813T9+nXNmjVL0o3ODG69p+5OHTlyRJLUtGnT2zZ3vXn+5cuX1aBBA/Px0aNHs9xfOXToUAUGBkqSrl69qm3btikkJESHDh1SVFSUrl69qpIlS6pOnTp65ZVXsnS+kdd9ZIiPj9fcuXO1efNmnTlzRtevX5e3t7caNGigwMBAPfbYY9keX2xsrObMmaMtW7bo7NmzSklJkYeHh0qVKqX69evrqaeekr+/f67PUYZb70nLOEcyZNdUNj4+Xj/88INCQkJ09uxZpaenq1y5cmrWrJl69eqlcuXK3dE+9u/fr//973/auXOnIiIiFBMTI2dnZ1WtWlUdOnRQt27dVLRoUauO72YJCQmaN2+eNmzYoLCwMF2/fl3u7u4qWbKkateurXbt2qlly5Z53m5+rFmzRm+//bY8PDy0a9euTPNmzZqlr776SjVq1NCyZcsUEhKi+fPn6/Dhw4qPj1dgYKA+/PDDTOvs3btXCxYsUGhoqKKjo+Xo6KhKlSqpXbt26tGjh1xdXbPUkJSUpB07duh///uf9u/fr6ioKF2+fFnu7u6qWbOmXnrpJbVt29aq+kNDQxUUFKS9e/cqLi5Obdu21YQJEyRJdevWVWJioqZOnSp/f39NnTpV69evV0REhDw9PdWiRQsNGDBA3t7ekqTIyEjNmDFDISEhioqKkqenp9q3b6+BAweqRIkS2dazc+dO/fTTT9q/f79iYmLk6OgoT09PVahQQU2bNtWLL74oLy+vPL9O169f1+LFi7V+/Xr9/fffSkhIMLfbsmVLPffccypTpkyO6//yyy9auHCh2ay9SpUq6tq1qwICAnLdb3h4uIKCgvT777/r/PnzSktLU7ly5dSkSRO99tprqlChQo7rpqam6tdff9Xq1at1+PBhXbx4Ue7u7ipXrpyaN2+u5557TpUqVbL6Odi9e7f69euny5cvq2PHjvr666/l5OQk6cZ7KigoSBs3btTp06fN95SXl5f5nnryySet3heAO0foA2zE5s2bNWjQIPM+QBcXF6Wnp+vYsWM6duyYlixZounTp6tGjRo5bmPHjh364IMPdP36dRUvXlx2dnYKDw/XvHnztGbNGgUFBalKlSp5qiskJEQXL16UJL344ov5P8DbiIiIyNPy9vb2KlWqlK5du6arV69me++Vi4uL+ffixYs1evToTPPs7e0VGRmptWvXau3atRo4cKDeeuutfO9DuvEldeDAgbp06ZKkG/eEOTk5KTw8XOHh4fr111/18ccf65VXXsm0XlhYmF555RXFxMRIkhwcHOTq6qrY2FhFR0fryJEjunDhgtWhL+MLWmxsrCTJw8NDRYr83z8Zt96LduDAAfXu3VtxcXGSbgRse3t7nTp1SqdOndLSpUs1YcIENW/ePF/7uHz5cqYvxA4ODnJxcdHly5e1d+9e7d27VytWrFBQUJDc3NysOkZJiouLU7du3RQWFibpxpXnEiVK6NKlS4qNjdXx48d18ODBuxb6rDVhwgRNnjxZklSiRAnZ22fujDs9PV1ffPGF5s2bZ05zcXFRUlKSDh06pEOHDmnp0qWaOXNmli/6ISEhevvtt83HRYsWlaOjo2JiYhQSEqKQkBC9/PLLGjFiRK41Ll68WMOGDVN6errc3Nyy1JghJiZGzz//vM6ePStnZ2cZhqGIiAgtWrRIO3fu1M8//6yYmBj16tVL0dHRKl68uAzDUGRkpObNm6e9e/fq559/NgNHhjlz5mjMmDHm42LFipmfaeHh4dq5c6eqVaumVq1a5Xoctzp+/Lj69u1rNhe3t7eXm5ubYmJiFBUVpdDQUKWmpqpfv35Z1jUMQ++++65WrlxpnsPXrl3TwYMH9cknn+jEiRMaMmRItvtdsWKFPv74YyUnJ0uSHB0dVaRIEYWFhSksLEy//PKLxo4dqzZt2mRZNyIiQv369dOhQ4ck3TjP3dzcFB8fr5iYGB04cEBRUVEaNWqUVc/B2rVr9d577yk5OVndu3fXxx9/bL6+sbGx6tatm06fPm3uq0SJErp48aJiY2P1999/6/Dhw4Q+4G4zANyXJkyYYFgsFsNisdx22ZMnTxq1a9c2LBaL8fzzzxv79+83DMMw0tPTjd9//91o1aqVYbFYjGbNmhlxcXGZ1j18+LC5n/r16xsBAQHGkSNHDMMwjNTUVGPjxo1GkyZNDIvFYjz11FNGUlJSno5j5MiRhsViMTp06GCkpqYa8+bNM55//nmjdu3aRp06dYwuXboYEydONC5dupSn7Wbo16+fYbFYDD8/P+Pnn382UlNT87T+nDlzDIvFYjz77LO5LhccHGx8/vnnxu7du42LFy+a08+fP2988803RvXq1Q2LxWLs2rUr3/s4fvy4+Tp+8MEHxtGjR83jiYyMNMaMGWNUr17dqF69urFjx45M6w4YMMB8jXbv3m2kpaUZhmEYycnJxpkzZ4ygoCBj4sSJVj0nGS5dumSeG4cPH85xudjYWKNp06aGxWIx2rRpY2zfvt1IT083DMMw/vrrL+PZZ581LBaLUadOHSMsLCxf+7hy5Yrx+uuvG8uXLzcuXLhgPi+JiYnGypUrzXN8yJAh2a5fv359w2KxGOvXr880fcyYMYbFYjGaNm1qhISEGMnJyYZh3Dj3L1y4YCxevNgYOXLk7Z+sW/Tt29eq1/xWq1evNiwWi9GwYcMs82bOnGk+j35+fsbIkSON6OhowzD+73XOMG7cOHM7P/zwgxEbG2sYhmGkpKQYoaGhRkBAgHm+ZBxzhi1bthhDhgwxfv/9d3M9wzCMmJgYY9q0aUbNmjUNi8VirFixIsf6a9SoYdSoUcMYNGiQWVdaWlqm179OnTqGxWIx/P39jU6dOhm7d+820tPTjZSUFGPdunVGvXr1DIvFYnz88cdGhw4djK5du5rnSFJSkrFo0SKjRo0ahsViMebMmZOpjpiYGHPe8OHDjfPnz5vzrly5Yuzdu9cYOXKksXv3bqtelwzR0dFGs2bNzHMmODjYuHr1qlnT8ePHjSlTphiLFi3KtF7v3r0Ni8ViNGjQwKhTp46xcOFCc72oqChj4MCB5udYxufvzbZt22a+90ePHm2cOXPGSEtLM9LT042wsDDj/fffNywWi1G7dm3j1KlTmdZNTEw0OnfubFgsFqNu3brGjz/+aH6GpaSkGKdPnzaCgoKMqVOnZlrv888/NywWizFgwIBM0+fPn29+3n3//fdZav3vf/9r/nuzadMmIyUlxTCMG++p8+fPG4sWLTJGjRqVh2cdQEEg9AH3qbyEvnfeecf8Rza78HTy5Enzi9q4ceMyzbs59DVv3ty4fPlylvX/+usv8x/5hQsX5uk4evXqZVgsFuPVV181Xn31VXNf/v7+5pcyi8VitGrVyjhx4kSetm0YhnHw4EHz2CwWi/Gvf/3LGDBggDFt2jRj27ZtxpUrV3Jd39pAdjsZX7Jv/YKUl30EBgYaFosl15Axfvx4w2KxGN27d880PSN0bdq0KX8HkA1rA9k333xjfuG8OXhkiIuLMxo3bmxYLBbj3Xffzdc+bufkyZNG9erVjRo1ahjx8fFZ5ucU+l5++WXDYrEY8+fPz/e+s5MR+h5//HGjSZMmOf53K2tCn8ViMT799NMc933mzBnjscceM2rUqGH89ddf2S5z7do1o23btobFYjGCg4PzdGw//fSTYbFYjICAgBzrt1gsxmuvvWaG/+xkhL4GDRoYkZGRWeZPnz7d3Fbbtm3NkHSzzz77LNtaNm3aZH4mFqRhw4YZFovFqFevXpYfMHKTEfosFovxv//9L8v8lJQU8/X45ptvMs1LS0sz2rVrZ1gsFiMoKCjHfQwePNiwWCzGRx99lGn6tGnTzHMxNDTU6pqzC33ffvutua0lS5Zku96LL76Yr38rABQuBmcHHnDJycnmgNP/+c9/sr23xdfX17xfatWqVTlu69VXX822aVytWrXMZnm5rZ+dy5cvS5J27dqlHTt26LnnntOWLVu0e/du7d27V1999ZWKFy+u8PBw9e3b12y6ZK0aNWrohx9+MO+Vi4+P19q1azV27Fi99tpratiwoXr27KmtW7fmabt5ldH8LzQ0NF/rh4eHa/v27bKzs9Mbb7yR43IZr+PevXvNprySzNc9Ojo6X/u/E7/99ptZW3b3FHl6eurVV1+VJK1bty5fvbTejq+vr3x8fJSSkqL9+/dbvV7G+V5Yz1tqaqpiYmJy/C+/evfuneO8pUuXKi0tTY0bN1atWrWyXaZYsWLq2LGjJGnbtm152nfGuX7o0KFcO0fq3bu37Ozsbru9Ll26mPft3ezmpsA9evTI0hRautHbqSQdO3Ys0/SM1zUpKUlXrly5bQ3WSElJ0YoVKyRJgYGBqlixYp63Ub169WybkxYpUsRs7njrsezatUunT5+Wm5tbrkO+ZHw23Pp6Ll26VJL0zDPPqF69enmuWbrRA/PHH3+sKVOmyNnZWZMmTcqxuX7GZ1FUVFS+9gWgcHBPH/CAO378uJKSkiRJTZo0yXG5Jk2aaOHChTp79qwuXbqU7ThguY2R16hRI23evFkHDx7MU33p6enm/+vUqaOvvvrKnOfk5KTnnntOqamp+uijjxQWFqaVK1fmecD5evXqafny5frzzz+1detW/fXXXzpy5Iji4uKUlpam7du3a/v27erRo4c++eSTPG37ZhEREfrpp5+0fft2nTlzRgkJCUpLS8u0TExMjFJTUzPdm2aNm8NibsdvGIakG2EiMjJSvr6+km58ET9x4oRGjBihI0eOqE2bNqpdu7aKFy+epzry6uLFizp37pyk259/48aNU1JSko4fP67HH388z/tKTU1VcHCw1q5dq2PHjik+Pj7bHwkiIyOt3mbLli21ZcsWTZs2TefPn1fHjh1Vr169AhsnrzAGX/f29tajjz6a4/yMc2n37t1q2rRpjstl/Ghw/vz5LPPi4+O1YMECbdmyRadOnVJCQoJSU1MzLZMRaLOrxd7eXnXr1rXqeHIKpjd3sFKzZs1sl8kYw/T69etKTk427+t77LHHVKZMGUVGRiogIEBdu3ZV06ZNVaVKlRzvLbydY8eOmc9ZXu8DzFC7du0c52UE34z7eTNkvJ7Xrl3L9d7SjNcnMjJSaWlpcnBwUHx8vHm/an5rvn79ut566y2FhITIw8ND06ZNU506dXJcvmXLlvr999/1/fffKzw8XB06dFC9evVy7GwHwN1B6AMecBmdYEjKtbe4smXLZlonuy+1ua2fMS8hISHTl6vbuTl0vPbaa9ku8/zzz2vs2LGKjY3Vtm3b8hz6pBudBdSrVy/TL9mnT5/W6tWrNXPmTF25ckXz5s1TzZo19dxzz+V5+9u3b9dbb72V6epa8eLFzc4hUlNTzQ5rrl27lqfORKT/+1XcMAyrrwDdfJVlwIABOnHihDZt2qQff/xRP/74o+zt7WWxWPTkk08qICAg15798iu/519eXb58Wb169cp0Fc/JyUmenp5ycHCQdCOApqamZnqNbqdbt25mpybBwcEKDg6WJFWuXFnNmjXTSy+9lKXH1Xvtdr1NZpxL165d07Vr1267vVuXOXz4sF577TXFx8eb01xcXMwOYwzDMF/DnLbv6uqar8+Im2W8rtYuk5KSYu7T2dlZ3377rd5++22dOnXK7ISpePHiql+/vjp27KhnnnnG6holZXpfPvLII1avd7PcfoTJOJZbr4RnvJ4ZIft2DMMwO+MqiJo3b95s/j1+/PhcA58kvfLKKzp8+LB++eUXLVu2TMuWLZOdnZ35ngoICFC1atXyVQuA/CP0ATbEmqZUuS1n7fp5cXMQyKnnT3t7e/n6+io2Njbbqw75VbFiRb355ptq0qSJunXrptTUVC1ZsiTPoS8xMVHvvfeeEhMTVbduXb399tuqXbt2puZmBw8eNJs7ZVyNy4uMK6KlS5fOc3M76caX3GnTpmn//v3auHGjQkNDdeDAAR09elRHjx7V7Nmz9emnn+rll1/O87atdafnX26+/fZb7d+/X66urnrvvffUunXrLCHzmWee0d9//52n59/e3l5ffPGFevXqpTVr1ig0NFR//fWXTp48qZMnT2revHnq16+fBg4cmOeaC8vtrlRlnEt9+/bVoEGD8rRt4//3LhkfH6+qVavq3XffVYMGDTL9iBETE2NeQczpuc7v1bSCVL9+fW3YsEEbNmzQ9u3b9eeff+rkyZPasmWLtmzZounTp2vmzJn5+jGkMD4rc5LRmqB+/fr66aef8r2d/NZct25dxcbG6syZMxo+fLiCgoJy/YHHwcFBY8aMUa9evbR27Vrt3btX+/bt04kTJ3TixAnNnTtX/fv3V//+/fN7KADy4d5/KgO4Izf/6n/hwoUcl7t5SIOcxtvLbdiDjCZzefkFX1KmqyS5fenIT1CyVq1atcyx7U6dOpXn9Xfu3KnY2FgVLVpU06ZNU+PGjbPcX3Qn92dJ/9dMLSYmxhz2ID9q1aqld955Rz/++KN2796tGTNmqGbNmkpJSdHnn39udjNfUAry/MtNxn2D7733nrp165btl847eQ2qVKmit956S7Nnz9Yff/yh+fPnq1mzZjIMQ5MnT9bu3bvzve27LeNc+vvvv/O87pEjR3Ty5ElJ0uTJk9W6dessV63v9Fy/m4oVK6bOnTvriy++0OrVq7Vt2zYNHTpULi4uCgsL02effWb1tkqXLm3+HR4eXhjlZivj9fznn3/MQG+tgqjZ29tb8+bNU6VKlXTq1Cn95z//sWqInGrVqql///6aPXu2du/erXnz5qlJkyYyDEMTJ07U3r1781UPgPwh9AEPuGrVqpkDUu/YsSPH5bZv3y5J8vHxyfF+pVsHg85u3hNPPJGn+m6+p+iff/7Jdpn09HTzvpPc7lW6Exkh7dbAmnFFIrfQmRFmypcvn+Nzl/H8ZseafWQ0SzUMwww4d8rJyUktWrTQtGnTZGdnp7S0NO3cudPq9W++WpNT7R4eHuZrltv5lzGvaNGimZp2WbOPa9eumU1nc7oX8OjRo3cUlm/m4OAgf39/TZkyxRxX8ffffy+Qbd8NGefS9u3bzefNWhnnuouLS44Dded2rt/vSpcurcDAQPXt21fSjR90br0vNyd+fn7m50hISEih1XirjNfz0qVLeW4F4OHhYd73eyc1ly1bVnPnzpWvr6/CwsLUo0ePXH/kuZWDg4MaNmyoqVOnmj8iPMjnEfAgIvQBDzgnJye1a9dOkjR37lyzt8ybnTlzxuxM4umnn85xW3PnzlVCQkKW6QcPHjR7v+zUqVOe6qtevbo5IPzs2bOzXeaXX34x7xFq3bp1nra/ffv2235pO336tP78809JWUODq6urJOXaw1/Gl5Tw8PBsn5+wsDAtXrw4x/Wt2UelSpXUsGFDSTeusNzuC9XNX+bT09OzdLJxs6JFi5rh6uZ7oG7H2dnZXC+78ypDxjm1fPnybK8kXrx4UXPnzpUkdejQQY6OjnnaR8bg4NKNcHer9PR0jR071ppDyiK33mIdHR3N5ysvz9u99tJLL8nBwUHXrl3TF198keuPDampqZnO6YxzPTExMdvXMjY2Nsf38f3kdr0AZ/xQZm9vb3WzxyJFipg9ZP7www/m4OOFrUmTJuYPK19++WW2n0E3uzXov/TSS5KkX3/99Y6urpUpU0bz5s1TlSpVdObMGXXv3j3bq4fWvqfuhybAwMOEdxxgA/r37y9nZ2dFRUUpMDDQ7GHTMAzt2LFDPXv21PXr1+Xt7W12nZ+dhIQEvf766+YX6/T0dIWEhOjNN99UWlpapqEf8mLo0KGyt7fXvn379OGHH5pNRZOTk7VixQqNGjVK0o3hFzp06JCnbX/22Wdq3769xo8fr3379mX6whEXF6f58+ere/fuSk5Olp2dnQIDAzOtn3HV6fz58zkO69CoUSM5OjoqKSlJgwYNMr8Mp6amasOGDfrPf/6Ta5NXa/YhSZ9++qlcXFwUFxenl19+WStWrNDVq1fN+TExMfrtt9/Up0+fTM3SEhIS1KZNG02YMEEHDhzI9BycOHFC7777rtLS0uTo6KjGjRvnuP9bOTg4mPdhLl++PMcvc4GBgSpdurQSExP12muvaceOHWbQOHDggHr27Kno6Gi5uLhkuY/Hmn3Y29ubXfN/++232rx5sxn0T506pf79+2v79u157jxHuhFYv/zyS+3ZsydTxzjh4eH66KOPzB8jWrRokedt3yu+vr7q06ePpBvPad++fXXgwAHzNUlLS9OxY8c0bdo0dejQIVMQqFWrlnk1e/DgwTp+/LikG58FO3bsUPfu3QtlyI2C9uOPPyowMFDLli3LdJ9wSkqKNmzYoO+//17SjSEf8hI++vfvL29vbyUkJOiVV17RihUrzI6DkpOTdfz4cY0bN04LFy4ssGNxdHTU559/LgcHB/3zzz/q2rWrQkJCMr1XwsPDtWTJEnXr1k0zZ87MtH737t1lsViUmpqq119/XfPnzzd/YElNTdXp06c1bdo08znJTenSpTVv3jxVq1ZN586dU48ePbL8ONCxY0d9+eWXCg0NzfSeOnfunIYOHaqLFy/Kzs4u05AckjRixAj5+fnJz88v0+cegIJBRy6ADfD19dV3332nQYMG6dChQ3rxxRdVvHhxpaenm73reXl5acqUKfL09MxxO6NGjdIHH3yg5557Tq6urkpJSTGHg/Dy8tKkSZPMX8jzokGDBho5cqSGDx+u4OBgLV++XCVKlFBiYqL5BdJisej777/P8xWVIkWK6OTJk/r+++/1/fffy87OTm5ubkpKSjJrl27c2zNs2DD5+/tnWr9mzZqqUaOGDh06pNdff11ubm5m1+L9+vXTSy+9JG9vbw0cOFBjx47V1q1b1bZtWxUvXlzJyclKSUmRt7e3RowYkWNnH9bsI+M5mDVrlgYOHKjIyEi9//77sre3V4kSJZScnJypV8pbw3FERIQmT56syZMny8HBQW5ubkpMTDS/GDo4OGj48OEqV65cnp7frl27auTIkVq2bJl+/fVXeXl5mUFtxowZkm7co/f999+rT58+OnPmjAIDA+Xs7Cw7OzuzZhcXF40fPz7bsc2s2cf777+v0NBQxcfHq3fv3nJ0dFTRokWVkJAge3t7ffLJJ1q0aFG2VwJzEx8fr9mzZ2v27Nmyt7eXm5ubkpOTM/VKOWDAgFy72r8fDRw4UOnp6Zo2bZpCQkIUEhIiJycnubi4ZBl+4eYrXcWKFdNHH32kIUOGaP/+/ercubNcXFyUlpampKQklShRQt98802u4wTeDzJ+8Lq5WbGzs7MuX75s3hfn4+OjYcOG5Wm7Xl5emjVrlt58802Fh4fr/fffN99vN2/77bffLtDjadasmb777jsNGTJEx48f15tvvqkiRYrI1dVV165dy/RZd+tnXLFixTRjxgz17dtXhw8f1ogRIzRy5EiVKFFCV69eNc+FjM+h2/Hy8tLcuXMVGBioY8eOqUePHpo7d658fHwkZf+eSkpKyhQA33777RyH4QBQOAh9gI1o2bKlfvvtN82ePVtbt27VhQsX5ODgIIvFotatWyswMDDXwCdJjRs31qJFizRt2jT98ccfSkpK0iOPPKLWrVurX79+t+0qPjcvvfSSatWqpTlz5mjXrl2KiopS0aJF9cQTT6hjx47q2rWrihUrluftLlu2TL///rt27typQ4cO6cyZM+Y4V15eXqpcubIaNWqkF198McfAM3PmTE2cOFHbtm1TRESE2WTp5mZUvXv3lo+Pj4KCgnTkyBGlpaWpfPnyat26td54443bjg1nzT6kG/fvrF27VosXL9aMPPO+AAAgAElEQVTGjRt1/PhxXblyRU5OTqpcubJq1aqlNm3aZPqV3NXVVdOnT9euXbu0d+9eRUREKDY21uwVtWHDhuav/XnVvXt3OTk5acmSJTpx4oQiIyNlGEaWq2q1atXSqlWr9MMPPygkJETnzp1TWlqaKlWqpObNm6tXr145Pv/W7KNKlSpaunSpJk6cqO3bt+vSpUtydnZW48aNFRgYKH9/fy1atCjPxzdlyhRt375de/bsUXh4uGJjY5Wenq4KFSqobt266tq1q+rXr5/n7d5rdnZ2euedd/T0009rwYIF2rVrly5cuKCEhAS5urrK19dXjRo1Utu2bbPcp5sxWPqMGTP0119/mT9sNGvWTH369MnX+/Rue+655+Tu7q5du3bp6NGjio6O1pUrV+Tm5qYqVaqobdu26tatW7YDvt+OxWLRqlWr9PPPP2vdunU6ceKErl69ao6f2KpVKz377LMFfkzt27eXv7+/fvrpJ23ZskVhYWG6cuWKnJ2dVbFiRdWvX19t2rTJdrzVsmXLatGiRQoODtaqVat09OhRJSQkyNPTU4888oiaN2+ep16NS5YsqaCgIL322ms6fPiwunfvrrlz56pSpUqaOnWqduzYodDQ0CzvqXr16qlbt25Wj+EIoODYGYXZZR6A+96RI0fMJpu7d+9mAF0AAAAbwz19AAAAAGDDCH0AAAAAYMMIfQAAAABgwwh9AAAAAGDD6MgFAAAAAGwYV/oAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAAAMCGEfoAAAAAwIYR+gAAAADAhhH6AAAPhSFDhuiFF16412Xc1sKFC7Vhw4Z7XQYAwIYQ+gAAuI8Q+gAABY3QBwAAAAA2jNAHAHgoHTlyRK+++qpq166tBg0a6N1331VMTIw5/5VXXtGnn35qPt66dav8/Pw0evRoc9ratWv1xBNP6Nq1a+a0xYsX6+mnn9YTTzyhVq1aacaMGZn2e/z4cfXq1UsNGzZUnTp19NRTT2n+/PmSpB49eujQoUP65Zdf5OfnJz8/Py1btqywngIAwEOiyL0uAACAuy0uLk49evRQlSpVNHbsWF29elVjx45Vz549tXTpUjk5Ocnf31/r1q0z19mzZ4+KFi2qPXv2mNN2796txx9/XM7OzpKkmTNn6ttvv9Xrr7+uhg0b6tChQxo/frycnZ3VvXt3SVLfvn1VuXJlff3113JyctLJkyd19epVSdJnn32mAQMGqEKFCurXr58kycfH5249LQAAG0XoAwA8dGbPni1JmjVrllxdXSVJvr6+CggI0Lp169S5c2f5+/tr6tSpiouLU8mSJbVnzx699NJL+vnnn3X16lUVL15coaGhatSokSQpISFBkydPVt++fdW/f39JUtOmTXXt2jVNmTJF3bp106VLl3T27FlNnjxZfn5+kqTGjRubdVWtWlXOzs4qWbKk6tSpczefEgCADaN5JwDgobN//341bdrUDHySVKtWLZUvX16hoaGSpLp168rBwUGhoaFKTk7W/v37FRAQIE9PT+3bt08JCQk6duyY/P39JUl//vmnEhMT1bFjR6Wmppr/NWrUSDExMYqIiJCHh4fKlSunzz77TL/99ptiY2PvyfEDAB4uXOkDADx0oqOjVa1atSzTS5UqpUuXLkmSXF1dVb16de3Zs0eenp4qVqyY/Pz8VL9+fe3Zs0epqalKT09XvXr1JEnx8fGSpKeffjrbfV64cEHly5fXrFmz9N133+mjjz7S9evXVa9ePX3yySd6/PHHC+loAQAPO0IfAOChU7p06WyvssXExKhGjRrmY39/f+3Zs0ceHh6qV6+e7O3tVb9+fW3YsEGpqamqWrWqPD09JUnu7u6SpGnTpsnLyyvLtn19fSVJVapU0cSJE5WSkqI9e/bom2++Ue/evbVlyxbZ29MABwBQ8PjXBQDw0Kldu7a2bdumhIQEc9r+/fsVHh6u+vXrm9P8/f115MgRbd682WzG2aBBA+3fv187duzItGzdunVVrFgxRUVFqWbNmln+u7kpqSQ5OjqqcePG6tmzp6Kjo3X58mVJkpOTk5KSkgrz8AEADxmu9AEAHjo9e/bUggUL9Prrr+v1119XYmKixo4dK4vFovbt25vL+fv7Ky0tTX/++aeGDBkiSapevbqKFCmiAwcO6NVXXzWXLVGihPr3769Ro0YpPDxcDRo0UHp6usLCwrRr1y5NnjxZR48e1VdffaWnnnpKFSpU0OXLlzVjxgxVr15dHh4ekm5cEdy2bZu2bt0qDw8PPfroo+bVRAAA8oPQBwB46JQsWVJz587VmDFj9O6778rR0VFPPvmkhg4dKicnp0zLVa5cWRcuXDCbfdrb26tu3braunVrpit9kvTGG2/I29tbQUFBmjNnjooWLapKlSqpU6dOkm40K/Xy8tLUqVMVFRWlEiVK6F//+pfee+89cxv9+vXThQsXNGjQICUkJGj06NF64YUX7sKzAgCwVXaGYRj3uggAAAAAQOHgnj4AAAAAsGGEPgAAAACwYYQ+AAAAALBhhD4AAAAAsGE20XtndPSVe10CAAAAANwzpUu75TiPK30AAAAAYMMIfQAAAABgwwh9AAAAAGDDCH0AAAAAYMMIfQAAAABgwwh9AAAAAGDD7mrou3Dhgnr06KGnnnpKTz/9tIKCgiRJFy9eVM+ePdW+fXv17NlTly5dkiQZhqH//ve/ateunZ555hkdOnTobpYLAAAAAA+8uxr6HBwcNGTIEK1evVoLFy7UTz/9pH/++UfTp09X48aNtW7dOjVu3FjTp0+XJG3ZskVhYWFat26dRo4cqeHDh9/NcgEAAADggXdXQ5+3t7dq1KghSXJ1dVXlypUVGRmpjRs3qkuXLpKkLl26aMOGDZJkTrezs1OdOnV0+fJlRUVF3c2SAQAAAOCBds/u6Tt37pyOHDmi2rVrKzY2Vt7e3pJuBMO4uDhJUmRkpMqWLWuuU7ZsWUVGRt6TegEAAADgQVTkXuz06tWrGjhwoD766CO5urrmuJxhGFmm2dnZZZnm6lpURYo4FGiNAAAAAGAL7nroS0lJ0cCBA/XMM8+offv2kiQvLy9FRUXJ29tbUVFRKlmypKQbV/YiIiLMdSMiIswrgjdLSEi6O8UDAAAAwH2odGm3HOfd1eadhmHo448/VuXKldWzZ09zeuvWrRUcHCxJCg4OVps2bTJNNwxD+/btk5ubW7ahDwAAAACQPTsjuzaUhWTPnj165ZVXZLFYZG9/I28OHjxYtWrV0qBBg3ThwgWVK1dO48ePl4eHhwzD0IgRI7R161Y5Ozvriy++UM2aNbNsNzr6yt06BAAAAOC+Nv2dnrdfCPet3t/Oydd6uV3pu6uhr7AQ+gAAAIAbCH0PtsIIffes904AAAAAQOEj9AEAAACADSP0AQAAAIANuyfj9AEAACBvFo7ZdK9LQD69PKTlvS4BDzmu9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANI/QBAAAAgA0j9AEAAACADSP0AQAAAIANK3I3dzZ06FBt2rRJXl5eWrlypSRp0KBBOnXqlCTpypUrcnNz0/Lly3Xu3Dl16tRJvr6+kqTatWtrxIgRd7NcAAAAAHjg3dXQ98ILL6h79+768MMPzWnfffed+feYMWPk6upqPvbx8dHy5cvvZokAAAAAYFPuavPOBg0ayN3dPdt5hmFo9erV6ty5890sCQAAAABs2l290pebPXv2yMvLS5UqVTKnnTt3Tl26dJGrq6sGDRokf3//bNd1dS2qIkUc7lKlAAAAgPU8PFzudQl4gBTG+XLfhL6VK1dmusrn7e2tkJAQeXp66uDBg3rrrbe0atWqTM0/MyQkJN3NUgEAAACrXbyYeK9LwAMkv+dL6dJuOc67L3rvTE1N1fr169WpUydzmpOTkzw9PSVJTzzxhHx8fMwOXwAAAAAA1snTlb6zZ8/qwIEDio6OVlJSkjw8PFSpUiXVqlVLxYoVy3cR27dvV+XKlVW2bFlzWlxcnNzd3eXg4KCzZ88qLCxMFSpUyPc+AAAAAOBhdNvQFxkZqcWLFys4OFjh4eEyDENOTk5ycnLS1atXZRiGihQpoubNmysgIECtW7fOcVuDBw/WH3/8ofj4eLVo0UIDBgxQQECAfvvtNz399NOZlt29e7cmTJggBwcHOTg46PPPP5eHh8edHzEAAAAAPETsDMMwcpo5atQoLVq0SNWrV1f79u1Vt25dVa9eXS4uN24uNAxD4eHhOnDggLZt26YNGzaobNmyGjlypGrVqnXXDiI6+spd2xcAAMC9sHDMpntdAvLp5SEt7+r+pr/T867uDwWr97dz8rVebvf05Xql78qVK1q2bJmqVKmS7Xw7Ozs9+uijevTRR/XUU09p+PDhCg4O1smTJ+9q6AMAAAAAZC/X0DdmzJg8bczR0VEBAQF3VBAAAAAAoODcUe+d169f1+nTp5VLC1EAAAAAwD1kdeibPn26xo8fbz4ODQ1VixYt1LFjR7Vr105hYWGFUR8AAAAA4A5YHfqWLl2qRx55xHw8evRo+fn5adasWSpdurTGjRtXKAUCAAAAAPLP6nH6IiMjVbFiRfPvgwcPasGCBapbt66uXbumYcOGFVqRAAAAAID8sfpKX7FixZSYmChJ2rlzp9zc3FSnTh1JUvHixXX16tXCqRAAAAAAkG9WX+mrX7++Zs+eLRcXFwUFBally5ays7OTJJ0+fVplypQptCIBAAAAAPlj9ZW+IUOGKDExUYGBgUpPT9c777xjzluxYoX8/f0LpUAAAAAAQP5ZfaWvQoUKWrJkidLT02Vvnzkrjhs3Tq6urgVeHAAAAADgzlgd+jLcGvgk0bQTAAAAAO5TuYa+N998M08bmzp16h0VAwAAAAAoWLmGvtTU1EyPjx07ppiYGFWpUkUlS5ZUXFycTpw4oVKlSql69eqFWigAAAAAIO9yDX0zZ840/16xYoWmTJmioKAgVa5c2Zx+4sQJ9e/fX507dy68KgEAAAAA+WJ1752TJk3SoEGDMgU+SapSpYoGDhyoiRMnFnhxAAAAAIA7Y3Xoi4yMzHV+dHT0HRcDAAAAAChYVoe+unXraty4cTpx4kSm6f/884++/fZb1atXr8CLAwAAAADcGauHbBg5cqTeeOMNde7cWT4+PmZHLmfOnFHFihU1YsSIwqwTAAAAAJAPeRqc/bffftOaNWt04MABxcTEqE6dOqpZs6Y6duyY7fh9AAAAAIB7K0+Ds9vb26tTp07q1KlTYdUDAAAAAChAeQp9GWJjY5WUlJRl+iOPPHLHBQEAAAAACo7VoS8uLk4jR47Uhg0bsgzabhiG7OzsdOTIkQIvEAAAAACQf1aHvk8++UShoaHq16+fqlatKkdHx8KsCwAAAABQAKwOfX/88YeGDRumZ599tjDrAQAAAAAUIKu73HR3d1eJEiUKsxYAAAAAQAGzOvT169dPc+bMybYDFwAAAADA/cnq5p179uzRmTNn1Lp1a9WtW1dubm6Z5tvZ2emLL74o8AIBAAAAAPlndej7+++/VbJkSUnShQsXdOHChUzz7ezsbruNoUOHatOmTfLy8tLKlSslSRMnTtSiRYvMbQ8ePFhPPvmkJGnatGlasmSJ7O3t9cknn6h58+bWlgsAAAAAUB5C39KlS+94Zy+88IK6d++uDz/8MNP0wMBA9erVK9O0f/75R6tWrdKqVasUGRmpnj17au3atXJwcLjjOgAAAADgYWH1PX0FoUGDBnJ3d7dq2Y0bN+rpp5+Wk5OTKlSooIoVK2r//v2FXCEAAAAA2Barr/RJUnJysrZs2aJTp05l26FL//7981XE/PnzFRwcrCeeeEJDhgyRu7u7IiMjVbt2bXOZMmXKKDIyMtv1XV2LqkgRrgACAADg/uPh4XKvS8ADpDDOF6tD3/nz5/Xvf/9bMTExSktLk7OzsxITEyVJzs7OKlasWL5CX7du3dSvXz/Z2dlp/PjxGjNmjEaPHi3DMLIsm9N9gwkJ9CgKAACA+9PFi4n3ugQ8QPJ7vpQu7ZbjPKubd44ePVq+vr7auXOnDMPQvHnztHfvXn3++efy9PTUnDlz8lVcqVKl5ODgIHt7ewUEBOjAgQOSpLJlyyoiIsJcLjIyUt7e3vnaBwAAAAA8rKwOfX/++ae6d+8uF5cblxtTU1Pl4uKil19+Wd27d9fIkSPzVUBUVJT594YNG1StWjVJUuvWrbVq1SolJyfr7NmzCgsLU61atfK1DwAAAAB4WFndvDMxMVElSpSQvb293N3dFR0dbc577LHHNGHChNtuY/Dgwfrjjz8UHx+vFi1aaMCAAfrjjz909OhRSVL58uU1YsQISVK1atX01FNPqVOnTnJwcNCwYcPouRMAAAAA8sjq0FepUiWzuaWfn5+WLFmiVq1ayd7eXr/++qu8vLxuu41x48ZlmRYQEJDj8n379lXfvn2tLREAAAAAcAurm3e2b99e+/btkyQNGDBAO3fulL+/vxo0aKDg4GC9+eabhVYkAAAAACB/rL7Sd3Ooywh6ISEhSkpKUpMmTbjfDgAAAADuQ3kap+9mlSpVUs+ePQuyFgAAAABAAbO6eef+/fu1bds28/Hly5f16aefqlu3bpowYYLS09MLpUAAAAAAQP7laZy+jHv6Mh6vXbtW5cuXV1BQkCZPnlwoBQIAAAAA8s/q0Hfy5EnVrFlTkpSUlKQ1a9boo48+0jfffKP33ntPv/76a6EVCQAAAADIH6tDX1JSkpydnSVJe/fuVXJystq0aSNJqlq1qiIjIwunQgAAAABAvlkd+ipVqqSdO3dKklavXq1atWrJzc1NkhQTE2P+DQAAAAC4f1jde2fv3r31wQcfKDg4WBEREZowYYI5b/v27XrssccKpUAAAAAAQP5ZHfo6deqkKlWq6PDhw6pevXqmkFelShU988wzhVIgAAAAACD/8jROn5+fn/z8/LJMDwwMLKh6AAAAAAAFyOrQFxwcfNtlunTpckfFAAAAAAAKltWhb8iQIdlOt7OzM/8m9AEAAADA/cXq0Ld3794s0y5duqRt27Zp7ty5+uabbwq0MAAAAADAnbM69Lm4uGQ7LSAgQMnJyRo9erR++OGHgqwNAAAAAHCHrB6nLze+vr7666+/CmJTAAAAAIACdMeh7/Lly/rxxx9Vrly5gqgHAAAAAFCArG7e2bp160ydtkhSSkqK4uLiJEnfffddwVYGAAAAALhjVoe+Dh06ZJlWtGhRlS1bVq1atVKZMmUKtDAAAAAAwJ2zOvR9+OGHhVkHAAAAAKAQWB36Mly8eFEHDhzQpUuX5O7urlq1asnd3b0wagMAAAAA3CGrQ59hGPr66681b948paSkmNOdnJzUo0cPvf/++4VSIAAAAAAg/6wOfVOmTNHcuXPVu3dvderUSV5eXoqNjdVvv/2m6dOny9XVVX379i3MWgEAAAAAeWR16FuyZIn69++vN99805zm6empgQMHysnJSYsWLSL0AQAAAMB9xupx+qKjo1WzZs1s59WsWVPR0dEFVhQAAAAAoGBYHfp8fHy0bt26bOetX79ePj4+BVYUAAAAAKBgWN2884033tCQIUMUHR2tDh06qFSpUoqNjdWaNWsUEhKi0aNH33YbQ4cO1aZNm+Tl5aWVK1dKkr788kuFhITI0dFRPj4+Gj16tEqUKKFz586pU6dO8vX1lSTVrl1bI0aMyOdhAgAAAMDDyerQ16VLFxUvXlwTJkzINGZftWrVNHHiRLVt2/a223jhhRfUvXv3TOs3bdpU7777rooUKaKvv/5a06ZNM3sC9fHx0fLly/NyPAAAAACAm+RpnL527dqpXbt2un79uqKjo1W6dGkVK1bM6vUbNGigc+fOZZrWrFkz8+86depozZo1eSkJAAAAAJALq+/pu1mxYsVUoUKFPAU+ayxdulQtWrQwH587d05dunRR9+7dtWfPngLdFwAAAAA8DPJ0pe/EiRNav369IiIilJSUlGmenZ2dvvjii3wXMmXKFDk4OOjZZ5+VJHl7eyskJESenp46ePCg3nrrLa1atUqurq5Z1nV1LaoiRRzyvW8AAACgsHh4uNzrEvAAKYzzxerQt2zZMn3yySdydnZW+fLl5ejomGm+nZ1dvov45ZdftGnTJv3www/mdpycnOTk5CRJeuKJJ+Tj46NTp05lO2xEQkJSlmkAAADA/eDixcR7XQIeIPk9X0qXdstxntWhb+rUqerUqZNGjRqlokWL5quQ7GzZskUzZszQjz/+KGdnZ3N6XFyc3N3d5eDgoLNnzyosLEwVKlQosP0CAAAAwMPA6tAXHR2tF1988Y4C3+DBg/XHH38oPj5eLVq00IABAzR9+nQlJyerZ8+ekv5vaIbdu3drwoQJcnBwkIODgz7//HN5eHjke98AAAAA8DCyOvQ1btxYhw8fVuPGjfO9s3HjxmWZFhAQkO2yHTp0UIcOHfK9LwAAAADAbULf+fPnzb/79OmjIUOGyM7OTk2aNFGJEiWyLP/II48UfIUAAAAAgHzLNfS1bt06UwcthmHoq6++ytJpi2EYsrOz05EjRwqnSgAAAABAvuQa+qZOnXq36gAAAAAAFIJcQ1/Lli3vUhkAAAAAgMJgb+2C9erV08GDB7Odd+TIEdWrV6/AigIAAAAAFAyrQ19iYqLS09OznZecnKzk5OQCKwoAAAAAUDBybd4ZHR2tqKgo8/HJkyfl4OCQaZmkpCQtXbqUnjsBAAAA4D6Ua+hbuHChJk2aJDs7O9nZ2Wno0KFZljEMQ0WKFNHw4cMLq0YAAAAAQD7lGvoCAgLUvHlzGYahrl276r///a+qVauWaRlHR0f5+PjI1dW1UAsFAAAAAORdrqGvTJkyKlOmjCTp119/VcWKFeXk5HRXCgMAAAAA3LlcO3KJjo42/65WrZrVgS8mJubOqgIAAAAAFIhcQ1/79u01bNgw7d+//7Ybio+P188//6zOnTtr6dKlBVYgAAAAACD/cm3euXz5ck2aNEn//ve/VapUKdWpU0fVqlWTp6ennJycdOXKFZ07d06HDh3SwYMHVb58efXp00ddunS5W/UDAAAAAHKRa+jz8fHRV199pSFDhmjFihXauXOnfv75Z8XFxSktLU3Ozs7y9fVVvXr1NHjwYDVs2PBu1Q0AAAAAsEKuoS9DyZIlFRgYqMDAQHNaSkqKHB0dC6suAAAAAEAByPWevtwQ+AAAAADg/pfv0AcAAAAAuP8R+gAAAADAhhH6AAAAAMCGEfoAAAAAwIbdUei7fv26Tp8+LcMwCqoeAAAAAEABsjr0TZ8+XePHjzcfh4aGqkWLFurYsaPatWunsLCwwqgPAAAAAHAHrA59S5cu1SOPPGI+Hj16tPz8/DRr1iyVLl1a48aNK5QCAQAAAAD5Z9Xg7JIUGRmpihUrmn8fPHhQCxYsUN26dXXt2jUNGzas0IoEAAAAAOSP1Vf6ihUrpsTEREnSzp075ebmpjp16kiSihcvrqtXrxZOhQAAAACAfLP6Sl/9+vU1e/Zsubi4KCgoSC1btpSdnZ0k6fTp0ypTpkyhFQkAAAAAyB+rr/QNGTJEiYmJCgwMVHp6ut555x1z3ooVK+Tv718oBQIAAAAA8s/qK30VKlTQkiVLlJ6eLnv7zFlx3LhxcnV1tWo7Q4cO1aZNm+Tl5aWVK1dKki5evKh33nlH4eHhKl++vL777ju5u7vLMAyNGjVKmzdvVrFixTRmzBjVqFEjD4cHAAAAAA+3PI/Td2vgk6QyZcqoePHiVq3/wgsvaObMmZmmTZ8+XY0bN9a6devUuHFjTZ8+XZK0ZcsWhYWFad26dRo5cqSGDx+e13IBAAAA4KFm9ZU+Sdq8ebPWr1+vyMhIJSUlZZk/d+7c226jQYMGOnfuXKZpGzdu1Lx58yRJXbp0UY8ePfT+++9r48aN6tKli+zs7FSnTh1dvnxZUVFR8vb2zkvZAAAAAPDQsjr0TZo0SZMmTVKVKlXk4+MjZ2fnAisiNjbWDHLe3t6Ki4uTdGNoiLJly5rLlS1bVpGRkYQ+AAAAALCS1aFvwYIF6tevnwYOHFiY9WRiGEaWaRk9ht7M1bWoihRxuBslAQAAAHni4eFyr0vAA6QwzherQ5+dnZ3q169f4AVIkpeXl9lsMyoqSiVLlpR048peRESEuVxERES2V/kSErI2NQUAAADuBxcvJt7rEvAAye/5Urq0W47zrO7IpWvXrlq+fHm+Crid1q1bKzg4WJIUHBysNm3aZJpuGIb27dsnNzc3mnYCAAAA+H/s3XlYVHX///HXsIrixmaJdhflkmsq7ndquC8I7rklZZpbmGukpmnemlaWt1tZ7plmSpC7afT5/IYAACAASURBVKYtprl0WyqWGaakoIAKGKvz+8Nf85VYHGYGwfH5uC6uizmfc87nPcxhzrzmcxYUgNkjfaNGjdKsWbMUHBysxo0bq3Tp7EnSYDBo5MiRd1zP2LFjdejQISUmJqpFixZ68cUXNXToUL300kvauHGjHnzwQc2fP1+S1LJlS+3bt09t27aVm5ubZs2aVcCnBwAAAAD3N7ND386dO7Vu3TplZGTo/PnzcnZ2ztZubuibN29ertNXrVqVY5rBYNC0adPMLREAAAAA8A9mh745c+aoVatWmj59uumcOwAAAABA8Wb2OX2JiYnq27cvgQ8AAAAA7iFmh76AgAAdPXq0MGsBAAAAANiY2Yd3duzYUbNnz9a1a9fUpEkTlSlTJsc8DRs2tGlxAAAAAADrFOjqnZK0Zs0arVmzJke7wWDQqVOnbFcZAAAAAMBqZoe+zZs3F2YdAAAAAIBCYFboS09P13fffaemTZuqatWqhV0TAAAAAMBGzLqQi4uLi+bNm6erV68Wdj0AAAAAABsy++qdNWrU0OnTpwuzFgAAAACAjZkd+qZNm6a1a9cqPDxc8fHxhVkTAAAAAMBGzL6QS3BwsCRp8uTJec7D1TsBAAAAoHgxO/RNmTJFBoOhMGsBAAAAANiY2aFvwIABhVkHAAAAAKAQmH1OHwAAAADg3mP2SJ8kffXVV9qwYYOio6OVlpaWo33Pnj02KwwAAAAAYD2zR/p27dqlUaNGqWTJkjp79qyeeOIJ1apVS4mJiXJwcFD79u0Ls04AAAAAgAXMDn3vvfeehg4dqjlz5kiSnnvuOc2fP1979uyRu7u7KlasWGhFAgAAAAAsY3bo+/3339WoUSM5ODjIwcFBKSkpkqTy5ctr2LBhWrFiRaEVCQAAAACwjNmhr1SpUsrIyJDBYFCFChUUHR1tanNyclJCQkJh1AcAAAAAsILZF3KpWbOmzpw5oyeffFItWrTQ4sWLVbJkSTk7O+vdd99VrVq1CrNOAAAAAIAFzA59gwcPVkxMjCRp9OjRio6O1vjx4yVJjz32mF577bVCKRAAAAAAYDmzQ1+jRo1Mv3t4eGjVqlW6fv26/vrrL1WoUKFQigMAAAAAWMeim7Nfu3ZNUVFRcnV1JfABAAAAQDFWoNAXERGhNm3aqEmTJurWrZvOnDkjSRo7dqzWrVtXKAUCAAAAACxnduhbs2aNpkyZojZt2mjJkiUyGo2mtjp16ujzzz8vlAIBAAAAAJYz+5y+VatWacSIERoxYoSysrKytfn5+ens2bM2Lw4AAAAAYB2zR/piY2NVr169XNucnJyUmppqs6IAAAAAALZh9khfpUqVdOzYMTVt2jRH29GjR+Xn52dxEWfPntWYMWNMj8+fP6/Q0FAlJSVpw4YN8vDwkHTr3MGWLVta3A8AAAAA3G/MDn39+/fXm2++qZIlS6pDhw6SpBs3bmjbtm1avny5Jk2aZHERfn5+ioyMlCRlZWWpRYsWatu2rcLDwxUSEqLBgwdbvG4AAAAAuJ+ZHfoGDBighIQEvf3225ozZ44k6ZlnnpGTk5OGDBminj172qSgAwcOqHLlyvL19bXJ+gAAAADgfmZ26JOk0NBQDRgwQIcPH1ZiYqLKli0rf39/eXl52aygrVu3qkuXLqbHa9euVUREhGrVqqWwsDCVLVs2xzLu7q5ycnK0WQ0AAACArZQrV7KoS8A9pDC2F7NCX1pamsaNG6eQkBD5+/urXbt2Ni9EktLT0/Xll19q3LhxkqS+fftqxIgRMhgMmj9/vt544w3Nnj07x3LJyWmFUg8AAABgratXbxR1CbiHWLq9eHuXzrPNrKt3urq66vvvv1dGRoZFBZhr//79qlmzpmnk0MvLS46OjnJwcFCvXr30008/FWr/AAAAAGBvzL5lQ4sWLbR///7CrEVbt25V586dTY/j4uJMv+/evVtVqlQp1P4BAAAAwN6YfU5fhw4dNHPmTCUmJqpFixby8vKSwWDINk/Dhg0tLuSvv/7Sd999pxkzZpimvfnmm4qKipIk+fr6ZmsDAAAAANyZ2aEvNDRUkhQREaGIiIgc7QaDQadOnbK4EDc3Nx08eDDbtDfffNPi9QEAAAAAChD6Nm/eXJh1AAAAAAAKgdmhj/PpAAAAAODeY/aFXAAAAAAA9558R/oef/xxffLJJ6pTp46qV6+e48It/2TNOX0AAAAAANvLN/RNmTJFlSpVMv1+p9AHAAAAAChe8g19/fv3N/0+YMCAQi8GAAAAAGBb+Z7Tt2vXLl2/fv1u1QIAAAAAsLF8Q9/o0aMVHR1tenzz5k21b99eZ86cKey6AAAAAAA2kG/oMxqNOR6fO3dOaWlphVoUAAAAAMA2uGUDAAAAANgxQh8AAAAA2LF8r94pSevXr9fevXsl/d/hnuvWrZO3t3e2+QwGg0JDQwuhRAAAAACApfINfZ6entq/f3+2aV5eXvrqq69ynZ/QBwAAAADFS76h79tvv71bdQAAAAAACgHn9AEAAACAHSP0AQAAAIAdI/QBAAAAgB0j9AEAAACAHSP0AQAAAIAdI/QBAAAAgB3L95YNGzduLNDKevbsaVUxAAAAAADbyjf0TZkyxewVGQwGQh8AAAAAFDP5hr7jx4/frToAAAAAAIUg39Dn4uJyt+oAAAAAABSCfENfbhISEhQdHa20tLQcbU2bNrVJUQAAAAAA2zA79N24cUMTJkzQ3r17ZTQac53n1KlTNisMAAAAAGA9s2/Z8M477+jMmTNatmyZjEaj3n77bX3wwQcKDAyUr6+v1q5dW5h1AgAAAAAsYPZI3969ezVq1Cg1atRIklS5cmXVrl1b//73vzVz5kytXbtW9evXt6qYgIAAlSpVSg4ODnJ0dFR4eLiuXr2qMWPGKCYmRr6+vnr33XdVtmxZq/oBAAAAgPuF2SN9V65cUaVKleTo6Cg3NzddvXrV1BYQEKB9+/bZpKBVq1YpMjJS4eHhkqSlS5eqadOm2rVrl5o2baqlS5fapB8AAAAAuB+YHfoqVKhgCnoPPfSQvv76a1PbyZMn5erqavvqJO3Zs0fBwcGSpODgYO3evbtQ+gEAAAAAe2T24Z1NmzbV999/rzZt2mjgwIGaMmWKoqKi5OLiogMHDqh///42KWjw4MEyGAzq06eP+vTpo/j4ePn4+EiSfHx8lJCQkGMZd3dXOTk52qR/AAAAwJbKlStZ1CXgHlIY24vZoW/8+PFKSUmRJPXs2VMlSpTQzp07lZqaqgkTJmjAgAFWF7Nu3TpVqFBB8fHxevbZZ+Xn52fWcsnJOW8fAQAAABQHV6/eKOoScA+xdHvx9i6dZ5vZoc/d3V3u7u6mx126dFGXLl0sKigvFSpUkCR5enqqbdu2On78uDw9PRUXFycfHx/FxcXJw8PDpn0CAAAAgD0z+5y+unXr6qeffsq17eTJk6pbt65Vhdy4cUPJycmm37/99ltVqVJFAQEBioiIkCRFRESodevWVvUDAAAAAPcTs0f60tLS8rwpe3p6urKysqwqJD4+XiNHjpQkZWVlqUuXLmrRooVq166tl156SRs3btSDDz6o+fPnW9UPAAAAANxP8g19sbGxio2NNT0+c+ZMjnnS0tIUHh4uX19fqwqpXLmyPv/88xzTy5cvr1WrVlm1bgAAAAC4X+Ub+j799FMtXLhQBoNBBoNBkydPzjGP0WiUs7OzZsyYUWhFAgAAAAAsk2/o6927t1q1aiWj0ahevXpp9uzZqlKlSrZ5nJ2dValSJZUqVapQCwUAAAAAFFy+oc/Hx8d0j7xt27apUqVKcnFxuSuFAQAAAACsZ/aFXPz8/HTz5k3t3r1bR44c0bVr11S2bFn5+/vrqaeekoOD2RcCBQAAAADcJWaHvsTERA0ZMkQ///yzvLy85OXlpStXrmjFihWqXbu2PvjgA5UrV64wawUAAAAAFJDZw3Nz5szRpUuX9NFHH+mbb75RRESEvvnmG3300Ue6dOmS5syZU5h1AgAAAAAsYHbo27t3r8aPHy9/f/9s0/39/TVu3Dh9+eWXNi8OAAAAAGAds0NfWlpanodvli1bVmlpaTYrCgAAAABgG2aHvtq1a2vZsmU5wl1aWpqWL1+u2rVr27w4AAAAAIB1zL6Qy8SJEzVo0CC1atVKLVu2lKenpxISErRv3z6lpqZq9erVhVknAAAAAMACBRrp27lzp7p06aLo6Gjt2LFDv//+uwIDA7Vjxw7VqlWrMOsEAAAAAFgg35G+H374QTVq1FCpUqUkSd7e3po8efJdKQwAAAAAYL18R/qeeeYZ/fbbb3erFgAAAACAjeUb+oxG492qAwAAAABQCMw+pw8AAAAAcO+549U79+3bp7Nnz5q1suDgYKsLAgAAAADYzh1D36JFi8xakcFgIPQBAAAAQDFzx9C3evVqbscAAAAAAPeoO4a+EiVKqGTJknejFgAAAACAjXEhFwAAAACwY4Q+AAAAALBj+R7eGRUVdbfqAAAAAAAUAkb6AAAAAMCOEfoAAAAAwI4R+gAAAADAjhH6AAAAAMCO3fE+fXfDxYsXNXHiRF25ckUODg7q3bu3Bg0apAULFmjDhg3y8PCQJI0dO1YtW7Ys4moBAAAA4N5RLEKfo6OjwsLCVLNmTSUnJ6tHjx5q3ry5JCkkJESDBw8u4goBAAAA4N5ULEKfj4+PfHx8JEnu7u7y8/NTbGxsEVcFAAAAAPe+YndO34ULF3Tq1CnVrVtXkrR27VoFBgbqlVde0bVr14q4OgAAAAC4txSLkb6/paSkKDQ0VJMmTZK7u7v69u2rESNGyGAwaP78+XrjjTc0e/bsHMu5u7vKycmxCCoGAAAA8leuXMmiLgH3kMLYXopN6MvIyFBoaKgCAwPVrl07SZKXl5epvVevXho2bFiuyyYnp92VGgEAAICCunr1RlGXgHuIpduLt3fpPNuKxeGdRqNRkydPlp+fn5599lnT9Li4ONPvu3fvVpUqVYqiPAAAAAC4ZxWLkb4jR44oMjJSVatWVVBQkKRbt2fYsmWLoqKiJEm+vr6aMWNGUZYJAAAAAPecYhH6/P39dfr06RzTuScfAAAAAFinWBzeCQAAAAAoHIQ+AAAAALBjhD4AAAAAsGOEPgAAAACwY4Q+AAAAALBjhD4AAAAAsGOEPgAAAACwY4Q+AAAAALBjhD4AAAAAsGNORV0AANib4PUdiroEWCji6R13tb+TPbrc1f5gWzU2bSnqEgDALIz0AQAAAIAdI/QBAAAAgB0j9AEAAACAHSP0AQAAAIAdI/QBAAAAgB0j9AEAAACAHSP0AQAAAIAdI/QBAAAAgB0j9AEAAACAHSP0AQAAAIAdI/QBAAAAgB0j9AEAAACAHXMq6gKAe8FfS5oVdQmwgtvw74q6BAAAgCLDSB8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2LF7IvTt379f7du3V9u2bbV06dKiLgcAAAAA7hnFPvRlZWVpxowZ+vDDD7V161Zt2bJFZ86cKeqyAAAAAOCeUOxD3/Hjx/Wvf/1LlStXlouLizp37qw9e/YUdVkAAAAAcE8wGI1GY1EXkZ8dO3bo66+/1n/+8x9JUkREhI4fP66pU6cWcWUAAAAAUPwV+5G+3DKpwWAogkoAAAAA4N5T7EPfAw88oEuXLpkex8bGysfHpwgrAgAAAIB7R7EPfbVr11Z0dLTOnz+v9PR0bd26VQEBAUVdFgAAAADcE5yKuoA7cXJy0tSpU/X8888rKytLPXr0UJUqVYq6LAAAAAC4JxT7kT5JatmypXbu3Kndu3dr+PDhRV1ONtWqVdOECRNMjzMzM9WkSRO98MILBVpPbGysQkNDbV1eNgsWLNCyZcvybA8PD9eMGTPuSl/25vHHH1dQUJA6d+6srl27asWKFbp586Yk6aefftLMmTPzXPbChQvavHlznu3mbBsrV67UX3/9VaCaDx8+rM6dOysoKEipqanZ2i5fvqwxY8aoTZs26tSpk4YMGaLff/893+fQpUuXAvWfl/y2w6efftomfeCWK1euaNy4cWrdurW6d++uPn366IsvvijqsmADAwcO1Ndff51t2sqVK/Xaa69ZvL+ZPHlygW6ZdKd9ysGDBwu8r7S0L1jm731bly5dFBoaWuD9TF4OHjyoo0ePFmiZ8PBwNWnSREFBQerQoYNWrlxpalu3bp0iIiJsUhvujnr16mV7XJj/w7e/593pM5k9K/YjfcVdyZIl9euvvyo1NVUlSpTQt99+qwoVKhRoHZmZmapQoYL++9//FlKVKGwlSpRQZGSkJCk+Pl7jxo1TUlKSQkNDVbt2bdWuXTvPZWNiYrRlyxYFBgbmaDN321i9erW6du0qNzc3s2v+/PPP9dxzz6lHjx7ZphuNRo0aNUrBwcF65513JEmnTp1SfHy8HnnkEbPXXxjWr19fpP3bE6PRqJEjRyo4OFhvv/22pFvb4pdfflnElcEWOnfurG3btunJJ580Tdu2bZsmTpyY53tKZmamnJzy/ljw91W0cf+4fd82btw4rV+/Xs8++6xV68zMzNShQ4dUsmRJ1a9fv0DLdurUSVOnTlViYqI6dOig9u3b68EHH1Tfvn2tqgn27fb3vDt9JrNn98RIX3HXokULffXVV5KkrVu3qnPnzqa248eP6+mnn1ZwcLCefvppnT17VtKtbzRCQ0M1bNgwPffcc9lGSi5cuKB+/fqpW7du6tatm+nbsIMHD2rgwIEKDQ1Vhw4dNG7cONPVTRcuXKgePXqoS5cuevXVV3O96untVq9erU6dOikwMFBjxozJ0f7ll1+qV69eCg4OVkhIiK5cuSLp1gjeK6+8ooEDB6p169ZavXq1aZklS5aoffv2CgkJyTYqdKe+7I2np6def/11rV27VkajMdu32YcOHVJQUJCCgoIUHBys5ORkvf322zp8+LCCgoK0cuXKfLeNrKwszZkzR4GBgQoMDNSaNWu0evVqxcXFadCgQRo4cGCOeg4cOKDg4GAFBgbqlVdeUXp6uj799FPt2LFDixYt0rhx47LN//3338vJySnbTvTxxx+Xv7+/jEaj5syZoy5duigwMFDbtm3L0d8/v6174YUXdPDgQUm3vtl788031b17d4WEhOj48eOmben2+29evHhRgwcPVvv27bVw4ULT9L+/GUxJSdGgQYPUrVs3BQYGavfu3QV+ne5333//vZydnbO9zr6+vho4cGC+r+G0adPUvXt3de7cOVtwCAgI0Lx589SnTx91795dJ06c0ODBg9WmTRutW7fu7j0xSJLat2+vvXv3Kj09XdKt/UpcXJwaNGiQ7T3ln+83N2/e1GuvvabOnTvrhRde0JAhQ7Rjxw5Jt0YPf/rpJ0m3/hffeecdde3aVb179zbtI/Kyfft2denSRV27dlX//v1ztOe3rxw1apQGDx6sdu3aae7cuaZlNm3apPbt22vAgAHZRo3u1Bcs4+/vr3PnzuU4smPZsmVasGCBJOmPP/7Q4MGD1b17d/Xr10+//fabJCksLEyzZ8/WwIEDNWbMGK1fv14rV65UUFCQDh8+rJiYGA0aNEiBgYEaNGiQ/vzzz3xrKV++vP71r3/p8uXLkrIfXXTu3DmFhISoa9eu6tatm/744w+z9l0oPvL6DBoYGKjr16/LaDSqcePGptHdCRMm6Lvvvsvz8/Pt26wtjzC41zDSZwOdOnXS4sWL9dRTT+n06dPq0aOHjhw5Ikny8/PTRx99JCcnJ3333Xd65513TG+OP/74oz7//HOVK1dOFy5cMK3P09NTK1askKurq6KjozV27FiFh4dLkk6ePKmtW7fKx8dHffv21ZEjR+Tv768BAwZo1KhRkm5t/Hv37s33gjdLly7Vl19+KRcXF12/fj1He4MGDbRhwwYZDAZ9+umn+vDDDxUWFiZJ+v3337V69WolJyerY8eO6tu3r06fPq1t27YpIiJCWVlZ6tatm2rWrGlWX/aocuXKunnzpuLj47NNX758uaZOnaoGDRooJSVFrq6uGjdunJYvX673339f0q0POXltG5988okuXLigzz77TE5OTrp69arKlSunlStXatWqVfLw8MjWX1pamsLCwrRy5Uo98sgjmjhxoj7++GOFhIToyJEjatWqlTp06JBtmV9//dX02v3Trl27FBUVpcjISCUmJqpnz57y9/c3++9y48YNNWrUSBMmTNDIkSP17rvvavny5frtt9/08ssvq3Xr1pJuHX6xefNmubm5qWfPnmrZsmW2b+ZcXV21aNEiubu7KyEhQX369FHr1q25nUsB/Prrr6pRo0aBlxszZozKlSunrKwshYSEKCoqStWrV5d062rLn3zyiWbNmqWwsDCtW7dO6enp6ty5M9/E32Xly5dXnTp1tH//frVp00bbtm1Tx44dc/0fuf39ZseOHYqJidHmzZsVHx+vTp065TgaQLr1v1y3bl2NGTNGc+fO1YYNGzRixIg861m8eLGWLVumChUq5LofyG9feerUKUVERMjFxUUdOnTQwIED5ejoqAULFig8PFzu7u565plnTNvznfpCwWVmZmr//v3ZRo5z8+qrr2r69Ol6+OGH9b///U/Tp083fTkcHR2tlStXml67kiVLavDgwZKkYcOGKTg4WN26ddPGjRs1c+ZMLV68OM9+/vzzT6WlpalatWo52saPH6+hQ4eqbdu2SktL082bN/Pcd3E1+KKTmpqqoKAg0+Nr166ZPrfm9Rm0Xr16Onr0qCpWrKhKlSrp8OHDCg4O1v/+9z+99tprcnBwyPPzMwh9NlG9enVduHBBW7ZsUcuWLbO1JSUl6eWXX9a5c+dkMBiUkZFhamvevLnKlSuXY32ZmZmaMWOGoqKi5ODgoOjoaFNbnTp19MADD5j6jYmJkb+/vw4ePKgPP/xQqampunr1qqpUqZJv6KtWrZrGjx+v1q1bq02bNjnaL126pDFjxujy5ctKT09XpUqVTG0tW7aUi4uLPDw85OHhofj4eB0+fFht2rQxHV54e9936ste5TbaWr9+fb3xxhsKDAxUu3btVKpUqVyXzWvbOHDggJ5++mnTIVi5zXO733//XZUqVTIdltmtWzetXbtWISEhBXw2txw5ckSdO3eWo6OjvLy81LBhQ/3000+57nhz4+zsrBYtWkiSqlatKhcXFzk7O6tq1aqKiYkxzdesWTOVL19ektS2bVsdOXIkW+gzGo2aN2+efvjhBzk4OCg2NlZXrlyRt7e3Rc8L0vTp03XkyBE5OzvnOzqyfft2bdiwQZmZmbp8+bJ+++03U+j7O7RXrVpVN27ckLu7u6RbIf369esqU6ZM4T8RmPx9iGebNm20detWzZo1K9f5bn+/OXLkiDp06CAHBwd5e3urcePGuS7j7Oysp556SpJUq1Ytffvtt/nWUq9ePYWFhaljx45q27Ztjvb89pVNmzZV6dKlJUmPPvqoYmJidPXqVTVq1Mj0RVenTp1M+8o79QXz3f7B3N/fXz179lRcXFyu86akpOjYsWMaPXq0adrfI82S1KFDBzk6Oua67LFjx0whPygoSG+++Wau823btk0HDx7U77//rtdff12urq7Z2pOTkxUbG2t63f9uz2vf9fd7Fu6+2w8dlm594f3zzz9LyvszqL+/v3744QdVrFhRffv21YYNGxQbG6uyZcuqVKlSSkpKyvPzMzi802YCAgI0d+7cbId2StL8+fPVuHFjbdmyRUuWLMn2BpjX+VcrV66Ul5eXIiMjtWnTpmw7PxcXF9Pvjo6OysrKUlpamqZPn67//ve/2rx5s3r37q20tLR86126dKn69eunEydOqHv37srMzMzWPnPmTPXv31+bN2/WjBkzstX9zxr+XjavUZY79WWPzp8/L0dHR3l6emabPnToUM2cOVOpqanq3bu36dCXf8pr2zAajQUazbrTYb65qVKlik6cOGHx+hwdHU0XsZGUbVt0dnY21e/g4GDalhwcHJSVlWWa75/P8Z+PN2/erISEBIWHhysyMlJeXl533OaRXZUqVXTy5EnT42nTpmnlypVKTEzM8zU8f/68li9frpUrV2rz5s1q1apVjtdXyv7a/v34fvi/L27atGmjAwcO6MSJE0pNTc1zBP/29xtz3zP++b98+/9vbmbMmKGXXnpJFy9eVHBwsBITE7O157evzG2/J+W9z7lTXzDf3x/MIyMj9eqrr8rFxUVOTk65vj8YjUaVKVPGNH9kZKS2b99umq8g55zn9dp26tRJW7du1dq1azVnzhzT4Z13Ysm+EEUnr8+gDRs21JEjR3TkyBE1btxYHh4e2rFjh+mIo/w+P4PQZzM9e/bUiBEjcox4JCUlmS7s8tlnn5m1rqSkJHl7e8vBwUGRkZF33Jn+/YZbvnx5paSkaOfOnfnOf/PmTV28eFFNmjTRhAkTlJSUpBs3buRZtzlXxGrYsKG++OILpaamKjk5WXv37jW7L3uTkJCgadOmqX///jl2XH/88YeqVaumoUOHqlatWvr9999VqlQppaSkmLXu5s2ba/369aYP0FevXpWkPNfh5+enmJgYnTt3TpIUGRmphg0b5ttHkyZNlJ6erg0bNpimHT9+XIcOHVLDhg21fft2ZWVlKSEhQYcPH1adOnWyLe/r66uoqCjTa3/8+HGzntvtvv32W129elWpqanavXt3jpP9k5KS5OnpKWdnZ33//ffZRglhniZNmigtLU0ff/yxadrfV3HN6zVMSUmRm5ubSpcurStXrmj//v1FUjvMU6pUKTVq1EiTJk0y++q6DRo00K5du3Tz5k1duXJFhw4dskktf/zxh+rWravRo0erfPnyunTpUrb2gu4r69Spo0OHDikxMVEZGRmm8w7N6QvW8fT0VHx8vBITE5Wenm66poG7u7sqVapkCnpGo1FRUVG5ruOf+6x69epp69atkm59qdegQYN86z78fwAAIABJREFUa6hXr566du2a7boCf9fwwAMPmM7zTk9P119//WXWvgvFR16fQR988EElJiYqOjpalStXVv369bV8+XLT9lLQz8/3Gw7vtJEHHnhAgwYNyjH9+eefV1hYmFasWKEmTZqYta5+/frpxRdf1I4dO9S4cWOVLFky3/nLlCmjXr16KTAwUL6+vne8KlFWVpYmTJig5ORkGY1GhYSE5DjsatSoURo9erQqVKigunXrZjuvLDc1a9ZUp06dFBQUJF9fX9M/oDl92YO/D4HJzMyUo6OjgoKCcr3C2apVq3Tw4EE5ODjoscceU4sWLWQwGOTo6KiuXbuqe/fu+f59evXqpejoaHXt2lVOTk7q3bu3BgwYoN69e2vIkCHy9vbWmjVrTPO7urpq9uzZGj16tLKyslSrVq07nltlMBi0cOFCzZo1S0uXLpWrq6t8fX01adIkNWzYUMeOHVNQUJAMBoMmTJggb2/vbNtHgwYN5Ovrq8DAQFWpUiXP0YX8NGjQQBMnTtS5c+cUGBiYY5sODAzU8OHD1b17dz3++OPy8/MrcB/3O4PBoEWLFmn27Nn68MMP5eHhITc3N40fPz7P17B69eqqUaOGOnfubNrhonjr0qWLRo0apXnz5pk1f/v27XXgwAF16dJFDz/8sOrUqWM6tNIac+fO1blz52Q0GtWkSRNVr149W6As6L7Sx8dHo0aN0tNPPy1vb2/VqFHDNPqUW1+wHWdnZ40cOVK9e/dWpUqVsr3/vvnmm3rttde0ZMkSZWZmqlOnTrn+/Z966imFhoZqz549evXVVzVlyhRNmjRJy5Ytk4eHh2bPnn3HOoYMGaLu3bvnuCjH3LlzNXXqVM2fP1/Ozs6aP3++2rZtm+u+C8VTfp9B69SpY/pf9/f317x580yfOQv6+fl+YzAy5g0AAP6/lJQUlSpVSomJierVq5fWrVvHB2QAuMcx0gcAAEyGDRum69evKyMjQyNGjCDwAYAdYKQPAAAAAOwYF3IBAAAAADtG6AMAAAAAO0boAwAAAAA7RugDANxXwsPD1b17d9WrV08NGzZUcHBwtkvEx8fHa8GCBXe8VU1u0tPTtWDBAp06dcqWJQMAYBVCHwDgvvH+++9rypQp+ve//62FCxdqzpw5at26tb788kvTPPHx8Vq4cKFiYmIKvP6MjAwtXLiQ0AcAKFa4ZQMA4L7x0UcfqU+fPho7dqxpWkBAgEaNGlWEVQEAULi4ZQMA4L7xxBNPaMiQIRo5cmSu7RcuXFDr1q1zTD99+rRu3Liht956S99++60uXbokT09PtWzZUuPGjZO7u7skqVq1ajmW3bNnjypVqqS0tDTNnz9fW7duVXx8vPz8/DRu3Di1bNky27yLFi3S2bNn5ezsrIcfflgTJkxQo0aNbPQXAADcjxjpAwDcN2rUqKGPPvpIFStWVKtWrVS+fPls7T4+Pnrrrbc0fvx4TZ06VTVr1jS1paamKisrS2PGjJGHh4cuXryo9957T6NHj9ayZcskSatWrdKgQYM0fPhwtWrVyrROSQoNDdXx48f14osv6qGHHtL27ds1fPhwbdq0SY8//rj++OMPjR49WgMHDtSECROUnp6un3/+WdeuXbs7fxwAgN1ipA8AcN+IiorSyJEjdeHCBRkMBj366KNq166dBg8ebBqt++WXXxQYGKjVq1ercePGea4rMzNT//vf/9SvXz/t3btXFStWVEpKiurXr6/Zs2ere/fupnkPHDigkJAQrVmzJtuoXf/+/eXp6an//ve/2rFjh6ZNm6aDBw8W3h8AAHBf4kIuAID7RvXq1bV9+3YtWbJE/fr1k9Fo1OLFi9WjRw+lpKTccfmIiAgFBwerXr16qlmzpvr16ydJio6Ozne57777Tt7e3qpfv74yMzNNP02bNtXPP/8sSapataqSkpL08ssv65tvvtGNGzesfr4AAEgc3gkAuM+4uLgoICBAAQEBkqRPP/1UU6ZM0caNGzVo0KA8l/viiy/08ssvq2/fvhozZozKlSuny5cva+TIkUpLS8u3z8TERF2+fDnb4aJ/c3R0lCT5+flp8eLFWrp0qYYOHSonJye1bdtWkydPloeHhxXPGABwvyP0AQDua7169dJbb72ls2fP5jvfjh07VLduXb322mumaYcOHTKrj7Jly6pChQpatGhRvvO1atVKrVq1UlJSkr766ivNmjVLr7/+ut555x2z+gEAIDeEPgDAfSM+Pl6enp7ZpiUkJCgpKUleXl6SJGdnZ0nKMXqXmpoqFxeXbNM2b96c7XFeyzZt2lQrVqxQyZIl9eijj96xztKlSyswMFA//PCDjh07ZsYzAwAgb4Q+AMB9IzAwUK1bt1bz5s3l6empmJgYLV++XCVKlFBwcLAkqWLFiipRooQiIiJUunRpOTk5qXbt2mrWrJlmzJihJUuWqG7dutq3b58OHDiQbf0uLi6qVKmStm/fripVqsjV1VXVqlVT8+bN9e9//1vPPfechgwZoscee0zJycmKiopSWlqaxo0bp/Xr1+vHH3/Uk08+KR8fH0VHR2vHjh0KCgoqij8VAMCOcPVOAMB9Y+3atdqzZ49++eUXXbt2Td7e3qpXr55GjBiRbQTu888/16JFixQTE6OMjAydPn1aWVlZeuuttxQREaG0tDQ1b95czz//vHr37q333ntPTz31lCTpm2++0Zw5cxQdHa309HTTffrS09P13nvvafPmzbp48aLKli2r6tWra+DAgWrVqpWOHTumJUuW6OTJk6baOnbsqNGjR+cYYQQAoCAIfQAAAABgx7hlAwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAMAuLViwQI0bN861LSwsTN27d5ckhYeHq1q1akpJSbHZ+m3tk08+0e7du+9KXwAA+0PoAwDc11q1aqVPPvlEbm5uRV1Kngh9AABrOBV1AQAAFCUPDw95eHgUdRkAABQaRvoAAPe13A7v/PPPP/X888+rTp06CggIUHh4uEJDQzVw4MAcy588eVK9e/dW3bp1FRwcrMOHD+eY59NPP1Xnzp1Vq1YtPfXUU/rggw+ytf/6668aPHiwGjVqpCeeeEIdO3bU2rVrJUkDBw7UiRMn9Nlnn6latWqqVq2awsPDbfxXAADYM0b6AAB2LTMzM8c0o9GY5/xGo1HDhw9XUlKSZs2aJVdXVy1evFgJCQl66KGHss2bmpqql19+WSEhIfLy8tKiRYs0cuRIffXVV6bDRT/88EO98847ev7559WoUSOdOHFC8+fPl5ubmwYMGCBJGj58uPz8/PTmm2/KxcVFZ8+eNYXQadOm6cUXX1TlypU1YsQIScpRBwAA+SH0AQDs1tWrV1WzZs1c2/Kavm/fPkVFRenTTz9VnTp1JMk04pdb6Js0aZKaNm0qSfLx8VFwcLB++OEHtWjRQsnJyVq0aJGGDx+uUaNGSZKaN2+uv/76S0uWLFHfvn117do1nT9/XosWLVK1atUkybQ+SXrsscfk5uYmDw8PPfHEE9b9QQAA9yVCHwDAbpUuXVorVqzIMX3RokWKi4vLdZmffvpJ3t7epsAnSRUqVMg1JDo7O2e7guejjz4qSYqNjZUkHTt2TDdu3FCHDh2yjTg2adJEixcv1qVLl/Tggw/qwQcf1LRp0/TMM8+ocePG8vT0tOwJAwCQC0IfAMBuOTo6qnbt2jmmlytXLs/Qd/nyZZUvXz7HdA8Pjxy3dShVqpQcHP7v9HgXFxdJUlpamiQpMTFRktS5c+dc+7p48aJ8fX21bNkyvfvuu5o0aZJSU1NVv359TZkyRTVq1DDjWQIAkD9CHwAAt/H29jaFtdslJCTI1dW1QOsqW7asJOn999/PdfTukUcekXRrhHDBggXKyMjQ4cOH9dZbb2no0KHav39/tlAJAIAl2JMAAHCb2rVr6/Llyzp+/LhpWmxsrE6cOFHgddWrV08lSpRQXFycateunePH3d092/zOzs5q2rSpnn32WV2+fFnXr1+XdGsE8e/RQwAACoqRPgAAbtOyZUtVr15dL730ksaOHasSJUpo4cKF8vT0lMFgKNC6ypQpo1GjRuk///mPYmJi1LBhQ928eVPR0dE6ePCgFi1apKioKM2dO1cdO3ZU5cqVdf36dX3wwQeqXr26ypUrJ+nWiOA333yjr7/+WuXKlVOlSpVyPQQVAIDcEPoAALiNwWDQ4sWLNXXqVL3yyivy8vLSsGHDtHPnTpUoUaLA6xsyZIh8fHy0atUqrVixQq6urnr44YfVqVMnSbcOJ/X09NR7772nuLg4lSlTRo0bN9b48eNN6xgxYoQuXryol156ScnJyZo9e7a6d+9us+cMALBvBmN+NysCAABKSkpSmzZt1L9/f4WGhhZ1OQAAFAgjfQAA/MO6devk4OCgf/3rX0pISNDKlSuVnp6uHj16FHVpAAAUGKEPAIB/cHFx0QcffKCLFy/KYDCodu3aWrFihXx9fYu6NAAACozDOwEAAADAjnHLBgAAAACwY4Q+AAAAALBjdnFO3+XLSUVdAgAAAAAUGW/v0nm2MdIHAAAAAHaM0AcAAAAAdozQBwAAAAB2jNAHAAAAAHaM0AcAAAAAdozQBwAAAAB2jNAHAAAAAHas2NynLy0tTf3791d6erqysrLUvn17hYaG6vz58xo7dqyuXbumGjVqaO7cuXJxcSnqcgEAAADgnlBsRvpcXFy0atUqff7554qIiNDXX3+tH3/8UW+99ZZCQkK0a9culSlTRhs3bizqUgEAAADgnlFsQp/BYFCpUqUkSZmZmcrMzJTBYND333+v9u3bS5K6deumPXv2FGWZAAAAAHBPKTahT5KysrIUFBSkZs2aqVmzZqpcubLKlCkjJ6dbR6E+8MADio2NLeIqAQAAAODeUWzO6ZMkR0dHRUZG6vr16xo5cqTOnj2bYx6DwZBjmru7q5ycHO9GiQAAAABwTylWoe9vZcqUUePGjfXjjz/q+vXryszMlJOTky5duiQfH58c8ycnpxVBlQAAAABQPHh7l86zrdiEvoSEBDk5OalMmTJKTU3Vd999pyFDhqhx48bauXOnOnfurM8++0wBAQFFXSoAAABQbC0d82xRlwArDH1nhc3XWWxCX1xcnMLCwpSVlSWj0agOHTroqaee0mOPPaYxY8bo3Xff1eOPP65evXoVdakAAAAAcM8oNqGvevXqioiIyDG9cuXK3KYBAAAAACxUrK7eCQAAAACwLUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DFCHwAAAADYMUIfAAAAANgxQh8AAAAA2DGnoi5Aki5evKiJEyfqypUrcnBwUO/evTVo0CAtWLBAGzZskIeHhyRp7NixatmyZRFXCwAAAAD3jmIR+hwdHRUWFqaaNWsqOTlZPXr0UPPmzSVJISEhGjx4cBFXCAAAAAD3pmIR+nx8fOTj4yNJcnd3l5+fn2JjY4u4KgAAAAC499kk9KWnp+vUqVO6fPmy0tPTVbZsWT388MPy9fUt8LouXLigU6dOqW7dujp69KjWrl2riIgI1apVS2FhYSpbtmyOZdzdXeXk5GiLpwIAAAAARaZcuZI2X6fBaDQaLVkwLS1N27ZtU0REhI4ePaqMjIzsKzYY5O3trXbt2qlnz56qXr36HdeZkpKigQMHatiwYWrXrp2uXLmi8uXLy2AwaP78+YqLi9Ps2bNzLHf5cpIlTwEAAACwO0vHPFvUJcAKQ99ZYdFy3t6l82yzaKRv1apVWrp0qRwcHNS2bVtTqCtfvrxcXFx0/fp1xcTE6Oeff9bXX3+tHj166Mknn9TLL7+sRx55JNd1ZmRkKDQ0VIGBgWrXrp0kycvLy9Teq1cvDRs2zJJyAQAAAOC+ZVHo2717t2bPnq0nn3xSBoMhR3uZMmVUqVIlNW7cWIMHD9aVK1f00Ucf6Ztvvsk19BmNRk2ePFl+fn569tn/+2YiLi7OdK7f7t27VaVKFUvKBQAAAID7lsWHd9rS4cOH1b9/f1WtWlUODrduHTh27Fht2bJFUVFRkiRfX1/NmDHDFAJvx+GdAAAAwC0c3nlvKzaHd5ojPT1dLi4uZs3r7++v06dP55jOPfkAAAAAwDoO1q5g06ZNWrVqlenxmTNn1L59ez3xxBMaMGCArly5Ym0XAAAAAAALWR36VqxYISen/xswfP3111WiRAlNnz5diYmJevvtt63tAgAAAABgIasP74yJiZGfn58kKTExUT/88IM+/PBDNWvWTO7u7rneYgEAAAAAcHdYPdLn7OysrKwsSdKhQ4fk6uqqhg0bSpI8PT117do1a7sAAAAAAFjI6tBXs2ZNffrpp4qJidHHH3+sZs2aydnZWdKtUcDb77UHAAAAALi7rA59EydO1NGjR9W6dWv99ttvGj16tKltx44deuKJJ6ztAgAAAABgIavP6Xv88ce1b98+Xbx4Ud7e3tlu0zB8+HA98MAD1nYBAAAAALCQTe7T5+DgIF9f3xzTGeUDAAAAgKJlk9D322+/6YsvvtClS5eUlpaWrc1gMGjWrFm26AYAAAAAUEBWh77w8HBNmTJFbm5u8vX1NV3E5W8Gg8HaLgAAAAAAFrI69L333nvq1KmT/vOf/8jV1dUWNQEAAAAAbMTqq3devnxZPXr0IPABAAAAQDFkdehr2rSpTp48aYtaAAAAAAA2ZvXhnS+88ILCwsJkMBjUrFkzlSlTJsc8FStWtLYbAAAAAIAFrA59ffr0kSTNnTs3x0VbjEajDAaDTp06ZW03AAAAAAAL2ORCLgAAAACA4snq0NeqVSsblAEAAAAAKAw2uTm7JP3yyy86evSorl27prJly6pBgwaqUqWKrVYPAAAAALCA1aEvPT1dEyZM0K5du2Q0GuXm5qa//vpLBoNB7du319y5c+Xi4mKLWgEAAAAABWT1LRveeust7d+/XzNnztSxY8dMPzNnztT+/fs1b948W9QJAAAAALCA1aFv27ZtGj9+vHr06CE3NzdJkpubm3r06KFx48Zpy5YtVhcJAAAAALCM1aHv+vXreuihh3Jtq1y5sq5fv25tFwAAAAAAC1kd+h577DFt3Lgx17aNGzdyMRcAAAAAKEJWX8hl9OjRGjFihIKDg9WuXTt5eXkpPj5eu3bt0q+//qrFixfbok4AAAAAgAWsDn0tW7bU+vXrtXDhQn300UdKSEiQh4eH6tSpo9dff121atWyRZ0AAAAAAAvY5D59tWvX1vvvvy9JysrKkqOjoy1WCwAAAACwktXn9P0TgQ8AAAAAig+LRvpeeeUVjRgxQpUrV9Yrr7yS77wGg0GzZs2yqDgAAAAAgHUsCn2//PKL0tLSTL/nx2AwWNIFAAAAAMAGLAp9mzZtyvV3AAAAAEDxYvNz+gAAAAAAxYdFI30REREFmj84ONiSbgAAAAAAVrIo9IWFhZk9r8FgIPQBAAAAQBGxKPQdPXrU1nUAAAAAAAqBRaGvZMmStq4DAAAAAFAILAp9f/75Z4Hmr1ixoiXdAAAAAACsZFHoCwgIMOv+e0ajUQaDQadOnbKkGwAAAACAlSwKfe+9956t6wAAAAAAFAKLQl+rVq1sWsTFixc1ceJEXblyRQ4ODurdu7cGDRqkq1evasyYMYqJiZGvr6/effddlS1b1qZ9AwAAAIA9KxY3Z3d0dFRYWJi2b9+uTz75RB9//LHOnDmjpUuXqmnTptq1a5eaNm2qpUuXFnWpAAAAAHBPsWikr3Xr1lq0aJGqV69u1vl9e/bsybfdx8dHPj4+kiR3d3f5+fkpNjZWe/bs0Zo1ayTdusH7wIEDNWHCBEtKBgAAAID7kkWhr127dipXrpwkqX379jYt6MKFCzp16pTq1q2r+Ph4Uxj08fFRQkKCTfsCAAAAAHtnUeh7+eWXc/3dWikpKQoNDdWkSZPk7u5u9nLu7q5ycnK0WR0AAAAAUBTKlbP9PdEtCn2FISMjQ6GhoQoMDFS7du0kSZ6enoqLi5OPj4/i4uLk4eGR67LJyWl3s1QAAAAAKBRXr96waDlv79J5ttkk9O3bt09ffPGFYmNjlZaWM4CtXr063+WNRqMmT54sPz8/Pfvss6bpAQEBioiI0NChQxUREaHWrVvbolwAAAAAuG9YHfoWLlyohQsX6tFHH9VDDz0kNze3Aq/jyJEjioyMVNWqVRUUFCRJGjt2rIYOHaqXXnpJGzdu1IMPPqj58+dbWy4AAAAA3FesDn3r1q3TiBEjFBoaavE6/P39dfr06VzbVq1aZfF6AQAAAOB+Z3XoMxgMatCggS1qAQAAQB4+eeOroi4BFuoT1qqoS8B9zuqbsz/99NOKjIy0RS0AAAAAABuzeqRv1KhRmjVrloKDg9W4cWOVLp39qjEGg0EjR460thsAAAAAgAWsDn07d+7UunXrlJGRofPnz8vZ2TlbO6EPAAAAAIqO1aFvzpw5atWqlaZPn57nffQAAEBOJ3t0KeoSYIUam7YUdQkAYBarz+lLTExU3759CXwAAAAAUAxZHfoCAgJ09OhRW9QCAAAAALAxqw/v7Nixo2bPnq1r166pSZMmKlOmTI55GjZsaG03AAAAAAAL2OTqnZK0Zs0arVmzJke7wWDQqVOnrO0GAAAAAGABq0Pf5s2bbVEHAAAAAKAQWB36qlSpYos6AAAAAACFwKILuVy+fNmizq5cuWLRcgAAAAAAy1gU+tq1a6epU6fq+PHjd5w3MTFR69evV5cuXbRp0yZLugMAAAAAWMiiwzsjIyO1cOFC9evXT15eXnriiSdUpUoVlS9fXi4uLkpKStKFCxd04sQJ/fzzz/L19dULL7yg4OBgW9cPAAAAAMiHRaHvoYce0ty5cxUWFqbPP/9c33//vdavX6+EhARlZWXJzc1NjzzyiOrXr6+xY8eqUaNGtq4bAAAAAGAGqy7k4uHhoZCQEIWEhJimZWRkyNnZ2dq6AAAAAAA2YNE5ffkh8AEAAABA8WHz0AcAAAAAKD4IfQAAAABgxwh9AAAAAGDHCH0AAAAAYMcKJfSlpqbq3LlzMhqNhbF6AAAAAICZrA59S5cu1fz5802Pjxw5ohYtWqhDhw5q27atoqOjre0CAAAAAGAhq0Pfpk2bVLFiRdPj2bNnq1q1alq2bJm8vb01b948a7sAAAAAAFjIqpuzS1JsbOz/a+/O46Ks9/6PvwcBEfGgAnput9OhzH0NLI4LhGaajKKiueZ27CS4o2m3RGam0THNIyZ5SnPpuORNqKmomWLllua51ZOYG6aWG6CCKMjy+8Ofc0toyVyjA+Pr+Xj4eDDX9v0MfJ2Z93yv63vpT3/6k+XnQ4cOadmyZWrWrJmuX7+u6Ohow0UCAAAAAKxjeKTPzc1NWVlZkqRdu3apQoUKatq0qSSpfPnyunbtmtEmAAAAAABWMjzS99RTT2nBggVyd3fXokWLFBQUJJPJJEk6deqUqlatarhIAAAAAIB1DI/0TZw4UVlZWRo4cKDy8/M1ZswYy7o1a9bIz8/PaBMAAAAAACsZHumrWbOmVq1apfz8fDk5Fc6QM2fOlIeHh9EmAAAAAABWMhz6bvt14JPEqZ0AAAAAYGc2CX1JSUnavHmzzp8/r+zs7CLrFy9ebItmAAAAAADFZDj0xcbGKjY2Vo8//rhq1aqlcuXK2aIuAAAAAIANGA59y5YtU3h4uEaOHGmLegAAAAAANmR49k6TyaSnnnrKFrUAAAAAAGzMcOjr1auXVq9ebYtaAAAAAAA2Zvj0zuHDh2vatGkKDQ3V008/rQoVKhRabzKZFBERYbQZAAAAAIAVDIe+jRs3atmyZbp586ZOnz4tFxeXQusJfQAAAABgP4ZDX0xMjIKCgvTmm2+qcuXKtqgJAAAAAGAjhkNfenq6evfuTeADgP8vdHkHe5cAKyX0SrR3CQAA2JzhiVyCg4P1/fffGy7ktddeU0BAgEJCQizL5syZo9atW6tLly7q0qWLkpKSDLcDAAAAAI8SwyN9HTt21PTp03XlyhU988wz+sMf/lBkG39//989Trdu3dSvXz9NmDCh0PKBAwdqyJAhRssEAAAAgEeSTWbvlKQlS5ZoyZIlRdabTCYdPnz4d4/j7++vM2fOGC0HAAAAAHAHw6Fv7dq1tqjjnj799FMlJCSoYcOGmjhxojw9PYts4+FRVs7OZR5oHQAAx1exoru9S0ApQn/B/aKvoDgeRH8xFPpycnK0Y8cOBQQE6Mknn7RVTRa9e/dWeHi4TCaTZs+erXfeeUfTp08vsl1mZrbN2wYAPHouX86ydwkoRegvuF/0FRSHtf3Fx6fCPdcZmsjF1dVVM2fO1OXLl40c5p68vb1VpkwZOTk5qUePHjp48OADaQcAAAAAHJXh2Tvr16+vI0eO2KKWIi5cuGD5+csvv1Tt2rUfSDsAAAAA4KgMX9P3xhtvaPTo0SpfvrwCAwPl5eVl1XHGjh2rPXv2KD09XW3atNGIESO0Z88eJScnS5KqV6+uKVOmGC0XAAAAAB4phkNfaGioJGnSpEn33OZ+Zu+cOXNmkWU9evSwvjAa9rqVAAAgAElEQVQAAAAAgPHQFxUVJZPJZItaAAAAAAA2Zjj09evXzxZ1AAAAAAAeAMMTuQAAAAAASi7DI32StG3bNq1cuVIpKSnKzi56z7wtW7bYohkAAAAAQDEZHunbtGmThg8fLnd3d504cUJNmzZVw4YNlZ6eLicnJz3//PO2qBMAAAAAYAXDoS8uLk4vv/yyYmJiJEmDBw/W7NmztWXLFnl4eKhatWqGiwQAAAAAWMdw6Dt58qRatGghJycnOTk56dq1a5KkSpUq6ZVXXtHChQsNFwkAAAAAsI7h0Fe+fHndvHlTJpNJVatWVUpKimWds7Oz0tLSjDYBAAAAALCS4YlcGjRooGPHjql169Zq06aNPvjgA7m7u8vFxUXvv/++GjZsaIs6AQAAAABWMBz6hgwZorNnz0qSRo0apZSUFI0bN06S9MQTT2jy5MlGmwAAAAAAWMlw6GvRooXl58qVK2vRokW6evWqrl+/rqpVqxo9PAAAAADAAJvenP3KlStKTk5W2bJlCXwAAAAAUALY5ObsCQkJio2NtZzmuWrVKjVo0EBjx46Vv7+/evfubYtmALu5Pu8v9i4BBpQbtsPeJQAAANiN4ZG+JUuWKCoqSu3atdO8efNUUFBgWde4cWOtWbPGaBMAAAAAACsZHulbtGiRwsPDFR4erry8vELrfH19deLECaNNAAAAAACsZHik7/z582rWrNld1zk7O+vGjRtGmwAAAAAAWMlw6KtRo4b2799/13Xff/+9fH19jTYBAAAAALCS4dDXt29fffjhh/rkk0908eJFSVJWVpbWr1+vBQsWqG/fvoaLBAAAAABYx/A1ff369VNaWpree+89xcTESJJeeuklOTs7a+jQoQoLCzNcJAAAAADAOja5ZcPIkSPVr18/7d27V+np6fL09JSfn5+8vb1tcXgAAAAAgJUMhb7s7GxFRkZq4MCB8vPzU/v27W1VFwAAAADABgxd01e2bFnt2rVLN2/etFU9AAAAAAAbMjyRS5s2bbR9+3Zb1AIAAAAAsDHD1/R16NBBU6dOVXp6utq0aSNvb2+ZTKZC2/j7+xttBgAAAABgBcOhb+TIkZKkhIQEJSQkFFlvMpl0+PBho80AAAAAAKxgOPStXbvWFnXYRYd3P7d3CbBS4qtd7V0CAAAAUCoYDn21a9e2RR0AAAAAgAfA8EQuAAAAAICSy6qRvnr16mnFihVq3Lix6tatW2Till/jmj4AAAAAsA+rQl9UVJRq1Khh+fn3Qh8AAAAAwD6sCn19+/a1/NyvXz+bFQMAAAAAsC2rrunbtGmTrl69autaAAAAAAA2ZlXoGzVqlFJSUiyP8/Pz9fzzz+vYsWO2qgsAAAAAYANWhb6CgoIij0+dOqXs7GybFAUAAAAAsA1u2QAAAAAADozQBwAAAAAOzKrZOyVp+fLl2rp1q6T/O91z2bJl8vHxKbSdyWTSyJEjDZQIAAAAALCWVaHPy8tL27dvL7TM29tb27Ztu+v2hD4AAAAAsA+rQt+3335r6zr02muvadu2bfLy8tIXX3whSbp8+bLGjBmjs2fPqnr16nr//ffl6elp87YBAAAAwFGVmGv6unXrpo8++qjQsvnz5ysgIECbNm1SQECA5s+fb6fqAAAAAKB0KjGhz9/fv8go3pYtWxQaGipJCg0N1ZdffmmP0gAAAACg1LJ6IpeHITU1VVWqVJEkValSRWlpaXfdzsOjrJydyzzM0mBnFSu6P9T2rj/U1mBrD7u/oPSir6A46C+4X/QVFMeD6C8lOvTdr8xMbgr/qLl8OcveJaAUob/gftFXUBz0F9wv+gqKw9r+4uNT4Z7rSszpnXfj5eWlCxcuSJIuXLigypUr27kiAAAAAChdSnToCw4OVkJCgiQpISFBbdu2tXNFAAAAAFC6WHV656pVq4q1fVhY2O9uM3bsWO3Zs0fp6elq06aNRowYoZdfflmjR4/WqlWr9F//9V+aPXu2NeUCAAAAwCPLqtAXFRV139uaTKb7Cn0zZ8686/JFixbdd1sAAAAAgMKsCn0HDhywdR0AAAAAgAfAqtDn6upq6zoAAAAAAA+AzW7ZkJaWppSUFGVnF719QkBAgK2aAQAAAAAUg+HQl5WVpfHjx2vr1q0qKCi46zaHDx822gwAAAAAwAqGb9kwa9YsHTt2TB9//LEKCgr03nvv6Z///KfMZrOqV6+uTz/91BZ1AgAAAACsYDj0bd26VcOGDVOLFi0kSTVr1lSrVq307rvvKigoiNAHAAAAAHZkOPRdunRJNWrUUJkyZVSuXDldvnzZsi44OFhJSUlGmwAAAAAAWMlw6Ktataol6NWqVUtff/21Zd0PP/ygsmXLGm0CAAAAAGAlwxO5BAQEaNeuXWrXrp369++vqKgoJScny9XVVTt37lTfvn1tUScAAAAAwAqGQ9+4ceN07do1SVJYWJjc3Ny0ceNG3bhxQ+PHj1e/fv0MFwkAAAAAsI7h0Ofh4SEPDw/L45CQEIWEhBg9LAAAAADABgxf09ekSRMdPHjwrut++OEHNWnSxGgTAAAAAAArGQ592dnZ97wpe05OjvLy8ow2AQAAAACwklWnd54/f17nz5+3PD527FiRbbKzsxUfH6/q1atbXx0AAAAAwBCrQt9nn32m2NhYmUwmmUwmTZo0qcg2BQUFcnFx0ZQpUwwXCQAAAACwjlWhr2fPngoKClJBQYF69Oih6dOnq3bt2oW2cXFxUY0aNVS+fHmbFAoAAAAAKD6rQl+VKlVUpUoVSdL69etVo0YNubq62rQwAAAAAIBxhm/Z4Ovrq/z8fH355Zfat2+frly5Ik9PT/n5+enZZ5+Vk5PhuWIAAAAAAFYyHPrS09M1dOhQHTp0SN7e3vL29talS5e0cOFCNWrUSP/85z9VsWJFW9QKAAAAACgmw8NwMTExOnfunJYuXapvvvlGCQkJ+uabb7R06VKdO3dOMTExtqgTAAAAAGAFw6Fv69atGjdunPz8/Aot9/PzU2RkpL766iujTQAAAAAArGSTm7Pf6/RNT09PZWdnG20CAAAAAGAlw6GvUaNG+vjjj4uEu+zsbC1YsECNGjUy2gQAAAAAwEqGJ3J59dVXNWDAAAUFBSkwMFBeXl5KS0tTUlKSbty4ocWLF9uiTgAAAACAFWwy0rdx40aFhIQoJSVFiYmJOnnypMxmsxITE9WwYUNb1AkAAAAAsIJVI33fffed6tevr/Lly0uSfHx8NGnSJJsWBgAAAAAwzqqRvpdeeknHjx+3dS0AAAAAABuzKvQVFBTYug4AAAAAwANg+Jo+AAAAAEDJZfXsnUlJSTpx4sR9bRsaGmptMwAAAAAAA6wOfXPnzr2v7UwmE6EPAAAAAOzE6tC3ePFibscAAAAAACWc1aHPzc1N7u7utqwFAAAAAGBjTOQCAAAAAA6M0AcAAAAADsyq0zuTk5NtXQcAAAAA4AFgpA8AAAAAHBihDwAAAAAcmNWzdz5MwcHBKl++vJycnFSmTBnFx8fbuyQAAAAAKBVKReiTpEWLFqly5cr2LgMAAAAAShVO7wQAAAAAB1ZqQt+QIUPUrVs3rVixwt6lAAAAAECpUSpO71y2bJmqVq2q1NRUDRo0SL6+vvL397es9/AoK2fnMnasEA9bxYruD7W96w+1Ndjaw+4vKL3oKygO+gvuF30FxfEg+kupCH1Vq1aVJHl5eem5557TgQMHCoW+zMxse5UGO7l8OcveJaAUob/gftFXUBz0F9wv+gqKw9r+4uNT4Z7rSvzpnVlZWcrMzLT8/O2336p27dp2rgoAAAAASocSP9KXmpqqiIgISVJeXp5CQkLUpk0bO1cFAAAAAKVDiQ99NWvW1Jo1a+xdBgAAAACUSiX+9E4AAAAAgPUIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODASkXo2759u55//nk999xzmj9/vr3LAQAAAIBSo8SHvry8PE2ZMkUfffSR1q1bpy+++ELHjh2zd1kAAAAAUCqU+NB34MAB/elPf1LNmjXl6uqqTp06acuWLfYuCwAAAABKBVNBQUGBvYv4LYmJifr666/19ttvS5ISEhJ04MABRUdH27kyAAAAACj5SvxI390yqclkskMlAAAAAFD6lPjQ98c//lHnzp2zPD5//ryqVKlix4oAAAAAoPQo8aGvUaNGSklJ0enTp5WTk6N169YpODjY3mUBAAAAQKlQ4kOfs7OzoqOj9de//lUvvPCCOnbsqNq1a9u7rIeiXr166tKli+XfmTNntHv3bv3tb38r1nEOHjyoqVOn3nVdcHCw0tLSbFEuDLj9tw4JCdHIkSN1/fp1u9bTrFkzmx9z0qRJzLxbgtz+G585c0YhISGSVOj1ZcuWLVbfIuf8+fMaOXKkbQrFQ3Hx4kWNGTNG7dq10wsvvKChQ4fq5MmT99z+dv/59d967NixMpvN+uSTTwzXRD8qndLT0y2fW1q2bKnWrVtbHufk5Ni7PDxkd36eSEpKUvv27fXzzz8X+zhXr17Vp59+anUd8fHxmjJlitX7OwJnexdwPwIDAxUYGGjvMh46Nzc3rV69utCys2fPFusYubm5atSokRo1amTL0mBjd/6tIyMjtXz5cg0aNMjOVdnW7cmYUDq0bdtWbdu2tWrfqlWr6h//+IeNK8KDUlBQoOHDhys0NFSzZs2SJB0+fFipqan685///Jv73vm3vnjxovbv36+tW7fed9u5ublydr77RxH6UelUqVIly/vZnDlz5O7uriFDhti5Ktjbzp079dZbb2nBggWqVq1asfe/evWqli1bpr59+z6A6h4NJX6kD/d2+fJlhYeHy2w2q2fPnkpOTpZ060X29ddf1+DBgzVhwoRC396np6dr8ODBCg0NVXR0dKGJcsLDw9WtWzd16tRJK1assMtzguTn56dTp05JklavXq2wsDB16dJF0dHRysvLk3Trm7NZs2apc+fO6tmzpy5duiRJmjhxoqZOnapevXqpbdu2SkxMlCRduHBBffv2tYwm7t27V5999pmmTZtmaXflypWaPn16oVpGjx6tpKQky+OJEydq48aNOnPmjPr06aOuXbuqa9eu+v777yXdGinq37+/Ro4cqQ4dOigyMtLSx/r376+DBw9Kkt544w1LX+NDXcl057ei9+pXBQUFiomJUUhIiMxms9avXy+p8Ojh0aNHLX3YbDYrJSXFLs8H97Zr1y45Ozurd+/elmX16tVTvXr1NGDAAHXt2lVms1lffvllkX3v/FsPHjxYqamp6tKli/bu3avDhw+rZ8+eMpvNioiI0JUrVyTdei2YOXOm+vXrp8WLF9+zf9157Hu95qB0+fzzzy2vB5MnT1Z+fr5Onz6t9u3b6/Lly8rLy1OvXr20c+dOSdIrr7xiea/47LPPJN36omD8+PEym80KCQnR4sWL7fmUcB/27t2rqKgoffjhh6pVq5YkKS0tTSNGjFD37t3VvXt37du3T9Ktz7Cvvfaa+vfvr7Zt21r+vu+9955++ukndenSRTExMUXOfJsyZYri4+Ml3brdW69evdS5c2eFhYUpMzOzUD3btm3Tiy++qF9++UXBwcG6efOmJCkzM7PQY0dTKkb6HlU3btxQly5dJEk1atTQ3LlzC62fM2eO6tevrw8++EA7d+7UhAkTLN+u/ec//9G//vUvubm5affu3ZZ95s6dq+bNm2v48OHatm1boXA3bdo0VaxYUTdu3FBYWJjat2+vSpUqPYRnittyc3O1fft2tW7dWsePH9eGDRu0bNkyubi4aPLkyVq7dq1CQ0OVlZWlJk2aaMyYMXr33Xe1cuVKhYeHS7oV8P71r3/pxIkTGjZsmDp06KAvvvhCrVq10rBhw5SXl6fr16+rfv366ty5s8aPHy8XFxfFx8frzTffLFRPp06dtH79egUGBionJ0c7d+7U5MmTVVBQoIULF6ps2bJKSUnR2LFjLS+2P/zwg9atW6cqVaqod+/e2rdvn/z8/Aodd8yYMapYsaLy8vI0cOBAJScnq27dug/nlwyr3K1fbdq0ScnJyVq9erXS09MVFhZW5G+9fPlyvfTSS+rcubNycnKUn59vp2eAezl69KgaNGhQZHnZsmU1d+5ceXh4KC0tTS+++KLatm17zxm0582bp1deecXyPmQ2m/X666+rRYsWmj17tmJjYzVp0iRJt761X7p0qaRbXyrcrX/dycvL656vOSgdfvzxR23evFnLly+Xs7OzXn/9da1bt05ms1mDBg3Sm2++qbp166pevXoKCAiQJL3zzjuqWLGirl+/ru7du6t9+/ZKSUlRenq61q5dK+lWX0LJlZOTo/DwcC1evFiPP/64Zfnbb7+tAQMGyM/PTz///LOGDBmiDRs2SJJOnjypxYsXKzMzUx07dlTv3r0VGRmpo0ePWl5f7vxs++v2xowZo1mzZqlx48bKzMyUm5ubZf3mzZu1cOFCzZ8/X56ennr66aeVlJSkdu3aad26dWrfvr1cXFwe4G/Efgh9JdjdTu+80759+zRnzhxJUkBAgC5fvqyMjAxJt67Vu7OT3/bdd98pNjZWkhQUFCRPT0/LuiVLlmjz5s2SpF9++UWnTp0i9D0kdwZ8Pz8/hYWFaeXKlTp06JDCwsIs23h5eUmSXFxc9Oyzz0qSGjZsqG+//dZyrHbt2snJyUlPPPGEZQSwUaNG+u///m/l5uaqXbt2qlevniTpmWee0bZt2+Tr66ubN2+qTp06hepq06aNpk6dqpycHG3fvl1+fn5yc3NTRkaGpkyZouTkZDk5ORUavWncuLH++Mc/SpLq1q2rs2fPFgkCGzZs0MqVK5Wbm6uLFy/q+PHjhL4S7m79at++ferUqZPKlCkjb29v+fv76+DBg4X6UdOmTRUXF6dz586pffv2euyxx+z0DFBcBQUFmjlzpr777js5OTnp/PnzunTpknx8fH5334yMDGVkZKhFixaSpK5du2rUqFGW9S+88EKh7e/Wv+6Um5t7z9cclA47duzQwYMH1b17d0m33tNuv1f07t1biYmJio+P1+eff27Z55NPPtFXX30lSTp37px++ukn1apVSydPntTUqVMVGBioVq1aPfwng/vm7OysZs2aadWqVYqKirIs37FjR6Hr/DMzMy0jcoGBgXJ1dVXlypVVuXJlpaam3nd7J0+elI+Pjxo3bixJ8vDwsKzbvXu3Dh06pAULFliWh4WF6aOPPlK7du0UHx+vt956y9DzLckIfaXYb93DsFy5csU61u7du7Vjxw6tWLFC5cqVU//+/ZWdnW2TOvH77hbwCwoK1LVrV0VGRhbZ3sXFxfK3dnJyspz2KUmurq5Ftvf399fSpUuVlJSkV199VUOGDFFoaKh69OihuLg4+fr6qlu3bkX2K1u2rFq0aKGvv/5aGzZsUKdOnSTdeiP29vbW6tWrlZ+fb3lx/XX7ZcqUKVSbJJ0+fVoLFizQqlWr5OnpqYkTJ9LXSoG79au7vQb9mtlsVpMmTbRt2zYNGTJEU6dOtXyLj5Khdu3a2rhxY5Hla9euVVpamuLj4+Xi4qLg4GCb/V/99XvU3frXnX7rNQelR/fu3TV69Ogiy69du6aLFy8qLy9PWVlZcnd3144dO7R3716tXLlSbm5u6t27t7Kzs1WpUiWtWbNG27dv15IlS7Rp0yaH/qBe2jk5Oen999/XoEGDFBcXp1deeUWSlJ+frxUrVtx1gOLXnyNyc3OLbFOmTJlCZ47cfm0qKCi459kINWvW1OnTp3Xy5EnLXBdPPfWU3nzzTe3Zs0d5eXl68sknrX+yJRzX9JVi/v7+WrNmjaRboa1SpUqFvtG41z63T4lISkqyXGORkZEhT09PlStXTsePH9e///3vB1s8fldAQIA2btxo+Ybr8uXLxZ7I57azZ8/Ky8tLPXv2VPfu3fWf//xHktSkSROdO3dOX3zxheXamV/r1KmT4uPjtXfvXss3qhkZGfLx8ZGTk5NWr15dJNj9lmvXrqlcuXKqUKGCLl26pO3bt1v1nGB//v7+2rBhg/Ly8pSWlqa9e/cW+TB++vRp1axZUy+99JKCg4N15MgRO1WLe3nmmWeUk5OjlStXWpYdOHBAP//8s7y8vOTi4qJdu3YV6/WnQoUK+sMf/qC9e/dKunV9sr+/v9U1GnnNQckQEBCgDRs2WGYMT09Pt8zi+O6776pr166KiIhQdHS0pP/7XOLm5qajR49arglPS0tTQUGBOnbsqBEjRljez1BylStXTnFxcVq7dq3l2sxWrVpZTvGWbk0e9VvKly+va9euWR5Xr15dx48fV05OjjIyMizXgfr6+urChQs6cOCApFsjiLdDY7Vq1TRnzhxNmDBBR48etRwrNDRUY8eOveuX346Ekb5SbPjw4XrttddkNptVrlw5vfPOO7+7T0REhCIjI9W1a1f5+/tbZlBq06aNli9fLrPZrD//+c9q2rTpgy4fv+OJJ57Q6NGjNXjwYOXn58vFxUXR0dGqXr16sY+1Z88effzxx3J2dpa7u7tiYmIs6zp27KjDhw8XOtX3Ti1bttSECRMUHBxs+fatT58+GjFihBITE/X000/L3d39vmupW7eu6tevr06dOqlmzZpq3rx5sZ8PSobnnntO+/fvV5cuXWQymTR+/Hj5+PjozJkzlm3Wr1+vNWvWyNnZWd7e3oqIiLBjxbgbk8mk2NhYTZs2TfPnz1fZsmVVvXp1DR8+XG+//ba6deumevXqydfXt1jHjYmJ0RtvvKHr16+rZs2aRSaKKg4jrzkoGerUqaPhw4dr0KBBlve0yZMn69SpUzpy5Iiio6NVpkwZbdy4UQkJCerYsaNWrlypzp07y9fXV02aNJF06/KTSZMmWUZ0xo0bZ+dnhvtRsWJFffTRR+rbt68qVaqkSZMmacqUKTKbzcrLy5Ofn99v3lKhUqVKat68uUJCQtS6dWtNmDBBHTp0kNls1mOPPab69etLujVKOGvWLE2dOlU3btyQm5ubFi5caDmOr6+vZsyYoVGjRikuLk61atWS2WzW+++/f88vvx2FqeB+zs8B4LD+9re/aeDAgZxyBwAAHjmJiYnasmWL/v73v9u7lAeKkT7gEXX16lX16NFDderUIfABAIBHzltvvaXt27dr/vz59i7lgWOkDwAAAAAcGBO5AAAAAIADI/QBAAAAgAMj9AEAAACAAyP0AQAeKfHx8erWrZuaNWsmf39/hYaGFrqdQGpqqubMmVPo1hP3KycnR3PmzPnde04BAPAwEfoAAI+MDz/8UFFRUWrVqpViY2MVExOjtm3b6quvvrJsk5qaqtjY2GLdjPy2mzdvKjY2ltAHAChRuGUDAOCRsXTpUr344osaO3asZVlwcLCGDx9ux6oAAHiwuGUDAOCR0bRpUw0dOlQRERF3XX/mzBm1bdu2yPIjR44oKytLM2bM0Lfffqtz587Jy8tLgYGBioyMlIeHhySpTp06RfbdsmWLatSooezsbM2ePVvr1q1TamqqfH19FRkZqcDAwELbzp07VydOnJCLi4see+wxjR8/Xi1atLDRbwAA8ChipA8A8MioX7++li5dqmrVqikoKEiVKlUqtL5KlSqaMWOGxo0bp+joaDVo0MCy7saNG8rLy9OYMWNUuXJl/fLLL4qLi9OoUaP08ccfS5IWLVqkAQMGaNiwYQoKCrIcU5JGjhypAwcOaMSIEapVq5Y2bNigYcOG6X/+539Ur149/fTTTxo1apT69++v8ePHKycnR4cOHdKVK1cezi8HAOCwGOkDADwykpOTFRERoTNnzshkMunxxx9X+/btNWTIEMto3Y8//iiz2azFixfr6aefvuexcnNz9b//+7/q06ePtm7dqmrVqunatWtq3ry5pk+frm7dulm23blzpwYOHKglS5YUGrXr27evvLy89I9//EOJiYl64403tHv37gf3CwAAPJKYyAUA8MioW7euNmzYoHnz5qlPnz4qKCjQBx98oO7du+vatWu/u39CQoJCQ0PVrFkzNWjQQH369JEkpaSk/OZ+O3bskI+Pj5o3b67c3FzLv4CAAB06dEiS9OSTTyojI0MTJkzQN998o6ysLMPPFwAAidM7AQCPGFdXVwUHBys4OFiS9NlnnykqKkqrVq3SgAED7rnf5s2bNWHCBPXu3VtjxoxRxYoVdfHiRUVERCg7O/s320xPT9fFixcLnS56W5kyZSRJvr6++uCDDzR//ny9/PLLcnZ21nPPPadJkyapcuXKBp4xAOBRR+gDADzSevTooRkzZujEiRO/uV1iYqKaNGmiyZMnW5bt2bPnvtrw9PRU1apVNXfu3N/cLigoSEFBQcrIyNC2bds0bdo0vfXWW5o1a9Z9tQMAwN0Q+gAAj4zU1FR5eXkVWpaWlqaMjAx5e3tLklxcXCSpyOjdjRs35OrqWmjZ2rVrCz2+174BAQFauHCh3N3d9fjjj/9unRUqVJDZbNZ3332n/fv338czAwDg3gh9AIBHhtlsVtu2bdWyZUt5eXnp7NmzWrBggdzc3BQaGipJqlatmtzc3JSQkKAKFSrI2dlZjRo10l/+8hdNmTJF80AfURcAAAFgSURBVObNU5MmTZSUlKSdO3cWOr6rq6tq1KihDRs2qHbt2ipbtqzq1Kmjli1bqlWrVho8eLCGDh2qJ554QpmZmUpOTlZ2drYiIyO1fPly/fvf/1br1q1VpUoVpaSkKDExUV26dLHHrwoA4ECYvRMA8Mj49NNPtWXLFv3444+6cuWKfHx81KxZM4WHhxcagVuzZo3mzp2rs2fP6ubNmzpy5Ijy8vI0Y8YMJSQkKDs7Wy1bttRf//pX9ezZU3FxcXr22WclSd98841iYmKUkpKinJwcy336cnJyFBcXp7Vr1+qXX36Rp6en6tatq/79+ysoKEj79+/XvHnz9MMPP1hq69ixo0aNGlVkhBEAgOIg9AEAAACAA+OWDQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MAIfQAAAADgwAh9AAAAAODACH0AAAAA4MD+H4LzDK0M+eOzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3cc1d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(state_firearms[\"totals\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From what we've seen thus far, Kentucky is by a large distance the state with the most background check, while Mariana islands account for a measly(< 1000) number. So, what does this mean? or what inferences can we draw from this?, None in a nutshell, as there is a need for additional information in order to make generalizations about this result. This might include details like census data, crime rate statistics, or even political affiliations/leanings. That being said, let's check the top states by firearm types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Hawaii                     3.0\n",
       "Mariana Islands          249.0\n",
       "Virgin Islands          3112.0\n",
       "District of Columbia    4570.0\n",
       "Guam                    7642.0\n",
       "Name: handgun, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group states by handguns.\n",
    "handgun = state_firearms[\"handgun\"].sort_values()\n",
    "handgun_low = handgun.head()\n",
    "handgun_high = handgun.tail()\n",
    "handgun_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Hawaii                    35.0\n",
       "Mariana Islands          182.0\n",
       "Virgin Islands           431.0\n",
       "District of Columbia     605.0\n",
       "Guam                    6035.0\n",
       "Name: long_gun, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group states by longguns.\n",
    "long_gun = state_firearms[\"long_gun\"].sort_values()\n",
    "long_gun_low = long_gun.head()\n",
    "long_gun_high = long_gun.tail()\n",
    "long_gun_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Oregon                  1.0\n",
       "District of Columbia    1.0\n",
       "Illinois                1.0\n",
       "Hawaii                  2.0\n",
       "Virgin Islands          5.0\n",
       "Name: other, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group states by other.\n",
    "other = state_firearms[\"other\"].sort_values()\n",
    "other_low = other.head()\n",
    "other_high = other.tail()\n",
    "other_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e68627320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(state_firearms[\"handgun\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is impressive. The mere fact that Kentucky is not listed amongst the top 6 for handguns is a major eye opener, and it's also noticeable that Mariana islands has a relatively sizable number of handguns when compared to say Hawai, despite being lowest in total background checks(intriguing, let's dive further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3c664cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(state_firearms[\"long_gun\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, Kentucky still isn't amongst the top 6, with Pennsylvania taking the price for this, It should be noted though that Texas has been consistently in the top 2 thus far for every category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3ca24470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(state_firearms[\"other\"], y_scale=1, y_scale_two=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we have accessories or firearms categorized as other most prevalent in California, followed by, you guessed right, Texas. It should also be noted that both Hawai and Mariana islands showed quite a considerable increase for this class of firearms, could there be more stringent conditions for acquiring the former classes of weapons in these states? remember, the golden rule(by me), no assumptions except the data says so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faada358080>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faada3d4358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_older[df_older[\"state\"] == \"Texas\"][\"totals\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which states have the most and least private sales of the different firearm categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>prepawn_long_gun</th>\n",
       "      <th>prepawn_other</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>450952.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>2222037.0</td>\n",
       "      <td>2626029.0</td>\n",
       "      <td>39578.0</td>\n",
       "      <td>8621</td>\n",
       "      <td>5.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6706079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>5069.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>434602.0</td>\n",
       "      <td>572174.0</td>\n",
       "      <td>15970.0</td>\n",
       "      <td>3760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1137643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>147410.0</td>\n",
       "      <td>6701.0</td>\n",
       "      <td>1781468.0</td>\n",
       "      <td>1480762.0</td>\n",
       "      <td>56832.0</td>\n",
       "      <td>13129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4425714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>81433.0</td>\n",
       "      <td>13595.0</td>\n",
       "      <td>965584.0</td>\n",
       "      <td>1663256.0</td>\n",
       "      <td>13053.0</td>\n",
       "      <td>7214</td>\n",
       "      <td>518.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3752633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>1441076.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5910880.0</td>\n",
       "      <td>5936770.0</td>\n",
       "      <td>342635.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19014063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               permit  permit_recheck    handgun   long_gun     other  \\\n",
       "state                                                                   \n",
       "Alabama      450952.0           749.0  2222037.0  2626029.0   39578.0   \n",
       "Alaska         5069.0            22.0   434602.0   572174.0   15970.0   \n",
       "Arizona      147410.0          6701.0  1781468.0  1480762.0   56832.0   \n",
       "Arkansas      81433.0         13595.0   965584.0  1663256.0   13053.0   \n",
       "California  1441076.0             0.0  5910880.0  5936770.0  342635.0   \n",
       "\n",
       "            multiple  admin  prepawn_handgun  prepawn_long_gun  prepawn_other  \\\n",
       "state                                                                           \n",
       "Alabama         8621    5.0            186.0             213.0            9.0   \n",
       "Alaska          3760    0.0            115.0              99.0            0.0   \n",
       "Arizona        13129    0.0            134.0              90.0            7.0   \n",
       "Arkansas        7214  518.0            147.0             193.0            4.0   \n",
       "California         0    0.0              0.0               0.0            0.0   \n",
       "\n",
       "              ...     returned_other  rentals_handgun  rentals_long_gun  \\\n",
       "state         ...                                                         \n",
       "Alabama       ...                0.0              0.0               0.0   \n",
       "Alaska        ...               10.0              0.0               0.0   \n",
       "Arizona       ...                1.0              0.0               0.0   \n",
       "Arkansas      ...                0.0              0.0               0.0   \n",
       "California    ...                0.0              0.0               0.0   \n",
       "\n",
       "            private_sale_handgun  private_sale_long_gun  private_sale_other  \\\n",
       "state                                                                         \n",
       "Alabama                    170.0                  144.0                11.0   \n",
       "Alaska                     184.0                  236.0                10.0   \n",
       "Arizona                    261.0                  159.0                31.0   \n",
       "Arkansas                   171.0                  182.0                13.0   \n",
       "California                   0.0                    0.0                 0.0   \n",
       "\n",
       "            return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "state                                                             \n",
       "Alabama                          5.0                        6.0   \n",
       "Alaska                           3.0                        5.0   \n",
       "Arizona                         13.0                       13.0   \n",
       "Arkansas                         9.0                       12.0   \n",
       "California                       0.0                        0.0   \n",
       "\n",
       "            return_to_seller_other    totals  \n",
       "state                                         \n",
       "Alabama                        6.0   6706079  \n",
       "Alaska                         1.0   1137643  \n",
       "Arizona                        3.0   4425714  \n",
       "Arkansas                       4.0   3752633  \n",
       "California                     0.0  19014063  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_firearms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4kAAAMRCAYAAABBG/N7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xl8TNf/P/BXVrLat6o1TGIJQuyqxFZLlaKtT6OoNbGUT0tp0cXHVlTtQiSItYpYK8SaVIIIEhF7UyERWUhkz2TO7w+/Od+MzCSZrNTr+Xh4yMzd3vfeM/fe973nnGsghBAgIiIiIiIiAmBY1gEQERERERHR64NJIhEREREREUlMEomIiIiIiEhikkhEREREREQSk0QiIiIiIiKSmCQSERERERGRxCSRiCgPrq6usLW1xYIFC8o6FHrFggULYGtrC1dX17IO5V9fTl6nbf2m+Pnnn2Fra4upU6eWdShUjBwcHGBra4szZ86UdSgFNmDAANja2mL79u1lHQq9QYzLOgB6fdna2hZ62kWLFuHjjz8uxmhKnp+fH7y9vXH16lXExsbCzMwM1atXR8uWLfHhhx+iQ4cOhZrvP//8g127duHixYuIjIxEeno6KlasiCpVqqBBgwZwdHREhw4d0KhRI43prl27Bj8/P1StWhXDhw8vjlXMpTSWQcVv165diIuLw3vvvYdWrVqVdThE/2qOjo548eJFvuN98cUX+P7770shIiKiksckkXSqWrWq1u9TU1ORmpqa5zjly5cvsbiKW1paGmbMmIGTJ0/K76ysrJCamoq7d+/i7t27yM7OLlSSuGvXLixYsABZWVka805JSUFsbCxu3bqFP//8E3Z2djh48KDGtNeuXcOaNWtgZ2dXokliSS/jTVezZk00aNBAZ1kvC7t378atW7dgZWXFJJGolJiZmcHCwkLncEtLS43PVatWRYMGDVC9evWSDo2IqNgxSSSd/vrrL63fr169GmvWrMlznDdFdnY2JkyYgIsXL6JixYqYOnUq+vXrh0qVKkEIgadPn+LChQsaSV5BBQQE4McffwQAtGjRAq6urujQoQPMzMwAALGxsQgODsaJEycQGRlZnKtFxWjevHllHQIRvQaGDRum15NCV1dXVs8lojcWk0R6q23evBkXL16Eubk5duzYoVHl08DAADVq1MDgwYMLNW9PT08AwLvvvgsvL69cT1erVauGPn36oE+fPkhPTy/8ShARERERFSMmiVQqoqOjsXnzZvj7+yM6OhqGhoaoU6cOunfvjtGjR6NixYq5pgkPD8egQYMAAJcvX0Z0dDQ2btyIixcv4vnz56hevTqcnJzg6uqKypUr6x1Teno6Nm/eDACYMGFCrjaBRRUeHg4A6Ny5c77Vb3MOT0pKQtu2beXnW7du5WofOnv2bIwaNQoAkJKSAn9/f5w5cwZhYWF4+vQpUlJSULlyZbRq1Qqff/452rdvrzG9vstQe/bsGbZt24Zz587h4cOHSE9PR/Xq1dG2bVuMGjUKTZo00bp+8fHx8PT0xPnz5xEZGYmsrCxUrFgRVatWRZs2bdC3b184OjrmuY10xe/t7Q1ra2ts2LAB/v7+iIuLQ8WKFdGlSxe4urqiTp06uaZ/tWw9ffoU7u7uCAwMRFxcHGxsbGT1X1dXV5w6dUqjvVFkZCR69uwJANi+fbvGtnzV/PnzsX37djRr1gz79++X30dGRuLUqVPw8/PDw4cP8fTpUxgaGuLdd99Fly5dMHr06FzV1LZs2YJFixbJz4sWLdL4rF4fa2trje+uXbuGnTt3IigoCLGxsTAxMUH9+vXRo0cPfPHFF7Cyssp7gxdQcnIyvLy84Ovri4iICKSnp6NChQqoXLkyWrZsiV69eqFbt24a08TExODs2bM4e/Ys/v77b8TExEAIgRo1aqBjx44YPXo06tWrV+iYCltmi8rb2xu7d+/GvXv3oFQqYWNjg08++QSffvqp1vGfP38ut8Pdu3cRExODjIwMVKtWDY6Ojhg1ahSaNm2qddoFCxZg27Zt6NGjB9atW4ezZ89i69atuHnzJtLS0lCvXj18+OGHGD16NExMTHTGfOLECXh5eeHmzZtQqVSoX78+Bg8ejM8//zzf9VWpVNi9ezf27duHBw8ewNjYGDY2Nvjss88waNAgrb8jIPdvWdf++Oijj3Dr1q1cx6VXp69Vqxbc3Nzg6+uLJ0+ewMLCAo6Ojpg8eTLs7OzyXY/i9PPPP2PHjh3o06cPVq1apTFswoQJOHv2LL788kt888032LFjBw4fPoyIiAgkJSVhw4YN6N69uxxfpVLh6NGjOHz4MMLCwpCYmAgLCws0adIEgwYNwsCBA2FomLsvwmfPnuHcuXM4c+YM7t27h5iYGGRmZqJatWpo27YtRo4cqXObvxq/t7c39u3bhzt37uD58+eYO3cunJ2dERoaiqFDhwIAgoOD8eTJE7i5uSEgIACJiYmoVasWBg8ejC+//BKmpqYAgJCQELi7u+Pq1at4/vw53n33XXzyyScYOXKk1vVQqVTw9vbGoUOHcOvWLbx48QIWFhaoVKkSFAoFunTpgqFDh8LIyEjv/fT06VNs374d/v7+ePjwITIyMlC9enU0atQIvXr1Qv/+/WUNoFelpaVh8+bNOHbsGB49eoTy5cvD3t4eLi4u+Z7TgoODsWvXLly5ckXjuNyrVy+MGDEiV/XlnJKSkrBz50553ExJSUG1atVQr1499OzZEwMGDNB6XaXLli1bsHjxYgDAzJkz8eWXX8phDx8+hIeHBwIDAxEdHQ2VSoVKlSqhevXqaN++PQYOHFikPizo9cUkkUrcuXPnMG3aNNmO0dzcHCqVCrdv38bt27fxxx9/YOPGjWjWrJnOeQQEBGDmzJlIT0+HhYUFDAwM8PjxY3h5eeH48ePYunUrbGxs9IrrzJkzeP78OQBgyJAhhV/BfDx58kSv8Q0NDVG1alWkpaUhJSUFxsbGuQ725ubm8u+9e/dqJArm5uYwNDRETEwMfHx84OPjg6lTp2LSpEmFXgYABAYGYurUqUhMTAQAGBsbw9TUFI8fP8bjx49x+PBhfP/997kuKCMiIvD5558jLi4OAGBkZARLS0vEx8cjNjYW4eHhiI6O1itJzOnOnTtYuHAhnj9/DjMzMxgZGeHp06fYv38/jh8/jk2bNuU5b39/f8yePRvp6ekwNzeHsXH+h8U6deqgdevWCA4OxsGDB3UmiUqlEseOHQPw8iI3p8mTJ+PWrVvys7W1NZKTk3Hnzh3cuXMHBw4cgIeHh0ZiYG5ujqpVq+L58+dQKpWwsLDIdfGS8wJLpVJhyZIl2LJli8Y8MjIyEBYWhrCwMOzbtw/u7u5o2LBhvuudl4SEBAwfPhwREREAXj6Jt7a2RmJiIuLj43H37l3cuHEjV5L4008/4dSpU/Kzuj1wREQEIiIi4O3tjdWrV+O9997TO6bCltmiEEJgxowZOHToEIyMjGBubo60tDTcuHEDN27cwN27dzFnzpxc061duxbbtm2Tny0sLCCEkLEePXoU8+fPz7dDsBUrVmDDhg0wMDCAlZUVMjIycOfOHSxfvhzXrl3DunXrtE6nTjQByGlv376NBQsWwN/fH3Xr1tW5zMzMTEyePBnnzp0D8LIMWllZ4fr167h69SquXbuW73YrDo8ePYKLiwuio6NRvnx5GBoa4tmzZzh58iT8/Pzg6emJ1q1bl0osBZWVlYUxY8YgICBAHhsNDAw0xnn+/DkmT56My5cvy+8sLS2RmJiIgIAABAQE4PDhw1i7dm2uG5KrV6/Gjh07NKZTqVR49OgRHj16hMOHD2PBggXyppku3377Lby9veW+1ZbIAcClS5fw3//+F6mpqbCyskJWVhYiIiKwYsUKXLt2DevXr8eRI0cwe/ZsZGVlyXEePHiAxYsXIzIyMlcVfyEEJk+erHGcsLS0RHp6ujxOnDhxAgMGDMizzag2x48fx+zZs+X1iYmJCcqXLy+3z9mzZ1G/fn2t55Dnz59j2LBhuHv3LkxNTWFsbIzExET4+/sjICAAq1atkjcTc1KpVFi4cCG8vLzkd7qOy/Xr1881/eXLlzF16lQkJCQA+L9zalRUFKKiohAQEAArK6tc5xxthBBYtmwZ3N3dYWJigoULF2LgwIFyeFBQEMaOHYu0tDQAL4+h5ubmiImJQUxMDEJDQ6FSqfDtt9/muyx6AwkiPa1atUooFAqhUCjyHffBgweiZcuWQqFQiMGDB4uQkBAhhBAqlUr89ddfonv37kKhUIguXbqIhIQEjWlv3rwpl9OmTRsxbNgwER4eLoQQQqlUilOnTolOnToJhUIh+vbtKzIyMvRaj/nz5wuFQiH69OkjlEql8PLyEoMHDxYtW7YUrVq1EoMGDRKrV68WiYmJes1XzdXVVSgUCmFrayt2794tlEqlXtN7enoKhUIhBg4cmOd43t7e4qeffhKXL18Wz58/l99HRUWJZcuWCTs7O6FQKMTFixcLvYy7d+/K/Thz5kxx69YtuT4xMTFi8eLFws7OTtjZ2YmAgACNaadMmSL30eXLl0V2drYQQojMzEzx8OFDsXXrVrF69eoCbRO1xMREjbLRu3dvcenSJTn80qVLonfv3kKhUIgOHTqI+Ph4jelzlq1WrVqJESNGyLIlxMtyq+bi4iIUCoX43//+pzGP3bt3y+Wnp6drjfP06dNCoVCIpk2biri4OI1hs2bNEh4eHuLBgwey7GZmZorLly8LZ2dnoVAoRO/evbWWm4EDBwqFQiE8PT3z3E7q32q7du2Ep6en3A5ZWVkiODhYfPrpp3I5+v5+XrV48WKhUChE586dxZkzZ0RmZqYQ4uVvNTo6Wuzdu1fMnz8/13RLliwRa9asEbdv3xapqalymps3b4pJkybJ+LX9Dv/3v/8JhUIhXFxccg0rSpktDHU5adu2rWjZsqXYuXOnSElJEUIIERsbK6ZPny7LXGhoaK7p3d3dxdKlS0VoaKh48eKFEOLlcfLBgwdizpw5QqFQiGbNmom///5b53Zo27ataNKkiVi3bp08FiQmJoqFCxfKZfv4+OSa/sCBA3L4rFmzxNOnT4UQQiQnJwt3d3fRpEkT4ejoqHNbL126VE6/cuVKua+eP38ufv31Vxmbtt9Rzt/yzZs3dW5fXWU+5/Rt27YVgwcPFleuXBEqlUpkZ2eLy5cvy/PMgAEDdM4/L23atNEae35++uknoVAoxJQpU3INGz9+vDz+tGjRQuzatUuWl8TERPlbVSqV8ngwYMAAcfLkSTleamqqOHLkiHjvvfeEQqEQ33//fa7luLm5iWXLlokbN27IcpWdnS3u378vvvvuO6FQKETz5s3Fw4cPdcbfqlUrYWdnJ9atWyf3bWpqqoiOjhZCCBESEiL3gaOjo3BxcZHzS0lJEWvXrpXD161bJ+zt7cWcOXNETEyMEEKIpKQk8eOPP8pxwsLCNOI4efKkLP/bt2+X6yGEEPHx8eL06dNi+vTp8vhRUIGBgaJJkyZCoVCIIUOGiICAAHmMePHihQgODhY//vijvG5Ra9WqlSxvvXr1En5+fkKpVAqVSiXCw8PFgAEDhEKhEJ06ddJ6XFX/Jtq1aye2bNmicVy+cuWKGDZsmDxnqo+javfu3ZPL7927t/D19ZXLSEtLE2FhYWLZsmXi1KlTGtP1799fKBQK4eXlJb/LzMwUM2bMkPvYz88vV6zq352zs7MICwsTKpVKCCFERkaGuHfvnli/fr3YuXOnXtud3hxMEklv+iSJ6gujLl26aL3Ie/DggbC3txcKhUL8+uuvGsNyXsi/9957IikpKdf0169fl0nQnj179FqPMWPGCIVCIUaOHClGjhypcZJr1qyZ/Ny9e3dx//59veYthBA3btyQ66ZQKET79u3FlClThJubm/D399c40WlT0AQuP+oTkrYLlYIuY9SoUUKhUGi9yFdbuXKlPJnk1LlzZ6FQKMTZs2cLtwJa5LwwbNWqlXj06FGucR49eiRPpnmVrfxuMOhKEhMTE0Xz5s2FQqEQR48e1TrtV199JRQKhZgwYYJe65eRkSF69OghFAqFOH36dK7hBUkSHz16JJo2bSqaNWsmrl27pnWc9PR0mUzv27dPrxhfpU44d+zYUaT55KRSqcTw4cNzXdyo5ZUkFqXMFoa6nCgUCnHy5Mlcw5VKpdzWixcv1nv+X3/9tc5p1dshrzKhPsa9ehxQKpXi/fffz7Ocurm5yfm/uq0TEhLk70DXeuWMrySTxG7dumk9T1y+fFmOc+fOHZ3L0EWdJLZs2VJ06tRJ679Jkyblmq4gSaJCoRCHDx/Wuez9+/cLhUIhevbsKZ49e6Z1nLt374qmTZuKJk2aaD0W5mXatGlCoVCIpUuX6oxfoVCIDRs26JxHziTxs88+kzcCcxo7dqwcZ/r06bmGq1Qq0a9fP62xLFmyRCgUCvHf//5Xr3XLS87lDR48WKSlpRV4WvV5pU2bNjJRzunhw4fC1tZWKBQKcf78+VzDmjRpIpo1ayauX7+udf5paWmiZ8+eQqFQCG9vb41h6uuWbt265bqxnpdXk8SUlBQ5r44dO2q9cZWamir3mbabU/Tvp72+AFExyMzMhK+vL4CX7496tZ0UADRo0EBWczl69KjOeY0cOVJru6kWLVrIamh5Ta9NUlISAODixYsICAjARx99hPPnz+Py5csIDg7GL7/8AgsLCzx+/BguLi7IzMzUa/7NmjXDli1bZF39Z8+ewcfHB8uXL8eXX36Jdu3aYfTo0fDz89NrvvpSV++7cuVKoaZ//PgxLly4AAMDA4wbN07neOr9GBwcLKvuAJD7PTY2tlDLz8/gwYNRu3btXN/Xrl27QGVr9OjRsp2MPqytrWWboUOHDuUa/uLFC5w+fRoANKrvFISpqSk6deoEoPD77cCBA1AqlWjXrh1atmypdZxy5cqhb9++AF5Wuy0K9e+zOPezgYEB3n//fQD6bYeiltmiaNy4sdYqZkZGRvK3eOfOHb3nW5DfcYUKFfCf//xH6zAnJycAwO3btzW+Dw4ORnR0NABoVEnPadSoUVqP3wBw+vRpZGZmwtjYWOe2njhxYqHaiunL2dlZ63nC0dERFSpUAJB7/fWRlpaGuLg4rf/UVZr1Vbt2bfTv31/n8L179wIAPvnkE51tzBo1aoTmzZsjOzsbFy5c0Gv5Bfl9mZmZYcSIEQWa35gxY7RWRe3SpYv8e/z48bmGGxgYoHPnzgBy7yN12VM3WSgO165dw7179wC8rEpbmNd2DR48GDVr1sz1fZ06ddC4cWMAuddl3759yM7ORseOHdGiRQut8y1fvjw++OADAJrH5ZiYGHm9MGXKFFSqVEnvmIGXTQO++OIL+Pn5oU6dOti1axeaN2+ea7xy5cqhXLlyAEru/E2vN7ZJpBJz9+5dZGRkAIC84NWmU6dO2LNnDyIjI5GYmChP5jnl9Y7CDh064Ny5c7hx44Ze8alUKvl/q1at8Msvv8hhpqam+Oijj6BUKvHdd98hIiICR44cybc90Ktat26NgwcP4urVq/Dz88P169cRHh6OhIQEeUK/cOECRowYobWdUkE9efIEO3fuxIULF/Dw4UMkJycjOztbY5y4uDgolcoCtbnLKefFQ17rL4QA8LIdXkxMDBo0aADg5cXt/fv38fPPPyM8PBw9evRAy5Yt9W47okt+ZWPnzp2IjIzE8+fPtV5kFaWN0qBBg+Dj4wM/Pz8kJCRodKB0/PhxZGRkwMrKCj169NA6/YULF7Bv3z6EhoYiNjZWa6Kib5tWNfV+Cw4Olhdf2qjbmkRFRRVqOWrdunXD+fPn4ebmhqioKHzwwQdo3bq11t/zq0JCQvD777/j6tWriIqKQlpamixPavpsh6KW2aLQdeEHQHZEpG4L/ar79+9j165duHz5Mh49eoTU1FR5nFLLazs0adJE5w0P9bJfTWbUx01LS0utF4rAy+Nh69atcfbs2VzDbt68CQCwsbHR2YFYlSpVYGNjU6jkWB+6boYAL9c/MTGx0MkcgFyd7hQHBweHXG0Q1bKyshASEgIA2Lhxo0bb4lep10vb7/jevXvYtWsXgoKCdJarmJgYnfO2s7PL1UZdF13lX/2eWVNTU52dnKjHUd/AVevatStWrVqFwMBAjBw5EoMHD0b79u1Rq1atAsWkzdWrVwG8LPd5dTyWl/zK2507d3KVN/Wx6fLly3kel9Xngpz7M2fb3lfbdhdUZGSkbDvetGlTbNq0Sec7gA0NDdG1a1ecPHkSkyZNwmeffYZu3bqhefPmhbqxSm8eJolUYuLj4+XfNWrU0Dlezjtx8fHxWi8q85pePSw5ORmZmZkFPnjlTFJy9uSV0+DBg7F8+XLEx8fD399f7yQReHmHtHXr1hrJyD///IM///wT7u7uePHiBby8vGBvb1+ghuavunDhAiZNmqSRYFhYWKB8+fIwMDCAUqmUF6VpaWl692T59OlTAC8vqAt6JzfnKz2mTJmC+/fv4+zZs9i+fTu2b98OQ0NDKBQKvP/++xg2bJjWHkgLqiBlA3h591RbkliYnnHVunbtisqVKyMhIQFHjx7VuNuufrrYt29feTc2p3nz5mHPnj3ys5GRESpUqCB7n0xJSUFaWppM4vSl3m8FnUdhl6M2fPhw2eGCt7c3vL29AQANGzaUPQ9quzjcsGEDfvvtN5mwGRoawtraWm4HdedK+sRX1DJbFHnd/FA/TVMqlbmGeXt7Y86cORrvZLWyspJlJzMzE0lJSXluh7yWrb459Oqy1cfp6tWr60xWAN2/M3XnGfm9ML5GjRolniQWZNsX5p23JalKlSo6hz179kzG+2ripMur5Xjfvn2YN2+e3O8GBgawtLTMVa7yepKeV4yv0rUP1Nvf3NxcZznTtY+aNm2K77//HkuWLEFgYCACAwMBvHyNVMeOHfHRRx9pPKksCPWTsVq1aunshCc/hfm9FeW4rD6WmZubF/q8pb7RUKFCBXh6eubbA+oPP/yAmJgYhISEwM3NDW5ubjAxMUGzZs3g5OSEYcOGFekcSq83JolUKvK6+CjIeAWdXh85L3p09YxqaGiIBg0aID4+vshPWnKqV68eJk6ciE6dOmH48OFQKpX4448/9E4SU1NT8c033yA1NRUODg746quv0LJlS427vjdu3JC9t776dKYg1Hecq1WrVqgqiWZmZnBzc0NISAhOnTqFK1euIDQ0FLdu3cKtW7fg4eGBuXPn6nw1QH6KWjaKUg3O2NgY/fv3h5eXF7y9vWWSGBUVJXsi1LZP//zzT5kgfvnllxg6dCjq16+vEYv61RmF2WcA5JPk8ePH4+uvvy7UPPRhaGiIhQsXYsyYMTh+/DiuXLmC69ev48GDB3jw4AG8vLzg6uqKqVOnymlCQkJkgvjxxx9jxIgRUCgUGk+7PTw8sGTJEr22Q1HLbGmLjo7GvHnzkJWVhW7dumHChAm57tareykubHnIT2F/R+p48pu+pOJ+0+V1/Mn5tG/Lli3o2LGjXvN+9OgRfvzxRyiVSjg5OWHChAlo2rSpRrk6duwYpk+fnuf+KY2qwvn5/PPP0adPHxw/fhyXLl1CcHAwYmNjcejQIRw6dAjdunXDqlWrtN6Qy0tJXFvkRb1PXVxcMG3atFJdNgD07t0b58+fR2JiIubOnYtff/01z9fiVKtWDb///jsCAwNx9uxZBAcHIzw8HNeuXcO1a9ewceNG/Prrr7LaMv27sE0ilZicdx/VbV60yVl9StcdqbyqWKmryVhaWupVBSLnU428ThQleXHTokUL+Y6qv//+W+/pAwMDER8fj3LlysHNzQ0dO3bMVS2oqO041FVR4uLi5FODwmjRogWmT5+O7du34/Lly9i0aRPs7e2RlZWFn376CZGRkYWab0HKBlC0J4Z5USeBN27cwP379wG8fIoohMC7776LNm3a5JpG/VqMfv364dtvv4WNjU2uC7GitgGpVq0agMK1fysKGxsbTJo0CR4eHrh06RJ27NiBLl26QAiBtWvXanTjf+zYMQgh0LJlSyxatAhNmzbNVR26MNuhuMpsafH19UVGRgZq1qyJNWvWoHXr1rmOZcXZHisn9XFa/X5KXdRPQHRNr2t4ftPnLPfq5gnavHjxIs/5/xtVrlxZPuUqTFtKX19fZGZmonbt2li9ejVatWqVq1y9SW3NqlatCmdnZ6xatQr+/v44evQonJ2dAQBnz56Fp6dngeelfvIdFRWVq+ptSVIfmwpzXFYf01NTUwt9XGvfvj3c3NxgZmaGEydO4Kuvvsq3vwUDAwN07NgRs2fPxt69e3Hp0iX89ttvqFu3LpKTk+WNavr3YZJIJaZx48byrl5AQIDO8dQN7evWrauz/dLFixd1Tq8epqs9jS452wOoG7C/SqVSyfe+vfvuu3rNv6DUSd2rJ2/1xUFeF27q5Lt27do6t11eHRkUZBnqarJCCJncFJWpqSm6du0KNzc3GBgYIDs7W1Yh0ldBykadOnX0erGwPuzt7dGoUSMA/1fFVP3/Rx99pPUGhHq/6XqJdVZWFoKCgnQusyDVo9T7LTAwsMwSJSMjIzg6OmL9+vVy+//1119yeH7bAcj72KFLSZTZkqTeDo0bN9Z5V1/fDkkKSn3cTE5ORlhYmNZxMjMzERwcrHWY+j2e9+7d01nO4uPj8eDBA63DzM3N5TrrupkYFxdXrDU53hSmpqZy/xSmHKu356tP53MqzO/rddGoUSPMnTtXVjXNeWzJj4ODA4CX5T7njauSpj42XbhwQWfbZF1atWol/9bWPrigOnTogE2bNsHc3BynTp3C1KlT9eqYz9zcHH379sXSpUsBvKwKrW+fEPRmYJJIJcbU1BS9evUCAGzbtk1rm4qHDx/i4MGDAJBnD2/btm1DcnJyru9v3Lghe/vq16+fXvHZ2dmhWbNmAF5WadPmwIEDss2OunfAgrpw4UKuzmNe9c8//8gG9Dlfmg68fDIK5H0HXd2+8PHjx1q3T0REhOwdT5uCLKN+/fpo164dgJcv/M7rqTCg2SmHSqXS2v5KrVy5cjLhKWyVpgMHDmiNKTo6GgcOHACgf9nQl/pp4qFDhxBaNM3sAAAgAElEQVQSEiKfKOqqPqzeb7qeDnh4eGi06X2Ver/l1U5pyJAhMDY2Rnp6OhYsWJDnjYDs7Gyt5UcfeV1kmJiYyP2bcz/ntx2OHDmC8PBwvWMpSpktC+rtcP/+fa2/l6CgIJw5c6ZElt26dWvZLnz9+vVax/Hy8tLZ4Uv37t1hamoKpVIJd3d3reNs2rRJ53HAwMAACoUCAHDixAmt42zYsOGtra76ySefAACuX7+e57EceHk8yPlUTF2u7t69q/VcdOnSJZw7d64Yoy0Z+SUw6p5J9TmHtGzZUpa7JUuW5PkUuzgNHToURkZGSEtLw8KFC/Ms10qlUuO4XL16dVmtc/Xq1Xj27Fmh42jbti02bdoECwsLnDlzBpMmTcq1nbOysvKML2ePsIVt10mvN+5VKlGTJ0+GmZkZnj59ilGjRsm7TUIIBAQEYPTo0UhPT0f16tUxcuRInfNJTk7G2LFjcevWLQAvk48zZ85g4sSJyM7O1niVhj5mz54NQ0NDXLt2Dd9++62snpiZmYlDhw5hwYIFAF6+zqJPnz56zfuHH35A7969sXLlSly7dk3jAJyQkIAdO3bA2dkZmZmZMDAwwKhRozSmV3ehHRUVpfM1GR06dICJiQkyMjIwbdo0WWVTqVTC19cXX3zxRZ5VcAuyDACYO3cuzM3NkZCQgE8//RSHDh1CSkqKHB4XF4djx45hwoQJ+OGHH+T3ycnJ6NGjB1atWoXQ0FCNbXD//n18/fXXyM7OhomJid7tbdQMDQ0xevRojSdvQUFBGD16NFJTU1GpUqVc27a4DRw4EIaGhoiKisJPP/0E4OWd6nr16mkdP+drW7Zs2SI7m0hISMCKFSuwYsWKPJ98qvfbqVOndCaTdevWhYuLC4CXydaECRMQEhIiT/oqlQp37tzBxo0b8cEHHxT5bnr//v2xZMkSBAUFaXSe8fjxY3z33Xcyzq5du8ph6u1w9epV/PLLL/JmRXJyMrZs2YJZs2YV+glwYctsWVBvh6ioKHz33XeyamlGRga8vb0xceJEvTucKigjIyPZTtTX1xfff/+9XH5qaio8PT2xfPlyna/AqFy5smyL6+HhgTVr1sj9mJSUhJUrV8LT0zPPXm4HDBgA4GWPwO7u7rLqWmxsLBYsWIBdu3bJGyNvG3VPnsDLzq6WLFmCR48eyeHp6ekICgrCggUL4OTkpNHpi7pcPXr0CHPmzJFPetPT07F//364uLjo3K+vk2+++QYzZ87E6dOnNW7oJCUlYfPmzfJVQ/q0izMwMMDcuXNhZGSEsLAwODs74+LFizKZTklJQVBQEGbNmoXQ0NBiW5cGDRpgwoQJAICDBw/CxcUFoaGh8ricnZ2N27dvw83NDX369Mn1BP/bb7+Fubk5oqKi8Nlnn8lX0AAv9+uNGzcwf/58nDp1Kt9YHB0dsXnzZlhaWuL8+fNwcXHRSJZv3bqF/v37w93dHXfu3JHbRgiBkJAQ2dNv5cqVYW9vrzHvAQMGwNbWtlCd/dHrgx3XUIlq0KABfvvtN0ybNg1hYWEYMmQILCwsoFKpZK9dVapUwfr16/N858+CBQswc+ZMfPTRR7C0tERWVpY8mFWpUgVr1qzRu8E68PJu2vz58/Hjjz/C29sbBw8ehLW1NVJTU+XJVqFQYN26dXo/6TI2NsaDBw+wbt06rFu3DgYGBrCyskJGRobGgbh8+fKYN28eHB0dNaa3t7dHs2bNEBYWhrFjx8LKykqe0F1dXTF06FBUr14dU6dOxfLly+Hn54eePXvCwsICmZmZyMrKQvXq1fHzzz9rdBai7zLU22Dz5s2YOnUqYmJiMGPGDNkLZWZmpkZ7hFeT6SdPnmDt2rVYu3YtjIyMYGVlhdTUVHliMzIywo8//ljo7sznzp2LhQsX4vPPP5dVd9XxmJubY9WqVSXe+1rNmjXRrl07BAYGyhsheXVCNHz4cBw5cgRhYWFYtGgRFi9eDGtrayQlJUEIgf79+6Ny5crw8vLSOv3QoUOxZ88e3Lp1C126dEGVKlXkzYBDhw7JC+pJkyYhOzsb69evx7lz53Du3DmYmprC3NwcycnJGk93itqBw7Nnz+Dh4QEPDw8YGhrCysoKmZmZGr3zTZkyRaPb+F69euH999/HuXPnsHnzZmzevBkVKlTAixcvoFKp0LZtW3Tt2hXLly/XO56ilNnS1rx5c3z66afYs2cPDh48iIMHD8LKygppaWlQKpVo1KgRRo8eXeyvX1AbMmQIbt68ie3bt+OPP/7Avn37YG1tjZSUFCiVSrz//vuoW7euzvI4bdo03L59G/7+/li9ejXWrl0ry7NKpcKIESPw+PFjnD59Wutx2tnZGceOHUNoaCiWLl2KZcuWwdraGomJiTAyMsIPP/yAnTt3ypuEbxNjY2OsWbMG33zzDc6dOyd/YxYWFjA2Npa/FbWcv+OWLVti6NCh+OOPP7B//37s379fnt+USiUUCgVGjBiBuXPnlsWqFVhGRgZ8fHxkrSMLCwsYGhpq1IDp1q2bbJ9YUO3atcPy5csxe/ZshISE4IsvvoCJiQnMzMw0ammoz4PFZerUqVCpVHBzc8OZM2dw5syZAh+XbWxs4ObmhilTpiAiIgIuLi4wNjaGhYWFPH8Aeb+KJycHBwd4enriyy+/hL+/PyZMmIANGzbIp4T379/H0qVLsXTpUpiYmMDCwgIpKSny+sjc3BzLli0r1PUXvf6YJFKJ69atG44dOwYPDw/4+fkhOjoaRkZGUCgUcHJywqhRo/J9KWzHjh3x+++/w83NDZcuXUJGRgbeeecdODk5wdXVVa8uul81dOhQtGjRAp6enrh48SKePn2KcuXKoXnz5vjggw/w2WefFepFu/v378dff/2FwMBAhIWF4eHDh7LKVpUqVdCwYUN06NABQ4YM0Zkgubu7Y/Xq1fD398eTJ0/w+PFjANCogjJ+/HjUrVsXW7duRXh4OLKzs1G7dm04OTlh3Lhxeb7/qqDLAF5WS/Px8cHevXtx6tQp3L17Fy9evICpqSkaNmyIFi1aoEePHvLuNfCyWuTGjRtx8eJFBAcH48mTJ4iPj5e9xrZr1w7Ozs6y2k9hKBQK7N+/H+vXr4e/vz/i4uJQrVo1dOnSBZMmTSrS6zX0MWjQINmu0sTEJM8qrmZmZvDy8sL69evh4+OD6OhoGBgYoE2bNhgyZAg+/vhj+RRbm6ZNm8qkKjQ0VL53E0CuC8avvvoK/fv3x86dO3Hx4kVERUUhOTkZlpaWqF+/Pjp06ICePXvmuhOsr/Xr1+PChQsICgrC48ePER8fD5VKhTp16sDBwQGfffZZrk58DA0NsXbtWnh6euLgwYP4559/oFKp0LRpU3z44YdwdnbG9u3bCx1TYcpsWfnpp5/QrFkz7NmzB/fu3UN2djYaNmyIPn36YMyYMXq1tyqMuXPnwtHREdu3b0d4eDiysrKgUCgwaNAgODs7Y/HixTqnNTU1xcaNG7F7927s27cPDx48QHZ2Nlq1aoX//Oc/cl8C0PrkytTUFFu2bMGmTZvw559/IioqCoaGhujevTvGjRuHNm3aYOfOnSW27q87a2trbNy4EefOnYO3tzeuX7+OuLg4ZGZmolq1amjcuDG6du2Knj175qo58r///Q/NmzfH77//Lqsz29jYyHJVlLZtpWXmzJlo3749Ll26hL///htxcXFIS0tDtWrV0KxZMwwcOBD9+vUr1I2uvn37wsHBAVu3boW/vz8ePXqEzMxM1KlTB40aNULv3r317u8gPwYGBpg+fTr69++PXbt24eLFi4iOjpbH5QYNGsjjsrZlt2vXDj4+Pti+fTvOnDmDiIgIpKWl4Z133kG9evXQs2dPvd6jqL7+GTNmDAICAjB+/Hi4ublBoVBg9erVuHjxIq5fv46YmBg8e/YMJiYmaNCgATp37owvvvgC77zzTjFuHXqdGIi3taI/vfbCw8NlFdLLly+/EdViqHQkJSXJFyB7e3vn2fEJEZWtrKwsdOrUCUlJSVi/fr3e7buJiKj0sU0iERERlZhdu3YhKSkJZmZm8uYOERG93pgkEhERUZFMmjQJp06d0uhYJCYmBqtWrcKSJUsAAJ999lmJdcBDRETFi20SiYiIqEh8fX3h6+sL4GVnFoaGhhrtmrt06YLp06eXVXhERKQnJolERATgZWdF+vb0aWlpCR8fnxKKqHTNnj0b58+f12uaRYsWabzW4231888/46+//sLt27cRHx+P9PR0VK1aVXZCNGDAAL5LjYjoDcIkkV5bTZo00fmSbXq7WVtbs2yUAJVKJd+RV1Cl9RLq0pCYmKj3+uf3ou+3xaeffopPP/20rMMgIqJiwt5NiYiIiIiISGLdDyIiIiIiIpKYJBIREREREZHEJJGIiIiIiIgkJolEREREREQkMUkkIiIiIiIiiUkiERERERERSUwSiYiIiIiISGKSSERERERERBKTRCIiIiIiIpKYJBIREREREZHEJJGIiIiIiIgkJolEREREREQkMUkkIiIiIiIiiUkiERERERERSUwSiYiIiIiISGKSSERERERERBKTRCIiIiIiIpKYJBIREREREZHEJJGIiIiIiIgkJolEREREREQkMUkkIiIiIiIiiUkiERERERERSUwSiYiIiIiISGKSSERERERERBKTRCIiIiIiIpKYJBIREREREZHEJJGIiIiIiIgkJolEREREREQkMUkkIiIiIiIiiUkiERERERERSUwSiYiIiIiISGKSSERERERERBKTRCIiIiIiIpKYJBIREREREZHEJJGIiIiIiIgkJolEREREREQkMUkkIiIiIiIiiUkiERERERERSUwSiYiIiIiISGKSSERERERERBKTRCIiIiIiIpKYJBIREREREZHEJJGIiIiIiIgkJolEREQ6zJo1Cx9//HFZh5GvPXv2wNfXt6zDICKifwkmiURERG84JolERFScmCQSERERERGRxCSRiIiogMLDwzFy5Ei0bNkSbdu2xddff424uDg5/PPPP8fcuXPlZz8/P9ja2mLRokXyOx8fHzRv3hxpaWnyu71796J///5o3rw5unfvjk2bNmks9+7duxgzZgzatWuHVq1aoW/fvtixYwcAYMSIEQgLC8OBAwdga2sLW1tb7N+/v6Q2ARERvQWMyzoAIiKiN0FCQgJGjBgBGxsbLF++HCkpKVi+fDlGjx6Nffv2wdTUFI6Ojjhx4oScJigoCOXKlUNQUJD87vLly2jatCnMzMwAAO7u7lixYgXGjh2Ldu3aISwsDCtXroSZmRmcnZ0BAC4uLmjYsCGWLl0KU1NTPHjwACkpKQCAH374AVOmTEGdOnXg6uoKAKhbt25pbRYiIvoXYpJIRERUAB4eHgCAzZs3w9LSEgDQoEEDDBs2DCdOnMCAAQPg6OiIDRs2ICEhAZUrV0ZQUBCGDh2K3bt3IyUlBRYWFrhy5Qo6dOgAAEhOTsbatWvh4uKCyZMnAwA6d+6MtLQ0rF+/HsOHD0diYiIiIyOxdu1a2NraAgA6duwo42rUqBHMzMxQuXJltGrVqjQ3CRER/UuxuikREVEBhISEoHPnzjJBBIAWLVqgdu3auHLlCgDAwcEBRkZGuHLlCjIzMxESEoJhw4ahUqVKuHbtGpKTk3H79m04OjoCAK5evYrU1FR88MEHUCqV8l+HDh0QFxeHJ0+eoGLFiqhVqxZ++OEHHDt2DPHx8WWy/kRE9Pbgk0QiIqICiI2NRePGjXN9X7VqVSQmJgIALC0tYWdnh6CgIFSqVAnly5eHra0t2rRpg6CgICiVSqhUKrRu3RoA8OzZMwBA//79tS4zOjoatWvXxubNm/Hbb7/hu+++Q3p6Olq3bo05c+agadOmJbS2RET0NmOSSEREVADVqlXT+hQvLi4OzZo1k58dHR0RFBSEihUronXr1jA0NESbNm3g6+sLpVKJRo0aoVKlSgCAChUqAADc3NxQpUqVXPNu0KABAMDGxgarV69GVlYWgoKCsGzZMowfPx7nz5+HoSErBRERUfHimYWIiKgAWrZsCX9/fyQnJ8vvQkJC8PjxY7Rp00Z+5+joiPDwcJw7d05WK23bti1CQkIQEBCgMa6DgwPKly+Pp0+fwt7ePte/nFVbAcDExAQdO3bE6NGjERsbi6SkJACAqakpMjIySnL1iYjoLcIniURERAUwevRo7Nq1C2PHjsXYsWORmpqK5cuXQ6FQoHfv3nI8R0dHZGdn4+rVq5g1axYAwM7ODsbGxggNDcXIkSPluNbW1pg8eTIWLFiAx48fo23btlCpVIiIiMDFixexdu1a3Lp1C7/88gv69u2LOnXqICkpCZs2bYKdnR0qVqwI4OUTR39/f/j5+aFixYp499135dNKIiIifTFJJCIiKoDKlStj27ZtWLx4Mb7++muYmJjg/fffx+zZs2FqaqoxXsOGDREdHS2roRoaGsLBwQF+fn4aTxIBYNy4cahevTq2bt0KT09PlCtXDvXr10e/fv0AvKzmWqVKFWzYsAFPnz6FtbU12rdvj2+++UbOw9XVFdHR0Zg2bRqSk5OxaNEifPzxx6WwVYiI6N/IQAghyjoIIiIiIiIiej2wTSIRERERERFJTBKJiIiIiIhIYpJIREREREREEpNEIiIiIiIikt7K3k1jY1+UdQhERERERERlplo1K53D+CSRiIiIiIiIJCaJREREREREJDFJJCIiIiIiIolJIhEREREREUlMEomIiIiIiEhikkhEREREREQSk0QiIiIiIiKSmCQSERERERGRxCSRiIiIiIiIJCaJREREREREJDFJJCIiIiIiIolJIhEREREREUlMEomIiIiIiEhikkhEREREREQSk0QiIiIiIiKSmCQSERERERGRxCSRiIiIiIiIJCaJREREREREJDFJJCIiIiIiIolJIhEREREREUlMEomIiIiIiEhikkhEREREREQSk0QiIiIiIiKSmCQSERERERGRxCSRiIiIiIiIJCaJREREREREJDFJJCIiIiIiIolJIhEREREREUlMEomIiIiIiEhikkhEREREREQSk0QiIiIiIiKSmCQSERERERGRxCSRiIiIiIiIJCaJREREREREJDFJJCIiIiIiIolJIhEREREREUmvZZJ4/vx59OnTB7169cLGjRtzDc/MzMS0adPQq1cvDBs2DI8ePdIYHhUVBQcHB2zevLm0QiYiIiIiIvpXeO2SxOzsbPz8889wd3fH0aNHceTIEdy7d09jnL1798La2honT57EqFGjsGzZMo3hixYtwnvvvVeaYRMREREREf0rvHZJYkhICOrVq4c6derA1NQU/fv3x6lTpzTGOX36NAYPHgwA6NOnDwICAiCEAAD4+vri3XffRePGjUs9diIiIiIiojedcVkH8KqYmBjUrFlTfq5RowZCQkJyjVOrVi0AgLGxMaysrPDs2TOUL18emzZtgoeHBzw8PHQuw9KyHIyNjUpmBYiIiIiIiN5gr12SqH4imJOBgUGBxlm9ejVGjhwJCwuLPJeRnJxRtCCJiIiIiIjeYNWqWekc9toliTVr1sSTJ0/k55iYGFSvXj3XONHR0ahZsyaUSiVevHiBihUr4vr16/Dx8cGyZcuQlJQEQ0NDlCtXDs7OzqW9GkRERERERG+k1y5JtLe3R0REBCIjI1GjRg0cPXoUy5cv1xjHyckJBw4cgIODA3x8fNChQwcYGBhg586dcpzVq1fD3NycCSIREREREZEeXrsk0djYGPPmzcPYsWORnZ2NIUOGoHHjxli5ciWaN2+OHj16YOjQoZgxYwZ69eqFChUqYMWKFWUdNhERERER0b+CgdDWwO9fLjb2RVmHQEREREREVGbyapP42r0Cg4iIiIiIiMoOk0QiIiIiIiKSmCQSERERERGRpHfHNZGRkQgNDUVsbCwyMjJQsWJF1K9fHy1atED58uVLIkYiIiIiIiIqJQVKEmNiYrB37154e3vj8ePHEELA1NQUpqamSElJgRACxsbGeO+99zBs2DA4OTmVdNxERERERERUAvLt3XTBggX4/fffYWdnh969e8PBwQF2dnYwNzcHAAgh8PjxY4SGhsLf3x++vr6oWbMm5s+fjxYtWpTKSuiLvZsSEREREdHbLK/eTfNNEmfNmoVx48bBxsamQAvLysqCt7c3TExMMGjQIP0iLSVMEomIiIiI6G1WpCTx34hJIhERERERvc1K9D2J6enp+Oeff/AW5ppERERERET/OnoliRs3bsTKlSvl5ytXrqBr16744IMP0KtXL0RERBR3fERERERERFSK9EoS9+3bh3feeUd+XrRoEWxtbbF582ZUq1YNv/76a7EHSERERERERKVHr/ckxsTEoF69evLvGzduYNeuXXBwcEBaWhrmzZtXIkESERERERFR6dDrSWL58uWRmpoKAAgMDISVlRVatWoFALCwsEBKSkrxR0hERERERESlRq8niW3atIGHhwfMzc2xdetWdOvWDQYGBgCAf/75BzVq1CiRIImIiIiIiKh06PUkcdasWUhNTcWoUaOgUqkwffp0OezQoUNwdHQs9gCJiIiIiIio9BTqPYkqlQqGhpr5ZUxMDCwtLWFhYVFswZUUvieRiIiIiIjeZnm9J1Gv6qZqryaIAFjVlIiIiIiI6F8g3yRx4sSJes1ww4YNhQ6GiIiIiIiIyla+SaJSqdT4fPv2bcTFxcHGxgaVK1dGQkIC7t+/j6pVq8LOzq7EAiUiIiIiIqKSl2+S6O7uLv8+dOgQ1q9fj61bt6Jhw4by+/v372Py5MkYMGBAyURJREREREREpUKv3k3XrFmDadOmaSSIAGBjY4OpU6di9erVxRocERERERERlS69ksSYmJg8h8fGxhYpGCIiIiIiIipbeiWJDg4O+PXXX3H//n2N7+/du4cVK1agdevWxRocERERERERlS693pMYGRmJcePG4Z9//kHdunVlxzUPHz5EvXr1sGnTJtSpU6ck4y0WfE8iERERERG9zfJ6T6JeSSIAqFQqHD9+HKGhoYiLi0PVqlVhb2+PDz74QOv7E19HTBKJiIiIiOhtVqxJ4r8Bk0QiIiIiInqb5ZUk5vsKDF3i4+ORkZGR6/t33nmnsLMkIiIiIiKiMqZXkpiQkID58+fD19cXSqVSY5gQAgYGBggPDy/WAImIiIiIiKj06JUkzpkzB1euXIGrqysaNWoEExOTkoqLiIiIiIiIyoBeSeKlS5cwb948DBw4sKTiISIiIiIiojKkV3ekFSpUgLW1dUnFQkRERERERGVMryTR1dUVnp6eWjusISIiIiIiojefXtVNg4KC8PDhQzg5OcHBwQFWVprdphoYGGDhwoXFGiARERERERGVHr2SxDt37qBy5coAgOjoaERHR2sMNzAwKL7IiIiIiIiIqNQZCCFEWQdR2mJjX5R1CERERERERGWmWjUrncP0apNIRERERERE/256VTcFgMzMTJw/fx5///231g5sJk+eXCyBERERERERUenTq7ppVFQU/vOf/yAuLg7Z2dkwMzNDamoqAMDMzAzly5dHQEBAiQVbXFjdlIiIiIiI3mbFVt100aJFaNCgAQIDAyGEgJeXF4KDg/HTTz+hUqVK8PT0LHKwREREREREVHb0ShKvXr0KZ2dnmJubAwCUSiXMzc3x6aefwtnZGfPnzy+RIImIiIiIiKh06JUkpqamwtraGoaGhqhQoQJiY2PlsCZNmiAsLKzYAyQiIiIiIqLSo1eSWL9+fTx58gQAYGtriz/++APZ2dkQQuDw4cOoUqVKiQRJREREREREpUOvJLF37964du0aAGDKlCkIDAyEo6Mj2rZtC29vb0ycOLFEgiQiIiIiIqLSoVfvpq+KiIjAmTNnkJGRgU6dOqFFixbFGVuJYe+mRERERET0Nsurd9MiJYlvKiaJRERERET0Niu2V2CEhITA399ffk5KSsLcuXMxfPhwrFq1CiqVqvBR5nD+/Hn06dMHvXr1wsaNG3MNz8zMxLRp09CrVy8MGzYMjx49AgD89ddf+Pjjj/Hhhx/i448/fiPe2UhERERERPQ60fs9ieo2ierPPj4+qF27NrZu3Yq1a9cWOaDs7Gz8/PPPcHd3x9GjR3HkyBHcu3dPY5y9e/fC2toaJ0+exKhRo7Bs2TIAQKVKlbB+/XocPnwYixcvxsyZM4scDxERERER0dtEryTxwYMHsLe3BwBkZGTg+PHj+O6777Bs2TJ88803OHz4cJEDCgkJQb169VCnTh2Ympqif//+OHXqlMY4p0+fxuDBgwEAffr0QUBAAIQQaNq0KWrUqAEAaNy4MTIzM5GZmVnkmIiIiIiIiN4WxvqMnJGRATMzMwBAcHAwMjMz0aNHDwBAo0aNEBMTU+SAYmJiULNmTfm5Ro0aCAkJyTVOrVq1AADGxsawsrLCs2fPULlyZTmOj48PmjRpAlNT01zLsLQsB2NjoyLHSkRERERE9G+jV5JYv359BAYGol27dvjzzz/RokULWFm9bPAYFxcn/y4Kbf3oGBgY6DXO3bt3sWzZMnh4eGhdRnJyRhGjJCIiIiIienMVW8c148ePx8aNG+Hk5IQ//vgDY8aMkcMuXLiAJk2aFD7K/69mzZp48uSJ/BwTE4Pq1avnGic6OhoAoFQq8eLFC1SsWBEA8OTJE0yePBlLlixB3bp1ixwPERERERHR20SvJ4n9+vWDjY0Nbt68CTs7O42k0MbGBh9++GGRA7K3t0dERAQiIyNRo0YNHD16FMuXL9cYx8nJCQcOHICDgwN8fHzQoUMHGBgYICkpCePHj8d///tftGnTpsixEBERERERvW1ey/cknjt3DgsXLkR2djaGDBkCFxcXrFy5Es2bN0ePHj2QkZGBGTNmIDw8HBUqVMCKFStQp04drFu3Dhs3bkS9evXkvDw8PFClShWN+fM9iURERERE9A3rEFEAACAASURBVDbLq7qpXkmit7d3vuMMGjSooLMrM0wSiYiIiIjobVZsSaKdnZ32meToNCY8PFyP0MoGk0QiIiIiInqb5ZUk6tUmMTg4ONd3iYmJ8Pf3x7Zt2+RL7YmIiIiIiOjNVGxtEnfs2IGTJ09iy5YtxTG7EsUniURERERE9DYrtldg5KVBgwa4fv16cc2OiIiIiIiIykCxJIlJSUnYvn07atWqVRyzIyIiIiIiojKiV5tEJycnjU5qACArKwsJCQkAgN9++634IiMiIiIiIqJSp1eS2KdPn1zflStXDjVr1kT37t1Ro0aNYguMiIiIiIiISl+xdVzzJmHHNURERERE9DYrtldgqD1//hyhoaFITExEhQoV0KJFC1SoUKHQARIREREREdHrQa8kUQiBpUuXwsvLC1lZWfJ7U1NTjBgxAjNmzCj2AImIiIiIiKj06JUkrl+/Htu2bcP48ePRr18/VKlSBfHx8Th27Bg2btwIS0tLuLi4lFSsREREREREVML0apPo5OSETz75BBMnTsw1bMOGDfj9999x+vTpYg2wJLBNIhERERERvc3yapOo13sSY2NjYW9vr3WYvb09YmNj9YuMiIiIiIiIXit6JYl169bFiRMntA47efIk6tatWyxBERERERERUdnQq03iuHHjMGvWLMTGxqJPnz6oWrUq4uPjcfz4cZw5cwaLFv0/9u48rqpq7+P49zCKEzgwJFpmkpSpaE6EOYDgSKJoNlmkpTnPVy2vljk89ng151mztJuzlZZDZCqVQ9N1SEvzUuoVRBkEFRDczx8+7hsxyJEDKHzer5evl2evdfb+bc7xcL6uvdaeVlh1AgAAAACKgNX3Sdy1a5fmzJmjkydPmtt8fHw0dOhQtW3b1uYFFgbmJAIAAAAozfKak2h1SLwlNTVVcXFxcnd3V5kyZe64uOJASAQAAABQmuUVEq263PTPypQpoxo1atzp0wEAAAAAdyGrQ+Jvv/2mXbt2KSYmRmlpaVnaLBaLpk6darPiAAAAAABFy6qQuGnTJo0fP14uLi7y9vaWo6NjlnaLxWLT4gAAAAAARcuqOYkhISGqX7++pkyZImdn58Ksq1AxJxEAAABAaZbXnESr7pMYFxen8PDwezogAgAAAAByZ1VI9Pf3188//1xYtQAAAAAAitlt5yT+5z//Mf/er18/jR07VhaLRU888YQqVqyYrX+1atVsWyEAAAAAoMjcdk6ir69vlgVpbnX/6yI1hmHIYrHo+PHjhVCmbTEnEQAAAEBpVqD7JC5atMimxQAAAAAA7l5WrW5aUjCSCAAAAKA0s9nqpo0aNdLRo0dzbDt+/LgaNWpkXWUAAAAAgLuKVSHx6tWrunHjRo5t6enpSk9Pt0lRAAAAAIDicds5iXFxcbpw4YL5+PTp07K3t8/SJy0tTRs3bmRlUwAAAAC4x902JK5du1bz5s2TxWKRxWLRuHHjsvUxDEMODg568803C6NGAAAAAEARue3CNbGxsYqJiZFhGHrmmWc0efJk+fj4ZOnj6Oio+++/X+XLly/UYm2FhWsAAAAAlGZ5LVxj1eqmJ0+e1AMPPCAnJyebFFZcCIkAAAAASrMCrW4aFxdn/t3HxyffAfHixYv56gcAAAAAuHvcNiSGhIRowoQJOnz48G13lpCQoI8++kidO3fWxo0bbVIgAAAAAKDo3PZy0z/++EPz5s3TZ599pqpVq8rPz08+Pj6qVKmSnJyclJycrLNnz+rYsWM6evSovL291a9fP4WFhcnOzqo7bBQZLjcFAAAAUJrZZE5ifHy8PvnkE+3fv1/Hjh1TfHy8MjMz5eLiogcffFCNGjVSSEiImjZtarPCCwshEQAAAEBpZrOFa/7q+vXrcnR0vNOnFxtCIgAAAIDSrEAL1+TlXgyIAAAAAIDc3Z2TBgEAAAAAxYKQCAAAAAAwERIBAAAAACZCIgAAAADAVOCQmJqaqt9//10FWCQVAAAAAHCXsCokLlmyRLNnzzYff//992rZsqXat2+v4OBgRUdH27o+AAAAAEARsiokbty4UdWqVTMfT5s2TXXq1NHy5cvl7u6umTNn2qSovXv3ql27dgoODtaSJUuytaenp2vYsGEKDg5Wjx49dPbsWbNt8eLFCg4OVrt27bRv3z6b1AMAAAAApYVVITE2NlYPPPCA+fejR49qxIgReuKJJ/TKK6/o+++/L3BBmZmZmjRpkpYtW6Zt27Zp69atOnXqVJY+69evV8WKFbVr1y5FRERoxowZkqRTp05p27Zt2rZtm5YtW6a33npLmZmZBa4JAAAAAEoLq0JimTJldPXqVUnS/v37VaFCBfn5+UmSypUrpytXrhS4oMOHD+uBBx5QjRo15OTkpE6dOikyMjJLny+//FJdu3aVJLVr107ffvutDMNQZGSkOnXqJCcnJ9WoUUMPPPCADh8+XOCaAAAAAKC0cLCm8+OPP64VK1aobNmyWrVqlVq3bi2LxSJJ+v333+Xp6VnggmJjY+Xl5WU+9vT0zBb0YmNjdd9990mSHBwcVKFCBSUkJCg2NlYNGjTI8tzY2Nhsxyhf3lkODvYFrhUAAAAAShqrQuLYsWM1fPhwRURE6OGHH9bw4cPNtk8++USNGzcucEE5rZJ6K4jerk9+nitJKSlpBagQAAAAAO5t7u4Vcm2zKiTWqFFDGzZs0I0bN2Rnl/VK1ZkzZ6p8+fJ3VuGfeHl5KSYmxnwcGxsrDw+PbH3Onz8vLy8vZWRkKDk5WW5ubvl6LgAAAAAgd3d0n8S/BkTp5qWd5cqVK3BB9erVU3R0tM6cOaP09HRt27ZNgYGBWfoEBgZq8+bNkqQdO3aoefPmslgsCgwM1LZt25Senq4zZ84oOjpa9evXL3BNAAAAAFBaWDWSKEl79uzRrl27FBsbq7S07Jdtvv/++wUryMFBEyZM0CuvvKLMzEyFh4fLx8dHs2fP1mOPPaagoCB1795do0ePVnBwsFxdXTVr1ixJko+Pjzp06KCOHTvK3t5eEyZMkL09cw8BAAAAIL8sRk4T+XIxb948zZs3Tw899JDuv/9+OTo6ZuszZ84cmxZYGOLikou7BAAAAAAoNnnNSbQqJAYEBKhnz54aMmSITQorLoREAAAAAKVZXiHRqjmJFotFjz/+eIELAgAAAADcnawKic8884w+/vjjwqoFAAAAAFDMrLrcVJKmTp2qgwcPqlmzZqpQIesQpcVi0cCBA21aYGHgclMAAAAApZnN5iTu2LFDo0aN0vXr11WuXLlsC9dYLBZ9++23d15pESEkAgAAACjNbBYSAwMDVbduXb311luqXLmyTYorDoREAAAAAKWZzRauSUhI0LPPPntPB0QAAAAAQO6sComBgYH64YcfCqsWAAAAAEAxc7Cmc4cOHTRt2jQlJSWpefPmqlixYrY+TZo0sVlxAAAAAICiZdWcRF9f37x3ZrHo+PHjBS6qsDEnEQAAAEBpltecRKtGEj/99NMCFwMAAAAAuHvlOySmp6frm2++kb+/vx5++OHCrAkAAAAAUEzyvXCNk5OTZs6cqcTExMKsBwAAAABQjKxa3fTRRx/VL7/8Uli1AAAAAACKmVUhceLEiVqzZo02bdqkS5cuFVZNAAAAAIBickerm1osllz7sLopAAAAANzdbLa66fjx4/MMiAAAAACAe5tVI4klBSOJAAAAAEqzvEYSrZqTCAAAAAAo2ay63FSSvvrqK61bt07R0dFKS0vL1h4ZGWmTwgAAAAAARc+qkcSdO3dq0KBBKlu2rE6fPi0/Pz899thjSkhIkJ2dndq1a1dYdQIAAAAAioBVIXHRokXq27evpk+fLknq3bu3Zs+ercjISJUvX17VqlUrlCIBAAAAAEXDqpD473//W02bNpWdnZ3s7Ox05coVSVKlSpX02muvaeXKlYVSJAAAAACgaFgVEsuVK6fr16/LYrHI09NT0dHRZpuDg4Pi4+NtXR8AAAAAoAhZtXBN3bp1derUKT355JNq2bKlFixYoLJly8rR0VHvvvuuHnvsscKqEwAAAABQBKwKiX369NG5c+ckSUOHDlV0dLRGjRolSapdu7befPNNmxcIAAAAACg6FsMwjILs4PLly7p27Zo8PT1tVVOhi4tLLu4SAAAAAKDYuLtXyLXNqjmJf5aUlKQTJ07I2dn5ngqIAAAAAIDcWR0St2zZorZt26p58+bq2rWrTp06JUkaMWKE/vnPf9q8QAAAAABA0bEqJH7wwQcaP3682rZtq4ULF+rPV6rWr19fn3zyic0LBAAAAAAUHasWrlm1apUGDBigAQMGKDMzM0tbrVq1dPr0aZsWBwAAAAAoWlaNJMbGxqphw4Y5tjk4OCg1NdUmRQEAAAAAiodVIbF69er68ccfc2z74YcfVKtWLZsUBQAAAAAoHlaFxOeff16LFy/We++9p7i4OEnS1atX9dlnn2nFihV6/vnnC6VIAAAAAEDRsPo+iXPmzNHSpUuVkZEhwzBksVjk4OCgV199VUOGDCmsOm2K+yQCAAAAKM3yuk+i1SFRkuLj4/Xdd98pISFBrq6uaty4sapWrVqgIosSIREAAABAaWaTkJiWlqaRI0cqIiJCjRs3tllxxYGQCAAAAKA0yysk5ntOorOzs/bv36/r16/bpCgAAAAAwN3HqoVrWrZsqb179xZWLQAAAACAYuZgTef27dtr8uTJSkhIUMuWLVW1alVZLJYsfZo0aWLTAgEAAAAARceqhWt8fX3z3pnFouPHjxe4qMLGnEQAAAAApVlecxKtGkn89NNPC1wMAAAAAODuZVVI9PHxKaw6AAAAAAB3AasWrgEAAAAAlGy3HUl85JFHtHbtWtWvX1++vr7ZFqr5q3thTiIAAAAAIGe3DYnjx49X9erVzb/fLiQWRGJiooYPH65z587J29tb7777rlxdXbP127x5sxYuXChJ6t+/v7p27apr165p6NCh+uOPP2Rvb682bdpo1KhRhVYrAAAAAJREVq1uWtjeeecdubm5qW/fvlqyZImSkpI0evToLH0SExMVHh6ujRs3ymKxqFu3btq0aZOcnJz0r3/9S82bN1d6eroiIiLUr18/tWrVKttxWN0UAAAAQGmW1+qmt52TuHPnTl2+fNmmBeUmMjJSYWFhkqSwsDB98cUX2fpERUUpICBAbm5ucnV1VUBAgPbt2ycXFxc1b95ckuTk5KRHH31UsbGxRVI3AAAAAJQUt73cdOjQoeacREm6ceOGOnTooPnz56t27do2LebSpUvy8PCQJHl4eCg+Pj5bn9jYWHl5eZmPPT09s4XBy5cva/fu3XrppZdyPE758s5ycLC3YeUAAAAAUDLcNiT+9WpUwzD0+++/Ky0t7Y4OGBERoYsXL2bbPmzYsHw9P6erY/88TzIjI0MjRoxQr169VKNGjRz3kZJyZ7UDAAAAQEmQ1+WmVt0n0Rbee++9XNuqVKmiCxcuyMPDQxcuXFDlypWz9fHy8tLBgwfNx7GxsWratKn5+O9//7tq1qypiIgIW5YNAAAAAKXCXXWfxMDAQG3ZskWStGXLFgUFBWXr06JFC0VFRSkpKUlJSUmKiopSixYtJEmzZs1SSkqKXn/99SKtGwAAAABKituuburr66tu3brJ09NT0s3LPRctWqTu3bvL3d09684sFg0ZMuSOi0lISNCwYcN0/vx53XfffZo9e7bc3Nx05MgRffTRR5oyZYokacOGDVq8eLEk6bXXXlN4eLhiYmLUqlUr1apVS05OTpKkF154QT169Mh2HFY3BQAAAFCa5XW56W1DYkBAgFX3RoyKisp/ZcWEkAgAAACgNCtQSCyJCIkAAAAASrMC3ScRAAAAAFB6EBIBAAAAACZCIgAAAADAREgEAAAAAJgIiQAAAAAAEyERAAAAAGByuF2HDRs2WLXD7t2733ExAAAAAIDiddv7JPr6+uZ/ZxaLjh8/XuCiChv3SQQAAABQmuV1n8TbjiQePnzYpsUAAAAAAO5etw2JTk5ORVEHAAAAAOAucNuQmJP4+HhFR0crLS0tW5u/v3+BiwIAAAAAFA+rQuLVq1c1evRo7d69W7lNZbwX5iQCAAAAAHJm1S0wZs2apVOnTmn58uUyDEP/+Mc/tHTpUoWGhsrb21tr1qwprDoBAAAAAEXAqpC4e/du9e/fX02bNpUk1ahRQy1atNA777yj1q1bExIBAAAA4B5nVUi8ePGiqlevLnt7e7m4uCgxMdFsCwwM1J49e2xeIAAAAACg6FgVEj09Pc1geP/992vfvn1m288//yxnZ2fbVgcAAAAAKFJWLVzj7++v/fv3q23bturVq5fGjx+vEydOyMnJSd9++62ef/75wqoTAAAAAFAELEZuy5TmICUlRVeuXJGnp6ckaevWrdqxY4dSU1MVEBCgF154QQ4Od3RXjSIVF5dc3CUAAAAAQLFxd6+Qa5tVIbGkICQCAAAAKM3yColWzUls0KCBjhw5kmPbzz//rAYNGlhXGQAAAADgrmJVSExLS1NuA4/p6enKzMy0SVEAAAAAgOJx2wmEsbGxio2NNR+fOnUqW5+0tDRt2rRJ3t7etq0OAAAAAFCkbhsS169fr3nz5slischiseiNN97I1scwDDk6OmrSpEmFUiQAAAAAoGjcduGaCxcu6MKFCzIMQz169NC0adPk4+OTpY+jo6OqV6+ucuXKFWqxtsLCNQAAAABKM5utbnr69GlVr15dTk5ONimsuBASAQAAAJRmeYVEq25qWKtWLd24cUNffPGFvv/+eyUlJcnV1VWNGzdWmzZtZGdn1To4AAAAAIC7jFUjiQkJCXr11Vd19OhRVa1aVVWrVtXFixd18eJF1atXT0uXLpWbm1th1msTjCQCAAAAKM1sdp/E6dOnKyYmRqtXr1ZUVJS2bNmiqKgorV69WjExMZo+fXqBiwUAAAAAFB+rQuLu3bs1atQoNW7cOMv2xo0ba+TIkfryyy9tWhwAAAAAoGhZFRLT0tJyvZzU1dVVaWlpNikKAAAAAFA8rAqJ9erV0/Lly7OFwbS0NK1YsUL16tWzaXEAAAAAgKJl1cI1R44c0UsvvSRnZ2e1atVKVapUUXx8vPbs2aPU1FS9//77euyxxwqzXptg4RoAAAAApZnN7pMoSXFxcVqyZImOHDmiuLg4ubu7q0GDBurTp488PDwKXGxRICQCAAAAKM0KFBIPHTqkRx99VOXKlbN5YcWFkAgAAACgNCvQLTBefPFF/fbbbzYtCAAAAABwd7ptSLTyalQAAAAAwD3MqtVNAQAAAAAlm0N+Ou3Zs0enT5/O1w7DwsIKVBAAAAAAoPjcduEaX1/f/O/MYtHx48cLXFRhY+EaAAAAAKVZXgvX5Gsk8V65/yEAAAAAoGDyFRLLlCmjsmXLFnYtAAAAAIBixsI1AAAAAAATIREAAAAAYLrtwjUlEQvXAAAAACjN8lq45q4aSUxMTNTLL7+skJAQvfzyy0pKSsqx3+bNmxUSEqKQkBBt3rw5W/trr72mzp07F3a5AAAAAFDi3FUhccmSJfL399fOnTvl7++vJUuWZOuTmJioefPmad26dVq/fr3mzZuXJUzu3LlT5cqVK8qyAQAAAKDEuKtCYmRkpMLCwiRJYWFh+uKLL7L1iYqKUkBAgNzc3OTq6qqAgADt27dPknTlyhWtXLlS/fv3L9K6AQAAAKCkyNctMIrKpUuX5OHhIUny8PBQfHx8tj6xsbHy8vIyH3t6eio2NlaSNHv2bPXu3VtlypTJ8zjlyzvLwcHehpUDAAAAQMlQ5CExIiJCFy9ezLZ92LBh+Xp+TuvsWCwWHT9+XH/88Ydef/11nT17Ns99pKSk5a9YAAAAACiB8lq4pshD4nvvvZdrW5UqVXThwgV5eHjowoULqly5crY+Xl5eOnjwoPk4NjZWTZs21Y8//qijR48qMDBQGRkZio+PV69evfTBBx8UxmkAAAAAQIl0V81JDAwM1JYtWyRJW7ZsUVBQULY+LVq0UFRUlJKSkpSUlKSoqCi1aNFCzz33nKKiovTll1/qww8/VM2aNQmIAAAAAGCluyok9u3bV19//bVCQkL09ddfq2/fvpKkI0eO6I033pAkubm5acCAAerevbu6d++ugQMHys3NrTjLBgAAAIASw2LkNMmvhIuLSy7uEgAAAACg2OQ1J/GuGkkEAAAAABQvQiIAAAAAwERIBAAAAACYCIkAAAAAABMhEQAAAABgIiQCAAAAAEyERAAAAACAiZAIAAAAADAREgEAAAAAJkIiAAAAAMBESAQAAAAAmAiJAAAAAAATIREAAAAAYCIkAgAAAABMhEQAAAAAgImQCAAAAAAwERIBAAAAACZCIgAAAADAREgEAAAAAJgIiQAAAAAAEyERAAAAAGAiJAIAAAAATIREAAAAAICJkAgAAAAAMBESAQAAAAAmQiIAAAAAwERIBAAAAACYCIkAAAAAABMhEQAAAABgIiQCAAAAAEyERAAAAACAiZAIAAAAADAREgEAAAAAJkIiAAAAAMBkMQzDKO4iAAAAAAB3B0YSAQAAAAAmQiIAAAAAwERIBAAAAACYCIkAAAAAABMhsYS6ePGiRo4cqaCgIHXr1k09e/bUrl27irss3EUaNmwoSTp79qw6d+4sSTpw4ID69esnSYqMjNSSJUvuaN+xsbEaMmSIbQpFsalTp47+53/+x3y8fPlyzZ07t1CPGRgYqPj4+EI9BgrXrc+WWzZt2qRJkyYVyrH+/Flz5MgRTZ48uVCOU9rVqVNHo0ePNh9nZGSoefPm5u+L/CqK3w1z587V8uXLc2235fvxdsfCfz3yyCPq0qWLOnfurCFDhujatWvFWs9fP6ds4Y033tCpU6dsvt/iQkgsgQzD0MCBA9W4cWNFRkZq06ZNmjlzpmJiYoq7NNxDgoKC1Ldv3zt6rqenp+bMmWPjilDUnJyctHPnTkIb7lp//qypV6+exo8fX8wVlUxly5bVyZMnlZqaKkn6+uuv5enpadU+MjIy+N1QipUpU0Yff/yxtm7dKkdHR3300UfFXZLNTZkyRbVr1y7uMmyGkFgC7d+/X46Ojnr22WfNbd7e3urVq1e2/0Hr16+fDhw4IEmaOHGiunXrpk6dOmX5EA8MDNTMmTPVs2dPdevWTceOHVOfPn3Utm1b/fOf/yy6E0OR+vN7ZezYsZo8ebKeeeYZBQUFafv27ZJu/ofE9OnT1blzZ4WGhuqzzz6TlHV08uTJk+revbu6dOmi0NBQRUdHF8v5wHoODg7q2bOnVq1ala0tPj5egwcPVnh4uMLDw/X999/rxo0bCgwM1OXLl81+wcHBunjxor788kv16NFDYWFhioiI0MWLFyVJCQkJ6t27t8LCwjRhwgT9+a5MAwYMMD+T1q5dW/gnjEKX2/sgNDRUly9flmEYatasmbZs2SJJGj16tL755hudPXtWzz33nLp27aquXbvqhx9+kJT7lRCwvZYtW+qrr76SJG3btk2dOnUy2w4fPqxnnnlGYWFheuaZZ3T69GlJN3+PDBkyRK+99pp69+6d5fXK7TU9cOCAevXqpSFDhqh9+/YaOXKk+bkwb948hYeHq3Pnzvr73/+u293F7f3331fHjh0VGhqq4cOHZ2vP7f04d+5cjRs3Tr169VJQUJDef/998zkLFy5Uu3btFBERoX//+9/5Phb+q3Hjxvr9998lSR9//LH5HWHChAnKzMyUdHOkb9asWXrqqaf09NNPm69Nbt9HLly4oOeff94crfzuu++0fv16TZ061TzuunXrNG3atCy1DBs2THv27DEfjx07Vjt27Lij92evXr105MgRSbl/p76nGChxVq1aZUyZMiXHto0bNxpvvfWW+bhv377G/v37DcMwjISEBMMwDCMjI8N44YUXjOPHjxuGYRht2rQx1qxZYxiGYUyZMsXo3LmzkZycbFy6dMlo3rx5YZ4KCpGfn59hGIZx5swZo1OnToZhGMb+/fuNvn37GoaR9b0yZswYY/DgwUZmZqZx8uRJo23btoZhGMb27duNiIgIIyMjw4iLizNatWplxMbGZtnnpEmTjI8//tgwDMNIS0szrl27VqTniTvn5+dnJCcnG23atDEuX75sLFu2zJgzZ45hGIYxYsQI49ChQ4ZhGMa5c+eM9u3bG4ZhGG+//baxYcMGwzAM46effjJeeuklwzAMIzEx0bhx44ZhGIaxbt06Y9q0aWb/uXPnGoZhGLt37zYefvhh49KlS4Zh/Pcz6dq1a0anTp2M+Pj4IjhrFJSvr6/x1FNPmX9atWplfpbk9j74+9//buzevdv45ZdfjG7duhlvvPGGYRiGERwcbKSkpBhXr141UlNTDcMwjH//+99G165dDcPI/fMLtuXn52ccP37cGDx4sJGammo89dRTWX7eycnJxvXr1w3DMIyvv/7aGDRokGEYN3+PPPnkk+a/5T+/Xrm9pvv37zcaNWpknD9/3sjMzDSefvpp87Pm1n4MwzBGjRplREZGZqt1zpw5xrJlywzDMIyAgAAjLS3NMAzDSEpKMmu63ftxzpw5Rs+ePY20tDTj0qVLRtOmTY309HTjyJEjRufOnY2rV68aycnJRtu2bfM8Fv7r1neO69evG6+99pqxZs0a49SpU0a/fv2M9PR0wzAMY+LEicbmzZsNwzCMhx9+2Hx9p0+fbsyfP98wjNy/jyxfvtxYsGCBYRg3v8cmJycbV65cMYKCgsz99+zZ0zhx4kSWenbu3Gn87W9/Mwzj5neUli1bGteuXbuj9+cLL7xgHD582DCM3L9T30scijukovC99dZb+v777+Xo6Kjnn38+136ff/651q1bp4yMDMXFxem3336Tr6+vpJuXHkrSww8/rKtXr6p8+fKSJGdnZ12+fFkVK1Ys/BNBsWrbtq3s7OxUu3Zt83/0vv/+e3Xq1En29vaqWrWqmjRpMyhhegAAIABJREFUoiNHjqhOnTrm8/z8/LRo0SLFxMQoJCRENWvWLKYzwJ0oX768unTpovfff19lypQxt3/zzTdZ5l6kpKQoJSVFHTt21Pz58xUeHq5t27apY8eOkqSYmBgNHz5ccXFxSk9PV/Xq1SVJhw4d0rx58yRJrVu3lqurq7nPDz74wJxLff78ef3++++qVKlSoZ8zCubWZWW3bNq0SUePHpWU+/ugcePGOnTokKpVq6Znn31W69atU2xsrFxdXVWuXDklJydr0qRJOnHihOzs7LgioRj4+vrq7Nmz2rp1q1q1apWlLTk5WWPGjNHvv/8ui8Wi69evm20BAQFyc3PLtr+MjIxcX9P69evLy8vLPO65c+fUuHFjHThwQMuWLVNqaqoSExPl4+OjwMDAXGuuU6eORo0apaCgILVt2zZbe27vR0lq1aqVnJycVLlyZVWuXFmXLl3Sd999p7Zt28rFxUWSshz7dscq7VJTU9WlSxdJN/+9d+/eXevWrdPRo0fVvXt3s0+VKlUkSY6OjmrTpo0k6bHHHtPXX39t7iun7yP16tXT66+/royMDLVt21aPPPKIJKl58+b66quvVKtWLV2/fj3L9xPp5gj55MmTlZ6err1796px48YqU6ZMnp85ub0//yyv79T3CkJiCeTj46OdO3eajydOnKj4+Hh1795d9vb2unHjhtmWlpYmSTpz5oxWrFihDRs2yNXVVWPHjjXbpJv/WCXJzs5OTk5O5nY7OztlZGQU9inhLvDn1/0W4zaX+kg3LyNr0KCBvvrqK/Xp00eTJ0+Wv79/YZSIQvLSSy+pW7du6tatm7ntxo0bWrt2bZbgKN28ROiPP/5QfHy8vvjiC/Xv31+SNHnyZEVERCgoKEgHDhwwg2FuDhw4oG+++UZr166Vi4uLevXqleUzCfem3N4HTZo00Ycffqjz589r+PDh+uKLL7R9+3bzi9d7772nqlWr6uOPP9aNGzdUv3794jyNUiswMFDvvPOO3n//fSUmJprbZ8+erWbNmmn+/Pk6e/asXnzxRbPtVqD6q7xe0z//vrG3t1dmZqbS0tL01ltvaePGjbrvvvs0d+7c234mLFmyRIcOHdKXX36pBQsWaNu2bVna8/pc+msNt77rWCyWfB/LwYGv2bf89T+PpJvfIbp27aqRI0dm6+/o6Gj+rO3s7MzLUKWcv480adJEq1ev1p49e/S3v/1Nffr0UVhYmHr06KFFixapVq1aWX6H3eLs7KymTZtq3759+vzzz83LqK19f/7Z7b5T3yuYk1gCNW/eXGlpafrwww/Nbbcmm3t7e+vEiRO6ceOGzp8/r8OHD0uSrly5IhcXF1WoUEEXL17U3r17i6V23FuaNGmizz//XJmZmYqPj9d3332X7cvbmTNnVKNGDb344osKDAzUL7/8UkzV4k65ubmpffv22rBhg7mtRYsWWr16tfn4+PHjkm5+gWrbtq2mTZumhx56yBz5S05ONhe6uDXfTLr5Hvr0008lSXv27FFSUpLZ39XVVS4uLvrtt9/0008/Fe5Jokjk9j647777lJCQoOjoaNWoUUONGjXSihUr9Pjjj5vPc3d3l52dnT7++ONsX8pQNLp3764BAwZkG4358+u6efPmfO3L2tf01pfsSpUq6cqVK9qxY0ee/W99z2nevLlGjx6t5ORkXb16Nde6//x+zE2TJk20a9cupaamKiUlRbt37873sZCdv7+/duzYoUuXLkmSEhMTde7cuTva17lz51SlShU9/fTTCg8P17FjxyRJDRo0UExMjLZu3WrOh/2rTp06adOmTfruu+/UokULSQX7zCkp36n5L44SyGKxaP78+Zo2bZqWLVumypUry8XFRaNGjdLjjz8ub29vhYaGysfHR3Xr1pV0c7j80UcfVadOncxf0MDtBAcH68cff1SXLl1ksVg0evRoubu76+zZs2afzz77TJ988okcHBxUtWpVDRw4sBgrxp3q3bu31qxZYz5+4403NGnSJIWGhiozM1ONGzc2Fzrq2LGjunfvnuX2GYMGDdLQoUPl6empBg0amO+RgQMHauTIkeratauaNGmiatWqSbp5CdBHH32k0NBQPfjgg/Lz8yvCs0Vhye19IN28hOvWlS6NGzfWzJkzzZD43HPPafDgwdq+fbuaNWumsmXLFkv9pZ2Xl5deeumlbNtfeeUVjR07VitXrlTz5s3ztS9rX9OKFSuqR48eCg0Nlbe3t+rVq5dn/8zMTI0ePVopKSkyDEMRERHZpsbk9X7MSd26ddWxY0d16dJF3t7e5vszP8dCdrVr19awYcPUu3dv3bhxQ46OjpowYYK8vb2t3tfBgwe1fPlyOTg4qGzZspo+fbrZ1qFDBx0/fjzLdIY/CwgI0JgxYxQYGGiOEhbkM6ekfKe2GPm5XgwAAAAA7jH9+vVTREQEU12sxOWmAAAAAEqUy5cvq127dnJ2diYg3gFGEgEAAAAAJkYSAQAAAAAmQiIAAAAAwERIBAAAAACYCIkAAORh06ZN6tatmxo2bKgmTZooLCxM06ZNM9svXbqkuXPn3nb5/Jykp6dr7ty55n0mAQC4GxASAQDIxeLFizV+/Hi1aNFC8+bN0/Tp0xUUFKQvv/zS7HPp0iXNmzfvjm4Cff36dc2bN4+QCAC4qzgUdwEAANytVq9erZ49e2rEiBHmtsDAQA0aNKgYqwIAoHBxCwwAAHLh5+enV199VQMHDsyx/ezZswoKCsq2/ZdfftHVq1c1Y8YMff3114qJiVGVKlXUqlUrjRw5UuXLl5ck1alTJ9tzIyMjVb16daWlpWn27Nnatm2bLl26pFq1amnkyJFq1apVlr7z58/X6dOn5ejoqJo1a2r06NFq2rSpjX4CAIDSiJFEAABy8eijj2r16tWqVq2aWrdurUqVKmVp9/Dw0IwZMzRq1ChNmDBBdevWNdtSU1OVmZmp4cOHq3Llyjp//rwWLVqkoUOHavny5ZKkVatW6aWXXlL//v3VunVrc5+SNGTIEB0+fFiDBw/W/fffr88//1z9+/fXxo0b9cgjj+iPP/7Q0KFD1atXL40ePVrp6ek6evSokpKSiuaHAwAosRhJBAAgFydOnNDAgQN19uxZWSwWPfTQQwoJCVGfPn3M0cBff/1VoaGhev/999WsWbNc95WRkaF//etfeu6557R7925Vq1ZNV65cUaNGjTRt2jR169bN7Pvtt98qIiJCH3zwQZZRweeff15VqlTRnDlztH37dk2cOFEHDhwovB8AAKBUYuEaAABy4evrq88//1wLFy7Uc889J8MwtGDBAoWHh+vKlSu3ff6WLVsUFhamhg0bqm7dunruueckSdHR0Xk+75tvvpG7u7saNWqkjIwM84+/v7+OHj0qSXr44YeVnJysMWPGKCoqSlevXi3w+QIAIHG5KQAAeXJyclJgYKACAwMlSevXr9f48eO1YcMGvfTSS7k+b9euXRozZoyeffZZDR8+XG5uboqLi9PAgQOVlpaW5zETEhIUFxeX5fLVW+zt7SVJtWrV0oIFC7RkyRL17dtXDg4OCg4O1htvvKHKlSsX4IwBAKUdIREAACv06NFDM2bM0OnTp/Pst337djVo0EBvvvmmue3gwYP5Ooarq6s8PT01f/78PPu1bt1arVu3VnJysr766itNnTpVb7/9tmbNmpWv4wAAkBNCIgAAubh06ZKqVKmSZVt8fLySk5NVtWpVSZKjo6MkZRsdTE1NlZOTU5Ztn376aZbHuT3X399fK1euVNmyZfXQQw/dts4KFSooNDRUhw4d0o8//piPMwMAIHeERAAAchEaGqqgoCAFBASoSpUqOnfunFasWKEyZcooLCxMklStWjWVKVNGW7ZsUYUKFeTg4KB69erpiSee0KRJk7Rw4UI1aNBAe/bs0bfffptl/05OTqpevbo+//xz+fj4yNnZWXXq1FFAQIBatGih3r1769VXX1Xt2rWVkpKiEydOKC0tTSNHjtRHH32kn376SU8++aQ8PDwUHR2t7du3q0uXLsXxowIAlCCsbgoAQC7WrFmjyMhI/frrr0pKSpK7u7saNmyoAQMGZBnh++STTzR//nydO3dO169f1y+//KLMzEzNmDFDW7ZsUVpamgICAvTKK6/o6aef1qJFi9SmTRtJUlRUlKZPn67o6Gilp6eb90lMT0/XokWL9Omnn+r8+fNydXWVr6+vevXqpdatW+vHH3/UwoUL9fPPP5u1dejQQUOHDs02ggkAgDUIiQAAAAAAE7fAAAAAAACYCIkAAAAAABMhEQAAAABgIiQCAAAAAEyERAAAAACAiZAIAAAAADAREgEAAAAAJkIiAAAAAMBESAQAAAAAmAiJAAAAAAATIREAAElz585Vs2bNcmwbO3asunXrJknatGmT6tSpoytXrths/7a2du1affHFF0VyLABAyUNIBADACq1bt9batWvl4uJS3KXkipAIACgIh+IuAACAe0nlypVVuXLl4i4DAIBCw0giAABWyOly0//85z965ZVXVL9+fQUGBmrTpk0aMmSIevXqle35P//8s55++mk1aNBAYWFh+u6777L1Wb9+vTp16qTHHntMbdq00dKlS7O0nzx5Un369FHTpk3l5+enDh06aM2aNZKkXr166dixY9q8ebPq1KmjOnXqaNOmTTb+KQAASjJGEgEA+JOMjIxs2wzDyLW/YRjq37+/kpOTNXXqVDk7O2vBggWKj4/X/fffn6VvamqqxowZo4iICFWtWlXz58/XwIED9dVXX5mXry5btkyzZs3SK6+8oqZNm+rYsWOaPXu2XFxc9MILL0iS+vfvr1q1aul///d/5eTkpNOnT5uhdeLEiRo8eLBq1KihAQMGSFK2OgAAyAshEQCA/5eYmKi6devm2Jbb9j179ujEiRNav3696tevL0nmiGJOIfH111+Xv7+/JMnDw0NhYWE6dOiQWrZsqZSUFM2fP1/9+/fXoEGDJEkBAQG6du2aFi5cqGeffVZJSUk6c+aM5s+frzp16kiSuT9Jql27tlxcXFS5cmX5+fkV7AcCACiVCIkAAPy/ChUqaOXKldm2z58/XxcuXMjxOUeOHJG7u7sZECXJ09Mzx1Dp6OiYZYXThx56SJIUGxsrSfrxxx919epVtW/fPsuIZvPmzbVgwQLFxMTovvvu03333aeJEyfqxRdfVLNmzVSlSpU7O2EAAHJASAQA4P/Z29urXr162ba7ubnlGhLj4uJUqVKlbNsrV66c7TYZ5cqVk53df5cDcHJykiSlpaVJkhISEiRJnTp1yvFY58+fl7e3t5YvX653331Xr7/+ulJTU9WoUSONHz9ejz76aD7OEgCAvBESAQAoAHd3dzPc/Vl8fLycnZ2t2perq6skafHixTmODj744IOSbo5Azp07V9evX9d3332nGTNmqG/fvtq7d2+WEAoAwJ3gNwkAAAVQr149xcXF6fDhw+a22NhYHTt2zOp9NWzYUGXKlNGFCxdUr169bH/Kly+fpb+jo6P8/f318ssvKy4uTpcvX5Z0c4Ty1ugkAADWYiQRAIACaNWqlXx9fTVs2DCNGDFCZcqU0bx581SlShVZLBar9lWxYkUNGjRIU6ZM0blz59SkSRPduHFD0dHROnDggObPn68TJ07onXfeUYcOHVSjRg1dvnxZS5cula+vr9zc3CTdHHGMiorSvn375ObmpurVq+d4SSwAADkhJAIAUAAWi0ULFizQhAkTNG7cOFWtWlWvvfaaduzYoTJlyli9v1dffVUeHh5atWqVVq5cKWdnZ9WsWVMdO3aUdPPy1ipVqmjRokW6cOGCKlasqGbNmmnUqFHmPgYMGKDz589r2LBhSklJ0bRp09StWzebnTMAoGSzGHnd/AkAAFgtOTlZbdu21fPPP68hQ4YUdzkAAFiFkUQAAAron//8p+zs7PTAAw8oPj5e7733ntLT0xUeHl7cpQEAYDVCIgAABeTk5KSlS5fq/PnzslgsqlevnlauXClvb+/iLg0AAKtxuSkAAAAAwMQtMAAAAAAAJkIiAAAAAMBUKuckxsUlF3cJAAAAAFBs3N0r5NrGSCIAAAAAwERIBAAAAACYCIkAAAAAABMhEQAAAABgIiQCAAAAAEyERAAAAACAiZAIAAAAADAREgEAAAAAJkIiAAAAAMBESAQAAAAAmAiJAAAAAAATIREAAAAAYCIkAgAAAABMDsVdAAAAAIDitWT4y8VdAu5Q31krbb5PRhIBAAAAACZCIgAAAADAREgEAAAAAJgIiQAAAAAAEyERAAAAAGAiJAIAAAAATIREAAAAAICJkAgAAAAAMBESAQAAAAAmQiIAAAAAwERIBAAAAACYCIkAAAAAABMhEQAAAABgIiQCAAAAAEyERAAAAACAiZAIAAAAADAREgEAAAAAJkIiAAAAAMBESAQAAAAAmAiJAAAAAABTkYbEcePGyd/fX507dza3TZ8+Xe3bt1doaKgGDhyoy5cvm22LFy9WcHCw2rVrp3379pnb9+7dq3bt2ik4OFhLliwxt585c0Y9evRQSEiIhg0bpvT09KI5MQAAAAAoIYo0JHbr1k3Lli3Lsi0gIEBbt27Vp59+qpo1a2rx4sWSpFOnTmnbtm3atm2bli1bprfeekuZmZnKzMzUpEmTtGzZMm3btk1bt27VqVOnJEkzZsxQRESEdu7cqYoVK2rDhg1FeXoAAAAAcM8r0pDYpEkTubq6ZtnWokULOTg4SJL8/PwUExMjSYqMjFSnTp3k5OSkGjVq6IEHHtDhw4d1+PBhPfDAA6pRo4acnJzUqVMnRUZGyjAM7d+/X+3atZMkde3aVZGRkUV5egAAAABwz3Mo7gL+bOPGjerQoYMkKTY2Vg0aNDDbPD09FRsbK0ny8vLKsv3w4cNKSEhQxYoVzcDp5eVl9v+r8uWd5eBgX1inAQAAAABFws2trM33edeExIULF8re3l5PPfWUJMkwjGx9LBaLbty4keP2nOS2PSUlrQCVAgAAAMDdITHx6h09z929Qq5td0VI3Lx5s7766iu99957ZrDz8vIyLz2Vbo4senh4SFKO2ytVqqTLly8rIyNDDg4OiomJMfsDAAAAAPKn2G+BsXfvXi1dulQLFy6Ui4uLuT0wMFDbtm1Tenq6zpw5o+joaNWvX1/16tVTdHS0zpw5o/T0dG3btk2BgYGyWCxq1qyZduzYIelm8AwMDCyu0wIAAACAe1KRjiSOGDFCBw8eVEJCglq2bKnBgwdryZIlSk9P18svvyxJatCggSZNmiQfHx916NBBHTt2lL29vSZMmCB7+5vzCCdMmKBXXnlFmZmZCg8Pl4+PjyRp9OjRGj58uN5991098sgj6tGjR1GeHgAAAADc8yxGTpP/Sri4uOTiLgEAAAC4aywZ/nJxl4A71HfWyjt6Xl5zEov9clMAAAAAwN2DkAgAAAAAMBESAQAAAAAmQiIAAAAAwERIBAAAAACYCIkAAAAAABMhEQAAAABgIiQCAAAAAEyERAAAAACAiZAIAAAAADAREgEAAAAAJkIiAAAAAMBESAQAAAAAmAiJAAAAAAATIREAAAAAYHKwpnN6erqOHz+uuLg4paeny9XVVTVr1pS3t3dh1QcAAAAAKEK3DYlpaWn67LPPtGXLFv3www+6fv16lnaLxSJ3d3eFhISoe/fu8vX1LbRiAQAAAACFK8+QuGrVKi1ZskR2dnYKDg42Q2ClSpXk5OSky5cv69y5czp69Kj27dun8PBwPfnkkxozZowefPDBojoHAAAAAICN5BkSv/jiC02bNk1PPvmkLBZLtvaKFSuqevXqatasmfr06aOLFy9q9erVioqKIiQCAAAAwD0oz5D4wQcfWLWzqlWratiwYQUqCAAAAABQfAq8uml6erot6gAAAAAA3AXyHRI3btyoVatWmY9PnTqldu3ayc/PTy+88IIuXrxYKAUCAAAAAIpOvkPiypUr5eDw36tT3377bZUpU0ZvvfWWEhIS9I9//KNQCgQAAAAAFJ183yfx3LlzqlWrliQpISFBhw4d0rJly/TEE0+ofPnymjZtWqEVCQAAAAAoGvkeSXR0dFRmZqYk6eDBg3J2dlaTJk0kSVWqVFFSUlLhVAgAAAAAKDL5Dol169bV+vXrde7cOX344Yd64okn5OjoKOnmKGPVqlULrUgAAAAAQNHId0j829/+ph9++EFBQUH67bffNHToULNt+/bt8vPzK5QCAQAAAABFJ99zEh955BHt2bNH58+fl7u7u5ycnMy2/v37y8vLq1AKBAAAAAAUnXyHREmys7OTt7d3tu2MIgIAAABAyWBVSPztt9+0a9cuxcTEKC0tLUubxWLR1KlTbVocAAAAAKBo5Tskbtq0SePHj5eLi4u8vb3NRWtusVgsNi8OAAAAAFC08h0SFy1apI4dO2rKlClydnYuzJoAAAAAAMUk36ubxsXFKTw8nIAIAAAAACVYvkOiv7+/fv7558KsBQAAAABQzPIdEvv166cNGzZoxYoVOnHihP7zn/9k+3M748aNk7+/vzp37mxuS0xM1Msvv6yQkBC9/PLLSkpKkiQZhqHJkycrODhYoaGhOnbsmPmczZs3KyQkRCEhIdq8ebO5/ejRowoNDVVwcLAmT54swzDye3oAAAAAAEkWI59JytfX979P+ssiNYZhyGKx6Pjx43nu49ChQypbtqzGjBmjrVu3SpLeeecdubm5qW/fvlqyZImSkpI0evRo7dmzRx988IGWLl2qf/3rX5oyZYrWr1+vxMREhYeHa+PGjbJYLOrWrZs2bdokV1dXde/eXW+88Yb8/Pz06quvqlevXmrVqlW2OuLikvNzygAAAECpsGT4y8VdAu5Q31kr7+h57u4Vcm2zauGagmrSpInOnj2bZVtkZKQ++OADSVJYWJh69eql0aNHKzIyUmFhYbJYLPLz89Ply5d14cIFHTx4UAEBAXJzc5MkBQQEaN++fWratKlSUlLUsGFDc1+RkZE5hkQAAAAAQM7yHRJbt25dKAVcunRJHh4ekiQPDw/Fx8dLkmJjY+Xl5WX28/LyUmxsbLbtnp6eOW6/1T8n5cs7y8HBvjBOBwAAAACKjJtbWZvvM98h8ZZff/1VP/zwg5KSkuTq6qrHH39cPj4+Ni8sp6tgLRaL1dtzkpKSVvACAQAAAKCYJSZevaPn2eRy0/T0dI0ePVo7d+6UYRhycXHRtWvXZLFY1K5dO73zzjtycnKyurgqVarowoUL8vDw0IULF1S5cmVJN0cCY2JizH4xMTHy8PCQl5eXDh48aG6PjY1V06ZNc+0PAAAAAMi/fK9uOmPGDO3du1eTJ0/Wjz/+aP6ZPHmy9u7dq5kzZ95RAYGBgdqyZYskacuWLQoKCsqy3TAM/fTTT6pQoYI8PDzUokULRUVFKSkpSUlJSYqKilKLFi3k4eGhcuXK6aeffpJhGFn2BQAAAADIn3yPJH722WcaNWqUwsPDzW0uLi4KDw9XamqqFi5cqLFjx+a5jxEjRujgwYNKSEhQy5YtNXjwYPXt21fDhg3Thg0bdN9992n27NmSpFatWmnPnj0KDg6Wi4uLpk6dKklyc3PTgAED1L17d0nSwIEDzUVs3nzzTY0bN06pqalq2bKlWrZsad1PAwAAAABKuXzfAqN+/fqaP3++nnzyyWxte/fu1aBBg3T48GGbF1gYuAUGAAAA8F/cAuPeVRi3wMj35aa1a9fWhg0bcmzbsGFDoSxeAwAAAAAoWvm+3HTo0KEaMGCAwsLCFBISoqpVq+rSpUvauXOnTp48qQULFhRmnQAAAACAIpDvkNiqVSt99NFHmjdvnlavXq34+HhVrlxZ9evX19tvv63HHnusMOsEAAAAABQBq+6TWK9ePS1evFiSlJmZKXt7bkgPAAAAACVJvuck/hUBEQAAAABKnjxHEseNG6cBAwaoRo0aGjduXJ47slgs5m0qAAAAAAD3pjxD4q+//qq0tDTz73mxWCy2qwoAAAAAUCzyDIkb/6+9Ow+v8c7/P/462RBCLIlQ2iljae0a29cWQVCJJLZaiyqdotQ2NaWqaiijjDZaNa29VbUFTcUSW22torUUrVZsI8cWEdIsks/vD7/e0xRxkORYno/rynXl3l/3ybnuc9753J/PvXTpTX8HAAAAADyc7rpPIgAAAADg4ZNlS2JkZOQd7SwsLOyewgAAAAAAnCvLInHEiBEO78hms1EkAgAAAMADLssicc+ePbmVAwAAAABwH8iySPT09MytHAAAAACA+0CWReJ///vfO9pZyZIl7ykMAAAAAMC5siwSAwMDHXr+oTFGNptNhw4dyrZgAAAAAIDcl2WROGPGjNzKAQAAAAC4D2RZJAYEBORSDAAAAADA/cDF2QEAAAAAAPePLFsSmzZtqunTp6tixYoO9U+MiYnJ1nAAAAAAgNyVZZEYFBQkb29vSVKLFi1yJRAAAAAAwHmyLBJfe+21m/4OAAAAAHg40ScRAAAAAGDJsiXxzzZv3qx169bJbrcrJSXlhuXz5s3LtmAAAAAAgNzncJEYERGhiIgIlS1bVo8//rjy5cuXk7kAAAAAAE7gcJG4cOFC9evXTwMHDszJPAAAAAAAJ3K4T6LNZtMzzzyTk1kAAAAAAE7mcJHYqVMnrVixIiezAAAAAACczOHbTQcMGKDx48crLCxMderUkZeXV6blNptN/fv3z/aAAAAAAIDc43CRuGbNGi1cuFBpaWk6efKk3N3dMy2nSAQAAACAB5/DReLEiRMVEBCgt956S0WKFMnJTAAAAAAAJ3G4T2J8fLw6d+5MgQgAAAAADzGHi8TAwEDt2bMnJ7MAAAAAAJzM4dtNW7VqpQkTJighIUF169ZVwYIFb1inVq1a2RoOAAAAAJC7bMYY48iKFStWzHpHNpsOHTqULaFy2rlzic6OAAAAANw3Zg7u5ey6k0Z8AAAgAElEQVQIuEt9p86+q+18fLxuuczhlsRVq1bd1cEdNWfOHC1evFg2m03ly5fXhAkTdPbsWQ0ZMkQJCQl6+umnNWnSJHl4eCg1NVV///vfdfDgQXl7e2vq1KkqVaqUJOmjjz7SkiVL5OLiolGjRqlhw4Y5mhsAAAAAHiYOF4nlypXLsRB2u13z5s3TV199pbx582rQoEGKiorS5s2b1bNnT7Vu3VqjR4/WkiVL1KVLFy1evFgFCxbUunXrFBUVpcmTJ+vf//63jh49qqioKEVFRclut6tXr15as2aNXF1dcyw7AAAAADxMshy45ty5c3e10/Pnz9/xNunp6UpOTta1a9eUnJwsHx8f7dy5Uy1atJAkhYeHKyYmRpK0YcMGhYeHS5JatGihHTt2yBijmJgYtW7dWh4eHipdurSeeOIJ7du3767OAQAAAAAeRVm2JAYFBSkkJETt27dX1apVs9xRfHy81qxZowULFigkJEQvvfSSwyGKFy+uF154QU2aNFGePHlUv359VapUSQULFpSb2/WIfn5+stvtkq63PJYoUeL6Cbi5ycvLS/Hx8bLb7apWrVqm/f6+zR8VKJBHbm60LgIAAAB4sHl7e2b7PrMsElesWKGIiAh16dJFxYoVU/Xq1VWuXDkVLlxYHh4eSkxM1KlTp3Tw4EEdOHBAjz32mF566SWFhYXdUYiEhATFxMQoJiZGXl5eGjRokLZs2XLDejabTZJ0s7F2bDbbLef/2ZUrKXeUDwAAAADuR5cuJd3Vdnc9cM3jjz+uSZMmacSIEVq5cqV27typzz//XBcvXlR6erry5cunJ598UjVr1tSQIUNUu3btuwq4fft2lSpVSkWKFJF0vQVz7969unz5sq5duyY3NzfFxcXJ19dX0vVWxTNnzsjPz0/Xrl1TYmKivL295efnp7i4OGu/drvd2gYAAAAAcHsODVxTpEgR9ezZUz179rTmpaWlyd3dPVtClCxZUj/88IN+++035c2bVzt27FDlypVVp04drVmzRq1bt9by5csVGBgoSQoMDNTy5ctVo0YNrVmzRnXr1pXNZlNgYKCGDh2qXr16yW63KzY29ra3yQIAAAAA/sfh0U3/LLsKREmqVq2aWrRoofDwcLm5uempp57Sc889p4CAAA0ePFj//ve/9dRTT6lDhw6SpPbt22v48OFq3ry5ChUqpKlTp0q6PgJrq1at9Oyzz8rV1VWjR49mZFMAAAAAuAM2c7OOfA+5c+cSnR0BAAAAuG/MHNzL2RFwl/pOnX1X22XVJzHLR2AAAAAAAB4tFIkAAAAAAAtFIgAAAADAck9FYnJyso4fP37T5xMCAAAAAB48DheJM2fO1LRp06zp3bt3q1GjRmrZsqWaN2+u2NjYnMgHAAAAAMhFDheJS5cuVcmSJa3pCRMmqEKFCvrkk0/k4+OjKVOm5EhAAAAAAEDucfg5iXa7XU888YT1+4EDB7Rw4ULVqFFDv/32m0aPHp1jIQEAAAAAucPhlsS8efMqKSlJkrRz5055eXmpevXqkqT8+fPr6tWrOZMQAAAAAJBrHG5JfOaZZzRr1ix5enpq7ty5CggIkM1mkyQdP35cxYsXz7GQAAAAAIDc4XBL4ogRI5SUlKSePXsqIyNDgwcPtpatXLlS/v7+ORIQAAAAAJB7HG5JLF26tJYsWaKMjAy5uGSuLadMmaICBQpkezgAAADcnUXvbHJ2BNyD50YEODsCHmEOF4m/+3OBKIlbTQEAAADgIXFHReLmzZu1bt062e12paSk3LB83rx52RYMAAAAAJD7HC4SIyIiFBERobJly+rxxx9Xvnz5cjIXAAAAAMAJHC4SFy5cqH79+mngwIE5mQcAAAAA4EQOj25qs9n0zDPP5GQWAAAAAICTOVwkdurUSStWrMjJLAAAAAAAJ3P4dtMBAwZo/PjxCgsLU506deTl5ZVpuc1mU//+/bM9IAAAAAAg9zhcJK5Zs0YLFy5UWlqaTp48KXd390zLKRIBAAAA4MHncJE4ceJEBQQE6K233lKRIkVyMhMAAAAAwEkc7pMYHx+vzp07UyACAAAAwEPM4SIxMDBQe/bsycksAAAAAAAnc/h201atWmnChAlKSEhQ3bp1VbBgwRvWqVWrVraGAwAAAADkrjsa3VSS5s+fr/nz59+w3Gaz6dChQ9mXDAAAAACQ6xwuEletWpWTOQAAAAAA9wGHisTU1FRt375d9erVU/ny5XM6EwAAAADASRwauMbDw0NTpkzRpUuXcjoPAAAAAMCJHB7d9Omnn9aRI0dyMgsAAAAAwMkcLhLffPNNffrpp1q2bJkuXLiQk5kAAAAAAE7i8MA1YWFhkqSRI0fech1GNwUAAACAB5vDReKoUaNks9lyMgsAAAAAwMkcLhK7deuWkzkAAAAAAPcBh/skAgAAAAAefg63JErSpk2b9MUXXyg2NlYpKSk3LI+Jicm2YAAAAACA3OdwS+LatWs1YMAAeXp66tdff1X16tVVuXJlxcfHy8XFRS1atLinIJcvX9bAgQPVsmVLtWrVSnv37tWlS5fUq1cvBQUFqVevXkpISJAkGWM0btw4NW/eXCEhITp48KC1n+XLlysoKEhBQUFavnz5PWUCAAAAgEeNw0XijBkz1LdvX02cOFGS9MILL2jatGmKiYlRgQIFVLJkyXsK8s9//lMNGzZUdHS0VqxYobJly2rmzJmqV6+e1q5dq3r16mnmzJmSpC1btig2NlZr167V22+/rTFjxkiSLl26pIiICH3xxRdavHixIiIirMISAAAAAHB7DheJx44dU+3ateXi4iIXFxddvXpVklS4cGH97W9/0+zZs+86xJUrV7Rr1y61b99ekuTh4aGCBQsqJibGevRGWFiY1q9fL0nWfJvNpurVq+vy5cs6e/astm7dqvr168vb21uFChVS/fr19fXXX991LgAAAAB41DjcJzF//vxKS0uTzWZT8eLFFRsbq9q1a1/fiZubLl68eNchTp48qSJFiugf//iHDh8+rEqVKmnkyJG6cOGCfH19JUm+vr7WMex2u/z8/Kzt/fz8ZLfbb5hfvHhx2e32G45XoEAeubm53nVeAAAAICd5e3s6OwIeEDnxXnG4SKxUqZKOHj2qhg0bqlGjRvrggw/k6ekpd3d3/fvf/1blypXvOsS1a9f0448/6o033lC1atU0btw469bSmzHG3DDPZrPdcv6fXbly46A7AAAAwP3i0qUkZ0fAA+Ju3ys+Pl63XObw7aa9e/eWt7e3JGnQoEF64oknNGzYMA0aNEiurq5Wv8C74efnJz8/P1WrVk2S1LJlS/34448qWrSozp49K0k6e/asihQpYq0fFxdnbR8XFydfX98b5tvtdqslEgAAAABwew4XibVr11Z4eLgkqUiRIpo7d66+/fZbbd68WV9++aXKli171yF8fHzk5+enX3/9VZK0Y8cOlS1bVoGBgYqMjJQkRUZGqmnTppJkzTfG6Pvvv5eXl5d8fX3VoEEDbd26VQkJCUpISNDWrVvVoEGDu84FAAAAAI+aO3pO4u8SEhJ05swZPfnkkypYsGC2BHnjjTc0bNgwpaWlqXTp0powYYIyMjL06quvasmSJSpRooSmTZsmSWrcuLE2b96s5s2bK1++fBo/frwkydvbW/369bMGwOnfv7/V+gkAAAAAuD2buVlHvluIjIxURESETp8+LUlasmSJKlWqpCFDhqhWrVrq3LlzjgXNTufOJTo7AgAAQI5a9M4mZ0fAPXhuRECuHm/m4F65ejxkn75T7+4pE9nSJ3H+/PkaNWqUmjVrpg8//DDTIDFVq1bVypUr7yocAAAAAOD+4fDtpnPnzlW/fv3Ur18/paenZ1pWpkwZqz8hAAAAAODB5XBLot1uV40aNW66zM3NTcnJydkWCgAAAADgHA4XiaVKldLevXtvumzPnj0qU6ZMtoUCAAAAADiHw0Vi165d9dFHH2nOnDk6d+6cJCkpKUlfffWVZs2apa5du+ZYSAAAAABA7nC4T2K3bt108eJFvfvuu5o4caIk6fnnn5ebm5v69OljPXYCAAAAAPDguqPnJA4cOFDdunXTd999p/j4eBUqVEj+/v4qVqxYTuUDAAAAAOQih4rElJQUDR06VD179pS/v7+CgoJyOhcAAAAAwAkc6pOYJ08e7dy5U2lpaTmdBwAAAADgRA4PXNOoUSNt2bIlJ7MAAAAAAJzM4T6JLVu21Lhx4xQfH69GjRqpWLFistlsmdapVatWtgcEAAAAAOQeh4vEgQMHSpIiIyMVGRl5w3KbzaZDhw5lXzIAAAAAQK5zuEhctWpVTuYAAAAAANwHHC4Sy5Url5M5AAAAAAD3AYcHrgEAAAAAPPyybEl86qmntGjRIlWtWlUVK1a8YaCaP6NPIgAAAAA82LIsEkeNGqVSpUpZv9+uSAQAAAAAPNiyLBK7du1q/d6tW7ccDwMAAAAAcK4s+ySuXbtWly9fzq0sAAAAAAAny7JIHDRokGJjY63pjIwMtWjRQkePHs3pXAAAAAAAJ8iySDTG3DB9/PhxpaSk5GgoAAAAAIBz8AgMAAAAAICFIhEAAAAAYMlydFNJ+vzzz7Vx40ZJ/7v9dOHChfLx8cm0ns1m08CBA3MgIgAAAAAgt2RZJBYtWlRbtmzJNK9YsWLatGnTTdenSAQAAACAB1uWReK2bdtyKwcAAAAA4D5An0QAAAAAgIUiEQAAAABgoUgEAAAAAFgoEgEAAAAAFopEAAAAAICFIhEAAAAAYMnyERhLliy5o521b9/+nsIAAAAAAJwryyJx1KhRDu/IZrNRJAIAAADAAy7LInHfvn25lUOSlJ6ernbt2ql48eL66KOPdPLkSQ0ZMkQJCQl6+umnNWnSJHl4eCg1NVV///vfdfDgQXl7e2vq1KkqVaqUJOmjjz7SkiVL5OLiolGjRqlhw4a5eg4AAAAA8CDLsk+ih4fHHf3cq3nz5qls2bLW9OTJk9WzZ0+tXbtWBQsWtG5/Xbx4sQoWLKh169apZ8+emjx5siTp6NGjioqKUlRUlD7++GO99dZbSk9Pv+dcAAAAAPCouOOBay5evKg9e/Zox44dN/zci7i4OG3atMm6ZdUYo507d6pFixaSpPDwcMXExEiSNmzYoPDwcElSixYttGPHDhljFBMTo9atW8vDw0OlS5fWE088keutoQAAAADwIMvydtM/SkpK0vDhw7Vx40YZY266zqFDh+46yPjx4zV8+HBdvXpVkhQfH6+CBQvKze16RD8/P9ntdkmS3W5XiRIlrp+Am5u8vLwUHx8vu92uatWqWfssXry4tc0fFSiQR25urnedFQAAAMhJ3t6ezo6AB0ROvFccLhKnTp2qo0eP6pNPPlGvXr00ZcoUFSxYUCtXrtSePXs0adKkuw6xceNGFSlSRJUrV9Y333xzy/VsNpsk3bRItdlst5z/Z1eupNx1VgAAACCnXbqU5OwIeEDc7XvFx8frlsscLhI3btyoAQMGqHbt2pKk0qVLq0qVKmrQoIHGjRunTz/9VDVr1ryrgHv27NGGDRu0ZcsWpaSk6MqVK/rnP/+py5cv69q1a3Jzc1NcXJx8fX0lXW9VPHPmjPz8/HTt2jUlJibK29tbfn5+iouLs/Zrt9utbQAAAAAAt+dwn8Tz58+rVKlScnV1Vb58+XTp0iVrWWBgoDZv3nzXIYYOHaotW7Zow4YNmjJliurWrat3331XderU0Zo1ayRJy5cvV2BgoHW85cuXS5LWrFmjunXrymazKTAwUFFRUUpNTdXJkycVGxurqlWr3nUuAAAAAHjUOFwkFi9e3CoMH3/8cX399dfWsh9//FF58uTJ9nDDhw/X7Nmz1bx5c126dEkdOnSQJLVv316XLl1S8+bNNXv2bA0bNkySVK5cObVq1UrPPvusXnzxRY0ePVqurvQ9BAAAAABHOXy7ab169bRz5041a9ZM3bt316hRo3T48GF5eHhox44d6tq1a7YEqlOnjurUqSPp+i2tvz/24o/y5Mmj995776bbv/zyy3r55ZezJQsAAAAAPGocLhKHDRtmjTzavn175c2bV2vWrFFycrKGDx+ubt265VhIAAAAAEDucLhILFCggAoUKGBNBwcHKzg4OEdCAQAAAACcw+E+idWqVdP+/ftvuuzHH3/M9HxCAAAAAMCDyeEiMSUl5abPIZSk1NRUpaenZ1soAAAAAIBzZHm7qd1ul91ut6aPHj16wzopKSlatmyZHnvssexPBwAAAADIVVkWiYsXL1ZERIRsNptsNptGjhx5wzrGGLm7u2vs2LE5FhIAAAAAkDuyLBI7duyogIAAGWPUoUMHTZgwQeXKlcu0jru7u0qVKqX8+fPnaFAAAAAAQM7Lskj09fWVr6+vJOmrr75SqVKl5OHhkSvBAAAAAAC5z+FHYJQpU0YZGRlav369du/erYSEBBUqVEj+/v5q0qSJXFwcHgMHAAAAAHCfcrhIjI+PV58+fXTgwAEVK1ZMxYoV0/nz5zV79mxVqVJF//nPf+Tt7Z2TWQEAAAAAOczh5r+JEycqLi5OCxYs0NatWxUZGamtW7dqwYIFiouL08SJE3MyJwAAAAAgFzhcJG7cuFHDhg2Tv79/pvn+/v4aOnSoNmzYkO3hAAAAAAC5y+EiMSUl5Za3kxYqVEgpKSnZFgoAAAAA4BwOF4lVqlTRJ598ckMxmJKSolmzZqlKlSrZHg4AAAAAkLscHrjm73//u3r06KGAgAA1btxYRYsW1cWLF7V582YlJydr3rx5OZkTAAAAAJAL7qglcc2aNQoODlZsbKyio6N17NgxhYSEKDo6WpUrV87JnAAAAACAXJBlS+KuXbv09NNPK3/+/JIkHx8fjRw5MleCAQAAAAByX5Ytic8//7x++eWX3MoCAAAAAHCyLItEY0xu5QAAAAAA3Acc7pMIAAAAAHj43XZ0082bN+vXX391aGdhYWH3HAgAAAAA4Dy3LRKnT5/u0I5sNhtFIgAAAAA84G5bJM6bN4/HWwAAAADAI+K2RWLevHnl6emZG1kAAAAAAE7GwDUAAAAAAAtFIgAAAADAkuXtpocPH86tHAAAAACA+wAtiQAAAAAAC0UiAAAAAMBCkQgAAAAAsFAkAgAAAAAsFIkAAAAAAAtFIgAAAADAQpEIAAAAALDcF0XimTNn1L17d7Vq1UqtW7fW3LlzJUmXLl1Sr169FBQUpF69eikhIUGSZIzRuHHj1Lx5c4WEhOjgwYPWvpYvX66goCAFBQVp+fLlTjkfAAAAAHhQ3RdFoqurq0aMGKHVq1dr0aJF+uyzz3T06FHNnDlT9erV09q1a1WvXj3NnDlTkrRlyxbFxsZq7dq1evvttzVmzBhJ14vKiIgIffHFF1q8eLEiIiKswhIAAAAAcHv3RZHo6+urSpUqSZIKFCigMmXKyG63KyYmRmFhYZKksLAwrV+/XpKs+TabTdWrV9fly5d19uxZbd26VfXr15e3t7cKFSqk+vXr6+uvv3baeQEAAADAg8bN2QH+7NSpUzp06JCqVaumCxcuyNfXV9L1QvLixYuSJLvdLj8/P2sbPz8/2e32G+YXL15cdrv9hmMUKJBHbm6uOXwmAAAAwN3x9vZ0dgQ8IHLivXJfFYlXr17VwIED9frrr6tAgQK3XM8Yc8M8m812y/l/duVKyr0FBQAAAHLQpUtJzo6AB8Tdvld8fLxuuey+uN1UktLS0jRw4ECFhIQoKChIklS0aFGdPXtWknT27FkVKVJE0vWWw7i4OGvbuLg4+fr63jDfbrdbLZEAAAAAgNu7L4pEY4xGjhypMmXKqFevXtb8wMBARUZGSpIiIyPVtGnTTPONMfr+++/l5eUlX19fNWjQQFu3blVCQoISEhK0detWNWjQwCnnBAAAAAAPovvidtPdu3drxYoVKl++vEJDQyVJQ4YMUd++ffXqq69qyZIlKlGihKZNmyZJaty4sTZv3qzmzZsrX758Gj9+vCTJ29tb/fr1U/v27SVJ/fv3l7e3t3NOCgAAAAAeQDZzs458D7lz5xKdHQEAACBHLXpnk7Mj4B48NyIgV483c3Cv26+E+1LfqbPvarsHok8iAAAAAMD5KBIBAAAAABaKRAAAAACAhSIRAAAAAGChSAQAAAAAWCgSAQAAAAAWikQAAAAAgIUiEQAAAABgoUgEAAAAAFgoEgEAAAAAFopEAAAAAICFIhEAAAAAYKFIBAAAAABYKBIBAAAAABY3ZwcAAACO+7FdsLMj4C49vfRLZ0cAAIfQkggAAAAAsNCSCABOFvZ5S2dHwD2I7BTt7AgAAGQrWhIBAAAAABZaEu9Ay0nLnR0B9yD67+G5erzfPvy/XD0esk++l7c7OwIAAIDT0JIIAAAAALBQJAIAAAAALBSJAAAAAAALRSIAAAAAwEKRCAAAAACwUCQCAAAAACwUiQAAAAAAC0UiAAAAAMBCkQgAAAAAsFAkAgAAAAAsFIkAAAAAAAtFIgAAAADAQpEIAAAAALBQJAIAAAAALBSJAAAAAADLQ1kkbtmyRS1atFDz5s01c+ZMZ8cBAAAAgAfGQ1ckpqena+zYsfr4448VFRWlL7/8UkePHnV2LAAAAAB4IDx0ReK+ffv0xBNPqHTp0vLw8FDr1q0VExPj7FgAAAAA8ECwGWOMs0Nkp+joaH399df65z//KUmKjIzUvn37NHr0aCcnAwAAAID730PXknizmtdmszkhCQAAAAA8eB66ItHPz09xcXHWtN1ul6+vrxMTAQAAAMCD46ErEqtUqaLY2FidPHlSqampioqKUmBgoLNjAQAAAMAD4aErEt3c3DR69Gi9+OKLevbZZ9WqVSuVK1fO2bFyRIUKFTR8+HBr+tq1a6pbt65eeuklSVJMTIxTHgGycOFCRUZG5vpxcefi4+MVGhqq0NBQ1a9fXw0bNrSmU1NTnR0Pd+l214bc9s033+TosS9fvqxPP/3Umj516pRWrVqVY8eD9NRTTyk0NFStW7dWmzZtNHv2bGVkZGS5zalTpxQcHJxLCXE/qVChgt555x1r+pNPPtH777+fLftOSUlRy5YtdeTIEWvef/7znzsai+L48eMKDQ3Nljy4vfHjx2vOnDnWdO/evTVy5Ehr+p133tHs2bPvaJ+BgYG6ePHiDfPv9bvwnDlz9Ntvv9319g8yN2cHyAmNGzdW48aNnR0jx3l6eurnn39WcnKy8ubNq23btql48eLW8qZNm6pp06a5munatWvq3Llzrh4Td69w4cJasWKFJOn999+Xp6enevfu7eRUuFe3uzY8bC5fvqyFCxeqa9eukqTTp0/ryy+/VEhIiJOTPbzy5s1rXTsuXLigoUOHKjExUQMHDnRysuvS09Pl6urq7Bj4/zw8PLR27Vr17dtXRYoUydZ958mTR6+//rreeustffrppzp79qwWLVqkpUuXOrT9tWvXsjUPbq9GjRqKjo5Wz549lZGRofj4eF25csVavnfvXr3++uvZcqx7/S48b948tWnTRvny5cuWPA+Sh64l8VHTqFEjbdq0SZIUFRWl1q1bW8uWLVumsWPHSpJWr16t4OBgtWnTxvoi9fPPP6t9+/YKDQ1VSEiIYmNjJUmzZ89WcHCwgoODrf/0/Pk/wH/8L2D37t01ZcoUdevWTfPmzdP777+vTz75JIfPHDlt+fLl1vtjzJgxysjI0MmTJxUUFKRLly4pPT1dnTp10o4dOyRJf/vb39S2bVu1bt1aixcvlnT9w3f48OEKCQlRcHCw5s2b58xTeqRkdW3Yt2+fOnXqpLCwMHXq1Em//vqrpJtfE5KSktS3b1+1adNGwcHB+uqrryRJERERateunYKDg/XGG29Yg4YdP35cPXv2VJs2bRQeHq4TJ05IkpKSkjRw4EC1bNlSQ4cOtdb/439/9+/fr+7du0uSvv32W6tVOywszPoC8fHHH6tdu3YKCQnRe++9J0l69913deLECYWGhmrixIl699139d133yk0NFRz5sy55bUO2aNo0aJ6++239emnn8oYo/T0dE2cONH6O33++ec3bHPq1Cl16dJF4eHhCg8P1549eyRJY8aMsR5b1b9/f/3jH/+QJC1evFhTp06VJPXr18+61ixatMjaZ40aNTRt2jR16NBBe/fu1YEDB9StWze1bdtWvXv31tmzZ3P6pcAtuLm56bnnntPcuXNvWHbx4kW98sorateundq1a6fdu3dLkkJCQnT58mUZY1SnTh3rDqXhw4dr+/btmfbRqFEj+fj4KDIyUuPHj9eAAQNUqFAhZWRkaMKECQoODlZISIiio6MlSdu3b1fPnj01ePBghYeHZ9rX8ePHFRYWpoMHD+bESwFJNWvW1N69eyVd/9wpV66c8ufPr4SEBKWmpuqXX35R2bJl1aNHD4WHhyskJETr16+XpFt+JknSggULrPV/+eUXSZm/C48YMULjxo1Tp06d1LRpU+v9kJGRoTFjxqh169Z66aWX1KdPH0VHR2vevHk6e/asevToYX02/f4PyODgYP3rX/+yjl2jRg1NnTpVbdq0UceOHXX+/PmcfyFzmsEDq3r16ubQoUPmlVdeMcnJyaZNmzZm586dpm/fvsYYY5YuXWreeustY4wxwcHBJi4uzhhjTEJCgjHGmLFjx5oVK1YYY4xJSUkxv/32m9m/f78JDg42V69eNVeuXDHPPvusOXjwoDl58qRp3bq1deyPP/7YvPfee8YYY7p162befPNNa9l7771nPv744xw/f2SvP/7djhw5Yl5++WWTlpZmjDFm1KhRZuXKlcYYYz777DPz6quvmhkzZpgxY8ZY28fHxxtjjElKSjKtWrUyly5dMt9//73p3bu3tc7v7z3krNtdGxITE62/7bZt28yAAQOMMTe/JkRHR5uRI0da+758+bIx5n9/b2OMGTZsmImJiTHGGNO+fdGN6ysAABHmSURBVHuzdu1aY4wxycnJJikpyezcudPUrFnTnDlzxqSnp5uOHTuaXbt2GWOMadKkiblw4YIxxph9+/aZbt26GWOMeemll8x3331njDHmypUrJi0tzXz99ddm1KhRJiMjw6Snp5u+ffuab7/99obr0x/P9VbnhXtTvXr1G+b5+/ubc+fOmc8//9xMnz7dGHP99Q4PDzcnTpzI9HdKSkoyycnJxhhjjh07ZsLDw40xxnz55ZfmnXfeMcYY065dO9OhQwdjjDEjRowwW7ZsMcb8773322+/mdatW5uLFy8aY4wpX768iYqKMsYYk5qaap577jnrvRUVFWVGjBiR/S8EHFK9enWTmJhomjRpYi5fvpzpO8SQIUOs68Hp06dNy5YtjTHGvPHGG2bjxo3myJEjpm3bttZ1qHnz5ubKlSs3HCMuLs40bNjQuoYYc/3v/sILL5hr166Zs2fPmkaNGpnz58+bbdu2merVq5vTp08bY4yJjY01bdq0MUePHjWhoaHm8OHDOfp64Pq1//Tp02bhwoXms88+M1OnTjWbNm0y3333nenSpYtJS0sziYmJxhhjLly4YJo1a2YyMjJu+ZnUpEkTM2/ePGOMMQsWLDCvv/66MSbzd+HXXnvNvPLKKyY9Pd38/PPPplmzZsYYY1avXm1efPFFk56ebs6ePWv8/f3N6tWrrf3+fh2Ji4szjRs3NhcuXDBpaWmme/fuZt26dcaY69ef3z8HJ06caF0DH2QP5e2mj5KKFSvq1KlT+vLLL7O8xbZGjRoaMWKEWrVqpebNm0uSqlevrhkzZiguLk5BQUH6y1/+ot27d6tZs2by9PSUJDVv3lzffffdbQf/efbZZ7PvpOB027dv1/79+9WuXTtJUnJysvz8/CRJnTt3VnR0tJYtW6bly5db28yZM0cbNmyQJMXFxenEiRN6/PHHdezYMY0bN06NGzdWgwYNcv9kHlFZXRsSExP12muv6fjx47LZbEpLS5N082tC+fLlNXHiRP3rX/9SkyZN5O/vL+l6P8OPP/5YycnJunTpksqVK6fatWvLbrdb15g8efJYx6xatar1HqpYsaJOnz5t7etmatasqXfeeUchISEKCgpS/vz5tW3bNm3btk1hYWGSrv9HOTY2ViVKlMjytbjZeSH7mf/fOrxt2zYdOXJEa9askXT9/Xb8+PFMr/u1a9c0duxYHT58WC4uLlbrrr+/v+bOnaujR4/qr3/9qxISEnT27Fnt3bvX6rM0f/58rVu3TpJ05swZHT9+XIULF5arq6tatGghSTp27Jh++ukn9erVS9L1lgIfH5/ceBlwCwUKFFBoaKjmzZunvHnzWvO3b9+uo0ePWtNXrlzRlStX5O/vr127dqlkyZLq3LmzvvjiC9ntdhUqVEj58+e/Yf/FixdX3bp1FRAQYM3bvXu3QkJC5OrqKh8fHz3zzDM6cOCA3N3dVb16dZUsWdJa98KFC+rfv7+mT5+usmXL5syLAEuNGjW0d+9e7d27V7169ZLdbteePXvk5eWlGjVqyBijKVOmaNeuXXJxcZHdbtf58+dv+ZkkSUFBQZKkypUrW9eIP2vWrJlcXFz017/+1Wrt2717t1q2bCkXFxf5+PioTp06N912//79ql27tnXLdEhIiHbt2qVmzZrJ3d1dTZo0sY6/bdu2bHutnIUi8SEQGBioSZMmad68ebp06dJN1xk7dqx++OEHbdq0SWFhYYqMjFRISIiqVaumTZs2qXfv3ho3btxNnzMpXb9V5I+DEqSkpGRa/ijeq/2wa9eunV599dUb5l+9elXnzp1Tenq6kpKS5Onpqe3bt+u7777TF198obx586pz585KSUlR4cKFtXLlSm3ZskXz58/X2rVr9fbbbzvhbB5Nt7o2TJs2TXXq1NH06dN16tQpPf/885J002tCvXr1tGzZMm3evFnvvvuu6tevrz59+uitt97S0qVLVaJECb3//vs3XBP+zMPDw/rd1dVV6enp1u+/X3f+uI++ffuqcePG2rx5szp27KjZs2fLGKO+ffuqU6dOmfZ96tSpLI99q/NC9jl58qRcXV1VtGhRGWM0atQoNWzYMNM6f/w7zZkzR8WKFdOKFSuUkZGhqlWrSrr+RT8hIUFff/21/P39lZCQoNWrV8vT01MFChTQN998o+3bt2vRokXKly+funfvbr1v8uTJY/VDNMaoXLlymW5HhfP16NFDbdu2Vdu2ba15GRkZWrRoUabCUZJq1aqlzz77TGfOnNHgwYO1fv16RUdHZ/nPJRcXF7m4/K8n1a2+00g3fm/x8vKSr6+v9uzZQ5GYC36/5fSnn35SuXLl5Ofnp1mzZqlAgQJq166dVq1apYsXL2rZsmVyd3dXYGCgUlJS9OSTT97wmTRgwABJkru7u6Tr74PfP2P+7I+fRb/L6n3iKHd3d+u57Fkd/0FCn8SHQPv27dWvXz9VqFDhluucOHFC1apV06BBg1S4cGHFxcXp5MmTKl26tJ5//nkFBgbqyJEjqlWrltavX6/ffvtNSUlJWr9+vfz9/VW0aFFduHBB8fHxSk1Ntfo64eFUr149rV692uorFh8fr//+97+SpEmTJik8PFz9+/e3Ro9LTExUoUKFlDdvXv3888/av3+/pOt9TYwxatWqlV555RX6eOSyW10bEhMTrYFs/tgafLNrgt1uV758+RQaGqrevXvrxx9/tL6UFy5cWFevXrVajAoUKCA/Pz+r70hqauptR4V77LHHdODAAUnS2rVrrfknTpxQhQoV1LdvX1WuXFnHjh1TgwYNtHTpUl29elXS9efgXrhwQfnz57fmSbph+mbnhexz8eJFvfnmm+ratatsNpsaNGighQsXWi3Ux44dU1JSUqZtEhMT5ePjIxcXF61YsSLTF6oaNWpo7ty5qlWrlvz9/TVr1iyrMPj9WpMvXz798ssv+v7772+a6cknn9TFixetfk9paWn6+eefc+L0cQe8vb3VsmVLLVmyxJrXoEEDLViwwJo+dOiQJKlEiRKKj49XbGysSpcurZo1a2rWrFl65plnHD6ev7+/oqKilJ6ervPnz2vPnj2qXLnyTdf18PDQBx98oKVLl2bq54acUbNmTW3cuFGFChWSq6urvL29lZiYqO+//17Vq1dXYmKiihYtKnd3d+3cuVOnT5+WpJt+Jt2rZ555RmvXrlVGRobOnz+vb7/91lr2x8+TqlWrateuXbp48aLS09MVFRWlWrVq3fPx71e0JD4E/Pz81KNHjyzXmTRpko4fPy5jjOrWrauKFStq5syZWrlypdzc3FSsWDH1799f3t7eatu2rTp06CDp+pfMp59+WtL1QQQ6duyoUqVKqUyZMjl+XnCeChUqaMCAAerVq5cyMjLk7u6uMWPG6Pjx4zpy5IhGjx4tV1dXrVmzRpGRkWrVqpW++OILtWnTRmXKlFG1atUkXb8VbOTIkTLGyGazadiwYU4+s0fLra4NL774okaMGKHZs2erbt261vyvvvrqhmvC/v37NWnSJLm4uMjNzU1jxoxRwYIF1aFDB4WEhOixxx5TlSpVrH1MmjRJo0eP1rRp0+Tu7q5p06ZlmXHAgAEaOXKkPvroI+t9I0lz587VN998Y90W1KhRI3l4eOiXX36xWhI9PT31r3/9S48//rhq1qyp4OBgNWzYUEOGDJGrq6vatGmjtm3bKiUl5Ybzwr1JTk5WaGiorl27JldXV4WGhlq3dnbo0EGnT59W27ZtZYxR4cKF9cEHH2TavkuXLnrllVcUHR2tOnXqWF0cpOtf2LZu3aonnnhCJUuWVEJCglUkNmrUSJ9//rlCQkL05JNPqnr16jfN5+Hhoffee0/jxo1TYmKi0tPT1aNHj4f2kVgPkhdeeCHTI2tGjhypsWPHKiQkROnp6fL397cGGqlatap1F5O/v7+mTJlyR0Viy5Yt9cMPPyg0NFQ2m00jRoxQ0aJFb7l+/vz5NWPGDL3wwgvy9PTMdOsqslf58uUVHx+faVDE8uXL6+rVqypSpIhCQkL08ssvq23btnrqqaes750//fTTDZ9J96pFixbasWOHgoOD9Ze//EVVq1aVl5eXJKljx47q06ePfHx8NH/+fA0ZMkQ9evSQMUaNGjVSs2bN7vn49yubyY42VgAAAAB4AF29elX58+dXfHy8OnTooIULFz7y/ZhpSQQAAADwyPrb3/6my5cvKy0tTf369XvkC0SJlkQAAAAAwB8wcA0AAAAAwEKRCAAAAACwUCQCAAAAACwUiQAAZGHZsmVq27atatSooVq1aiksLEwTJkywll+4cEHvv/9+pofFOyo1NVXvv/++9Ww4AADuBxSJAADcwkcffaRRo0apQYMGioiI0MSJE9W0aVNt2LDBWufChQuKiIiwHvZ8J9LS0hQREUGRCAC4r/AIDAAAbmHBggV67rnnNGTIEGteYGCgBgwY4MRUAADkLB6BAQDALVSvXl19+vRR//79b7r81KlTatq06Q3zjxw5oqSkJE2ePFnbtm1TXFycihYtqsaNG2vo0KEqUKCAJKlChQo3bBsTE6NSpUopJSVF06ZNU1RUlC5cuKAyZcpo6NChaty4caZ1p0+frl9//VXu7u76y1/+ouHDh6t27drZ9AoAAB5FtCQCAHALTz/9tBYsWKCSJUsqICBAhQsXzrTc19dXkydP1rBhwzR69GhVqlTJWpacnKz09HQNHjxYRYoU0ZkzZzRjxgwNGjRIn3zyiSRp7ty56tGjh15++WUFBARY+5SkgQMHat++fXrllVf0+OOPa/Xq1Xr55Ze1dOlSPfXUUzpx4oQGDRqk7t27a/jw4UpNTdWBAweUkJCQOy8OAOChRUsiAAC3cPjwYfXv31+nTp2SzWZT2bJlFRQUpN69e1utgT/99JNCQkI0b9481alT55b7unbtmn744Qd16dJFGzduVMmSJXX16lXVrFlTEyZMUNu2ba11d+zYoZ49e2r+/PmZWgW7du2qokWL6r333lN0dLTefPNNffPNNzn3AgAAHkkMXAMAwC1UrFhRq1ev1ocffqguXbrIGKMPPvhA7dq109WrV2+7fWRkpMLCwlSjRg1VqlRJXbp0kSTFxsZmud327dvl4+OjmjVr6tq1a9ZPvXr1dODAAUlS+fLllZiYqNdee01bt25VUlLSPZ8vAAASt5sCAJAlDw8PBQYGKjAwUJK0ePFijRo1SkuWLFGPHj1uud26dev02muvqXPnzho8eLC8vb117tw59e/fXykpKVkeMz4+XufOnct0++rvXF1dJUllypTRBx98oJkzZ6pv375yc3NT8+bNNXLkSBUpUuQezhgA8KijSAQA4A506NBBkydP1q+//prletHR0apWrZrGjBljzfv2228dOkahQoVUvHhxTZ8+Pcv1AgICFBAQoMTERG3atEnjx4/X22+/ralTpzp0HAAAboYiEQCAW7hw4YKKFi2aad7FixeVmJioYsWKSZLc3d0l6YbWweTkZHl4eGSat2rVqkzTt9q2Xr16mj17tjw9PVW2bNnb5vTy8lJISIh27dqlvXv3OnBmAADcGkUiAAC3EBISoqZNm6p+/foqWrSoTp8+rVmzZilv3rwKCwuTJJUsWVJ58+ZVZGSkvLy85ObmpipVquj//u//NHbsWH344YeqVq2aNm/erB07dmTav4eHh0qVKqXVq1erXLlyypMnjypUqKD69eurQYMGeuGFF9SnTx/99a9/1ZUrV3T48GGlpKRo6NCh+vzzz/X999+rYcOG8vX1VWxsrKKjoxUaGuqMlwoA8BBhdFMAAG7h008/VUxMjH766SclJCTIx8dHNWrUUL9+/TK18K1cuVLTp0/X6dOnlZaWpiNHjig9PV2TJ09WZGSkUlJSVL9+fb344ovq2LGjZsyYoSZNmkiStm7dqokTJyo2NlapqanWcxJTU1M1Y8YMrVq1SmfOnFGhQoVUsWJFde/eXQEBAdq7d68+/PBD/fjjj1a2Vq1aadCgQTe0YAIAcCcoEgEAAAAAFh6BAQAAAACwUCQCAAAAACwUiQAAAAAAC0UiAAAAAMBCkQgAAAAAsFAkAgAAAAAsFIkAAAAAAAtFIgAAAADAQpEIAAAAALD8PxIEwhZzxrchAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3c7ab6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(state_firearms[\"private_sale_handgun\"], y_scale=10, y_scale_two=1)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would seem as though Washington D.C accounts for most private sales of hand guns folllowed by New york, the rest are not too significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAMRCAYAAACu2ZhFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XdYU3fbB/Bv2FucWBUXGnAAokidteIerbutj1q1bpyt41Uf66yrddTiRMGBe1LRKnUiiAsXqOCsishGxYDMnPcPyHmIhJhAEK3fz3V5SXLWnbPvc35DIgiCACIiIiIiIvrk6ZV2AERERERERPRhYIJIREREREREAJggEhERERERUR4miERERERERASACSIRERERERHlYYJIREREREREAJggEhGp5OHhAXt7eyxcuLC0Q6G3LFy4EPb29vDw8CjtUD6oWOjD5OLiAnt7e5w5c6a0QyEdOX78OOzt7fH555+XdigaCw8Ph729Pezt7ZGamlra4dAHzqC0A6APj729fZGnXbx4MXr37q3DaEpeUFAQ/Pz8cP36dSQkJMDU1BSVKlWCs7MzvvrqKzRr1qxI833y5Al27dqFS5cuISoqCunp6bC2tkb58uVRq1YtuLq6olmzZqhTp47SdDdu3EBQUBAqVKiA/v376+InFvA+lkG6t2vXLiQmJqJ169Zo1KhRaYdD9Mk4efIkxo4dq9G4fn5+qFevXglHRERUcpggUgEVKlRQ+X1aWhrS0tLUjmNiYlJicenamzdvMHXqVJw4cUL8ztLSEmlpabh//z7u37+PnJycIiWIu3btwsKFC5GVlaU079TUVCQkJCAyMhLHjh2Dg4MD/vzzT6Vpb9y4gdWrV8PBwaFEE8SSXsbHrnLlyqhVq1ah+3pp2L17NyIjI2FpackEkaiUWFtbw8Cg8Nunt4fVqlULaWlpMDMzK+nQiIh0ggkiFXD+/HmV33t6emL16tVqx/lY5OTkYNSoUbh06RKsra0xYcIEdO3aFWXLloUgCIiPj0dISIhSgqepCxcuYO7cuQAAJycneHh4oFmzZjA1NQUAJCQk4Nq1a/j7778RFRWly59FOjR79uzSDoGIPkBbtmzR6g3hwYMHSzAaIiLdY4JInyRvb29cunQJZmZm2LFjh1IxT4lEAhsbG/Tq1atI8968eTMAoFq1avD19S3wVrVixYro1KkTOnXqhPT09KL/CCIiIiIiHWOCSCUqJiYG3t7eCA4ORkxMDPT09GBra4u2bdti6NChsLa2LjBNREQEevbsCQC4cuUKYmJi4OXlhUuXLuHly5eoVKkS3N3d4eHhgXLlymkdU3p6Ory9vQEAo0aNKlAHsLgiIiIAAC1btnxnkdv8w1NSUtC0aVPxc2RkZIH6oDNmzMCQIUMAAKmpqQgODsaZM2dw+/ZtxMfHIzU1FeXKlUOjRo0wYMCAAhXotV2GwosXL7Bt2zYEBgbi6dOnSE9PR6VKldC0aVMMGTKk0KfpSUlJ2Lx5M86dO4eoqChkZWXB2toaFSpUQJMmTdClSxe4urqqXUeFxe/n5wcrKyusX78ewcHBSExMhLW1NVq1agUPDw/Y2toWmP7tfSs+Ph6bNm3CxYsXkZiYCDs7O7HIr4eHB06dOoXvv/8e//3vfwEAUVFRaN++PQBg+/btSuvybQsWLMD27dvRoEEDpTcIUVFROHXqFIKCgvD06VPEx8dDT08P1apVQ6tWrTB06FBUqlRJaV5btmzB4sWLxc+LFy9W+qz4PVZWVkrf3bhxAzt37kRoaCgSEhJgaGiImjVrol27dvj+++9haWmpfoVrSCaTwdfXFydPnsTjx4+Rnp6OMmXKoFy5cnB2dkaHDh3w5ZdfKk0TFxeHs2fP4uzZs/jnn38QFxcHQRBgY2OD5s2bY+jQoahRo0aRYyrqPlsSrl+/ju3btyM0NBRJSUkwMzND3bp10b17d/Tr109lcUXFNlcUQ7927Ro2bdqEGzduICUlBVWqVEHnzp0xatQomJubF7rs4OBgbN68GeHh4cjIyEC1atXQpUsXjBgxArt27VJahi7Ex8dj7dq1CAwMFI9JNzc3jB49GjY2NkrHb/5t8PbvVeXt4zf//q6r9VUSXFxckJaWhvXr16Nt27bi98nJyWjevDkAwN/fHxYWFvDy8kJwcDDi4uJgZmaGS5cuKc0rOTlZ3K8VddsrVaoENzc3DBkyBA4ODipjiIyMxJkzZxASEoLo6GgkJCTAyMhIPB8MGjSo0PNB/vhdXFzg5eWFM2fOICYmBm/evMG1a9dgbm6O+fPnY8eOHejUqRP++OMPHDlyBDt37sT9+/chCALq1auH0aNHo2XLlgAAuVyOvXv3Yv/+/fjnn38gCAIaNWqECRMmFFqEPjExET4+PggKCsKzZ8+Urimurq7o2rUrGjdurPU2AoBLly5h7969uH79OhITE2FsbAwbGxs0adIE3bt3V3u+v3v3boH7lLZt22Ls2LFq71MyMzOxf/9+HD9+HPfu3YNMJoOVlRUcHR3xzTffoF27dmpjvnPnDnbu3IkrV64gLi4OEokElStXhqOjI7p06aK0v73L69ev4eHhgcuXL8PGxgYbN25Uuj84efIk9u3bh9u3b+Ply5cwNjZGuXLlUKtWLbRs2RJ9+/Z978cWlRwmiFRiAgMDMWnSJLHeopmZGeRyOe7evYu7d+9i//798PLyQoMGDQqdx4ULFzBt2jSkp6fD3NwcEokE0dHR8PX1xfHjx7F161bY2dlpFdeZM2fw8uVLAECfPn2K/gPfITY2Vqvx9fT0UKFCBbx58wapqakwMDAokEDnr8Oyb98+pSTBzMwMenp6iIuLQ0BAAAICAjBhwgSlhhW0XQYAXLx4ERMmTMCrV68A5NavMTIyQnR0NKKjo+Hv74///ve/GDBggNJ0jx8/xoABA5CYmAgA0NfXh4WFBZKSkpCQkICIiAjExMRolSDmd+/ePSxatAgvX76Eqakp9PX1ER8fj4MHD+L48ePYuHGj2nkHBwdjxowZSE9Ph5mZmdo6RQq2trZo3Lgxrl27hj///LPQG4bs7Gz89ddfAIAePXooDRs3bhwiIyPFz1ZWVpDJZLh37x7u3buHQ4cOwcfHB/Xr1xfHMTMzQ4UKFfDy5UtkZ2fD3NxcLLKsoKf3v0ap5XI5li5dii1btijNIyMjA7dv38bt27dx4MABbNq0CbVr137n71YnOTkZ/fv3x+PHjwHkvoG3srLCq1evkJSUhPv37+PWrVsFEsR58+bh1KlT4mdF/d/Hjx/j8ePH8PPzg6enJ1q3bq11TEXdZ0vC6tWr4enpKX62srJCamoqQkNDERoaikOHDmHDhg0oW7ZsofPYvXs35s+fj5ycHFhZWSE7OxtPnjzBhg0bEBISgp07d8LIyKjAdGvXrsWqVauUlv3kyRN4enri9OnT6Nixo05/a2RkJAYPHiyeX42NjZGamoojR47g1KlT+O2333S6vMIUdX2Vpjt37mDhwoVISUmBiYmJyvNRSEgIJk6ciJSUFADK+/WhQ4fg7++PWbNmqaxX3r9/f/FarKenBwsLC6SkpODWrVu4desW/Pz8sG3bNlSuXLnQGGNiYjB37lzExsbCyMhI7TqcN28edu7cCX19fZiamkImk+Hy5csIDQ3FihUr0KFDB0yYMAGnTp2CoaEhDA0NkZaWhvPnz+PKlSvYsmULmjRpojTPR48eYcCAAUhOTgag+poSHx+vdYKYmZmJWbNmKT2YMDMzQ2ZmptgeQUhIiFJ7BfmdPHkSP/74IzIzM2FpaQm5XI7o6Ghs374dQUFB2LdvH8qUKVNguujoaIwaNQr3798HkHvuNDc3R1JSkvjwrGfPnli8eLHS+R0ABEHA8uXLsXHjRvE7xTXh0aNHePToEY4cOYI7d+5otA7i4+MxYsQIREZGonbt2vD29kaVKlXE4b/88gt8fX2V1o9cLsfTp0/x9OlTBAYGonnz5pBKpRotjz4CApGG/vjjD0EqlQpSqfSd4z569EhwdnYWpFKp0KtXLyEsLEwQBEGQy+XC+fPnhbZt2wpSqVRo1aqVkJycrDTtnTt3xOU0adJE6NevnxARESEIgiBkZ2cLp06dElq0aCFIpVKhS5cuQkZGhla/Y8GCBYJUKhU6deokZGdnC76+vkKvXr0EZ2dnoVGjRkLPnj0FT09P4dWrV1rNV8HDw0OQSqWCvb29sHv3biE7O1ur6Tdv3ixIpVLh66+/Vjuen5+fMG/ePOHKlSvCy5cvxe+fP38uLFu2THBwcBCkUqlw6dKlIi/j/v374nacNm2aEBkZKf6euLg4YcmSJYKDg4Pg4OAgXLhwQWna8ePHi9voypUrQk5OjiAIgpCZmSk8ffpU2Lp1q+Dp6anROlF49eqV0r7RsWNH4fLly+Lwy5cvCx07dhSkUqnQrFkzISkpSWn6/PtWo0aNhEGDBon7liDk7rcKY8aMEaRSqfDLL78ozWP37t3i8tPT01XGefr0aUEqlQr169cXEhMTlYZNnz5d8PHxER49eiTuu5mZmcKVK1eEgQMHClKpVOjYsaPK/ebrr78WpFKpsHnzZrXrSXGsurm5CZs3bxbXQ1ZWlnDt2jXh22+/FZej7fHztiVLlghSqVRo2bKlcObMGSEzM1MQhNxjNSYmRti3b5+wYMGCAtMtXbpUWL16tXD37l0hLS1NnObOnTvC2LFjxfhVHYe//PKLIJVKhTFjxhQYVpx9tijUxeLn5yfubz/99JMQExMjCIIgpKenC7t37xYcHR0FqVQqDBs2rMC0imO0cePGQr169YQFCxYI8fHxgiAIgkwmE7y8vMRj3MfHp8D0gYGB4rLHjBkjREVFCYIgCBkZGYKfn5/QqFEjoWnTphqdBzSRlpYmtGvXTpBKpULr1q2Fs2fPisd8RESE8M0334jLk0qlwp07d1T+XnWx5D9+394viru+3uXEiROFxv4ujRo1EqRSqXD69Gml75OSkpTORz179hSuXbsmyOVyQRCUz0eRkZGCk5OTIJVKhenTpwv37t0T9+vY2Fhh0aJFgr29veDg4KB0TlQYOXKksGfPHuHZs2dCVlaWIAi5++GZM2eE7t27C1KpVBg6dKja+Bs1aiS4u7sL586dE5f99OlTcX7z5s0TpFKp4OrqKjRo0EDYunWrkJqaKo6nOO+0bNlSWLJkidC4cWPBz89PPAdFRkYKXbp0KXQ/UJyTu3XrpvKasmXLFmHNmjUabpX/mTFjhrgdfvnlF/FYkcvlQlxcnHD06FFh+vTpStMcO3ZMkEqlQsOGDQVnZ2dh8uTJwtOnT8X1unfvXvH4XrRoUYFlymQyoVOnToJUKhX69+8vhISEiNeT169fC9u3bxdcXV0FqVQqrF27tsD0q1evVjq33L9/XxyWlJQknD59Whg3bpzSNGFhYeI0MplM/P7hw4fiPdm3334rvHjxQmm6/Medp6en0nX15cuXQkhIiDBz5kzhyZMnGq1v+jgwQSSNaZMg/vjjj2ICqOoG79GjR+LJc8WKFUrD8p+MWrduLaSkpBSY/ubNm+LFfs+ePVr9jmHDhglSqVQYPHiwMHjwYHFZioua4nPbtm2Fhw8fajVvQRCEW7duib9NKpUKn3/+uTB+/Hhhw4YNQnBwsPD69Wu102uavL3LihUrBKlUKowfP77IyxgyZIgglUpV3uArrFq1SpBKpcLAgQOVvm/ZsqUglUqFs2fPFu0HqJA/QWzUqJHw7NmzAuM8e/ZMvKFRt2+96+FCYQniq1evhIYNGwpSqVQ4evSoymknTpwoSKVSYdSoUVr9voyMDPEm++2bSUHQLEF89uyZUL9+faFBgwbCjRs3VI6Tnp4uJtIHDhzQKsa3KW76duzYUaz55CeXy4X+/fsLUqlU8PX1LTBcXVJWnH22KAqLJSsrS2jTpo0glUqFESNGiDf9+R06dEjcH8+fP680THGMqtoHFRQ3tr179y4wrFevXoJUKhX69eun8mFD/oRHFwmir6+vIJVKhXr16qlMoF6/fi2uj5JMEIu6vt4l//pyc3MTWrRoofKfqnOCJgliy5YtlR70vU3x8EhVsqGgOOcPHjxYq9+WlJQkxvjgwYNC43dyclKbBCgSRKlUKuzatavA8Li4OKFevXriOOfOnSswzs2bN8Xhb8fSrFkzQSqVCsHBwVr9PnVu3LghLm/jxo0aT6dIEAu7xgrC/84zLVu2LDDM09NTTMgKuw4FBweLDz0UD9EEIfchsOJeZe7cuRrHrCpBvHHjhuDm5iZer/IvR2H79u1irPTp0Hv3O0Yi7WRmZuLkyZMAgO+//75AvSggt9lvRV2So0ePFjqvwYMHq6wX4eTkJBY9Uze9KoriOZcuXcKFCxfQo0cPnDt3DleuXMG1a9fw66+/wtzcHNHR0RgzZgwyMzO1mn+DBg2wZcsWsez+ixcvEBAQgOXLl+OHH36Am5sbhg4diqCgIK3mqy1Fkb6rV68Wafro6GiEhIRAIpFgxIgRhY6n2I7Xrl0TizABELd7QkJCkZb/Lr169ULVqlULfF+1alWN9q2hQ4cWqZiZlZWVWK/j8OHDBYa/fv0ap0+fBgB8/fXXWs3byMgILVq0AFD07Xbo0CFkZ2fDzc0Nzs7OKscxNjZGly5dAOQWtS0OxfGpy+0skUjQpk0bANqth+Lus7p0/fp1xMTEAMgtViyRSFTGUbNmTQDAkSNHVM5HIpFg9OjRKocp6ifdvXtX6fuoqCjcvn0bADBy5Ejo6+sXmLZ9+/Y6LQ52/PhxALnnHVX1Oy0sLDB48GCdLa8wRVlf2nr58iUSExNV/itqw2PffvutymKIAPD06VNcvnwZEokEw4cPL3QeiuLsoaGhWsVRrlw5ODk5Acg9JgrTpUsXVK9e/Z3zK1u2LPr27Vvg+0qVKon7XP369VUWH3dychKvHW9vp5K4phw4cABAbqNyP/zwQ5HmMWbMGJXfK/a3hIQEsViswr59+wDk3iMVdh1q2bIlKlasCJlMhhs3bojfHz58GFlZWTA3N8fkyZOLFDOQWw1IUSS8T58+WLNmTYGqC8D/1vvLly+L1LI7fZxYB5F07v79+8jIyAAA8WZXlRYtWmDPnj2IiorCq1evVF4c1fVB2KxZMwQGBuLWrVtaxSeXy8X/GzVqhF9//VUcZmRkhB49eiA7OxszZ87E48ePceTIEfTu3VurZTRu3Bh//vknrl+/jqCgINy8eRMRERFITk5GTk4OQkJCEBISgkGDBmHWrFlazTu/2NhY7Ny5EyEhIXj69ClkMhlycnKUxklMTER2drZGdezyy39jru73C4IAILfeXVxcHGrVqgUg90bx4cOHmD9/PiIiItCuXTs4OzvrrBL7u/aNnTt3IioqCi9fvlTZGFJRGzIAcm/sAwICEBQUhOTkZKVGCI4fP46MjAxYWloW2sBASEgIDhw4gPDwcCQkJKhMUrStw6qg2G7Xrl0TG4NQ5c2bNwCA58+fF2k5Cl9++SXOnTuHDRs24Pnz5+jcuTMaN25c6M1ufmFhYWKjEM+fP8ebN2/E/UlBm/VQ3H1WlxTnJQsLCzg6OhY6XsuWLfH48eNCz2PVqlVD+fLlVQ5TNGaUlZWl1M9e/npH6hrW+Pzzz3Hv3j31P0RDioTUzc2t0HHUDdOVoqwvbb3dwI4uqDsf5d+vFQ83VFFc27KyspCQkKDUUJcgCDh+/DiOHDmCiIgIJCUlqUwi1R1vmp4zGzRoUOj1RrFt1B0T5cuXR0pKivgwV+HLL7/Eli1bMGfOHNy6dUu8phSnf8nr16+L8367np8mjIyMCm0YKH9jYy9fvhSvE9HR0eJ6njdvHhYuXFjo/F+8eCFO83bMbm5usLCw0DpmILfrlSVLliA7OxujR4/Gjz/+WOi4bm5uMDMzwz///IPvvvsO33zzDZo1a1asRsTow8cEkXQuKSlJ/NvGxqbQ8fJXhk9KSlJ5Q6luesUwmUyGzMxMjd8G5U9QCnti2KtXLyxfvhxJSUkIDg7WOkEEcp9kN27cWOmi+uTJExw7dgybNm3C69ev4evrC0dHxwINmWgiJCQEY8eOVUouzM3NYWJiAolEguzsbLGxiDdv3mjdYmV8fDyA3BsLRUMz75L/hmP8+PF4+PAhzp49i+3bt2P79u3Q09ODVCpFmzZt0K9fP5UtjWpKk30DyG1ERVWCWJQWcBW++OILlCtXDsnJyTh69CgGDRokDlO8VezSpQuMjY0LTDt79mzs2bNH/Kyvr48yZcrA0NAQQG7rtG/evBETOG0ptpum8yjqchT69+8vNnrj5+cHPz8/AEDt2rXRqlUr9O3bt0BLuQCwfv16/P7772KypqenBysrK3E9KBpS0ia+4u6zuqQ4D1aqVEnl20MFxXnw7TcMCuoeqOS/Cc/Ozhb/VszL2NhYbaL+dmu5RZWRkSGeh9TNU90xqytFWV8fgsKSWqBo+3X+4yYzMxNjx47FuXPnxO8MDQ1hbW0trpOUlBRkZmaqfaOu6TlTk22gyThvb6OJEyfi0aNHOHfuHHx9feHr6ws9PT3Y29ujTZs2+Oabb1SWKlFH8TYyf4Ms2jAzMyv0+M7/5j7/b1FsTwDiNfpd8p+nihszkNvoDJB7r6MuOQRyj9ulS5di5syZYqNGAMQWirt164aOHTsWKcGmDxcTRCpR6m6MNBlP0+m1kf8mpbAWUPX09FCrVi0kJSUV+w1LfjVq1MDo0aPRokUL9O/fH9nZ2di/f7/WCWJaWhqmTJmCtLQ0uLi4YOLEiQWepN66dUtspfXttzKaUDyNrlixYpGKIZqammLDhg0ICwvDqVOncPXqVYSHhyMyMhKRkZHw8fHBzz//jG+//VbreQPF3zdUFbvTlIGBAbp16wZfX1/4+fmJCeLz589x5coVAAVbLwWAY8eOicnhDz/8gL59+6JmzZpKsSi6xyjKNgMgvkEeOXJksYofaUpPTw+LFi3CsGHDcPz4cVy9ehU3b94UW9Lz9fWFh4cHJkyYIE4TFhYmJoe9e/fGoEGDIJVKlW7gfXx8sHTpUq3WQ3H32ZJQEuewd1Gss/e1bE23UVH36U+BuptrxTFduXJlBAYGaj1vRVdDhoaGmDhxIjp37oxq1aop7R/Dhw9/Z7WH4pwzdcHMzAwbN27EzZs3la4pERERiIiIgLe3N+bOnauyeOu7vM/jNH8pnxMnTmhUbFeV4sT89ddf4/Dhw/D398cXX3yBrl27qh2/Y8eOaN68OY4fP46LFy/i+vXriI6Oxt9//42///4bTk5O2Lhxo8qHsfRxYrpPOpf/SaiiDo4q+YuyFPZkUl1xl7i4OAC5Rbi0qUuW/22GuhNsSd7MODk5iUWU/vnnH62nv3jxIpKSkmBsbIwNGzagefPmBYrZaPqkuTAVKlQQ51PY2w1NODk54ccff8T27dtx5coVbNy4EY6OjsjKysK8efMQFRVVpPlqsm8AxXtTqI4iAbx16xYePnwIIPftoSAIqFatWoEm2gGIXV907doV//d//wc7O7sCN13FrV9TsWJFANBZ0UFN2dnZYezYsfDx8cHly5exY8cOtGrVCoIgYM2aNWLiDOSuB0EQ4OzsjMWLF6N+/foFiqQVZT3oap/VBcV5UNG/Y2EU+7Eu91PFstPT08WuPlTJ/yajOExMTMTzj7p5qhum2P6K6gmqvH79uogRftwUx3R8fLzGb5zyU9TFHjZsGEaMGAFbW9sC177iXi/eJ2dnZ/z000/YsWMHQkNDxe6ysrKyMGfOHKXimO+iWLfaTFNcimUCRTtP6yLmOXPm4D//+Q+ys7MxZcoU+Pv7v3MaS0tL9OvXD8uXL8fp06dx6tQpjBs3Dvr6+ggLC8Py5cuLHA99eJggks7VrVtXLFp34cKFQscLCQkBAFSvXr3QYlBvdxKsaljDhg21ii9/vawHDx6oHEcul4v9ulWrVk2r+WtKcUP1dnKreJKs7qZSkXhXrVq10HWnWL+qaLIMRdFYQRDExKa4jIyM8MUXX2DDhg2QSCTIycnBxYsXizQvTfYNW1vbEnui6ejoiDp16gD4X7FSxf89evRQ+fBBsd0Kq7+UlZWF0NDQQpepSREexXa7ePFiqSVJ+vr6cHV1xbp168T1f/78eXH4u9YDoP7cUZiS2GeLSnFekslkCA8PL3Q8xXGqrk6WtvL3oZk/MX+bumNIW4r+bC9fvlzoOOqGKRrCyP9w5203b94sYnQfN8V+LZfLcezYMa2nVzyEyL9f5JeYmCj2xfexMTIyQps2bbB+/XoAuUU51e1nb3NxcQEAnD17ViyBUNKqV68uPszStpE94H/7w+XLlyGTyYoUg0QiwZw5czBo0CDk5ORg2rRpYvUATVWrVg3jx48X39jmP8fTx48JIumckZEROnToAADYtm1bgYrmQG6rbIpOabt161bovLZt26byBHjr1i2xOMy7ika8zcHBQbyZ8fHxUTnOoUOHxDpE7u7uWs0/JCSkQEMxb3vy5IlY0fzti7ai0rm6p+WK+oTR0dEq18/jx4/FVtJU0WQZNWvWFBuVWLNmjdq3wYByXQq5XK62jo+xsbGY7BS12NKhQ4dUxhQTE4NDhw4B0H7f0JbiLeLhw4cRFhYmvkksrMiwYrsV1oqij4+PUh3etym2m6pjSqFPnz4wMDBAeno6Fi5cqPYhQE5OTpFvMBTUtfJraGgobt/82/ld60HRkIa2irPP6lrjxo3x2WefAQBWr16tcjv4+/uLJQi6d++us2Xb2tqK57hNmzapPB+dPn1ap2+ZO3XqBCC3ZcTIyMgCw1NTU7Ft27ZCp1c09JGWlqZUV07h9evX2LFjh46i/bjY2dmJJRJWr16tNokGCu7XivOGqu0CACtWrPjg6mS+TZtrijZ14RTJzbNnzwq9H9A1iUSCfv36Acht1OxdxeHf3p49evSAoaEhUlNTi/3WbtasWRgyZAjkcjlmzJiBgwcPFhjnXS25m5iYACj9IsikW0wQqUSMGzcOpqamiI+Px5AhQ8RKzYIg4MKFCxg6dCjS09NRqVIltU2fy2QyDB8+XLywyeVynDlzBqNHj0ZOTo5SdxnamDFjBvT09HDjxg383//9n3jBzcxUB9d5AAAgAElEQVTMxOHDh8VWxRo0aCDe+Ghqzpw56NixI1atWoUbN24onVyTk5OxY8cODBw4EJmZmZBIJBgyZIjS9HXr1gWQW5+tsDohzZo1g6GhITIyMjBp0iSxmGZ2djZOnjyptulsTZcBAD///DPMzMyQnJyMb7/9FocPH0Zqaqo4PDExEX/99RdGjRqFOXPmiN/LZDK0a9cOf/zxB8LDw5XWwcOHDzF58mTk5OTA0NAQzZs3L3T56ujp6WHo0KFKb9xCQ0MxdOhQpKWloWzZsgXWra59/fXX0NPTw/PnzzFv3jwAuU+kC2vdLX/XLFu2bBEbHkhOTsbKlSuxcuVKtW88Fdvt1KlThSaS1atXF5tdP3LkCEaNGoWwsDAxQZHL5bh37x68vLzQuXNntW+YNNGtWzcsXbq0QNP60dHRmDlzphjnF198IQ5TrIfr16/j119/FR9UyGQybNmyBdOnTy/ym9+i7rO6pq+vLzb+EBgYiKlTp4pvcjIyMrBv3z6xBePWrVsX+TgozMSJEwHkruNJkyaJxdGysrLg7++PqVOnatTSrKb69OmDqlWrIjs7G6NGjUJQUJD4Rubu3bsYNmyY0nZ4m1QqFbtAmDt3LkJDQyGXyyEIAq5du4bBgwcXu0Glj9ns2bNhZmaGxMREfPPNN/D391dqUCYhIQFHjx7FiBEjMH/+fKVpFcebj48Pjhw5InZVEBMTgxkzZuDAgQMffN2xly9fon379vD09MStW7eUult48OABJk+eDLlcDiMjI7UtXL/NyclJTNaWLVuGRYsWiceKIAhISEjAoUOHMGXKFJ3+nuHDh6NOnTqQy+Xw8PDAhg0blIrVy2QynD9/Hv/9738LNJJnY2ODsWPHAgB27tyJKVOmiA8ngdzrSUBAgNqufvKbMWMGhg0bBrlcjpkzZxZ4uPzrr79i7Nix+Ouvv5RifPPmDQ4cOCDWq1d0TaQQHh4Oe3t72NvbY/v27RrFQh8ONlJDJaJWrVr4/fffMWnSJNy+fRt9+vSBubk55HK5eJEvX7481q1bh7JlyxY6n4ULF2LatGno0aMHLCwskJWVJdZRKV++PFavXq2ypch3adq0KRYsWIC5c+fCz88Pf/75J6ysrJCWliZeeKRSKdauXav1UzEDAwM8evQIa9euxdq1ayGRSGBpaYmMjAyl+jUmJiaYPXs2XF1dlaZ3dHREgwYNcPv2bQwfPhyWlpZi8SsPDw/07dsXlSpVwoQJE7B8+XIEBQWhffv2MDc3R2ZmJrKyslCpUiXMnz9fqWEQbZehWAfe3t6YMGEC4uLiMHXqVLG1ybdbvHs7kY6NjcWaNWuwZs0a6Ovrw9LSEmlpaWKyqK+vj7lz54pvWbT1888/Y9GiRRgwYIBYXFcRj5mZGf74448Sq3+oULlyZbi5ueHixYviQxB1DQ71798fR44cwe3bt7F48WIsWbIEVlZWSElJgSAI6NatG8qVKwdfX1+V0/ft2xd79uxBZGQkWrVqhfLly4sPAg4fPiy+KRg7dixycnKwbt06BAYGIjAwEEZGRjAzM4NMJlN6El/cxhlevHgBHx8f+Pj4QE9PD5aWlsjMzFS6mR8/frxSn4wdOnRAmzZtEBgYCG9vb3h7e6NMmTJ4/fo15HI5mjZtii+++KJIT8eLs8/qWo8ePfD06VOsXr0a/v7+8Pf3R5kyZZTOM87Ozvjtt990vuw2bdpg/Pjx8PT0FBuSsLKywps3b5CVlQUnJye4u7vj999/L9I59G1mZmZYvXo1hg4ditjYWAwfPhwmJiYwMDCATCaDmZkZfvvtN/HGVtUDrLlz5+KHH35AdHQ0BgwYILbI/ObNG3z22Wf45ZdfMG7cuGLH+jFycHDAxo0bMXHiRMTGxmLKlCnifp2RkaF0vL1dKmfs2LE4e/YsYmNjMXnyZEybNg3m5uZiSYTRo0cjMjISZ8+efZ8/SWsxMTFYvXo1Vq9erfKaYmBggAULFmjdWu7s2bORnp4Of39/bN26FVu3boW5uTlycnLEh15FbUimMBYWFvD29sb48eMRFhaGFStWYMWKFWLpCplMJj7Uy19nUWH06NF4/fo1vL29xXPL29dBbe5dpk2bBgMDA2zYsAE///wzcnJy8N133wHIfah48uRJsX9rMzMzGBoaKtVvdnR0FB9K0b8DE0QqMV9++SX++usv+Pj4ICgoCDExMdDX14dUKoW7uzuGDBmiNjkEgObNm2Pv3r3YsGEDLl++jIyMDFSpUgXu7u7w8PBQ2zT4u/Tt2xdOTk7YvHkzLl26hPj4eBgbG6Nhw4bo3LkzvvvuO7HohDYOHjyI8+fP4+LFi7h9+zaePn0qnkjLly+P2rVro1mzZujTp0+hydGmTZvg6emJ4OBgxMbGik808xcHHDlyJKpXr46tW7ciIiICOTk5qFq1Ktzd3TFixIh3FkPSZBlAblG5gIAA7Nu3D6dOncL9+/fx+vVrGBkZoXbt2nByckK7du2UOj22sLCAl5cXLl26hGvXriE2NhZJSUli67Bubm4YOHBgsTrqlkqlOHjwINatW4fg4GAkJiaiYsWKaNWqFcaOHVusLjS00bNnT7EepaGhodpiraampvD19cW6desQEBCAmJgYSCQSNGnSBH369EHv3r3V9olVv359MaEKDw8X+9UEoFR/RiKRYOLEiejWrRt27tyJS5cu4fnz55DJZLCwsEDNmjXRrFkztG/fvth139atW4eQkBCEhoYiOjoaSUlJkMvlsLW1hYuLC7777rsCDfbo6elhzZo12Lx5M/788088efIEcrkc9evXx1dffYWBAwcW64lzUfbZkjJ+/Hi0bNkS27dvR2hoKJKTk2FqagonJyd0794d/fr1E7v20LVx48bByckJW7ZsEd/k16hRA927d8ewYcPEelvadoFTmPr16+Pw4cNYs2YNzp07h8TERFhZWcHd3R2jR49WSgoVD6Tya9KkCXbv3o21a9fiypUrkMlksLGxQfv27TFmzJh3Fhn+t3N1dUVAQAD27t2LM2fOFNivnZ2d0a5dO7Rq1UppukqVKmH//v3w9PREYGAgEhMTYWBggNatW2PAgAFo27YtRo0aVUq/SjNlypSBl5eX2IKmqmvK999/L9YL14aRkRGWLVuGnj17Yt++fbh+/TqSk5Nhbm6O6tWrw9XVVadFwBUqV66M3bt34/jx4zh69CjCw8Px4sUL6OnpoUqVKrC3t0fbtm1V9qcrkUgwbdo0dO7cGTt27MCVK1eQmJgIIyMj2NnZwdnZGZ07d9Yqnp9++gn6+vpYu3Yt5s6di5ycHAwYMADDhg2DnZ0dLl26hAcPHiAxMRGpqakoW7Ys7O3t0aVLF/Tp06fEzmNUOiQC252mD0xERIRYbPTKlSsqbyTo05SSkiJ2/F0SnVUTfUpGjhyJwMBADB06FNOnTy/x5R07dgyTJk2CtbW1ThvIISIi3WIdRCIiok/MnTt3xMYx3seb1OzsbLERkLffcBER0YeFCSIREdG/0NKlS7Fnzx7ExcWJRZBlMhkOHTqEH374ATk5OWjYsCFatGihk+WdPn0av/76KyIiIsT61nK5HDdu3MCwYcMQFhYGAwMD/PDDDzpZHhERlQzWQSQiIvoXunXrlvjWztDQUGyYRJEs2traYuXKlcVuqEjh5cuXYh1ZiUQiNsiTv2GqOXPmiF1wEBHRh4kJIhHRJ04mk2ndoqeFhQUCAgJKKKL3a8aMGSr73lNn8eLFSl13fIhGjRqFmjVr4saNG0hMTERKSgosLCxgZ2cHd3d3/Oc//xFbvgWA+/fva901TN26dbFlyxYAud3vjBs3DhcvXkR0dDSSk5Ohr6+PmjVromnTphg0aBDs7e11+AuJiKgkMEGkD069evUK7UCbPm1WVlbcN0qAXC5HYmKiVtPk77LlY/fq1Sutf/+7Oo/+ELRq1Uqr+n7Z2dlar4cKFSqIf1epUgXjx4/H+PHjtZoHERF9WNiKKREREREREQFgIzVERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREREREREAJohERERERESUhwkiERERERERAWCCSERERERERHmYIBIREakwffp09O7du7TDeKc9e/bg5MmTpR0GERH9SzBBJCIi+ogxQSQiIl1igkhEREREREQAmCASERFpJCIiAoMHD4azszOaNm2KyZMnIzExURw+YMAA/Pzzz+LnoKAg2NvbY/HixeJ3AQEBaNiwId68eSN+t2/fPnTr1g0NGzZE27ZtsXHjRqXl3r9/H8OGDYObmxsaNWqELl26YMeOHQCAQYMG4fbt2zh06BDs7e1hb2+PgwcPltQqICKiT4BBaQdARET0oUtOTsagQYNgZ2eH5cuXIzU1FcuXL8fQoUNx4MABGBkZwdXVFX///bc4TWhoKIyNjREaGip+d+XKFdSvXx+mpqYAgE2bNmHlypUYPnw43NzccPv2baxatQqmpqYYOHAgAGDMmDGoXbs2fvvtNxgZGeHRo0dITU0FAMyZMwfjx4+Hra0tPDw8AADVq1d/X6uFiIj+hZggEhERvYOPjw8AwNvbGxYWFgCAWrVqoV+/fvj777/RvXt3uLq6Yv369UhOTka5cuUQGhqKvn37Yvfu3UhNTYW5uTmuXr2KZs2aAQBkMhnWrFmDMWPGYNy4cQCAli1b4s2bN1i3bh369++PV69eISoqCmvWrIG9vT0AoHnz5mJcderUgampKcqVK4dGjRq9z1VCRET/UixiSkRE9A5hYWFo2bKlmBwCgJOTE6pWrYqrV68CAFxcXKCvr4+rV68iMzMTYWFh6NevH8qWLYsbN25AJpPh7t27cHV1BQBcv34daWlp6Ny5M7Kzs8V/zZo1Q2JiImJjY2FtbY3PPvsMc+bMwV9//YWkpKRS+f1ERPTp4BtEIiKid0hISEDdunULfF+hQgW8evUKAGBhYQEHBweEhoaibNmyMDExgb29PZo0aYLQ0FBkZ2dDLpejcePGAIAXL14AALp166ZymTExMahatSq8vb3x+++/Y+bMmUhPT0fjxo0xa9Ys1K9fv4R+LRERfcqYIBIREb1DxYoVVb69S0xMRIMGDcTPrq6uCA0NhbW1NRo3bgw9PT00adIEJ0+eRHZ2NurUqYOyZcsCAMqUKQMA2LBhA8qXL19g3rVq1QIA2NnZwdPTE1lZWQgNDcWyZcswcuRInDt3Dnp6LAhERES6xSsLERHROzg7OyM4OBgymUz8LiwsDNHR0WjSpIn4naurKyIiIhAYGCgWJW3atCnCwsJw4cIFpXFdXFxgYmKC+Ph4ODo6FviXvzgrABgaGqJ58+YYOnQoEhISkJKSAgAwMjJCRkZGSf58IiL6hPANIhER0TsMHToUu3btwvDhwzF8+HCkpaVh+fLlkEql6Nixozieq6srcnJycP36dUyfPh0A4ODgAAMDA4SHh2Pw4MHiuFZWVhg3bhwWLlyI6OhoNG3aFHK5HI8fP8alS5ewZs0aREZG4tdff0WXLl1ga2uLlJQUbNy4EQ4ODrC2tgaQ+6YxODgYQUFBsLa2RrVq1cS3lERERNpigkhERPQO5cqVw7Zt27BkyRJMnjwZhoaGaNOmDWbMmAEjIyOl8WrXro2YmBix6Kmenh5cXFwQFBSk9AYRAEaMGIFKlSph69at2Lx5M4yNjVGzZk107doVQG7R1vLly2P9+vWIj4+HlZUVPv/8c0yZMkWch4eHB2JiYjBp0iTIZDIsXrwYvXv3fg9rhYiI/o0kgiAIpR0EERERERERlT7WQSQiIiIiIiIATBCJiIiIiIgoDxNEIiIiIiIiAsAEkYiIiIiIiPJ8cq2YJiS8Lu0QiIiIiIiISk3FipaFDuMbRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKA8TRCIiIiIiIgLABJGIiIiIiIjyMEEkIiIiIiIiAEwQiYiIiIiIKM8HlyCeO3cOnTp1QocOHeDl5VVgeGZmJiZNmoQOHTqgX79+ePbsmdLw58+fw8XFBd7e3u8rZCIiIiIion+FDypBzMnJwfz587Fp0yYcPXoUR44cwYMHD5TG2bdvH6ysrHDixAkMGTIEy5YtUxq+ePFitG7d+n2GTURERERE9K/wQSWIYWFhqFGjBmxtbWFkZIRu3brh1KlTSuOcPn0avXr1AgB06tQJFy5cgCAIAICTJ0+iWrVqqFu37nuPnYiIiIiI6GNnUNoB5BcXF4fKlSuLn21sbBAWFlZgnM8++wwAYGBgAEtLS7x48QImJibYuHEjfHx84OPjU+gyLCyMYWCgXzI/gIiIiIiI6CP2QSWIijeB+UkkEo3G8fT0xODBg2Fubq52GTJZRvGCJCIiIiIi+ohVrGhZ6LAPKkGsXLkyYmNjxc9xcXGoVKlSgXFiYmJQuXJlZGdn4/Xr17C2tsbNmzcREBCAZcuWISUlBXp6ejA2NsbAgQPf988gIiIiIiL6KH1QCaKjoyMeP36MqKgo2NjY4OjRo1i+fLnSOO7u7jh06BBcXFwQEBCAZs2aQSKRYOfOneI4np6eMDMzY3JIRERERESkhQ8qQTQwMMDs2bMxfPhw5OTkoE+fPqhbty5WrVqFhg0bol27dujbty+mTp2KDh06oEyZMli5cmVph01ERERERPSvIBFUVer7F0tIeF3aIRAREREREZUadXUQP6huLoiIiIiIiKj0MEEkIiIiIiIiAEwQiYiIiIiIKI9WjdRERUUhPDwcCQkJyMjIgLW1NWrWrAknJyeYmJiUVIxERERERET0HrwzQYyLi8O+ffvg5+eH6OhoCIIAIyMjGBkZITU1FYIgwMDAAK1bt0a/fv3g7u7+PuImIiIiIiIiHVPbiunChQuxd+9eODg4oGPHjnBxcYGDgwPMzMwAAIIgIDo6GuHh4QgODsbJkydRuXJlLFiwAE5OTu/tR2iDrZgSEREREdGnTF0rpmoTxOnTp2PEiBGws7PTaEFZWVnw8/ODoaEhevbsqX2k7wETRCIiIiIi+pQVOUH8N2KCSEREREREn7IS6wcxPT0dT548wSeWYxIREREREf0raZwgenl5YdWqVeLnq1ev4osvvkDnzp3RoUMHPH78uCTiIyIiIiIiovdE4wTxwIEDqFKlivh58eLFsLe3h7e3NypWrIgVK1aUSIBERERERET0fmjcD2JcXBxq1Kgh/n3r1i3s2rULLi4uePPmDWbPnl1iQRIREREREVHJ0/gNoomJCdLS0gAAFy9ehKWlJRo1agQAMDc3R2pqaslESERERERERO+Fxm8QmzRpAh8fH5iZmWHr1q348ssvIZFIAABPnjyBjY1NiQVJREREREREJU/jN4jTp09HWloahgwZArlcjh9//FEcdvjwYbi6upZIgERERERERPR+aN0Polwuh56ecl4ZFxcHCwsLmJub6zS4ksB+EImIiIiI6FOmrh9EjYuYKrydHAJg8VIiIiIiIqJ/AbUJ4ujRo7Wa2fr164sVDBEREREREZUetQlidna20ue7d+8iMTERdnZ2KFeuHJKTk/Hw4UNUqFABDg4OJRooERERERERlSy1CeKmTZvEvw8fPox169Zh69atqF27tvj9w4cPMW7cOHTv3r3koiQiIiIiIqISp3ErpqtXr8akSZOUkkMAsLOzw4QJE+Dp6anz4IiIiIiIiOj90ThBjIuLUzs8ISGh2MEQERERERFR6dE4QXRxccGKFSvw8OFDpe8fPHiAlStXonHjxjoPjoiIiIiIiN4fjftBjIqKwogRI/DkyRNUr15dbKTm6dOnqFGjBjZu3AhbW9uSjrfY2A8iERERERF9ytT1g6hxgggAcrkcx48fR3h4OBITE1GhQgU4Ojqic+fOKvtH/BAxQSQiIiIiok+ZzhLEfwMmiERERERE9ClTlyCq7eaiMElJScjIyCjwfZUqVYoyOyIiIiIiIvoAaJwgJicnY8GCBTh58iSys7OVhgmCAIlEgoiICJ0HSERERERERO+HxgnirFmzcPXqVXh4eKBOnTowNDQsybiIiIiIiIjoPdM4Qbx8+TJmz56Nr7/+uiTjISIiIiIiolKicdOjZcqUgZWVVUnGQkRERERERKVI4wTRw8MDmzdvVtk4DREREREREX38NC5iGhoaiqdPn8Ld3R0uLi6wtFRuGlUikWDRokU6D5CIiIiIiIjeD40TxHv37qFcuXIAgJiYGMTExCgNl0gkuo2MiIiIiIiI3iuJIAhCaQfxPiUkvC7tEIiIiIiIiEpNxYqWhQ7TuA4iERERERER/btpXMQUADIzM3Hu3Dn8888/KhurGTdunM4CIyIiIiIiovdL4yKmz58/x3/+8x8kJiYiJycHpqamSEtLAwCYmprCxMQEFy5cKNFgdYFFTImIiIiI6FOmkyKmixcvRq1atXDx4kUIggBfX19cu3YN8+bNQ9myZbF582adBEtERERERESlQ+ME8fr16xg4cCDMzMwAANnZ2TAzM8O3336LgQMHYsGCBSUWJBEREREREZU8jRPEtLQ0WFlZQU9PD2XKlEFCQoI4rF69erh9+3aJBEhERERERETvh8YJYs2aNREbGwsAsLe3x/79+5GTkwNBEODv74/y5cuXWJBERERERERU8jROEDt27IgbN24AAMaPH4+LFy/C1dUVTZs2hZ+fH0aPHl1iQRIREREREVHJ07gV07c9fvwYZ86cQUZGBlq0aAEnJyddx1Yi2IopERERERF9ytS1YlrkBPFjxQSRiIiIiIg+ZTrp5iIsLAzBwcHi55SUFPz888/o378//vjjD8jl8uJFmefcuXPo1KkTOnToAC8vrwLDMzMzMWnSJHTo0AH9+vXDs2fPAADnz59H79698dVXX6F3794fRZ+MREREREREHxKt+kFU1EFUfA4ICEDVqlWxdetWrFmzptjB5OTkYP78+di0aROOHj2KI0eO4MGDB0rj7Nu3D1ZWVjhx4gSGDBmCZcuWAQDKli2LdevWwd/fH0uWLMG0adOKHQ8REREREdGnROME8dGjR3B0dAQAZGRk4Pjx45g5cyaWLVuGKVOmwN/fv9jBhIWFoUaNGrC1tYWRkRG6deuGU6dOKY1z+vRp9OrVCwDQqVMnXLhwAYIgoH79+rCxsQEA1K1bF5mZmcjMzCx2TERERERERJ8KA01HzMjIgKmpKQDg2rVryMzMRLt27QAAderUQVxcXLGDiYuLQ+XKlcXPNjY2CAsLKzDOZ599lhu8gQEsLS3x4sULlCtXThwnICAA9erVg5GRUYFlWFgYw8BAv9ixEhERERER/dtonCDWrFkTFy9ehJubG44dOwYnJydYWuZWbkxMTBT/Lg5V7eVIJBKtxrl//z6WLVsGHx8flcuQyTKKGSUREREREdHHSyeN1IwcORJeXl5wd3fH/v37MWzYMHFYSEgI6tWrV7woAVSuXBmxsbHi57i4OFSqVKnAODExMQCA7OxsvH79GtbW1gCA2NhYjBs3DkuXLkX16tWLHQ8REREREdGnROM3iF27doWdnR3u3LkDBwcHpYTQzs4OX331VbGDcXR0xOPHjxEVFQUbGxscPXoUy5cvVxrH3d0dhw4dgouLCwICAtCsWTNIJBKkpKRg5MiR+Omnn9CkSZNix0JERERERPSp+eD6QQwMDMSiRYuQk5ODPn36YMyYMVi1ahUaNmyIdu3aISMjA1OnTkVERATKlCmDlStXwtbWFmvXroWXlxdq1KghzsvHxwfly5dXmj/7QSQiIiIiok+ZuiKmGieIfn5+7xynZ8+emkdVSpggEhERERHRp0wnCaKDg4PqGeRrICYiIkLL0N4/JohERERERPQpU5cgalwH8dq1awW+e/XqFYKDg7Ft2zaxw3oiIiIiIiL6OOmkDuKOHTtw4sQJbNmyRQchlSy+QSQiIiIiok+ZTrq5UKdWrVq4efOmLmZFREREREREpaTYCWJKSgq2b9+Ozz77TBfxEBERERERUSnRuA6iu7u7UoM0AJCVlYXk5GQAwO+//67byIiIiIiIiOi90jhB7NSpU4HvjI2NUblyZbRt2xY2NjY6DYyIiIiIiIjeL500UvMxYSM1RERERET0KdNJNxcKL2vdp8UAACAASURBVF++RHh4OF69eoUyZcrAyckJZcqUKVaAREREREREVPo0ThAFQcBvv/0GX19fZGVlid8bGRlh0KBBmDp1aokESERERERERO+HxgniunXrsG3bNowcORJdu3ZF+fLlkZSUhL/++gteXl6wsLDAmDFjSjJWIiIiIiIiKkEa10F0d3fHN998g9GjRxcYtn79euzduxenT5/WeYC6xjqIRERERET0KVNXB1HjfhATEhLg6OiocpijoyMSEhK0j4yIiIiIiIg+GBoniNWrV8fff/+tctiJEydQvXp1nQVFRERERERE75/GdRBHjBiB6dOnIyEhAZ06dUKFChWQlJSE48eP48yZM1i8eHFJxklEREREREQlTKt+EE+cOIE//vgD9+/fF7+rW7cuJk6ciPbt25dIgLrGOohERERERPQpU1cHUasEUSE9PR0JCQmoWLEiTExMihXc+8YEkYiIiIiIPmXqEkSNi5jmZ2JiAltb2yIHRERERERERB8erRLEhw8f4sSJE4iNjUVGRobSMIlEgkWLFuk0OCIiIiIiInp/NE4QDx48iFmzZsHU1BRVq1aFoaGh0nCJRKLz4IiIiIiIiOj90bgOYseOHeHk5ISFCxfC2Ni4pOMqMayDSEREREREnzJ1dRA17gcxISEBffr0+aiTQyIiIiIiIiqcxgli8+bNcefOnZKMhYiIiIiIiEqR2jqIz58/F/8eNWoUpk+fDolEghb/z959h0dV5X8c/0w6JSRAGl0RJEpHqkFKQqgioelakCiIUqQICCILgpSFZUUgVCmCsovSlUgNHamKSxGUslFAMgQSQgKk398fhPkRU5yESQLk/XoenoeZc+6535sMM/Ph3Hvus8+qRIkSGfqXLVvW9hUCAAAAAPJFttcg+vr6plt85m7XPy9IYxiGTCaTTp06lUdl2g7XIAIAAAAozHJ9H8R58+bZvBgAAAAAwIPJ6lVMHxXMIAIAAAAozGyyimm9evV04sSJTNtOnTqlevXq5bwyAAAAAMADw+qAeOvWLaWmpmbalpiYqMTERJsVBQAAAADIf9legxgZGakrV65YHp8/f1729vbp+iQkJGj16tWsYAoAAAAAD7lsA+JXX32lkJAQmUwmmUwmffDBBxn6GIYhBwcHffTRR3lVIwAAAAAgH2S7SI3ZbFZERIQMw9Df/vY3TZgwQVWrVk3Xx9HRURUrVlTx4sXzvFhbYJEaAAAAAIVZdovUWL2K6ZkzZ1SpUiU5OTnZrLCCQEAEAAAAUJjlehXTyMhIy9+rVq1qdTi8evWqlaUBAAAAAB4U2QbE1q1ba8yYMTp27NhfDhQdHa0VK1bo+eef1+rVq21WIAAAAAAgf2R7iunvv/+ukJAQfffdd/Lw8FCdOnVUtWpVlSxZUk5OToqNjdXFixd18uRJnThxQuXKldPbb7+toKAg2dlZfQeNfMUppgAAAAAKs/u+BjEqKkrffPONDhw4oJMnTyoqKkopKSkqUqSIHn/8cdWrV0+tW7dWw4YNbVp4XiAgAgAAACjMbLJIzZ8lJSXJ0dEx10UVFAIiAAAAgMIs14vUZOdhDIcAAAAAgKw9mBcKAgAAAADyHQERAAAAACCJgAgAAAAASENABAAAAABIus+AGB8fr99++025XAgVAAAAAPAAsTogLliwQDNmzLA8/uGHH9SsWTO1bdtWgYGBCg8Pz4v6AAAAAAD5xOqAuHr1apUtW9byePLkyapWrZoWLVokT09PffLJJzYpaPfu3WrTpo0CAwO1YMGCDO2JiYkaPHiwAgMD1b17d128eNHSNn/+fAUGBqpNmzbas2ePTeoBAAAAgMLC6oBoNptVqVIly99PnDih9957T88++6x69+6tH3744b6LSUlJ0fjx47Vw4UKFhoZqw4YNOnv2bLo+K1euVIkSJbR161YFBwdr2rRpkqSzZ88qNDRUoaGhWrhwocaNG6eUlJT7rgkAAAAACgurA6KLi4tu3bolSTpw4IBcXV1Vp04dSVKxYsV08+bN+y7m2LFjqlSpkipUqCAnJyd16NBBYWFh6fps375dnTt3liS1adNG+/fvl2EYCgsLU4cOHeTk5KQKFSqoUqVKOnbs2H3XBAAAAACFhYO1HZ955hktXrxYRYsW1dKlS9WiRQuZTCZJ0m+//SZvb+/7LsZsNsvHx8fy2NvbO0PIM5vNKlOmzJ3iHRzk6uqq6Ohomc1m1a5dO922ZrM5wz6KF3eWg4P9fdcKAAAAAI8aqwPiyJEjNWTIEAUHB+vJJ5/UkCFDLG3ffPON6tevf9/FZLYa6t0Q+ld9rNlWkuLiEu6jQgAAAAB4uHl6umbZZnVArFChglatWqXU1FTZ2aU/M/WTTz5R8eLFc19hGh8fH0VERFgem81meXl5Zehz+fJl+fj4KDk5WbGxsXJ3d7dqWwAAAABA1nJ8H8Q/h0PpzumcxYoVu+9iatasqfDwcF24cEGJiYkKDQ2Vv79/uj7+/v5au3atJGnz5s1q3LixTCaT/P39FRoaqsTERF24cEHh4eGqVavWfdcEAAAAAIWF1TOIkrRr1y5t3bpVZrNZCQkZT9VctmzZ/RXj4KAxY8aod+/eSklJUdeuXVW1alXNmDFDNWrUUEBAgLp166bhw4crMDBQbm5umj59uiSpatWqateundq3by97e3uNGTNG9vZcawgAAAAA1jIZmV28l4mQkBCFhIToiSeeUMWKFeXo6Jihz8yZM21eoK1FRsYWdAkAAAAAUGCyuwbR6oDo5+enl156SQMHDrRZYQWBgAgAAACgMMsuIFp9DaLJZNIzzzxjk4IAAAAAAA8eqwPi3/72N61fvz4vawEAAAAAFCCrTzGVpEmTJunQoUNq1KiRXF3TT0uaTCb179/f5gXaGqeYAgAAACjMbHIN4ubNmzVs2DAlJSWpWLFiGRapMZlM2r9///1Vmg8IiAAAAAAKM5sERH9/f1WvXl3jxo1TqVKlbFZcfiMgAgAAACjMbLJITXR0tF5++eWHOhwCAAAAALJmdUD09/fXjz/+mJe1AAAAAAAKkIO1Hdu1a6fJkycrJiZGjRs3VokSJTL0adCggU2LAwAAAADkH6uvQfT19c1+IJNJp06dsklReYlrEAEAAAAUZtldg2j1DOK3335rk2IAAAAAAA8mqwJiYmKivv/+ezVp0kRPPvlkXtcEAAAAACgAVi1S4+TkpE8++UTXr1/P63oAAAAAAAXE6lVMn376af3yyy95WQsAAAAAoABZHRDHjh2r5cuXa82aNbp27Vpe1gQAAAAAKAA5XsXUZDJl2YdVTAEAAADgwWaTVUxHjx6dbTgEAAAAADzcrJ5BfFQwgwgAAACgMMtuBtHqaxABAAAAAI82q08xlaSdO3fq66+/Vnh4uBISEjK0h4WF2awwAAAAAED+snoGccuWLRowYICKFi2q8+fPq06dOqpRo4aio6NlZ2enNm3a5GWdAAAAAIA8ZnVAnDdvnvr06aMpU6ZIkt58803NmDFDYWFhKl68uMqWLZtnRQIAAAAA8p7VAfF///ufGjZsKDs7O9nZ2enmzZuSpJIlS+qdd97RkiVL8qxIAAAAAEDeszogFitWTElJSTKZTPL29lZ4eLilzcHBQVFRUXlRHwAAAAAgn1i9SE316tV19uxZPffcc2rWrJnmzJmjokWLytHRUZ9++qlq1KiRl3UCAAAAAPKY1QGxV69eunTpkiRp0KBBCg8P17BhwyRJVapU0UcffZQnBQIAAAAA8ofJMAwjtxvfuHFDt2/flre3ty1rylORkbEFXQIAAAAAFBhPT9cs26y+BvFeMTExOn36tJydnR+qcAgAAAAAyFqOAuK6devUqlUrNW7cWJ07d9bZs2clSe+9957+85//5EmBAAAAAID8YXVA/OKLLzR69Gi1atVKc+fO1b1nptaqVUvffPNNnhQIAAAAAMgfVi9Ss3TpUvXr10/9+vVTSkpKurbKlSvr/PnzNi8OAAAAAJB/rJ5BNJvNqlu3bqZtDg4Oio+Pt1lRAAAAAID8Z3VALF++vI4ePZpp248//qjKlSvbrCgAAAAAQP6zOiC++uqrmj9/vj7//HNFRkZKkm7duqXvvvtOixcv1quvvppnRQIAAAAA8l6O7oM4c+ZMffbZZ0pOTpZhGDKZTHJwcNBbb72lgQMH5mWdNsN9EAEAAAAUZtndBzFHAVGSoqKidOTIEUVHR8vNzU3169eXh4fHfReZXwiIAAAAAAqz+w6ICQkJGjp0qIKDg1W/fn2bFpffCIgAAAAACrPsAqJV1yA6OzvrwIEDSkpKsllRAAAAAIAHi9WL1DRr1ky7d+/Oy1oAAAAAAAXIwdqObdu21YQJExQdHa1mzZrJw8NDJpMpXZ8GDRrYvEAAAAAAQP6wepEaX1/f7AcymXTq1CmbFJWXuAYRAAAAQGGW3TWIVs8gfvvttzYpBgAAAADwYLI6IFatWjUv6wAAAAAAFDCrF6kBAAAAADzasp1BfOqpp/TVV1+pVq1a8vX1zbAozZ89DNcgAgAAAAAyl21AHD16tMqXL2/5+18FxPtx/fp1DRkyRJcuXVK5cuX06aefys3NLUO/tWvXau7cuZKkvn37qnPnzrp9+7YGDRqk33//Xfb29mrZsqWGDRuWZ7UCAAAAwKPI6lVM89rUqVPl7u6uPn36aMGCBYqJidHw4cPT9bl+/bq6du2q1atXy2QyqUuXLlqzZo2cnJz03//+V40bN1ZiYqKCg4P19ttvq3nz5hn2wyqmAAAAAAqz7FYxzfYaxC1btujGjRs2LygzYWFhCgoKkiQFBQVp27ZtGfrs3btXfn5+cnd3l5ubm/z8/LRnzx4VKVJEjRs3liQ5OTnp6aefltlszpe6AQAAAOBRke0ppoMGDbJcgyhJqampateunWbPnq0qVarYtJBr167Jy8tLkuTl5aWoqKgMfcxms3x8fCyPvb29MwTBGzduaMeOHerZs2em+yle3FkODvY2rBwAAAAAHg3ZBsQ/n31qGIZ+++03JSQk5GpnwcHBunr1aobnBw8ebNX2mZ0Ne+91kcnJyXrvvffUo0cPVahQIdMx4uJyVzsAAAAAPAqyO8XU6vsg2sLnn3+eZVvp0qV15coVeXl56cqVKypVqlSGPj4+Pjp06JDlsdlsVsOGDS2P//73v+uxxx5TcHCwLcsGAAAAgELhgbkPor+/v9atWydJWrdunQICAjL0adq0qfbu3auYmBjFxMRo7969atq0qSRp+vTpiouL06hRo/K1bgAAAAB4VGS7iqmvr6+6dOkib29vSXdO8Zw3b566desmT0/P9AOZTBo4cGCuC4mOjtbgwYN1+fJllSlTRjNmzJC7u7uOHz+uFStWaOLEiZKkVatWaf78+ZKkd955R127dlVERISaN2+uypUry8nJSZL02muvqXv37hn2wyqmAAAAAAqz7E4xzTYg+vn55ejeh3v37s1ZZQWAgAgAAACgMMt1QHwUERABAAAAFGa5vg8iAAAAAKDwICACAAAAACQREAEAAAAAaQiIAAAAAABJBEQAAAAAQBoCIgAAAABAkuSQXeOqVatyNFi3bt3uqxgAAAAAQMHJ9j6Ivr6+1g9kMunUqVM2KSovcR9EAAAAAIVZdvdBzHYG8dixYzYvBgAAAADwYMo2IDo5OeVXHQAAAACAApZtQMxMVFSUwsPDlZCQkKGtSZMmNikKAAAAAJD/rA6It27d0vDhw7Vjxw5lddniw3ANIgAAAAAgc1bf5mL69Ok6e/asFi1aJMMw9K9//UufffaZOnbsqHLlymn58uV5WScAAAAAII9ZHRB37Nihvn37qmHDhpKkChUqqGnTppo6dapatGhBQAQAAACAh5zVAfHq1asqX7687O3tVaRIEV2/ft3S5u/vr127duVJgQAAAACA/GF1QPT29raEwooVK2rPnj2Wtp9//lnOzs62rw4AAAAAkG+sXqSmSZMmOnDggFq1aqUePXpo9OjROn36tJycnLR//369+uqreVknAAAAACCPmYysliT9k7i4ON28eVPe3t6SpA0bNmjz5s2Kj4+Xn5+fXnvtNTk45PiuGfkuMjK2oEsAAAAAgALj6emaZZvVAfFRQUAEAAAAUJhlFxCtvgaxdu3aOn78eKZtP//8s2rXrp3zygAAAAAADwyrA2JCQoKymmxMTExUSkqKzYoCAAAAAOS/bC8aNJvNMpvNlsdnz57N0CchIUFr1qxRuXLlbF8dAAAAACDfZBsQV65cqZCQEJlMJplMJn344YcZ+hiGIUdHR40fPz7PigQAAAAA5L1sF6m5cuWKrly5IsMw1L17d02ePFlVq1ZN18fR0VHly5dXsWLF8rxYW2CRGgAAAACFmU1WMT1//rzKly8vJycnmxVWEAiIAAAAAAqz7AKi1TcurFy5slJTU7Vt2zb98MMPiomJkZubm+rXr6+WLVvKzs7q9W4AAAAAAA8gq2cQo6Oj9dZbb+nEiRPy8PCQh4eHrl69qqtXr6pmzZr67LPP5O7untf13jdmEAEAAAAUZja5D+KUKVMUERGhL7/8Unv37tW6deu0d+9effnll4qIiNCUKVNsUiwAAAAAoGBYHRB37NihYcOGqX79+umer1+/voYOHart27fbvDgAAAAAQP6xOiAmJCRkeQqpm5ubEhISbFYUAAAAACD/WR0Qa9asqUWLFmUIggkJCVq8eLFq1qxp8+IAAAAAAPnH6kVqjh8/rp49e8rZ2VnNmzdX6dKlFRUVpV27dik+Pl7Lli1TjRo18rre+8YiNQAAAAAKM5vcB1GSIiMjtWDBAh0/flyRkZHy9PRU7dq11atXL3l5edmk2LxGQAQAAABQmOU6IB4+fFhPP/20ihUrlieFFQQCIgAAAIDCLNe3uXj99dd17tw5mxcEAAAAAHjwZBsQc3D2KQAAAADgIWf1KqYAAAAAgEebw1912LVrl86fP2/VYEFBQfddEAAAAACgYGS7SI2vr6/1A5lMOnXqlE2KykssUgMAAACgMMtukZq/nEF8WO5vCAAAAAC4P38ZEF1cXFS0aNH8qAUAAAAAUIBYpAYAAAAAIImACAAAAABIk+0iNY8iFqkBAAAAUJhlt0jNAzODeP36db3xxhtq3bq13njjDcXExGTab+3atWrdurVat26ttWvXZmh/55139Pzzz+d1uQAAAADwyHlgAuKCBQvUpEkTbdmyRU2aNNGCBQsy9Ll+/bpCQkL09ddfa+XKlQoJCUkXJLds2aJixYrlZ9kAAAAA8Mh4YAJiWFiYgoKCJElBQUHatm1bhj579+6Vn5+f3N3d5ebmJj8/P+3Zs0eSdPPmTS1ZskR9+/bN17oBAAAA4FHxl7e5yC/Xrl2Tl5eXJMnLy0tRUVEZ+pjNZvn4+Fgee3t7y2w2S5JmzJihN998Uy4uLtnup3hxZzk42NuwcgAAAAB4NORrQAwODtbVq1czPD948GCrts9sPR2TyaRTp07p999/16hRo3Tx4sVsx4iLS7CuWAAAAAB4BGW3SE2+BsTPP/88y7bSpUvrypUr8vLy0pUrV1SqVKkMfXx8fHTo0CHLY7PZrIYNG+ro0aM6ceKE/P39lZycrKioKPXo0UNffPFFXhwGAAAAADySHphrEP39/bVu3TpJ0rp16xQQEJChT9OmTbV3717FxMQoJiZGe/fuVdOmTfXKK69o79692r59u/7973/rscceIxwCAAAAQA49MAGxT58+2rdvn1q3bq19+/apT58+kqTjx4/rww8/lCS5u7urX79+6tatm7p166b+/fvL3d29IMsGAAAAgEeGycjswr5HWGRkbEGXAAAAAAAFJrtrEB+YGUQAAAAAQMEiIAIAAAAAJBEQAQAAAABpCIgAAAAAAEkERAAAAABAGgIiAAAAAEASAREAAAAAkIaACAAAAACQREAEAAAAAKQhIAIAAAAAJBEQAQAAAABpCIgAAAAAAEkERAAAAABAGgIiAAAAAEASAREAAAAAkIaACAAAAACQREAEAAAAAKQhIAIAAAAAJBEQAQAAAABpCIgAAAAAAEkERAAAAABAGgIiAAAAAEASAREAAAAAkIaACAAAAACQREAEAAAAAKQhIAIAAAAAJBEQAQAAAABpCIgAAAAAAEkERAAAAABAGgIiAAAAAEASAREAAAAAkIaACAAAAACQREAEAAAAAKQhIAIAAAAAJEkmwzCMgi4CAAAAAFDwmEEEAAAAAEgiIAIAAAAA0hAQAQAAAACSCIgAAAAAgDQExIdctWrV9I9//MPyeNGiRZo1a1ae7tPf319RUVF5ug/c+d0OHz7c8jg5OVmNGzfW22+/naNxzGazBg4caOvy0pk1a5YWLVqUZfuaNWs0fvz4fNkX0nvqqafUqVMnPf/88xo4cKBu375doPXUrVvX5mN++OGHOnv2rM3HRc7d/f1evHhRzz//vCTp4MGDlvetsLAwLViwIFdj58d7GfJHRESE+vbtq9atW6tVq1aaMGGCEhMTC7osPMD+/Nlhy+8Vf3bve83x48c1YcKEPNnPg4yA+JBzcnLSli1bCGyPoKJFi+rMmTOKj4+XJO3bt0/e3t45GiM5OVne3t6aOXNmXpSIh4CLi4vWr1+vDRs2yNHRUStWrCjokmxu4sSJqlKlSkGXASsEBASoT58+udqW97JHg2EYGjBggFq1aqUtW7Zo8+bNunXrlqZPn56uX3JycgFViMLu3veamjVravTo0QVcUf5zKOgCcH8cHBz00ksvaenSpRoyZEi6tqioKI0dO1Z//PGHJGnUqFGqW7euWrVqpXXr1qlEiRKSpMDAQP3nP//RsWPHNHfuXCUlJcnd3V3Tpk2Th4eHoqOjNXToUEVFRalWrVq6984o/fr1U0REhBISEvT666/rpZdeyr+DLwSaNWumnTt3qm3btgoNDVWHDh30ww8/SJKOHTumSZMmKT4+Xi4uLpo0aZIqV66sNWvWaOfOnUpMTNStW7c0adIkvfPOO9qwYYMuXryo999/3zKL9Pe//1316tXTwYMHFRISopIlS+rXX39V9erVNW3aNJlMJoWEhGjHjh1KSEhQ3bp1NX78eJlMpixrXrZsmVasWCF7e3tVqVIlw4f+9u3bM32dzZo1S3/88YcuXryoP/74Qz179tTrr78uSZo7d67WrVunMmXKqFSpUqpevbpV+0J69evX1y+//CJJWr9+vb744gslJSWpdu3aGjt2rOzt7VW3bl29/vrr2rFjh1xcXDRnzhx5eHho5MiRKl68uE6cOKHIyEgNHz5cbdu21ZUrVzRkyBDFxcUpJSVFH330kf73v//pzJkzGjVqlCTp66+/1rlz5/TBBx9Yahk8eLA6d+6s5s2bS5JGjhypli1bqnr16jl+jfbo0UPvv/++atasqbFjx+r48eNKSEhQmzZtmHF6wKxZs0YnTpzQmDFjsnxNGYahqVOnas+ePTKZTOrbt6/at2+vixcvWt7Lzpw5ow8++EBJSUlKTU3VrFmz9NhjjxX04cEKBw4ckLOzs7p27SpJsre316hRoxQQEKDy5cvr4MGDls+vZcuWaeHChdq4caMSExMVGBho+Tc9e/ZsffvttypTpoxKliyp6tWrq1evXjp16pTGjh2r27dvq2LFipo0aZLc3NzUo0cP1apVSwcPHlRsbKwmTpyo+vXrF+SPAjaS1feKjh07avny5XJ1dVXjxo31wQcfKCgoSMOHD1fnzp1VsWLFTD9v7n2vOXjwoBYvXqz58+cX8FHmMwMPtTp16hixsbFGy5YtjRs3bhgLFy40Zs6caRiGYbz33nvG4cOHDcMwjEuXLhlt27Y1DMMwPv74Y2PVqlWGYRjGTz/9ZPTs2dMwDMO4fv26kZqaahiGYXz99dfG5MmTLf1nzZplGIZh7Nixw3jyySeNa9euGYZhGNHR0YZhGMbt27eNDh06GFFRUflw1IVDnTp1jFOnThnvvvuuER8fb7zwwgvGgQMHjD59+hiGYRixsbFGUlKSYRiGsW/fPmPAgAGGYRjG6tWrjeeee87yu7lw4YLRoUMHwzAM49atW0Z8fLxhGIbxv//9z+jcubNhGIZx4MABo169esbly5eNlJQU48UXX7S8du6OYxiGMWzYMCMsLCxDrTNnzjQWLlxoGIZh+Pn5GQkJCYZhGEZMTIylpnHjxhmGkfXrbObMmcZLL71kJCQkGNeuXTMaNmxoJCYmGsePHzeef/5549atW0ZsbKzRqlWrbPeF9OrUqWMYhmEkJSUZ77zzjrF8+XLj7Nmzxttvv20kJiYahmEYY8eONdauXWsYhmE8+eSTlt/xlClTjNmzZxuGYRgjRoww3n33XSMlJcU4c+aM0apVK8MwDGPRokXGnDlzDMMwjOTkZCM2Nta4efOmERAQYBn/pZdeMk6fPp2uni1bthjvv/++YRiGkZCQYDRr1sy4fft2rl6jr732mnHs2DHDMP7/9ZqcnGy89tprxqlTp2z/Q0WW7v5+733fufd96973gqxeU5s2bTKCg4ON5ORkIzIy0mjevLlhNpvTjTl+/Hhj/fr1hmHcef3cvn07X48Tubd06VJj4sSJGZ7v1KmTsXTp0nSfX3v27DFGjx5tpKamGikpKUafPn2MQ4cOGceOHTNeeOEF4/bt20ZsbKwRGBho+Vx4/vnnjYMHDxqGYRiffvqpMWHCBMMw7rxP3P282blzp+W7Dx4Ovr6+xgsvvGD507x587/8XvH3v//d2LFjh/HLL78YXbp0MT788EPDMAwjMDDQiIuLy/LzJqv3r8KEGcRHQPHixdWpUyctW7ZMLi4ulue///77dNflxMXFKS4uTu3bt9fs2bPVtWtXhYaGqn379pLuXBMwZMgQRUZGKjExUeXLl5ckHT58WCEhIZKkFi1ayM3NzTLmF198oa1bt0qSLl++rN9++00lS5bM82Mu9bX8PgAAIABJREFULHx9fXXx4kVt2LDBMtNyV2xsrEaMGKHffvtNJpNJSUlJljY/Pz+5u7tnGC85OVnjx4/X6dOnZWdnp/DwcEtbrVq15OPjY9nvpUuXVL9+fR08eFALFy5UfHy8rl+/rqpVq8rf3z/LmqtVq6Zhw4YpICBArVq1ytCe1etMkpo3by4nJyeVKlVKpUqV0rVr13TkyBG1atVKRYoUkaR0+/6rfUGKj49Xp06dJN2ZQezWrZu+/vprnThxQt26dbP0KV26tCTJ0dFRLVu2lCTVqFFD+/bts4zVqlUr2dnZqUqVKrp69aqkO6ffjBo1SsnJyWrVqpWeeuopSVLjxo21c+dOVa5cWUlJSapWrVq6upo1a2a57mj37t2qX7++XFxcFBsbm+PX6L02btyor7/+WsnJyYqMjNS5c+fk6+trqx8nbCyz19QPP/ygDh06yN7eXh4eHmrQoIGOHz+e7jVUp04dzZs3TxEREWrdujWzhw8RwzAyPQvl7vP3fn7t27dP+/btU1BQkCTp1q1bCg8P182bNxUQEGD5znP3PSs2NlaxsbFq2LChJKlz584aNGiQZR+BgYGSpOrVq+vSpUt5d5CwubuXS9x192wEKevvFfXr19fhw4dVtmxZvfzyy/r6669lNpvl5uamYsWKZft5U9gREB8RPXv2VJcuXdSlSxfLc6mpqfrqq6/ShUbpzoW+v//+u6KiorRt2zb17dtXkjRhwgQFBwcrICDAcjpXdg4ePKjvv/9eX331lYoUKaIePXooISHB9gdXyPn7+2vq1KlatmyZrl+/bnl+xowZatSokWbPnq2LFy9aTseUZAlTf/b555/Lw8ND69evV2pqqmrVqmVpc3Jysvzd3t5eKSkpSkhI0Lhx47R69WqVKVNGs2bN+svf8YIFC3T48GFt375dc+bMUWhoaLr27F5nf67h7jUoWZ3Smtm+HBx4W7vXnz9UpTtfxDp37qyhQ4dm6O/o6Gj5edvZ2SklJcXSdu/v564GDRroyy+/1K5du/T++++rV69eCgoKUvfu3TVv3jxVrlw53fvSXc7OzmrYsKH27NmjjRs3qkOHDpJy/hq914ULF7R48WKtWrVKbm5uGjlyJO9JD7jMXlPGPZcxZKVjx46qXbu2du7cqV69emnChAlq0qRJXpQIG6tataq2bNmS7rm4uDhFRETIzs4u3eeXYRjq06eP/va3v6Xr//nnn+dq33dfb39+b8PDLavvFQ0aNNC///1vXb58WUOGDNG2bdu0adMmy38sZvd5U9ixSM0jwt3dXW3bttWqVasszzVt2lRffvml5fGpU6ck3fmy3apVK02ePFlPPPGEZcYvNjbWsgjKunXrLNs1aNBA3377rSRp165diomJsfR3c3NTkSJFdO7cOf300095e5CFVLdu3dSvX78MMzD3/r7Wrl1r1VixsbHy9PSUnZ2d1q9f/5cfkHe/XJcsWVI3b97U5s2bs+2fmpqqy5cvq3Hjxho+fLhiY2N169atLOu+93WWlQYNGmjr1q2Kj49XXFycduzYYfW+kLkmTZpo8+bNunbtmiTp+vXruf7f9EuXLql06dJ68cUX1bVrV508eVKSVLt2bUVERGjDhg2W1Sz/rEOHDlqzZo2OHDmipk2bSsr5a/ReN2/eVJEiReTq6qqrV69q9+7duTomFKwGDRpo48aNSklJUVRUlI4cOZLhi9uFCxdUoUIFvf766/L397dcW4sHX5MmTXT79m3L+39KSor+8Y9/qHPnzhn+c7Np06ZavXq1bt68KenO6pLXrl1TvXr1LNfG37x5Uzt37pQkubq6qkSJEjpy5IikO9daN2jQIP8ODgUiq+8VZcqUUXR0tMLDw1WhQgXVq1dPixcv1jPPPGPZLrefN486/qv9EfLmm29q+fLllscffvihxo8fr44dOyolJUX169e3LAncvn17devWLd0tMgYMGKBBgwbJ29tbtWvX1sWLFyVJ/fv319ChQ9W5c2c1aNBAZcuWlXTnFLEVK1aoY8eOevzxx1WnTp18PNrCw8fHRz179szwfO/evTVy5EgtWbJEjRs3tmqsV155Re+++642bdqkRo0aqWjRotn2L1GihLp3766OHTuqXLlyqlmzZrb9U1JSNHz4cMXFxckwDAUHB1sWQ7orq9dZVqpXr6727durU6dOKleunOWN3Zp9IXNVqlTR4MGD9eabbyo1NVWOjo4aM2aMypUrl+OxDh06pEWLFsnBwUFFixbVlClTLG3t2rXTqVOn0p2Wfi8/Pz+NGDFC/v7+lv/Zz+lr9F6+vr56+umn1aFDB8uXATx8AgMDdfToUXXq1Ekmk0nDhw+Xp6dnuveK7777Tt98840cHBzk4eGh/v37F2DFyAmTyaTZs2dr3LhxmjNnjlJTU9W8eXO999572rBhQ7q+TZs21blz5ywziEWLFtU///lP1apVS/7+/nrhhRdUrlw51ahRQ66urpKkKVOmWBapqVChgiZPnpzvx4j8ld33ilq1aik1NVXSnVNOP/nkE8v3iPv5vHnUmQxrzuUAACCH3n77bQUHB3PqHwCbu3nzpooVK6bbt2/r1Vdf1ccff2xZ4RrA/WEGEQBgUzdu3FD37t1VrVo1wiGAPDFmzBidPXtWCQkJ6ty5M+EQsCFmEAEAAAAAklikBgAAAACQhoAIAAAAAJBEQAQAAAAApCEgAgCQhTVr1qhLly6qW7euGjRooKCgoHTL5l+7dk2zZs36y9u1ZCYxMVGzZs2y3KMWAIAHAQERAIBMzJ8/X6NHj1bTpk0VEhKiKVOmKCAgQNu3b7f0uXbtmkJCQnTp0qUcj5+UlKSQkBACIgDggcJtLgAAyMSXX36pl156Se+9957lOX9/fw0YMKAAqwIAIG9xmwsAADJRp04dvfXWW+rfv3+m7RcvXlRAQECG53/55RfdunVL06ZN0759+xQREaHSpUurefPmGjp0qIoXLy5JqlatWoZtw8LCVL58eSUkJGjGjBkKDQ3VtWvXVLlyZQ0dOlTNmzdP13f27Nk6f/68HB0d9dhjj2n48OFq2LChjX4CAIDCiBlEAAAy8fTTT+vLL79U2bJl1aJFC5UsWTJdu5eXl6ZNm6Zhw4ZpzJgx6W7UHR8fr5SUFA0ZMkSlSpXS5cuXNW/ePA0aNEiLFi2SJC1dulQ9e/ZU37591aJFC8uYkjRw4EAdO3ZM7777ripWrKiNGzeqb9++Wr16tZ566in9/vvvGjRokHr06KHhw4crMTFRJ06cUExMTP78cAAAjyxmEAEAyMTp06fVv39/Xbx4USaTSU888YRat26tXr16WWYBf/31V3Xs2FHLli1To0aNshwrOTlZ//3vf/XKK69ox44dKlu2rG7evKl69epp8uTJ6tKli6Xv/v37FRwcrC+++CLdbOCrr76q0qVLa+bMmdq0aZPGjh2rgwcP5t0PAABQKLFIDQAAmfD19dXGjRs1d+5cvfLKKzIMQ3PmzFHXrl118+bNv9x+3bp1CgoKUt26dVW9enW98sorkqTw8PBst/v+++/l6empevXqKTk52fKnSZMmOnHihCTpySefVGxsrEaMGKG9e/fq1q1b9328AABInGIKAECWnJyc5O/vL39/f0nSypUrNXr0aK1atUo9e/bMcrutW7dqxIgRevnllzVkyBC5u7srMjJS/fv3V0JCQrb7jI6OVmRkZLpTVu+yt7eXJFWuXFlz5szRggUL1KdPHzk4OCgwMFAffvihSpUqdR9HDAAo7AiIAABYqXv37po2bZrOnz+fbb9Nmzapdu3a+uijjyzPHTp0yKp9uLm5ydvbW7Nnz862X4sWLdSiRQvFxsZq586dmjRpkj7++GNNnz7dqv0AAJAZAiIAAJm4du2aSpcune65qKgoxcbGysPDQ5Lk6OgoSRlmBePj4+Xk5JTuuW+//Tbd46y2bdKkiZYsWaKiRYvqiSee+Ms6XV1d1bFjRx0+fFhHjx614sgAAMgaAREAgEx07NhRAQEB8vPzU+nSpXXp0iUtXrxYLi4uCgoKkiSVLVtWLi4uWrdunVxdXeXg4KCaNWvq2Wef1fjx4zV37lzVrl1bu3bt0v79+9ON7+TkpPLly2vjxo2qWrWqnJ2dVa1aNfn5+alp06Z688039dZbb6lKlSqKi4vT6dOnlZCQoKFDh2rFihX66aef9Nxzz8nLy0vh4eHatGmTOnXqVBA/KgDAI4RVTAEAyMTy5csVFhamX3/9VTExMfL09FTdunXVr1+/dDN733zzjWbPnq1Lly4pKSlJv/zyi1JSUjRt2jStW7dOCQkJ8vPzU+/evfXiiy9q3rx5atmypSRp7969mjJlisLDw5WYmGi5D2JiYqLmzZunb7/9VpcvX5abm5t8fX3Vo0cPtWjRQkePHtXcuXP1888/W2pr166dBg0alGHmEgCAnCAgAgAAAAAkcZsLAAAAAEAaAiIAAAAAQBIBEQAAAACQhoAIAAAAAJBEQAQAAAAApCEgAgAAAAAkERABAAAAAGkIiAAAAAAASQREAAAAAEAaAiIAAAAAQBIBEQAAzZo1S40aNcq0beTIkerSpYskac2aNapWrZpu3rxps/Ft7auvvtK2bdvyZV8AgEcPAREAACu1aNFCX331lYoUKVLQpWSJgAgAuB8OBV0AAAAPi1KlSqlUqVIFXQYAAHmGGUQAAKyU2Smmf/zxh3r37q1atWrJ399fa9as0cCBA9WjR48M2//888968cUXVbt2bQUFBenIkSMZ+qxcuVIdOnRQjRo11LJlS3322Wfp2s+cOaNevXqpYcOGqlOnjtq1a6fly5dLknr06KGTJ09q7dq1qlatmqpVq6Y1a9bY+KcAAHiUMYMIAECa5OTkDM8ZhpFlf8Mw1LdvX8XGxmrSpElydnbWnDlzFBUVpYoVK6brGx8frxEjRig4OFgeHh6aPXu2+vfvr507d1pOWV24cKGmT5+u3r17q2HDhjp58qRmzJihIkWK6LXXXpMk9e3bV5UrV9Y///lPOTk56fz585bAOnbsWL377ruqUKGC+vXrJ0kZ6gAAIDsERAAAJF2/fl3Vq1fPtC2r53ft2qXTp09r5cqVqlWrliRZZhIzC4ijRo1SkyZNJEleXl4KCgrS4cOH1axZM8XFxWn27Nnq27evBgwYIEny8/PT7du3NXfuXL388suKiYnRhQsXNHv2bFWrVk2SLONJUpUqVVSkSBGVKlVKderUub8fCACgUCIgAgAgydXVVUuWLMnw/OzZs3XlypVMtzl+/Lg8PT0t4VCSvL29Mw2Ujo6O6VYyfeKJJyRJZrNZknT06FHdunVLbdu2TTeT2bhxY82ZM0cREREqU6aMypQpo7Fjx+r1119Xo0aNVLp06dwdMAAAmSAgAgAgyd7eXjVr1szwvLu7e5YBMTIyUiVLlszwfKlSpTLcCqNYsWKys/v/S/+dnJwkSQkJCZKk6OhoSVKHDh0y3dfly5dVrlw5LVq0SJ9++qlGjRql+Ph41atXT6NHj9bTTz9txVECAJA9AiIAALnk6elpCXb3ioqKkrOzc47GcnNzkyTNnz8/01nBxx9/XNKdmcdZs2YpKSlJR44c0bRp09SnTx/t3r07XQAFACA3+CQBACCXatasqcjISB07dszynNls1smTJ3M8Vt26deXi4qIrV66oZs2aGf4UL148XX9HR0c1adJEb7zxhiIjI3Xjxg1Jd2Ym785KAgCQU8wgAgCQS82bN5evr68GDx6s9957Ty4uLgoJCVHp0qVlMplyNFaJEiU0YMAATZw4UZcuXVKDBg2Umpqq8PBwHTx4ULNnz9bp06c1depUtWvXThUqVNCNGzf02WefydfXV+7u7pLuzDTu3btXe/bskbu7u8qXL5/pabAAAGSGgAgAQC6ZTCbNmTNHY8aM0QcffCAPDw+988472rx5s1xcXHI83ltvvSUvLy8tXbpUS5YskbOzsx577DG1b99e0p1TWkuXLq158+bpypUrKlGihBo1aqRhw4ZZxujXr58uX76swYMHKy4uTpMnT1aXLl1sdswAgEebycjuBk8AACBHYmNj1apVK7366qsaOHBgQZcDAECOMIMIAMB9+M9//iM7OztVqlRJUVFR+vzzz5WYmKiuXbsWdGkAAOQYAREAgPvg5OSkzz77TJcvX5bJZFLNmjW1ZMkSlStXrqBLAwAgxzjFFAAAAAAgidtcAAAAAADSEBABAAAAAJIK4TWIkZGxBV0CAAAAABQYT0/XLNuYQQQAAAAASCIgAgAAAADSEBABAAAAAJIIiAAAAACANAREAAAAAIAkAiIAAAAAIA0BEQAAAAAgiYAIAAAAAEhDQAQAAAAASCIgAgAAAADSEBABAAAAAJIIiAAAAACANAREAAAAAIAkyaGgCwAAAABQcBYMeaOgS0Au9Zm+xOZjMoMIAAAAAJBEQAQAAAAApCEgAgAAAAAkERABAAAAAGkIiAAAAAAASQUYEHfv3q02bdooMDBQCxYsyNCemJiowYMHKzAwUN27d9fFixclSdHR0erRo4fq1q2r8ePHp9vmxIkT6tixowIDAzVhwgQZhpEvxwIAAAAAj4ICCYgpKSkaP368Fi5cqNDQUG3YsEFnz55N12flypUqUaKEtm7dquDgYE2bNk2S5OzsrEGDBun999/PMO5HH32k8ePHa8uWLQoPD9fu3bvz5XgAAAAA4FFQIAHx2LFjqlSpkipUqCAnJyd16NBBYWFh6fps375dnTt3liS1adNG+/fvl2EYKlq0qOrXry9nZ+d0/a9cuaK4uDjVrVtXJpNJQUFBGcYEAAAAAGStQAKi2WyWj4+P5bG3t7fMZnOGPmXKlJEkOTg4yNXVVdHR0VaP6ePjk2FMAAAAAEDWHApip5ldG2gymXLcJzf9ixd3loODvTVlAgAAAMADy929qM3HLJCA6OPjo4iICMtjs9ksLy+vDH0uX74sHx8fJScnKzY2Vu7u7laPGRERkWFMSYqLS7DBEQAAAABAwbp+/VautvP0dM2yrUBOMa1Zs6bCw8N14cIFJSYmKjQ0VP7+/un6+Pv7a+3atZKkzZs3q3HjxtnOIHp5ealYsWL66aefZBiG1q1bp4CAgDw9DgAAAAB4lBTIDKKDg4PGjBmj3r17KyUlRV27dlXVqlU1Y8YM1ahRQwEBAerWrZuGDx+uwMBAubm5afr06Zbt/f39FRcXp6SkJG3btk2LFy9WlSpV9NFHH+mDDz5QfHy8mjVrpmbNmhXE4QEAAADAQ8lkFLKbBUZGxhZ0CQAAAMADY8GQNwq6BORSn+lLcrXdA3eKKQAAAADgwUNABAAAAABIIiACAAAAANIQEAEAAAAAkgiIAAAAAIA0BEQAAAAAgCQCIgAAAAAgDQERAAAAACCJgAgAAAAASENABAAAAABIIiACAAAAANIQEAEAAAAAkgiIAAAAAIA0BEQAAAAAgCQCIgAAAAAgDQERAAAAACCJgAgAAAAASENABAAAAABIIiACAAAAANIQEAEAAAAAkgiIAAAAAIA0BEQAAAAAgCQCIgAAAAAgDQERAAAAACCJgAgAAAAASENABAAAAABIIiACAAAAANIQEAEAAAAAkgiIAAAAAIA0BEQAAAAAgCTJITcbJSYm6tSpU4qMjFRiYqLc3Nz02GOPqVy5crauDwAAAACQT6wOiAkJCfruu++0bt06/fjjj0pKSkrXbjKZ5OnpqdatW6tbt27y9fW1ebEAAAAAgLxjVUBcunSpFixYIDs7OwUGBloCYMmSJeXk5KQbN27o0qVLOnHihPbs2aOuXbvqueee04gRI/T444/n9TEAAAAAAGzAqoC4bds2TZ48Wc8995xMJlOG9hIlSqh8+fJq1KiRevXqpatXr+rLL7/U3r17CYgAAAAA8JCwKiB+8cUXORrUw8NDgwcPzlVBAAAAAICCYbNVTBMTE201FAAAAACgAOQ4IK5evVpLly61PD579qzatGmjOnXq6LXXXtPVq1dtWiAAAAAAIH/kOCAuWbJEDg7/f2bqxx9/LBcXF40bN07R0dH617/+ZdMCAQAAAAD5I8f3Qbx06ZIqV64sSYqOjtbhw4e1cOFCPfvssypevLgmT55s8yIBAAAAAHkvxzOIjo6OSklJkSQdOnRIzs7OatCggSSpdOnSiomJsW2FAAAAAIB8keOAWL16da1cuVKXLl3Sv//9bz377LNydHSUdGd20cPDw+ZFAgAAAADyXo4D4vvvv68ff/xRAQEBOnfunAYNGmRp27Rpk+rUqWPTAgEAAAAA+SPH1yA+9dRT2rVrly5fvixPT085OTlZ2vr27SsfHx+bFggAAAAAyB85DoiSZGdnp3LlymV4ntlDAAAAAHh45Sognjt3Tlu3blVERIQSEhLStZlMJk2aNMkmxQEAAAAA8k+OA+KaNWs0evRoFSlSROXKlbMsUHOXyWSyWXEAAAAAgPyT44A4b948tW/fXhMnTpSzs3Oud7x7925NnDhRqamp6t69u/r06ZOuPTExUe+//75Onjwpd3d3TZ8+XeXLl5ckzZ8/X6tWrZKdnZ1Gjx6t5557TpL0+eefa+XKlTKZTHryySc1efLk+6oRAAAAAAqTHK9iGhkZqa5du95X8EpJSdH48eO1cOFChYaGasOGDTp79my6PitXrlSJEiW0detWBQcHa9q0aZKks2fPKjQ0VKGhoVq4cKHGjRunlJQUmc1mLVu2TKtXr9aGDRuUkpKi0NDQXNcIAAAAAIVNjgNikyZN9PPPP9/XTo8dO6ZKlSqpQoUKcnJyUocOHRQWFpauz/bt29W5c2dJUps2bbR//34ZhqGwsDB16NBBTk5OqlChgipVqqRjx45JuhM84+PjlZycrPj4eHl5ed1XnQAAAABQmOT4FNO3335bI0eOlMlk0rPPPqsSJUpk6FO2bNlsxzCbzeluh+Ht7W0Jeff2KVOmzJ0iHRzk6uqq6Ohomc1m1a5dO922ZrNZdevW1ZtvvqmWLVvK2dlZfn5+atq0aYZ9Fy/uLAcH+xwdMwAAAAA8aNzdi9p8zBwHxJdeekmSNHXq1AwL0hiGIZPJpFOnTmU7hmEYGZ7LbKzM+mT1fExMjMLCwhQWFiZXV1cNGjRI69evV6dOndL1jYtLyLA9AAAAADxsrl+/lavtPD1ds2zL1SI198vHx0cRERGWx2azOcPpoD4+Prp8+bJ8fHyUnJys2NhYubu7Z7nt999/r/Lly6tUqVKSpNatW+vo0aMZAiIAAAAAIHM5DogtWrS4753WrFlT4eHhunDhgry9vRUaGqp//etf6fr4+/tr7dq1qlu3rjZv3qzGjRvLZDLJ399fQ4cO1RtvvCGz2azw8HDVqlVLdnZ2+u9//6vbt2/LxcVF+/fvV40aNe67VgAAAAAoLHIcEO/69ddf9eOPPyomJkZubm565plnVLVqVet26uCgMWPGqHfv3kpJSVHXrl1VtWpVzZgxQzVq1FBAQIC6deum4cOHKzAwUG5ubpo+fbokqWrVqmrXrp3at28ve3t7jRkzRvb29qpdu7batGmjzp07y8HBQU899ZTldFgAAAAAwF8zGZld1JeNxMREDR8+XFu2bJFhGCpSpIhu374tk8mkNm3aaOrUqXJycsqreu9bZGRsQZcAAAAAPDAWDHmjoEtALvWZviRX22V3DWKOb3Mxbdo07d69WxMmTNDRo0ctfyZMmKDdu3frk08+yVWRAAAAAICCleOA+N1332nYsGHq2rWrihQpIkkqUqSIunbtqqFDh2rDhg02LxIAAAAAkPdyHBBv3LihihUrZtpWoUIF3bhx476LAgAAAADkvxwHxCpVqmjVqlWZtq1atcrqhWoAAAAAAA+WHK9iOmjQIPXr109BQUFq3bq1PDw8dO3aNW3ZskVnzpzRnDlz8qJOAAAAAEAey3FAbN68uVasWKGQkP9r797jczjz/4+/75wcQxwiKUKXBm2dz351iBBEEoJQShelWofSOrS2NItVqqtUSx1WKdW1rVKHpoIG1Srq1A2talWjWLmLBEmQxJ3r90f1/jZNQm5y50Zfz8fD45GZueaaz9y5c5v3PTPXzNWKFSuUlJSksmXLqm7duvrHP/7BswcBAAAA4C51S89BrFOnjhYuXChJstlscnd3L9CiAAAAAACFz+F7EP+IcAgAAAAA94Z8nUH829/+pmHDhikgIEB/+9vfbtjWYrFo2rRpBVIcAAAAAKDw5Csgfv/990pPT7f/fCMWi+X2qwIAAAAAFLp8BcTVq1fn+jMAAAAA4N5x2/cgAgAAAADuDfk6g7h27VqHOo2MjLylYgAAAAAArpOvgDh+/Ph8d2ixWAiIAAAAAHAXyldAPHDggLPrAAAAAAC4WL4CYvHixZ1dBwAAAADAxfIVEP/3v/851GnFihVvqRgAAAAAgOvkKyAGBwfn6/mGxhhZLBYdOXLktgsDAAAAABSufAXEBQsWOLsOAAAAAICL5SsgBgUFObkMAAAAAICrubm6AAAAAADAnSFfZxDbtWunefPmqVatWvm6HzEuLq5AigMAAAAAFJ58BcQOHTrIx8dHktSxY0enFgQAAAAAcI18BcQXXngh158BAAAAAPcO7kEEAAAAAEjK5xnEP/rss8+0ZcsWWa1Wpaen51i+fPny2y4MAAAAAFC4HA6Ic+fO1dy5c1W9enVVqVJFxYoVc0ZdAAAAAIBC5nBAXLlypYYNG6aRI0c6ox4AAAAAgIs4fA+ixWJRo0aNnFELAAAAAMCFHA6IvXv31rp165xRCwAAAADAhRy+xHTEiBGaNm2aIiMj1axZM3l7e2dbbrFYNHz48AIrEAAAAABQOBwOiJs2bdLKlSuVmZmpkydPytPTM9tyAiIAAAAA3J0cDogzZsxQUFCQJk+erLJlyzqjJgAAAACACzh8D2JycrL69OlDOAQAAACAe4yT6kN2AAAgAElEQVTDATE4OFgHDhxwRi0AAAAAABdy+BLT0NBQTZ8+XRcvXlTz5s1VqlSpHG2aNGlSIMUBAAAAAAqPxRhjHFmhVq1aN+7QYtGRI0duqyhnOns2xdUlAAAAAHeMRc8NdHUJuEVDZi+9pfV8fb3zXObwGcQNGzbcUhEAAAAAgDubwwExMDDQGXUAAAAAAFwsX4PUnD179pY6P3fu3C2tBwAAAAAofPkKiB06dFB0dLTi4+Nv2jY5OVn/+c9/FB4ertWrV992gQAAAACAwpGvS0zXrVunuXPn6rHHHlP58uVVv359BQYGqkyZMvLy8lJKSopOnTqlb775RocPH1alSpX01FNPKTIy0tn1AwAAAAAKiEOjmCYlJWn9+vXavXu3vvnmGyUlJclms6lYsWL6y1/+ooYNG6pDhw5q2rSpM2u+LYxiCgAAAPwfRjG9e7l8FNOyZctqwIABGjBggH1eZmamPD09b6kwAAAAAMCdI1/3IN4I4RAAAAAA7g23HRBv1Y4dO9SxY0eFhIRo0aJFOZZnZGTo2WefVUhIiHr27KlTp07Zly1cuFAhISHq2LGjPv/8c/v8S5cuaeTIkerUqZNCQ0N18ODBQtkXAAAAALgXuCQg2mw2TZkyRYsXL1ZMTIw+/vhjHTt2LFubVatWqVSpUtqyZYsGDBigmTNnSpKOHTummJgYxcTEaPHixZo8ebJsNpsk6eWXX1arVq0UGxurdevWqXr16oW+bwAAAABwt3JJQIyPj1fVqlUVEBAgLy8vhYWFKS4uLlubrVu3qlu3bpKkjh07ateuXTLGKC4uTmFhYfLy8lJAQICqVq2q+Ph4paamau/evYqKipIkeXl5qVSpUoW+bwAAAABwt3JJQLRarfL397dP+/n5yWq15mhz3333SZI8PDzk7e2t5OTkPNc9efKkypYtq7/97W+KjIzUhAkTdPny5cLZIQAAAAC4Bzg0imlerl69KqvVqipVqshisdy0fW5P1vjjenm1yWv+tWvX9O233+qll15SvXr1NHXqVC1atEjPPvtstrYlSxaRh4f7TWsEAAAAgDuZj0/xAu/T4YC4aNEiXblyRaNGjZIk7d+/X0OHDlVKSooqVaqkxYsX6/77779hH/7+/kpMTLRPW61WVahQIUebM2fOyN/fX9euXVNKSop8fHzyXNff31/+/v6qV6+eJKlTp065Dn6Tmpru6C4DAAAAwB3nwoVbu2LyRs9BdPgS09WrV6tixYr26enTp6tmzZp6++235evrq1mzZt20jzp16ighIUEnT55URkaGYmJiFBwcnK1NcHCwPvroI0nSpk2b1Lx5c1ksFgUHBysmJkYZGRk6efKkEhISVLduXfn6+srf31/Hjx+XJO3atYtBagAAAADAAQ6fQbRarapatar958OHD2vlypVq0KCBrly5oujo6Jtv1MND0dHRGjx4sGw2m3r06KHAwEDNmTNHtWvXVrt27RQVFaVx48YpJCREpUuX1uzZsyVJgYGBCg0NVefOneXu7q7o6Gi5u/96yehLL72ksWPHKjMzUwEBAZo+fbqjuwcAAAAAf1oOB8SiRYvaB3/ZvXu3vL29Vb9+fUlSiRIllJaWlq9+2rRpozZt2mSb99tlq5JUpEgRvfHGG7muO3ToUA0dOjTH/AcffFBr1qzJ1/YBAAAAANk5HBAbNWqkJUuWqHjx4lq2bJmCgoLsA8ycOHFCfn5+BV4kAAAAAMD5HL4Hcfz48bp8+bIGDBigrKwsPffcc/Zl69evV+PGjQu0QAAAAABA4XD4DGJAQIA+/PBDZWVlyc0te76cNWuWSpYsWWDFAQAAAAAKzy0/B/GP4VASl5cCAAAAwF3slgLiZ599pi1btshqtSo9PedzBZcvX37bhQEAAAAACpfDAXHu3LmaO3euqlevripVqqhYsWLOqAsAAAAAUMgcDogrV67UsGHDNHLkSGfUAwAAAABwEYdHMbVYLGrUqJEzagEAAAAAuJDDAbF3795at26dM2oBAAAAALiQw5eYjhgxQtOmTVNkZKSaNWsmb2/vbMstFouGDx9eYAUCAAAAAAqHwwFx06ZNWrlypTIzM3Xy5El5enpmW05ABAAAAIC7k8MBccaMGQoKCtLkyZNVtmxZZ9QEAAAAAHABh+9BTE5OVp8+fQiHAAAAAHCPcTggBgcH68CBA86oBQAAAADgQg5fYhoaGqrp06fr4sWLat68uUqVKpWjTZMmTQqkOAAAAABA4bEYY4wjK9SqVevGHVosOnLkyG0V5Uxnz6a4ugQAAADgjrHouYGuLgG3aMjspbe0nq+vd57LHD6DuGHDhlsqAgAAAABwZ3MoIGZkZOjLL79UixYtVKNGDWfVBAAAAABwAYcGqfHy8tKsWbN04cIFZ9UDAAAAAHARh0cxfeihh3T06FFn1AIAAAAAcCGHA+Lf//53vffee1qzZo3Onz/vjJoAAAAAAC7g8CA1kZGRkqQJEybk2eZOHsUUAAAAAJA7hwPixIkTZbFYnFELAAAAAMCFHA6I/fr1c0YdAAAAAAAXc/geRAAAAADAvcnhM4iStH37dn3wwQdKSEhQenp6juVxcXG3XRgAAAAAoHA5HBA3b96s0aNHq1OnTjp+/LjCwsJ07do1ff755ypXrpxCQkKcUScAAADy6f1Xtru6BNyGR8cHuboE/Ik5fInpggULNGTIEM2YMUOS9MQTT2jOnDmKi4tTyZIlVbFixQIvEgAAAADgfA4HxJ9++klNmzaVm5ub3NzclJaWJkkqU6aMnn76aS1durTAiwQAAAAAOJ/DAbFEiRLKzMyUxWKRn5+fEhIS7Ms8PDyUlJRUkPUBAAAAAAqJw/cgPvzwwzp27JhatWql1q1b66233lLx4sXl6emp119/XbVr13ZGnQAAAAAAJ3M4IA4aNEinT5+WJI0aNUoJCQkaO3asJOmBBx7QpEmTCrRAAAAAAEDhcDggNm3a1P5z2bJltWzZMl26dElXrlyRn59fgRYHAAAAACg8Dt+D+HsXL17Ud999pyJFihAOAQAAAOAud0sBce3atWrfvr2aN2+ubt266dixY5Kk0aNHa+XKlQVaIAAAAACgcDgcEN99911NnDhR7du31/z582WMsS+rW7eu1q9fX6AFAgAAAAAKh8P3IC5btkzDhg3TsGHDZLPZsi2rVq2ajh8/XmDFAQAAAAAKj8NnEK1Wqxo0aJDrMg8PD129evW2iwIAAAAAFD6HA2LlypV18ODBXJcdOHBA1apVu+2iAAAAAACFz+GA2LdvXy1cuFDvvPOOzp49K0m6fPmyPvnkEy1ZskR9+/Yt8CIBAAAAAM7n8D2I/fr1U1JSkl577TXNmDFDkvTXv/5VHh4eevLJJxUVFVXgRQIAAAAAnM/hgChJI0eOVL9+/bRv3z4lJyerdOnSaty4scqXL1/Q9QEAAAAAColDATE9PV1jxozRgAED1LhxY3Xo0MFZdQEAAAAACplD9yAWKVJEu3fvVmZmprPqAQAAAAC4iMOD1LRu3Vo7duxwRi0AAAAAABdy+B7ETp06aerUqUpOTlbr1q1Vvnx5WSyWbG2aNGlSYAUCAAAAAAqHwwFx5MiRkqS1a9dq7dq1OZZbLBYdOXLkpv3s2LFDL7/8srKystSzZ08NGTIk2/KMjAw9//zz+uabb+Tj46PZs2ercuXKkqSFCxfqww8/lJubmyZOnKhWrVrZ17PZbOrRo4f8/Py0cOFCR3cPAAAAAP60HA6IGzZsuO2N2mw2TZkyRUuXLpWfn5+ioqIUHBysBx54wN5m1apVKlWqlLZs2aKYmBjNnDlTr7/+uo4dO6aYmBjFxMTIarVq4MCB2rRpk9zd3SVJy5cvV/Xq1ZWamnrbdQIAAADAn4nDATEwMPC2NxofH6+qVasqICBAkhQWFqa4uLhsAXHr1q0aMWKEJKljx46aMmWKjDGKi4tTWFiYvLy8FBAQoKpVqyo+Pl4NGjRQYmKitm/frqefflrvvPPObdcJAAAAAH8mt/QcxNtltVrl7+9vn/bz81N8fHyONvfdd58kycPDQ97e3kpOTpbValW9evWyrWu1WiVJ06ZN07hx45SWlpbntkuWLCIPD/eC3B0AAACgwPj4FHd1CbhLOOO9kq+A+OCDD+r9999X3bp1VatWrRyD0vzRze5BNMbkmPfHPvNqk9f8bdu2qWzZsqpdu7b27NmT57ZTU9NvWBsAAADgShcuXHZ1CbhL3Op7xdfXO89l+QqIEydOtA8QM3HixJsGxJvx9/dXYmKifdpqtapChQo52pw5c0b+/v66du2aUlJS5OPjk+e6W7du1datW7Vjxw6lp6crNTVVY8eO1cyZM2+rVgAAAAD4s8hXQOzbt6/95379+t32RuvUqaOEhASdPHlSfn5+iomJ0WuvvZatTXBwsD766CM1aNBAmzZtUvPmzWWxWBQcHKwxY8Zo4MCBslqtSkhIUN26ddWgQQONGTNGkrRnzx4tWbKEcAgAAAAADshXQNy8ebOaN2+uUqVKFcxGPTwUHR2twYMH2x9LERgYqDlz5qh27dpq166doqKiNG7cOIWEhKh06dKaPXu2pF8HyQkNDVXnzp3l7u6u6Oho+wimAAAAAIBbZzG53dT3B7+/B1GSsrKyFBoaqnnz5mUbefRucPZsiqtLAAAAcKr3X9nu6hJwGx4dH1So21v03MBC3R4KzpDZS29pvRvdg+iWnw7+mCGNMTpx4oTS0xnwBQAAAADuFfkKiAAAAACAex8BEQAAAAAgKZ+D1EjSf/7zH23btk3S/11yunLlSvn6+mZrZ7FYNHLkyAIsEQAAAABQGPIVEMuVK6cdO3Zkm1e+fHlt37491/YERAAAAAC4++QrIO7cudPZdQAAAAAAXIx7EAEAAAAAkgiIAAAAAIDrCIgAAAAAAEkERAAAAADAdQREAAAAAIAkAiIAAAAA4Lp8Pebiww8/dKjTqKioWyoGAAAAAOA6+QqIEydOzHeHFouFgAgAAAAAd6F8BcT4+Hhn1wEAAAAAcLF8BUQvLy9n1wEAAAAAcLF8BcTcJCUlKSEhQenp6TmWtWjR4raKAgAAAAAUPocD4uXLlzVu3Dht27ZNxphc2xw5cuS2CwMAAAAAFC6HH3Mxe/ZsHTt2TG+//baMMXrttdf0r3/9SxEREapUqZLee+89Z9QJAAAAAHAyhwPitm3bNHToUDVt2lSSFBAQoJYtW+rVV19VUFAQAREAAAAA7lIOB8Rz586pcuXKcnd3V7FixXThwgX7suDgYH322WcFWiAAAAAAoHA4HBD9/PzsobBKlSr6/PPP7cu+/fZbFSlSpOCqAwAAAAAUGocHqWnRooV2796t9u3b6/HHH9fEiRP13XffycvLS7t27VLfvn2dUScAAAAAwMkcDohjx45VWlqaJCkqKkpFixbVpk2bdPXqVY0bN079+vUr8CIBAAAAAM7ncEAsWbKkSpYsaZ8ODw9XeHh4gRYFAAAAACh8Dt+DWK9ePR06dCjXZd9++63q1at320UBAAAAAAqfwwExPT1dxphcl2VkZMhms912UQAAAACAwpevS0ytVqusVqt9+tixYznapKena82aNapUqVLBVQcAAAAAKDT5CoirVq3S3LlzZbFYZLFYNGHChBxtjDHy9PTUlClTCrxIAAAAAIDz5Ssg9urVS0FBQTLGqGfPnpo+fboCAwOztfH09FTlypVVokQJpxQKAAAAAHCufAXEChUqqEKFCpKkTz75RJUrV5aXl5dTCwMAAAAAFC6HH3NRrVo1ZWVl6dNPP9X+/ft18eJFlS5dWo0bN1bbtm3l5ubwuDcAAAAAgDuAwwExOTlZTz75pA4fPqzy5curfPnyOnfunJYuXao6deroX//6l3x8fJxRKwAAAADAiRw+3TdjxgwlJiZqxYoV+uKLL7R27Vp98cUXWrFihRITEzVjxgxn1AkAAAAAcDKHA+K2bds0duxYNW7cONv8xo0ba8yYMdq6dWuBFQcAAAAAKDwOB8T09PQ8LyEtXbq00tPTb7soAAAAAEDhczgg1qlTR2+//XaOIJienq4lS5aoTp06BVYcAAAAAKDwODxIzfPPP6/+/fsrKChIbdq0Ubly5ZSUlKTPPvtMV69e1fLly51RJwAAAADAyW7pDOKmTZsUHh6uhIQExcbG6qefflJERIRiY2NVu3ZtZ9QJAAAAAHCyfJ1B3Lt3rx566CGVKFFCkuTr66sJEyY4tTAAAAAAQOHK1xnEv/71r/rxxx+dXQsAAAAAwIXyFRCNMc6uAwAAAADgYg7fgwgAAAAAuDflexTTzz77TMePH89X28jIyFsuCAAAAADgGvkOiPPmzctXO4vFQkAEAAAAgLtQvgPi8uXLC/QRFjt27NDLL7+srKws9ezZU0OGDMm2PCMjQ88//7y++eYb+fj4aPbs2apcubIkaeHChfrwww/l5uamiRMnqlWrVjpz5oyef/55nTt3Tm5uburVq5f69+9fYPUCAAAAwL0u3wGxaNGiKl68eIFs1GazacqUKVq6dKn8/PwUFRWl4OBgPfDAA/Y2q1atUqlSpbRlyxbFxMRo5syZev3113Xs2DHFxMQoJiZGVqtVAwcO1KZNm+Tu7q7x48fr4YcfVmpqqnr06KFHHnkkW58AAAAAgLy5ZJCa+Ph4Va1aVQEBAfLy8lJYWJji4uKytdm6dau6desmSerYsaN27dolY4zi4uIUFhYmLy8vBQQEqGrVqoqPj1eFChX08MMPS5JKliypatWqyWq1Fvq+AQAAAMDdyiUB0Wq1yt/f3z7t5+eXI8xZrVbdd999kiQPDw95e3srOTk5X+ueOnVKR44cUb169Zy4FwAAAABwb8nXJabfffddgW40t+cqWiyWfLW52bppaWkaOXKkXnzxRZUsWTJH25Ili8jDw/1WygYAAACczsenYG7rwr3PGe+VfN+DWJD8/f2VmJhon7ZarapQoUKONmfOnJG/v7+uXbumlJQU+fj43HDdzMxMjRw5UhEREerQoUOu205NTXfCHgEAAAAF48KFy64uAXeJW32v+Pp657nMJZeY1qlTRwkJCTp58qQyMjIUExOj4ODgbG2Cg4P10UcfSZI2bdqk5s2by2KxKDg4WDExMcrIyNDJkyeVkJCgunXryhijCRMmqFq1aho4cKArdgsAAAAA7mouOYPo4eGh6OhoDR48WDabTT169FBgYKDmzJmj2rVrq127doqKitK4ceMUEhKi0qVLa/bs2ZKkwMBAhYaGqnPnznJ3d1d0dLTc3d21b98+rVu3TjVq1FDXrl0lSaNHj1abNm1csYsAAAAAcNexmNxu6ruHnT2b4uoSAAAAnOr9V7a7ugTchkfHBxXq9hY9x9V3d6shs5fe0np33CWmAAAAAIA7DwERAAAAACCJgAgAAAAAuI6ACAAAAACQREAEAAAAAFxHQAQAAAAASCIgAgAAAACuIyACAAAAACQREAEAAAAA1xEQAQAAAACSCIgAAAAAgOsIiAAAAAAASQREAAAAAMB1BEQAAAAAgCQCIgAAAADgOgIiAAAAAEASAREAAAAAcB0BEQAAAAAgiYAIAAAAALiOgAgAAAAAkERABAAAAABcR0AEAAAAAEgiIAIAAAAAriMgAgAAAAAkERABAAAAANcREAEAAAAAkgiIAAAAAIDrCIgAAAAAAEkERAAAAADAdQREAAAAAIAkycPVBQDAn1nkfzq5ugTchrW9Ywt1e9/2CC/U7aHgPLT6Y1eXAAD5QkAEnODK/P/n6hJwi4oN/dLVJQAAALgMl5gCAAAAACQREAEAAAAA1xEQAQAAAACSuAcx3zq9+pGrS8BtiH2+m6tLAAAAAO54nEEEAAAAAEgiIAIAAAAAriMgAgAAAAAkERABAAAAANcREAEAAAAAkgiIAAAAAIDrCIgAAAAAAEkERAAAAADAdQREAAAAAIAkAiIAAAAA4DqXBcQdO3aoY8eOCgkJ0aJFi3Isz8jI0LPPPquQkBD17NlTp06dsi9buHChQkJC1LFjR33++ef57hMAAAAAkDeXBESbzaYpU6Zo8eLFiomJ0ccff6xjx45la7Nq1SqVKlVKW7Zs0YABAzRz5kxJ0rFjxxQTE6OYmBgtXrxYkydPls1my1efAAAAAIC8uSQgxsfHq2rVqgoICJCXl5fCwsIUFxeXrc3WrVvVrVs3SVLHjh21a9cuGWMUFxensLAweXl5KSAgQFWrVlV8fHy++gQAAAAA5M3DFRu1Wq3y9/e3T/v5+Sk+Pj5Hm/vuu0+S5OHhIW9vbyUnJ8tqtapevXrZ1rVarZJ00z4lydfX+5Zq3v/Pv97SeviTij7k6gpwl9j5zE5Xl4C7SJsdn7m6BNwlRrwW4eoScBeZsOJDV5eAO4hLziAaY3LMs1gs+Wrj6HwAAAAAQP64JCD6+/srMTHRPm21WlWhQoUcbc6cOSNJunbtmlJSUuTj45PnuvnpEwAAAACQN5cExDp16ighIUEnT55URkaGYmJiFBwcnK1NcHCwPvroI0nSpk2b1Lx5c1ksFgUHBysmJkYZGRk6efKkEhISVLdu3Xz1CQAAAADIm0sCooeHh6KjozV48GB17txZoaGhCgwM1Jw5c+wDy0RFRenChQsKCQnR0qVLNXbsWElSYGCgQkND1blzZw0ePFjR0dFyd3fPs897RWJiooYOHaoOHTqoffv2mjp1qjIyMrRmzRpNmTIlR/s333xTb7/99g37HD9+vGJjY51VMgpIzZo1NW7cOPv0tWvX1Lx5cz311FMuqWfPnj1O3falS5f03nvv2adPnTqlDRs2OG17uLnk5GR17dpVXbt21SOPPKJWrVrZpzMyMlxdHlzoZp9PcXFxLnns1MqVK7V27dpC3y5yqlmzpl555RX79Ntvv60333yzQPpOT09Xp06ddPToUfu8f/3rX4qOjs53HydOnFDXrl0LpB7c3LRp0/TOO+/YpwcNGqQJEybYp1955RUtXbrUoT6Dg4OVlJSUY/7tfv688847unLlyi2vfzdzySA1ktSmTRu1adMm27xRo0bZfy5SpIjeeOONXNcdOnSohg4dmq8+7wXGGI0YMUJ9+vTR/PnzZbPZ9NJLL2n27Nn3VAhG7ooXL64ffvhBV69eVdGiRbVz5075+fm5uiynuXTpklauXKm+fftKkk6fPq2PP/5YEREMuOAqZcqU0bp16yT9+uVT8eLFNWjQIBdXhTvBzT6f2rVrp3bt2hVqTdeuXVOfPn0KdZvIm5eXlzZv3qwhQ4aobNmyBdp3kSJF9OKLL2ry5Ml677339Msvv+j999/X6tWr87X+tWvXCrQe3FyDBg0UGxurAQMGKCsrS8nJyUpNTbUvP3jwoF588cUC2dbtfv4sX75cXbp0UbFixQqknruJS84gwjG7d+9WkSJF1KNHD0mSu7u7XnzxRa1ZsybbNxvbt2/Xo48+muNblA8++EA9evRQly5d9Mwzz2RbZ9++ferdu7fatWtnP5tojNGMGTMUHh6uiIgIffLJJ5J+PXPUr18/jRo1Sh07dtTMmTO1fv16RUVFKSIiQj///LOkXx9R0rNnT0VGRmrAgAE6d+6cU1+fP4PWrVtr+/btkqSYmBiFhYXZl8XHx6t3796KjIxU7969dfz4cUnSDz/8oKioKHXt2lURERFKSEjQ5cuXNWTIEHXp0kXh4eH23+3cuXPVo0cPhYeH66WXXrIP+nTixAkNGDBAXbp0Ubdu3ey/48uXL2vkyJHq1KmTxowZY2//+2/xDh06pMcff1yS9NVXX9nPOEVGRtr/M1i8eLF69OihiIgI+xdCr732mn7++Wd17dpVM2bM0GuvvaZ9+/apa9eueuedd3LdL7jORx99ZP99TJo0SVlZWTp58qQ6dOigCxcuyGazqXfv3tq1a5ck6emnn1b37t0VFhamVatWSfr1IG3cuHGKiIhQeHi4li9f7spdgoNu9Pn0+6tcNm7cqPDwcHXp0sX+BVBef89Lly5VeHi4wsPD7WcbTp06pfDwcHvfvz8T9fjjj2vWrFnq16+fli9fnq+raFA4PDw89Oijj2rZsmU5liUlJemZZ55Rjx491KNHD+3fv1+SFBERoUuXLskYo2bNmtnPBo8bN05ffvlltj5at24tX19frV27VtOmTdOIESNUunRpZWVlafr06fZjmd+Ocb788ksNGDBAzz33nP1xar85ceKEIiMj9c033zjjpYCkhg0b6uDBg5J+/fsPDAxUiRIldPHiRWVkZOjHH39U9erV1b9/f3Xr1k0RERH69NNPJSnPYxhJWrFihb39jz/+KCn758/48eM1derUHMe8WVlZmjRpksLCwvTUU0/pySefVGxsrJYvX65ffvlF/fv3tx/L/PZldXh4uP75z3/at92gQQPNnj1bXbp0Ua9eve6N416DO96yZcvMyy+/nGN+165dzbJly8zkyZPN5s2bTZ8+fcyFCxeMMca88cYbZvHixcYYY5KSkuzrzJo1yyxfvtwYY8wLL7xgnnnmGWOz2cwPP/xg2rdvb4wxJjY21gwYMMBcu3bNnD171rRp08ZYrVaze/du06hRI2O1Wk16erpp2bKlmTNnjjHGmHfeecdMnTrVGGPMhQsXTFZWljHGmA8++MBMnz7dSa/Mn0P9+vXNkSNHzDPPPGOuXr1qunTpYnbv3m2GDBlijDEmJSXFZGZmGmOM2blzpxkxYoQxxpgpU6aYdevWGWOMSU9PN1euXDGxsbFmwoQJ9r4vXbpkjDEmOTnZPm/s2LEmLi7OGGNMVFSU2bx5szHGmKtXr5rLly+b3bt3m4YNG5ozZ84Ym81mevXqZfbu3WuMMaZt27bm/Pnzxhhj4uPjTb9+/Ywxxjz11FNm3759xhhjUlNTTWZmpvn888/NxIkTTVZWlrHZbGbIkCHmq6++MidPnjRhYWH2ehfvuYMAABEiSURBVH6/r3ntFwrP7z9bjh49aoYOHWp//02cONGsX7/eGGPMv//9b/Pss8+aBQsWmEmTJtnX/+29dvnyZRMaGmouXLhgvv76azNo0CB7m4sXLxbW7uA23ezzafXq1Wby5MnGGGPCw8NNYmKiMeb/fse5/T0fOnTIhIeHm7S0NJOammo6d+5svvnmmxyfDYsXLzZvvPGGMcaYfv36mb///e/2Zb9/n8K16tevb1JSUkzbtm3NpUuXsv3eRo8ebf//4/Tp06ZTp07GGGNeeukls23bNnP06FHTvXt3+/9bISEhJjU1Ncc2EhMTTatWrez/5xhjTExMjHniiSfMtWvXzC+//GJat25tzp07Z3bu3Gnq169vTp8+bYwxJiEhwXTp0sUcO3bMdO3a1Xz33XdOfT3w67HC6dOnzcqVK82///1vM3v2bLN9+3azb98+89hjj5nMzEyTkpJijDHm/Pnzpn379iYrKyvPY5i2bdvaj21XrFhhXnzxRWNM9s+fvI55N27caAYPHmxsNpv55ZdfTOPGjc3GjRvt/f52TJOYmGjatGljzp8/bzIzM83jjz9utmzZYowxpkaNGvbjphkzZph58+Y59fUrDC67xBT5Z4zJ9ZEdv83fs2ePDh8+rCVLlqhkyZI52v3www96/fXXlZKSorS0NLVs2dK+rH379nJzc9MDDzxg/8Zj//79CgsLk7u7u8qXL68mTZro0KFDKlmypOrUqWMfHbZKlSp65JFHJEk1atTQnj17JP16v+Rzzz2ns2fPKiMjQ5UrVy7w1+TPplatWjp16pQ+/vjjHJdRp6Sk6IUXXtCJEydksViUmZkpSapfv74WLFigxMREdejQQffff79q1KihGTNm6J///Kfatm2rxo0bS/r17PDixYt19epVXbhwQYGBgWratKmsVqtCQkIk/Xopz2/q1q1rf+5orVq1dPr0aXtfuWnYsKFeeeUVRUREqEOHDipRooR27typnTt3KjIyUtKv3wwmJCTYn3+al9z2C67x5Zdf6tChQ/arG65evWp/X/Tp00exsbFas2aNfcAx6dd7OrZu3Srp18+Kn3/+WVWqVNFPP/2kqVOnqk2bNtk+o3Dnu9Hn0+81aNBA48ePV2hoqP1zJbe/5/3796t9+/YqXry4JCkkJET79u276cBznTt3LridQoEqWbKkunbtquXLl6to0aL2+V9++aWOHTtmn05NTVVqaqoaN26svXv3qmLFiurTp48++OADWa1WlS5dWiVKlMjRv5+fn5o3b66goCD7vP379ysiIkLu7u7y9fVVo0aNdPjwYXl6eqp+/fqqWLGive358+c1fPhwzZs3T9WrV3fOiwC7Bg0a6ODBgzp48KAGDhwoq9WqAwcOyNvbWw0aNJAxRrNmzdLevXvl5uYmq9Wqc+fO5XkMI0kdOnSQJNWuXVtbtmzJdbt5HfN26tRJbm5u8vX1VbNmzXJd99ChQ2ratKn9MumIiAjt3btX7du3l6enp9q2bWvf/s6dd//zjbnE9C4QGBiow4cPZ5uXmpqqxMREubm5KSAgQGlpafrpp59yXX/8+PGKjo7Whg0bNGLEiGyDSnh5eeVob3J5pmRu7d3c3OzTbm5ustlskqSpU6eqb9++2rBhg6ZMmcIgFgUkODhYr776arbLtyRpzpw5atasmT7++GPNnz/f/npHRERo/vz5Klq0qAYNGqRdu3bpL3/5i9asWaMaNWrotdde09y5c5Wenq7JkyfrjTfe0IYNG9SrVy+lp6ffsJbfvw/c3d3tv3t3d3f7++f3fQwZMkRTp07V1atX1atXL/34448yxmjIkCFat26d1q1bpy1btqhnz543fR1y2y+4To8ePey/w02bNmn48OGSpLS0NJ09e1Y2m02XL1+W9OvB4L59+/TBBx9o/fr1qlmzptLT01WmTBmtX79ejRo10rvvvuvQABO4M+T1+fR7U6ZM0bPPPqszZ84oMjJSycnJuf495/V/kIeHh7KysuzTf/yc+jPeJ3Q36d+/v1avXp3tNpesrCy9//779s+Qzz//XCVLllSTJk20f/9+7d+/X82aNVPZsmUVGxt7wy8i3dzc5Ob2f4e1NzqW+eN7xdvbW35+fjpw4MBt7CHy67fLTL///nsFBgaqXr16+vrrr3Xw4EE1bNhQGzZsUFJSktasWaN169apfPnySk9Pz/UY5jeenp6Ssh+P/pGjx7z55enpaT+Rc6Pt300IiHeBFi1a6MqVK/Zr8G02m1555RV169ZNxYoVU8WKFfXmm2/qhRde0A8//JBj/bS0NPn6+iozMzNfo0E2adJEGzdulM1mU1JSkvbt26e6devmu96UlBT7IAWMIldwoqKiNGzYMNWsWTPb/N+/3r8/U3Py5EkFBATor3/9q4KDg3X06FFZrVYVK1ZMXbt21aBBg/Ttt9/aD7LKlCmjtLQ0bdq0SdKv3/j6+/vbr/3PyMi46WhelSpVsn+ZsXnzZvv8n3/+WTVr1tSQIUNUu3Zt/fTTT2rZsqVWr16ttLQ0Sb8+u/T8+fMqUaKEfZ6kHNO57Rdco0WLFtq4caP9vtPk5GT973//kyS9+uqr6tatm4YPH24PfCkpKSpdurSKFi2qH374QYcOHZL0631IxhiFhobqmWee4f6fu1Ben0+/9/PPP6tevXoaNWqUypQpo8TExFz/nps0aaJPP/1UV65c0eXLl/Xpp5+qcePGKleunM6fP6/k5GRlZGTY73vE3cHHx0edOnXShx9+aJ/XsmVLrVixwj595MgRSdJ9992n5ORkJSQkKCAgQA0bNtSSJUvUqFGjfG+vcePGiomJkc1m07lz53TgwAHVrl0717ZeXl566623tHr16mz3tcE5GjZsqG3btql06dJyd3eXj4+PUlJS9PXXX6t+/fpKSUlRuXLl5Onpqd27d+v06dOSlOsxzO1q1KiRNm/erKysLJ07d05fffWVfdnvjz/q1q2rvXv3KikpSTabTTExMWrSpMltb/9OxSWmdwGLxaJ58+Zp8uTJeuutt5SVlaU2bdpo9OjR+vjjjyVJ1apV08yZMzVq1CgtWLAg2/qjRo1Sz549ValSJdWoUSPbwXZuQkJCdPDgQXXt2lUWi0Xjxo2Tr6+vffCTmxkxYoRGjRolPz8/1atXT6dOnbq1HUc2/v7+6t+/f475gwcP1vjx47V06VI1b97cPv+TTz7R+vXr5eHhofLly2v48OE6dOiQXn31Vbm5ucnDw0OTJk1SqVKl1LNnT0VERKhSpUqqU6eOvY9XX31V0dHRmjNnjjw9PTVnzpwb1jhixAhNmDBBCxcuVL169ezzly1bpj179tgv7WjdurW8vLz0448/qnfv3pJ+HQ3xn//8p6pUqaKGDRsqPDxcrVq10ujRo+Xu7q4uXbqoe/fuSk9Pz7FfcI2aNWtqxIgRGjhwoLKysuTp6alJkybpxIkTOnr0qP0xRJs2bdLatWsVGhqqDz74QF26dFG1atXs75EzZ85owoQJ9svmf3usEe4eeX0+/d6rr76qEydOyBij5s2bq1atWlq0aFGOv2cfHx91797dfkVBVFSUHnroIUnS8OHD1atXL1WuXFnVqlVz+n6hYD3xxBPZHmM0YcIETZkyRREREbLZbGrcuLF9UJG6devazxg3btxYs2bNciggdurUSf/973/txzLjx49XuXLl8mxfokQJLViwQE888YSKFy+e7XJVFKwaNWooOTk526BTvx2fli1bVhERERo6dKi6d++uBx980P63/v333+c4hrldHTt21K5duxQeHq77779fdevWlbe3tySpV69eevLJJ+Xr66t3331Xo0ePVv/+/WWMUevWrdW+ffvb3v6dymIK4twqAAAAANxl0tLSVKJECSUnJ6tnz55auXKlfH19XV2WS3EGEQAAAMCf0tNPP61Lly4pMzNTw4YN+9OHQ4kziAAAAACA6xikBgAAAAAgiYAIAAAAALiOgAgAAAAAkERABAAgT2vWrFH37t3VoEEDNWnSRJGRkZo+fbp9+fnz5/Xmm2/e0uN8MjIy9Oabb9qf/QYAwJ2AgAgAQC4WLlyoiRMnqmXLlpo7d65mzJihdu3aaevWrfY258+f19y5c+0PcnZEZmam5s6dS0AEANxReMwFAAC5WLFihR599FGNHj3aPi84OFgjRoxwYVUAADgXj7kAACAX9evX15NPPqnhw4fnuvzUqVNq165djvlHjx7V5cuXNXPmTO3cuVOJiYkqV66c2rRpozFjxqhkyZKSpJo1a+ZYNy4uTpUrV1Z6errmzJmjmJgYnT9/XtWqVdOYMWPUpk2bbG3nzZun48ePy9PTU/fff7/GjRunpk2bFtArAAD4M+IMIgAAuXjooYe0YsUKVaxYUUFBQSpTpky25RUqVNDMmTM1duxYRUdH6+GHH7Yvu3r1qmw2m5577jmVLVtWZ86c0YIFCzRq1Ci9/fbbkqRly5apf//+Gjp0qIKCgux9StLIkSMVHx+vZ555RlWqVNHGjRs1dOhQrV69Wg8++KB+/vlnjRo1So8//rjGjRunjIwMHT58WBcvXiycFwcAcM/iDCIAALn47rvvNHz4cJ06dUoWi0XVq1dXhw4dNGjQIPtZwO+//14RERFavny5mjVrlmdf165d03//+1899thj2rZtmypWrKi0tDQ1bNhQ06dPV/fu3e1td+3apQEDBujdd9/Ndjawb9++KleunN544w3Fxsbq73//u/bs2eO8FwAA8KfEIDUAAOSiVq1a2rhxo+bPn6/HHntMxhi99dZb6tGjh9LS0m66/tq1axUZGakGDRro4Ycf1mOPPSZJSkhIuOF6X375pXx9fdWwYUNdu3bN/q9FixY6fPiwJKlGjRpKSUnRCy+8oC+++EKXL1++7f0FAEDiElMAAPLk5eWl4OBgBQcHS5JWrVqliRMn6sMPP1T//v3zXG/Lli164YUX1KdPHz333HPy8fHR2bNnNXz4cKWnp99wm8nJyTp79my2S1Z/4+7uLkmqVq2a3nrrLS1atEhDhgyRh4eHQkJCNGHCBJUtW/Y29hgA8GdHQAQAIJ969uypmTNn6vjx4zdsFxsbq3r16mnSpEn2eV999VW+tlG6dGn5+flp3rx5N2wXFBSkoKAgpaSkaPv27Zo2bZr+8Y9/aPbs2fnaDgAAuSEgAgCQi/Pnz6tcuXLZ5iUlJSklJUXly5eXJHl6ekpSjrOCV69elZeXV7Z5GzZsyDad17otWrTQ0qVLVbx4cVWvXv2mdXp7eysiIkJ79+7VwYMH87FnAADkjYAIAEAuIiIi1K5dOz3yyCMqV66cTp8+rSVLlqho0aKKjIyUJFWsWFFFixbV2rVr5e3tLQ8PD9WpU0f/7//9P02ZMkXz589XvXr19Nlnn2nXrl3Z+vfy8lLlypW1ceNGBQYGqkiRIqpZs6YeeeQRtWzZUk888YSefPJJPfDAA0pNTdV3332n9PR0jRkzRv/5z3/09ddfq1WrVqpQoYISEhIUGxurrl27uuKlAgDcQxjFFACAXLz33nuKi4vT999/r4sXL8rX11cNGjTQsGHDsp3ZW79+vebNm6fTp08rMzNTR48elc1m08yZM7V27Vqlp6frkUce0eDBg9WrVy8tWLBAbdu2lSR98cUXmjFjhhISEpSRkWF/DmJGRoYWLFigDRs26MyZMypdurRq1aqlxx9/XEFBQTp48KDmz5+vb7/91l5baGioRo0alePMJQAAjiAgAgAAAAAk8ZgLAAAAAMB1BEQAAAAAgCQCIgAAAADgOgIiAAAAAEASAREAAAAAcB0BEQAAAAAgiYAIAAAAALiOgAgAAAAAkERABAAAAABc9/8B3judGdRMy/UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3cbfd208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(state_firearms[\"private_sale_long_gun\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same is also found for the long guns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3ce11898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(state_firearms[\"private_sale_other\"], y_scale=1, y_scale_two=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And do you know it, same applies to \"Other\". Finaaly, let's try and investigate if there are any relationships between the demand of the various firearm types, as assumed by the checks carried out on them. First let's view the top and bottom 6 states with the highest \"totals\" with the firearm categories side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column to hold the sum of all firearm categories, then check for highest total and lowest.\n",
    "state_firearms[\"total_firearms\"] = state_firearms[\"handgun\"] + state_firearms[\"long_gun\"] + state_firearms[\"other\"]\n",
    "all_firearms = state_firearms[[\"handgun\", \"long_gun\", \"other\", \"total_firearms\"]].sort_values(by=\"total_firearms\")\n",
    "high_firearms = all_firearms.tail()\n",
    "low_firearms = all_firearms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAAG/CAYAAAAU1UXYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xd0VFUb7/HfpNGCCSl0FEESago9BIj0IhYiRUGqEnilN2ki8krvUl4hAtJFkSJF6YoiAQSRgBcQARtNIAkkBNI49w8ucx3SJmHS4PtZi7Vmztlnn+fM7Bnmyd5nb5NhGIYAAAAAAHmWXU4HAAAAAAB4NCR2AAAAAJDHkdgBAAAAQB5HYgcAAAAAeRyJHQAAAADkcSR2AAAAAJDHkdgBjzlvb+90/x06dChbYrl9+7amTJmihg0bqmrVqmratKmWLVuW7nE7d+5Uhw4dVLNmTdWoUUNt2rTR+++/r7i4OEnSnTt3NG/ePP3666+ZimvhwoU6cuRIpo7NDoGBgZo9e7b5+ZYtW/Tll18mK9ehQwcNGzYsw/V/+umn8vb2VmJiYprlMlv/w/FnlatXr2revHm6cuVKumUfXPPD/9q0aWMuU6dOHc2bNy8rQ86Vbt++LW9vb23YsCFbzpcVr/PUqVPVuHFjm9Zpa6tWrdK+fftyOoxU5YXXEIAlh5wOAEDW+uyzz8yP7969q27duuk///mPnn/+efP25557LsvjSEhI0Jtvvqno6GgNGzZMxYoV0x9//KHY2Ng0j9uwYYNGjRqlN954Q3379pUknT59Whs3btTt27eVL18+3blzR/Pnz1e5cuXk5eWV4dgWLVokOzs71axZM1PXltVCQ0Pl7u5ufr5161YlJibq5ZdfztY4Jk6cqHz58mXrOTPin3/+0fz589WgQQMVL17cqmPWrFkje3t78/P8+fObHy9ZskQeHh42jxOQ7id29erVU1BQUE6HkqIuXbroxRdfzOkwAGQAiR3wmPPz8zM/vn37tiTp6aefttieHVavXq3ff/9dX3/9tYoUKSLp/l/q07Nq1So1b95cY8eONW8LCgpS7969ZRhGlsWbm1SpUiWnQ5AkVahQIadDsDlfX185OKT8X2HVqlXTPDYpKUn37t2To6NjVoQm6f4fY/6dbAJZLT4+Xg4ODipZsqRKliyZ0+EAyACGYgIwO3HihLp06SJfX1/VqVNHI0aMUEREhHn/uXPn5O3tra+//lqDBw+Wv7+/AgMDtWjRonTrXr9+vdq0aWNO6qx169atVHtNTCaT4uLiFBAQIEkaMmSIeUjdtWvXZBiGpkyZojZt2sjPz09BQUHJrikwMFCxsbGaOXOm+diff/5Z0v0f7gsWLFCTJk1UtWpVtWzZUlu2bLGI4dChQ3rttdfk7++vGjVqqG3bttq9e3eq19OuXTtNmDDB/Hz37t3y9vbWrFmzzNs2b94sHx8fxcfHm2N8MJRx8ODB+vbbb7V//35zvKGhoRbn2Lhxo5o0aaLq1aurT58+unbtWrqvsyT98ccf6tq1q3x9fdW6dWt98803FvtTGoq5efNmNW3aVD4+PurevbvCw8Pl7e2tbdu2Jav/448/Vv369VW7dm0NHz5cMTExFvsjIiI0evRoBQQEyMfHR506ddLJkyctynz66adq1aqVfHx8VKdOHXXp0kUXLlzQuXPn1K5dO0lSx44d5e3trWrVqll13al5eIjggAED1KVLF23btk0tW7ZUtWrVdPbsWUnSn3/+qf79+6tmzZry8/NT79699ddff5mPTUpK0uTJk/XCCy/Iz89Pzz//vEaOHGnRFh+c88MPP9Ts2bNVv359NWjQwOLcO3bsUIsWLeTn56e+ffsqJiZGZ8+eVadOneTn56cOHTro/Pnz6V7b9evXNWrUKNWrV08+Pj5q1aqV1q5da1EmMTFRU6ZMUZ06dRQYGKjJkycrISHBokx61y3d/4PSxIkTFRQUZB6CPX/+/FRj++uvv9S4cWP16dNH8fHxunfvnubNm6fGjRuratWqCgwMVEhIiG7evJnudYaFhenFF19UtWrVFBwcrPDwcPO+cePGpdgjtXLlSvn5+SVrn/+W3uv30UcfqW3btubvyH79+unixYvm/cHBwbpw4YJWr15t/hxv377dvH/VqlVq2bKlqlatqiZNmmjFihXJYli8eLHq168vf39/DRw4UPv27ZO3t7dOnDhhLhMTE6P33nvP/Jnq2LGjDh8+bFFPcHCwRo4cqeXLl6tx48by9fVVdHR0ikMxb9y4oVGjRqlu3bry8fHRG2+8oVOnTlmUWb16tfnzUbduXXXt2lV//PFHqq8lANuhxw6ApPvD2Lp27apKlSpp1qxZunXrlmbMmKGzZ8/q888/t+jVmDRpkpo1a6a5c+fq4MGDmjVrltzc3NS+ffsU646NjdXZs2f14osvauDAgdq7d6/y58+vFi1aaNSoUSpUqFCqcVWuXFmbNm1ShQoV1KxZM3l6elrsd3Jy0uLFi/XWW29p4MCBqlevniTJ1dVV9+7d082bN/Wf//xHnp6eun79upYsWaKePXtq48aNMplMCg0NVadOnRQcHGwe2vigZ2rs2LHavn27+vXrp4oVK2rfvn0aPny43NzcFBgYqMjISPXp00etW7fWgAEDlJSUpDNnzujWrVupXk+tWrUUFhZmfn7kyBHly5fP4h6/I0eOyNfXV05OTsmOHzRokK5cuaJ79+5p1KhRkqQSJUpYHHv58mWNHj1aMTExmjRpksaPH5/mD+kHBg8erI4dOyokJESffPKJ+b1KLbE+evSo3nnnHbVp00Yvv/yyzpw5o6FDh6ZY9ssvv1SVKlU0ceJEXbx4UVOnTpWbm5v5Gu7evauuXbsqPj5eI0eOlKurq1atWqXu3btr586dcnNz0/79+zVx4kQNGjRIPj4+unXrln766SfFxMTIy8tLkydP1qhRozRhwgRVqFBBdnbp/+3y3r17FvcW2tvby2QypVr+t99+0/z589WvXz+5urqqaNGiunbtml5//XWVKlVKEydOlIODg/73v//pzTff1LZt2+To6KikpCTdunVLffv2lYeHh65fv25utw/fy7Zu3TpVrlxZEyZMsIjtwoULWrx4sYYPH66bN29qwoQJGjdunM6dO6c33nhDISEhmjp1qoYPH67169eneg0xMTHq3LmzYmNjNXDgQD399NO6cOFCsnsTP/roIzVo0EAzZ85UeHi45s6dq6efflqdO3eWJKuv+6233tLZs2fVr18/eXt769KlS8kS9n9fY/fu3eXr66uZM2fK0dFRq1ev1ooVKzRs2DCVK1dOERER+uGHH8z32Kbm5s2bGjNmjN5++20VKVJEoaGheuutt7Rr1y65uLioffv2Wrt2rU6cOGHxR4CNGzeqRYsWcnZ2zvTr988//6hbt24qUaKEoqOjtXLlSnXq1Ek7duxQ/vz5NWXKFPXp00dVq1ZVz549JUlly5aVJM2dO1cff/yxevfurerVq+unn37S1KlT5ezsrODgYEnSpk2bNH36dPXo0UP169fXwYMH9f777yeLdfjw4Tpy5IiGDBmiEiVKaPXq1erZs6fWrl1r0SO9f/9+nT9/XiNHjpSDg0OKQ67v3LmjLl26KCkpSaNHj5aLi4tWrFihbt26aefOnXJ1ddW+ffs0efJkDRkyRFWrVtWtW7d09OjRNJNkADZkAHhixMTEGF5eXsb69euT7ZswYYJRu3Zt4/bt2+Zthw8fNry8vIydO3cahmEYv/32m+Hl5WX07t3b4thhw4YZjRo1SvW8f/75p+Hl5WX4+fkZ/fv3N3744Qdj7dq1Ro0aNYzhw4enGfOff/5pvPDCC4aXl5fh5eVlNGvWzJgyZYpx48YNc5kbN24YXl5extatW9OsKzEx0fjjjz8MLy8v4/jx4+btfn5+xqJFiyzK/vrrr4aXl5exbds2i+0DBgwwXn/9dcMwDOPHH380vL29jbi4uDTP+2+7du0yKlasaNy6dcswDMN49dVXjfHjxxtVq1Y119OqVStj9uzZ5mPq1atnzJo1y/w8JCTE6NmzZ7K627dvb9SuXduIjo42b1u0aJFRqVIlIyEhIdWY1qxZY3h5eRmbN282b7t69WqyttK+fXtj6NCh5ue9e/c22rZta1HXvHnzkr0X9erVM1q2bGkkJSWZt40dO9aizaxatcqoVq2a8ddff5m3xcXFGQ0bNjS/FgsWLDA6duyY6nWEh4cbXl5exrFjx1It8/A1P/xv06ZN5jK1a9c25s6da37ev39/o2LFisb58+ct6po4caIRGBho8br/888/ho+Pj7Fhw4YUz5+YmGicO3fO8PLyMk6ePGlxzkaNGiV7v/r3729UqVLFuHLlinnbuHHjDC8vL2PHjh3mbdu2bTO8vLyMS5cupXrtS5YsMSpXrpzsOh548D3x1ltvWWzv1q2b0a1btwxd9/bt2w0vLy/jwIEDqcbz4HX+9ddfjcDAQGPo0KFGYmKief+IESOMd955J9XjUzJlyhTDy8vL2L17t3lbZGSk4efnZyxYsMC87aWXXjLef/998/PTp08bXl5exsGDB1OtO73X72GJiYlGdHS0UaVKFYv3qkWLFsb48eMtyt64ccOoUqWKsWTJEovtkyZNsvi8tGzZ0hg4cKBFmeHDhxteXl5GeHi4YRj///Owfft2c5mEhASjcePGRr9+/czb2rZta/j7+xtRUVEW9U2ZMsXinMuWLTN8fHws2tbdu3eN+vXrG/PmzTMMwzDmzp1rdO7c2arXBYDtMRQTgKT7wzCDgoJUsGBB87ZatWrJ09NTR48etSjbrFkzi+fNmzfXxYsXdePGjRTrNv7fvXAeHh6aPXu26tWrp44dO2r48OHavHlzmrMYlilTRps2bdKSJUvUvXt3FSxYUEuXLtVLL71k1RDDPXv2qEOHDqpRo4YqV65sjv3ChQtpHnfgwAE5OTmpUaNGSkxMNP8LCAjQL7/8Ikl69tlnlS9fPg0ePFh79+5VdHR0uvHUqFFDhmHop59+0u3bt3Xq1Cl17txZ+fPnV3h4uCIiInT+/PlMT+Ti5+dn0dNQvnx5JSUl6fr16+keGxgYaH5ctGhRubi4pPnenDhxItlQrSZNmqRYNiAgwKIH7bnnntPVq1fNbePAgQPy9fVV8eLFza/1gwltHvTuVKpUSeHh4Zo6daqOHDmSbFhgZnz++ef64osvzP/Sm8jimWee0bPPPmuxLSwsTEFBQcqfP7859iJFisjLy8uiZ2r37t1q3769qlevrsqVK6tVq1aSpN9//92ivvr166d431/58uVVrFgxi1gkqW7dusm2Xb16NdVrOHjwoPz9/ZNdx8P+3R6k++/Zv9uDNdd98OBBlSxZ0jxcOjVnzpxRly5dFBQUpGnTpllMaFOxYkXt3LlTCxYs0MmTJ3Xv3r0063rA0dFRjRo1Mj93dXVVrVq1LIZjtmvXTtu2bTMPe96wYYPKlCmj2rVrp1qvNa/fjz/+qC5duqh27dqqXLmyatSooYSEhGTvdUrHJSQkqGXLlsm+dy5evKjIyEjdvn1b58+fT/bZe/h5eHi4HB0d1bRpU/M2BwcHNW/ePNl3ur+/v1xcXNKMLSwsTP7+/vL09DTHZW9vr+rVq1t8Rn/66SdNmzZNR48etclnFID1GIoJQNL9oUP+/v7Jtru7uye7l+XfMzRKkpubm6T7Q7Me3ifJ/IOhZs2aFj/Y6tatK8MwdO7cuTRnMXRwcFD9+vVVv359SdLevXvVt29frVixItWhf9L9oYL9+vVT69at1adPH7m5uSkxMVGdO3c2/5BLTWRkpOLj41OdZCYiIkLu7u5asmSJFixYoAEDBki6/6N87NixKlWqVIrHFSlSRM8995yOHj0qBwcHubi4qHz58qpevbqOHDmiyMhI2dnZZXpym8KFC1s8fzCxR3rD1iTpqaeeSnZsascZhqGIiIhk90w+aAvWxJWYmKikpCQ5ODgoMjJSR48eTXGimAdDYxs1aqTx48dr9erVWrp0qQoXLqxXXnlFw4YNy/QEI1WqVEl18pSUpDQsNTIy0pwYPuzB0OHDhw+rf//+atOmjXloYFxcnLp27ZrsNU5t6GtK78/D2615v6OiolJtn+md79+fG2uuOyoqKtnw6ZT8+OOPio6OVrt27ZINoX3jjTcUHx+v9evXa+7cuXJ3d1fnzp319ttvpzls1tXVNVld7u7uFsuivPTSS5o+fbp2796t5s2ba8uWLercuXOa9ab3+l24cEFvvfWW6tSpo4kTJ8rDw0MODg4pvtcPi4yMlCSLhPTfrly5Yv7j28OftYefX7t2Ta6urhbfudL99vXwd7o1s79GRkbq559/TvEzWrFiRUlS06ZNNW7cOK1Zs0ZLlixR4cKFFRwcrKFDh+bqGXWBxwWJHQBJ93toHp7IQbp/s/zDf8l9uGfuwXGp/YBzcXFJ8YfDg94aa+6F+rfGjRurXLly6U4SsXPnTpUoUUIzZ840b7NmYgnp/o/CfPnyafXq1Snuf/Cjt2bNmvrkk08UGxurAwcOaNKkSRo5cqRWrlyZat01atTQkSNHLJZYqFGjhg4fPqyIiAhVrFgx1ft7cguTySQ3NzfzD9EHUmpD1nBxcVH16tU1evToZPv+nbS1b99e7du31/Xr17Vjxw5NmTJFLi4u6t+/f6bOm1Ep/eB3cXFR7dq11aNHj2T7HrSTnTt3qkyZMpo+fbp534OJV6w5hy25urpaPaFOWqy5bldXV/NkRGnp1KmT/vzzT/Xu3VsrVqwwJwrS/T/shISEKCQkRH///bc2btyouXPnqnTp0mku+REVFaV79+5ZfL/cuHHD4nvKxcVFzZo108aNG+Xk5KSIiAi1bds2zVjTe/2+/fZbSdL8+fPN98nevn073aVdHsQjScuXL0/x3uOyZcuaeywf/qw9/NzT01NRUVFKSkqySO6uX7+e7Dvdmjbn4uKiWrVqacSIEcn2/fsz2rFjR3Xs2FHXrl3T9u3bNXXqVLm6uurtt99O9xwAHg1DMQFIknx8fLRv3z7duXPHvO3IkSO6du2aatSoYVF2165dFs937typUqVKpdhb98Dzzz+vw4cPW0wGERYWJnt7+zTX0UtpeGdsbKxF7+CDH08P/zX87t27yaaif3hWSynlnqm6desqLi5OcXFxqlatWrJ/D/fyFCxYUE2bNtUrr7yi3377LdXrke4ngydOnNCBAwfMiV2tWrV07NgxHT58ON1hmA/3nOSUatWqae/evRbb9uzZk6m6AgICdOHCBZUpUybZa53SMgseHh7q3LmzfH19za93RnonbSkgIEC//vqrKlasmCz2B0MjrW2L2SEgIEDHjh1Ld1igNfWkd90BAQG6dOmSDh06lGZddnZ2mjp1qmrWrKk333wz1aHSpUuXVv/+/VW0aFGdO3cuzToTEhIsZnaNiorSjz/+KB8fH4ty7dq10w8//KDFixcrICAg3Sn+03v97t69K3t7e4tkauvWrcnKpfS9U7NmTTk6Our69espfu8UKFBAhQoVUrly5ZJ91h7+LPr4+CghIcGiXFJSknbt2pXsO90aAQEBOn/+vJ555hmrPqOenp7q0qWLqlatmu57BcA26LEDIEl68803tW7dOvXq1Us9e/Y0z4pZpUqVZMOCfvnlF33wwQdq1KiRwsLCtHnzZn3wwQdp1t+rVy999dVXGjRokDp06KC///5bM2fO1Ouvv57mUK0uXbqocuXKatSokYoVK6Z//vlHK1asUGxsrDp27ChJcnZ2lqenp7Zt26ZnnnlGTk5OqlSpkgIDA7V27VpNnTpVDRs21I8//qivvvoq2TnKlSunvXv3qk6dOipQoIDKly+vihUrKjg4WP369VOvXr1UuXJl3b17V2fPntXFixc1fvx47dy5U9u2bVOTJk1UvHhxXb58WevXr7e45ykltWrVUnx8vI4fP67x48dLur9mWmJiok6dOpXuX7bLlSunVatWac+ePSpatKiKFy9u1XA3W+vVq5c6d+6sYcOGmWfF3LRpk6SM9zq1b99e69atU5cuXdSjRw+VLl3aPPSrdOnS6ty5s2bOnKm4uDjVrFlTrq6uOnHihH7++WeNGTNG0v0f/Y6OjtqwYYOcnJzk5OSULWsA9urVS19//bV69Ohhbs/Xrl3ToUOH1LBhQzVt2lT16tXTunXrNGPGDAUGBurQoUMW09tnpw4dOmjt2rXq1q2b+vbtq6efflp//PGHrly5ooEDB1pdjzXX3aRJE9WsWVP9+/fX22+/LW9vb129elUnTpywWJtSut8zN2fOHPXp00c9evTQmjVrVLJkSY0YMUIlSpRQtWrV5OzsrO+//17Xrl1Ldx1MZ2dnTZw4UVFRUXJ1dVVoaKgcHBzUqVMni3J169ZViRIldOzYMc2YMeORX7969erpww8/1LvvvquXXnpJp06d0qpVq1SgQAGLesqVK6eDBw/qwIEDKly4sJ5++ml5enqqV69eGjt2rH7//Xf5+/srMTFRFy5c0PHjx83LnvTu3VsjRozQlClTzLNiPljG4EEPZbVq1dS4cWO9++67ioiIUIkSJbRmzRpduXJFc+bMSfc6U7rudevW6Y033lCPHj1UqlQpRURE6NixYypbtqxef/11TZs2TUlJSapRo4ZcXV11/PhxnThxQq+++qq5nsDAQLVq1UrvvvtuhmMAkDYSOwCSpGLFimn58uWaNm2aBg0apPz585vX2nq4d2r06NH6+uuv1b9/fxUoUEADBw5Uhw4d0qy/bNmyWrp0qaZOnaq+ffvqqaeeUocOHTR48OA0jwsJCTEP54mMjJSbm5uqVq2qtWvXWvxonzBhgmbMmKHu3bsrPj5e+/fvV/PmzTVw4ECtWbNGn376qWrUqKEFCxbohRdesDjHyJEj9cEHH6hXr166e/euPvvsM/n5+WnixIn65JNP9MUXX+ivv/6Ss7OzKlSoYE4oy5Ytq6SkJM2YMcN8z13jxo01ZMiQNK+pePHiKlWqlG7duiVvb29J9/967+Pjo8OHD6f71/SuXbvq119/1ciRI3Xr1i0NHTpUISEhaR6TFWrUqKFp06bpww8/1I4dO+Tr66v33ntPvXr1yvBQ0gIFCmjlypXmNdwiIyPl7u4uX19f8yQjPj4+WrFihTZv3qzY2FiVLFlSQ4YM0euvvy7p/g/58ePHa+HChdq6davs7Ows1vTKKsWKFdPnn3+u2bNn64MPPlBMTIyKFi2q2rVrm3ujW7durfPnz2vt2rVavXq1atasqfnz56e4jlpWK1SokFavXq3p06dr5syZunPnjsqUKaNu3bplqB5rrtve3l6LFi3SnDlztHjxYkVFRalEiRKpDnd0cnLSggUL9Oabb6pHjx5atWqV/P39tXHjRq1evVoJCQkqW7aspk2blmxyl4e5uLjogw8+0OTJk/X777/Ly8tLixcvlqurq0U5k8mkxo0ba+PGjWrevHm6153e6+fr66vx48dr0aJF2rZtm6pUqaIFCxaoe/fuFvUMGDBA48aNU79+/XT79m19+OGHatmypQYOHKiSJUtq1apVWrRokQoUKKBy5cpZtJVXXnlF165d0/Lly/Xpp58qMDBQgwcP1jvvvGPx2Zs+fbqmTZumOXPm6Pbt26pUqZKWLFlisdSBtR5c95w5czRr1izzZ9TPz8/8nerr66tVq1Zp06ZNunPnjkqVKqXhw4dbLIWTmJho9QQ4ADLGZDy4yQUA0nHu3Dm1bt1an3zyiXm9OODf1q1bp7Fjx2rfvn0WMzgCudkLL7ygmjVrmnvQ86IZM2bo008/1eHDh5NNmALgyUCPHQAgU5KSkvTf//5X9evXl7Ozs06ePKmFCxeqefPmJHXI9e7du6eTJ09q3759On/+fKaGJ+aUS5cuadWqVapVq5acnJx06NAhLVu2TN27dyepA55gJHYAgEwxmUy6fv26xo0bp5s3b6pIkSJ6+eWXNWzYsJwODUjXnTt31L59e7m6umrUqFEpTgCSW+XLl0+nT5/W+vXrFRMTo2LFiikkJER9+/bN6dAA5CCGYgIAAABAHsdyBwAAAACQx5HYAQAAAEAel2fusbt2LTqnQ3isODvnU0xM9i7iC1iL9onciraJ3Iq2idyM9mk7np6FU91Hj90TysGBWbOQe9E+kVvRNpFb0TaRm9E+sweJHQAAAADkcSR2AAAAAJDHkdgBAAAAQB5HYgcAAAAAeRyJHQAAAADkcSR2AAAAAJDHkdgBAAAAQB5HYveILl++pC5dOti83okT39c33+y2eb0AAAAAHj8OOR2ALdWa+Z1N6/txaEOb1gcAAAAAWeGxSuxyyr179zR16gSdOBEuT09PTZkyUzt2fK3NmzcqISFBpUuX1tixHyh//vyaOPF9FSpUSKdPn9KNGzf09tv91ahRUxmGodmzp+mnn46oRImSMgzDXH9Y2H7NmzdbLi6u8vauqEuXLmratDlasmSRChQoqE6dukiSunTpoGnT5kiShg0bIB8fP4uY8uXLnyOvDwAAAICsxVBMG/j7778UHNxeq1Z9Lmfnwvr2270KCmqkxYtXaPnyT/XMM89q69ZN5vLXr1/X//63WNOmzdbChfMlSd99943+/PMPLV++ViNGvKuTJ8MlSXFxcZo+fbJmzJirjz5aosjIyEzHBAAAAODxRI+dDZQoUVIVKnhLkry9K+ry5Us6f/6cPv74I8XEROvOnTuqXbuuuXzDhs/Lzs5Ozz5bThEREZKkn38+pqZNW8je3l4eHp6qXr2WJOnPP39XyZKlVLJkKUlSs2YttHnzxkzFBAAAAODxRGJnA46OjubHdnb2SkqK06RJ4zVp0gxVqOClr77aomPHjqZYXvr/Qy5NJlOyuv89JPNh9vb2Mox75ufx8fFpxgQAAADg8URil0ViY2/Lw8NDiYmJ2rnza3l6Fk2zvJ+fv778coNatnxBkZGR+umnI2rWrIWeeaasLl26qMuXL6lEiZLas2eX+ZgSJUrqwIHvJUlnzpymVw4AAABPhOsNatu8To/vD9u8zuxEYpdF3nrrPwoJ6a5ixYqrfPnnFBsbm2b5hg0b6ejRH9Wt22sqU+awpgqHAAAgAElEQVRp+ftXlyTly5dfQ4aM0NCh/eXi4qrKlauYj3n++cbavn2bunfvpEqVKqtMmaez9JoAAAAA5E4mI62xfrnItWvROR1CjomNjVXBggVlGIZmzpyqMmXKqGPHzo9Up6trQUVFpZ1sAjmF9onciraJ3Iq2idwsK9rnk9pj5+lZONV99NjlAVu2bNTXX29TYmKCKlTw1ssvv5rTIQEAAADIRbI1sRs1apS+/fZbubu7a+vWrRb7lixZomnTpiksLExubm7ZGVau17Fj50fuoQMAAADw+MrWdeyCg4O1ePHiZNsvX76sAwcOqGTJktkZDgAAAAA8FrI1satVq5ZcXFySbZ88ebKGDx+e4nT/AAAAAIC0ZWtil5I9e/aoaNGiqlixYk6HAgAAAAB5Uo5OnnLnzh0tXLhQS5cuTbess3M+OTjYZ0NUTwZ7ezu5uhbM6TCAFNE+kVvRNpFb0TaRm2VF+7xu09ruy+ufoRxN7P7880/9/fffevnllyVJV65cUXBwsNatWydPT0+LsjExcTkR4mOLaZGRm9E+kVvRNpFb0TaRm+WV9pkXYsy1yx14e3srLCzM/Lxx48b64osvMj0rpueC0rYKTZJ0re/f6ZZp1qyBdu363qbnBQAAAICMyNZ77IYMGaLXXntNFy5cUMOGDbVu3brsPD0AAAAAPJaytcdu1qxZae7fu3dvNkVie4Zh6H//m6uDB3+QyWRSt25vqkmT5vrppyNaujRUrq6uOn/+nLy9K+m99z6QyWRSWNh+zZs3Wy4urvL2rqhLly5q2rQ5KdYfGRmp8ePH6Natm6pYsbIOHQrTkiWrdOdOrN55Z5BWrvxckrRmzUrduROrN9/srX79QlS5clUdO3ZE0dExGjVqrHx9/bPzZQEAAACQDXJ8VszHxb59e3X27BktW/ap5sz5nxYs+FDXr9+/rfPs2TMaMGCoVq1ap0uXLio8/Lji4uI0ffpkzZgxVx99tESRkZFp1v/JJ6GqUaOWli5drYYNG+nq1StWxZWUlKSPP16hgQOHaOnSjx/5OgEAAADkPiR2NhIe/rOaNm0he3t7ubm5y9+/uk6f/kWSVKlSFRUtWkx2dnaqUMFLV65c0p9//q6SJUupZMlSkqRmzVqkU/9xNWnSXJJUt249FS78lFVxBQU1kiR5e1fSlSuXMnt5AAAAAHIxEjsbMYzU9zk5OZkf29nZKSkpSUZaB6RYf8rl7e3tLfbFx1vOHvrg3HZ29kpKSsrQOQEAAADkDSR2NuLn56+9e3cpKSlJkZGR+vnnY6pUqUqq5Z95pqwuXbqoy5fv96Lt2bMrzfp9fPy0d+/9MocPH1R09C1JkpubuyIjI3TzZpTi4+N14MB+G10RAAAAgLwiR5c7sDVrlifIKg0bNtLJkyfUvfvrMplMevvtAXJ399Aff/yeYvl8+fJryJARGjq0v1xcXFW5cupJoCT17NlL778/Rnv37pKfX3W5u3uoYMGCcnBwUPfuvRQS0l0lSpTUM8+Utf3FAQAAAMjVTEZGxwTmkGvXonM6BJuLjY1VwYIFZRiGZs6cqjJlyqhjx84plo2Pj5ednZ0cHBx08mS4ZsyYomXL1mT63HlloUg8mWifyK1om8itaJvIzbKifV5vUNum9UmSx/eHbV6nreXaBcqfdFu2bNTXX29TYmKCKlTw1ssvv5pq2atXr+i990bq3j1Djo6OGjFiTDZGCgAAACA3o8cul9m2bbPWrVtrsa1aNV8NHTrCpufhL3vIzWifyK1om8itaJvIzeixsx167PKQF154SS+88FJOhwEAAAAgD2FWTAAAAADI40jsAAAAACCPI7EDAAAAgDyOxC4LREdHa8OGdebnP/10RO+8MygHIwIAAADwOHusJk9p/FU9m9a3t/WBTB0XExOtjRvXKTi4vU3iSExMlIPDY/VWAQAAALAhsgUbWLt2lbZt2yxJevHFV/TLLyd08eJFde/eSbVq1VFAQKBiY2P17rvv6Pz5c/L2rqT33vtAJpNJp0+f0vz5sxUbGytXV1eNHv2+PDw81K9fiKpV89WJE8cVGNhQr7/+Rg5fJQAAAIDcisTuEZ0+fUpffbVFoaHLZRiGQkK66733/qvz589p2bI1ku4PxTx79oxWrvxcHh6e+s9/3lR4+HFVqVJVc+ZM1+TJM1WkSBHt2bNToaELNHr0OEn3h3TOnx+ak5cHAAAAIA8gsXtE4eE/q2HDRipQoIAkKSiokY4f/zlZuUqVqqho0WKSpAoVvHTlyiUVLuys8+fPafDgvpKke/eS5O7uYT6mSZNm2XAFAAAAAPI6ErtHZlhVysnJyfzYzs5OSUlJMgzp2WfLadGiT1I85kGyCAAAAABpYVbMR+TrW13ff/+t7t69qzt37ui7775RtWq+io2NTffYp59+RlFRkTp5MlzS/UlSzp8/l9UhAwAAAHjM0GP3iLy9K6pVqzbq1aurpPuTp1SsWEnVqvmqS5cOqls3UAEBgSke6+joqAkTpmrOnBmKiYlRUlKSOnR4XeXKlc/OSwAAAACQx5kMw7BuLGEOu3YtOqdDeKy4uhZUVFT6vYpATqB9IreibSK3om0iN8uK9nm9QW2b1idJHt8ftnmdtubpWTjVfQzFBAAAAIA8jsQOAAAAAPI4EjsAAAAAyONI7AAAAAAgjyOxAwAAAIA8jsQOAAAAAPI4EjsAAAAAyOMeqwXKbb2ehTVrWURHR2vXru0KDm6fapnLly/pxIlwNW/eMs26Ll++pHfeGaSVKz9Ptcy4caP1++/n1br1i4qOjpavr79q1aqTbpwAAAAAHl+PVWKXE2JiorVx47p0E7vdu7enm9il58aN6zp5Mlzr129Ns1xSUpLs7e0zfZ7ExEQ5ONA0AAAAgLyCX++PaOHCebp48aK6d+9k7jk7ePAHmUwmdev2ppo0aa6FC+frjz8uqHv3TmrV6gU1bNhIH3zwnu7evSNJGjz4HVWr5pvuuYYM6afIyEh1795JgwcP19atX6pevfpq1Kip2rV7US+88JIOHz6oV1/toEqVqmjmzKmKiopU/vz5NWLEu3rmmbLav/87LV++RIaRpEKFntK4cR/Izc1dS5Ys0vXr13XlyiW5uLiqdu26+v77b5WUdE8XLpzTa691VkJCgnbs+EqOjk6aMeNDPfWUi9atW6svv1wve3t7lS37rMaPn5ylrzcAAACA5EjsHlGfPv11/vw5LVu2Rt9+u0ebNq3XsmWf6ubNKL31Vlf5+lZXnz79tHbtKk2bNkeSdPfuXc2evUD58uXTX3/9qfffH6MlS1ame64pU2bpnXcGadmyNZKkrVu/tNjv5OSkjz5aIkkaOPA/GjZslMqUeVq//HJSM2dO0dy5C+Xj46fQ0GUqUqSQVq5co9WrV6h//8GSpDNnTumjjxYrX778+uqrLTp//pw++WS14uLi9dprr+g//+mvTz5Zo7lzZ2r79m3q0KGTVq1apnXrNsvJyUnR0dG2fGkBAAAAWInEzobCw39W06YtZG9vLzc3d/n7V9fp07+oYMFCFuUSExM1e/ZUnT37q+zs7PXXX3/Y5PxNmjSXJMXGxurEiXCNHTvSvC8hIV6SdO3aPxo3bpSioiJ0926cSpQoZS5Tv35D5cuX3/y8evWaKliwkAoWLKRChZwVGNhQklSu3HM6d+43SVL58hX03/++qwYNnleDBs/b5DoAAAAAZAyJnQ0ZhnXlPvtstYoUcdeyZZ/q3r17atIk0Cbnz5+/wP+L454KF3Y29+z92+zZ0/Taa53Vpk0r7d37nZYuDU12/AOOjo7mx3Z2dnJ0dDI/TkpKlCRNnz5Hx48f0/79+7Rs2WKtXPk59+cBAAAA2Sxbf4GPGjVK3377rdzd3bV16/0JQKZOnapvvvlGjo6OevrppzV58mQ99dRT2RnWIylYsKBiY2MlSX5+/vryyw1q1aqNbt26pZ9/Pqa33x6oa9eumctI0u3bMfL0LCY7Ozt9/fVWJSUl2TSmQoWcVaJEKe3du1uNGzeVYRj67bezqlDBS7dvx8jDo6gkafv2bY90nnv37umff66qevWa8vHx065dO3Tnzh0VLlzYFpcBAABykK1nG5esm3EcQOZka2IXHBysN954QyNGjDBvCwwM1NChQ+Xg4KDp06dr0aJFGj58eKbqz4kvCxcXV1Wr5qsuXTqobt1AlS9fQd27vy6TyaS33x4gd3cPubi4yt7eXt26va7Wrduobdv2evfdd/TNN7tVvXpNFShQIP0TZdB7732gGTOmaPnyJUpKSlSTJs1VoYKXevYM0dixI1WiRDF5e1fRpUsXM32Oe/fu6b//Havbt2NkGIY6dOhEUgcAAADkAJNhWDuA0Db+/vtv9enTx9xj92+7du3S9u3bNXPmzGT7rl1jYg5bcnUtqKio2PQLAjmA9onciraJ3Cor2iY9drAV2qfteHqm3olil41xpGv9+vVq2LBhTocBAAAAAHlKrpnl4qOPPpK9vb1eeumlFPc7O+eTg0PmF93OS374Yb9mzbLstSxVqrTmzp1ns3PY29vJ1bWgzeoDbIn2idyKtoncKiva5nWb1nYfn58nE+0ze+SKxG7jxo369ttvtWzZMplMphTLxMTEZXNUOadKlepasmR1su227MJmOBFyM9oncivaJnKrvNI280KMsD3ap+2kNRQzxxO77777Th9//LFWrVqVJZOIAAAAAMDjLlsTuyFDhujw4cOKjIxUw4YN1b9/f4WGhio+Pl49evSQJPn6+uq///1vdoYFAAAAAHlatiZ2s2bNSratffv22RkCAAAAADx2ctWsmAAAAACAjCOxAwAAAIA8jsQOAAAAAPI4EjsAAAAAyONI7AAAAAAgj8vxdewAAHiSXW9Q2+Z1enx/2OZ1AgByN3rsAAAAACCPI7EDAAAAgDyOxA4AAAAA8jgSOwAAAADI40jsAAAAACCPI7EDAAAAgDyOxA4AAAAA8jgSOwAAAADI40jsAAAAACCPI7EDAAAAgDyOxA4AAAAA8jgSOwAAAADI40jsAAAAACCPI7EDAAAAgDyOxA4AAAAA8jgSOwAAAADI40jsAAAAACCPI7EDAAAAgDyOxA4AAAAA8jgSOwAAAADI40jsAAAAACCPc8hI4TNnzig8PFzXr19XXFycXF1dVbZsWfn7+8vFxSWrYgQAAAAApCHdxO6vv/7SmjVrtGXLFt24cUN2dnYqXLiwnJycFB0drTt37sjOzk61atVS+/bt1bp1a9nZ0REIAAAAANklzcRuzJgx2rJli2rUqKG+ffvK399fFSpUkL29vblMRESETpw4of3792v69OmaN2+eJk6cqJo1a2Z58AAAAACAdBK7fPny6euvv1apUqVSLePm5qagoCAFBQVp1KhR2r59u/755x+bBwoAAAAASFmaid17772Xocrs7OzUunXrRwoIAAAAAJAxj3Qz3M2bN3Xq1CnFx8fbKh4AAAAAQAZZndjNnTtXM2bMMD8PCwvT888/r+DgYDVt2lRnz57NkgABAAAAAGmzOrHbsmWLypUrZ34+depU1ahRQ59++qmeffZZzZw5M0sCBAAAAACkzerE7p9//lGZMmUkSZcvX9bp06c1YMAA+fn5qUePHjp+/Hi6dYwaNUoBAQFq06aNeVtUVJR69Oih5s2bq0ePHrp582YmLgMAAAAAnlxWJ3aFChVSdHS0JOngwYNycXGRj4+PpPuzZ969ezfdOoKDg7V48WKLbaGhoQoICNDOnTsVEBCg0NDQjMQPAAAAAE88qxO7WrVqKTQ0VN9++62WLl2qxo0bm/dduHBBxYsXt6oOFxcXi2179uzRK6+8Ikl65ZVXtHv3bmtDAgAAAAAoneUO/m306NEaPny4Bg8erEqVKmnw4MHmfV9++aVq1aqVqQBu3LihokWLSpKKFi2qiIiIFMs5O+eTg4N9ivuQcfb2dnJ1LZjTYQApon0it8qKtnndprXdx+fnyUPbRG5G+8weVid2xYoV04oVK1Lct2TJEjk5OdksqJTExMRlaf1PGlfXgoqKis3pMIAU0T6RW+WVtpkXYoRt0TaRm9E+bcfTs3Cq+x5pHbsHnJ2dM53Yubu7659//pF0f4IWNzc3W4QEAAAAAE8Mq3vsJGn79u3atWuXrly5ori45D1oX3zxRYYDaNy4sTZt2qSQkBBt2rRJTZo0yXAdAJCW6w1q27xOj+8P27xOAACAzLI6sZs3b54WLFigihUrqnz58pnqoRsyZIgOHz6syMhINWzYUP3791dISIgGDRqkL774QiVKlNCHH36Y4XoBAAAA4ElmdWL3xRdfKCQkREOGDMn0yWbNmpXi9uXLl2e6TgAAAAB40ll9j93t27cVEBCQlbEAAAAAADLB6sSudevW+u6777IyFgAAAABAJlg9FDMgIEAzZsxQVFSU6tWrp6eeeipZmaCgIJsGBwAAAABIn9WJ3YMFyTdu3KiNGzcm228ymXTq1CnbRQYAAAAAsIrVid2ePXuyMg4AAAAAQCZZndiVKlUqK+MAAAAAAGRShhYoT0xM1M6dO3X06FFFRUXJ1dVVNWrUUPPmzeXgkKGqAAAAAAA2YnU2duPGDfXs2VNnzpxRqVKl5OHhoZ9//lmrV69WxYoVtXTpUrm5uWVlrAAAAACAFFid2E2ePFlRUVH6/PPP5ePjY94eHh6uAQMGaPLkyZo+fXqWBAkAQEZ5Liht8zoTxkTYvE4AAGzB6nXsvvvuOw0bNswiqZMkHx8fDRkyRPv27bN5cAAAAACA9Fmd2MXHx6tQoUIp7itUqJASEhJsFhQAAAAAwHpWJ3a+vr76+OOPFRsba7E9NjZWH3/8sXx9fW0eHAAAAAAgfVbfYzdy5Eh17dpVzz//vAIDA+Xu7q6IiAjt379fhmFo5cqVWRknAAAAACAVVvfYVapUSTt27FCHDh0UERGhAwcO6MaNG3rttde0Y8cOVaxYMSvjBAAAAACkIkOLz7m5uWnYsGFZFQsAAAAAIBOs7rEDAAAAAOROafbYtWvXTlOmTNFzzz2nV199VSaTKc3KvvjiC5sGBwAAAABIX5qJXYUKFZQvXz7z4/QSOwAAAABA9kszsZs8ebL58ZQpU7I8GAAAAABAxnGPHQAAAADkcWn22E2bNi1Dlb3zzjuPFAwAAAAAIOPSTOy2b99udUUmk4nEDgAAAAByQJqJ3d69e7MrDgAAAABAJnGPHQAAAADkcWn22O3bty9DlQUFBT1SMAAAAACAjEszsevdu7dMJpMMw0i3IpPJpFOnTtksMAAAAACAddJM7Pbs2ZNdcQAAAAAAMinNxK5UqVLZFQcAAAAAIJPSTOzu3LmjAgUKmB+n50FZAAAAAED2STOxq169uj777DP5+PjI399fJpMpzcq4xw4AAAAAsl+aid2kSZNUpkwZ8+P0EjsAAAAAQPZLM7Fr27at+XFwcHCWBwMAAAAAyDgWKAcAAACAPC7NHrt/i4+P1/Lly7V7925dvXpVcXFxycqEhYXZNDgAAAAAQPqsTuzef/99bdmyRU2aNFHdunXl6Oho00CWLVumdevWyWQyycvLS5MnT1a+fPlseg4AAAAAeBxZndjt2rVLo0eP1uuvv27zIK5evaoVK1boq6++Uv78+TVw4EBt27aN+/oAAAAAwApW32Pn4uKikiVLZlkgSUlJunv3rhITE3X37l0VLVo0y84FAAAAAI8TqxO7vn37aunSpYqNjbV5EMWKFVPPnj3VqFEj1a9fX87Ozqpfv77NzwMAAAAAjyOrh2K2bdtWv/32mxo1aqQqVaqocOHCFvtNJpPmzJmTqSBu3rypPXv2aM+ePSpcuLAGDhyoL7/8Ui+//LK5jLNzPjk42GeqfiRnb28nV9eCOR0GkCJbt8/rNqvp/+Pz82TKiu9O2idsgbaJ3Iz2mT2sTuyWLl2qJUuWyMPDQ7GxsUpISLBZEAcOHFDp0qXl5uYmSWrevLmOHTtmkdjFxCSfhROZ5+paUFFRtu99BWwhL7TP3B4fJM8sqDMp6V6eeO/zQoywrbzwvSnRNp9UtE/b8fQsnOo+qxO70NBQdenSRaNHj5bJZLJJYA+ULFlSx48f1507d5Q/f36FhYWpatWqNj0HAAAAADyurE7sDMNQo0aNbJ7USZKvr69atGihtm3bysHBQZUqVVLHjh1tfh4AAAAAeBxZndgFBwdrx44dqlevXpYEMmDAAA0YMCBL6gYAAACAx5nViV2xYsW0bNkyde/eXXXr1k1x8pROnTrZPEAAAAAAQNqsTuymTJkiSbpy5YoOHjyYbD+JHQAAAADkDKsTu9OnT2dlHAAAAABymOeC0javM2FMhM3rRHJWL1AOAAAAAMid0kzsjhw5kuEKo6OjdebMmUwHBAAAAADImDSHYg4aNEilS5dWu3bt1KxZM7m4uKRa9ujRo/rqq6/05Zdfavjw4fL29rZ5sMjdrjeobfM6Pb4/bPM6AQAAgMdNmond7t27tWLFCi1YsEDjxo1T2bJlVaFCBRUpUkROTk66deuW/v77b506dUp3795VUFCQPvnkE1WrVi274gcAAACAJ16aiV3+/PkVEhKiXr16KSwsTAcPHtQvv/yi8+fPKy4uTq6urnr22Wf14osvqkmTJnJ3d8+uuAEAAAAA/49Vs2KaTCbVq1cvyxYnBwAAAABkHrNiAgAAAEAeR2IHAAAAAHkciR0AAAAA5HEkdgAAAACQx5HYAQAAAEAeZ3Vi17VrV507dy7FfRcuXFDXrl1tFhQAAAAAwHpWJ3aHDx/W7du3U9wXExOjI0eO2CwoAAAAAID1HnkoZnx8vA4ePCgPDw9bxAMAAAAAyKA0FyifP3++FixYIOn+IuUdO3ZMteybb75p28gAAAAAAFZJM7Fr2LChihQpIsMwNGHCBPXo0UOlS5e2KOPo6Khy5cqpZs2aWRooAAAAACBlaSZ2Pj4+8vHxkSQVKlRIQUFBcnNzy5bAAAAAAADWSTOx+7e2bdtmZRwAAAAAgEyyOrFLSEjQihUrtGvXLl25ckVxcXHJyoSFhdk0OAAAAABA+qxO7CZPnqzPPvtMzz//vOrUqSNHR8esjAsAAAAAYCWrE7vt27dr6NCh6tmzZ1bGAwAAAADIIKvXsTMMQ97e3lkZCwAAAAAgE6xO7Dp06KCtW7dmZSwAAAAAgEyweiimu7u7tmzZoi5duigwMFCFCxe22G8ymdSpUyebBwgAAAAASJvVid2kSZMkSZcuXdKPP/6YbD+JHQAAAJB9as38zuZ1/p7f5lUim1id2J0+fTor4wAAAAAAZJLV99gBAAAAAHInq3vs9u3bl26ZoKCgRwoGAAAAAJBxVid2vXv3lslkkmEYFttNJpP58alTp2wXGQAAAADAKlYndnv27Em27ebNm9q/f782btyoyZMn2zQwAAAAAIB1rE7sSpUqleK2ypUry97eXgsXLtTChQttGhwAAAAAIH02mTylcuXKOnTo0CPVcevWLQ0YMEAtW7ZUq1atdOzYMVuEBgAAAACPPat77FITHx+vDRs2yNPT85HqmThxoho0aKC5c+cqPj5ed+/efdTQAAAAAOCJYHVi9+qrr1pMlCJJCQkJunjxom7fvv1I99jFxMToxx9/1JQpUyRJTk5OcnJyynR9AAAAAPAksTqxq1ChQrLEzsnJSS1btlTTpk1VoUKFTAfx119/yc3NTaNGjdLp06dVpUoVjRkzRgULFsx0nQAAAADwpLA6sXvQm5YVEhMT9X/+z//R2LFj5evrqwkTJig0NFSDBg0yl3F2zicHB/ssi+FJY29vJ1dX2ybO121a2322jhF5g63bJ20TtsJ3J3Ir2iZyM9pn9sjwPXbx8fH69ddfdfPmTbm4uMjLy+uRh00WL15cxYsXl6+vrySpZcuWCg0NtSgTExP3SOeAJVfXgoqKis3pMNKVF2KE7eWF9pnb44P0aHd+pywp6V6eeO/zQoxPMs8FpW1eZ8KYiDzxvueFGGF7fHfajqdn4VT3ZSix+/jjjxUaGqqYmBjzQuWFCxdW79699dZbbz1CgJ4qXry4zp8/r3LlyiksLEzly5fPdH0AUlZr5nc2r/PHoQ1tXicA5Ca2/u78Pb9NqwMASRlI7JYtW6ZZs2bptddeU+vWreXu7q4bN27oq6++0qxZs+Tk5KSuXbtmOpCxY8dq2LBhSkhIUJkyZVjwHAAAAACsZHVit2bNGoWEhGjw4MHmbeXKlVOtWrX01FNPaeXKlY+U2FWqVEkbNmzI9PEAAAAA8KSyeoHyy5cvq06dOinuq127tq5cuWKzoAAAAAAA1rM6sStZsqT279+f4r4ffvhBJUuWtFlQAAAAAADrWT0Us0uXLpowYYJu3rypFi1ayMPDQzdu3ND27du1ceNGjRkzJivjBAAAAACkwurE7o033pCTk5Pmz5+v9evXy2QyyTAMFS1aVOPHj1f79u2zMk4AAAAAQCoytNxBhw4d1L59e125ckXXrl0zL1NgMpmyKj4AAAAAQDoyvEC5yWRSiRIlVKJEiayIBwAAAACQQRlK7K5evapvvvlGV69eVVxcnMU+k8mk4cOH2zQ4AAAAAED6rE7stm3bphEjRkiSihQpIkdHR4v9JHYAAAAAkDOsTuxmz56tFi1aaPz48XJ2ds7KmAAAAAAAGWD1OnZRUVFq164dSR0AAAAA5DJWJ3bNmjXToUOHsjIWAAAAAEAmWD0U87333tOYMWM0ZswY1a1bV0899VSyMkFBQTYNDgAAAACQPqsTu99//13h4eH6+++/tX79+mT7TSaTTp06ZdPgAAAAAADpszqxGzVqlJydnbVo0SI988wzyWbFBAAAAADkjAz12M2bN08NGjTIyngAAAAAABlk9eQp1apV0+XLl7MyFgAAAABAJmRoKObIkSOVL1++VCdPKVCggE2DAwAAAACkz+rELjg4WJI0clKzsSkAAB4sSURBVOTIVMsweQoAAAAAZD+rE7tJkybJZDJlZSwAAAAAgEzIcI9dahISEh45GAAAAABAxlk9eUpKDMNQWFiY3n33XdWvX99WMQEAAAAAMsDqHrt/O/5/27v3qKrKxI3jz4EDgnlBSXQws0RMQzMML0Ql460pRyXQcmzM7lleMM1sRjPTlDR+uRxBaVbldabyiiJhJposi7C8a5mlYyopKd64JODh/P5wzZkYiZpC3rM5389arRV7w+YB9+Lw8L77fXfv1rp165SRkaH8/Hw1bNhQ9957b3VnAwAAAAD8Ar+42B08eFDp6elKT09Xbm6ufHx8VFZWphdeeEEPPvig7PZf1REBAAAAAL9RlW3s2LFjrjL3zTffyNvbW1FRURo9erS6dOmi6Oho3XzzzZQ6AAAAADCoykbWu3dv2Ww2dezYUVOnTlWfPn3UsGFDSVJBQUGNBAQAAAAAVK3KxVOCg4PldDp18OBB5eTkaOfOnbp06VJNZQMAAAAA/AJVjtht2rRJO3fu1Lp16/TBBx9o3bp1atiwoXr37q277rqLfe0AAAAAwA387MNx4eHhCg8P18SJE5Wdna1169Zpw4YNWrFihWw2m5YtWyY/Pz916NChJvICAAAAAP7LL171xMvLS1FRUYqKilJpaamysrKUnp6ujRs3at26dbrhhhuUkZFxNbMCAAAAACrxq5az9PX1Va9evdSrVy8VFxdr48aNev/996s7GwAAAADgF/jN+xTUrVtX/fv3V//+/asjDwAAAADgf1TlqpgAAAAAAPdHsQMAAAAAi6PYAQAAAIDFUewAAAAAwOLcqtg5HA7FxMToqaeeMh0FAAAAACzDrYrd4sWLFRISYjoGAAAAAFiK2xS7kydP6qOPPtLAgQNNRwEAAAAAS/nN+9hVlxkzZmj8+PEqKiqq9Hy9enVkt3vXcKray9vbSwEBdav1mqer9WqXVXdGVL+r8W9U3fcn9yaqCz874a64N+HOuD9rhlsUu82bN6tx48Zq3769cnJyKn2fwsKSGk5VuwUE1NW5c8WmY/wsK2T0dFfj38gK96e754PU5Cpc0+Eot8S/vRUyonpxb8KdcX9WnyZN6v/kObcodjt27NCmTZuUlZWlkpISFRYW6rnnnlNiYqLpaAAAAADg9tyi2I0bN07jxo2TJOXk5Ojtt9+m1AEAAADAL+QWxQ6AdTVJvq7ar1k28Uy1XxMAAKA2c7ti17VrV3Xt2tV0jF+t8/9lVfs1Pxt3V7VfEwAAAEDt4TbbHQAAAAAAfh2KHQAAAABYnNtNxcSVeIYJAAAAQFUYsQMAAAAAi6PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAsjmIHAAAAABZHsQMAAAAAi6PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAsjmIHAAAAABZHsQMAAAAAi6PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAsjmIHAAAAABZHsQMAAAAAi6PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAszm46gCSdOHFCzz//vE6fPi0vLy/df//9GjZsmOlYAIAa0vn/sqr9mkf8qv2SAAC4Lbcodt7e3nrhhRcUFhamwsJCxcXFKSoqSq1btzYdDQAAAADcnltMxQwKClJYWJgkqV69emrVqpXy8vIMpwIAAAAAa3CLYvdjx48f15dffqmOHTuajgIAAAAAluAWUzH/raioSKNHj9Zf//pX1atXr8K5evXqyG73NpSs9vH29lJAQN1qvebpar3aZdWdEdZQ3fcn9yaqCz874a64N+HOuD9rhtsUu7KyMo0ePVr9+vVTnz59rjhfWFhiIFXt5XCU69y5YtMxfpYVMqL6WeH+dPd8uDqscG9K3J+eiHsT7oz7s/o0aVL/J8+5xVRMp9OpiRMnqlWrVnrkkUdMxwEAAAAAS3GLYrd9+3atWbNGn376qQYMGKABAwZoy5YtpmMBAAAAgCW4xVTMiIgIffXVV6ZjAAAAAIAlucWIHQAAAADg16PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAsjmIHAAAAABZHsQMAAAAAi6PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAsjmIHAAAAABZHsQMAAAAAi6PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAsjmIHAAAAABZHsQMAAAAAi6PYAQAAAIDFUewAAAAAwOIodgAAAABgcRQ7AAAAALA4ih0AAAAAWBzFDgAAAAAszm46AAAAAH6bTv+8tdqvuazarwjgamLEDgAAAAAsjmIHAAAAABbnNlMxs7KyNH36dJWXl2vQoEF68sknTUcCAAAA8BsxVbhmuEWxczgcmjp1qhYsWKCmTZtq4MCB6tGjh1q3bm06GgADqvsFgB/+AACgtnOLYrdnzx61bNlSLVq0kCT17dtXmZmZFLuriL+cAAAAALWHWzxjl5eXp2bNmrnebtq0qfLy8gwmAgAAAADrsDmdTqfpEBkZGdq6daumT58uSUpNTdXevXv14osvGk4GAAAAAO7PLUbsmjVrppMnT7rezsvLU1BQkMFEAAAAAGAdblHsOnTooCNHjujYsWMqLS1Venq6evToYToWAAAAAFiCWyyeYrfbNXnyZD3++ONyOByKi4tTaGio6VgAAAAAYAlu8YwdAAAAAODXc4upmAAAAABqh6VLl6qwsFCSNHnyZA0cOFDZ2dmGU9V+bjEVEzXnwIED+vzzzyVJERERatu2reFEwH+cP39e3377rUpKSlzHOnfubDARcNnBgwf1zTffqLS01HUsJibGYCIAcF/vvfee/vznP2vr1q3Ky8vTlClTNHnyZK1atcp0tFqNYudBFi1apOXLl6t3796SpPHjx+v+++/X0KFDDScDpOXLl2vx4sU6efKk2rZtq927d+vWW2/V4sWLTUeDh0tKSlJOTo4OHTqk7t27KysrS7fddhvFDsbt2rVL06ZN0+HDh1VWViaHwyF/f3/t2LHDdDR4OJvNJknKyspSbGys2rdvr/LycsOpaj+mYnqQFStWaNmyZYqPj1d8fLzee+89LVu2zHQsQJK0ePFirVixQsHBwVqyZIlWr16txo0bm44F6IMPPtCiRYt07bXXKiEhQWvWrKkwcgeYMnXqVL3++utq2bKldu/erVdeeYU/1sIttG3bVk888YQ2b96su+66S0VFRa6yh6uHETsP4+3tXen/A6b5+vqqTp06kqTS0lKFhIToX//6l+FUgFSnTh15eXnJbrersLBQgYGBOnbsmOlYgCSpZcuWcjgc8vb2VlxcnAYPHmw6EqCEhATt379f119/vfz9/XXmzBlNnz7ddKxaj2LnQWJjYzVo0CDXVMyNGzcqLi7OcCrgsmbNmunChQvq1auXHnnkETVo0EBBQUGmYwFq3769Lly4oEGDBik2NlZ169bVLbfcYjoWIH9/f5WWlqpdu3aaNWuWgoKCVFxcbDoWIG9vb9144406evSoDh8+bDqOx2C7Aw+zf/9+bd++XU6nU507d9bNN99sOhJwhW3btqmgoEB33nmnfH19TccBXI4fP67CwkIWnoJbyM3NVWBgoC5duqSFCxeqoKBAQ4YMUcuWLU1Hg4dbsWKFFixYoLy8PLVp00Z79+7VrbfeqiVLlpiOVqtR7DxAYWGh6tWrp3PnzlV6PiAgoIYTAf/xU/flv3F/wpRDhw4pJCRE+/fvr/R8WFhYDScCAGvo16+fli9frgceeEBr1qzR119/rfnz5+v11183Ha1WYyqmBxg3bpzeeOMNxcbGVnhw1el0ymazKTMz02A6eLp/35dOp1MnTpxQgwYNJEkXLlzQ7373O23atMlwQniqhQsXatq0aXr11VevOGez2VixFcbEx8drzpw56tevX6Xn09LSajgRUJGvr6/8/PwkXX5uPjQ0lCmZNYAROwBuYfLkyerZs6e6d+8uSdqyZYuys7P1wgsvGE4GAO7l+++/V1BQkHJzcys937x58xpOBFQ0fPhwzZo1S2+//ba2b9+ugIAAFRcX66233jIdrVaj2HmYvLw85ebmyuFwuI6xATTcQWxs7BUbl1Z2DDBhx44dV/zsZB87APh52dnZKigoUHR0NM/NX2VMxfQgr732mjIyMhQSElJhqwOKHdxBo0aNNG/ePPXv3182m01r165Vo0aNTMcCNH78eB07dkxt27Z1/ey02WwUOxi3YcMGJSYmKj8/X06n0/WIBRuUw7ScnBx17dpVkhQZGSlJWrt2rfr3728yVq3HiJ0Hufvuu5WWlsZfS+CWzp07p6SkJH3++eeSpIiICI0cOZLFU2DcPffco/fff5/NdeF2evfurZSUFIWEhJiOAlQwePBghYWFafz48SouLtaLL74op9OpefPmmY5WqzFi50FatGihsrIyih3cUkBAgCZNmmQ6BnCF0NBQnTp1in0V4XYCAwMpdXBL//znP/Xmm28qJiZG5eXleuaZZ5jlUAModh5g2rRpstls8vf3V0xMjCIjIyuUO36ZhknTp0/XxIkTNXz48ErPp6Sk1HAioKKzZ8+qb9++uuWWW+Tj4+M6zr0J09q3b68xY8aoV69eFV7X+/TpYzAVcHmrrQMHDqhZs2Y6deqUa7owMx+uLoqdB2jfvr0kKSQkRF26dJGXl5e8vLxcy9ACJg0YMECS9OijjxpOAlRu1KhRpiMAlSoqKpK/v78+/vjjCscpdjBt0KBBevTRR/XAAw+ouLhYs2bN0pAhQ/TOO++Yjlar8YydBygrK9Ps2bO1cuVKBQcHu/YLi42N1dixYyv8BRowweFwaMKECUpMTDQdBQAsweFwaMmSJXr44YdNRwGucOzYMbVo0aLCsezsbNdCKrg6vEwHwNX32muv6cKFC8rMzNTq1auVmpqqzMxMFRYWatasWabjAfL29tbZs2dVWlpqOgpwhV27dikuLk7h4eFq37692rVrp06dOpmOBQ/n7e2tzMxM0zGASrVo0ULp6emaP3++JOnEiROqX7++4VS1HyN2HqBPnz764IMPrpjX7HA4dM8992jDhg2GkgH/MXnyZO3fv189evRQ3bp1XccfeeQRg6mAy/spzp49W/Hx8Vq5cqVSU1P17bffauzYsaajwcPNnj1bBQUFuvfee+Xv7+86HhYWZjAVIE2dOlWXLl3SZ599poyMDJ07d06PPfaYVq5caTparcYzdh7AZrNV+rCqt7c3D7HCbQQFBSkoKEhOp1NFRUWm4wAVtGzZUg6HQ97e3oqLi9PgwYNNRwJc+9XNmTPHdcxms2nx4sWmIgGSpJ07d2r16tWulTADAgJUVlZmOFXtR7HzACEhIUpNTb1imdk1a9boxhtvNJQKqGjkyJGSLq+kZbPZdM011xhOBFzm7++v0tJStWvXTrNmzVJQUJCKi4tNxwK0ZMkS0xGAStntdpWXl7sGEM6ePSsvL54Au9qYiukB8vLyNHLkSPn5+SksLEw2m0179+7VxYsXlZycrKZNm5qOCOjgwYN6/vnndf78eUlSo0aNNHPmTIWGhhpOBk+Xm5ura6+9VmVlZVq4cKEKCgo0ZMgQtWzZ0nQ0eLiCggIlJSXps88+kyR16dJFI0aM4FkmGHPp0iXZ7Xalpqbqww8/1L59+xQXF6eMjAyNHDlSffv2NR2xVqPYeZDs7Gx98803cjqdCg0NZWUiuJXBgwdrzJgx6tatmyQpJydHs2fP1rvvvms4GTzdhx9+qO7du1fYJwxwB6NGjVJoaKjuu+8+SZdn4hw4cEBJSUmGk8FT3XfffVq9erUk6euvv9Ynn3wip9Op22+/XW3atDGcrvZjKqYHiYyMpMzBbRUXF7tKnSR17dqV6W5wC5s2bdKMGTMUERGhvn376o477pDdzssnzDt69Kjmzp3renvkyJGuvUEBE348XhQaGsqsmxrGKxMAt9CiRQslJye7filZu3atrrvuOsOpACkhIUFlZWXKyspSWlqaXn75Zd1+++2aPn266WjwcH5+fvr8888VEREhSdq+fbv8/PwMp4InO3PmjBYsWPCT51np+uqi2AFwCzNmzNDcuXM1atQoOZ1ORUREKCEhwXQsQJLk4+Oju+66SzabTSUlJcrMzKTYwbiXX35Zzz//vAoLCyVJDRo00Kuvvmo4FTxZeXk5K1sbxDN2AABUISsrS++//75ycnLUpUsX3XvvvYqKimI6JoxZtGiRhg0bpu3bt+u2225zFbt69eoZTgZP9+Nn7FDzeFUCYNTw4cOrPJ+SklJDSYDKrV69Wn379tXUqVNZQAVuYdWqVRo2bJheeeUVrV69mkIHt8F4kVmM2AEwatu2bVWe79KlSw0lAQBrGDt2rHbt2qUzZ86oRYsWV5xPS0szkAqQzp07p4CAANMxPBbFDoBR3333nYKDg03HAK4QHh7u2lz3x5xOp2w2m3bs2GEgFXDZqVOn9Nhjj2n+/PlXnGvevLmBRABMo9gBMOrH8/FHjRpVYeluAAAA/DI8YwfAqB//benYsWMGkwCVmzlzpuLi4tS6dWvTUQBJUnx8vObMmaN+/fpVep6pmIBnotgBMOrHU90qm/YGmNaqVStNmjRJDodDsbGx+uMf/6j69eubjgUPNnHiREksLgWgIqZiAjCqXbt28vf3l9PpVElJiWtzXZ5jgrs5fPiwVq1apfT0dHXq1EmDBg1St27dTMcCAEASxQ4AgJ/lcDi0efNmrVq1SidPntQf/vAH7dixQ/7+/po9e7bpePAwLOwDoDJMxQQAoAoJCQnavHmzunXrpuHDh+uWW25xnbv77rsNJoOn2rlzp+kIANwQI3YAAFRhxYoV6tu3r/z9/a84V1BQwPN2MC4/P18lJSWut9lCBvBMjNgBAFCFG2+80bV665o1a/TFF1/ooYceUvPmzSl1MCozM1MzZ87U999/r8aNG+u7775TSEiI0tPTTUcDYICX6QAAALizKVOmyN/fXwcOHNCbb76p4OBgTZgwwXQsQHPmzNF7772nG264QZs2bdLChQvVqVMn07EAGEKxAwCgCna7XTabTRs3btRDDz2kYcOGqaioyHQsQHa7XY0aNVJ5ebnKy8vVrVs3ffnll6ZjATCEqZgAAFThmmuu0RtvvKG0tDQtXbpUDodDly5dMh0LUIMGDVRUVKTOnTvrueeeU+PGjWW386sd4KlYPAUAgCqcOnVK69atU4cOHRQREaHvvvtO27ZtU0xMjOlo8FDffvutTp8+rXbt2snPz0/l5eVKS0tTbm6uoqOj1b59e9MRARhAsQMAALCQp556Ss8++6zatm1b4fjevXuVnJyslJQUQ8kAmMR4PQAAVdiwYYMSExOVn58vp9PJJtAwLjc394pSJ0kdOnRQbm6ugUQA3AHFDgCAKrz22mtKSUlRSEiI6SiAJFXYs+6/Xbx4sQaTAHAnrIoJAEAVAgMDKXVwKx06dNCyZcuuOL58+XKFhYUZSATAHfCMHQAAVXjllVd0+vRp9erVS76+vq7jffr0MZgKnuz06dMaOXKkfHx8XEVu3759KisrU1JSkpo0aWI4IQATKHYAAFThL3/5S6XHExISajgJUNGnn36qr7/+WpLUunVrRUZGGk4EwCSKHQAAAABYHIunAABQhTNnzmjZsmXKzc2tsDE5I3YAAHdCsQMAoArPPPOMbrvtNkVGRsrb29t0HAAAKkWxAwCgCj/88IPGjx9vOgYAAFViuwMAAKoQHR2tLVu2mI4BAECVWDwFAIAqhIeH64cffpCPj498fHzkdDpls9m0Y8cO09EAAHCh2AEAAACAxTEVEwCAKjidTq1Zs0bJycmSpBMnTmjPnj2GUwEAUBHFDgCAKkyZMkW7du3SunXrJEl169bVyy+/bDgVAAAVUewAAKjCnj179NJLL6lOnTqSpIYNG6qsrMxwKgAAKqLYAQBQBbvdLofDIZvNJunyhuVeXrx8AgDcC/vYAQBQhaFDh2rEiBHKz8/X7NmztX79eo0ZM8Z0LAAAKmBVTAAAfsahQ4f06aefyul0KjIyUiEhIaYjAQBQAcUOAIBKlJSU6J133tHRo0fVpk0bDRw4UHY7E10AAO6JYgcAQCXGjBkju92uiIgIZWVlqXnz5po4caLpWAAAVIo/PQIAUIlDhw4pLS1NkjRw4EANGjTIcCIAAH4ay3oBAFCJH0+7ZAomAMDdMRUTAIBKtGvXTv7+/pIkp9OpkpIS+fn5yel0ymazaceOHYYTAgDwHxQ7AAAAALA4pmICAAAAgMVR7AAAAADA4ih2AIBaZ9WqVYqNjVV4eLg6d+6smJgYJSQkuM7n5+dr7ty5On78+P987dLSUs2dO1dffvlldUYGAOA3odgBAGqVN954Q5MmTdIdd9yhpKQkzZw5Uz179tSmTZtc75Ofn6+kpCTl5ub+z9cvKytTUlISxQ4A4FZYvxkAUKssXbpUDzzwgMaOHes61qNHD40cOdJgKgAAri5WxQQA1Cq33nqrnnjiCY0YMaLS88ePH1fPnj2vOP7VV1+puLhYiYmJ+vjjj3Xy5EkFBgaqe/fuGjdunOrVqydJuummm6742MzMTF133XUqKSnRnDlzlJ6ervz8fLVq1Urjxo1T9+7dK7xvcnKyDh8+LB8fH91www0aP368unTpUk3fAQCAJ2LEDgBQq9x8881aunSpgoODFR0drUaNGlU4HxQUpMTERD333HOaPHmywsLCXOcuXrwoh8OhZ599Vo0bN9aJEyeUkpKi+Ph4vfXWW5KkRYsWadiwYXr66acVHR3tuqYkjR49Wnv27NGoUaN0/fXXKyMjQ08//bRWrlypdu3a6ejRo4qPj9fQoUM1fvx4lZaWat++fTp//nzNfHMAALUWI3YAgFrlwIEDGjFihI4fPy6bzaaQkBD16dNHjz32mGvU7eDBg+rXr58WL16srl27/uS1Ll26pN27d2vIkCHavHmzgoODVVRUpE6dOikhIUGxsbGu983OztbDDz+sJUuWVBh9e/DBBxUYGKi//e1vWr9+vV566SXl5ORcvW8AAMAjsXgKAKBWadu2rTIyMjR//nwNGTJETqdT8+bNU1xcnIqKin7241NTUxUTE6Pw8HCFhYVpyJAhkqQjR45U+XGffPKJmjRpok6dOunSpUuu/yIjI7Vv3z5JUps2bVRQUKAJEyZo69atKi4u/s1fLwAAElMxAQC1kK+vr3r06KEePXpIkpYvX65JkyZpxYoVGjZs2E9+3IcffqgJEyboT3/6k5599lkFBATo1KlTGjFihEpKSqr8nGfPntWpU6cqTO38N29vb0lSq1atNG/ePP3973/Xk08+Kbvdrt69e2vixIlq3Ljxb/iKAQCejmIHAKj1Bg0apMTERB0+fLjK91u/fr06duyoKVOmuI5t27btF32Ohg0bqmnTpkpOTq7y/aKjoxUdHa2CggJ99NFHmjFjhqZNm6bZs2f/os8DAEBlKHYAgFolPz9fgYGBFY6dOXNGBQUFuvbaayVJPj4+knTFKNzFixfl6+tb4VhaWlqFt3/qYyMjI7VgwQLVrVtXISEhP5uzfv366tevnz777DPt3LnzF3xlAAD8NIodAKBW6devn3r27KmoqCgFBgYqNzdXb7/9tvz8/BQTEyNJCg4Olp+fn1JTU1W/fn3Z7XZ16NBBt99+u6ZOnar58+erY8eO2rJli7Kzsytc39fXV9ddd50yMjIUGhqqOnXq6KabblJUVJTuuOMOPfroo3riiSfUunVrFRYW6sCBAyopKdG4ceP07rvvateuXbrzzjsVFBSkI0eOaP369RowYICJbxUAoBZhVUwAQK3yj3/8Q5mZmTp48KDOnz+vJk2aKDw8XM8880yFkbS1a9cqOTlZubm5Kisr01dffSWHw6HExESlpqaqpKREUVFRevzxx3X//fcrJSVFv//97yVJW7du1cyZM3XkyBGVlpa69rErLS1VSkqK0tLSdOLECTVs2FBt27bV0KFDFR0drZ07d2r+/Pn64osvXNnuuecexcfHXzFSCADA/4JiBwAAAAAWx3YHAAAAAGBxFDsAAAAAsDiKHQAAAABYHMUOAAAAACyOYgcAAAAAFkexAwAAAACLo9gBAAAAgMVR7AAAAADA4ih2AAAAAGBx/w8KbZb8+Iql5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc23359acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a clustered bar plot to show the firearm numbers by categories.\n",
    "(high_firearms/1e06).plot(kind=\"bar\", figsize=(15, 6))\n",
    "plt.title(\"Top 6 States with highest Firearm checks by categories.\", fontsize=15)\n",
    "plt.xlabel(\"States\", fontsize=15)\n",
    "plt.ylabel(\"Amount (millions)\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surely, there ARE some inconsistencies, especially since the sum of all three firearm categories do not seem to add up to the main \"totals\" column. Let's just file that under further investigations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc231671d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(low_firearms/1e03).plot(kind=\"bar\", figsize=(15, 6))\n",
    "plt.title(\"Top 6 States with highest Firearm checks by categories.\", fontsize=15)\n",
    "plt.xlabel(\"States\", fontsize=15)\n",
    "plt.ylabel(\"Amount (thousands)\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there relationships between firearm categories and total firearms?(Not part of research questions, but...), Let's see;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>handgun</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777428</td>\n",
       "      <td>0.833962</td>\n",
       "      <td>0.716187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_gun</th>\n",
       "      <td>0.777428</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.620138</td>\n",
       "      <td>0.720044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>0.833962</td>\n",
       "      <td>0.620138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.567329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totals</th>\n",
       "      <td>0.716187</td>\n",
       "      <td>0.720044</td>\n",
       "      <td>0.567329</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           handgun  long_gun     other    totals\n",
       "handgun   1.000000  0.777428  0.833962  0.716187\n",
       "long_gun  0.777428  1.000000  0.620138  0.720044\n",
       "other     0.833962  0.620138  1.000000  0.567329\n",
       "totals    0.716187  0.720044  0.567329  1.000000"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_firearms[firearm_cat].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems as though all weapon firearm category types have a positive relationship with total checks. In an unrelated check, unsuprisingly, hand guns and long guns have strong positive correlations with total checks. Let's do a regression(lm plot), just to be sure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc232d202b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(state_firearms[firearm_cat].corr(), cmap=\"coolwarm\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc230b89898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=state_firearms, x=\"handgun\", y=\"long_gun\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, we can infer that a positive relationship exists between handguns and longguns. However, that wasn't our true goal so..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc232f4fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=state_firearms, x=\"handgun\", y=\"totals\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc23119b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=state_firearms, x=\"long_gun\", y=\"totals\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above, being just a formality confirmed the suspicions, there is a positive relationship between the weapon categories and total checks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "The dataset contained details about background checks carried out in the United States of America(USA) by the Federal Bureau of Intelligence(FBI) within a specified time period. It showed among other things the distribution of different categories of firearms by states and also the different timelines associated with them. Thus, insigghts were drawn from the dataset, which though fallible, were fascinating. It is my view that a more in depth approach achieved via supporting datasets will help in understanding and making inferences from this dataset.\n",
    "## Submitting your Project \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}