Investment_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='Green'>Problem Statement</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Spark Funds** is an asset management company. Spark Funds wants to make investments in a few companies. The CEO of Spark Funds wants to understand the global trends in investments so that she can take the investment decisions effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='Green'>Business and Data Understanding:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spark Funds has **two minor constraints** for investments:\n",
    "\n",
    "1. It wants to invest between **5 to 15 million USD** per round of investment\n",
    "2. It wants to invest only in **English-speaking countries** because of the ease of communication with the companies it would invest in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='Green'>Business Objective:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(i) Identify *most suitable investment* type for making investments.<br>\n",
    "(ii) Best *English speaking countries* to invest in.<br>\n",
    "(iii) Identify *best sectors* within these countries and the most suitable funding type FT."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='Royalblue4'>Checkpoint 1: Data Cleaning</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Supress Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "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>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>homepage_url</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>founded_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>#fame</td>\n",
       "      <td>http://livfame.com</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>16</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>http://www.qounter.com</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>DE</td>\n",
       "      <td>DE - Other</td>\n",
       "      <td>Delaware City</td>\n",
       "      <td>04-09-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>(the) one of them,inc.</td>\n",
       "      <td>http://oneofthem.jp</td>\n",
       "      <td>Apps|Games|Mobile</td>\n",
       "      <td>operating</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",
       "      <td>3</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>http://www.0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>22</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>01-01-2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/004-technologies</td>\n",
       "      <td>004 technologies</td>\n",
       "      <td>http://004gmbh.de/en/004-interact</td>\n",
       "      <td>Software</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>IL</td>\n",
       "      <td>Springfield, Illinois</td>\n",
       "      <td>Champaign</td>\n",
       "      <td>01-01-2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             permalink                    name  \\\n",
       "0                  /organization/-fame                   #fame   \n",
       "1               /organization/-qounter                :qounter   \n",
       "2  /organization/-the-one-of-them-inc-  (the) one of them,inc.   \n",
       "3                /organization/0-6-com                 0-6.com   \n",
       "4       /organization/004-technologies        004 technologies   \n",
       "\n",
       "                        homepage_url  \\\n",
       "0                 http://livfame.com   \n",
       "1             http://www.qounter.com   \n",
       "2                http://oneofthem.jp   \n",
       "3                 http://www.0-6.com   \n",
       "4  http://004gmbh.de/en/004-interact   \n",
       "\n",
       "                                       category_list     status country_code  \\\n",
       "0                                              Media  operating          IND   \n",
       "1  Application Platforms|Real Time|Social Network...  operating          USA   \n",
       "2                                  Apps|Games|Mobile  operating          NaN   \n",
       "3                                        Curated Web  operating          CHN   \n",
       "4                                           Software  operating          USA   \n",
       "\n",
       "  state_code                 region           city  founded_at  \n",
       "0         16                 Mumbai         Mumbai         NaN  \n",
       "1         DE             DE - Other  Delaware City  04-09-2014  \n",
       "2        NaN                    NaN            NaN         NaN  \n",
       "3         22                Beijing        Beijing  01-01-2007  \n",
       "4         IL  Springfield, Illinois      Champaign  01-01-2010  "
      ]
     },
     "execution_count": 1056,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import python packages namely numpy and pandas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Loading 'companies.txt' into dataframe - companies\n",
    "#file path variable for loading 'companies.txt'\n",
    "file_path_companies = r\"C:\\Users\\Chaitanya\\Desktop\\UpGrad\\2. Statistics Essentials\\4. Assignment\\companies.txt\"\n",
    "\n",
    "# Using encoding \"ISO-8859-1\" along with the correct separator to read the file 'companies.txt'\n",
    "companies = pd.read_csv(file_path_companies, sep=\"\\t\", encoding = 'ISO-8859-1')\n",
    "\n",
    "#Making sure that permalink and name columns are proper encoded and decoded into ascii and all lowercase()\n",
    "companies['permalink'] = companies['permalink'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "companies['name'] = companies['name'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "\n",
    "#Display head(default 5 observations)\n",
    "companies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 66368 entries, 0 to 66367\n",
      "Data columns (total 10 columns):\n",
      "permalink        66368 non-null object\n",
      "name             66367 non-null object\n",
      "homepage_url     61310 non-null object\n",
      "category_list    63220 non-null object\n",
      "status           66368 non-null object\n",
      "country_code     59410 non-null object\n",
      "state_code       57821 non-null object\n",
      "region           58338 non-null object\n",
      "city             58340 non-null object\n",
      "founded_at       51147 non-null object\n",
      "dtypes: object(10)\n",
      "memory usage: 5.1+ MB\n"
     ]
    }
   ],
   "source": [
    "companies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66368, 10)"
      ]
     },
     "execution_count": 1058,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 1059,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for any null values in 'permalink' column within the companies file\n",
    "companies['permalink'].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique companies present in companies dataframe: 66368\n"
     ]
    }
   ],
   "source": [
    "#permalink is the Unique ID of company in companies frame. permalink doesn't have any null values. \n",
    "#Determining the total unique companies present in companies.\n",
    "\n",
    "uniq_compID_companies_val = companies['permalink'].nunique()\n",
    "print(\"Number of unique companies present in companies dataframe:\",uniq_compID_companies_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In the companies dataframe, which column can be used as the unique key for each company? Write the name of the column.**<br>\n",
    "Solution: **permalink** can be used as the unique key for each company as it represent the unique ID of company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>funding_round_code</th>\n",
       "      <th>funded_at</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>14-10-2014</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-03-2014</td>\n",
       "      <td>700000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>/funding-round/650b8f704416801069bb178a1418776b</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>30-01-2014</td>\n",
       "      <td>3406878.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>19-03-2008</td>\n",
       "      <td>2000000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink  \\\n",
       "0                  /organization/-fame   \n",
       "1               /organization/-qounter   \n",
       "2               /organization/-qounter   \n",
       "3  /organization/-the-one-of-them-inc-   \n",
       "4                /organization/0-6-com   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "2  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "3  /funding-round/650b8f704416801069bb178a1418776b            venture   \n",
       "4  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "\n",
       "  funding_round_code   funded_at  raised_amount_usd  \n",
       "0                  B  05-01-2015       10000000.000  \n",
       "1                  A  14-10-2014                nan  \n",
       "2                NaN  01-03-2014         700000.000  \n",
       "3                  B  30-01-2014        3406878.000  \n",
       "4                  A  19-03-2008        2000000.000  "
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading data from 'rounds2 file'\n",
    "##file path variable for 'rounds2.csv'\n",
    "file_path_rounds2 = r\"C:\\Users\\Chaitanya\\Desktop\\UpGrad\\2. Statistics Essentials\\4. Assignment\\rounds2.csv\"\n",
    "# Using encoding \"ISO-8859-1\" to read the file 'rounds2.csv'\n",
    "rounds2 = pd.read_csv(file_path_rounds2, encoding = 'ISO-8859-1')\n",
    "\n",
    "#Making sure that company_permalink column is proper encoded and decoded to ascii and all lowercase()\n",
    "rounds2['company_permalink']=rounds2['company_permalink'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "\n",
    "#Display head(default 5 observations)\n",
    "rounds2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1062,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114949, 6)"
      ]
     },
     "execution_count": 1062,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 114949 entries, 0 to 114948\n",
      "Data columns (total 6 columns):\n",
      "company_permalink          114949 non-null object\n",
      "funding_round_permalink    114949 non-null object\n",
      "funding_round_type         114949 non-null object\n",
      "funding_round_code         31140 non-null object\n",
      "funded_at                  114949 non-null object\n",
      "raised_amount_usd          94959 non-null float64\n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "rounds2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1064,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 1064,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null values in'company_permalink' column of rounds2 dataframe (True: Null present, False: Null absent)\n",
    "rounds2['company_permalink'].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique companies present in rounds2 dataframe: 66368\n"
     ]
    }
   ],
   "source": [
    "#company_permalink is the Unique ID of company in the rounds2 frame. The company_permalink doesn't have any null values.\n",
    "#Determining the number of unique companies within rounds2 frame\n",
    "uniq_compID_rounds2_val = rounds2['company_permalink'].nunique()\n",
    "print('Number of unique companies present in rounds2 dataframe:',uniq_compID_rounds2_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set of companies in the rounds2 file which are not present in companies dataframe:\n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "#Finding all the companies that are in the rounds2 file which are not present in companies\n",
    "\n",
    "#Creating dataframes for unique company IDs in companies and rounds2 dataframes\n",
    "companies_ID_df = pd.DataFrame(companies['permalink'].unique())\n",
    "rounds2_ID_df = pd.DataFrame(rounds2['company_permalink'].unique())\n",
    "\n",
    "#Converting into sets for unique values\n",
    "set_compID_companies = set(companies_ID_df)\n",
    "set_compID_rounds2 = set(rounds2_ID_df)\n",
    "\n",
    "#Since the data types of the unique IDs of companies in both the dataframes is same, we can now compare them\n",
    "print('Set of companies in the rounds2 file which are not present in companies dataframe:')\n",
    "print(set_compID_rounds2.difference(set_compID_rounds2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Are there any companies in the rounds2 file which are not present in companies? Answer yes or no: Y/N**<br>\n",
    "Solution: **N**<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>funding_round_code</th>\n",
       "      <th>funded_at</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>homepage_url</th>\n",
       "      <th>category_list</th>\n",
       "      <th>status</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>founded_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>05-01-2015</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>#fame</td>\n",
       "      <td>http://livfame.com</td>\n",
       "      <td>Media</td>\n",
       "      <td>operating</td>\n",
       "      <td>IND</td>\n",
       "      <td>16</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>14-10-2014</td>\n",
       "      <td>nan</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>http://www.qounter.com</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>DE</td>\n",
       "      <td>DE - Other</td>\n",
       "      <td>Delaware City</td>\n",
       "      <td>04-09-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-03-2014</td>\n",
       "      <td>700000.000</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>http://www.qounter.com</td>\n",
       "      <td>Application Platforms|Real Time|Social Network...</td>\n",
       "      <td>operating</td>\n",
       "      <td>USA</td>\n",
       "      <td>DE</td>\n",
       "      <td>DE - Other</td>\n",
       "      <td>Delaware City</td>\n",
       "      <td>04-09-2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>/funding-round/650b8f704416801069bb178a1418776b</td>\n",
       "      <td>venture</td>\n",
       "      <td>B</td>\n",
       "      <td>30-01-2014</td>\n",
       "      <td>3406878.000</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>(the) one of them,inc.</td>\n",
       "      <td>http://oneofthem.jp</td>\n",
       "      <td>Apps|Games|Mobile</td>\n",
       "      <td>operating</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",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>A</td>\n",
       "      <td>19-03-2008</td>\n",
       "      <td>2000000.000</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>http://www.0-6.com</td>\n",
       "      <td>Curated Web</td>\n",
       "      <td>operating</td>\n",
       "      <td>CHN</td>\n",
       "      <td>22</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>01-01-2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink  \\\n",
       "0                  /organization/-fame   \n",
       "1               /organization/-qounter   \n",
       "2               /organization/-qounter   \n",
       "3  /organization/-the-one-of-them-inc-   \n",
       "4                /organization/0-6-com   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "2  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "3  /funding-round/650b8f704416801069bb178a1418776b            venture   \n",
       "4  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "\n",
       "  funding_round_code   funded_at  raised_amount_usd  \\\n",
       "0                  B  05-01-2015       10000000.000   \n",
       "1                  A  14-10-2014                nan   \n",
       "2                NaN  01-03-2014         700000.000   \n",
       "3                  B  30-01-2014        3406878.000   \n",
       "4                  A  19-03-2008        2000000.000   \n",
       "\n",
       "                             permalink                    name  \\\n",
       "0                  /organization/-fame                   #fame   \n",
       "1               /organization/-qounter                :qounter   \n",
       "2               /organization/-qounter                :qounter   \n",
       "3  /organization/-the-one-of-them-inc-  (the) one of them,inc.   \n",
       "4                /organization/0-6-com                 0-6.com   \n",
       "\n",
       "             homepage_url                                      category_list  \\\n",
       "0      http://livfame.com                                              Media   \n",
       "1  http://www.qounter.com  Application Platforms|Real Time|Social Network...   \n",
       "2  http://www.qounter.com  Application Platforms|Real Time|Social Network...   \n",
       "3     http://oneofthem.jp                                  Apps|Games|Mobile   \n",
       "4      http://www.0-6.com                                        Curated Web   \n",
       "\n",
       "      status country_code state_code      region           city  founded_at  \n",
       "0  operating          IND         16      Mumbai         Mumbai         NaN  \n",
       "1  operating          USA         DE  DE - Other  Delaware City  04-09-2014  \n",
       "2  operating          USA         DE  DE - Other  Delaware City  04-09-2014  \n",
       "3  operating          NaN        NaN         NaN            NaN         NaN  \n",
       "4  operating          CHN         22     Beijing        Beijing  01-01-2007  "
      ]
     },
     "execution_count": 1067,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating the 'master_frame'\n",
    "#Merging two dataframes such that the variable columns of companies frame are added to the rounds2 frame\n",
    "\n",
    "master_frame = pd.merge(rounds2, companies, left_on= 'company_permalink', right_on= 'permalink', how= 'inner')\n",
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 114949 entries, 0 to 114948\n",
      "Data columns (total 16 columns):\n",
      "company_permalink          114949 non-null object\n",
      "funding_round_permalink    114949 non-null object\n",
      "funding_round_type         114949 non-null object\n",
      "funding_round_code         31140 non-null object\n",
      "funded_at                  114949 non-null object\n",
      "raised_amount_usd          94959 non-null float64\n",
      "permalink                  114949 non-null object\n",
      "name                       114948 non-null object\n",
      "homepage_url               108815 non-null object\n",
      "category_list              111539 non-null object\n",
      "status                     114949 non-null object\n",
      "country_code               106271 non-null object\n",
      "state_code                 104003 non-null object\n",
      "region                     104782 non-null object\n",
      "city                       104785 non-null object\n",
      "founded_at                 94428 non-null object\n",
      "dtypes: float64(1), object(15)\n",
      "memory usage: 14.9+ MB\n"
     ]
    }
   ],
   "source": [
    "master_frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1069,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114949, 16)"
      ]
     },
     "execution_count": 1069,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1070,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of observations in the master_frame:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "114949"
      ]
     },
     "execution_count": 1070,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of observations in the master_frame\n",
    "print('Total number of observations in the master_frame:')\n",
    "master_frame.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merge the two data frames so that all variables (columns) in the companies frame are added to the rounds2 data frame. Name the merged frame <br>master_frame. How many observations are present in master_frame?<br>**\n",
    "Solution: **114949** observations are present in master_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using str.encode() and str.decode() for remaining columns so as to have correct values in the columns of master_frame\n",
    "\n",
    "master_frame['funding_round_type'] = master_frame['funding_round_type'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "master_frame['funding_round_permalink'] = master_frame['funding_round_permalink'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "master_frame['category_list'] = master_frame['category_list'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "\n",
    "#Retaining uppercase for country_code\n",
    "master_frame['country_code'] = master_frame['country_code'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_permalink              0\n",
       "funding_round_permalink        0\n",
       "funding_round_type             0\n",
       "funding_round_code         83809\n",
       "funded_at                      0\n",
       "raised_amount_usd          19990\n",
       "permalink                      0\n",
       "name                           1\n",
       "homepage_url                6134\n",
       "category_list               3410\n",
       "status                         0\n",
       "country_code                8678\n",
       "state_code                 10946\n",
       "region                     10167\n",
       "city                       10164\n",
       "founded_at                 20521\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1072,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Cleaning:\n",
    "\n",
    "#Sum of all the null values in the columns of master_frame\n",
    "master_frame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1073,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_permalink          0.000\n",
       "funding_round_permalink    0.000\n",
       "funding_round_type         0.000\n",
       "funding_round_code        72.910\n",
       "funded_at                  0.000\n",
       "raised_amount_usd         17.390\n",
       "permalink                  0.000\n",
       "name                       0.000\n",
       "homepage_url               5.340\n",
       "category_list              2.970\n",
       "status                     0.000\n",
       "country_code               7.550\n",
       "state_code                 9.520\n",
       "region                     8.840\n",
       "city                       8.840\n",
       "founded_at                17.850\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1073,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking percentage of null values in columns\n",
    "round((100*(master_frame.isnull().sum())/len(master_frame.index)),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Treating the missing values and dropping unnecessary/redundant columns**\n",
    "1. Deleting 'permalink' as it is same as the unique ID of company i.e. 'company_permalink'. Also, all other columns are merged, in accordance to these values making 'permalink' redundant. Hence, can be dropped.\n",
    "2. The 'funding_round_code' column has around 72.91% of missing values and is not necessary for our analysis. Hence, can be dropped.\n",
    "3. The column 'raised_amount_usd' also has 17.39% of NaN values but we do need this column to drive our business decisions. So, it is retained.\n",
    "4. Dropping some other columns like 'status', 'funded_at', 'homepage_url','state_code','region','city'and 'founded_at' as they aren't useful to drive our business decisions. Note that 'status', state_code','city' and 'region' would have been helpful, in case our goals required decisions based out of these finer details within the countries but since we are explicitly considering the decisions on the basis of countries, these columns can be dropped.\n",
    "5. Retaining the column 'funding_round_permalink' because it is the unique ID of funding round.\n",
    "6. The columns 'country_code' and 'category_list' have 7.55% and 2.97% of NaN values respectively. But we cannot drop them as they are essential for the data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>nan</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>application platforms|real time|social network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.000</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>application platforms|real time|social network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>/funding-round/650b8f704416801069bb178a1418776b</td>\n",
       "      <td>venture</td>\n",
       "      <td>3406878.000</td>\n",
       "      <td>(the) one of them,inc.</td>\n",
       "      <td>apps|games|mobile</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.000</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink  \\\n",
       "0                  /organization/-fame   \n",
       "1               /organization/-qounter   \n",
       "2               /organization/-qounter   \n",
       "3  /organization/-the-one-of-them-inc-   \n",
       "4                /organization/0-6-com   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "2  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "3  /funding-round/650b8f704416801069bb178a1418776b            venture   \n",
       "4  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "\n",
       "   raised_amount_usd                    name  \\\n",
       "0       10000000.000                   #fame   \n",
       "1                nan                :qounter   \n",
       "2         700000.000                :qounter   \n",
       "3        3406878.000  (the) one of them,inc.   \n",
       "4        2000000.000                 0-6.com   \n",
       "\n",
       "                                       category_list country_code  \n",
       "0                                              media          IND  \n",
       "1  application platforms|real time|social network...          USA  \n",
       "2  application platforms|real time|social network...          USA  \n",
       "3                                  apps|games|mobile          NaN  \n",
       "4                                        curated web          CHN  "
      ]
     },
     "execution_count": 1074,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping the columns with large number of missing values and also, the unnecssary ones to drive correct business decisions\n",
    "master_frame = master_frame.drop(['permalink','status', 'funding_round_code',\\\n",
    "                                  'funded_at','homepage_url','state_code','region','city','founded_at'], axis=1)\n",
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 114949 entries, 0 to 114948\n",
      "Data columns (total 7 columns):\n",
      "company_permalink          114949 non-null object\n",
      "funding_round_permalink    114949 non-null object\n",
      "funding_round_type         114949 non-null object\n",
      "raised_amount_usd          94959 non-null float64\n",
      "name                       114948 non-null object\n",
      "category_list              111539 non-null object\n",
      "country_code               106271 non-null object\n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 7.0+ MB\n"
     ]
    }
   ],
   "source": [
    "master_frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>nan</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>application platforms|real time|social network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.000</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>application platforms|real time|social network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/-the-one-of-them-inc-</td>\n",
       "      <td>/funding-round/650b8f704416801069bb178a1418776b</td>\n",
       "      <td>venture</td>\n",
       "      <td>3406878.000</td>\n",
       "      <td>(the) one of them,inc.</td>\n",
       "      <td>apps|games|mobile</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.000</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink  \\\n",
       "0                  /organization/-fame   \n",
       "1               /organization/-qounter   \n",
       "2               /organization/-qounter   \n",
       "3  /organization/-the-one-of-them-inc-   \n",
       "4                /organization/0-6-com   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "2  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "3  /funding-round/650b8f704416801069bb178a1418776b            venture   \n",
       "4  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "\n",
       "   raised_amount_usd                    name  \\\n",
       "0       10000000.000                   #fame   \n",
       "1                nan                :qounter   \n",
       "2         700000.000                :qounter   \n",
       "3        3406878.000  (the) one of them,inc.   \n",
       "4        2000000.000                 0-6.com   \n",
       "\n",
       "                                       category_list country_code  \n",
       "0                                              media          IND  \n",
       "1  application platforms|real time|social network...          USA  \n",
       "2  application platforms|real time|social network...          USA  \n",
       "3                                  apps|games|mobile          NaN  \n",
       "4                                        curated web          CHN  "
      ]
     },
     "execution_count": 1076,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#master_frame with the relevant data that will help us get insights and drive business decisions\n",
    "#Display head(5 observations)\n",
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_permalink          0.000\n",
       "funding_round_permalink    0.000\n",
       "funding_round_type         0.000\n",
       "raised_amount_usd         17.390\n",
       "name                       0.000\n",
       "category_list              2.970\n",
       "country_code               7.550\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1077,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rechecking percentage of null values in master_frame columns\n",
    "round(100*(master_frame.isnull().sum())/len(master_frame.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/22dacff496eb7acb2b901dec1dfe5633</td>\n",
       "      <td>venture</td>\n",
       "      <td>nan</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>application platforms|real time|social network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.000</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>application platforms|real time|social network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.000</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>/organization/004-technologies</td>\n",
       "      <td>/funding-round/1278dd4e6a37fa4b7d7e06c21b3c1830</td>\n",
       "      <td>venture</td>\n",
       "      <td>nan</td>\n",
       "      <td>004 technologies</td>\n",
       "      <td>software</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                company_permalink  \\\n",
       "0             /organization/-fame   \n",
       "1          /organization/-qounter   \n",
       "2          /organization/-qounter   \n",
       "4           /organization/0-6-com   \n",
       "5  /organization/004-technologies   \n",
       "\n",
       "                           funding_round_permalink funding_round_type  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "1  /funding-round/22dacff496eb7acb2b901dec1dfe5633            venture   \n",
       "2  /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "4  /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "5  /funding-round/1278dd4e6a37fa4b7d7e06c21b3c1830            venture   \n",
       "\n",
       "   raised_amount_usd              name  \\\n",
       "0       10000000.000             #fame   \n",
       "1                nan          :qounter   \n",
       "2         700000.000          :qounter   \n",
       "4        2000000.000           0-6.com   \n",
       "5                nan  004 technologies   \n",
       "\n",
       "                                       category_list country_code  \n",
       "0                                              media          IND  \n",
       "1  application platforms|real time|social network...          USA  \n",
       "2  application platforms|real time|social network...          USA  \n",
       "4                                        curated web          CHN  \n",
       "5                                           software          USA  "
      ]
     },
     "execution_count": 1078,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since our analysis does require a specific catergory from the category_list and the country_code.\n",
    "#The rows with NaN values for these columns can be dropped.\n",
    "\n",
    "master_frame = master_frame[~(master_frame['category_list'].isnull() | master_frame['country_code'].isnull())]\n",
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104532, 7)"
      ]
     },
     "execution_count": 1079,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1080,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_permalink          0.000\n",
       "funding_round_permalink    0.000\n",
       "funding_round_type         0.000\n",
       "raised_amount_usd         15.310\n",
       "name                       0.000\n",
       "category_list              0.000\n",
       "country_code               0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1080,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rechecking percentage of null values in master_frame columns\n",
    "round(100*(master_frame.isnull().sum())/len(master_frame.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1081,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.94"
      ]
     },
     "execution_count": 1081,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of total data retained\n",
    "round(100*len(master_frame.index)/114949,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1082,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         88529.000\n",
       "mean       10473849.722\n",
       "std       111811808.001\n",
       "min               0.000\n",
       "25%          370518.000\n",
       "50%         1800000.000\n",
       "75%         7100000.000\n",
       "max     21271935000.000\n",
       "Name: raised_amount_usd, dtype: float64"
      ]
     },
     "execution_count": 1082,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for steps to be taken on NaN values in 'raised_amount_usd' column of the master_frame\n",
    "#stats for 'raised_amount_usd'\n",
    "master_frame.loc[:,'raised_amount_usd'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1083,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot of 'raised_amount_usd' (from master_frame)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(num= 1, figsize=(16,6), dpi= 200)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(y= master_frame['raised_amount_usd']/1000000)\n",
    "plt.title(\"Boxplot of raised_amount_usd(in millions)\")\n",
    "plt.ylabel('raised_amount_usd (in millions)')\n",
    "\n",
    "\n",
    "#Since the values are too large, taking 'log' along the y scale\n",
    "plt.subplot(1,2,2)\n",
    "sns.boxplot(y= master_frame['raised_amount_usd']/1000000)\n",
    "plt.title(\"(Logarithmic) Boxplot of raised_amount_usd (in millions)\")\n",
    "plt.yscale('log')\n",
    "plt.ylabel('logarithmic_raised_amount_usd(in millions)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1084,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median of 'raised_amount_usd' column (without skipping NaNs): nan\n",
      "Median of 'raised_amount_usd' column (skipping NaNs): 1800000.0\n",
      "Median of 'raised_amount_usd' column (in millions usd): 1.8\n"
     ]
    }
   ],
   "source": [
    "#Find median of the column 'raised_amount_usd': (Check for all values and also for list where we skip NaN values)\n",
    "print(\"Median of 'raised_amount_usd' column (without skipping NaNs):\", \\\n",
    "      master_frame.loc[:,\"raised_amount_usd\"].median(skipna=False))\n",
    "\n",
    "#Median of 'raised_amount_usd'\n",
    "print(\"Median of 'raised_amount_usd' column (skipping NaNs):\",\\\n",
    "      master_frame.loc[:,\"raised_amount_usd\"].median(skipna=True))\n",
    "\n",
    "#In millions (usd)\n",
    "print(\"Median of 'raised_amount_usd' column (in millions usd):\",\\\n",
    "      master_frame.loc[:,\"raised_amount_usd\"].median(skipna=True)/1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1085,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of 'raised_amount_usd' column (skipping NaNs): 10473849.72231698\n",
      "Mean of 'raised_amount_usd' column (in millions usd): 10.47384972231698\n"
     ]
    }
   ],
   "source": [
    "#Find mean of the column 'raised_amount_usd':\n",
    "print(\"Mean of 'raised_amount_usd' column (skipping NaNs):\",master_frame.loc[:,\"raised_amount_usd\"].mean())\n",
    "\n",
    "#In millions\n",
    "print(\"Mean of 'raised_amount_usd' column (in millions usd):\",master_frame.loc[:,\"raised_amount_usd\"].mean()/1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1086,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         88529.000\n",
       "mean       10473849.722\n",
       "std       111811808.001\n",
       "min               0.000\n",
       "25%          370518.000\n",
       "50%         1800000.000\n",
       "75%         7100000.000\n",
       "max     21271935000.000\n",
       "Name: raised_amount_usd, dtype: float64"
      ]
     },
     "execution_count": 1086,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.loc[:,'raised_amount_usd'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean does not lie in between the variation from 25% to 75% as we see from the above results. Moreover, there is a large difference between the mean (10.4738 million usd) and the median (1.8 million usd). Thus, imputing this mean/median would make the data quite biased. Hence, dropping the NaNs from 'raised_amount_usd' column of the master_frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/-qounter</td>\n",
       "      <td>/funding-round/b44fbb94153f6cdef13083530bb48030</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.000</td>\n",
       "      <td>:qounter</td>\n",
       "      <td>application platforms|real time|social network...</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.000</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>/organization/01games-technology</td>\n",
       "      <td>/funding-round/7d53696f2b4f607a2f2a8cbb83d01839</td>\n",
       "      <td>undisclosed</td>\n",
       "      <td>41250.000</td>\n",
       "      <td>01games technology</td>\n",
       "      <td>games</td>\n",
       "      <td>HKG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>/funding-round/2b9d3ac293d5cdccbecff5c8cb0f327d</td>\n",
       "      <td>seed</td>\n",
       "      <td>43360.000</td>\n",
       "      <td>ondine biomedical inc.</td>\n",
       "      <td>biotechnology</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>/funding-round/954b9499724b946ad8c396a57a5f3b72</td>\n",
       "      <td>venture</td>\n",
       "      <td>719491.000</td>\n",
       "      <td>ondine biomedical inc.</td>\n",
       "      <td>biotechnology</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/383a9bd2c04f7038bb543ccef5ba3eae</td>\n",
       "      <td>seed</td>\n",
       "      <td>3000000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>venture</td>\n",
       "      <td>20000000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>venture</td>\n",
       "      <td>1700000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>venture</td>\n",
       "      <td>8900000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      company_permalink  \\\n",
       "0                   /organization/-fame   \n",
       "2                /organization/-qounter   \n",
       "4                 /organization/0-6-com   \n",
       "6      /organization/01games-technology   \n",
       "7   /organization/0ndine-biomedical-inc   \n",
       "8   /organization/0ndine-biomedical-inc   \n",
       "9                  /organization/0xdata   \n",
       "10                 /organization/0xdata   \n",
       "11                 /organization/0xdata   \n",
       "12                 /organization/0xdata   \n",
       "\n",
       "                            funding_round_permalink funding_round_type  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638            venture   \n",
       "2   /funding-round/b44fbb94153f6cdef13083530bb48030               seed   \n",
       "4   /funding-round/5727accaeaa57461bd22a9bdd945382d            venture   \n",
       "6   /funding-round/7d53696f2b4f607a2f2a8cbb83d01839        undisclosed   \n",
       "7   /funding-round/2b9d3ac293d5cdccbecff5c8cb0f327d               seed   \n",
       "8   /funding-round/954b9499724b946ad8c396a57a5f3b72            venture   \n",
       "9   /funding-round/383a9bd2c04f7038bb543ccef5ba3eae               seed   \n",
       "10  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44            venture   \n",
       "11  /funding-round/ae2a174c06517c2394aed45006322a7e            venture   \n",
       "12  /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e            venture   \n",
       "\n",
       "    raised_amount_usd                    name  \\\n",
       "0        10000000.000                   #fame   \n",
       "2          700000.000                :qounter   \n",
       "4         2000000.000                 0-6.com   \n",
       "6           41250.000      01games technology   \n",
       "7           43360.000  ondine biomedical inc.   \n",
       "8          719491.000  ondine biomedical inc.   \n",
       "9         3000000.000                  h2o.ai   \n",
       "10       20000000.000                  h2o.ai   \n",
       "11        1700000.000                  h2o.ai   \n",
       "12        8900000.000                  h2o.ai   \n",
       "\n",
       "                                        category_list country_code  \n",
       "0                                               media          IND  \n",
       "2   application platforms|real time|social network...          USA  \n",
       "4                                         curated web          CHN  \n",
       "6                                               games          HKG  \n",
       "7                                       biotechnology          CAN  \n",
       "8                                       biotechnology          CAN  \n",
       "9                                           analytics          USA  \n",
       "10                                          analytics          USA  \n",
       "11                                          analytics          USA  \n",
       "12                                          analytics          USA  "
      ]
     },
     "execution_count": 1087,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping the rows containing NaNs in the 'raised_amount_usd' column of master_frame\n",
    "master_frame = master_frame[~(master_frame['raised_amount_usd'].isnull())]\n",
    "\n",
    "#Display master_frame head(10 observations)\n",
    "master_frame.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1088,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_permalink         0.000\n",
       "funding_round_permalink   0.000\n",
       "funding_round_type        0.000\n",
       "raised_amount_usd         0.000\n",
       "name                      0.000\n",
       "category_list             0.000\n",
       "country_code              0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1088,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rechecking percentage of null values in master_frame columns\n",
    "round(100*(master_frame.isnull().sum())/len(master_frame.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1089,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.02"
      ]
     },
     "execution_count": 1089,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of total data retained\n",
    "round(100*len(master_frame.index)/114949,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1090,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88529, 7)"
      ]
     },
     "execution_count": 1090,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='RoyalBlue4'> Checkpoint 2: Funding Type Analysis</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1091,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "venture                  47809\n",
       "seed                     21095\n",
       "debt_financing            6506\n",
       "angel                     4400\n",
       "grant                     1939\n",
       "private_equity            1820\n",
       "undisclosed               1345\n",
       "convertible_note          1320\n",
       "equity_crowdfunding       1128\n",
       "post_ipo_equity            598\n",
       "product_crowdfunding       330\n",
       "post_ipo_debt              151\n",
       "non_equity_assistance       60\n",
       "secondary_market            28\n",
       "Name: funding_round_type, dtype: int64"
      ]
     },
     "execution_count": 1091,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unique investment types in funding_round_type\n",
    "master_frame['funding_round_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spark Funds wants to choose one of these four investment types for each potential investment they will make. Hence, we carry out funding type analysis for these four funding round types namely venture, seed, angel and private equity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1092,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "venture           47809\n",
       "seed              21095\n",
       "angel              4400\n",
       "private_equity     1820\n",
       "Name: funding_round_type, dtype: int64"
      ]
     },
     "execution_count": 1092,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only these four types of investments\n",
    "master_frame = master_frame[(master_frame['funding_round_type']=='venture') | \\\n",
    "                            (master_frame['funding_round_type']=='seed') \\\n",
    "                            | (master_frame['funding_round_type']=='angel') \\\n",
    "                            | (master_frame['funding_round_type']=='private_equity')]\n",
    "master_frame['funding_round_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1093,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75124, 7)"
      ]
     },
     "execution_count": 1093,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "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>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>seed</td>\n",
       "      <td>43360.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>venture</td>\n",
       "      <td>719491.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  funding_round_type  raised_amount_usd\n",
       "0            venture       10000000.000\n",
       "2               seed         700000.000\n",
       "4            venture        2000000.000\n",
       "7               seed          43360.000\n",
       "8            venture         719491.000"
      ]
     },
     "execution_count": 1094,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#New dataframe 'investments_four_types': global dataframe containing only 4 funding round types and their records\n",
    "#This is the global dataframe and shall be used to draw Plot 1\n",
    "\n",
    "investments_four_types = master_frame[['funding_round_type','raised_amount_usd']]\n",
    "investments_four_types.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1095,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funding_round_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>private_equity</td>\n",
       "      <td>73938486.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>venture</td>\n",
       "      <td>11724222.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>angel</td>\n",
       "      <td>971573.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>seed</td>\n",
       "      <td>747793.682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    raised_amount_usd\n",
       "funding_round_type                   \n",
       "private_equity           73938486.275\n",
       "venture                  11724222.690\n",
       "angel                      971573.891\n",
       "seed                       747793.682"
      ]
     },
     "execution_count": 1095,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating the most representative value of investment amounts for each of these 4 investment types:\n",
    "\n",
    "#Datatype to float using proper formatting options\n",
    "pd.options.display.float_format  = '{:.3f}'.format\n",
    "\n",
    "avg_typewise = investments_four_types.groupby('funding_round_type')\n",
    "\n",
    "#DataFrame 'investments_avg_typeswise' contains the 4 funding round types \n",
    "#and their most representative values of investment amounts.\n",
    "\n",
    "investments_avg_typewise = pd.DataFrame(avg_typewise['raised_amount_usd'].mean().sort_values(ascending=False))\n",
    "investments_avg_typewise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1096,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average funding amount of venture type (in_usd): 11724222.69\n",
      "Average funding amount of venture type (in millions_usd): 11.72422269\n"
     ]
    }
   ],
   "source": [
    "#Average funding amount of venture type\n",
    "venture_val = round(investments_avg_typewise.loc['venture','raised_amount_usd'],3)\n",
    "print('Average funding amount of venture type (in_usd):',venture_val)\n",
    "print('Average funding amount of venture type (in millions_usd):',venture_val/1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average funding amount of angel type (in_usd): 971573.891\n",
      "Average funding amount of angel type (in millions_usd): 0.9715738909999999\n"
     ]
    }
   ],
   "source": [
    "#Average funding amount of angel type\n",
    "angel_val = round(investments_avg_typewise.loc['angel','raised_amount_usd'],3)\n",
    "print('Average funding amount of angel type (in_usd):',angel_val)\n",
    "print('Average funding amount of angel type (in millions_usd):',angel_val/1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1098,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average funding amount of seed type (in_usd): 747793.682\n",
      "Average funding amount of seed type (in millions_usd): 0.7477936820000001\n"
     ]
    }
   ],
   "source": [
    "#Average funding amount of seed type\n",
    "seed_val = round(investments_avg_typewise.loc['seed','raised_amount_usd'],3)\n",
    "print('Average funding amount of seed type (in_usd):',seed_val)\n",
    "print('Average funding amount of seed type (in millions_usd):',seed_val/1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1099,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average funding amount of private equity type (in_usd): 73938486.275\n",
      "Average funding amount of private equity type (in millions_usd): 73.938486275\n"
     ]
    }
   ],
   "source": [
    "#Average funding amount of private equity type\n",
    "private_eq_val = round(investments_avg_typewise.loc['private_equity','raised_amount_usd'],3)\n",
    "print('Average funding amount of private equity type (in_usd):',private_eq_val)\n",
    "print('Average funding amount of private equity type (in millions_usd):',private_eq_val/1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering that Spark Funds wants to invest between 5 to 15 million USD per  investment round, which investment type is the most suitable for them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1100,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funding_round_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>venture</td>\n",
       "      <td>11724222.690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    raised_amount_usd\n",
       "funding_round_type                   \n",
       "venture                  11724222.690"
      ]
     },
     "execution_count": 1100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the most suitable investment type for Sparks Funds\n",
    "#Range: in between 5 and 15 million USD per investment round.\n",
    "#'suitable_investment_type' dataframe contains the average investments in between 5 and 15 million USD per round range\n",
    "suitable_investment_type = investments_avg_typewise.loc[(investments_avg_typewise['raised_amount_usd'] >= 5000000) & \\\n",
    "                                            (investments_avg_typewise['raised_amount_usd'] <= 15000000)]\n",
    "suitable_investment_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Therefore, the most suitable investment type for Sparks Funds is: venture\n"
     ]
    }
   ],
   "source": [
    "#Since, venture type is the only investment type whose average value of investment lies between 5 and 15 million usd\n",
    "print('Therefore, the most suitable investment type for Sparks Funds is:',suitable_investment_type.index[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, considering that Spark Funds wants to invest between 5 to 15 million USD per investment round, the most suitable investment type is **venture**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='Royalblue4'>Checkpoint 3: Country Analysis</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criteria:<br>\n",
    "(i) Spark Funds wants to see the top nine countries which have received the highest total funding (across ALL sectors for the chosen investment type).<br>\n",
    "(ii) For the chosen investment type, make a data frame named top9 with the top nine countries (based on the total investment amount each country has received).\n",
    "\n",
    "Steps:\n",
    "1. Spark Funds wants to invest in countries with the highest amount of funding for the chosen investment type. As per our analysis until now, we deduced that the most suitable investment type for Sparks Funds is **venture** type. As this is a part of its broader strategy, let us further analyze where are the investments occuring in this venture investment type.\n",
    "2. For venture investment type, let's make a dataframe named 'top9' with the top nine countries (based on the total investment amount each country has received).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This point onwards our focus is going to be exclusively on the venture type of investment. Hence, eliminating the other funding_round_types from the table. Retain only venture type of investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retaining only venture funding_type in the master_frame. \n",
    "master_frame = master_frame[master_frame['funding_round_type']=='venture']\n",
    "\n",
    "#Dropping the 'funding_round_type' column since now on all the funding_round_types in the master_frame are 'venture' type\n",
    "master_frame = master_frame.drop('funding_round_type', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/0-6-com</td>\n",
       "      <td>/funding-round/5727accaeaa57461bd22a9bdd945382d</td>\n",
       "      <td>2000000.000</td>\n",
       "      <td>0-6.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>/organization/0ndine-biomedical-inc</td>\n",
       "      <td>/funding-round/954b9499724b946ad8c396a57a5f3b72</td>\n",
       "      <td>719491.000</td>\n",
       "      <td>ondine biomedical inc.</td>\n",
       "      <td>biotechnology</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>20000000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>1700000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      company_permalink  \\\n",
       "0                   /organization/-fame   \n",
       "4                 /organization/0-6-com   \n",
       "8   /organization/0ndine-biomedical-inc   \n",
       "10                 /organization/0xdata   \n",
       "11                 /organization/0xdata   \n",
       "\n",
       "                            funding_round_permalink  raised_amount_usd  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638       10000000.000   \n",
       "4   /funding-round/5727accaeaa57461bd22a9bdd945382d        2000000.000   \n",
       "8   /funding-round/954b9499724b946ad8c396a57a5f3b72         719491.000   \n",
       "10  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44       20000000.000   \n",
       "11  /funding-round/ae2a174c06517c2394aed45006322a7e        1700000.000   \n",
       "\n",
       "                      name  category_list country_code  \n",
       "0                    #fame          media          IND  \n",
       "4                  0-6.com    curated web          CHN  \n",
       "8   ondine biomedical inc.  biotechnology          CAN  \n",
       "10                  h2o.ai      analytics          USA  \n",
       "11                  h2o.ai      analytics          USA  "
      ]
     },
     "execution_count": 1103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1104,
   "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>country_code</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>420068029342.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>39338918773.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GBR</td>\n",
       "      <td>20072813004.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>IND</td>\n",
       "      <td>14261508718.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>CAN</td>\n",
       "      <td>9482217668.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>FRA</td>\n",
       "      <td>7226851352.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ISR</td>\n",
       "      <td>6854350477.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>DEU</td>\n",
       "      <td>6306921981.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>JPN</td>\n",
       "      <td>3167647127.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code  raised_amount_usd\n",
       "0          USA   420068029342.000\n",
       "1          CHN    39338918773.000\n",
       "2          GBR    20072813004.000\n",
       "3          IND    14261508718.000\n",
       "4          CAN     9482217668.000\n",
       "5          FRA     7226851352.000\n",
       "6          ISR     6854350477.000\n",
       "7          DEU     6306921981.000\n",
       "8          JPN     3167647127.000"
      ]
     },
     "execution_count": 1104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame 'top9' contains all the top nine countries (based on the total investment amount each country has received) \n",
    "#(across ALL sectors in the venture type of investment)\n",
    "\n",
    "by_country_code = master_frame.groupby('country_code')\n",
    "top9 = by_country_code['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "top9 = pd.DataFrame(top9.iloc[:9])\n",
    "\n",
    "#Required for plotting graphs later\n",
    "top9.reset_index(inplace=True) \n",
    "top9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country_code', 'raised_amount_usd'], dtype='object')"
      ]
     },
     "execution_count": 1105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for columns after reset index\n",
    "top9.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_code</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>english_official_lang_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>420068029342.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>39338918773.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GBR</td>\n",
       "      <td>20072813004.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>IND</td>\n",
       "      <td>14261508718.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>CAN</td>\n",
       "      <td>9482217668.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>FRA</td>\n",
       "      <td>7226851352.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ISR</td>\n",
       "      <td>6854350477.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>DEU</td>\n",
       "      <td>6306921981.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>JPN</td>\n",
       "      <td>3167647127.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code  raised_amount_usd english_official_lang_flag\n",
       "0          USA   420068029342.000                        Yes\n",
       "1          CHN    39338918773.000                         No\n",
       "2          GBR    20072813004.000                        Yes\n",
       "3          IND    14261508718.000                        Yes\n",
       "4          CAN     9482217668.000                        Yes\n",
       "5          FRA     7226851352.000                         No\n",
       "6          ISR     6854350477.000                         No\n",
       "7          DEU     6306921981.000                         No\n",
       "8          JPN     3167647127.000                         No"
      ]
     },
     "execution_count": 1106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List of all the countries from the top9 dataframe that have English as their official language\n",
    "eng_off_lang_country = [\"USA\",\"GBR\",\"IND\",\"CAN\"]\n",
    "\n",
    "#Flag English speaking countries (shall be used for plotting plot 2)\n",
    "top9['english_official_lang_flag'] = np.where(top9.country_code.isin(eng_off_lang_country),'Yes','No')\n",
    "top9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1107,
   "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>raised_amount_usd</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>USA</td>\n",
       "      <td>420068029342.000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GBR</td>\n",
       "      <td>20072813004.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IND</td>\n",
       "      <td>14261508718.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              raised_amount_usd  rank\n",
       "country_code                         \n",
       "USA            420068029342.000     1\n",
       "GBR             20072813004.000     2\n",
       "IND             14261508718.000     3"
      ]
     },
     "execution_count": 1107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining the top 3 English Speaking Countries (top3_english derived from top9)\n",
    "top3_english = top9[(top9['english_official_lang_flag'])=='Yes']\n",
    "\n",
    "#Dropping the English Official Language Flag column from the dataframe \n",
    "#as all English speaking countries are filtered\n",
    "top3_english = top3_english.drop('english_official_lang_flag', axis=1)\n",
    "\n",
    "#Retaining top 3 observations\n",
    "top3_english = pd.DataFrame(top3_english.loc[:3])\n",
    "top3_english.set_index('country_code',inplace=True)\n",
    "\n",
    "#Assigning rank in rank column\n",
    "top3_english['rank'] = (top3_english['raised_amount_usd'].rank(ascending=False))\n",
    "top3_english['rank'] = pd.to_numeric(top3_english['rank'], downcast='integer')\n",
    "top3_english"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country codes of top 3 English_speaking countries for the most suited funding type (venture) for Spark Funds:\n",
      "USA GBR IND "
     ]
    }
   ],
   "source": [
    "#Retaining the top 3 most investment-friendly English Speaking Countries for the most suited funding type (venture) \n",
    "#for Spark Funds\n",
    "\n",
    "#Print the names of top 3 English-speaking countries\n",
    "print(\"Country codes of top 3 English_speaking countries for the most suited funding type (venture) for Spark Funds:\")\n",
    "for row in top3_english.index: \n",
    "    print(row, end = \" \") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, the top 3 English-speaking countries for the most suitable funding type (venture) for Sparks Funds are **USA (United States), GBR (Great Britain) and IND (India).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>20000000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>1700000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>8900000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>5000000.000</td>\n",
       "      <td>1 mainstream</td>\n",
       "      <td>apps|cable|distribution|software</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             company_permalink  \\\n",
       "0          /organization/-fame   \n",
       "10        /organization/0xdata   \n",
       "11        /organization/0xdata   \n",
       "12        /organization/0xdata   \n",
       "22  /organization/1-mainstream   \n",
       "\n",
       "                            funding_round_permalink  raised_amount_usd  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638       10000000.000   \n",
       "10  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44       20000000.000   \n",
       "11  /funding-round/ae2a174c06517c2394aed45006322a7e        1700000.000   \n",
       "12  /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e        8900000.000   \n",
       "22  /funding-round/b952cbaf401f310927430c97b68162ea        5000000.000   \n",
       "\n",
       "            name                     category_list country_code  \n",
       "0          #fame                             media          IND  \n",
       "10        h2o.ai                         analytics          USA  \n",
       "11        h2o.ai                         analytics          USA  \n",
       "12        h2o.ai                         analytics          USA  \n",
       "22  1 mainstream  apps|cable|distribution|software          USA  "
      ]
     },
     "execution_count": 1109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only these 3 countries in the master_frame for venture type of investment\n",
    "master_frame = master_frame[(master_frame['country_code']==\"USA\") | (master_frame['country_code']==\"GBR\") | \\\n",
    "                            (master_frame['country_code']==\"IND\")]\n",
    "master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38803, 6)"
      ]
     },
     "execution_count": 1110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_frame.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='Royalblue4'> Checkpoint 4: Sector Analysis 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1111,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>20000000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>1700000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>8900000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>5000000.000</td>\n",
       "      <td>1 mainstream</td>\n",
       "      <td>apps</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>/organization/10-minutes-with</td>\n",
       "      <td>/funding-round/0faccbbcc5818dc5326469f13f5a8ac8</td>\n",
       "      <td>4000000.000</td>\n",
       "      <td>10 minutes with</td>\n",
       "      <td>education</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>/organization/1000memories</td>\n",
       "      <td>/funding-round/502bd0e50c27616995e4bdad24605ef8</td>\n",
       "      <td>2520000.000</td>\n",
       "      <td>1000memories</td>\n",
       "      <td>curated web</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>/organization/1000museums-com</td>\n",
       "      <td>/funding-round/13be128d655076a025221d7fddc90d68</td>\n",
       "      <td>265940.000</td>\n",
       "      <td>1000museums.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>/organization/1000museums-com</td>\n",
       "      <td>/funding-round/6aeb32ab3fc05db2b606d7a2466e8c09</td>\n",
       "      <td>2906832.000</td>\n",
       "      <td>1000museums.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>/organization/1000museums-com</td>\n",
       "      <td>/funding-round/89317984885b44f02e4befb9dc2e588c</td>\n",
       "      <td>1500000.000</td>\n",
       "      <td>1000museums.com</td>\n",
       "      <td>curated web</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                company_permalink  \\\n",
       "0             /organization/-fame   \n",
       "10           /organization/0xdata   \n",
       "11           /organization/0xdata   \n",
       "12           /organization/0xdata   \n",
       "22     /organization/1-mainstream   \n",
       "28  /organization/10-minutes-with   \n",
       "34     /organization/1000memories   \n",
       "38  /organization/1000museums-com   \n",
       "39  /organization/1000museums-com   \n",
       "41  /organization/1000museums-com   \n",
       "\n",
       "                            funding_round_permalink  raised_amount_usd  \\\n",
       "0   /funding-round/9a01d05418af9f794eebff7ace91f638       10000000.000   \n",
       "10  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44       20000000.000   \n",
       "11  /funding-round/ae2a174c06517c2394aed45006322a7e        1700000.000   \n",
       "12  /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e        8900000.000   \n",
       "22  /funding-round/b952cbaf401f310927430c97b68162ea        5000000.000   \n",
       "28  /funding-round/0faccbbcc5818dc5326469f13f5a8ac8        4000000.000   \n",
       "34  /funding-round/502bd0e50c27616995e4bdad24605ef8        2520000.000   \n",
       "38  /funding-round/13be128d655076a025221d7fddc90d68         265940.000   \n",
       "39  /funding-round/6aeb32ab3fc05db2b606d7a2466e8c09        2906832.000   \n",
       "41  /funding-round/89317984885b44f02e4befb9dc2e588c        1500000.000   \n",
       "\n",
       "               name category_list country_code  \n",
       "0             #fame         media          IND  \n",
       "10           h2o.ai     analytics          USA  \n",
       "11           h2o.ai     analytics          USA  \n",
       "12           h2o.ai     analytics          USA  \n",
       "22     1 mainstream          apps          USA  \n",
       "28  10 minutes with     education          GBR  \n",
       "34     1000memories   curated web          USA  \n",
       "38  1000museums.com   curated web          USA  \n",
       "39  1000museums.com   curated web          USA  \n",
       "41  1000museums.com   curated web          USA  "
      ]
     },
     "execution_count": 1111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the sector analysis, we have come up with the business rule that the first string \n",
    "#before the vertical bar ('|') will be considered the primary sector\n",
    "\n",
    "#1. Extracting the primary sector from the 'category_list' of the master_frame\n",
    "#Before that, let's create a shallow copy of the master_frame called 'venture_master_frame to avoid 'SettingsWithCopyWarning'\n",
    "#on subsequent iterations\n",
    "\n",
    "#venture_master_frame is derived from the master_frame for venture type of investment for top 3 English Speaking countries\n",
    "venture_master_frame = master_frame.copy()\n",
    "\n",
    "#Extracting primary sector\n",
    "venture_master_frame['category_list'] = venture_master_frame['category_list'].apply(lambda x: x.split('|')[0])\n",
    "\n",
    "#Display master_frame(10 obervations)\n",
    "venture_master_frame.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38803, 6)"
      ]
     },
     "execution_count": 1112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venture_master_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_list</th>\n",
       "      <th>Automotive &amp; Sports</th>\n",
       "      <th>Blanks</th>\n",
       "      <th>Cleantech / Semiconductors</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Health</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>News, Search and Messaging</th>\n",
       "      <th>Others</th>\n",
       "      <th>Social, Finance, Analytics, Advertising</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3d</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3d printing</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3d technology</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>accounting</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",
       "      <td>5</td>\n",
       "      <td>active lifestyle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ad targeting</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",
       "      <td>7</td>\n",
       "      <td>advanced materials</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>adventure travel</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>advertising</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",
       "      <td>10</td>\n",
       "      <td>advertising exchanges</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            category_list  Automotive & Sports  Blanks  \\\n",
       "1                      3d                    0       0   \n",
       "2             3d printing                    0       0   \n",
       "3           3d technology                    0       0   \n",
       "4              accounting                    0       0   \n",
       "5        active lifestyle                    0       0   \n",
       "6            ad targeting                    0       0   \n",
       "7      advanced materials                    0       0   \n",
       "8        adventure travel                    1       0   \n",
       "9             advertising                    0       0   \n",
       "10  advertising exchanges                    0       0   \n",
       "\n",
       "    Cleantech / Semiconductors  Entertainment  Health  Manufacturing  \\\n",
       "1                            0              0       0              1   \n",
       "2                            0              0       0              1   \n",
       "3                            0              0       0              1   \n",
       "4                            0              0       0              0   \n",
       "5                            0              0       1              0   \n",
       "6                            0              0       0              0   \n",
       "7                            0              0       0              1   \n",
       "8                            0              0       0              0   \n",
       "9                            0              0       0              0   \n",
       "10                           0              0       0              0   \n",
       "\n",
       "    News, Search and Messaging  Others  \\\n",
       "1                            0       0   \n",
       "2                            0       0   \n",
       "3                            0       0   \n",
       "4                            0       0   \n",
       "5                            0       0   \n",
       "6                            0       0   \n",
       "7                            0       0   \n",
       "8                            0       0   \n",
       "9                            0       0   \n",
       "10                           0       0   \n",
       "\n",
       "    Social, Finance, Analytics, Advertising  \n",
       "1                                         0  \n",
       "2                                         0  \n",
       "3                                         0  \n",
       "4                                         1  \n",
       "5                                         0  \n",
       "6                                         1  \n",
       "7                                         0  \n",
       "8                                         0  \n",
       "9                                         1  \n",
       "10                                        1  "
      ]
     },
     "execution_count": 1113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#file path for mapping.csv file\n",
    "file_path_mapping = r\"C:\\Users\\Chaitanya\\Desktop\\UpGrad\\2. Statistics Essentials\\4. Assignment\\mapping.csv\"\n",
    "\n",
    "#Reading mapping.csv file\n",
    "mapping = pd.read_csv(file_path_mapping, encoding=\"ISO-8859-1\")\n",
    "\n",
    "#Replacing content within the mapping file\n",
    "mapping['category_list'] = mapping['category_list'].replace({'0':'na'}, regex=True)\n",
    "\n",
    "#Proper str.encode() and str.decode() to get proper output in ascii\n",
    "mapping['category_list'] = mapping['category_list'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "\n",
    "#Removing null observations from the category_list\n",
    "mapping = mapping[~(mapping['category_list'].isnull())]\n",
    "\n",
    "mapping.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "687\n"
     ]
    }
   ],
   "source": [
    "#Total number of observtions in the mapping dataframe\n",
    "print(len(mapping.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    687\n",
       "Name: Blanks, dtype: int64"
      ]
     },
     "execution_count": 1115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of '0' in 'Blanks' column of 'mapping' dataframe\n",
    "mapping['Blanks'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_list</th>\n",
       "      <th>Automotive &amp; Sports</th>\n",
       "      <th>Cleantech / Semiconductors</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Health</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>News, Search and Messaging</th>\n",
       "      <th>Others</th>\n",
       "      <th>Social, Finance, Analytics, Advertising</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3d printing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3d technology</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>accounting</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",
       "      <td>5</td>\n",
       "      <td>active lifestyle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ad targeting</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",
       "      <td>7</td>\n",
       "      <td>advanced materials</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>adventure travel</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>advertising</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",
       "      <td>10</td>\n",
       "      <td>advertising exchanges</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            category_list  Automotive & Sports  Cleantech / Semiconductors  \\\n",
       "1                      3d                    0                           0   \n",
       "2             3d printing                    0                           0   \n",
       "3           3d technology                    0                           0   \n",
       "4              accounting                    0                           0   \n",
       "5        active lifestyle                    0                           0   \n",
       "6            ad targeting                    0                           0   \n",
       "7      advanced materials                    0                           0   \n",
       "8        adventure travel                    1                           0   \n",
       "9             advertising                    0                           0   \n",
       "10  advertising exchanges                    0                           0   \n",
       "\n",
       "    Entertainment  Health  Manufacturing  News, Search and Messaging  Others  \\\n",
       "1               0       0              1                           0       0   \n",
       "2               0       0              1                           0       0   \n",
       "3               0       0              1                           0       0   \n",
       "4               0       0              0                           0       0   \n",
       "5               0       1              0                           0       0   \n",
       "6               0       0              0                           0       0   \n",
       "7               0       0              1                           0       0   \n",
       "8               0       0              0                           0       0   \n",
       "9               0       0              0                           0       0   \n",
       "10              0       0              0                           0       0   \n",
       "\n",
       "    Social, Finance, Analytics, Advertising  \n",
       "1                                         0  \n",
       "2                                         0  \n",
       "3                                         0  \n",
       "4                                         1  \n",
       "5                                         0  \n",
       "6                                         1  \n",
       "7                                         0  \n",
       "8                                         0  \n",
       "9                                         1  \n",
       "10                                        1  "
      ]
     },
     "execution_count": 1116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total number of '0' and the total no. of observations in the 'mapping' dataframe are 687.\n",
    "#Hence, dropping the 'Blanks' column from 'mapping' dataframe, as it has 100% '0' values\n",
    "mapping = mapping.drop('Blanks', axis=1)\n",
    "mapping.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the mapping file 'mapping.csv' to map each primary sector to one of the eight main sectors (Note that ‘Others’ is also considered one of the main sectors). Now we are only left with eight main sectors after dropping 'Blanks'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_list</th>\n",
       "      <th>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>adventure travel</td>\n",
       "      <td>automotive &amp; sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>aerospace</td>\n",
       "      <td>automotive &amp; sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>auto</td>\n",
       "      <td>automotive &amp; sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>automated kiosk</td>\n",
       "      <td>automotive &amp; sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>automotive</td>\n",
       "      <td>automotive &amp; sports</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category_list          main_sector\n",
       "7   adventure travel  automotive & sports\n",
       "13         aerospace  automotive & sports\n",
       "44              auto  automotive & sports\n",
       "45   automated kiosk  automotive & sports\n",
       "46        automotive  automotive & sports"
      ]
     },
     "execution_count": 1117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using melt() function to unpivot the 'mapping' file and get the 'main_sector' column\n",
    "pd.set_option('display.max_columns', 5400)\n",
    "\n",
    "#In the melt() function, we won't declare 'value_vars' as we need all the columns (considered by default)\n",
    "#Note, we have already dropped the column 'Blanks'. So, now we need all the other columns.\n",
    "mapping = pd.melt(mapping, id_vars = ['category_list'], var_name='main_sector')\n",
    "\n",
    "#Retain category list with value=1 (True) implies the presence of the primary sector (category_list) and its main sector\n",
    "mapping = mapping[~(mapping['value'] == 0)]\n",
    "\n",
    "#Dropping the 'value' column from mapping DataFrame as all the values having value=1 are retained in the dataframe\n",
    "#Meaning, now we have primary sectors corresponding to its main sectors in the mapping dataframe\n",
    "mapping = mapping.drop('value',axis=1)\n",
    "\n",
    "#Proper str.encode() and str.decode() to get proper output in ascii\n",
    "mapping['main_sector'] = mapping['main_sector'].str.encode('ISO-8859-1').str.decode('ascii','ignore').str.lower()\n",
    "\n",
    "mapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(687, 2)"
      ]
     },
     "execution_count": 1118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique primary sectors in venture_master_frame: 563\n",
      "Number of unique primary sectors in mapping: 687\n"
     ]
    }
   ],
   "source": [
    "#Check for uniqueness in primary sectors of both the dataframes, venture_master_frame and mapping.\n",
    "\n",
    "temp1=set(venture_master_frame['category_list'])\n",
    "temp2=set(mapping['category_list'])\n",
    "\n",
    "print('Number of unique primary sectors in venture_master_frame:',len(temp1))\n",
    "print('Number of unique primary sectors in mapping:',len(temp2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1120,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "      <td>entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>20000000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>1700000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>8900000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>5000000.000</td>\n",
       "      <td>1 mainstream</td>\n",
       "      <td>apps</td>\n",
       "      <td>USA</td>\n",
       "      <td>news, search and messaging</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            company_permalink  \\\n",
       "0         /organization/-fame   \n",
       "1        /organization/0xdata   \n",
       "2        /organization/0xdata   \n",
       "3        /organization/0xdata   \n",
       "4  /organization/1-mainstream   \n",
       "\n",
       "                           funding_round_permalink  raised_amount_usd  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638       10000000.000   \n",
       "1  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44       20000000.000   \n",
       "2  /funding-round/ae2a174c06517c2394aed45006322a7e        1700000.000   \n",
       "3  /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e        8900000.000   \n",
       "4  /funding-round/b952cbaf401f310927430c97b68162ea        5000000.000   \n",
       "\n",
       "           name category_list country_code  \\\n",
       "0         #fame         media          IND   \n",
       "1        h2o.ai     analytics          USA   \n",
       "2        h2o.ai     analytics          USA   \n",
       "3        h2o.ai     analytics          USA   \n",
       "4  1 mainstream          apps          USA   \n",
       "\n",
       "                               main_sector  \n",
       "0                            entertainment  \n",
       "1  social, finance, analytics, advertising  \n",
       "2  social, finance, analytics, advertising  \n",
       "3  social, finance, analytics, advertising  \n",
       "4               news, search and messaging  "
      ]
     },
     "execution_count": 1120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging the mapping.csv and master_frame (venture_master_frame)\n",
    "venture_master_frame = venture_master_frame.merge(mapping, how = 'left', on ='category_list')\n",
    "venture_master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique primary sectors in venture_master_frame: 563\n"
     ]
    }
   ],
   "source": [
    "#Number of distinct primary sectors in category_list\n",
    "temp= set(venture_master_frame['category_list'])\n",
    "print('Number of unique primary sectors in venture_master_frame:',len(temp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38803 entries, 0 to 38802\n",
      "Data columns (total 7 columns):\n",
      "company_permalink          38803 non-null object\n",
      "funding_round_permalink    38803 non-null object\n",
      "raised_amount_usd          38803 non-null float64\n",
      "name                       38803 non-null object\n",
      "category_list              38803 non-null object\n",
      "country_code               38803 non-null object\n",
      "main_sector                38788 non-null object\n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "venture_master_frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38803, 7)"
      ]
     },
     "execution_count": 1123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venture_master_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 1124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding if Null values are present in the main_sector\n",
    "venture_master_frame['main_sector'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7345</td>\n",
       "      <td>/organization/clean-vehicle-solutions</td>\n",
       "      <td>/funding-round/7b991c526219c2edff8789942215d93b</td>\n",
       "      <td>125000.000</td>\n",
       "      <td>clean vehicle solutions</td>\n",
       "      <td>natural gas uses</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8258</td>\n",
       "      <td>/organization/consensus-point</td>\n",
       "      <td>/funding-round/11ad1d209af6e06e87467f3432aa735f</td>\n",
       "      <td>788374.000</td>\n",
       "      <td>consensus point</td>\n",
       "      <td>enterprise 2.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8259</td>\n",
       "      <td>/organization/consensus-point</td>\n",
       "      <td>/funding-round/7cdac9335cdc78285e8dcd1932f8729b</td>\n",
       "      <td>2600000.000</td>\n",
       "      <td>consensus point</td>\n",
       "      <td>enterprise 2.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10162</td>\n",
       "      <td>/organization/dolores-speech-products</td>\n",
       "      <td>/funding-round/ba7393d89b2a5ad0c076e2295794c4ce</td>\n",
       "      <td>250000.000</td>\n",
       "      <td>dolores speech products</td>\n",
       "      <td>adaptive equipment</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12375</td>\n",
       "      <td>/organization/fia-formula-e</td>\n",
       "      <td>/funding-round/19a09624fcf806a4a64809462a4e391c</td>\n",
       "      <td>68569234.000</td>\n",
       "      <td>fia formula e</td>\n",
       "      <td>racing</td>\n",
       "      <td>GBR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           company_permalink  \\\n",
       "7345   /organization/clean-vehicle-solutions   \n",
       "8258           /organization/consensus-point   \n",
       "8259           /organization/consensus-point   \n",
       "10162  /organization/dolores-speech-products   \n",
       "12375            /organization/fia-formula-e   \n",
       "\n",
       "                               funding_round_permalink  raised_amount_usd  \\\n",
       "7345   /funding-round/7b991c526219c2edff8789942215d93b         125000.000   \n",
       "8258   /funding-round/11ad1d209af6e06e87467f3432aa735f         788374.000   \n",
       "8259   /funding-round/7cdac9335cdc78285e8dcd1932f8729b        2600000.000   \n",
       "10162  /funding-round/ba7393d89b2a5ad0c076e2295794c4ce         250000.000   \n",
       "12375  /funding-round/19a09624fcf806a4a64809462a4e391c       68569234.000   \n",
       "\n",
       "                          name       category_list country_code main_sector  \n",
       "7345   clean vehicle solutions    natural gas uses          USA         NaN  \n",
       "8258           consensus point      enterprise 2.0          USA         NaN  \n",
       "8259           consensus point      enterprise 2.0          USA         NaN  \n",
       "10162  dolores speech products  adaptive equipment          USA         NaN  \n",
       "12375            fia formula e              racing          GBR         NaN  "
      ]
     },
     "execution_count": 1125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the unique primary sectors from category_list which don't have a main_sector \n",
    "#(15 observations total: NaN in main_sector)\n",
    "\n",
    "venture_master_frame[venture_master_frame['main_sector'].isnull()].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique entries of primary sector in the category_list that don't have a main sector:\n",
      "['natural gas uses' 'enterprise 2.0' 'adaptive equipment' 'racing'\n",
      " 'specialty retail' 'biotechnology and semiconductor' 'rapidly expanding'\n",
      " 'product search' 'greentech' 'retirement']\n",
      "\n",
      "Number of unique entries of primary sector in the category_list that don't have a main sector: 10\n"
     ]
    }
   ],
   "source": [
    "#Unique entries in category_list (primary sectors) that don't have a main sector\n",
    "print(\"Unique entries of primary sector in the category_list that don't have a main sector:\")\n",
    "print(venture_master_frame[venture_master_frame['main_sector'].isnull()]['category_list'].unique())\n",
    "\n",
    "#Number of unique entries of primary sector\n",
    "print(\"\\nNumber of unique entries of primary sector in the category_list that don't have a main sector:\",\\\n",
    "      venture_master_frame[venture_master_frame['main_sector'].isnull()]['category_list'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1127,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "      <td>entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/3bb2ee4a2d89251a10aaa735b1180e44</td>\n",
       "      <td>20000000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/ae2a174c06517c2394aed45006322a7e</td>\n",
       "      <td>1700000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>8900000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>5000000.000</td>\n",
       "      <td>1 mainstream</td>\n",
       "      <td>apps</td>\n",
       "      <td>USA</td>\n",
       "      <td>news, search and messaging</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            company_permalink  \\\n",
       "0         /organization/-fame   \n",
       "1        /organization/0xdata   \n",
       "2        /organization/0xdata   \n",
       "3        /organization/0xdata   \n",
       "4  /organization/1-mainstream   \n",
       "\n",
       "                           funding_round_permalink  raised_amount_usd  \\\n",
       "0  /funding-round/9a01d05418af9f794eebff7ace91f638       10000000.000   \n",
       "1  /funding-round/3bb2ee4a2d89251a10aaa735b1180e44       20000000.000   \n",
       "2  /funding-round/ae2a174c06517c2394aed45006322a7e        1700000.000   \n",
       "3  /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e        8900000.000   \n",
       "4  /funding-round/b952cbaf401f310927430c97b68162ea        5000000.000   \n",
       "\n",
       "           name category_list country_code  \\\n",
       "0         #fame         media          IND   \n",
       "1        h2o.ai     analytics          USA   \n",
       "2        h2o.ai     analytics          USA   \n",
       "3        h2o.ai     analytics          USA   \n",
       "4  1 mainstream          apps          USA   \n",
       "\n",
       "                               main_sector  \n",
       "0                            entertainment  \n",
       "1  social, finance, analytics, advertising  \n",
       "2  social, finance, analytics, advertising  \n",
       "3  social, finance, analytics, advertising  \n",
       "4               news, search and messaging  "
      ]
     },
     "execution_count": 1127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only the rows that have main_sector values\n",
    "venture_master_frame = venture_master_frame[~(venture_master_frame['main_sector'].isnull())]\n",
    "venture_master_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38788, 7)"
      ]
     },
     "execution_count": 1128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venture_master_frame.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, Sector 1 Analysis is done. We have merged the venture_master_frame (derived from the master_frame) with the mapping dataframe to successfully map exact primary sector to its main sector. The primary sectors are present in a separate column named category_list of the venture_master_frame, and every primary sector has a main sector. Also, main sector is displayed in a separate column within the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='Royalblue4'>Checkpoint 5: Sector Analysis 2 </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*'venture_master_frame'*: A data frame with each company’s main sector (main_sector) mapped to it. Here, we carry on the sector analysis based on the eight main sectors in this dataframe.<br>\n",
    "\n",
    "(i) The most suitable funding type for Spark Funds: *venture type* (type 'FT')<br>Also, the range of funding preferred by Spark Funds is 5 to 15 million USD.<br><br>\n",
    "(ii) The top 3 English speaking countries that satisfy the funding type requirements are: <br>\n",
    "*Country 1: USA, Country 2: GBR and Country 3: IND*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating DataFrames D1, D2 and D3 for all 3 countries to calculate the total number and amount of investments in each main sector. Also, to infer additional insights from the data. But before that, let's create dataframes C1, C2 and C3 from the venture_master_frame for the respective top 3 English speaking countries based on the funding value requirement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating DataFrame C1, C2 and C3 for top 3 English speaking countries that satisfy the funding amount requirements \n",
    "#under the venture type investment (The range of funding preferred by Spark Funds is 5 to 15 million USD)\n",
    "\n",
    "#Dataframe C1, country_1: 'USA'\n",
    "C1 = venture_master_frame[(venture_master_frame['country_code'] == 'USA') & \\\n",
    "                          (venture_master_frame['raised_amount_usd'] >= 5000000) & \\\n",
    "                         (venture_master_frame['raised_amount_usd'] <= 15000000)]\n",
    "\n",
    "#Dataframe C1, country_1: 'GBR'\n",
    "C2 = venture_master_frame[(venture_master_frame['country_code'] == 'GBR') & \\\n",
    "                          (venture_master_frame['raised_amount_usd'] >= 5000000) & \\\n",
    "                         (venture_master_frame['raised_amount_usd'] <= 15000000)]\n",
    "\n",
    "#Dataframe C1, country_1: 'IND'\n",
    "C3 = venture_master_frame[(venture_master_frame['country_code'] == 'IND') & \\\n",
    "                          (venture_master_frame['raised_amount_usd'] >= 5000000) & \\\n",
    "                         (venture_master_frame['raised_amount_usd'] <= 15000000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>8900000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/1-mainstream</td>\n",
       "      <td>/funding-round/b952cbaf401f310927430c97b68162ea</td>\n",
       "      <td>5000000.000</td>\n",
       "      <td>1 mainstream</td>\n",
       "      <td>apps</td>\n",
       "      <td>USA</td>\n",
       "      <td>news, search and messaging</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>/organization/128-technology</td>\n",
       "      <td>/funding-round/fb6216a30cb566ede89e0bee0623a634</td>\n",
       "      <td>11999347.000</td>\n",
       "      <td>128 technology</td>\n",
       "      <td>service providers</td>\n",
       "      <td>USA</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>/organization/1366-technologies</td>\n",
       "      <td>/funding-round/424129ce1235cfab2655ee81305f7c2b</td>\n",
       "      <td>15000000.000</td>\n",
       "      <td>1366 technologies</td>\n",
       "      <td>manufacturing</td>\n",
       "      <td>USA</td>\n",
       "      <td>manufacturing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>/organization/1366-technologies</td>\n",
       "      <td>/funding-round/6d3f3797371956ece035b8478c1441b2</td>\n",
       "      <td>5000000.000</td>\n",
       "      <td>1366 technologies</td>\n",
       "      <td>manufacturing</td>\n",
       "      <td>USA</td>\n",
       "      <td>manufacturing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  company_permalink  \\\n",
       "3              /organization/0xdata   \n",
       "4        /organization/1-mainstream   \n",
       "17     /organization/128-technology   \n",
       "20  /organization/1366-technologies   \n",
       "21  /organization/1366-technologies   \n",
       "\n",
       "                            funding_round_permalink  raised_amount_usd  \\\n",
       "3   /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e        8900000.000   \n",
       "4   /funding-round/b952cbaf401f310927430c97b68162ea        5000000.000   \n",
       "17  /funding-round/fb6216a30cb566ede89e0bee0623a634       11999347.000   \n",
       "20  /funding-round/424129ce1235cfab2655ee81305f7c2b       15000000.000   \n",
       "21  /funding-round/6d3f3797371956ece035b8478c1441b2        5000000.000   \n",
       "\n",
       "                 name      category_list country_code  \\\n",
       "3              h2o.ai          analytics          USA   \n",
       "4        1 mainstream               apps          USA   \n",
       "17     128 technology  service providers          USA   \n",
       "20  1366 technologies      manufacturing          USA   \n",
       "21  1366 technologies      manufacturing          USA   \n",
       "\n",
       "                                main_sector  \n",
       "3   social, finance, analytics, advertising  \n",
       "4                news, search and messaging  \n",
       "17                                   others  \n",
       "20                            manufacturing  \n",
       "21                            manufacturing  "
      ]
     },
     "execution_count": 1130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='darkslateblue'>**Investment Counts and Main Sectors**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determining the total number (or count) of investments for each main sector in a separate column in new dataframes.<br>\n",
    "*'tic_usa'*: Total Investment count for each sector in Country 1: USA<br>\n",
    "*'tic_gbr*': Total Investment count for each sector in Country 2: GBR<br>\n",
    "*'tic_ind'*: Total Investment count for each sector in Country 3: IND<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**USA**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1131,
   "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>Total_Investment_Count_USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>health</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manufacturing</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entertainment</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>automotive &amp; sports</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_Investment_Count_USA\n",
       "main_sector                                                        \n",
       "others                                                         2950\n",
       "social, finance, analytics, advertising                        2714\n",
       "cleantech / semiconductors                                     2350\n",
       "news, search and messaging                                     1583\n",
       "health                                                          909\n",
       "manufacturing                                                   799\n",
       "entertainment                                                   591\n",
       "automotive & sports                                             167"
      ]
     },
     "execution_count": 1131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'tic_usa': Total Investment count for each sector in Country 1: USA \n",
    "tic_usa = C1.groupby('main_sector')['main_sector'].count().rename('Total_Investment_Count_USA')\n",
    "\n",
    "#Sorting the total investment counts in descending order\n",
    "tic_usa = tic_usa.sort_values(ascending=False)\n",
    "tic_usa = pd.DataFrame(tic_usa)\n",
    "tic_usa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of investments count (total of all sectors) in country_1: USA:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12063"
      ]
     },
     "execution_count": 1132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total investment count (total of all sectors) in country_1: USA\n",
    "print('Total number of investments count (total of all sectors) in country_1: USA:')\n",
    "tic_usa.Total_Investment_Count_USA.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top sector names(no. of investments-wise) First, Second and Third (Country_1: USA)\n"
     ]
    },
    {
     "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>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               main_sector\n",
       "0                                   others\n",
       "1  social, finance, analytics, advertising\n",
       "2               cleantech / semiconductors"
      ]
     },
     "execution_count": 1133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top sector names(no. of investments-wise) First, Second and Third (Country_1: USA)\n",
    "print('Top sector names(no. of investments-wise) First, Second and Third (Country_1: USA)')\n",
    "pd.DataFrame(tic_usa[:3].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of investments in First, Second and Third best sector in Country_1: USA:\n"
     ]
    },
    {
     "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>Total_Investment_Count_USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>2350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_Investment_Count_USA\n",
       "main_sector                                                        \n",
       "others                                                         2950\n",
       "social, finance, analytics, advertising                        2714\n",
       "cleantech / semiconductors                                     2350"
      ]
     },
     "execution_count": 1134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of investments in First, Second and Third best sector in Country_1: USA\n",
    "print(\"Number of investments in First, Second and Third best sector in Country_1: USA:\")\n",
    "pd.DataFrame(tic_usa.iloc[:3,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**GBR**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1135,
   "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>Total_Investment_Count_GBR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entertainment</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manufacturing</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>health</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>automotive &amp; sports</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_Investment_Count_GBR\n",
       "main_sector                                                        \n",
       "others                                                          147\n",
       "social, finance, analytics, advertising                         133\n",
       "cleantech / semiconductors                                      130\n",
       "news, search and messaging                                       73\n",
       "entertainment                                                    56\n",
       "manufacturing                                                    42\n",
       "health                                                           24\n",
       "automotive & sports                                              16"
      ]
     },
     "execution_count": 1135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'tic_gbr': Total Investment count for each sector in Country_2: GBR \n",
    "tic_gbr = C2.groupby('main_sector')['main_sector'].count().rename('Total_Investment_Count_GBR')\n",
    "\n",
    "#Sorting the total investment counts in descending order\n",
    "tic_gbr = tic_gbr.sort_values(ascending=False)\n",
    "tic_gbr = pd.DataFrame(tic_gbr)\n",
    "tic_gbr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of investments count (total of all sectors) in country_2: GBR:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "621"
      ]
     },
     "execution_count": 1136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total investment count (total of all sectors) in country_2: GBR\n",
    "print('Total number of investments count (total of all sectors) in country_2: GBR:')\n",
    "tic_gbr.Total_Investment_Count_GBR.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top sector names(no. of investments-wise) First, Second and Third (Country_2: GBR)\n"
     ]
    },
    {
     "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>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               main_sector\n",
       "0                                   others\n",
       "1  social, finance, analytics, advertising\n",
       "2               cleantech / semiconductors"
      ]
     },
     "execution_count": 1137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top sector names(no. of investments-wise) First, Second and Third (Country_2: GBR)\n",
    "print('Top sector names(no. of investments-wise) First, Second and Third (Country_2: GBR)')\n",
    "pd.DataFrame(tic_gbr[:3].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of investments in First, Second and Third best sector in Country_2: GBR:\n"
     ]
    },
    {
     "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>Total_Investment_Count_GBR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_Investment_Count_GBR\n",
       "main_sector                                                        \n",
       "others                                                          147\n",
       "social, finance, analytics, advertising                         133\n",
       "cleantech / semiconductors                                      130"
      ]
     },
     "execution_count": 1138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of investments in First, Second and Third best sector in Country_2: GBR\n",
    "print(\"Number of investments in First, Second and Third best sector in Country_2: GBR:\")\n",
    "pd.DataFrame(tic_gbr.iloc[:3,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**IND**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1139,
   "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>Total_Investment_Count_IND</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entertainment</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manufacturing</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>health</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>automotive &amp; sports</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_Investment_Count_IND\n",
       "main_sector                                                        \n",
       "others                                                          110\n",
       "social, finance, analytics, advertising                          60\n",
       "news, search and messaging                                       52\n",
       "entertainment                                                    33\n",
       "manufacturing                                                    21\n",
       "cleantech / semiconductors                                       20\n",
       "health                                                           19\n",
       "automotive & sports                                              13"
      ]
     },
     "execution_count": 1139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'tic_ind': Total Investment count for each sector in Country_3: IND\n",
    "tic_ind = C3.groupby('main_sector')['main_sector'].count().rename('Total_Investment_Count_IND')\n",
    "\n",
    "#Sorting the total investment counts in descending order\n",
    "tic_ind = tic_ind.sort_values(ascending=False)\n",
    "tic_ind = pd.DataFrame(tic_ind)\n",
    "tic_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of investments count (total of all sectors) in country_3: IND:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "328"
      ]
     },
     "execution_count": 1140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total investment count (total of all sectors) in country_3: IND\n",
    "print('Total number of investments count (total of all sectors) in country_3: IND:')\n",
    "tic_ind.Total_Investment_Count_IND.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top sector names(no. of investments-wise) First, Second and Third (Country_3: IND)\n"
     ]
    },
    {
     "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>main_sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>news, search and messaging</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               main_sector\n",
       "0                                   others\n",
       "1  social, finance, analytics, advertising\n",
       "2               news, search and messaging"
      ]
     },
     "execution_count": 1141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top sector names(no. of investments-wise) First, Second and Third (Country_3: IND)\n",
    "print('Top sector names(no. of investments-wise) First, Second and Third (Country_3: IND)')\n",
    "pd.DataFrame(tic_ind[:3].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of investments in First, Second and Third best sector in Country_3: IND:\n"
     ]
    },
    {
     "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>Total_Investment_Count_IND</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_Investment_Count_IND\n",
       "main_sector                                                        \n",
       "others                                                          110\n",
       "social, finance, analytics, advertising                          60\n",
       "news, search and messaging                                       52"
      ]
     },
     "execution_count": 1142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of investments in First, Second and Third best sector in Country_3: IND\n",
    "print(\"Number of investments in First, Second and Third best sector in Country_3: IND:\")\n",
    "pd.DataFrame(tic_ind.iloc[:3,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='darkslateblue'>**Investment Amounts and Main Sectors**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determining the total amount invested in each main sector in a new dataframe and sorting it in descending order.<br>\n",
    "*'tai_usa'*: Total amount invested in each main sector for Country 1: USA<br>\n",
    "*'tai_gbr*': Total amount invested in each main sector for Country 2: GBR<br>\n",
    "*'tai_ind'*: Total amount invested in each main sector for Country 3: IND<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**USA**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1143,
   "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>Total_amount_invested_USA_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>26321007002.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>23807376964.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>21633430822.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>13971567428.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>health</td>\n",
       "      <td>8211859357.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manufacturing</td>\n",
       "      <td>7258553378.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entertainment</td>\n",
       "      <td>5099197982.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>automotive &amp; sports</td>\n",
       "      <td>1454104361.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_amount_invested_USA_usd\n",
       "main_sector                                                           \n",
       "others                                                 26321007002.000\n",
       "social, finance, analytics, advertising                23807376964.000\n",
       "cleantech / semiconductors                             21633430822.000\n",
       "news, search and messaging                             13971567428.000\n",
       "health                                                  8211859357.000\n",
       "manufacturing                                           7258553378.000\n",
       "entertainment                                           5099197982.000\n",
       "automotive & sports                                     1454104361.000"
      ]
     },
     "execution_count": 1143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total amount invested in each main sector (sorted_descending order of total funding values): \n",
    "#For Country_1: USA\n",
    "\n",
    "tai_usa = C1.groupby('main_sector')['raised_amount_usd'].sum().rename('Total_amount_invested_USA_usd')\n",
    "tai_usa = pd.DataFrame(tai_usa.sort_values(ascending=False))\n",
    "tai_usa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total amount of investment (in usd) Country_1: USA:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "107757097294.0"
      ]
     },
     "execution_count": 1144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total amount of investment (in usd) Country_1: USA\n",
    "print('Total amount of investment (in usd) Country_1: USA:')\n",
    "round(tai_usa['Total_amount_invested_USA_usd'].sum(),3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**GBR**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_amount_invested_GBR_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>1283624289.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>1163990056.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>1089404014.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>615746235.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entertainment</td>\n",
       "      <td>482784687.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manufacturing</td>\n",
       "      <td>361940335.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>health</td>\n",
       "      <td>214537510.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>automotive &amp; sports</td>\n",
       "      <td>167051565.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_amount_invested_GBR_usd\n",
       "main_sector                                                           \n",
       "others                                                  1283624289.000\n",
       "cleantech / semiconductors                              1163990056.000\n",
       "social, finance, analytics, advertising                 1089404014.000\n",
       "news, search and messaging                               615746235.000\n",
       "entertainment                                            482784687.000\n",
       "manufacturing                                            361940335.000\n",
       "health                                                   214537510.000\n",
       "automotive & sports                                      167051565.000"
      ]
     },
     "execution_count": 1145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total amount invested in each main sector (sorted_descending order of total funding values): \n",
    "#For Country_2: GBR\n",
    "\n",
    "tai_gbr = C2.groupby('main_sector')['raised_amount_usd'].sum().rename('Total_amount_invested_GBR_usd')\n",
    "tai_gbr = pd.DataFrame(tai_gbr.sort_values(ascending=False))\n",
    "tai_gbr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total amount of investment (in usd) Country_2: GBR:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5379078691.0"
      ]
     },
     "execution_count": 1146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total amount of investment (in usd) Country_2: GBR\n",
    "print('Total amount of investment (in usd) Country_2: GBR:')\n",
    "round(tai_gbr['Total_amount_invested_GBR_usd'].sum(),3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**IND**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_amount_invested_IND_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>main_sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>others</td>\n",
       "      <td>1013409507.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>550549550.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>433834545.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entertainment</td>\n",
       "      <td>280830000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>manufacturing</td>\n",
       "      <td>200900000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>health</td>\n",
       "      <td>167740000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>165380000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>automotive &amp; sports</td>\n",
       "      <td>136900000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total_amount_invested_IND_usd\n",
       "main_sector                                                           \n",
       "others                                                  1013409507.000\n",
       "social, finance, analytics, advertising                  550549550.000\n",
       "news, search and messaging                               433834545.000\n",
       "entertainment                                            280830000.000\n",
       "manufacturing                                            200900000.000\n",
       "health                                                   167740000.000\n",
       "cleantech / semiconductors                               165380000.000\n",
       "automotive & sports                                      136900000.000"
      ]
     },
     "execution_count": 1147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total amount invested in each main sector (sorted_descending order of total funding values): \n",
    "#For Country_3: IND\n",
    "\n",
    "tai_ind = C3.groupby('main_sector')['raised_amount_usd'].sum().rename('Total_amount_invested_IND_usd')\n",
    "tai_ind = pd.DataFrame(tai_ind.sort_values(ascending=False))\n",
    "tai_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total amount of investment (in usd) Country_3: IND:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2949543602.0"
      ]
     },
     "execution_count": 1148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total amount of investment (in usd) Country_3: IND\n",
    "print('Total amount of investment (in usd) Country_3: IND:')\n",
    "round(tai_ind['Total_amount_invested_IND_usd'].sum(),3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging the total investment counts and total amount invested in each main sector in dataframes (for each of the top 3 English speaking countries). Creating dataframes D1, D2 and D3 for USA, GBR and IND respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12063"
      ]
     },
     "execution_count": 1149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataframe D1 for Country_1: USA\n",
    "\n",
    "#Merging 'C1' and 'tic_usa'\n",
    "D1 = pd.merge(C1, tic_usa, how='inner', left_on ='main_sector', right_index = True)\n",
    "#Merging further with 'tai_usa'\n",
    "D1 = pd.merge(D1, tai_usa, how='inner', left_on ='main_sector', right_index = True)\n",
    "D1.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1150,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "      <th>Total_Investment_Count_USA</th>\n",
       "      <th>Total_amount_invested_USA_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/0xdata</td>\n",
       "      <td>/funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e</td>\n",
       "      <td>8900000.000</td>\n",
       "      <td>h2o.ai</td>\n",
       "      <td>analytics</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>23807376964.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>/organization/21e6</td>\n",
       "      <td>/funding-round/bdf9f5bf67ee51155eae223acac57ec5</td>\n",
       "      <td>5050000.000</td>\n",
       "      <td>21 inc</td>\n",
       "      <td>big data</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>23807376964.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>/organization/33across</td>\n",
       "      <td>/funding-round/a45d977b48cb54216eb705d59b24ca19</td>\n",
       "      <td>13100000.000</td>\n",
       "      <td>33across</td>\n",
       "      <td>advertising</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>23807376964.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>/organization/33across</td>\n",
       "      <td>/funding-round/bfc2233768b7c79ed58ad7561423e555</td>\n",
       "      <td>9000000.000</td>\n",
       "      <td>33across</td>\n",
       "      <td>advertising</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>23807376964.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>/organization/360commerce</td>\n",
       "      <td>/funding-round/1c51042e815e96ed2653ae9ced99dfc4</td>\n",
       "      <td>12000000.000</td>\n",
       "      <td>360commerce</td>\n",
       "      <td>information technology</td>\n",
       "      <td>USA</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>23807376964.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             company_permalink  \\\n",
       "3         /organization/0xdata   \n",
       "57          /organization/21e6   \n",
       "99      /organization/33across   \n",
       "101     /organization/33across   \n",
       "103  /organization/360commerce   \n",
       "\n",
       "                             funding_round_permalink  raised_amount_usd  \\\n",
       "3    /funding-round/e1cfcbe1bdf4c70277c5f29a3482f24e        8900000.000   \n",
       "57   /funding-round/bdf9f5bf67ee51155eae223acac57ec5        5050000.000   \n",
       "99   /funding-round/a45d977b48cb54216eb705d59b24ca19       13100000.000   \n",
       "101  /funding-round/bfc2233768b7c79ed58ad7561423e555        9000000.000   \n",
       "103  /funding-round/1c51042e815e96ed2653ae9ced99dfc4       12000000.000   \n",
       "\n",
       "            name           category_list country_code  \\\n",
       "3         h2o.ai               analytics          USA   \n",
       "57        21 inc                big data          USA   \n",
       "99      33across             advertising          USA   \n",
       "101     33across             advertising          USA   \n",
       "103  360commerce  information technology          USA   \n",
       "\n",
       "                                 main_sector  Total_Investment_Count_USA  \\\n",
       "3    social, finance, analytics, advertising                        2714   \n",
       "57   social, finance, analytics, advertising                        2714   \n",
       "99   social, finance, analytics, advertising                        2714   \n",
       "101  social, finance, analytics, advertising                        2714   \n",
       "103  social, finance, analytics, advertising                        2714   \n",
       "\n",
       "     Total_amount_invested_USA_usd  \n",
       "3                  23807376964.000  \n",
       "57                 23807376964.000  \n",
       "99                 23807376964.000  \n",
       "101                23807376964.000  \n",
       "103                23807376964.000  "
      ]
     },
     "execution_count": 1150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe D1 (display head)\n",
    "D1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "621"
      ]
     },
     "execution_count": 1151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataframe D2 for Country_2: GBR\n",
    "\n",
    "#Merging 'C2' and 'tic_gbr'\n",
    "D2 = pd.merge(C2, tic_gbr, how='inner', left_on ='main_sector', right_index = True)\n",
    "#Merging further with 'tai_gbr'\n",
    "D2 = pd.merge(D2, tai_gbr, how='inner', left_on ='main_sector', right_index = True)\n",
    "D2.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1152,
   "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>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "      <th>Total_Investment_Count_GBR</th>\n",
       "      <th>Total_amount_invested_GBR_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>/organization/365scores</td>\n",
       "      <td>/funding-round/48212f931f542fdef78810bc87aef086</td>\n",
       "      <td>5500000.000</td>\n",
       "      <td>365scores</td>\n",
       "      <td>android</td>\n",
       "      <td>GBR</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>1089404014.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>/organization/acal-enterprise-solutions</td>\n",
       "      <td>/funding-round/0f9a693d9686330c5c2724215e0048e2</td>\n",
       "      <td>10720178.000</td>\n",
       "      <td>acal enterprise solutions</td>\n",
       "      <td>information technology</td>\n",
       "      <td>GBR</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>1089404014.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>836</td>\n",
       "      <td>/organization/adbrain</td>\n",
       "      <td>/funding-round/71f1b5903874817292d67e1ac6aa66b2</td>\n",
       "      <td>7500000.000</td>\n",
       "      <td>adbrain</td>\n",
       "      <td>advertising</td>\n",
       "      <td>GBR</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>1089404014.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>914</td>\n",
       "      <td>/organization/adinsight</td>\n",
       "      <td>/funding-round/5bcca548aeccd5acdea0d0edb086db8d</td>\n",
       "      <td>6772812.000</td>\n",
       "      <td>responsetap</td>\n",
       "      <td>advertising</td>\n",
       "      <td>GBR</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>1089404014.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>920</td>\n",
       "      <td>/organization/adjug</td>\n",
       "      <td>/funding-round/83653befbf3c473ec97a1617479448c8</td>\n",
       "      <td>6500000.000</td>\n",
       "      <td>adjug</td>\n",
       "      <td>advertising</td>\n",
       "      <td>GBR</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>1089404014.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           company_permalink  \\\n",
       "108                  /organization/365scores   \n",
       "396  /organization/acal-enterprise-solutions   \n",
       "836                    /organization/adbrain   \n",
       "914                  /organization/adinsight   \n",
       "920                      /organization/adjug   \n",
       "\n",
       "                             funding_round_permalink  raised_amount_usd  \\\n",
       "108  /funding-round/48212f931f542fdef78810bc87aef086        5500000.000   \n",
       "396  /funding-round/0f9a693d9686330c5c2724215e0048e2       10720178.000   \n",
       "836  /funding-round/71f1b5903874817292d67e1ac6aa66b2        7500000.000   \n",
       "914  /funding-round/5bcca548aeccd5acdea0d0edb086db8d        6772812.000   \n",
       "920  /funding-round/83653befbf3c473ec97a1617479448c8        6500000.000   \n",
       "\n",
       "                          name           category_list country_code  \\\n",
       "108                  365scores                 android          GBR   \n",
       "396  acal enterprise solutions  information technology          GBR   \n",
       "836                    adbrain             advertising          GBR   \n",
       "914                responsetap             advertising          GBR   \n",
       "920                      adjug             advertising          GBR   \n",
       "\n",
       "                                 main_sector  Total_Investment_Count_GBR  \\\n",
       "108  social, finance, analytics, advertising                         133   \n",
       "396  social, finance, analytics, advertising                         133   \n",
       "836  social, finance, analytics, advertising                         133   \n",
       "914  social, finance, analytics, advertising                         133   \n",
       "920  social, finance, analytics, advertising                         133   \n",
       "\n",
       "     Total_amount_invested_GBR_usd  \n",
       "108                 1089404014.000  \n",
       "396                 1089404014.000  \n",
       "836                 1089404014.000  \n",
       "914                 1089404014.000  \n",
       "920                 1089404014.000  "
      ]
     },
     "execution_count": 1152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe D2 (display head)\n",
    "D2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328"
      ]
     },
     "execution_count": 1153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataframe D3 for Country_3: IND\n",
    "\n",
    "#Merging 'C3' and 'tic_ind'\n",
    "D3 = pd.merge(C3, tic_ind, how='inner', left_on ='main_sector', right_index = True)\n",
    "#Merging further with 'tai_ind'\n",
    "D3 = pd.merge(D3, tai_ind, how='inner', left_on ='main_sector', right_index = True)\n",
    "D3.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>funding_round_permalink</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>name</th>\n",
       "      <th>category_list</th>\n",
       "      <th>country_code</th>\n",
       "      <th>main_sector</th>\n",
       "      <th>Total_Investment_Count_IND</th>\n",
       "      <th>Total_amount_invested_IND_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/-fame</td>\n",
       "      <td>/funding-round/9a01d05418af9f794eebff7ace91f638</td>\n",
       "      <td>10000000.000</td>\n",
       "      <td>#fame</td>\n",
       "      <td>media</td>\n",
       "      <td>IND</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>33</td>\n",
       "      <td>280830000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>926</td>\n",
       "      <td>/organization/adlabs-imagica</td>\n",
       "      <td>/funding-round/508d3c83daaae9fda3ba6f9682c78f6c</td>\n",
       "      <td>8180000.000</td>\n",
       "      <td>adlabs imagica</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>IND</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>33</td>\n",
       "      <td>280830000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2313</td>\n",
       "      <td>/organization/apalya</td>\n",
       "      <td>/funding-round/21e89efcaeab1bb1f61323686af357bf</td>\n",
       "      <td>7500000.000</td>\n",
       "      <td>apalya</td>\n",
       "      <td>content</td>\n",
       "      <td>IND</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>33</td>\n",
       "      <td>280830000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2315</td>\n",
       "      <td>/organization/apalya</td>\n",
       "      <td>/funding-round/fc622ba39384746a32c974c1c5891438</td>\n",
       "      <td>8000000.000</td>\n",
       "      <td>apalya</td>\n",
       "      <td>content</td>\n",
       "      <td>IND</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>33</td>\n",
       "      <td>280830000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6969</td>\n",
       "      <td>/organization/chef-s-basket</td>\n",
       "      <td>/funding-round/b4d86bd15405c6a70ebbf724befc214a</td>\n",
       "      <td>6000000.000</td>\n",
       "      <td>chef's basket</td>\n",
       "      <td>restaurants</td>\n",
       "      <td>IND</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>33</td>\n",
       "      <td>280830000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 company_permalink  \\\n",
       "0              /organization/-fame   \n",
       "926   /organization/adlabs-imagica   \n",
       "2313          /organization/apalya   \n",
       "2315          /organization/apalya   \n",
       "6969   /organization/chef-s-basket   \n",
       "\n",
       "                              funding_round_permalink  raised_amount_usd  \\\n",
       "0     /funding-round/9a01d05418af9f794eebff7ace91f638       10000000.000   \n",
       "926   /funding-round/508d3c83daaae9fda3ba6f9682c78f6c        8180000.000   \n",
       "2313  /funding-round/21e89efcaeab1bb1f61323686af357bf        7500000.000   \n",
       "2315  /funding-round/fc622ba39384746a32c974c1c5891438        8000000.000   \n",
       "6969  /funding-round/b4d86bd15405c6a70ebbf724befc214a        6000000.000   \n",
       "\n",
       "                name  category_list country_code    main_sector  \\\n",
       "0              #fame          media          IND  entertainment   \n",
       "926   adlabs imagica  entertainment          IND  entertainment   \n",
       "2313          apalya        content          IND  entertainment   \n",
       "2315          apalya        content          IND  entertainment   \n",
       "6969   chef's basket    restaurants          IND  entertainment   \n",
       "\n",
       "      Total_Investment_Count_IND  Total_amount_invested_IND_usd  \n",
       "0                             33                  280830000.000  \n",
       "926                           33                  280830000.000  \n",
       "2313                          33                  280830000.000  \n",
       "2315                          33                  280830000.000  \n",
       "6969                          33                  280830000.000  "
      ]
     },
     "execution_count": 1154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have three separate data frames D1, D2 and D3 for each of the three countries containing the observations of funding type FT (venture) falling within the 5-15 million USD range. They contain all the columns of the master_frame. (Here, venture_master_frame is derived from the master_frame). Also, in addition to this, it also contains the total number (or count) of investments for each main sector and the total amount invested in each main sector in separate columns.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For the top sector count-wise (as per the top sector based out of investment count), which company received the highest investment?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**USA**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating new dataframes 'D1_1_top','D2_1_top' and 'D3_1_top' containing only the first top sector for the respective countries \n",
    "#Using dataframes D1, D2 and D3.\n",
    "\n",
    "#The top main_sector sector for USA, GBR and IND is 'others'. Hence, retaining only those records.\n",
    "D1_1_top = D1[(D1['main_sector'] == 'others')]\n",
    "D2_1_top = D2[(D2['main_sector'] == 'others')]\n",
    "D3_1_top = D3[(D3['main_sector'] == 'others')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1156,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/virtustream</td>\n",
       "      <td>virtustream</td>\n",
       "      <td>64300000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/capella</td>\n",
       "      <td>capella photonics</td>\n",
       "      <td>54968051.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/airtight-networks</td>\n",
       "      <td>airtight networks</td>\n",
       "      <td>54201907.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/decarta</td>\n",
       "      <td>decarta</td>\n",
       "      <td>52100000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/black-duck-software</td>\n",
       "      <td>black duck software</td>\n",
       "      <td>51000000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   company_permalink                 name  raised_amount_usd\n",
       "0          /organization/virtustream          virtustream       64300000.000\n",
       "1              /organization/capella    capella photonics       54968051.000\n",
       "2    /organization/airtight-networks    airtight networks       54201907.000\n",
       "3              /organization/decarta              decarta       52100000.000\n",
       "4  /organization/black-duck-software  black duck software       51000000.000"
      ]
     },
     "execution_count": 1156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the top sector (in USA) count-wise (as per the top sector based out of investment count)\n",
    "#Determining the company that received the highest investment\n",
    "\n",
    "D1_1_top = D1_1_top.groupby(['company_permalink','name'])['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "D1_1_top = pd.DataFrame(D1_1_top)\n",
    "D1_1_top.reset_index(inplace=True)\n",
    "D1_1_top.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1157,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/virtustream</td>\n",
       "      <td>virtustream</td>\n",
       "      <td>64300000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           company_permalink         name  raised_amount_usd\n",
       "0  /organization/virtustream  virtustream       64300000.000"
      ]
     },
     "execution_count": 1157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only maximum investment records\n",
    "D1_1_top = D1_1_top[D1_1_top['raised_amount_usd'] == D1_1_top.raised_amount_usd.max()]\n",
    "D1_1_top = D1_1_top.sort_values('company_permalink', ascending=True)\n",
    "D1_1_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the top sector (in USA) count-wise (as per the top sector based out of investment counts), the company that received the highest investment:\n",
      "\n",
      "Unique ID of company: /organization/virtustream\n",
      "Company name: virtustream\n"
     ]
    }
   ],
   "source": [
    "print('For the top sector (in USA) count-wise (as per the top sector based out of investment counts), \\\n",
    "the company that received the highest investment:\\n')\n",
    "\n",
    "print('Unique ID of company:',D1_1_top['company_permalink'][0])\n",
    "print('Company name:',D1_1_top['name'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**GBR**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1159,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/electric-cloud</td>\n",
       "      <td>electric cloud</td>\n",
       "      <td>37000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/sensage</td>\n",
       "      <td>sensage</td>\n",
       "      <td>36250000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/enigmatic</td>\n",
       "      <td>enigmatec</td>\n",
       "      <td>32500000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/silverrail-technologies</td>\n",
       "      <td>silverrail technologies</td>\n",
       "      <td>29000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/opencloud</td>\n",
       "      <td>opencloud</td>\n",
       "      <td>27972766.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       company_permalink                     name  \\\n",
       "0           /organization/electric-cloud           electric cloud   \n",
       "1                  /organization/sensage                  sensage   \n",
       "2                /organization/enigmatic                enigmatec   \n",
       "3  /organization/silverrail-technologies  silverrail technologies   \n",
       "4                /organization/opencloud                opencloud   \n",
       "\n",
       "   raised_amount_usd  \n",
       "0       37000000.000  \n",
       "1       36250000.000  \n",
       "2       32500000.000  \n",
       "3       29000000.000  \n",
       "4       27972766.000  "
      ]
     },
     "execution_count": 1159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the top sector (in GBR) count-wise (as per the top sector based out of investment count)\n",
    "#Determining the company that received the highest investment\n",
    "\n",
    "D2_1_top = D2_1_top.groupby(['company_permalink','name'])['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "D2_1_top = pd.DataFrame(D2_1_top)\n",
    "D2_1_top.reset_index(inplace=True)\n",
    "D2_1_top.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/electric-cloud</td>\n",
       "      <td>electric cloud</td>\n",
       "      <td>37000000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              company_permalink            name  raised_amount_usd\n",
       "0  /organization/electric-cloud  electric cloud       37000000.000"
      ]
     },
     "execution_count": 1160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only those records\n",
    "D2_1_top = D2_1_top[D2_1_top['raised_amount_usd'] == D2_1_top.raised_amount_usd.max()]\n",
    "D2_1_top = D2_1_top.sort_values('company_permalink',ascending=True)\n",
    "D2_1_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the top sector (in GBR) count-wise (as per the top sector based out of investment counts), the company that received the highest investment:\n",
      "\n",
      "Unique ID of Company: /organization/electric-cloud\n",
      "Company name: electric cloud\n"
     ]
    }
   ],
   "source": [
    "print('For the top sector (in GBR) count-wise (as per the top sector based out of investment counts), \\\n",
    "the company that received the highest investment:\\n')\n",
    "\n",
    "print('Unique ID of Company:',D2_1_top['company_permalink'][0])\n",
    "print('Company name:',D2_1_top['name'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**IND**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1162,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/firstcry-com</td>\n",
       "      <td>firstcry.com</td>\n",
       "      <td>39000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/myntra</td>\n",
       "      <td>myntra</td>\n",
       "      <td>38000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/commonfloor</td>\n",
       "      <td>commonfloor</td>\n",
       "      <td>32900000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/pepperfry-com</td>\n",
       "      <td>pepperfry.com</td>\n",
       "      <td>28000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/itzcash-card-ltd</td>\n",
       "      <td>itzcash card ltd.</td>\n",
       "      <td>25000000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                company_permalink               name  raised_amount_usd\n",
       "0      /organization/firstcry-com       firstcry.com       39000000.000\n",
       "1            /organization/myntra             myntra       38000000.000\n",
       "2       /organization/commonfloor        commonfloor       32900000.000\n",
       "3     /organization/pepperfry-com      pepperfry.com       28000000.000\n",
       "4  /organization/itzcash-card-ltd  itzcash card ltd.       25000000.000"
      ]
     },
     "execution_count": 1162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the top sector (in IND) count-wise (as per the top sector based out of investment count)\n",
    "#Determining the company that received the highest investment\n",
    "\n",
    "D3_1_top = D3_1_top.groupby(['company_permalink','name'])['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "D3_1_top = pd.DataFrame(D3_1_top)\n",
    "D3_1_top.reset_index(inplace=True)\n",
    "D3_1_top.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1163,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/firstcry-com</td>\n",
       "      <td>firstcry.com</td>\n",
       "      <td>39000000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            company_permalink          name  raised_amount_usd\n",
       "0  /organization/firstcry-com  firstcry.com       39000000.000"
      ]
     },
     "execution_count": 1163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only those records\n",
    "D3_1_top = D3_1_top[D3_1_top['raised_amount_usd'] == D3_1_top.raised_amount_usd.max()]\n",
    "D3_1_top = D3_1_top.sort_values('company_permalink',ascending=True)\n",
    "D3_1_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the top sector (IND) count-wise (as per the top sector based out of investment counts), the company that received the highest investment:\n",
      "\n",
      "Unique ID of Company: /organization/firstcry-com\n",
      "Company name: firstcry.com\n"
     ]
    }
   ],
   "source": [
    "print('For the top sector (IND) count-wise (as per the top sector based out of investment counts), \\\n",
    "the company that received the highest investment:\\n')\n",
    "\n",
    "print('Unique ID of Company:',D3_1_top['company_permalink'][0])\n",
    "print('Company name:',D3_1_top['name'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For the second-best sector count-wise (as per the top sector based out of investment count), which company received the highest investment?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**USA**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating new dataframes 'D1_2_top','D2_2_top' and 'D3_2_top' containing only the second-best sector for the respective countries \n",
    "#Using dataframes D1, D2 and D3.\n",
    "\n",
    "D1_2_top = D1[(D1['main_sector'] == 'social, finance, analytics, advertising')]\n",
    "D2_2_top = D2[(D2['main_sector'] == 'social, finance, analytics, advertising')]\n",
    "D3_2_top = D3[(D3['main_sector'] == 'social, finance, analytics, advertising')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1166,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/shotspotter</td>\n",
       "      <td>sst inc. (formerly shotspotter)</td>\n",
       "      <td>67933006.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/demandbase</td>\n",
       "      <td>demandbase</td>\n",
       "      <td>63000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/intacct</td>\n",
       "      <td>intacct</td>\n",
       "      <td>61800000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/netbase</td>\n",
       "      <td>netbase solutions</td>\n",
       "      <td>60600000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/lotame</td>\n",
       "      <td>lotame</td>\n",
       "      <td>59700000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           company_permalink                             name  \\\n",
       "0  /organization/shotspotter  sst inc. (formerly shotspotter)   \n",
       "1   /organization/demandbase                       demandbase   \n",
       "2      /organization/intacct                          intacct   \n",
       "3      /organization/netbase                netbase solutions   \n",
       "4       /organization/lotame                           lotame   \n",
       "\n",
       "   raised_amount_usd  \n",
       "0       67933006.000  \n",
       "1       63000000.000  \n",
       "2       61800000.000  \n",
       "3       60600000.000  \n",
       "4       59700000.000  "
      ]
     },
     "execution_count": 1166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the second-best sector (in USA) count-wise (as per the top sector based out of investment count)\n",
    "#Determining the company that received the highest investment\n",
    "\n",
    "D1_2_top = D1_2_top.groupby(['company_permalink','name'])['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "D1_2_top = pd.DataFrame(D1_2_top)\n",
    "D1_2_top.reset_index(inplace=True)\n",
    "D1_2_top.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1167,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/shotspotter</td>\n",
       "      <td>sst inc. (formerly shotspotter)</td>\n",
       "      <td>67933006.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           company_permalink                             name  \\\n",
       "0  /organization/shotspotter  sst inc. (formerly shotspotter)   \n",
       "\n",
       "   raised_amount_usd  \n",
       "0       67933006.000  "
      ]
     },
     "execution_count": 1167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only maximum investment records\n",
    "D1_2_top = D1_2_top[D1_2_top['raised_amount_usd'] == D1_2_top.raised_amount_usd.max()]\n",
    "D1_2_top = D1_2_top.sort_values('company_permalink',ascending=True)\n",
    "D1_2_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the second-best sector (in USA) count-wise (as per the top sector based out of investment counts), the company that received the highest investment:\n",
      "\n",
      "Unique ID of Company: /organization/shotspotter\n",
      "Company name: sst inc. (formerly shotspotter)\n"
     ]
    }
   ],
   "source": [
    "print('For the second-best sector (in USA) count-wise (as per the top sector based out of investment counts), \\\n",
    "the company that received the highest investment:\\n')\n",
    "\n",
    "print('Unique ID of Company:',D1_2_top['company_permalink'][0])\n",
    "print('Company name:',D1_2_top['name'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**GBR**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1169,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/celltick-technologies</td>\n",
       "      <td>celltick technologies</td>\n",
       "      <td>37500000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/mythings</td>\n",
       "      <td>mythings</td>\n",
       "      <td>34000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/zopa</td>\n",
       "      <td>zopa</td>\n",
       "      <td>32900000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/imagini</td>\n",
       "      <td>visualdna</td>\n",
       "      <td>28550000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/marketinvoice</td>\n",
       "      <td>marketinvoice</td>\n",
       "      <td>25553007.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink                   name  \\\n",
       "0  /organization/celltick-technologies  celltick technologies   \n",
       "1               /organization/mythings               mythings   \n",
       "2                   /organization/zopa                   zopa   \n",
       "3                /organization/imagini              visualdna   \n",
       "4          /organization/marketinvoice          marketinvoice   \n",
       "\n",
       "   raised_amount_usd  \n",
       "0       37500000.000  \n",
       "1       34000000.000  \n",
       "2       32900000.000  \n",
       "3       28550000.000  \n",
       "4       25553007.000  "
      ]
     },
     "execution_count": 1169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the second-best sector (in GBR) count-wise (as per the top sector based out of investment count)\n",
    "#Determining the company that received the highest investment\n",
    "\n",
    "D2_2_top = D2_2_top.groupby(['company_permalink','name'])['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "D2_2_top = pd.DataFrame(D2_2_top)\n",
    "D2_2_top.reset_index(inplace=True)\n",
    "D2_2_top.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1170,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/celltick-technologies</td>\n",
       "      <td>celltick technologies</td>\n",
       "      <td>37500000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_permalink                   name  \\\n",
       "0  /organization/celltick-technologies  celltick technologies   \n",
       "\n",
       "   raised_amount_usd  \n",
       "0       37500000.000  "
      ]
     },
     "execution_count": 1170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only those records\n",
    "D2_2_top = D2_2_top[D2_2_top['raised_amount_usd'] == D2_2_top.raised_amount_usd.max()]\n",
    "D2_2_top = D2_2_top.sort_values('company_permalink',ascending=True)\n",
    "D2_2_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the second-best sector (in GBR) count-wise (as per the top sector based out of investment counts), the company that received the highest investment:\n",
      "\n",
      "Unique ID of Company: /organization/celltick-technologies\n",
      "Company name: celltick technologies\n"
     ]
    }
   ],
   "source": [
    "print('For the second-best sector (in GBR) count-wise (as per the top sector based out of investment counts), \\\n",
    "the company that received the highest investment:\\n')\n",
    "D2_2_top['company_permalink'][0]\n",
    "\n",
    "print('Unique ID of Company:',D2_2_top['company_permalink'][0])\n",
    "print('Company name:',D2_2_top['name'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='maroon'>**IND**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1172,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/manthan-systems</td>\n",
       "      <td>manthan systems</td>\n",
       "      <td>50700000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>/organization/komli-media</td>\n",
       "      <td>komli media</td>\n",
       "      <td>28000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>/organization/shopclues-com</td>\n",
       "      <td>shopclues.com</td>\n",
       "      <td>25000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>/organization/intarvo</td>\n",
       "      <td>intarvo</td>\n",
       "      <td>21900000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>/organization/grameen-financial-services</td>\n",
       "      <td>grameen financial services</td>\n",
       "      <td>21556050.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          company_permalink                        name  \\\n",
       "0             /organization/manthan-systems             manthan systems   \n",
       "1                 /organization/komli-media                 komli media   \n",
       "2               /organization/shopclues-com               shopclues.com   \n",
       "3                     /organization/intarvo                     intarvo   \n",
       "4  /organization/grameen-financial-services  grameen financial services   \n",
       "\n",
       "   raised_amount_usd  \n",
       "0       50700000.000  \n",
       "1       28000000.000  \n",
       "2       25000000.000  \n",
       "3       21900000.000  \n",
       "4       21556050.000  "
      ]
     },
     "execution_count": 1172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the second-best (in IND) sector count-wise (as per the top sector based out of investment count)\n",
    "#Determining the company that received the highest investment\n",
    "\n",
    "D3_2_top = D3_2_top.groupby(['company_permalink','name'])['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "D3_2_top = pd.DataFrame(D3_2_top)\n",
    "D3_2_top.reset_index(inplace=True)\n",
    "D3_2_top.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1173,
   "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>company_permalink</th>\n",
       "      <th>name</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>/organization/manthan-systems</td>\n",
       "      <td>manthan systems</td>\n",
       "      <td>50700000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               company_permalink             name  raised_amount_usd\n",
       "0  /organization/manthan-systems  manthan systems       50700000.000"
      ]
     },
     "execution_count": 1173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retaining only those records\n",
    "\n",
    "D3_2_top = D3_2_top[D3_2_top['raised_amount_usd'] == D3_2_top.raised_amount_usd.max()]\n",
    "D3_2_top = D3_2_top.sort_values('company_permalink',ascending=True)\n",
    "D3_2_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the second-best (in IND) sector count-wise (as per the top sector based out of investment counts), the company that received the highest investment:\n",
      "\n",
      "Unique ID of Company: /organization/manthan-systems\n",
      "Company name: manthan systems\n"
     ]
    }
   ],
   "source": [
    "print('For the second-best (in IND) sector count-wise (as per the top sector based out of investment counts), \\\n",
    "the company that received the highest investment:\\n')\n",
    "\n",
    "print('Unique ID of Company:',D3_2_top['company_permalink'][0])\n",
    "print('Company name:',D3_2_top['name'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='RoyalBlue4'> Checkpoint 6: Plots</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color='darkslateblue'>(1) Plot 1:<br>\n",
    "(i) A plot showing the fraction of total investments (globally) in angel, venture, seed, and private equity, and <br>(ii) The average amount of investment in each funding type.</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1175,
   "metadata": {
    "scrolled": false
   },
   "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>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>venture</td>\n",
       "      <td>10000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>seed</td>\n",
       "      <td>700000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>venture</td>\n",
       "      <td>2000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>seed</td>\n",
       "      <td>43360.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>venture</td>\n",
       "      <td>719491.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  funding_round_type  raised_amount_usd\n",
       "0            venture       10000000.000\n",
       "2               seed         700000.000\n",
       "4            venture        2000000.000\n",
       "7               seed          43360.000\n",
       "8            venture         719491.000"
      ]
     },
     "execution_count": 1175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For a plot showing the fraction of total investments (globally) in angel, venture, seed, and private equity\n",
    "#Bringing in: Dataframe 'investments_four_types' contains the total investments in angel, venture, seed,\n",
    "#and private equity (globally)\n",
    "\n",
    "investments_four_types.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1176,
   "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>raised_amount_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funding_round_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>venture</td>\n",
       "      <td>560523362596.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>private_equity</td>\n",
       "      <td>134568045021.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>seed</td>\n",
       "      <td>15774707732.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>angel</td>\n",
       "      <td>4274925121.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    raised_amount_usd\n",
       "funding_round_type                   \n",
       "venture              560523362596.000\n",
       "private_equity       134568045021.000\n",
       "seed                  15774707732.000\n",
       "angel                  4274925121.000"
      ]
     },
     "execution_count": 1176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using groupby to get the sum total of raised_amount_usd (globally) by investment type\n",
    "#Dataframe 'tia_typewise': Total investment amounts typewise in usd\n",
    "\n",
    "tia_typewise = investments_four_types.groupby('funding_round_type')['raised_amount_usd'].sum().sort_values(ascending=False)\n",
    "tia_typewise = pd.DataFrame(tia_typewise)\n",
    "tia_typewise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1177,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funding_round_type</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>private_equity</td>\n",
       "      <td>73938486.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>venture</td>\n",
       "      <td>11724222.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>angel</td>\n",
       "      <td>971573.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>seed</td>\n",
       "      <td>747793.682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  funding_round_type  raised_amount_usd\n",
       "0     private_equity       73938486.275\n",
       "1            venture       11724222.690\n",
       "2              angel         971573.891\n",
       "3               seed         747793.682"
      ]
     },
     "execution_count": 1177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bringing in: Dataframe 'investments_avg_typewise' contains the average value of total investments for each of the \n",
    "#four types\n",
    "\n",
    "#Reset_index\n",
    "investments_avg_typewise.reset_index(inplace=True)\n",
    "investments_avg_typewise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2400x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(num= 2, figsize=(12,7), dpi=200)\n",
    "\n",
    "plt.subplot(121)\n",
    "funding_labels = ['Venture','Private Equity','Seed','Angel'] \n",
    "defined_colors = ['Chocolate', 'Firebrick','Green','RoyalBlue']\n",
    "\n",
    "plt.pie(tia_typewise, labels = funding_labels, autopct='%1.1f%%', colors= defined_colors)\n",
    "plt.title('Fraction of Total investments(globally)', fontsize=13, fontweight='bold')\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "#Creating a copy of investments_four_types dataframe\n",
    "plot_avg_investments = investments_four_types.copy()\n",
    "#Using Seaborn barplot which by default returns mean values on the plot\n",
    "ax = sns.barplot(x= plot_avg_investments['funding_round_type'], y= plot_avg_investments['raised_amount_usd']/1000000, \\\n",
    "            order=[\"angel\",\"venture\",\"seed\",\"private_equity\"])\n",
    "\n",
    "#Assigning labels and titles\n",
    "ax.set_xticklabels(['Angel','Venture','Seed','Private Equity'])\n",
    "plt.title(\"Average amount of investment per funding type (in millions usd)\", fontsize=13, fontweight='bold')\n",
    "plt.xlabel('Investment Funding Type', fontsize=12, fontstyle='italic')\n",
    "plt.ylabel('Average amount raised in investments (in millions usd)',fontsize=12, fontstyle='italic')\n",
    "\n",
    "#Lines for determining the most-suitable investment type\n",
    "plt.axhline(linewidth='2', linestyle='dashed', label= 'Minimum Limit: 5 million USD', color='g', y=5)\n",
    "plt.axhline(linewidth='2', linestyle='dashed', label= 'Maximum Limit: 15 million USD', color='r', y=15)\n",
    "\n",
    "#To avoid overlap of entities in subplots\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color='darkslateblue'>(2) Plot 2:<br>Top 9 countries vs the total amount of investments of funding type FT (venture)</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1179,
   "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>country_code</th>\n",
       "      <th>raised_amount_usd</th>\n",
       "      <th>english_official_lang_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>420068029342.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>39338918773.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GBR</td>\n",
       "      <td>20072813004.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>IND</td>\n",
       "      <td>14261508718.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>CAN</td>\n",
       "      <td>9482217668.000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>FRA</td>\n",
       "      <td>7226851352.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ISR</td>\n",
       "      <td>6854350477.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>DEU</td>\n",
       "      <td>6306921981.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>JPN</td>\n",
       "      <td>3167647127.000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code  raised_amount_usd english_official_lang_flag\n",
       "0          USA   420068029342.000                        Yes\n",
       "1          CHN    39338918773.000                         No\n",
       "2          GBR    20072813004.000                        Yes\n",
       "3          IND    14261508718.000                        Yes\n",
       "4          CAN     9482217668.000                        Yes\n",
       "5          FRA     7226851352.000                         No\n",
       "6          ISR     6854350477.000                         No\n",
       "7          DEU     6306921981.000                         No\n",
       "8          JPN     3167647127.000                         No"
      ]
     },
     "execution_count": 1179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top9 DataFrame\n",
    "top9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set grid style\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "plt.figure(num=3, figsize=(12,6), dpi=200, edgecolor='k')\n",
    "\n",
    "#barplot using Seaborn\n",
    "sns.barplot(x = top9[\"country_code\"], y = top9[\"raised_amount_usd\"]/1000000000, \\\n",
    "            hue = top9[\"english_official_lang_flag\"].rename('English Speaking Country'))\n",
    "plt.ylabel('Raised amount for venture type_FT (in billions usd)', fontstyle= 'italic')\n",
    "plt.xlabel('Country codes', fontstyle= 'italic')\n",
    "plt.title(\"Top 9 countries vs Total amount of investments of 'Venture' type funding\", \\\n",
    "          fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color='darkslateblue'>(3) Plot 3:<br>Number of investments in the top 3 main sectors of top 3 countries for chosen investment type (venture)</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1181,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Total number of investment counts dataframes were 'tic_usa','tic_gbr' and 'tic_ind' \n",
    "#For all the 3 English speaking countries\n",
    "#For chosen investment type FT (venture)\n",
    "\n",
    "#Reset the index of 'tic_usa','tic_gbr' and 'tic_ind'\n",
    "tic_usa.reset_index(inplace=True)\n",
    "tic_gbr.reset_index(inplace=True)\n",
    "tic_ind.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming all the total investment counts columns of 3 countries to a similar name\n",
    "tic_usa.rename(columns={'Total_Investment_Count_USA':'total_investment_count'}, inplace=True)  #For USA\n",
    "tic_gbr.rename(columns={'Total_Investment_Count_GBR':'total_investment_count'}, inplace=True)  #For GBR\n",
    "tic_ind.rename(columns={'Total_Investment_Count_IND':'total_investment_count'}, inplace=True)  #For IND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_sector</th>\n",
       "      <th>total_investment_count</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>others</td>\n",
       "      <td>2950</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>2714</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>2350</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>others</td>\n",
       "      <td>147</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>133</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>cleantech / semiconductors</td>\n",
       "      <td>130</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>others</td>\n",
       "      <td>110</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>social, finance, analytics, advertising</td>\n",
       "      <td>60</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>news, search and messaging</td>\n",
       "      <td>52</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               main_sector  total_investment_count country\n",
       "0                                   others                    2950     USA\n",
       "1  social, finance, analytics, advertising                    2714     USA\n",
       "2               cleantech / semiconductors                    2350     USA\n",
       "3                                   others                     147     GBR\n",
       "4  social, finance, analytics, advertising                     133     GBR\n",
       "5               cleantech / semiconductors                     130     GBR\n",
       "6                                   others                     110     IND\n",
       "7  social, finance, analytics, advertising                      60     IND\n",
       "8               news, search and messaging                      52     IND"
      ]
     },
     "execution_count": 1183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'tic_usa','tic_gbr' and 'tic_ind' sort in descending and retain only 1st 3 sectors:\n",
    "\n",
    "tic_usa = tic_usa.sort_values(by= 'total_investment_count', ascending = False).head(3)\n",
    "#Adding country column to tic_usa\n",
    "tic_usa['country'] = 'USA'\n",
    "\n",
    "tic_gbr = tic_gbr.sort_values(by= 'total_investment_count', ascending = False).head(3)\n",
    "#Adding country column to tic_gbr\n",
    "tic_gbr['country'] = 'GBR'\n",
    "\n",
    "tic_ind = tic_ind.sort_values(by= 'total_investment_count', ascending = False).head(3)\n",
    "#Adding country column to tic_ind\n",
    "tic_ind['country'] = 'IND'\n",
    "\n",
    "#Merging the dataframes 'tic_usa','tic_gbr' and 'tic_ind' for total investment counts in 3 countries\n",
    "#Resultant dataframe is 'tic_all_3_countries'\n",
    "tic_all_3_countries = pd.merge(tic_usa, tic_gbr, how='outer')\n",
    "tic_all_3_countries = pd.merge(tic_all_3_countries, tic_ind, how = 'outer')\n",
    "tic_all_3_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1184,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACUsAAAYrCAYAAAAoJ34PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAewgAAHsIBbtB1PgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd1gUx/8H8DegKIioCHZjxS5qxF6iYkTAmhBRUIy9YsNGjEQliQ17I/aWqF9UQBFBQVRQRGkaO4KAICBKB4UD7vcHz92PY2evwB0lfl7P4/PI3N7e3N7u7OzMZ2bUhEKhEIQQQgghhBBCCCGEEEIIIYQQQgghhBDyH6de2RkghBBCCCGEEEIIIYQQQgghhBBCCCGEkIpAwVKEEEIIIYQQQgghhBBCCCGEEEIIIYSQrwIFSxFCCCGEEEIIIYQQQgghhBBCCCGEEEK+ChQsRQghhBBCCCGEEEIIIYQQQgghhBBCCPkqULAUIYQQQgghhBBCCCGEEEIIIYQQQggh5KtAwVKEEEIIIYQQQgghhBBCCCGEEEIIIYSQrwIFSxFCCCGEEEIIIYQQQgghhBBCCCGEEEK+ChQsRQghhBBCCCGEEEIIIYQQQgghhBBCCPkqULAUIYQQQgghhBBCCCGEEEIIIYQQQggh5KtAwVKEEEIIIYQQQgghhBBCCCGEEEIIIYSQrwIFSxFCCCGEEEIIIYQQQgghhBBCCCGEEEK+ChQsRQghhBBCCCGEEEIIIYQQQgghhBBCCPkqULAUIYQQQgghhBBCCCGEEEIIIYQQQggh5KtAwVKEEEIIIYQQQgghhBBCCCGEEEIIIYSQrwIFSxFCCCGEEEIIIYQQQgghhBBCCCGEEEK+ChQsRQghhBBCCCGEEEIIIYQQQgghhBBCCPkqULAUIYQQQgghhBBCCCGEEEIIIYQQQggh5KtAwVKEEEIIIYQQQgghhBBCCCGEEEIIIYSQrwIFSxFCCCGEEEIIIYQQQgghhBBCCCGEEEK+ChQsRQghhBBCiBIVFRUhNze3srPxVaFjTggh/w3Z2dmVnQVCCKn2qCwlpPqg67Vy0HEnhBBCCADUqOwMEEII+brFx8fDxMSEkz5q1Cjs27dPrn0EBwfD1taWk/7q1aty56+64juup0+fRr9+/SohR1+voKAgXLlyBWFhYfj48SPy8/Ohp6eHJk2aoHfv3hgzZgy6dOki9/74ftvFixfDzs5OmVknZRAeHg4nJyesWbOGrrUKUh2OeXUrk6dNm4aHDx8qfb+bN2/GDz/8oPT9VifJycm4fv06/P398e7dO3z8+BE1atSAvr4+jIyMYGJiAlNTU6ir07gmeZ07dw4bNmyQSNPU1MS///5bORkiZZKamopdu3ahRo0a+O233yo7O0SK3NxcXLx4EX5+fnj79i3S0tJQq1Yt6OnpoX379ujbty9++ukn1KlTR+a+7t69izlz5ig9j23atIG3t7fS91udFBUV4e7du/D19UVERARSUlKQk5OD+vXro1WrVujfvz8sLS3RtGnTys4qUaKPHz9ix44d0NXVhYODg9L3n5mZiT59+nDSDxw4gJEjRyr986q67OxscXkYGxuLtLQ01K5dG3p6ejA0NETfvn0xadIk1K5du7KzWm5//PEHTp8+zUl3d3dHx44dMWzYMCQnJ3Ne9/DwQKdOnRT6rNu3b2PevHmcdDMzM+zevVuhfVVlhYWFOH/+PP7++294eXlVdnaqhIiICFhZWXHS58yZg5UrVyrlMz58+IDt27ejSZMmsLe3V8o+CVdycjJcXV1x584dvH//HhkZGahTpw4aNmyILl26YMCAAfjxxx8rO5uVwtHRERcuXJBI69GjB/73v/9xtu3evTvy8/Ml0jZs2IApU6aoNI+EEPI1oWApQgghVdKNGzdw584dfPfdd5WdFULKpKioCGvXroWHhwfntaSkJCQlJSEiIgL6+voKBUuRqiklJQU7duyAu7s7hEJhZWfnq0DHnFQnAoEABw8exLFjx5CXlyfxWl5eHnJychAbG4urV6+ibdu2cHZ2RteuXSspt4RUnMLCQpw7dw579+5FRkYGs4OMVB2vX7/G/PnzkZCQIJGen5+PrKwsxMbG4tatW7C0tKykHBKguLN548aNeP78Oee1lJQUpKSkICQkBH/99RdmzpyJpUuXQkNDoxJySpSloKAAZ8+exf79+5GVlcUcTEaU6/nz51iwYAGSkpIk0vPz85GZmYmYmBj4+/tj8uTJlZTDiqOurg4LCwscP36c85qXl5fCwVI+Pj7M9LFjx5Ypf1VRSEgInJyc8PLlS+jr61d2dr4KAoEAp06dwoEDB5Cbm6uSYG1SzN/fH6tWrUJWVpZEenp6OtLT0xEVFYUnT558tcFShBBCqhYarkoIIaTK+v333zkdioRUFydOnGAGSpXWuXPnCsgNUZXCwkKcPHkSo0ePhpubGwXtVAA65qS6KSgowPLly3Hw4EG56jXR0dGYMmUK7t69WwG5I6TyhISEYMKECXByckJGRkZlZ4fIUFRUBDs7O06gVGnffPMNdHR0KihXpLSgoCDY2toyA6VKEwgE+Ouvv7Bo0SIUFBRUQO6IKjx48ADjx4/H5s2bOR3TRDUEAgEWL17MCZQqrW3bttDU1KygXFUuvkAmRWdMKigowK1btzjp9evXx5AhQ8qUt6rkw4cPsLe3h42NDV6+fFnZ2flqBAYGYsyYMdi+fTtyc3MrOzv/aR8+fMCKFStk3o8UDaIkhBBCVIWCpQghhFRZcXFxOHz4cGVng5AyuXbtmlzbdezYUcU5IaqUmJiIzZs3Izs7u7Kz8tWgY06qm2PHjuHmzZsKvScvLw9Lly6VGZRASHW2bds2vH79urKzQeT09OlTxMTEyNyOOr8qT3p6OpYsWaLwgCN/f3/s2LFDRbkiqrZ582a8efOmsrPxVXn8+LFcdbSv6Vm/S5cuaNeuHSf93bt3Ci2NHBQUhPT0dE66qanpfyLwzN3dHZ6enpWdja/O77//LlcdhpTf7du35QpIo/oiIYSQqoKW4SOEEFKlHT58GOPGjUOrVq0qOyuEKITVEKOjo4NJkyahcePGyMzMRF5eHvT09Co+c4SQClW/fn2sX7+ek966deuKz4wcbG1tYWpqyvv6nTt3mLMe2dnZoX79+rzv69Gjh1LyV51kZmbiyJEjnPSaNWvC3Nwc7du3R1paGry8vDizE+Tm5mLfvn3YsmVLRWW3WjI2NuZcX7ScFCHKFxsby0zv1q0bRo0ahRo1aiA1NRW9evWSe5/t27dn3h9FCgoKsHnzZk567969YW5uzvs+XV1dufPwX3LkyBFkZmZy0jt27Ihhw4ZBW1sbERER8Pf352xz+vRpTJ8+HU2aNKmIrJJqqnbt2sxr9msKCgLYz/oA0LNnT4wYMQIaGhpITU1F3759KzZjKqSuzh5zr6amJv7/2LFjsXv3bs42Xl5e6N69u1yfw7cE37hx4+R6P6ne+M4zvnRS9fCVj8OHD4exsTEKCwuRlpaGoUOHVmzGqqlffvkFhYWFEmnGxsaVlBtCCPlvomApQgghVVp+fj6cnJxw9OjRys4KIQphjaSaPXs2FixYUAm5IYRUJh0dHUydOrWysyG377//XurraWlpzGCpCRMmoEWLFqrKVrV069YtzhIEOjo6+PvvvyVG0y5ZsgQ2NjZ49uyZxLa+vr4Vks/qzNDQEIaGhpWdDUL+8/hmCTh69CgaNGhQpn02a9ZM6v0xLy+PGSzVvn37anVfrShXr17lpFlbW2P9+vUSHc1ubm5Yu3atxHYFBQXw9/fHlClTVJ5PUn1pamrStQd2eaiuro5jx479Z5chbdmyJTO9adOm4v+PGTOGGSzl7e2N1atXSwRWsRQWFjLrvs2aNUPv3r0VzDGpjvieJSmQt/pglY/NmjXDoUOHZJYBhIvqZYQQonoUkk0IIaTKCwgI4B1dRkhVJRQKOWmNGzeuhJwQQgipLE+fPuXMcjRv3jzOsgNaWlqwsbHhvD8rK4sTbEUIIZWhqKiIk6apqVnmQCmiXG/fvuV0UOrp6WHdunWcGTkmTpwIfX19zj7ev3+v0jwS8l/BKg/r1Knznw2UAoDOnTtz0gwMDFCvXj3x3y1btmTOLvj+/XtERETI/Izg4GCkpaVx0seMGUNBFl8JPT09ZmBU+/btKyE3pCxY5aOBgQFdw4QQQqosmlmKEEJItfDnn39i8ODBqFOnTrn2ExwcDFtbW066n58f7wgmRd4zbdo0PHz4UCLt4sWL6N69O4RCIW7evInLly/j5cuX+PTpEwwMDNC1a1dMmTIFAwcOlHifQCCAl5cX3N3dERkZiaysLDRq1AidOnWCpaUlhg4dWuaHzezsbLi6uuL69euIiYlBfn4+DAwM8O2338LCwkLh6ZBTU1Ph7u6O27dvIyYmBqmpqahTp454nyYmJnLld+3atXBzc5NI27x5M3744Qf4+vri5MmTePHiBQoLC6Gnp4euXbti7ty5ck/pLk1ISAh8fHwQHh6OuLg45OTkQEdHBw0bNoSRkRGGDh2KkSNHQlNTk3cfspYfcHBwgIODg/jv06dPo1+/fuXOOx/Wudu3b1+cOXMGAJCTkwNPT094enoiJiYG6enp4uNqYWEBMzMz5nTnpqamnKm1BwwYgJMnT8rMk42NDUJCQiTShg4dylymCiieXc7Lywt+fn54/vw5Pn78iBo1aqBhw4bo1KkThg0bBjMzM2hpacn8bJHHjx/Dy8sLT58+RWxsLLKzsyEQCKClpYWGDRuiXbt2GDhwIMzNzXmXSWSdqyWVPO7NmzfHrVu3xH+PGDECCQkJEtsHBQVBT09P/H09PDwQGRmJzMxMNG7cGN9++y1sbGxgZGQk8b7c3Fy4u7vj6tWrePv2LfLy8tC4cWMYGRnByspKoRG4OTk5cHd3h7+/P6KiovDx40fxMenevTuGDRsmXmZHGlWdd+U55iJfvnzBzZs3cefOHbx69QofPnxATk4O1NTUUKdOHTRv3hydO3eGiYkJvvvuO6VN9x8fHw8TExNOOqsMuHz5skQ5ARR3aoqWYktLS4O7uzt8fHwQFxeH7Oxs6Ovro1evXhg3bhy+++47peS5MiQlJcHT0xP3799HZGQk0tPToaGhAT09PbRp0wZDhgyBmZmZXIGnzs7OnHJl6dKlWLhwIQDg2bNn+OeffxAWFobExETUqlULLVu2hImJCaysrJS2ROqvv/6KNWvWICEhAbGxsYiLi2OeC0Dx0nylaWlplbvjbdCgQfj48aNEWlhYGOrUqYO8vDx4enriypUriIqKQlZWFho3bozevXtj6tSp6Nq1q8T7cnJy4ObmBk9PT7x9+xb5+flo0qQJevTogcmTJ6Nnz55y5amgoAB+fn7w8/PDq1evkJSUhJycHPF3NjAwgKGhIQYPHgwLCwtoa2vz7uvcuXPYsGGDRJqmpib+/fdfzrbLly+Hl5eXRNrOnTthYWEBAHjz5g1cXV1x//59vH//HkKhEE2bNsWgQYMwadIklXTUhIWF4fr163j27Jn4mhYIBNDW1kbDhg3Rvn17DBw4EGZmZgoHpYSGhsLLywsPHz5EcnIyPn/+jIYNG6JFixbiY8s3U4Q0MTExuHbtGoKCghAfH4/U1FSoq6tDV1cXhoaG6N+/P8aPH49GjRpx3nv37l3MmTOHd98XLlzAhQsXJP6Wdl4ps9yQdn54e3vj9OnTePnyJYRCofi+tWDBAokO5KKiIty/fx83btzAixcvkJCQgOzsbBQVFUFbWxuNGjWCoaEhhg4dilGjRpX72UaanJwceHt7486dO3jx4gU+fvwIgUAAPT09NGvWDP3798eoUaPQpUsX3n2wrq+S8vPzJeq/bdq0gbe3tzK/hkoUFhbi9u3b8PX1xdOnT/H+/Xt8+fIF9erVg4GBAYyNjTFs2DAMGTJE5r4yMzPRp08fiTRtbW2Eh4cDKH7u8vDwgKenJ+Li4pCVlSV+RpowYQIGDRqklO/Upk0bhISEIDU1FXFxcYiNjYWmpiZvvY2VbmBgoJS8lPTgwQP4+voiJCQEKSkpyMjIQK1ataCvr49OnTphxIgRMDU1Re3atRXa75MnT3D9+nWEhYUhJiYG2dnZqFOnDvT09NCtWzcMGTJE7v2OHz8eL1++lEiztbXFunXrlPIeY2NjTuDzo0ePoKurC6FQiMDAQFy+fBnPnj1DUlIStLW10bp1a4wYMQJWVlYSwSgivr6+WLRoEW/+Tp8+jdOnT4v/dnd3F5dVJ0+e5MzYJsr7kydP4OLigtDQUOTm5qJBgwYwNDTElClTMHLkSOb5DgAHDhzAyJEjefMDFN87PDw8EBgYiISEBGRmZqJ+/fpo1KgR+vXrh9GjRyu0ZHNOTg58fHwQGBgobl/JycmBuro66tSpg5YtW6Jz5874/vvvMWjQoHJ31LOOW0lZWVkS5WGnTp3g4eHBu31CQoL4XhoZGYmMjAzUqFEDenp6aNu2LQYPHgwzMzPmvbS0hQsXws/PTyJN9Jtcu3YNZ86cwatXrwAUB6N0794dCxcuRIcOHWTuu+T3UVdXlwiEaNeuHWe7sWPHisu/kry8vGQu08p3/5BnCb6oqChcuXIFgYGBeP/+PbKyslC/fn00btwYAwYMgKmpqVztRo6OjhL1EADYsGGDeHaX2NhYuLq6IiAgAO/fv0dBQQEaN26MgQMHwtLSkvee2r17d+Tn5zNf+/jxo8S5M2fOHKxcuVLqe0vmSd7PY73n2rVrWLFihUSaubk5du3ahTdv3uDgwYN48OABsrKyUK9ePbRt2xYTJ07ExIkTOZ+Zm5uLK1euwN/fH69fv8bHjx9Ru3ZtNGzYEN26dcOwYcNgamrKfOYpqXPnzpzlyctSB2d9t5KOHDki8bwoaof566+/ONv6+vrKrDPb29vD09NTIq1169YSg39nzZqFwMBAiW1cXFwwfPhwCIVC3Lt3DxcuXMCLFy+QnJwMXV1dtGvXDqampvjxxx8VuleK2pT9/Pzw7NkzfPr0Cerq6tDX10fHjh0xdOhQmc9Z8mJdNyU9fvxY4hwXnWMAcPjwYezYsUNie319fdy7d493f4q8R1XXNEtOTg4uXboEPz8/REVFIT09XXz+//jjjxgxYoTc+xKR91qOiIiAlZWVRFqPHj3wv//9DwDw+fNncTvc27dvkZaWhgYNGqBLly4wNzeHhYWFQkvZP3jwAB4eHuL6Xa1atdCqVSuYmZnhxx9/FC+Jzcr/kydPUKtWLbk/ixBCVI2CpQghhFQLSUlJ2L9/P9asWVPZWSmTxMRErF69mhNIlZCQgISEBNy4cQNTpkzBr7/+iho1auDt27dYuXIlnj59KrF9XFwc4uLicOPGDYwePRpbt25VuGE5JCQEq1at4owcFu3b3d0dAwcOxLZt2+RqMD916hT27t2L7OxsifT09HSkp6cjMjISFy5cgJGRETZs2MDpeJXHkSNH4OzsLJEmOnbLli1TeH8l/fvvv3BycsLjx485r4m+Q1RUFNzc3NC8eXOsWLECY8aMKddnVgWhoaFYtWoVJ2gnKSkJSUlJ8PPzw7Fjx3Dw4EHOyL4ff/yR0zARHByM5ORkqZ2RCQkJCA0N5aT/8MMPzO3v3buHDRs2IC4ujvNadnY2YmNj4ePjg927d2Pt2rUwNzfn/WwASE5Oxrp16xAQEMB8XTSDS0xMDPz8/LBz506sWLGiwpaaiIyMhL29vbghW0R0bXp4eGDJkiXiYI8nT55g5cqViI2Nldj+7du3ePv2LTw8PGBra4u1a9fKbPTw9PTEli1bkJKSIpGen5+PjIwMREdHw8PDA23btoWjoyMGDBhQpu9YnvOuvPz9/eHo6IgPHz4wX8/Pz0daWhqePn0KV1dXdOzYEdu3b5cZBFmRfH19sW7dOqSnp0uki8pDT09PDBo0CHv27EHdunUrKZeKy8zMhLOzMy5fvgyBQMB5XfT9AgMDsXPnTlhbW2PJkiVlatjNz8/Hli1b8Pfff0ukf/78Genp6fj3339x/PhxrFu3DhMmTCjzdyqpZs2aaN26NVq3bs27jUAgwLlz5zjpvXv3Vtko3JcvX2LlypWIjIyUSI+NjUVsbCzc3d1hb2+P2bNnAygO6lm1ahXi4+Mlto+OjkZ0dDTc3Nwwe/Zs2NvbSw00fP78OVauXImoqCjm6wKBAJmZmYiKioK3tzf27NmDP/74Q6WBgAUFBdi/fz/++usvzmjoN2/e4M2bNzh79izmzJmD5cuXK+UzExMT4eDggKCgIObrmZmZyMzMxNu3b3Hz5k3s3LkTq1evxqRJk2TuOyUlBY6Ojsyg0cTERCQmJuLRo0fYv38/pkyZAnt7e7nqk9nZ2di8eTPc3NxQWFjIef3z589ITk5GYGAg9u3bhwULFmD+/PkqOYcrstzYv38/9u3bJ5GWm5uL+Ph4rFq1Spz2+vVrrF27lrOcpkhGRgYyMjIQGRkJLy8v7NixAxs2bJAZYKCowsJCnD59Gi4uLpz7BVBcH0pOTkZ4eDgOHTqEkSNHYu3atWUKnKuOfH19sXXrVmb98tOnT/j06RNevnyJs2fPomvXrvjll19gbGxcps969OgRVqxYwal7xMfHIz4+HleuXMHw4cOxZcsW1K9fv0yfUZqenh709PSkBhneuHGD0xENoMzfkyUyMhIbNmzgDJIAisv57OxsxMTEwNvbG7t27cLGjRvlKudfv34NJycnznM18P/X2Nu3b3H16lU4OztjyZIl+Omnn5TynZTt06dPWLt2LWdp47y8PKSlpSE8PBzHjh3Dli1bMHz4cJXnx9/fH3Z2dhJlqqi84Htek0d+fj6cnZ3x999/o6CgQOK1lJQUpKSk4NmzZzh+/DhGjhyJ9evXy3wO8PLywqZNm5izEAHFxzA1NRWPHz/G+fPnYWRkBGdnZ7Rq1arM30NZ0tLS4OzsDHd3d87xyM/PF99f7t69i507d2Lq1Kmws7NTuN0HKA74LR34kZubi/fv33MGZ8hSp04dfPPNNxKDplgBLGZmZti8eTPn3uzt7Q0HBwfeOmJRUREn4AsoDtKSttxyXl4etm7divPnz3PqJqLz6+nTpzhy5AhMTU2xfv36MgWGFhUV4ejRo9i7dy/nu4mev8+dOwcbGxv88ssvSht0U1nCwsIwa9YsiRkLRceTFfDh7e2NP/74g3O/y8/PF9dnr169in379mHdunVSB2h26dIF/v7+4r8bNGigtIEssowfP54ZLHXz5k3MnDmT930CgQB37tzhpMvbdpiamorVq1dz2qo+fvyIjx8/Ijg4GMePH8eWLVuYAaulBQcHw9HRkTPIESgO6ImNjcWNGzewZ88erFq1CuPHj5crn/8lyr6mb926BUdHR06bmqity9fXF8OHD5cadKsqjx8/hr29Pd69eyeRLrrH+/v74+jRozh48CDvQHKRjIwM/PLLL5wlU0XtKY8fP8aJEyewY8cOuc5VQgipCqp3rY0QQsh/Fmuk0enTpzkdetVBcnIyrK2tmQ26JZ07dw779u1DdHQ0rK2tOYFSpXl7e8PR0VGhvAQHB2PWrFkyl1i4f/8+Jk+ezAloKKmoqAjr1q3Dn3/+yQmUYnny5AmmTp3KG6jC59atW5xAKZHOnTszRzPK68KFC7CysmIGSrEkJCTA3t4eTk5OzE7C6uLu3buwtbWV+vsCxTOvzJgxgzMKaOLEiZzR6EVFRZzZGEq7evUqZ3nC+vXrM2dZcXV1xdy5c5kdWaUlJydj+fLlOHjwIO82qampmDZtmkLnX05ODpycnHDgwAG531NWr169grW1NSdQqiShUIg9e/bgf//7H0JCQmBra8sJlCrt9OnT2L9/v9Rt9u/fD3t7e06jDkt0dDRmz56Ny5cvy9y2tPKed+Xh4+ODRYsW8QZKsbx69Qo2NjZ4/fq10vJRHq6urli0aBGz47uke/fuSZ1poKqJiorChAkTcOHCBWbAQ2l5eXk4ceIErK2tFV4uqKioCHZ2dpxAqdIyMzOxZs0aHDt2TKH9K6qwsBAfP37EzZs3YW1tjbCwMInXNTQ0sGTJEpV89rNnz2BtbS21XlVUVITt27fD3d0dDx48wPTp0zmBUqUdPXoUhw8f5n399evXmDp1Km+gFEtKSgoWLlyIBw8eyP0eRQiFQtjb2+PQoUPMZSNECgsL4eLiIvX7ySslJQU2Nja8gVIsWVlZWL9+PY4ePSp1u+joaFhZWTEDpUoTCAQ4ffo0Zs6ciczMTKnbJiYmYsqUKbh48aJcdaC8vDzs3r0bq1evlrmtoiqy3Lh+/TonUEqkZ8+e4gCjN2/eYNq0abyBUiwpKSmws7OTWX9SRHZ2NmbPno0tW7bIvF+I+Pr6wtLSUmXXWFVRVFSEP/74A4sWLZKrfgkUl5XTp0+Xed9gCQoKws8//yyz7uHv74+pU6dyZgBUts+fP+PFixdwdnZmzrRhamqq0OwJ0gQGBmLy5MnMQCmWxMREzJ8/X+oMokBxcP8PP/wg87la5MOHD/j111/h4OCg1HqlMnz69AlWVlacQKnS0tPTYWdnhydPnqg0P9HR0VixYgWzTNXW1i5zsFZWVhZmzZqFU6dOcQKDWHx9fTF58mSp9YRLly5hxYoVvIFSLE+ePMHkyZM5HcUV7eXLlxg/fjwuXrwo1/H48uULjh49ChsbGyQnJyv0We7u7sygD6A4MFKeGRdLK11GsNpi9PT0mDPmffjwgTlwSuTRo0fMcnDs2LG878nIyBCX0fLUTXx8fDB58mRmAIksjo6O2LFjh9R6R1FREc6cOSOeMae6+vDhAxYtWsRZ2hUA1NXVYWZmJpF2+PBhLFu2TK5n7ZiYGMybNw/nz5/n3ab0eVaRS/C1a9cO3bp146TfuHFD6vtEs2+VJk+wVFZWFqytrWW2VcXHx2PWrFkSgWQsHh4emDVrllzneUpKClavXo3du3fL3Pa/RpnX9OXLl7F48WKZbWr+/v5yPXcpU1BQEGxsbGTe/169eoUZM2bg8+fPvNukpqZi6tSpnECp0pKTkzFjxgy564GEEFLZKFiKEEJIlfTzzz9z0goKCrBx48aKz0w5rV27VitxTpMAACAASURBVO6OmSNHjsDW1hapqalybe/h4SG1wam0AwcO4MuXL3JtGx8fD3t7e95GpwMHDuDixYtyfzZQPIrRzs5Oocapmzdv8r4mayYhaa5cuQJHR8cyBT2dPXsWTk5OZf7syvTu3TssXbpUrsZZoLjh/J9//pFIMzAwYI4EvHr1qtR9sV4fO3YsZ2nDoKAgODo6yp1HkT179uDKlSvM17Zt2yYzsIjPvn37mEsJKJOdnZ3cDSabN2/G/PnzpTZilHTkyBHehpHLly/zdgLzKSgowLp16xRq+FDGeVdW6enpWL9+fZmu9aysLKxYsaLSgyPDw8Px22+/yb19cHCwzAasqiApKQnTp0+XGUDH8uLFC0yfPl3uYAAAOH/+PG7fvi339tu3b5c6/X95TZkyBYMGDcLixYs5naDq6ur4888/FVqORhELFy4UL3sni5OTExYsWCB3R/OhQ4eYM5YUFRVh5cqVcn9uSQUFBdi2bZvC75PHoUOHFFo27ODBgwqddyx//vlnmc57oHiWCL6A+uzsbCxYsEDhfYeGhkoNasrPz8fixYvLFDx65coVnDhxQuH38anockPeeugvv/xSpvNCNPhA0U5wloKCAixatAj3799X+L3p6emYO3euygMyKpOzs7PEsmTyKigowKZNm6QuL1OaQCDAsmXL5K73REZGYvXq1ZxBBcri6+uLnj17YsKECThy5AinY7BXr15Km+kgOjoadnZ2cg2mKamoqAiOjo54/vw58/Vbt25h1apVcgVIlsZa4riyLV68WO7AHYFAgO3bt6s0P4GBgczACAAYPnx4mZdpcnBwkDu4TSQxMRFz585l1heSk5Px+++/l+laSU1NlVjarKLFx8djxowZZSrvnz59ip9//lmh60ra/Uu0/LCidu3ahVevXon/WVtbM7fjC3CSFhxccqkyETU1Nd5gE6FQiFWrVin8nB4fH4958+bJ3S4GFLf/uLq6yr39iRMnkJiYqFC+qhLRkq4spQPtRDNlKnJNFhUVYcOGDbyDBkaOHClxnp09e1axL1BOrFmWIiIipF67rOutW7duaNOmjczP27ZtG96+fStX3vLy8pgzBImEhobil19+UfheeejQIYXO8epOmdf0ixcvFGpbfv78Oa5fvy73Z5dHUlISFi9eLPf5EBcXhzNnzvC+7uDgIPfzoEAgwKJFiyq9LY0QQuRBy/ARQgipkqysrBAcHMxpsH/06BHc3d2VtjRORRCNLqpVqxYsLCzQrl07JCcn4/Lly5zGrsLCQvFIFHV1dYwcORLdu3dHWloaLl26hIyMDM7+vby80Lt3b4XypKurCzMzM7Ru3Rrp6enw8/PDmzdvONuFh4fj8uXLnKULnj9/zpzFR01NDcOGDcO3336LwsJCBAYGcgIqPn/+DAcHB+ZyQ9LUrVsXEyZMgIGBAZ48eQJ/f/8yN/K9f/+eN/CgYcOGGDVqFL755htkZmYiICCA2Sl57tw59OjRAxMnThSnrV+/Xvx/VjDVuHHjJDq+pS3JpColH/D19fVhZmaGJk2aICYmBlevXmU2Gnp5eXECGC0tLTmzVjx79gxRUVHMEabPnj1jnmM//vijxN9fvnzB6tWrmbN7dO3aVdxY//TpU/j4+HAevH///XcMHjxYYpr27OxsXLt2jbO/Ro0aYcSIEWjZsiU0NDSQkpKCu3fvcmZaEQqF2LdvH44fPy5OGz9+PLp164b09HRmsNG0adPEv6+Ojg7n9dJE5UTdunUxZswYtGzZErGxsXBzc+MEKJTsyKhZsyYsLCxgaGiIhIQEXLp0CXl5eRLbCwQC5rTxycnJvEF/ffv2xcCBA1GjRg08evSIM6V8UVER1q5dCy8vL06wG4syzruyHnMfHx9m2dmrVy/07dsXenp6yMvLE09FX3pEqGi5JGmjmlWtZIBpixYtMGrUKDRs2BAvXryAt7c3szP22rVrSl/aSdnWrVvHHH1Zo0YNDBs2DEZGRlBTU8OzZ8/g5+fHaeSLi4vDypUrZc60I1Lyszp06IARI0agXr16iIyMxPXr1zkBiEKhEOvWrYOvry9nNj1l4Aukbtu2LTZt2qTSaetF53m9evUwZswYNG/eXLzcZunjXLKuoqmpiTFjxqB9+/a8ZdSXL1/g5+cHGxsbiXRvb2/m7Hlt27bFyJEj0bBhQ3z58gUvX76Er68vJx/Pnj1DWloaGjRoUK7vXlrJe1OfPn3Qp08f1KpVC3fv3mUGpH/+/Bm3bt0q85JE6enpzNHpTZs2xbBhw9CiRQvxPcnf3x/R0dES2xUWFmL//v1wcXHh7MPZ2ZkZkK6npwcLCws0b94cSUlJ8PT05Mze4O/vDw8PD2YHkYuLC7MuVLNmTYwaNQqdO3fG58+fERISguDgYM52+/btw/jx46Gnp4f27duL60vHjx/nBD317t1bIgip9FIQFV1uiNSrVw8TJkyAnp4eIiIiEBAQIJ5Z4eXLl8xZStu0aYMhQ4agadOmEAqFSEpKws2bNzmdLbm5uThy5Ah+/fVXhfJU2tGjR3lnh+rVqxcGDhwIHR0dxMTEwMfHhxPclZeXh0WLFuHq1aviJeGMjY3Fv1doaCino7tGjRoSQSi6urrl+g6qEhQUxDtbYKtWrWBiYoJGjRrhw4cP8PX1Zc485eTkhM6dO8PIyEjm5wkEAvHx1dLSwujRo2FoaIjs7Gz4+fkxy8J79+7Bw8NDJc+5fMGFNWrUwM8//1zmJb5Y1qxZwwy6adiwIUaPHo0WLVogJSWFeZzz8/OxefNmTkddamoqHBwcmM8H9evXx6hRo9C6dWvk5ubi3r17zOAJT09PGBkZYfr06eX8hsohuveIytFOnTohOzsbnp6ezN/r0aNH+PDhAxo1agQA6Nixo/jaPHz4MKcTv2/fvjA1NRX/Le8sQjVr1sTYsWPRpk0bvH37Fl5eXmV+5nZ3d2cGEGhqasLc3BwdO3ZEZmYmbt68yXlOjI+Px44dOzgzaV+5coV5fvXt2xfffvstGjRogM+fPyMmJgY3btzgbBsREYE7d+6UaWnf/v37i4/5w4cPOcE9tWvXlliatfSyYQ4ODswAlJo1a2LEiBHo2rUr1NTU8O+//+LWrVuc+n10dDTWrFmj8KzH9evXx8SJE1GvXj2Eh4fj/v37EueGKpiYmEBbW5tz/H18fPDrr79ylogvKipi1o369OnDuyTj//73P+ayZ6J2N0NDQ2RkZMDHx4cTiBITE4M9e/ZgzZo1cn2fkudnjx49MGDAAGhra+PBgwfMAGWBQAAfHx+JZ9lffvkFhYWFuHfvHqcdpU6dOhIz/nXt2lWufKmauro6Ro8ejc6dOyM+Ph7Xrl2TCF5LTU2VaAMrqXfv3hg8eDA0NTUREhKC27dvSwRUCYVCODg44Pr169DS0lJJ/rt27SrO36FDhzh130GDBkksKShannHMmDHYunWrxDUoFApx8+ZNTJ06lfM5QqGQOaOrvO0HJeu1ffv2Rf/+/aGpqYknT57A19eXc+/LycnBpk2bcOTIEYn0/Px8rFq1itk20KlTJ5iYmEBHRwfPnj1jtiFs2bIFQ4cOLdOscxYWFujQoQOA4nac0s9QzZs3l2iPqoy20JKUcU2LbNq0iRmMVLJ9vKioCI8ePUJgYKBSv4csJesGDRo0gLm5OZo1a4a4uDhcvXqVeT+9du0a5s6dy0n39vbmHXjWoUMHmJiYoG7duoiMjIS3t7d4WT5CCKkOKFiKEEJIlaSmpoaNGzfC0tKSEwyxbds2jBgxoso2xrM0bdoUp06dQqtWrcRppqamnI5EES0tLbi4uKB///7iNEtLS4wfP57zEKbo0oRGRkZwcXFBw4YNxWlLly7Frl27OA/bQPEyXqWDpY4cOcJ5YNfU1MTBgwcxZMgQcdqCBQvwzz//cGYECwsLw+PHj+WeMaNevXpwc3ND8+bNxWnv3r2T+FsR+/btYz4UWlhYYNOmTRKBFsuWLcOlS5fw22+/cY79jh07YGZmJu5cKNlwwgpCGTBgQJk7V5XN1NQU27Ztk+gYmTx5MqZMmcLp+H7x4gWEQiHU1NTEacOGDYOBgQGnw9LT0xNLly7lfB5rVqkuXbqgc+fOEmlubm7M6dvt7OywePFiibSIiAjMmjVLoiM/IyMDFy9elHi4j4mJ4XwnAwMDeHl5oW7duhLpq1evxp9//olTp05JpEdFRSE7O1t8bgwYMAADBgxAfHw8M3Dn+++/R79+/Tjp0nTs2BEnTpyQuDa//fZb3kZcPT09nDp1StwoJfrcGTNmcLZllRNnzpzhXAdqamr4448/JILY5syZAz8/PyxZskSiQe3du3fw9fVVaIa38px3ZT3mrJFvU6ZMwYYNGzjpy5cvh62trURwQs2aNREWFlapwVIitra2WLNmjUTgzpgxYzB//nzOtnwzM1QVAQEBzMbC5s2b49ChQ+jYsaNEelRUFBYsWMCZIS4gIAABAQES9x5Zli1bhvnz50uUaQsXLsSsWbM4+09MTMSNGzfKNZMhS15eHu+SS/r6+nj//j0KCws5nUnK1LVrVxw7dkwi+KhHjx68nR4GBgY4deqUREDs8OHDmecfq8xhBa0OHjwYLi4unOWXr127xlwmKi4uTunBUkDxde7s7IzRo0eL0+bPnw8nJyfmaPbnz5+X+X4eFRXF6Zxo1qwZPD09UadOHYn0VatWYePGjZwA89evX+PLly8SZenHjx9x6dIlzuf16tULf/31F+rVqydOW7x4MebPn88JaD9x4gQnWCo3N5d5DBo3boxjx47B0NBQIv3cuXOc8jUnJweurq6YN28emjVrJq4vXblyhRMU0L59e2ZHFFB55Yaenh7c3d0lOo/evXsnDlpg3WeMjIxw7tw5TqDlqlWrsHTpUolONTU1NURERMiVFz7p6enM5ZZq166NzZs3c8qwlStXYvXq1ZylXD58+IBjx47B3t4eAGBoaCj+jTU0NDjBUurq6ry/V1WydetWZvqiRYuwcOFCid/J3t4ee/bs4Sy5KZrdR9qI+9Lat2+Pw4cPSzyz2NnZYe/evTh06BBn+5MnT1ZosJSuri4KCwvx6dOnMj9XlRQUFMScnWzQoEHYu3evxDPWihUrsHLlSs7Mfg8fPsTz588llmFycXFhdrgNHz4cW7dulSjf7OzscP36daxdu5YTjL9//35MnDixyrQjNG/eHMeOHZOYeWTu3LmYNm0apx4nFArx4sULcbnTsmVL8bXn6urKCZbq1KmTwtemuro6jh07JlGfXrZsWZnuu0KhkLlsrYGBAU6ePCmxrJadnR2cnJw497pLly5h2bJlEr8Xq7ydN28es86wZMkSTJs2TeL8r1mzJkJCQsoULNWpUyd06tQJQPGMc6WDpWrWrMl7zH19fZkzbLVq1QqHDh3iDDZ6/fo15s+fz7l2Rfvp27evXHlu1KgR3NzcoK+vL0579+6dSupSJWlpaWHkyJGcWZ8/ffqEhw8fYsCAARLpYWFhzEDocePGMfdfVFTEDHpu2rQpTpw4IXFN2dnZwdHRkVNHOn/+PBYvXsype/HR0NCAk5OTxHPyvHnzsHv3bmZ5/uLFC4m/p0yZAqC4XlU6sEZLS6tK3kt37twpseTe8uXLJersf//9N3O2sw0bNoi/LwDMnj0bd+/excKFCyXa1BITE+Ht7S0xAFGZWrduLQ7KOXv2LOfZq0uXLszjrqenhyFDhnDqSD4+Psztw8PDOeevurq6Qs+Pmpqa2L59u8SzCFA8y9f8+fM5A7oCAgI4AxX5gm3nzZuH5cuXSzz7zpgxAzNnzpQYVJadnQ1XV1dOu5s8+vXrJ753vH79mhMspa+vX+XO8fJe06K0sLAwTnrdunVx8OBBibJ6/vz58Pf3x7JlyxSa2U4ZRowYgR07dkjMEmltbY3JkydzBoxFRkZCIBBwns/5ZnibM2cOVqxYAXX1/1/EatGiRZgzZ47cM6YRQkhlo2X4CCGEVFldunRhTuv96dMnmeuFVzXr16+XCJQCikdql2wkLGnOnDkSgVIA0K5dOxgbG3O2lXfJPqB4tpX9+/dLBGMAxQ+JK1euxKBBgzjvef36NeLj48V/Z2dnM0f9LVy4kNnpZG1tLdHAIqLIEn62tracBvyWLVvK/f6SUlNTmYE7vXv3xvbt25mzAP3444/M5WlSUlLg4eFRpnxUphYtWmDHjh2cEeTdunVjNkjm5+dzlojT0NBgNmp5enpy0oqKipid5KVnlQKKl8kobeDAgcwGm549e4o780oq3RBa8qG9ZFrJxqKSVq5ciZkzZ2LTpk34559/xDMryTNDVFmpqalh27ZtnGtz3LhxvA24a9askQiUAoqP1TfffMPZllVOuLm5cdIsLS2Zv4uJiQlzFJ0i17EyzruyYP3OfAEoBgYGcHJywqJFi7Br1y54enoqvASeqhgbG2PdunWcjvfhw4dzOhwA8AbiVBWspZBq166No0ePcgIegOJ74NGjR5nLwLACffmMHTsWCxYs4JwXLVu2xN69e5nnhiqmyX///j3vchUPHz7E6tWrYWNjg0+fPin9s4HiMnD79u2czrKJEyeiVq1azPesW7eO05k3fPhw5uhjVpmza9cuuLu7Y8uWLfj555/Rv39/rFu3jtMQCxQHVrLSFanzKGLRokWczgmguFOINdtKea4v1j1JTU2NWVapqalhzZo1mDlzprgjOSQkBLdu3eLky9PTkxN0qqmpiT179kgEEgDFDfi7du3i/NYvXrzAs2fPJNL8/f2Zs/M5OTlxAqWA4s7Akh0D2tra6NmzJ/N7K6qyyo2ZM2dyzvOS9VDWd+O7z2hqamLDhg2YNWsWtmzZgosXLyIsLEzhpa1Lc3V1ZQ4E2LhxI7OzTldXF3v37uUErQPFnSEV3YmjSo8ePWJ2cFlbW2PJkiWc+2qNGjVgb2/PDIh8+PCh3EsVigaSlH6GUVdXx7JlyzB8+HDOe168eKHQcuXy4pvJMDU1FSdOnIC5uTnc3d3L/Tms5yJdXV3s3LmTU4+uWbMmfv/9d4kZShs2bIhBgwZJ3Ps+f/7MXCqnc+fO2Lt3L6d8AwAzMzPOYB0AyMzMVGg5RVXbu3cvZ4kmHR0dLFmyhLm9qut2JiYmnIEHjRs3lmsW2dIiIiIQFRXFSf/99985bSAaGhpYv349J/3Lly+c53a+5zqW5s2bY+PGjeIARS8vL0RERDCfH1WNdf/S1tbGsWPHmLMyd+jQAUeOHGHWQRS5f82ZM0ciUAooezuKohRZio+1HLKmpibvDFgPHz5kzgC4efNmzjVVo0YNbNy4kdMel5ubq1Adf/r06czn5IULF4pnYyypqj+LydKrVy9OO16DBg0kynJW2824ceMkAqVEhg4dypytprz1H1VhtU+EhoYyn0VYM+j169dPHNwqj2XLljGfRYyNjZltEUKhkHPdsH4PY2NjrFixgvOc0a1bN+agvKr6e6iCMq5pPz8/5r43bNjADGodPnw4c3CpKjVp0gR79uzhPA916tSJWdctLCxEWlqaRFpqaioePXrE2XbgwIFYuXIl5z7csmVLHDhwgPk8TwghVREFSxFCCKnSli1bxnzAPH/+PHM5kKqoQYMGElM7l8TXUDVp0iRmeukGHgAKdWZYWFhInVJ52rRpzPSSS4uEh4czp3VmPWSKsBoaWCMr+ShzKanbt28zp0hevny51Bk8bGxs0LRpU+b+qhsrKyveh1a+2b5KjzYC2L95XFwcZymaoKAgzmxRoqWcSsrNzWXOhiPt3Bo7diznwTwmJkbi81q1asVpaE5OTsbo0aOxZcsWBAUFSXQwa2pqYs2aNbCyskLv3r0rZPR5ly5dxCOVS1JXV2eO9NfW1uYcPxF5yono6GhmY4+i13FoaChn9j8+yjrvFMU6rmfPnsXkyZNx6tQpTkeOsbExlixZAnNzcxgaGlaZBh6+mQgB9vFTxrFTlezsbAQFBXHSJ06ciLZt2/K+75tvvoGVlRUnPSwsjDmqmWX27Nm8r3Xq1AmDBw/mpLOW8ymv7OxsjB07FsuXL+cNVg4PD8esWbM4ATDK0KNHD2bnXM2aNZnLndSvX5+3w0reuommpiY6d+6MiRMnwsHBAadOnWL+3oWFhQgJCWF2zpZeZlQZ1NXVmcH5QHGnNes4lef6ateuHadcSUhIEM+8FxwcLPGba2lpYc2aNZg0aRK+/fZbzoyIIqwG7MGDB/PW+xo1asQJzGftp/TsU0Bxo/vQoUOZ+wWKZyzdvXs3vL29ERoaigsXLmDOnDm828ujMssNWfVQ1n0mPDwc5ubm2LdvHx4/fixxr2zcuDFWr16NiRMnonv37sxgLkWxOurat28vdZYiTU1NZlBGbm4ucznF6oo1yENTUxN2dnZS37ds2TLmEqzy1v1HjRrFLB9F+O5H5Z1ljKVx48aYPXs21q5dC1tbW04n4JcvX7B27VrmeaQIVnkxatQoZqcjUBy4uWXLFhw9ehSBgYG4f/8+jh8/LjEA5969e8xAQDs7O6lBPBMmTGAOTGIt21UZevbsiW7dujFfU2W9WBplPnOz7kn6+voYNmwYc3sNDQ1mYGfp/bDK20OHDmHatGn4+++/OTMJDhkyBIsXL4apqSnatWunkmWVZUlLS2Mej0mTJkkNXGrXrh2zIzs4OFjuNqDKXJJ70KBBnIFAQHGZXLI9RrS8WWnDhg3jfQ5nlTXNmjVjDiABiuu3rAF8rN+FD9+sOJqamszzklVuVScmJiZSX09ISGAG4iranvD48WOVPOuUl2hJsZIKCwvh6+vL2ZYVMMPXVsOira3NDDATsbCwYD6flawv5OfnM4O5pc2Ea25uzikTExMT8e7dO3myXe0p45pmHfOmTZtKnVVs0qRJvIOTVOGnn37irS/xLS1dur7BWm4cAHNApUi7du14+0IIIaSqoWApQgghVZqOjg4cHBw46UVFRdiwYQNnKbiqqF27drwz2LBmjNHX14eBgQFze9bIWUWOAWtmqpJ69+7NTC85sxRrZHbdunWljppidRTExsbK1UmloaHBOwNXWbAe8nR1dWUeGw0NDWbHoCo60VWNNfuCiJ6eHjOdFRDTunVr9OnTh5NeegRw6en3geLGp9IdJ69fv2YG4rE6qkXq1q3LXEag5MwYderUYTbapaSk4MSJE/j555/Ru3dvTJkyBTt27MDdu3crvHFT2jnOKicMDQ15G/vlKSdY1zEg/VjzBUSwRo2zKOu8U9To0aOZZWp4eDj+/PNPmJubY9CgQbCzs8PJkyfFy/9VNYoeP2UcO1V5/vw5M2hVnsY01kwcAoEA//77r8z31qtXj9nwWRKrTEtJSZE7qEJe3bt3h7OzM+bPn485c+bg+PHj2LRpE2e7Fy9eMJexKS/WjEAirDKnQ4cOvDM3sMocRc6/1NRU3L17F4cOHcK8efPQr18/TJ8+HTk5OeXar7yaNGnC/A4iyr6+dHV1OUvdAf+//JmtrS2MjY1hY2ODXbt2ITAwUK4Ocla5Lq1MB8CcibD0YIRXr15xtunatStv3RYorm+amZmhTZs2SplRCqi8cqNWrVriJVz4tG/fntlBGxsbi/3792PSpEno06cPZs6ciQMHDuDRo0dK7RgUCATMYHPW9y5t8ODBzKDg6li/5cPqyOrZsydv3UOkcePG6Nq1Kydd3mMja5msnj17MjuvSi4FrCzr16/HqlWrMGPGDKxbtw6enp6c8kEoFMLR0bHMdeDc3FxmBytfQJCIhYUFhgwZwvv8y3p2q1mzplzLaLKugYiIiCpRz5NWr2vQoAGzjFV13a70jLXlwbonSQtsBdjPGqVnOxw/fjzznv3w4UNs2rQJo0aNwpAhQ7B06VKcPXuWuWxfRXv69Cmzzaas96+8vDzOcWGpW7cumjVrJl8mVYAvAC49PR33798X/x0REYGkpCTOdtKWQGfd82SdX6w6jzzHEShul5S2VCkrKKw6tFVKI6s8KEt7QsuWLTllm0AgYNY1K1utWrWYMz2VXn7z1atXnCBNabOiscgKnFdXV2e2V5asL0RFRTEHlUj7PbS0tJjtuPJeF9WZsq7p0r89UNyuLu35R0dHR2abhDJJq2+wvifArW+8efOGs42amprMdvTSs1USQkhVVfHDKQghhBAFmZub4+LFi7h3755E+r///osLFy7IbBQpr/I2SvI9fABgBjvwNRQDKPcMJ7KmgdbV1YWuri5n6auSy6+Uno4XALKysqQ+gLEIhULExsYyOyFK0tPTU+oI0MTERE5a27ZtpXb6ldyutPT0dBQUFFTKKNWyknaOaWlpMdP5OhUsLS05IzK9vLzg4OAADQ0NfPnyhTlSlBW8xDq3AEidFYFP6SVMVq1ahZcvXyI0NJS5fX5+PsLCwhAWFobDhw+jVq1aGDp0KKysrOTqjCmvii4n+I41K1BElpiYGLk6V5R53ilCR0cHu3fvxvz585GVlcXc5uPHj7hx44Z49ommTZvCzMwMU6dOldqIVpFKL6FREt/xq6r4lgOSFdgB8HeEpKSkyHwva0RuaXy/d3p6ukqX4gSKZ18LDAzkzILyzz//YOHChUoLOgH4AxQB1ddNhEIhgoKC4O3tjfv371f66GVp1xbAvr7KWzY5ODggMjKSd5RuXl4eQkJCEBISAhcXF2hpaWHo0KGYMmUK76wJrHL9yJEjCi3XA3Dvn+np6ZxtZB0zVaisckNfX1+uOuLWrVtha2vLu4RaTk4O7t27J36e0dXVhYmJCaytrXlHdcvr48ePzEAyeY6NpqYmWrRogbdv33L2+V/BOnfkfX5s27Yt5zqV99jIuufUqFEDjRo1khiUArCvOWUzMDDAli1b8NNPP0mkp6am4tq1a5x0efDlW9r9Qx6s369ly5ZyLQ3H+p0FAgHS09OZgy0UUd42AmnlqJqaGmrXrs0JlFV1kJciS0bJwron4h7a+QAAIABJREFUPXz4UOE2g3fv3qGwsFA8A7Senh6cnZ2xZMkS3kDiDx8+wNvbW7xEVcuWLWFhYQFra2ups2yrCqv9ASjf/Uuecqi8154yjB07FmfOnOGkX79+Hd999x0AbvAJUBzoxTcLGcA+vwIDAxU+v+Rd9lRWvYe1XGJlB2WWt4ySda3wtSewZumVJSYmBt27d1f4fao2fvx4zjKwwcHByMzMFM96xmrr+u6773hngmVhzWBfGusZVVY7LQDm7KqyqGI5YGVjDbJUhLKu6dJt54B8bQ4tWrTgfQ5UtrK0I5X+rqzvWa9ePeYgq5JYQaqEEFIV0cxShBBCqoXffvuN2SC6a9cu5prxipLWkFHehzDWQ5YIq/NFGUtx8JEnoIf1+SVHv/MFG5SFPPtS9hJorM4keY8560FQKBQqfcYRVZP2faUtRcgyevRoTkPQp0+fxKNFb926xZkdpEmTJswlp1R5bmlpaeHkyZNYuHChXOdUXl4ebt68idmzZ8PGxgYJCQlKyxtLRZcTFX0dA8o97xRlbGwMV1dXDBs2TK5O78TERBw/fhyjRo3Crl27qsTI4Mo8fsrGKocB+c5rvm3kOQ/Leg8EKm5ZQ9Yo+k+fPsk9g5u8pAXYqbJu8urVK1haWmLGjBm4cOECb6BUs2bNKmwJTFnfTRXXl46ODs6ePYu5c+fKFYT3+fNn+Pj44Oeff4atrS1nBobCwkKlzYhY+lpizfBVGcuTVla5IW3WsZIaN24MV1dXWFlZybW0RmZmJtzc3PDTTz9h6dKl5bovl+fY8G2nzHpCZStP3b88x0ae64S1/4q63xgZGTFnTVNkqfSSWGUFIN+9Vxpl/34A5Hp2kxXoUN42gsq498iizOduZZUhrGftoUOH4sKFCxg4cKBc+3j37h1cXFwwcuRIlczWKUtVv3+pUo8ePZgzhvn6+orbmFhLpZqamkoNiFRW+4tAIJBrSUNZAQGVMXBO2vOpUCgsd7CUrPKgMtoTKpqxsTEnSEkgEODWrVviv1nL8kmbFY1FnvOH9exWsr7wX/s9ZLW/lPf8VtY1zSo/ylr/UxVltCOx6njS2jBFqtugPkLI14uCpQghhFQLrVq1wty5cznpGRkZ2L17d7n3L62xs7ydT4rOBKHKhlG+RuySWA97JTvylLm2ujyNXPI8gCmC9aAo72/MOn7q6upKD+hSNWnnmDyBJCXVrl0bFhYWnHTRCDvRiN6SJkyYwLwulHlusRp4NDU1sXTpUgQEBGD37t0YO3aszCVYACAkJATTpk1TaaNRRZcTqj7WLMo878qiTZs2+Ouvv+Dn54c1a9agb9++MhuyCgoK4OLigq1bt6o8f7JIa7CriOOnTHyNk/KUxXz3sdLLerLIc8/hy4Mio4PLg2/0JWt5kvJQtMxRxqxWUVFRsLGx4Szzpq6uDiMjI8yYMQO7du2Cn58f/P39K6yTT1ZjuKquL01NTdjb2yMwMBA7d+6EhYWFXOdxcHAwpk+fLnGuamhoKC2AqfR1Up56kzJVVrmhSD1UV1cXmzZtQmBgIP744w+YmJjI7IwBiutKc+fOLXNgLl8nSHnqt1Whk11ZlF33l/fYyFM/YuWjou43QPGMO6WV9X5T3vOQD+saKs/vB8j3G8oKhirv96qsew8fDQ0NuWbrkpeqnzU6duyIEydO4MaNG7C3t0evXr1kHtP8/Hzs2LED+/fvV1re5FGea6Oi7l+qNGbMGE5aVlYWAgIC8OTJE+agJFnBJso8V+Upq6viwBRpwSLKqKfJOn8qul2wMqipqWHcuHGcdNFsaPHx8ZzlCHV0dKTOisYizznICqRWVTttVQiWkhUMVd5zXFnXdFnrKBUVGA8opx2JdX7JE2hakd+TEELKo/qsF0MIIeSrN2/ePFy9epWzJrgypgiWVsmviKUQKgprPfWScnNzmdPrlmyMYzXMNWvWDP7+/uXPIIMyG8IA9hTX0dHREAqFMh8UWeu0N2zYUKlLI1UEZTe+W1pa4vz58xJp/v7++PLlCwICAjifzVqCD+Bv9PX19WV25pRV7dq1YWZmBjMzMwiFQrx69QqhoaHiZfhYy30kJCTg1KlTWLx4sdLyUZlYx1pNTQ3Pnj1TWWNwVQnoad68OWbOnImZM2ciNzcX4eHhCA0NRUREBMLDw5mNW6dPn8bUqVOVeh4qqqocP2XgW2ogKipK5rKHfDMsybN0TEJCAvLy8qQ2JvMt9VXWoNiCggIkJSUhISEBCQkJeP/+PWbNmsU7ypKvc7Y6LfXK59dff+U0vvfv3x9btmxhnhPlnbWjutDS0oKFhQUsLCwgFArx8uVLhISEiMsmVuBCTEyMeGYqkXr16nGW5VmxYgXmzZtXrvyx7hellw0rTSAQoKCgQKmjiSur3ChLPVRXVxeWlpawtLREQUEBnj59itDQUPFvypoVNywsDN7e3jA3N1f48/T09FCrVi3k5eVJpMszI11eXh6zo1qZy3FVtmbNmnG+Y3R0tFzvZdX95T02sj6joKAAHz584KSXZxBGeno63r9/L77ntGnTRrzUFQurM7KsgZd89XhZ5UVOTg5q167NW/9kXfvv3r1Dfn6+zOuT9fvVrl1brmMsqxOw5PJH/wXKfuZmnQ8jRozAoUOHlPo5okF1c+fORXZ2tviZLjw8HBEREZxyEQBcXFxgbW0t16AZZZB2/5K1FFRF379UYezYsThw4AAn3cvLi7nUW+PGjdG3b1+p+2SdX2ZmZkoZSMlSFZ/DVN2GKev84SvzHz9+XGUC9ZRh/PjxnHLr3r17yMnJYc4qNWrUKIUDl0ovhczCqquVDPzl+z28vLzkWvKzqpEVZFPec1xZ13T9+vU5y3rLMyu9qmeuVzbW+ZWRkYHs7GypsyTHxcWpMluEEKI01atnjxBCyFdNU1MTjo6O5doHXyOstMbO6rBeu7xES6PxCQsLY45q79ChA/P/IsnJyVJHH2VnZ0ss5acIZQdudOrUiZOWmZmJkJAQqe8rKCjgBP4AQJ8+fZSWt+qqe/funOP64cMHHD58mBN40qdPH96ZUwwNDZnp0jr78vPzFRqJWFBQgOjoaHHHjZqaGjp16gQbGxvs2LED/v7+cHNzYy5LEhgYyEmrig2n8mBdx0KhUGrH3ufPn+UaPaZq5Tnm2dnZePLkibg80tbWxqBBg7BkyRIcP34cwcHB2LRpE+d9RUVFCAoKKvPnEkmGhobMsr3kkgZ8WNvUqlUL3bt3l/negoICmb9jWFgYJ61FixZlmio/LS0NRkZGMDExga2tLRwcHLBv3z6EhobyvicyMpKZXt0DF6KiopjHdufOncxORIFAUCVGNauaQCBAVFSUOEhPTU0NnTt3xrRp07Bz507cuXMHrq6uzGCge/fuSfzNKtdZgQIlpaWlycxjx44dOWnPnz+XWq8LCAhAr169MGLECMydOxdbt27FpUuXOEsRKVKeV1a5oWg9ND8/H69evcKnT58AFAc69uzZE7NmzcL+/ftx//59nDx5khmswapnyENdXZ35+8szkCEgIIC5RNR/qX7LqvtHRETIXMo9KSkJL1++5KTLe2xYzw0lPX36lHkdsX5LeTg6OqJfv36YOHEiFi9ejM2bN+PYsWNS38O655T1flOnTh20aNGCkx4RESH1fc7OzujRowfGjh2LZcuWYf/+/RLXAuv3EwgEMo8vANy5c4eTZmxszEljXefS2gcSExOrRJ1YRBnPI8oe/MM6j2UFcGZmZio0w15mZiYiIiLE79HR0cF3332H5cuX4/Tp03j48CHWrFnDeZ9AIEBwcLDcn1NeHTp0YP5GZb1/aWtro3PnzjLfW1UGdLVp0wbdunXjpN+6dYs5C7WFhYXMvJfl/MrIyKgSS6uXBWvQhKrbMGXVf/juVdLqnl++fKm0mWbKWk62adMGPXr0kEjLy8vDnTt3xLOpl8SaSU2WN2/eIDExUeo24eHhnLSSv0H79u2Z142036OgoKBKzOrFOtfy8/OlnivyBJhVBFYbZnh4uNSyJjMzE8+fP1dltpSuffv2nDShUCizHb308yohhFRVVaPWTAghhMhp8ODBMDMzK/P7+UY8sBrCgeKZljw9Pcv8eVXNnTt38PjxY97XT506xUlTV1eX6Ezq2bMnp6GhsLAQrq6uvPtdv349evXqBXNzcyxZsgT79u1jNiywKLuRb8iQIcyGkl27dkl9oP3777+ZszsoOsX2f5WlpSUn7fDhw5w0vlmlgOKZEdq0acNJLz1rVUmXLl1C7969MXz4cMyZMwdbt26Fm5ubRKPP4cOHsXjxYpibm6Nnz54wMzPDr7/+yrvPLl26YPjw4Zx0Vqcy3/kpa9rwytaxY0fmlOHSjvWBAwfQq1cvfP/991i4cCF27dqFq1evVvjsL4oc84SEBGzcuBHTp0/HkCFD0Lt3b/z000+4fv06cx+ampqwsrJijpyTJ6iAyEdHRwe9evXipLu5uUkN2IuLi8PFixc56QMGDJB7BLOLiwtvWR8XF4e7d+9y0nv27CnXvktr0KABDAwMOOms7yDCupfq6+ujbdu2ZcpDVfH69WtmOt+MWX5+flW+HC2rgwcPYtGiRTAzM0PPnj1hbm6OjRs38m5vZGTEnBmmdJn07bffcrbx9fXlzDYl8unTJwwZMgTGxsaYPHkyHB0dcfr0aU6nCiswJDs7W2r92NPTE0KhEAkJCbhz5w6OHz+OAwcOcGasYdXH+H73yio3ZHWuFRQUYOvWrZg3bx5GjhyJHj16YNy4ccxZNET7GzBgAPO7lOc+M2TIEE7amzdv4O7uzvue/Px87Nu3j5Ouq6vLzF91xTo2fN+9pD179jDrONJmaiopNDQUDx484H39zJkzzPSyHntWYGNYWBjv9XH79m3ObAgAO5hIXqzyIiAggHcGhfz8fNy8efP/2Lvz+JjO/v/j76wSQTYS+177Wmrf94Qg1t5Var/TFretSldaLVrq1qLc9qJqVyXWEISWqqUaKrXvOyGCbPP7wzfzS8zJHknI6/l4eDwm1znXda4ZM2fOnPM5n48iIyMVEhKiTZs2WfxOrFu3rmG2q++++y7RoM21a9caBk8YHeMbHROfPHlSJpPJcOzly5cnuN3MYHRsnNLv0PT+zW30nXT+/PlEg9a7d++uatWqydfXV++9957+97//6ffffzcvP3PmjD799FP17NlT9evX12uvvabu3bsbBsVJT7OI9e7d2/D9k5HH9W5ubobBQitWrNDFixcT7Hf69Gn9/PPPFu0NGzZMVga4rBIsJRmX1QsPDzfcN3To0CHJ8WrUqGHRFhISkuCF+5iYGHXu3FnVq1dXp06dNGrUKM2ZMyfRGxieF6P/l6R+TxvtoxI6hymlzz4qqfdPqVKlDH8zJ3Y+Yc6cOapevbpatGghPz8/TZkyRevXr0/1jZUpkZb9pNF7ctmyZRY3guTLl0916tRJ8dxiYmISzbq3Y8cOw89K3N+ouXLlMgxgS+z/Y/369apRo4aaNGmi/v37m29uMKo28DwlVLI6off4yZMnEz2vnZGMjtlu376t9evXJ9jnhx9+MMx6mJVVrVrV8DOU0LGs9PTmmsDAwOc4KwBIP1nnqBkAgGQaM2ZMomleE5M/f37D9gULFuj69evx2sLCwjR48GDDk8gvqpiYGA0aNMjiR6fJZNK0adMMLxI3atRIuXPnNv/t6elpeAJg6tSphmmod+/era1btyoqKkqnT5/Wli1bNH36dK1duzYdnlHKeXp6qlGjRhbtf/zxh0aOHKmHDx9aLFuzZo2+/vpri/bChQunqlzKy8jHx8ciVfuzmQqcnJzUunXrRMdp3769RdvOnTsNg9muXLmimTNnmh/v3r1b8+fP11dffRVvLqdPn9a2bdt0+vRp85x+/fVXzZkzx/AiyN27dw2zMRgFPSSUnt6orEpWYmdnZxh4unTpUv34448W7cePH9fSpUsVExOjCxcuKCAgQLNmzdLcuXMzvDxYSl5zV1dXrVmzRr/99lu85RMnTtTJkycNxwkICDBM655UmQykTNeuXS3aHj9+rAEDBhj+35w+fVr9+/c3LJM4YMCAZG/38OHD+vDDDy1Oyt+8eVNDhgwxzLCSmjuEY7Vp08aibdOmTVq9erVF+3//+1/DizZt2rR5YbPYxUrohLBRQMmvv/6aYCbRl6E0X0hIiLZv364zZ86Yn09gYKBhwLr09IS70YXgZ7+TEroQ+fbbb1sETMXExOizzz4zZ/A6fPiwli9fri+++MKiFGXTpk3jlfmINWHCBMO73Ldu3Sp/f3+LdqPvf6P9eWLH3Zm130iMra2tDh48qMDAQF28eNF8rLJ8+XLDjBnS07vhjV47o+OM5OrcubPhnfmffvqpYYDw/fv3NWTIEMMLUX379s0y5ZvSQ/369Q2zs/3444/69ttvLS6WRkVF6ZtvvtGaNWss+jRt2tQwKCkhw4cP17FjxyzaFy5caBhw+OqrryZYsispLVu2tHgPREZGatSoURaZ+s6cOaOPP/7YYgwHBwc1a9YsVduXjC8oR0RE6D//+Y9FJq/o6GhNnDjR8DMf97szd+7chvuPEydOaMiQIYbZVTZv3qyxY8datOfNm1edOnWyaDd6zS9fvmx4EXDdunWaM2eORXtmSum+NCPUq1fP8Nj5vffeM8yoMW/ePJ05c0ZPnjzR8ePHtX79ek2ZMiVewKGLi4tWrlypAwcOxPteGzt2rM6fP284j/Xr1xse26Vlf5sa3bp1s2gLDw9Xv379DIP6QkJCNHDgQMMMZun1/ZWR2rZtm6xMjaVLlzbMJvesRo0aGQbqjBgxwjBAf9asWbp48aIeP36s4OBg/fzzz5o8eXKmBEsZfV4fPnxoeKwSy+g85t69ey0y7MXExGj69OnasmVL2ieaBGtra7Vt29aifeXKlVq4cKFFe0hIiBYtWiSTyaSLFy9q586d+t///qeZM2dmyDGH0TaSe87G29vbIkDxwIEDFuenvLy8Up0Zf/ny5Ybnp/7++2/D72sbGxuL8zlG59L27duniRMnWhzrXL9+3Rw0fvXqVe3Zs0fz58/XxIkTM/wYMKHjnm+++cbiN/v58+c1aNCgLJMlrk2bNobBq+PGjTPMYLhhw4Z0L0ebEfLmzWsYEB8UFKSpU6davG9PnTqld99996W9+QnAyydjr2wAAJAOPD09NWTIEH355Zcp7uvi4qLChQubS3DFunbtmtq3b6/27dsrf/78unTpkjZu3JhoausX1Y0bN9SlSxe1aNFCFSpUUEREhAICAhJMA9yrVy+LtgEDBljcFRoREaF3331XtWrVUp06dWRnZ6fg4GBt27bN4geSlZVVpp7kGzp0qPbu3Wtx0XXjxo367bff1Lp1axUtWlT379/X7t279ddffxmO8/777yfrrs7swMXFRS1bttTGjRsTXMfb21uOjo6JjvP6669rwYIFFnezzZo1S1u2bFHLli3l5uam8+fP65dffjFMG96nT594J3jeeust/fzzzxY/4CdPnqx169apYcOG8vDwUHR0tM6cOaOAgADDz75RkF3cQMJnx75x44bu3r2rEydOGJ4wzGx9+vTRunXr4n0OTCaTxo0bp1WrVqlJkyZycnLSqVOntGHDBsM7PgcOHJiRU5aUstc8Z86c6t69u0UQwp07d+Tr66vGjRurbNmycnV1VWhoqI4cOWJYrtTa2loNGjR4Ls8nu2rXrp0WLFhgcaH+0qVL6tSpk5o2baoqVapIkoKDgxUQEGB4scvLyyvFWTDWrFmj/fv3q02bNsqXL5/OnTsnf39/w7toixcvnuwsIkbefPNNLVu2zCJY6IMPPpC/v79q166tqKgo7dixw/CCes6cOeXn55fq7WcVRYoUMWxftGiR/vrrLzVo0EARERE6ePBgvCwSz8qs0h3pqXfv3obBK19++aVWrVqlBg0ayMPDQ5GRkTpz5oy2b99uWJLw2Ww5xYsXV6tWrbR169Z47X/++adatWolLy8vlS5dWqGhodq2bZthWY4KFSpYjJszZ069+eabFoFt9+/f1xtvvKHmzZurSpUqioiI0OHDhw1LydnZ2RkeTxqVotu1a5e++OILubq66tChQ3r77bfNGSQyc7+RmD59+mjYsGHx2qKiovSf//xH1atXV40aNZQvXz49fvxYf//9t3bu3Gl4AdzoOCO5ChcurO7du1sEPD9+/FhDhw7VokWLVL9+feXKlUtnz57Vli1bDAODixQpYvh/9SKztbXVkCFDDMtxzZgxQxs3blSzZs3k4eGhGzduaPv27bpw4YLFuo6OjhoxYkSKtn379m11795dLVq0UOXKlRUREaHdu3cnWJrurbfeStH4cXl4eKhdu3YWmWiOHTsmLy8v+fj4yNPTU6dPn9b69esN34O9e/eWm5tbqudQt25dValSRX/++afFHFq3bi0vLy8VL15ct2/fVmBgYIL7obp168ZrGzx4sLZt22bxXbpz5061atVKrVu3VvHixfXw4UPt3bvXMBhRehq8ZlRWt1KlSoYZfL744gsFBQWpRo0aevz4sX799dcEx85MRsfG27Zt08SJE5U7d2798ccfGjZsWLJKj6YXW1tb9enTx+KGo5s3b6pLly5q3ry5KleurKioKO3fv98wC1uuXLn0r3/9y/y3m5ubOnToYBHIeO3aNbVt21bNmjVT6dKl5ezsrHv37ungwYM6cOCAxbj29vaqXbt2Oj3T5OnYsaMWLlxoERh1/vx5dejQQc2aNTNnnzp27Jh27NhhGCDesWPHDP1/TC+xGXeSKslkFOxhxN7eXr169dK3334br/3atWvy9fU1v78iIiL022+/Gb4PXFxcDIPYnjejY5/IyEi9++67ql+/vk6dOqUiRYro3XffNS+vVKmSxe8Ek8kkPz8/tWnTRuXKlVNoaKh27NiRZDnC9PTWW29p5cqVFucJJkyYoHXr1qlJkybKnTu3Tp8+rY0bNxp+72TU+QSj/aS/v7/y588vR0dHHTx4UGPGjDEMiHZ1dVWjRo0UEBCQ6DaMblxIicmTJ8vf319NmzaVk5OTjh8/ri1bthgey7Zo0cIiELxLly6aM2eORea8BQsWaOfOnWrRooXy5s2rixcv6pdffjH87durV69kZ2xOLxUrVpSVlZXF+boDBw7Ix8dHbdq0kZOTk06ePJng65FZPDw85OXlZZFJKjw8XL1791bjxo3N2af27duXaMbRrK5fv36GAWCzZs3Srl271KJFCzk4OOjEiRNZ7v8JAJJCsBQA4IX05ptvat26damq892+fXtzNpq47t27px9++MGivVChQgoPD3/hSzA5OTnJZDIpPDxckZGR2rRpU4KlqGK1b99e9evXt2ivX7++fHx89Msvv1gsO3DggOHJqLi6deuWqeU9KlSooOHDh+urr76yWHb79m3DzDrPGjhwoFq1avU8pvfC6tq1a6LBUomV4Ivl5uamUaNGGZbJO3v2rGFpv7jKli2r3r17x2urUKGCevXqZZi149SpU4YXap7l4eER72R9LHt7exUtWtTiotrNmzc1ZcoUSU+DAyMjI7NcYF3p0qXVv39/zZo1y2JZcHCwgoODE+3foEEDw7tJn7eUvubvvvuuAgICLIJko6OjtWPHDu3YsSPJbb7++uvy9PRMvycB2dra6uuvv9Ybb7xhEQgSFRWlbdu2JVmutUyZMvriiy9Stf3Lly9r3rx5ia5jbW2tL774Ik1lTIoUKaIhQ4YYZicMCgoyDCyJ6/PPP8/w7AfPQ7Vq1VSgQAFdvXrVYtkff/yR7Dv7b9++nd5Ty3DVqlXT66+/blgWIyQkJMGShXEVKlTIMMvSmDFjdODAAYsgmIcPHyZa/lF6GtA0btw4wyxmfn5+CgwMtPheiImJSdZnddCgQYZ3jZcqVcqwb9zjcR8fH3OwVGbvNxLi5eWltWvXGmZoPXz4cLKCK6pUqWJYHiwlRo4cqUOHDhlmi0rOPHLmzKmZM2cmWA7lRdaxY0cFBQUZ/nY5d+6c5s+fn+QYX3zxhV555ZUUbzs6OlpbtmxJMtNH8+bNDbMRpsTo0aO1d+9ei2xyN2/eTPI5Vq1aVe+8806ati9JkyZNUufOnS2ypNy/fz/J0lC2traGZUmLFy+ujz/+2PD3wb1795JVcur1119P8LdI69atNXnyZMMsiLt27TLM7lepUqUEb6jJaKVLl7bY/5hMJi1YsMD8d7du3TI8yOatt97Sxo0bLc7ZREdHa+vWrRbBvc8aOXKkRXaq4cOHa8+ePRaZsyIjI5P1OZOeZs8zClh5nuzt7TV58mT16NHD4rOR3LlXqFAh0bK9WZ2Pj0+iwVJWVlYp+m05YMAAbdq0Sf/880+89qioqGS9nu+//75hdqrnrVSpUobt+/btM9+082w2PR8fHy1btsyiT1RUlDZs2GCRqdDW1lalS5dOtFRfeihWrJjefvttTZs2zWLZiRMndOLEiUT716pVS76+vs9revGULl3aItAjJiZGc+fONf995cqVBLNHdujQIdFgqWLFipkD9tPi+PHjSZ7ndnFx0YcffmjR7uzsrDFjxmjUqFEWy86dOxfvuRopWbJkptzUmjdvXtWrV89w/3Du3DnD81VZ6Tt49OjR2rdvn2Em3507dxpmrY8N0H+RNG7cWE2bNjV8Pgl93p2cnAyrNwBAVkMZPgDAC8nGxkZjx45N1QXMgQMHJpjh4Fm5cuXSzJkzDe9AfdG4uLho2rRpyS6ZVb16dX366acJLv/yyy8tsg8kR/369Q1PdGe0fv36acSIEakqbfTvf/9bw4cPfw6zerHVqVNHhQsXNlxWsmTJZAfIde3aVYMHD07x9gsVKpRgGvdRo0al+gJUrly5NH369AQzGiV1cdNkMlmU+cwqhg4dmqwgtmeVK1dO33zzzXOYUfKk5DV3dnbW7NmzUx3sVLt2bcMFIlV6AAAgAElEQVQTjki7MmXKaN68eakKBqpWrZoWLFiQoov6JUqUUM+ePZO1rpWVlT766KN0yT7Tv3//ZG83lrW1tcaMGZOmEoBZiY2NjT744IMUfecaHXslJ5DoRfDhhx+qefPmqerr7Oys7777zvC9X7BgQc2dOzfF5aptbGw0fvz4BC/y2Nvba8aMGakKFGnXrl2CF16SU+7r2e/PjN5vJIeVlZWmTJmS6hsBChUqpKlTp6a6dEssJycnzZs3T1WrVk1x33z58mnhwoUqU6ZMmuaQlU2YMEEdO3ZMcT97e3tNmjQpxQHigwcPlqura7LWrVixYqqyJj/Lzc1N8+fPT3EAQMWKFTVz5kzlyJEjzXMoWbKkvv322xR/zqytrTVu3LgE90Ndu3bVJ598kqryz2+++WaC5V2lp5mrBw0alOzx3nrrrTRnEElPyQm0vHbtWgbMJD47OzvNnTtXJUqUSHHfnj17Gt6oki9fPn3//fepzoDWtGnTeBl7MlKFChU0Z86cVM39tdde07x58zI840t6atmyZaLzf/XVVxM8l2DE3t5e8+bNU9GiRVM8l/79+xuW5MwIFSpUMCyrF9ezn9caNWqkKKjoo48+StWxQGq88847hp/VpLzyyiuaNm1ahpUaT+t+MqGy1LHS8putefPmyc5i7ejoqKlTpyZ4bqNDhw4aOXJkiufg6empWbNmZdo+5qOPPkp2+b+SJUsa3viaWdzd3fXtt98m+/dXiRIlNH78+Oc8q+fj66+/TjDg81lFihQxzOoqKcM+9wCQXARLAQBeWFWrVjW8qz4pjo6OWrBgQYJ3DMWqWLGiVq9erXLlyqV2illOo0aNNH/+/ARrwsdq166d5s+fn+iPPXt7e82cOVODBw9O1g9qW1tb9e3bV7Nnz072j+DnbeDAgVq6dGmy7wArXry4ZsyYoeHDh/PjzoCVlVWCJx1TejJy0KBBiZ4EelaTJk20fPnyBE+w2tra6r///a/GjBmToovIr776qlasWJHoyUY/P78kLwJkxgWK5LCystL48eP1ySefJHryL+76vr6+Wrp0abLWf15S+pqXLl1aq1atSlHAnK2trfr37685c+YkWT4SqVe1alX9/PPP6tq1a7Kyrzk6OsrPz0+LFy+2yDaQHB999JGGDRuW6LZcXV31zTffqEePHikeP7Htfvnll8n63BQpUkSzZ8+2yJL3omvVqpXGjRuX5DGAlZWVevbsaVH2TZJ+++23lyKlv729vaZPn66RI0emKCC/Vq1aWrFihSpWrJjgOpUrV9bKlStVr169ZI1ZqFAhzZkzJ8kgkgIFCmjZsmXq1KlTsm5WcHBw0IgRI/T1118nGARUtWpVvfHGG4mOY/T9mdH7jeTIkyePFi1apH79+qUok2SrVq0SPX5Jqbx582rJkiUaMWJEsr/Xvb29tXr16gy7sJpZ7OzsNGnSJE2dOjXZN85Ur15dP/74Y6qCrMqVK6clS5YkGYDWunVr/fDDD+mW4aRs2bL6+eefk7UPsLW1Vffu3bV06dJ0/Ww0bNhQy5YtS3YAYcGCBTV79mx16dIl0fV69OihFStW6LXXXkvWuIUKFdKUKVP08ccfJxmM2L9/f7399tuJ/sbLkSOHPvnkE33wwQfJ2n5GqVWrVpI3P2TWjRvu7u5atmyZunXrlqzvjly5cumTTz5J9Oaq2O+5Jk2aJHse9vb2Gjx4sL777rtMPRdRs2ZNrV+/Xp06dUpW4F/OnDk1aNAgLViwIE0lMrOCXLlyJRqwkpoARE9PTy1fvlydOnVK1vmZPHny6PPPP9d7772X4m2lFxsbG40bNy7RYwWjY5+xY8eqQ4cOiY7t4uKi77//PlXBS2nx6aef6rPPPkv291i7du20bNmyDH1PN2zYMMmApsTO2djb2yd6LiEtwVI5cuTQ999/n+Q5s+LFi2vRokVJfr8PGDBA06dPV8GCBZO1/QYNGmjlypUqVqxYsuec3kqWLJmsYFIvLy+tWLEiw7MDJqVGjRpavHixSpcuneh6TZs21ZIlSzL1XFpa5M6dW4sXL04yuK969er64Ycf5O7ubrg8LZm7AeB5oAwfAOCFNmLECG3fvj3FZVmKFCmilStXyt/fX+vXr9fp06d19+5deXh4qGzZsurUqZOaNm2a5ju8s6LatWtrw4YN+vnnn7Vx40adPXtW4eHhypcvn1577TV17tw52Zk07O3tNWjQIHXt2lXbtm1TYGCgzp8/rzt37ujJkyfKnTu3SpYsqTp16qhjx47JvjCRkWrUqKGVK1fq6NGj2rx5sw4dOqRz584pLCxMTk5Oyps3r6pWrarGjRurRYsWqbqbOTvp3Lmzpk+frpiYGHObjY1Nqi4yeXt7q0mTJtq5c6d27NihEydO6Pbt2woLC5Ojo6OKFCmi6tWrq3379qpWrVqS41lZWal3797q3LmztmzZor179+rvv//WzZs3FR4eLnt7e+XKlUvFihVT5cqV1aZNm2SN6+bmptWrV+uHH37Qtm3bdPbsWcXExMjd3V1FihRR48aNk333VWawtrZWjx491K5dO/Pn+J9//tGdO3cUHh5ufk1i72jNClknUvOae3h4aNq0aTp9+rQ2btyoI0eO6NSpU7p//74iIiKUM2dOubq6qkyZMqpZs6batWv3UpQ/exG4u7tr/PjxGjJkiPz9/bV3716FhITozp07srGxkZubm0qWLKmGDRvK29s7zf8vfn5+atKkiZYuXapff/1VN27ckKOjo4oVK6YWLVqoW7duz6UsR+fOndWmTRutW7dOe/bs0d9//627d+/KyspK7u7uqlSpkpo1ayZvb+8sV7YzvXTv3l1169bV0qVLtW/fPl26dEkRERHKlSuXeZ/++uuvq1SpUoqKipKLi0u8knL37t3Ttm3b5O3tnYnPIn1YW1trwIAB6tatm7Zu3aqgoCCdPHlSt27divedVKJECVWqVEleXl7JDu4uWbKkFixYoIMHDyogIEAHDhzQzZs3defOHdna2srd3V0VK1ZUixYt1Lp162Rnk8mdO7cmTJig/v37a+PGjeb/w3v37snW1lYuLi4qW7as6tatq/bt2yfrItinn36qevXqacWKFTp27JjCwsLk7OwsDw8P1a5dO8GLpxm930iOHDlyaNSoUerTp4/8/f21f/9+85weP36sHDlyKE+ePCpVqpSqVKkiHx+f53J8YG9vr4EDB6pnz57asmWLdu/ereDgYF2/fl1RUVFydXVVoUKFVK9ePbVu3TrJG0heNt7e3mrTpo327NmjgIAAHT16VJcuXdLjx4+VJ08eeXh46LXXXlPz5s1Vt27dNG2rdOnSWr16tZYvX27+3fXo0SPz7y5fX1/Vrl07nZ7Z/5c/f34tWLBAhw8f1vr16/XHH3/o2rVrCg8PV548eVS4cGHVq1dPHTt2VPHixdN9+9LToK2ffvpJe/fu1fbt23Xw4EHdvHlTDx48kKOjo/LmzWv+3mvRokWyA1gqVqyoJUuW6Pjx49q0aZMOHjyos2fPmsd1d3dX5cqV1aRJE7Vq1SrZ41pbW2vo0KHmAMYDBw7o2rVrsre3V5EiRdSsWTN16dJFHh4eaXlZnpvYrM+rVq1ScHCwwsLC5OLiYt6XJhVk8Ty5urrq888/V+/evbVt2zYFBQXpypUrunPnjqKjo+Xs7KyyZcuqUaNGat++fbIyshUuXFizZ8/W33//LX9/fx05ckRnz57V/fv3FRUVpZw5c8rNzU1ly5bVa6+9prZt22aZYKN8+fJpwoQJGjp0qDZs2KB9+/aZf3vZ2trKzc1NpUuXVsOGDbPUvNODj4+PNm3aZNFuZ2eX6izQbm5umjBhgvr162f+jX/t2jXz+8vV1VVly5ZV48aN5ePjkyWCFJo0aaKff/5Z8+bN02+//Wb+LRJ7fGaUfdTBwUFfffWVfH19tWrVKh07dkzXr19X7ty5VaxYMXl5ealDhw4JZsN+nqysrNS9e3d5eXlp+/btCgwMNB//PHz4UE5OTipatKhq1qypjh07ZtoNqZMnT1bTpk21evVqHT9+XA8fPpSLi4s8PT1Vt27dJAOeOnToYFj2tWLFiipZsmSa5mZvb68JEybIx8dHy5cv15EjR3T79m05OzvrlVdekZeXl3x9fZP9ndayZUs1bNhQO3bsiHcuLfa7slChQqpWrZp8fHzSJZNyeqhTp442btyoNWvWaOvWrbp48aIePXqkAgUKqFq1aurWrZs5CPvx48eZPFtLFSpU0Lp16/Tzzz9r06ZNOnnypEJDQ5UvXz5VrVpVnTt3NgcZXbp0KZNnm3ru7u6aO3euAgMDtWbNGh0/flw3b95Unjx59Morr8jX11fe3t6ytbU1LClpbW3NeXUAWY6VyWQyZfYkAAAAAABIL5MnT9acOXPitZUoUUKbN2/OpBkBAF5G9+/fN8x0NGPGDLVo0SITZgQAANJbcHCwYfanUaNGqV+/fskao1+/fgoKCorX5u3tralTp6bLHIGsxN/fX8OGDYvX5uLiov3792fSjADAGCGcAAAAAAAAAAAAAPCMtWvXWrTFljMGXnYTJkxQrly5VLBgQRUsWFCFChVS/vz5E812dvPmTYs2T0/P5zlNAEgVgqUAAAAAAAAAAAAAII4zZ87op59+smivUaOGChQokAkzAjLWjh07dOHChXhtVlZWmj17tho3bmyxvslk0saNGy3aX3nllec2RwBILevMngAAAAAAAAAAAAAAZJYHDx4oNDRUkhQTE6P9+/fLz89PkZGRFut27do1o6cHZIoSJUpYtJlMJk2ePFlHjx5VTEyMJCkqKkrHjh3TO++8o6NHj1r0qV+//nOfKwCkFJmlAAAAAAAAAAAAAGRbe/bs0bBhw+Tg4KDo6GjDIClJ8vDwoAQfsg1vb2/t2rXLoj0kJETdunWTjY2NnJyc9PDhQ0VHRxuO4eLiwmcGQJZEZikAAAAAAAAAAAAA2Vb+/PklSY8fP04wUEqShg0bJnt7+4yaFpCp2rdvr2rVqiW4PDo6Wvfv308wUEqS3n//fTk4ODyP6QFAmhAsBQAAAAAAAAAAACDb8vT0THKdzp07q1OnThkwGyBrsLa21vfff6+KFSumuK+NjY2GDRvGZwZAlkUZPgAAAAAAAAAAAADZloeHh6ytrRUTE2OxLF++fBo4cKB69uyZCTMDMpebm5tWrFihpUuXatWqVQoJCUl0fTs7OzVv3lz9+/dX5cqVM2iWAJByViaTyZTZkwAAAAAAAAAAAACAzHLw4EGdPn1aoaGhMplMcnZ2VpkyZVS1alXZ2Nhk9vSALOH69es6efKkrly5oocPH+rJkyfKmTOncufOreLFi6tSpUrKkSNHZk8TAJJEsBQAAAAAAAAAAAAAAACAbME6sycAAAAAAAAAAAAAAAAAABmBYCkAAAAAAAAAAAAAAAAA2QLBUgAAAAAAAAAAAAAAAACyBdvMngBeTDExMXr06JEkydbWVlZWVpk8IwAAAAAAAAAAAAAAALxMTCaToqKiJEmOjo6ytk57XiiCpZAqjx490t9//53Z0wAAAAAAAAAAAAAAAEA2UK5cOTk5OaV5HMrwAQAAAAAAAAAAAAAAAMgWyCyFVLG1/f9vnXLlysnOzi4TZwMAAAAAAAAAAAAAAICXTWRkpLnyWdxYlbQgWAqpYmVlZX5sZ2cne3v7TJwNAAAAAAAAAAAAAAAAXmZxY1XSgjJ8AAAAAAAAAAAAAAAAALIFgqUAAAAAAAAAAAAAAAAAZAsESwEAAAAAAAAAAAAAAADIFgiWAgAAAAAAAAAAAAAAAJAtECwFAAAAAAAAAAAAAAAAIFsgWAoAAAAAAAAAAAAAAABAtkCwFAAAAAAAAAAAAAAAAIBsgWApAAAAAAAAAAAAAAAAANkCwVIAAAAAAAAAAAAAAAAAsgWCpQAAAAAAAAAAAAAAAABkC7aZPQEAAAAAAAAAAAAAz19ERITCwsL08OFDRUREKCYmJrOnBAAAXnI2NjZycHBQnjx55OTkJCsrq8yeEsFSAAAAAAAAAAAAwMvMZDLp1q1bunXrVmZPBQAAZDNRUVF68uSJQkND5ejoqKJFi8raOnML4REsBQAAAAAAAAAAALzErl69qtDQ0HhtVlZWsrGxyaQZAQCA7CI6Olomk0mS9OjRI124cEHFihXL1AxTBEsBAAAAAAAAAAAAL6nHjx/HC5Ryd3dXnjx5lCNHjixRBgcAALzcYmJiFBYWpmvXrik6OlqPHj3Sw4cPlStXrkybU+bmtQIAAAAAAAAAAADw3Ny7d8/82MPDQx4eHnJwcCBQCgAAZAhra2vlyZNH+fPnN7c9ePAgE2dEsBQAAAAAAAAAAADw0goPDzc/dnFxycSZAACA7CxXrlzmYO1Hjx5l6lwIlgIAAAAAAAAAAABeUtHR0ZIkW1tb2djYZPJsAABAdmVtbW0+Fok9Psm0uWTq1gEAAAAAAAAAAAAAAAAggxAsBQAAAAAAAAAAAAAAACBbIFgKAAAAAAAAAAAAAAAAQLZAsBQAAAAAAAAAAAAAAACAbIFgKQAAAAAAAAAAAAAAAADZAsFSAAAAAAAAAAAAAADgpRQdHZ3ZUwCQxdhm9gQAAAAAAAAAAAAAAMhsZcuWNT+2srJSYGCg8ufPn6y+b7/9tnbs2GH+++TJk+k2r++++07Tp0+XJAUHB8vWNuMv8wcEBGjbtm06fPiwbt68qaioKLm5ualEiRJq2LChOnToIHd39wyfV2JiYmL0008/6cyZM/roo48yezoAshAySwEAAAAAAAAAAAAAEIfJZNLmzZuTte6DBw8UFBT0nGeUOa5fv67XX39d77zzjtauXatz587p4cOHevLkia5evap9+/Zp0qRJatWqlZYtW5bZ043n/fff17hx4xQWFpbZUwGQxZBZCgAAAAAAAAAAAACAZ2zevFm9e/dOcr2tW7cqIiLiuc3D2dlZRYsWlfQ041VGefTokfr166d//vlHuXLlUp8+fdSkSRMVLFhQNjY2unXrlg4ePKg5c+bo4sWLGjt2rGxtbdW1a9cMm2Nirl27ltlTAJBFESwFAAAAAAAAAAAAAMD/KVOmjEJCQnTkyBFdu3YtyVJ8/v7+kqR8+fLp5s2b6T6fXr16qVevXuk+blKWL1+uf/75R3Z2dlq0aJEqVaoUb7mzs7NKlSqlVq1aqXPnzrp8+bK+/vpreXl5KVeuXBk+XwBILsrwAQAAAAAAAAAAAADwfxo2bKjcuXMnqxTfnTt39Ntvv8nOzk7NmzfPoBlmjB07dkiS6tSpYxEoFZerq6tGjRolSQoNDX1pSxICeHmQWQoAAAAAAAAAAAAAgP9jZ2enFi1aaO3atUmW4tu0aZOioqLUuHFjubi4JDpuTEyMNm/erC1btujYsWO6c+eOYmJi5OLiosqVK6tDhw5q2bKlRam97777TtOnT5ckBQcHy9b26WX+/fv3mzNOBQcH68KFC5o7d6727dunW7duydnZWTVr1lTfvn1VtWrVFL8OsVmyIiMjk1y3Xr16KlOmjFxcXMzze1ZkZKRWrVolf39/hYSE6OHDh3J3d1fNmjXVs2dPVatWLdFtHD16VD/99JMOHTqkq1evys7OTmXKlJGPj4+6detm3u7o0aO1du1ac7+1a9ea/z558mS8MW/duqXFixdr165dOn/+vKKjo5U/f37VqVNHvXr1UunSpS3msWbNGo0ZM0aenp7aunWrvvrqK23YsEEREREqUqSIxowZo3r16iX5mgHIPARLAQAAAAAAAAAAAAAQh5eXl9auXasjR47o6tWrKlCggOF6sSX42rZtqzNnziQ43p07d+Tn56ejR49aLLt+/bquX7+u7du3y9fXVxMnTkzxfHft2qXhw4fr8ePH5rZbt26Zg7MmTJggX1/fFI1ZqFAhnTlzRr///ruCgoLUoEGDBNfNkyePfvnllwSXX716VQMHDlRISEi89mvXrmnDhg3asGGDBgwYoBEjRlgEi8XExGjq1Kn63//+F6/9yZMnOnTokA4dOqRffvlFc+bMSVH5v8DAQL333nu6f/9+vPbz58/r/PnzWrVqlUaMGKF+/foZ9jeZTBo+fLgCAgLMbadOnVLJkiWTPQcAmYMyfAAAAAAAAAAAAAAAxFGvXj25uLjIZDJpy5Ythutcv35df/zxh3LkyJFkCb4xY8bo6NGjsrGx0aBBg/TLL7/ot99+06ZNm/Tll1+qYMGCkp5mQfr1119TPN8RI0bIxcVFEydO1O7du7V792598skncnBwkMlk0vjx4/XgwYMUjdmpUydJUnR0tAYMGCA/Pz+tX79et2/fTtE44eHh6tu3r0JCQpQzZ06NGDFCW7Zs0f79+7VixQq1a9dOkjRnzhzNmTPHov+cOXPMgVK1a9fWokWL9Ouvv8rf3189e/aUJB06dEjjxo2TJH322Wc6dOiQatSoIUny8fExB1XFOnz4sAYPHqz79+/L3d1dn332mQIDA/Xrr7/q+++/V7ly5RQdHa2vvvpKy5YtM3xeN27cUEBAgP71r39p586d2rx5s7788kvlz58/Ra8PgIxHZikAAAAAAAAAAAAAAOKws7NTy5YttXLlSm3atMmwFJ+/v79MJpOaNGmSaEajU6dOKTAwUJI0ePBgvf322+Zlrq6uKlmypCpWrKgOHTpIkvbs2aO6deumeL4//fRTvAxYPXr0kMlk0ueff66wsDDt27dPrVu3TvaY3t7eCgoK0urVqxUTE6OdO3dq586dkqSSJUuqRo0aql27turXry83N7cEx5k7d67OnDkjOzs7LVy4MF5JQBcXF02ZMkXu7u5atGiRvvvuO/n6+ipfvnySngakzZgxQ5LUtGlTzZgxQzY2NpIkNzc3ffTRR7KystIPP/ygDRs2aMiQISpSpIjs7e3N69na2srJySnenMaPH6+IiAi5uLho+fLlKlKkiHlZs2bNVLduXb311ls6evSoJk2apNatWxs+x5o1a2rs2LHmv0uUKJHs1xdA5iGzVBybN29Wv379VLt2bVWqVElNmjTRsGHDkozcjYyM1A8//KAuXbqoevXqqlatmtq2baupU6fq3r17SW735MmTeu+999SoUSNVqlRJDRo0kJ+fn3bv3p1k37RuGwAAAAAAAAAAAABgycvLS5J09OhRXblyxWL5xo0bJcmcGSkh0dHR6tu3r1q3bq1//etfhuuUK1dOefLkkSTdvXs3xXP18fExLBXYuHFj8+NLly6leNwvv/xS48ePNwcvxTpz5oxWrlypkSNHqkGDBurfv7+OHz9u0d9kMmn58uWSnpYqjBsoFdeQIUPk4OCgiIgIrV271ty+fft2PXnyRFZWVvrggw/MAVBx9e/fX8WLF1ejRo10586dJJ/T8ePH9ddff0mS3nnnnXiBUrEcHR3NQVCPHj3SunXrDMdq1apVktsDkPWQWUpSRESERo4caZE+8erVq7p69ar8/f3VvXt3jRs3zqI+6pMnT9SvXz/9/vvv8dpPnTqlU6dOac2aNZo3b57KlCljuO3t27dr6NChioyMNLfdvHnTHJXbs2dPffTRR4Z907ptAAAAAAAAAAAAAICxOnXqyM3NTXfu3NGWLVvUp08f87ILFy7o2LFjcnJyiheQZKRs2bJ6//33E1z+8OFDHTlyRNbWT3OdREVFpXiuVapUMWx3d3c3P3706FGKx5Wkrl27ytfXV/v27TOXqjtz5ox5eXR0tPbs2aO9e/dq+PDhGjBggHnZ6dOndevWLUlS+fLl9fDhQ8NtWFlZqWzZsjp69Gi8cnmxiU1KliypokWLGvb19PRMsFSikQMHDpgfJ5Zpq0KFCipatKguXLigAwcOqG/fvhbrlC9fPtnbBZB1ECwl6auvvjLvPNu0aaO+ffuqcOHCunz5subNm6fNmzdr+fLlKlCgQLyUiJL0/vvv6/fff5ednZ0GDRqkdu3ayd7eXoGBgfr6669148YN+fn5acOGDcqZM2e8vsHBwRo+fLgiIyNVuXJljRo1Sq+88oouXbqk77//XgEBAVq8eLFKlCihHj16WMw7LdsGAAAAAAAAAAAAACTMxsZGrVq10k8//aRNmzbFC5aKzSrVsmVL5ciRI9ljhoSE6Pfff9fZs2d18eJFnTt3ThcuXFBMTIx5HZPJlOK5urq6Grbb29unadxYtra2atSokRo1aiRJun37tg4cOKC9e/dq27ZtunfvnmJiYjR58mQVKlRI3t7ekp4GlcWaMGGCJkyYkOS2rl69an58/fp1SVKxYsVSPfdnXbt2TZKUM2dO5c+fP9F1S5UqpQsXLsSbU1wuLi7pNi8AGSfbl+G7evWqli1bJulpesRp06apatWqcnd3V5UqVTRt2jQ1a9ZMkjRv3rx40bZ//vmnNm3aJEn68MMP5efnp8KFC8vDw0PdunXT/PnzZWdnp8uXL2vRokUW2/7vf/+rJ0+eqGjRolq0aJFq1aolV1dXVa5cWTNmzFDLli0lSd9++63CwsLi9U3rtgEAAAAAAAAAAAAAiYsN+nm2FJ+/v7+kp6XlkiMkJER9+vSRj4+PPvvsMy1evFiBgYE6d+6c3N3d1bFjRzk7O6d6nnZ2dqnumxru7u7y8vLS+PHjtXPnTg0cONC8bMaMGebHz17nTo64fUJDQyU9LYuXXmLHd3JySnLd2O2Gh4cbLk9JoByArCPbB0vt2LHDnMbwnXfeMVynffv2kqQHDx7ESyc4f/58SVKhQoXUrVs3i36VK1eWj4+PJGnlypXxlp0+fVq7d++WJA0cONBiR2xlZaXRo0fLyspK9+7ds0gbmJZtAwAAAAAAAAAAAACS9tprrylfvnySpM2bN0uSTp06pZCQELm6uqpevXpJjnHlyhW9+eab2rdvn+zs7NS6dWuNHj1a8+bN0+7duxUUFKRJkybJwcHhuT6XlAgKCtLUqVM1cRePhJsAACAASURBVOLEJNfNmTOnRowYoTZt2kh6+vrEltuLG+Q0d+5cnTx5Msl/AQEB5j6xr0lqSwgmNF9JCZYEjCs2sCo9g7UAZL5sHyzVo0cPBQYGauHChSpVqlSS69vY2Eh6mqIwKChIktS4cWNz+7OaN28uSbp8+bJOnDhhbo8NlLKysjJnrnpW4cKFVbZsWUmK94WQ1m0DAAAAAAAAAAAAAJJmbW1tDgKKDZaKLcHXunVr2draJjnGrFmzFBoaKhsbGy1ZskTffvut+vTpowYNGsjT01PS02vAsVmUsoK9e/dq1qxZWrBggbkUXlJq1aplfvzkyRNJUoECBcxtly5dSrS/UZnAggULSpIuXryYaN/Zs2dr3rx5OnLkSJLzjB0zPDzcXJIvIadPn47XB8DLIdsHS0lPd9B169Y1XBYZGakff/zRvF5sQNWlS5f04MEDSVLFihUTHLt8+fLmx3/99Zf5cWzwUv78+eXu7p5g/woVKkiSgoODzW1p3TYAAAAAAAAAAAAAIHm8vLwkSX/++aeuXr2qTZs2SZLatWuXrP6HDx+W9PT6bbVq1QzXOXTokB4/fizJOGgoo9WoUcP8eMmSJcnqc/78eUmSm5ub3NzcJEnlypUzV1mKmyDkWQ8fPlT9+vXVtGlTTZ482dz+6quvSnqarSpuGcS4wsLC9O233+qrr77Svn37kpxnzZo1zY9jA+CM/PXXX7p8+XK8eQB4OSQd5poNhYeH68aNGzp06JAWLlyokydPys7OTuPGjTPXeo3dKUpPS+ElxNPTUzY2NoqOjo7XJ3ZHnlhf6f9HqF6/fl2RkZGys7NL87bTW2Rk5HMbGwAAAAAAAAAAAKkXExNjDryIiYnJ5NkALwaTyRTv81KtWjUVKFBAV69e1cyZM3X27Fl5enrq1Vdfjbde3CCnuO3W1k9zmFy+fFnh4eEW5fZCQ0P12Wefmf+OiIhIdNzYZQm1x/XsOMndDzRs2FBFixbVhQsXNG/ePJUoUUIdO3ZMcP2//vpLK1eulCS9/vrr5u1YW1urU6dOWrx4sfbs2SN/f39zpq64pk6dqtu3b0uSypYta+7v4+OjadOmKSoqSpMmTdI333wjKyureH1nzpypqKgoWVlZqU2bNua+sRWann09K1SooAoVKuj48eOaOXOmmjVrpsKFC8cb8/Hjx+b/kxw5cqhdu3Ypet0BJCx2XxQREZGs9Z9HTArBUgb69u1rju6VnmaUmjp1qqpXr25uu3v3rvmxs7NzgmPZ2trK0dFRYWFhun//vkX/PHnyJDqX3LlzS3r6Znnw4IHc3NzSvO309vfffz+3sQEAAAAAAAAAAJA2tra2sra21qNHjzJ7KsALISoqyuLz0rx5cy1ZssQcENSyZUtzJqi4/WLF7V+rVi39/fffunv3rvz8/OTn56eiRYvqwYMH2r9/vxYuXKirV6+a13/w4EG8/nEDBR49emQu/Rdb6k56GhBk9BmPO6fIyMgU7QcmTZqkgQMH6sGDBxozZoxWr16tdu3aqXLlynJzc9OTJ0907tw57dixQ+vWrVNERITKly+vN954I952+vTpo23btunatWsaOXKkDh06JB8fH+XNm1dXrlzRTz/9pA0bNkh6GpjWpEkTc/88efKoT58+mjNnjjZv3qzw8HD16dNHxYoV040bN7R27Vrz/0nnzp3l6elp7ht7rf3gwYMKDg6Wq6urXF1dJUnvv/+++vXrp9DQUL3++ut6++23Vb9+fdnb2+uvv/7S999/b74OPnToUDk7O5vHjRvg8fjxY/atQApER0eb90XHjh3LtHkQLGXg2bqkV69e1eeff66PP/7YHDAV94snR44ciY7n4OCgsLCweF+Wsf2fjRp+VtyxY/ukddsAAAAAAAAAAAAAgORr1aqVlixZYs4qZJQdKSF9+/bVnj17dPbsWe3fv1/79++3WKdy5cpydnZWUFCQLl68mG7zTouyZctq9uzZ+vzzz3XixAkdPHhQBw8eTHD9Jk2a6OOPP1bOnDnjtbu4uGjmzJkaOnSoLly4oMWLF2vx4sUW/StVqqQpU6aYM3HF+ve//63Q0FCtWLFCu3fv1u7duy36Nm3aVO+99168tpo1a2rr1q26fv26unTpIknasGGDChYsqMqVK2vKlCn64IMPdPv2bY0fP95iTFtbWw0aNEjdu3dP+EUC8EIiWMrA/PnzVbhwYYWFhSkgIECTJ09WcHCw+vTpo4ULF6patWrmlH2SLNL8PSv2CzPuTj22f3L7xu2f1m2nt3LlypnLEwIAAAAAAAAAACDrOHv2rKKjo2VjYyNHR8fMng7wQoit4BPXa6+9psKFC+vSpUsqWrSoatSoYdgvVtz+jo6OWrFihebOnavt27fr4sWLMplMcnFxUdmyZdW2bVu1a9dOmzdvVlBQkC5duqRz586pfPnykhTvWqyjo6N5O3ETa9jb2xt+xuNmlrKzs0vxfqB69epatWqVdu3apV27dunIkSO6ffu2QkND5ejoqHz58qlmzZry9vZWrVq1EhynfPnyWr9+vVatWqWtW7cqJCREYWFhcnJyUrly5dS2bVt16tQp3rXwuMaNG6e2bdtq2bJlOnTokO7evStHR0dVqFBBXbp0Udu2bS369OjRQ/fv39fq1at169YtOTs76+7duypVqpSkpwFwr776qhYvXqxdu3aZg9QKFCigevXqqXv37uZ147K3tzc/dnBwYN8KpICNjY3s7Ozk4OCgEiVKJKtPZGRkulc8szLFjcaBoZCQEHXp0kVPnjzRq6++qmXLlikgIEDvvPOOJGndunXmLyojNWrUUFhYmN588019/PHHkiRfX18dP35czZs318yZMxPsu3DhQk2YMEGS9Ntvv8nV1TXN204PERER5pRolStXjveFAAAAAAAAAAAAgKzhn3/+UVRUlGxtbfXKK69k9nQAAEA2lprjkucRn/L80g29RMqUKaP27dtLkg4dOqQ7d+6Y65tKT2vGJiRuPdvY+qeSlCtXriT7StL9+/clPc0M5ezsLElp3jYAAAAAAAAAAAAAAACQHREslUwVK1Y0P7506ZKKFy9u/vvKlSsJ9rt+/bqio6MlSQULFjS3x6YTu3r1aqLbjV1eoEABcym9tG4bAAAAAAAAAAAAAAAAyI6yfbDU7Nmz9cYbb2jQoEGJrvfkyRPzYwcHB3l4eJizNZ04cSLBfsePHzc/jlsur2zZspKky5cvm7NHGQkODpYklStXztyW1m0DAAAAAAAAAAAAAAAA2VG2D5a6efOm/vjjD+3cuVPXr19PcL09e/ZIkpycnMyZnRo3bixJCgwMVExMjGG/gIAASVK+fPniBTw1atRIkhQTE6PAwEDDvhcvXlRISIgkqWHDhvGWpWXbAAAAAAAAAAAAAAAAQHaU7YOl2rVrJ0mKiorSlClTDNfZuHGjgoKCJEm+vr6yt7eXJHXs2FGSdO7cOS1btsyi359//qlffvlFkvTWW2/JysrKvKxIkSKqUaOGJGnGjBl68OBBvL4mk0kTJ06UyWSSq6urOnToEG95WrYNAAAAAAAAAAAAAAAAZEc2Y8eOHZvZk8hM+fPn18WLF3Xy5EmdPHlSwcHBKlCggBwcHHTp0iXNmzdPX3/9tUwmk4oVK6bJkyfLwcFB0tOAp+PHj+vs2bMKCgpSRESEChcurIiICPn7+2v06NF69OiRChcurC+++MIcZBWrTJkyWrVqle7evavAwEAVLVpUuXLl0pkzZzR27FhzZqhRo0aZA6tipXXbaRUdHa0bN25Ikjw9PWVjY5Ou4wMAAAAAAAAAACDt7ty5o5iYGFlbW8vd3T2zpwMAALKx1ByXPI/4FCuTyWRK8ygvuIiICI0YMUJbt25NcJ3y5ctr+vTpKly4cLz20NBQ9evXT8eOHTPslzdvXi1dutRcuu9Za9as0ccff6yoqCjD5X369NHo0aMNl6V122kRERFh3m7lypXTPRgLAAAAAAAAAAAAaffPP/8oKipKtra2euWVVzJ7OgAAIBtLzXHJ84hPyfaZpSTJxsZG3t7eKl++vMLDw3X//n1FRkYqT548ql69uvz8/PTJJ5/I1dXVoq+Dg4N8fX3l6uqq0NBQhYWFSZKKFi0qX19fTZ48WQULFkxw2+XLl1eLFi0UHh6u0NBQPXnyRLlz51bNmjU1evRo9erVK8G+ad12WpBZCgAAAAAAAAAAIOsjsxQAAMgqyCyFFxqZpQAAAAAAAAAAALI+MksBAICsIqtklrJO8wgAAAAAAAAAAAAAAAAA8AIgWAoAAAAAAAAAAAAAAABAtkCwFAAAAAAAAAAAAAAAAIBsgWApAAAAAAAAAAAAAAAAANkCwVIAAAAAAAAAAAAAAAAAsgWCpQAAAAAAAAAAAAAAAABkCwRLAQAAAAAAAAAAAAAAAMgWCJYCAAAAAAAAAAAAAAAAkC0QLAUAAAAAAAAAAAAAAAAgWyBYCgAAAAAAAAAAAACykJiYmMyeAgAALy3bzJ4AAAAAAAAAAAAAgKwjOiZGt+4/yuxppLu8eRxlY521c0lcvHhRn376qT777DMVLlzY3H7p0iU1b95ckjR+/Hh17do1s6YIAMALj2ApAAAAAAAAAAAAAGa37j+S9xerM3sa6c7/w87ydHHK7Gkk6OTJk+rWrZseP36c2VMBAOClRrAUkEFe1rswkLFehLteAAAAAAAAAABAyt27d49AKQAAMgDBUkAGeVnvwkDGyup3vQAAAAAAAAAAAAAAkJWRngQAAAAAAAAAAAAAAABAtkBmKQAAAAAAAAAAAABIZ4GBgVq5cqWOHj2qe/fuycnJSWXKlJG3t7e6dOkiOzs7SdKlS5fUvHnzeH1j/x40aJAGDx5sMfbNmzf1v//9T4GBgbp27Zpy586tihUrqmfPnmrUqFGCcwoPD9fSpUu1bds2nT17Vo8fP5aHh4fq1q2rPn36qFSpUhZ91qxZozFjxsjT01Nbt27VV199pQ0bNigiIkJFihTRmDFjVK9ePUnS3r17tXz5ch0+fFh3795Vzpw5VaRIETVq1Eg9e/aUm5tbql9PAADSC8FSAAAAAAAAAAAAAJBOHj16pBEjRiggICBe+71793TgwAEdOHBAP/74o2bNmqVChQqlePwjR45o8uTJunfvnrnt9u3b2r17t3bv3q0RI0Zo4MCBFv1OnjwpPz8/XblyJV77pUuXtHLlSnNQVM+ePQ23azKZNHz48HjP69SpUypZsqQkadq0aZo5c2a8PqGhoQoNDdVff/2lJUuWaN68eapSpUqKnzMAAOmJMnwAAAAAAAAAAAAAkE7iBhS1adNGy5cv1/79++Xv76+BAwfK1tZWISEh6tu3r8LCwlSoUCH9P/buOyzrev/j+JOpDBVRNDRHbnOkZo40t2iZpQ1HztTjSMuOZmqnTmGmNtQyVzZcDMVVjlREcYQIKkbmIgeoqCAgQ0BB4PcHF98fd9wgKEYdX4/r6jrw+X7G+7vu23Pxvt6fkJAQli1bZsyxbds2QkJCGDNmTJ75169fz61bt5g0aRK+vr7s27ePTz/9FCcnJyA7aSkiIsJkTHR0NK+//jpXrlzB2dmZDz/8kD179nDo0CFWr15N+/btycjIYObMmWzbts3seUVHR7N7924GDhyIv78/O3bsYNasWTzyyCMcO3bMSJTq3bs369evJzAwED8/P9zd3XF0dCQxMZGpU6eSmZlZLNdZRETkXqmylIiIiIiIiIiIiIiIiIhIMfD392fPnj0ADBs2jPfee8845uTkxOTJk2nUqBETJ04kPDycxYsX8+677+Lg4EDp0qWNvqVLl8bBwcHsGhYWFnz77be0atXKaOvTpw9ly5Zl3Lhx3LlzB39/f4YPH24cnzt3LrGxsZQrV461a9dSvXp141irVq1o2bIlEydOxNfXl08++YRu3bpRqlSpPGu3bNmSjz76yPj9scceA8DX1xeAGjVq8Pnnn2NhYQGAs7MzAwYMoHTp0kydOpXz589z6tQpGjVqVNhLKiIiUuxUWUpEREREREREREREREREpBj4+PgAUKFCBd555x2zfXr27EmXLl2M/hkZGUVa45lnnjFJlMrRsWNHI8Hp4sWLRntiYqJRLWrw4MEmiVI5LC0tmTJlCpC9pd+ftxDM4ebmZrY9LS0NgJSUFFJSUvIc79GjB4sXL2bLli3UrVu3oNMTERF54JQsJSIiIiIiIiIiIiIiIiJSDA4fPgxA586dsbW1zbdfz549AUhKSuL06dNFWqNZs2Zm262srChfvjwAycnJRvuxY8dIT08HoEGDBiQnJ5v9r0KFCri4uABw9OhRs2s0bNjQbHvLli0BuH79Oi+99BI//PAD586dM47b2dnRtWtX6tWrV+B1ERER+StoGz4RERERERERERERERERkft08+ZNkpKSAKhdu3aBfXMfv3r1apG2pXNycsr3mJWVFYBJtarcVabefPPNQq1x9erVIq3t5uZG586d8ff3Jzw8nE8//ZRPP/2UKlWq0L59ezp37kz79u2VKCUiIn8LqiwlIiIiIiIiIiIiIiIiInKfcldzsre3L7CvnZ2d2XGFUdSEo5s3bxapf0Fjcrb5+zMrKysWL17Mxx9/bJL4deXKFXx8fBg3bhwdO3Zk06ZNRY5FRESkuKmylIiIiIiIiIiIiIiIiIjIfcqdIJWSklJg36IkVt2v3IlZ27dvp1atWg9kHUtLS/r160e/fv24du0aBw4cIDAwkICAAOLj44mLi2PatGk4OjrSvXv3BxKDiIhIYaiylIiIiIiIiIiIiIiIiIjIfXJ0dKRs2bIAnDt3rsC+uY9XqVLlgcbl6upq/Hz58uUC+2ZlZRXLmo888givvvoq8+bN4+DBg8ydO9eoiLVq1apiWUNEROReKVlKREREREREREREREREROQ+WVhY8OSTTwLg7+9PWlpavn137twJZFeVqlevnjH+QWjRooUx9+7du/PtFxkZSfPmzenWrVuRE5omTpxI165d+fzzz/Mcs7Ky4vnnn6d9+/YAREdHF2luERGR4qZkKRERERERERERERERERGRYtCvXz8AYmNj+eKLL8z28fPzw9/fH4C+fftiY2MDZCcV5UhPTy+2mFxcXOjcuTMAGzZs4OjRo3n6ZGZmMnv2bFJTU7l06RKNGzcu0hq3bt3i8uXLbN68mbi4uDzH09LSjGpa1atXv4ezEBERKT5KlhIRERERERERERERERERKQZdunShS5cuAKxcuZK3336b0NBQEhISOHfuHPPnz+ftt98GoFq1akyaNMkY6+TkZPy8ZcsW4uLiuHnzZrHENW3aNBwdHUlPT2fkyJEsWbKE8PBw4uLiOHLkCOPGjWPXrl0APP/887Ro0aJI848cORLIrho1bNgwfH19iYyMJCYmhuDgYMaOHUtERAQAgwcPLpZzEhERuVfWJR2AiIiIiIiIiIiIiIiIiPx9VCxrx8//ebmkwyh2Fcva/SXrfPHFF7zzzjvs2bOH7du3s3379jx9GjVqxFdffYWjo6PRVqNGDVxdXbl69SqLFi1i0aJF9O3blzlz5tx3TDVq1OC7775jwoQJxMTE8OWXX/Lll1/m6de5c2dmzpxZ5PlbtWrF5MmTmTdvHmFhYbz55pt5+lhaWjJx4kQ6dux4T+cgIiJSXJQsJSIiIiIiIiIiIiIiIiIGK0tLKjs5lHQY/1gODg4sWbKE3bt3s2HDBn777Tfi4+MpX748derU4cUXX+S5557D1tbWZJy1tTVLly5l1qxZHD9+HIDk5ORii6t58+bs2LEDLy8v9uzZw4ULF0hOTqZs2bI0adKEvn378uyzz97z/KNHj+app57Cy8uLkJAQoqOjsbCwoFKlSrRq1YrXXnutyNv7iYiIPAgWWVlZWSUdhPzzpKWlGf9Ia9KkSZ5/zEleUfHJPPfJhpIOQ/7hfv7Py/o/qCIiIiIiIiIiIiJSaH/88Qd37tzB2tqaunXrlnQ4IiIi8hC7l3+XPIj8FMv7nkFEREREREREREREREREREREROQfQMlSIiIiIiIiIiIiIiIiIiIiIiLyUFCylIiIiIiIiIiIiIiIiIiIiIiIPBSULCUiIiIiIiIiIiIiIiIiIiIiIg8FJUuJiIiIiIiIiIiIiIiIiIiIiMhDQclSIiIiIiIiIiIiIiIiIiIiIiLyUFCylIiIiIiIiIiIiIiIiIiIiIiIPBSULCUiIiIiIiIiIiIiIiIiIiIiIg8FJUuJiIiIiIiIiIiIiIiIiIiIiMhDQclSIiIiIiIiIiIiIiIiIiIiIiLyUFCylIiIiIiIiIiIiIiIiIiIiIiIPBSULCUiIiIiIiIiIiIiIiIiIiIiIg8FJUuJiIiIiIiIiIiIiIiIiIiIiMhDQclSIiIiIiIiIiIiIiIiIiIiIiLyUFCylIiIiIiIiIiIiIiIiIiIiIiIPBSULCUiIiIiIiIiIiIiIiIiIiIiIg8FJUuJiIiIiIiIiIiIiIiIiIiIiMhDQclSIiIiIiIiIiIiIiIiIiIiIiLyUFCylIiIiIiIiIiIiIiIiIiIiEgxy8jIKOkQpATovv/9KVlKRERERERERERERERERAxZmRncSYz6n/svK1N/vM6tfv361K9fn/nz5xfbnBs3bjTmjYiIKLZ5AbKysli1ahUvvPACzZs3p3nz5vTo0YOzZ88yZMgQ6tevz8CBA4t1TXk4dOnShfr16/POO+8U25yxsbFMnjyZo0eP5jn2IN69/0U57/W0adNKOpQiCQwM5F//+lee9uL+fAwKCjLmO3jw4H3P97CxLukAREREREREREREREREROTvI+NmDFe+7FbSYRS7Km/7YV22ckmHIfdowYIFLF682KQtIiICV1fXEopIxLy4uDieffZZEhIS6NevX0mHI3+hdevW8f7771O1atWSDkXuQslSIiIiIiIiIiIiIiIiIiIPmerVqwNQvnz5Eo6kcDZs2ADA448/zuzZs3F1dSUlJQUHBwcqVapE9erVeeSRR0o4ShFISUkhISEh3+P/tHdPCu/atWv5HnN0dDTuvY2NzX2vVbp0aWO+0qVL3/d8DxslS4mIiIiIiIiIiIiIiIiIPGR27dpV0iEUSUxMDAAvvfQSDRo0AKBcuXIAzJ07t8TiEimqf9q7J8XDzc0NNze3YpvviSee0LN0HyxLOgAREREREREREREREREREZGCZGRkAODg4FDCkYiIyD+dKkuJiIiIiIiIiIiIiIiIiBST7du38+OPP3L8+HESEhJwdHSkVq1adOnShYEDB+Lo6Gh2XEZGBtu2bWPz5s2cOHGCpKQkypUrR6NGjXjhhRfo1asXFhYW+a4bGRmJj48P/v7+REZGkpGRQbVq1ejcuTMjRozAycnJpH/9+vUBGDt2LP/+97/zzHfs2DE2btzI0aNHiY6O5tatW5QpU4Y6derQrVs3+vfv/8C3frp8+TJdu3Y1aZs+fTrTp08HYNWqVbRu3ZohQ4YQHBxMixYt8Pb2Nvp+/fXXLFy40Gj/5Zdf8PDw4LfffiMpKYlKlSrRqVMnRo8eTeXKlc3GkJmZyY4dO9i5cyfHjx8nLi6OzMxMnJycaNKkCS+++CLdu3fPc2+KY22As2fPsmbNGg4ePMiVK1ewsLDgscceo2fPngwZMgQ7Ozuz444dO4anpydHjhwhNjYWOzs76tWrR69evXjllVeKZRswc27evMn69evZv38/YWFhxMfHY2Njg4uLC08++SSDBg2icePGecbl3MOxY8cyceJEfHx82LRpE+fOnePOnTvUqFGDXr16MXTo0Hyfu3td25yhQ4cSFBTEo48+yu7du/Ptt2XLFt555x2srKzYt28f7du3zzMPQN++fZkzZw5w93cvLi6OdevWsXPnTi5fvkxqaipVqlThmWeeYeTIkbi6uuYZ89tvv+Hp6cnhw4eJjo6mVKlSVKlShXbt2jF48GAeffTRQp13UUVGRrJmzRoCAwO5fPkySUlJ2NvbU7VqVdq3b8+QIUMKfL7379+Ph4cHp06dIjExkerVq/Pyyy8zePBgs/179erF2bNnefrpp1m+fHm+8y5atIgFCxZgb2/PL7/8YpJkmZKSgqenJ7t27eLChQvcunWLSpUq0bZtW15//XVq166dZ76NGzcyffp0KleujK+vL5999hlbt24lLS2NatWqMXToUN5//32T65Jzn3M+p3LmAPD19aVGjRomaxT1uyMoKMh4vpYvX87TTz9tHMtZe/ny5TRp0oTvvvuOXbt2ERkZia2tLQ0bNqR///706tUr32sYFRXF8uXLOXDgAJGRkdjb29OqVSvGjh1LhQoVjGd99+7dD+z5epCULCUiIiIiIiIiIiIiIiIiUgzeffddfvrpJ5O2+Ph4QkJCCAkJwdPTk1WrVlG9enWTPrGxsUyYMIGQkBCT9piYGPbt28e+ffvw8fFhwYIFeZKeIPuP7O+99x4pKSkm7WFhYYSFhbFx40Z++OEH6tWrd9dzyMjI4KOPPsLHxyfPsbi4OIKDgwkODmbDhg14enpSpkyZu875d7BgwQIWLVpk0nb58mU8PDzYvHkzq1evNrb3yxEXF8fYsWMJDQ3NM19UVBRRUVH4+fmZJMIU19oAq1evZs6cOdy5c8ek/cSJE5w4cYJNmzaxYsUKk2SUzMxMPvvsszyJJGlpaRw+fJjDhw/j4+PD0qVLC0xiuRfHjx9n7NixxpaJOdLT04mIiCAiIoIff/yRGTNm8Oqrr5qdIz09nVGjRhEQEGDSfvr0aU6fPs3PP/+Mh4dHnsSR4lg7tz59+hAUFMTly5f59ddfadasmdl+W7duBaBt27a4uLjcdd67CQ4O5u233yY2NtakPTw8nPDwcDZu3MiSJUto3bq1cWzdunX897//JTMz02hLT0833n9PT0+++uorunTpct/x5bZu3Trc3d1JT083aU9MTCQxJxLpPAAAIABJREFUMZFTp07h4+PD8uXLadSokUmfjIwM3N3dWbt2rUl7WFgYs2fPZs+ePdy+fTvPmi+++CJz584lKCiI2NhYKlSoYDa2bdu2AdCtWzeTRKkzZ84wduxYrly5YtL/8uXLrFu3zkhoGjJkiNl5s7KymDRpkkkC3dmzZ7l+/brZ/oV1r98ddxMZGcl//vMfk/O9desWQUFBBAUFERAQwKxZs/KMO3r0KGPGjCEpKcloS01NZfv27fj5+TFlypQinuHfj7bhExERERERERERERERERG5T1u3bjX+2D1s2DA2b97MoUOH8PX1ZfLkyVhbW3P16lXc3d1NxqWlpTF69GhCQkKwsLBg4MCB/PjjjwQFBfHjjz/Sv39/ILuKyBtvvJEncSYkJIRJkyaRkpJCtWrVmDt3LgcOHGDPnj28//77ODg4cP36dcaPH09aWtpdz2PFihVGolSvXr1Ys2YNAQEB7Nmzh2XLltG8eXMgO+nghx9+uO/rVpCqVasayQI53N3djbaWLVsWap5Tp06xaNEimjdvzvfff8+hQ4fYvn27kRCRmJjIzJkz84ybPn06oaGhWFlZMWHCBLZs2WKMnTVrFlWqVAFg06ZNBAYGFuvaW7duZebMmdy5c4eGDRuyZMkSDh48iK+vL2+99RbW1tacP38+T2WiBQsWGIlSbm5ueHl5ERQUhJ+fH9OnT6dMmTKcPHmSMWPGmE1GuVc3b95k3LhxxMTEULFiRWbOnImvry+HDh3ip59+4s0338TOzo7MzExmzZrFzZs3zc7j5eVFQEAAffr0YePGjRw6dAgfHx/atm1rXM8/P3fFtXZubm5uRtWunISoP7tx44aR1NW7d28g+33MSdQBWLZsGSEhIcyYMeOua166dInRo0cbSUDu7u74+/uzf/9+Pv30UypWrEhycjJvvfWWkUwVGRmJu7s7mZmZPPPMM3h6ehIQEMDevXuZO3culSpVIi0tjenTpxfqvAvrt99+44MPPiA9PZ3GjRvzzTff4O/vT0BAAGvWrKFPnz4AJCQkmE3GWbx4sZEo1b17d9avX8+hQ4dYu3YtnTp1IigoyGySYu/evbG0tCQjI4MdO3aYje3kyZOcO3cOyE6uyhEdHc3rr7/OlStXcHZ25sMPP2TPnj0cOnSI1atX0759ezIyMpg5c6bJPcwtOjqa3bt3M3DgQPz9/dmxYwezZs0yPsPHjBkDQJUqVQr9OXWv3x2F8cknnxAbG8vEiRPx9fUlMDCQJUuWGJWgNmzYwKFDh0zGREVFMWrUKJKSkqhYsSJz5swhICCA3bt3M3nyZKysrJg9e3aRY/m7UWUpEREREREREREREREREZH75OvrC2RXmHnvvfeM9vLlyzN69GjS09NZsGABAQEB3Lhxg/LlywPg4+PD77//DmQn5wwbNswY6+TkxIwZM6hZsyaffvopR48eZe3atQwaNMjo89FHH5GZmUmVKlXw8fHB2dnZODZkyBBcXV0ZP348Fy9eZNu2bfTt2zffc8jMzDQSbdq1a8fcuXNNtperWrUqrVq1ws3NjejoaA4cOMDEiRPv57IVyMLCwqQqDICtrW2etrtJTU3liSeeYPXq1cb2c+XLl+f9998nOjqanTt3cuTIEeLi4ozrd/bsWfbu3QvAm2++ybhx44z5ypcvT61atWjUqJGRjHHgwAEjoed+1759+7aRYNKoUSM8PT2NxJ0KFSowfvx4HBwcmD17NkePHiU4OJhWrVoRERHBN998A2Tf+9zbgjk5OTF8+HBatmxJv379OHXqFN7e3gwfPrxI1zI/P/30k1FdZ8GCBTz55JMm16tBgwaUKVOGWbNmkZKSwtGjR+nYsaPZ6/X6668zbdo0k/HffPMN3bt3Jyoqil27dvHWW28V+9q5OTo60rVrV7Zu3cqOHTt47733sLQ0rUWzY8cO0tPTsbOzo3v37gA4ODiYbBNYunTpQj+vs2bNIjU1FUdHR7y9vU22aevTpw916tShX79+xMfH4+3tzYQJE9i7dy/p6enY29uzePFibG1tjTHPP/88jzzyCIMGDSI+Pp6DBw/i5uZWqFju5vvvvycrKwtnZ2d++OEHypUrZxyrWLEizZs35+bNm/j5+XH06FGSk5ON6xAVFcV3330HwLPPPsv8+fONz5ny5cuzZMkSI7nnz1xdXXnqqacICgpi27ZtJp+FOXKS21xcXEzeyblz5xIbG0u5cuVYu3atSZWmVq1a0bJlS2PdTz75hG7dulGqVKk887ds2ZKPPvrI+P2xxx4DwNra2njHzX125edevzsKIzU1lSVLlphUFevSpQuPPvqokeC3a9cu2rRpYxyfN28eKSkp2Nvb4+npSc2aNY1jo0ePpkGDBowePbrQMfxdqbKUiIiIiIiIiIiIiIiIiMh9yqnalJCQkKf6E8DAgQNZtmwZ27ZtM9m6LqeKU8OGDU0SpXIbMWIE9evXB2DNmjVG+x9//MGZM2cAGD9+vEmiVI5u3brx1FNP0bp1a5NtusxJTk7m5Zdf5vnnn2fMmDEmiVI57OzsaNq0KZBdWeefYsSIEUYiQ245STNZWVlERkYa7RkZGYwYMYIePXowcOBAs3M2aNCAsmXLAgVfi6KuHRgYaFQOmjJlipEoldvAgQOpV68e7du3N7ZfXLt2LZmZmdjZ2eWpOJWjcePG9OrVy+hfXFxdXRk0aBADBw40SVbKLffWcfldLwsLC7OJGKVKlTISXy5fvvxA1v6znES469evExQUlOd4TlJO165di5zA92dJSUkcOHAAgKFDh5okSuVo3LgxPXv25MknnzSSeHKqg925c8fsebVs2ZJFixaxadMm2rdvf18x5taiRQteeeUVxo8fb5IolVurVq2A7Oc7ISHBaPfz8+PWrVtYWFgwZcqUPJ8zlpaWvP/++1hZWZmdN6dqVUhICNeuXTM5lpWVxc8//wxkV6HKmSMxMdGoFjV48GCz29lZWloa28vFxsaabLWXW3ElnOW41++Owqhbt67Z7Rfr1atnVMbL/T7dunXLqNg1dOhQk0SpHB06dCj2a1ASVFlKREREREREREREREREROQ+tWzZEn9/f06ePEm/fv145ZVX6NChg7HdkbOzc55qNvHx8YSFhQF3/wN8z549OXPmDGFhYUZ1kdxbv3Xu3DnfsR4eHoU6hzJlyuSbZAPZCRmnTp0yEnnM/WH/7+qJJ54w216hQgXj51u3bhk/169fn6lTp+Y7X3JyMr/++qtRbaiga1HUtXPuq729vUmST26lSpViy5YtJm3BwcEA1KpVy4jRnKZNm7J582bOnz9f5Eo1+enSpYvZpIwcMTExHDt2zPg9PT3dbL9HH33UbNIfYLSnpqY+kLX/rF27dri4uHD9+nV+/vlnkypFV65c4ejRo4DpVm/3Kjg42IiroHd53rx5Jr/nbPGWlpbGK6+8woABA+jUqROPP/64kYTUrVu3+47vz/JL7MwRHh5ubIUHpu9HzrZvderUoWrVqmbHV65cmSZNmvDrr7/mOebm5saMGTNITU3l559/ZsSIEcaxI0eOcPXqVcD0vhw7dsy4vg0aNMj33ahQoYJxz48ePcpzzz2Xp0/Dhg3zPe97cS/fHYWVk9hqjrOzM1euXDF5nw4fPmx8FnXt2jXfsT169GDnzp33FNPfhZKlRERERERERERERERERETu06BBg9ixYwfHjx/nxIkTnDhxAsjeoql9+/Z07dqV1q1bm2zlFRUVRVZWFgC1a9cucP7cx69du0b58uWJiooCsrcMy514Uxyio6MJCAjg/PnzREREEBERwfnz540qKIAR+z9BfglBubcty6/yVlhYGIcPH+bChQtcunSJ8PBwLl68aNK/oGtR1LVz7mu1atXybP1WkEuXLgFw4sQJWrRoUagxOc9SccnKyiIoKIjff/+diIgILl68yPnz54mOji7U+IJiyble+V3r+137z6ysrOjVqxcrVqzA19eX//73v0aFsK1bt5KVlUXFihVp167dPc2fW849B8xWlcpP06ZNee211/Dy8iI6OpoFCxawYMECKlSoQLt27ejcuTOdOnXC3t7+vmM0JzU1lQMHDhAWFkZERASXLl3i7NmzJCUlmfTLfc9ykpmqVatW4Ny1atUymyyVe4vEbdu2mSRL5VT7qlevHg0aNDDaL168aPz85ptvFurccuL8Mycnp0KNL6x7+e4orKK+T7kr3BX0HOYkZP6TKVlKREREREREREREREREROQ+2dnZ4eXlxapVq9iwYQPnz58H4MKFC1y4cIHVq1dTtWpVPvzwQ6NKyM2bN43xd0tmyL0VW05VlJytrcxt03avEhMT+fTTT/npp5/yVOBxcHCgbdu2XL9+nZMnTxbbmn8Fa+ui/2k8LCyM2bNnc/DgwTzHXFxcaNeuHf7+/iZbjBXH2vd6X3M/Tw9yTH78/PyYO3eu8eznsLCwoFatWjzxxBNs2rSpwDnu5T4V19rm9OnThxUrVhAfH09AQACdOnUC/j8p57nnnst3u7iiyP0MFfW+f/jhh7Ru3RoPDw+OHj1KZmYmsbGxbN68mc2bN+Pg4MC4ceP417/+dd9x5sjMzOTbb7/lhx9+ID4+3uSYjY0NzZs3p2zZsuzbty/P2JxEqrudp6OjY77HXnzxRbZu3crvv//OpUuXqFatGnfu3DGqHf252ldxvhs5WyAWl3v57iisor5Pue9lQffnQSXf/ZWULCUiIiIiIiIiIiIiIiIiUgxsbW0ZNWoUo0aNIjw8nICAAA4ePEhgYCDJyclERkYyfvx4vLy8aNq0KQ4ODsbYlJSUAuc2l1hVunRpIO+2ZPfqzp07jBw5kt9++w3I3h7q6aefpl69etSuXZuaNWtiaWnJO++8849LliqqK1euMHjwYBISErCxsaFLly40b96cunXrUrduXSpXrgxAhw4d7posVVT3el9Lly7NzZs36dWrV57t2h40Pz8/JkyYQFZWFk5OTri5udG4cWNq165NvXr1KFu2LBEREfeUsFSSazds2JB69eoRFhbGtm3b6NSpE2fPnuXMmTMAvPDCC8VyDrkTU1JTU02qjhVGz5496dmzJ3FxccbnTkBAAFFRUSQnJ/PFF19gY2PD8OHDiyXezz77jOXLlwNQs2ZNunbtSoMGDahduzZ169bF1taWdevWmU2WKleuHHD3z7zcVez+LPcWidu2bWPs2LH88ssv3LhxA0tLS3r37m3SP/f13b59+9+uMlJRvzselJzPHsi+P/k9h8X1nVOSlCwlIiIiIiIiIiIiIiIiIlLMatasSc2aNRk0aBBpaWl4e3sze/Zs0tPT8fb2pmnTpri6umJhYUFWVhbnzp0rcL7cFXOqVKli8r83b94kLi4OZ2dns2N//vlnLly4QN26dXFzc8t3jR07dhiJUlOmTGHUqFFm+924caPAWP8XLF26lISEBKysrPDw8KBZs2Z5+mRlZRV7ohT8/329fPkyWVlZWFhYmO3n7e1NUlISjRs35umnn6ZKlSqEhYVx+fLlAucvaM579fnnn5OVlUXVqlVZv3692WfxQT03D3rtF154gS+++IK9e/dy584d/Pz8gOxt0po0aXLP8+bm6upq/Hzx4sV85z148CBHjhyhevXqvPjii3nuo7OzM71796Z3795kZWUREBDApEmTSEhIYNWqVcWSLHXt2jVWrlwJQJcuXVi4cKHZ6lr5XXNXV1dCQ0O5cOFCgesU9Bz/eYvEsWPHsm3bNgDatGljJDPmXjP3vAUlSz2I96MoCvPd8aDk3novIiIi3y0HIyIiHlgMf5Wib2ooIiIiIiIiIiIiIiIiIiKG1NRURo4cSceOHfH09Mxz3NbWlmHDhlGvXj0AoqKigOwKK3Xr1gXA19e3wDVytpd67LHHjD9gt2jRwjh+4MCBfMcuX76cBQsW4OXlVeAax44dM35+7bXXzPZJTU3l119/BbK34vpflXMtGjZsaDZRCiAkJIRbt24B2QkWxSXnviYnJ3P06FGzfbKysli4cCFz5841kkSefPJJAE6cOMG1a9fynf+///0vrVu35uWXXy6Wbfhu3LhBeHg4AD169Mg3aS8wMND4ubienb9i7RdeeAFLS0sSExM5evQoe/fuNdrNuZdEm+bNmxvjCnqXfXx8WLRoEYsWLcLCwoKZM2fSo0cPJk2aZDaO9u3bG3FGR0cXOS5zQkNDjWvYv3//fLchzO+aP/PMMwCEh4fzxx9/mB178+ZNQkJCCoyjT58+QPbzHh4ezu7du4G8W/BB9juVc31z+pkTGRlJ8+bN6datG6tWrSpwfXOKeu/v9bvjQWnevDk2NjYA7N+/P99+Oe/AP5mSpURERERERERERERERERE7oOdnR1RUVFcu3aNtWvXcvv27Tx9EhISuHLlCgDVq1c32vv16wfAqVOnjGotf7ZixQrCwsIAePXVV432Zs2aUadOHQAWLVpEUlJSnrH79u0zqkX16tWrwPOwtPz/Px+fPXs2z/HMzExmzJhhJNikp6cXON8/Wc61iIyMNBKicktISGDGjBnG78V5Lbp27WokxM2dO9fsdmTe3t7ExMQA/39fc56lO3fu4O7uTkZGRp5xoaGhbNq0ifj4eJycnHB0dLzveHMny+RXIe306dMsW7bM+L24rtdfsXblypVp06YNABs3buS3337DwsIiz1ZvOayt/3+Dr8KuValSJdq3bw/AypUruXr1ap4+p0+fNqpaPffcc0D2OxkeHo6/v7/Zaj9ZWVnGloG5P3fuR+5rbu5zAmDDhg0cPHjQ+D33dejevTtly5YF4OOPPzZ7jebPn3/XbfpytkgEmDNnDsnJydjZ2dG9e/c8fV1cXOjcubMRm7kkxMzMTGbPnk1qaiqXLl2icePGBa5vTs61KWgLwdzu57vjQXBycqJHjx4ArFq1isjIyDx9QkND2bx58wON46+gZCkRERERERERERERERERkfs0cuRIAM6cOcPrr7/OgQMHiIqKIioqin379jFixAiSkpKwsrJi4MCBxrj+/fsbf5SfPXs27u7unD59moSEBE6fPo27uztz5swBsqt+DBs2zGTd999/H0tLSyIiIhgwYAB+fn7ExcURERHBihUrjIoz9erVMyqx5CcnWQNg8uTJ7N69m+joaK5evcquXbsYPHgwGzduNPrcLZkht127dtGzZ0969uzJrl27Cj2upLRr1w7Irlw0btw4jh07RlxcHOHh4Xh5edG3b19Onz5t9E9OTi62tUuXLs27774LZFevGjp0KIGBgcTFxXHu3DkWLFjA7NmzgewqPU8//TQAjz/+uPFs7dmzh6FDh/LLL78QFxfHxYsX8fDw4F//+hfp6emUKlWKKVOmmKwbFRVl3KO5c+cWOt6yZcsa28bt27ePmTNncu7cOW7cuMHp06f58ssvGTBggMnzUpRn5++wdk61op9++omMjAxatGhBtWrVzPYtV66c8fP27duJiYkp1HaNU6dOpXTp0sTHxzNgwAB++uknrl+/TmRkJBs2bGDUqFGkp6fj4uLCiBEjABgyZAi2trakpKTw+uuvs2nTJi5evEhcXByhoaFMnjyZ4OBgAAYPHmyy3r2+k08++SSlS5cGYOHChXh6enL58mViYmI4fPgwU6dO5b333jMZk/ualytXzni+g4KCGD58OIcPHyY+Pp7Tp0/z7rvv4uHhkW/Fqtxyqmb5+/sD0K1bNxwcHMz2nTZtGo6OjqSnpzNy5EiWLFlCeHg4cXFxHDlyhHHjxhnX4fnnnzep3FdYOUmOMTEx7N27l/j4eLMJULnd63fHgzJp0iTs7e1JTExk4MCBbN68mZiYGK5du4aHhwcjR47kzp07Rv+S3LLwfljfvYuIiIiIiIiIiIiIiIiIPCysHCtS5W2/kg6j2Fk5Vnyg8/ft25fQ0FC8vb05evQoo0aNytPHxsaGjz/+mPr16xtttra2fPPNN4wfP55ff/0VLy8vs9vltWvXji+++MKkag1A27ZtmT17Nu+//z5nz55l/PjxecbWqlWLb775xtheKT8dO3akV69ebNu2jYsXL/LGG2/k6VOpUiW6du2Kt7c3aWlpXL16FVdX1wLnBUhKSuLChQvGz393Y8aMYe/evZw7d46DBw+aVMnJ0axZM5ycnNi7d6/Zqj734+WXXyYmJob58+dz7Ngxhg8fnqdP8+bNmTdvnknbf/7zH9LT01m/fj1HjhwxEjFyc3BwYN68eTRo0MCkPT093bhH169fL1K8H374IUOHDiUlJYXVq1ezevXqPH1eeuklgoODuXz5srF1XnH4K9Z2c3PD3d3dSPrJbws+yE52a9asGb/++ivr169n/fr1tGrVymxcudWtW5fFixfz1ltvce3aNSOhKLdKlSrx7bffGglZjz32GJ988gnvvfcekZGRTJs2zezcAwYMyLO15r2+k+XLl2fatGm4u7uTmppqUmEth62trZGQBNlb7uXezvLVV18lLi6O+fPnc+TIkTyJXI0aNaJOnTr89NNPBcbywgsvMG/ePGObP3Nb8OWoUaMG3333HRMmTCAmJoYvv/ySL7/8Mk+/zp07M3PmzALXzc9TTz2FlZUVGRkZjBkzBshOgn3ppZfyHXOv3x0PStWqVVm4cCHjx48nKioqT1JlqVKleOmll4zE2T9/J/1T/DOjFhEREREREREREREREZEHwsLSCuuylUs6jH+kjz76iM6dO7N+/Xp+++03YmNjsbGxoXLlyjz99NMMHTqUmjVr5hlXsWJFvLy82LJlC1u2bOHkyZMkJSXh4uJC/fr1efnll+natavJNnm59enThxYtWrBixQoCAgK4evUqlpaW1KpVi2effZbBgwdjZ2dXqHOYO3curVu3ZtOmTYSFhXH79m0cHR157LHH6Ny5MwMGDCA1NZW1a9eSmZmJr69vnmpXd/NPqERSrlw5fHx8+Pbbb9m1axeXLl0iKysLJycn6tevT+/evXn++efZvn07e/fu5dKlS5w6dYqGDRsWWwxjxoyhQ4cOrFq1iqCgIKKjo7G1tTWqhL3yyit5EhVsbGz45JNP6NOnD2vWrOHYsWNcv34dS0tLqlWrxjPPPMOwYcN45JFHCly7qPeoSZMmbNq0iW+++YbAwECuX7+OtbU1Li4uNG3alP79+9O6dWs++OADfHx82LdvH2lpadja2hb5upTE2vb29nTr1o3NmzdjY2PDs88+W2D/+fPn8/HHH3P48GHS0tIKXc2qXbt27Ny5k+XLl7Nv3z4uX75MZmYm1atXp2vXrrz++utG9aIcL7zwAg0bNmT16tUEBwdz9epVMjIyqFixIi1atODVV1+lbdu2Ba5b1Ps9cOBAatasyYoVKwgNDSUxMZHSpUtTtWpVWrduzeDBg6lZsybbt28nPDycXbt25alsN2bMGNq2bcsPP/xAaGgoMTExuLq68txzzzF27FhmzZp11zhytkg8ePAgLi4uRpW1/DRv3pwdO3bg5eXFnj17uHDhAsnJyUaFsr59+9713hakTp06zJs3j0WLFhEREUHp0qW5cePGXcfd63fHg9KuXTu2bNnCt99+yy+//ML169dxdHSkbdu2jB8/npMnTxrJUsXxDpcEi6ysrKySDkL+edLS0jh+/DiQ/eXzT30B/kpR8ck898mGkg5D/uF+/s/LVHYyXzpSREREREREREREROTP/vjjD+7cuYO1tTV169Yt6XDkIXfjxg3atGnDggUL6NGjR0mHI/no3bs3rVq14oMPPijpUP5Wpk6dyo8//kj37t1ZuHBhSYdTLPROyr3w9vbmo48+wtramt9//71IyXb38u+SB5GfYj71WEREREREREREREREREREpBidPn0ayN4OS/6ekpOTuXjxou7Rn6SmprJr1y4ge9u0/xV6JyW3rKws3nrrLebOncvZs2fz7ff7778DULNmzX9EpUBzlCwlIiIiIiIiIiIiIiIiIiIPVGRkJLNmzaJJkyY0aNCgpMMRM1JTU/nggw+wtra+r63I/hetXLmS5ORkKleuTMeOHUs6nGKhd1L+zMLCgt9//51ly5axbNkys33Cw8PZtm0bkL1d3z+V9d27iIiIiIiIiIiIiIiIiIiI3LuEhATKlSvH559/XtKhSD6ysrK4du0aixcvxsXFpaTDKXELFy7E3t6esLAwNm3aBMCIESOwtv7fSLPQOynm9O7dm6VLl/LTTz9hb2/Pq6++iqurK4mJiQQHB7Nw4UJSU1MpX748Y8aMKelw79n/xlssIiIiIiIiIiIiIiIiIiJ/W48//jgeHh4lHYYUwN7eHi8vr5IO42/D39/f2G4MoEmTJgwePLgEIypeeifFnLFjxxIaGkpgYCDe3t54e3vn6VOpUiW+/vprKlSoUAIRFg8lS4mIiIiIiIiIiIiIiIiIiIjk0rp1a86fP4+trS1dunRh+vTp/zNVpUTyY2dnxw8//MCWLVvYvHkzJ0+eJDExkbJly/Loo4/So0cPXn75ZcqXL1/Sod4XvckiIiIiIiIiIiIiIiIiIiIiubz77ru8++67JR2GyF/O0tKSF198kRdffLGkQ3lgLEs6ABERERERERERERERERERERERkb+CkqVEREREREREREREREREREREROShoGQpERERERERERERERERERERERF5KChZSkREREREREREREREREREREREHgpKlhIRERERERERERERERERERERkYeCkqVEREREREREREREREREREREROShoGQpERERERERERERERERERERERF5KChZSkREREREREREREREREREREREHgpKlhIRERERERERERERERERERERkYeCkqVEREREREREREREREREREREROShoGQpERERERERERERERERERERERF5KChZSkREREREREREREREREREREREHgpKlhIRERERERERERERERERkQciIyMrHMZ5AAAgAElEQVSjpEMQeWhlZmaWdAgif0vWJR2AiIiIiIiIiIiIiIiIiPx9ZGRmEJsSW9JhFLsK9hWwsrQq6TDy2LhxI9OnTwfA19eXGjVqlHBExSM2NpZZs2bRv39/WrVqVdLhMGTIEIKDg2nRogXe3t4PdK3Bgwdz+PBhNm7cSKNGjR7oWn9H06ZNY9OmTVSuXJn9+/eXdDgPXO53+MyZMyUcTbZLly7x4YcfMmPGDB599NGSDkfkb0fJUiIiIiIiIiIiIiIiIiJiiE2Jpe/yviUdRrHb9PomKjlWKukwHgpxcXE8++yzJCQk0K9fv5IO5y+VlJTEsWPHcHFx4fHHHy/pcOQhdObMGfr168etW7dKOhSRvy0lS4mIiIiIiIiIiIiIiIiISLFJSUkhISGhpMMoEb/88gt37tzhmWeewcLCoqTDKREVKlSgevXquLi4lHQoD6X4+HglSonchZKlREREREREREREREREREREikHOtnPPPPNMCUdScqZMmcKUKVNKOgwRkXxZlnQAIiIiIiIiIiIiIiIiIiIi/3RZWVkcOHAAa2tr2rdvX9LhiIhIPlRZSkRERERERERERERERESkGIWGhrJmzRpCQkK4evUqNjY21KtXj969e9OvXz+srYv2Z9qzZ8+ycuVKAgMDiY6OxtbWlpo1a+Lm5sbgwYOxt7fPd+wff/zB2rVrOXz4MFevXiU5ORlHR0dq1KhBp06dGDRoEOXKlTMZc/nyZbp27QqAr68vdnZ2LFu2jH379nHt2jUcHBx44oknGDRoEB06dDAZW79+fZPfhw4dCkDfvn2ZM2dOsZ3XzZs3+fHHH9m6dSsREREkJSVRqVIlWrduzciRI6lTp06+YzMzM9mwYQMbNmzgjz/+IDMzk2rVqtGrVy+GDx9OqVKl8h1bkJMnT3L9+nVatmxJ2bJlizQ2ICCAtWvXcuzYMW7cuIG9vT3VqlWjQ4cODBkyBGdn53zHHjt2DE9PT44cOUJsbCx2dnbUq1ePXr168corr2BjY5NnzJAhQwgODmbChAmMHTsWDw8PfvzxRyIiIihVqhSNGjVi9OjRtGnTBsi+V0uXLuXQoUPEx8fzyCOP4ObmxoQJE/Lcp2nTprFp0yYqV65sVNrKLTMzEz8/PzZs2MCZM2eIiYmhbNmyNG3alNdeey3PM5Xj5s2beHl54efnx/nz57l9+zYVK1akZcuWDBo0iGbNmuUZcz/Pcm6RkZEsX76cgIAArly5QtmyZenQoQPjx4/Pd0xQUJDx/C9fvpynn37abL+cd2bs2LH8+9//NnvehXnWc59rjpzfJ0yYwJtvvmlybO/evaxbt47Q0FDi4+NxcHCgXr16PPfcc/k+N126dCEyMpKZM2dSrVo1Pv30U86ePUuZMmVo27Ytc+fOBSAhIYHVq1ezZ88eLly4wJ07d6hQoQJPPPEEffv2pVOnTvleN5G/gpKlRERERERERERERERERESKQWZmJvPnz2fZsmUm7bdv3yYkJISQkBC2bNnCt99+i6OjY6HmXL58OZ9//jkZGRkm8x0/fpzjx4+zZs0ali1bZjY5aOHChSxcuJCsrCyT9vj4eOLj4wkNDWXdunV4enpSpUoVs+ufOHECd3d34uPjjba0tDT27t3L3r17eeuttwpMGHkQ53XmzBkmTJjAxYsXTdojIyPZuHEjW7ZsYc6cOTz//PN5xqampjJq1CgCAgLyzHnmzBl27tyJl5cXpUuXLvI57du3D6DApBtzvvrqKxYvXmzSlpCQQEJCAr///jseHh58//33NG3a1KRPZmYmn332GcuXLzdpT0tL4/Dhwxw+fBgfHx+WLl1K5cqVza6dmprK0KFDCQkJMWkLCAjg0KFDLFy4EIB///vf3Lp1y+hz6dIlvv/+e0JCQvDw8Ch0AmBCQgKTJ0/mwIEDJu2xsbH4+/vj7+/P4MGD+eCDD0yOh4aG8tZbb3Ht2jWT9itXrrB582Y2b97MsGHDmDZtGpaW5jfYutdnef/+/UycOJGUlBSj7datW6xfv54dO3bQu3fvQp37vbifZz0/qampTJ48md27d5u0x8fHExwcTHBwMF5eXixdupSqVauanSM0NBR3d3fS09OB7Pvn4OAAZD8bQ4cO5cqVKyZjrl69ytWrV41r9vnnn2NhYVHouEWKk7bhExEREREREREREREREREpBt9++62RKNW6dWujatLPP//MkCFDAAgJCcHd3b1Q861bt445c+aQkZFBq1at+OGHHwgMDGTv3r3MnDkTFxcXIiMjGTlyJHFxcSZjd+zYwddff01WVhbt2rVj5cqV7N+/n/3797Nq1SqjssuVK1f48ssv843hvffeIzMzkw8++IA9e/bwyy+/8Pnnn+Pk5ATAokWLiIiIMPqHhISwbds24/dly5YREhLCjBkziuW8EhMTGTlyJBcvXsTe3p4pU6awa9cuAgICWLRoETVq1CA9PZ1p06Zx9uzZPOdz6tQpAgIC6NatG97e3hw8eBBvb2+eeuopIDuh5rvvvivU/fmznCpKHTt2LPSYY8eOGYlSvXv3Zv369QQGBuLn54e7uzuOjo4kJiYydepUMjMzTcYuWLDASJRyc3PDy8uLoKAg/Pz8mD59OmXKlOHkyZOMGTOG27dvm13fw8ODkJAQ+vfvz9atW9m/fz8ff/wxtra2ZGRk4O7uzuTJk6lSpQpLliwhMDCQbdu20bNnTyP+nTt3Fvp8cydKDRgwgB9//JHAwEDWrFlDu3btjJjWrVtnjImIiGD06NFGJaicex4YGMiKFSto1aoVACtXrmTevHn5rl3UZxngwoULvPHGG6SkpFC1alXmz59v3J+3336btLQ0vL29C33+RVHUZ71q1aqEhISYJGtu27aNkJAQxowZY7RNmjTJSJTq2bMna9euJSgoiJ9//pnRo0djbW1NWFgYI0aM4ObNm2ZjW7duHc7Oznz33XcEBASwePFi4zPuo48+4sqVK1SsWJHPPvsMPz8/AgMDWbt2rfFubNmyxeRzQuSvpmQpEREREREREREREREREZH7FBUVxaJFiwDo3Lkzy5cvp02bNjg7O1O7dm3ef/99Y0uurVu3cunSpQLnS0pKYvbs2QB069aNlStX0q5dO5ydnXF1deXVV1/Fx8eHMmXKcO3atTyViXISfurWrcvSpUtp06YNlStXpnLlyrRu3ZolS5bQqFEjgDxVfnJLT09n5cqVDB48mKpVq+Li4sILL7xgbKmXkZFhUqHGwcHBpCpT6dKlcXBwwNbWtljOa8GCBVy/fh1ra2u+//57Ro0aRfXq1alYsSLdunVj+fLllClThvT0dL7//nuz5/TKK6+waNEiWrRoQYUKFWjRogXff/+9UX3J19e3wHtjTnx8PL/99huVKlWiQYMGhR6Xs1aNGjX4/PPPadKkCc7OzlSrVo0BAwYYFZbOnz/PqVOnjHERERF88803QPaWel9//TVPPvkkTk5OVKtWjeHDh7NixQqsrKw4depUvgk9t2/fZujQocyYMYO6detSuXJl+vXrxyuvvAJgJCh5enrSpUsXnJ2dqVOnDl988YVxvf5cpSs/u3btMp61d955B3d3dxo2bIizszPNmzdn6dKlNGnSBIClS5ca4+bOnUt8fDw2NjasWLHCuOfOzs60bduWFStW0K1bNyD7uf/jjz/Mrl/UZxngs88+Iz09HScnJzw9PXnuueeM+zNu3Djmz59fqHO/F0V91i0sLO76/vn7+7Nnzx4Ahg0bxldffUWzZs1wcnKidu3aTJ482dhKLzw8PM/7l9unn37KM888Q8WKFenatSt169bl5s2bxvPw7rvv8uKLL1KtWjWcnZ1p1qwZixYt4rHHHgOyPwdFSoqSpURERERERERERERERERE7pOfnx+3b9/GwsKC9957Dysrqzx9Ro0aRc2aNenQoUOeikl/tnnzZpKTkwHy3VqsSpUqDB48GICNGzdy584dIHt7tk6dOtGnTx/eeOMNI1EiN0tLS1q2bAnAjRs38o2jffv2PP7443na27VrZ2y9dvny5QLPpbjOKysri+3btwPQq1cvWrRokWds1apVefXVV3niiScoV65cnuMWFhb8+9//ztNeqlQpY/u8uyWymXPgwAEyMjKKvAVfWloaACkpKSbbvOXo0aMHixcvZsuWLdStW9doX7t2LZmZmdjZ2Zk9H4DGjRvTq1cvo39+Ro8enact59kAePnll3F2djY5bmNjQ+PGjYHsRMHC2LJlC5B9j0aOHJnnuK2tLaNGjaJevXo0atSIxMREbty4YSSUvfbaa3m2IgSwsrLC3d0dGxsbsrKyWLNmjdn1i/osJyUlGdXChg4diqura56x3bp1o3379nc79SIrjmfdHB8fHwAqVKjAO++8Y7ZPz5496dKli9E/91aZOcqVK0fbtm3ztKenpxvbfsbGxuY5bmNjw2effYanp6dJtTmRv1rhNg4VEREREREREREREREREZF8BQYGAlCrVi2qV69utk/lypULvWVZcHAwAOXLl8fZ2dlIMPqznEo8ycnJnD59msaNG2NpacmECRPynTszM5OzZ88aiSFZWVlkZGSYTfAyl5wC2YktZcqU4caNG6SmphbqnO73vM6cOUNMTAyAsY2gOVOnTs332KOPPkrFihXNHstpT0lJITMz02wiV37uZQs+yE5K8vDw4Pr167z00kv079+fjh07Urt2bQDs7Ozo2rVrnnE517FWrVoA+V7Hpk2bsnnzZs6fP8+NGzcoX768yXFXV1dcXFzyjMudHGUuwQjA0dER+P+Er7s5dOgQAB06dMj32vbs2dPY4g+ykxBzkm/c3NzynbtixYq0bNmSwMBA49r8WVGf5cOHDxuJegUlwXXv3p1ffvkl3+P3ojiedXMOHz4MZFe/M5dEmaNnz57s2bOHpKQkTp8+bVShy1G/fn2z48qXL0+dOnU4e/YsX3zxBWFhYbi5udGmTRvs7f+PvTuPqqre/z/+YnIAVMQwhxCHpMGhNETNETXtZnq1cp5KzUztOjVaX7OcMjPLnEvNAechySlCUQkRnHLCIU1RUUFGBVQQzu8Pf2dfTudgMhTXeD7Waq3D3vvz2e89nLNcq9d6f5wl5fwcgL8TYSkAAAAAAAAAAAAAyCdzdx0vL68Cmc/c3SgxMdFmVxlbrl69anT7MUtOTlZISIjOnDmjCxcuKCoqSr///rtVFyNzIOWP/hiuyc4ctshprC35ua7sHYyqVq163+fMzs3NLcd92cNiuQlLZWVl6ZdffpGTk5OeffbZXNXTtm1b+fn5KTg4WOfPn9eUKVM0ZcoUVapUSU2bNpWfn5+aNm1qFWwx38fjx4/n6j7+8Xnm9HyzX7s5FHWvY/7M7du3lZycLCl3z+7KlSvGZ3OALCc1atRQWFiYxZjscvsuZ58npwCk9N/AWkEqiHf9j1JSUnTjxg1J93cvza5cuWIVlrrXvRw3bpwGDhyoW7duacOGDdqwYYOcnJxUv359tWjRQm3btpWnp2c+rgTIP8JSAAAAAAAAAAAAAJBP5iBIyZIlC2S+lJSUfI1JT0/X9OnTtXLlSqtgVPHixdWwYUNlZWUZnWZy4uTklOs67rfG3I4x32NJKlGiRJ7OX9DXI0lHjx5VQkKCfH19cwwW5cTBwUGzZ8/W2rVrtXLlSh0/flySdPnyZa1evVqrV6+Wu7u73n33XXXu3NkYl9/3w+x+7qOdnV2uz/VHSUlJuTqnWfaaXVxc7nms+btnazlDKffP3hwsyj63LaVKlcrVvPejIN71P8refczc5Skn2a/XVteye3WlatCggQICAjR37lz9/PPPunHjhjIyMhQeHq7w8HB9/vnn8vPz0/jx4212NQP+DoSlAAAAAAAAAAAAACCfzIGG3CxJdz/zPf3001q1alWux48ePVqBgYGSpCeeeEItW7aUt7e3Hn30UVWvXl2Ojo6aPn36n4alClp+rit7gOPWrVsFWld+7Nq1S9K9l2q7F3t7e3Xt2lVdu3bV1atXFRISorCwMIWGhiopKUkJCQl6//335erqqueee07S3fuYkpKi9u3b68svvyywa/mrZA/85ObZZQ/1pKam3jOkYw5WFVRgsUyZMsbntLS0HM99v8sQ2pLTvfgr3vXs9zKnQJlZboJVtnh5eWny5Mn69NNPdfDgQe3Zs0ehoaE6duyYTCaTgoOD9frrr2v9+vW56lAGFBTeOgAAAAAAAAAAAADIp0qVKkn67/JoOZk3b54WLFigX3/99b7mu3Tp0j2Ps7UE3qFDh4ygVK9evfTDDz9oxIgReuGFF+Tt7S1Hx7s9NRITE+85918hP9dVsWJF4/OFCxdyHBsZGanp06dr7dq1BRZeuxdzWKpFixb5nqtChQrq0qWLvvzyS+3Zs0fTpk0zQjpLliwxjsvPfSwMpUuXNjpD3evZpaWlafLkyfL399elS5dUuXJlY9/Zs2fveQ7zfvO9ya/s8/z+++85HpfTdz77so4ZGRk2j8necSu7v+Jdd3V1VenSpSXd/72U8nc/nZyc1LBhQ40cOVJr165VcHCwmjZtKkk6ceKE9u/fn+e5gfwgLAUAAAAAAAAAAAAA+VS/fn1J0pkzZ3T58mWbx6SkpGjGjBn6/PPPtWfPnnvO98wzz0iS4uLidPjw4RyPmzdvnnx8fNSxY0cjVHHo0CFjf48ePWyOy8rKUnh4uMXfBeVey7bl57oef/xxo8tNSEhIjmM3b96suXPnavz48UYw7K+SkJCg48ePq2LFivL29s71+OHDh6t169aaOnWq1T4HBwe9+OKLRrgkNjbW2Ge+j8ePH9fVq1dznH/s2LFq2LChXn755Twt3VdQ7OzsVK9ePUnSL7/8kuNx+/bt0/fff69PP/1UV69eVf369Y3OQz/99FOO465du2a89+bz5FeDBg2MjlhBQUE5Hrd7926b27N3h8opmHjw4EGb2/Pzruf0/bOzszPem+Dg4Ht2xDLfa2dn51y91zt37lSXLl3UsGFDi2UMzSpWrKjRo0cbf8fExNz33EBBIiwFAAAAAAAAAAAAAPn073//W46OjjKZTJoyZYrNjj5z5szRnTt3ZGdnpxdeeOGe83Xq1MnoKDR+/HibXWMuXLigRYsW6caNG0pPT5enp6ckWSxrdebMGZvzz5w5U+fPnzf+zqnzTV5kDyj9cd78XJejo6M6duwoSdq4caMiIyOtxsbExGjt2rWSpOeee05OTk4Fc1E52L17t0wmU56X4Lt165YuXbqkgIAAJSQkWO1PT083uvxUqVLF2N61a1dJ0p07d/TJJ58oMzPTauzhw4e1YcMGJSUlyc3NTa6urnmqsaC8/PLLkqSoqCj5+/tb7c/MzNScOXMk3e1mVK9ePbm7u6tNmzaSpBUrVujIkSNW47KysvTpp58qIyNDdnZ2euWVVwqkXmdnZ/3rX/+SJPn7++vUqVNWx/z666/avHmzzfGenp73DHrdunVL8+fPtzk2P+/6vTpamd+b+Ph4ffHFFzbPHRQUpODgYElS586dc/UdKleunI4cOaKkpCQtX77c5jEnTpwwPnt5ed333EBBIiwFAAAAAAAAAAAAAPn08MMP64033pAkbdu2TW+++aYOHTqkxMREnTx5UuPHj9fChQslSd26dVPVqlXvOZ+Hh4f+85//SJKOHj2qrl27KjAwUHFxcbp8+bJ++OEH9enTR0lJSbKzs9OHH35odJRp0qSJ8Xn8+PEKCAjQ1atXFRMTo5CQEA0ePFizZs2yOF9aWlqB3YsyZcoYn7du3aq4uDglJyfn+7okadiwYSpXrpwyMjL06quvyt/fX1euXFFMTIx++ukn9e3bV0lJSXJ2djbO81cyL8GX17DUgAEDJN3tGtWvXz8FBgYqOjpacXFxioiI0ODBgxUVFSVJ6t27tzHuySefNLqG7dixQ3379tUvv/yihIQEXbhwQcuWLdPrr7+ujIwMFS9eXO+8805+LrNAPP/882rYsKEkacKECZoyZYrOnj2rhIQE7d+/XwMHDjS6Q73zzjtG6Ofdd99V6dKljWe+YMECXbx4UYmJiQoPD1f//v2NZSdfe+011a1bt8Bqfuedd+Tm5qZbt26pb9++WrVqlWJjYxUTEyN/f38NGDAgx6UOS5curQYNGki6+4w++eQTnTt3TnFxcdqxY4e6d++uEydOGEvj/VFe33U3Nzfj848//qiEhASjq1irVq3UqlUrSdLixYs1YsQIHT58WMnJyTp79qymT5+uESNGSLob9ho1alSu7ledOnXk6+srSfr66681ZcoUnThxQgkJCTp37py+//57TZw4UZJUt27dAn1WQG78tT0HAQAAAAAAAAAAADxQyjmX04bXNhR2GQWunHO5v/wcw4YNU1JSkvz9/RUcHGx0Z8nuueee04cffnhf8w0cOFBpaWmaM2eOTp8+rbfeesvqGCcnJ3388cdq1qyZsa1mzZp6/fXXNX/+fMXHx9sMypQqVUpdunQxAlxRUVHy8PC430u9pxIlSujpp5/Wr7/+qrVr12rt2rXy9fXV0qVL83Vd0t2w1Xfffac33nhDsbGx+vTTT/Xpp59aXdvXX39t0Ynpr5CZmanQ0FA5OTmpcePGeZrD19dXo0eP1pdffpnjvbC3t9fw4cPVokULi+0ffvihMjIytHbtWu3fv98IXmXn4uKiL7/8Uo8//nie6itI9vb2+vrrrzVkyBAdPHhQCxcuNN6/7MeMHDnSovOap6enFixYoCFDhujatWv6/PPP9fnnn1vN379/f4sl3gpCuXLltGjRIg0aNEjXrl3T2LFjLfYXL15cI0aM0LRp02yOHzNmjPr162d0WsrebcnOzk7vvvuuQkJCFBYWZjU2r++6l5eXKlasqCtXrmjWrFmaNWuWOnfurM8++0yS9MUXX+jtt9/Wjh07tHXrVm3dutXq3LVq1dLXX3+dp25kU6dOVb9+/XT+/Hmbz1iSqlatqhkzZuR6bqCgEJYCAAAAAAAAAAAAYHCwd1B51/KFXcYDyd7eXmPHjtXzzz+v5cuX68CBA0pMTFTJkiVVq1YtdenSRe3bt7/v+ezs7DR8+HC1a9dOy5YtU0REhGJiYpSVlaVKlSqpUaNG6tu3r2rUqGE1dvTo0apVq5ZWrFihyMhIpaamytnZWVWqVFGzZs3Us2dPubm5afXq1UpJSVFgYKB8fHwK7F5Mnz5d48eP1759+5Senm7RuSo/1yXd7aq0detWLVmyREFBQTp//rzS09NVqVIlNW/eXAMGDFDFihUL7Fpy8uuvvyo5OVmNGzeWi4tLnucZNGiQGjRooOXLl+vgwYOKjY2VnZ2dypcvL19fX/Xs2VO1a9e2Gufk5KSJEyeqU6dOWrlypQ4dOqRr167J3t5enp6eatasmfr166cKFSrk5zILVNmyZbVs2TJt3LhRAQEBOnHihFJSUlS2bFk1aNBAr776qp566imrcXXr1tW2bdu0bNkybd++XefOnVNGRoYqVKggHx8fdevW7S/rUvTkk08qICBAy5Yt088//6yLFy+qZMmSatCggYYOHWpzKUmzxx9/XAEBAfr222+1a9cuXb16Va6urqpXr5769+8vHx8fhYSE3PPcuX3XHR0dNXfuXE2aNElHjx6VJKWmphr7XVxcNGfOHG3fvl3r1q0zls0rW7asHn30Uf373//WCy+8YCyXmVsVKlTQhg0b5O/vr+3bt+vs2bNKS0tTqVKlVKNGDT333HPq0aOHihcvnqf5gYJgZ8qpJxxwD+np6cYPa506dfL8Q1mUxCSl6oWJ6wq7DDzgtnz4sh52y/s/tgEAAAAAAAAARctvv/2mO3fuyNHRUTVr1izscgAAQBGWl3+X/BX5FPt8zwAAAAAAAAAAAAAAAAAADwDCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIA8ABxdy1R2CUAAAAAAAAAAAAAAPDAIiwFAA8Qe3u7wi4BAAAAAAAAAAAAAIAHlmNhFwAAyD1TVqYyU+IKuww8wBxcH5KdvUNhlwEAAAAAAAAAAAAAfyvCUgDwAMpMidPlr9oUdhl4gFUaESTH0g8XdhkAAAAAAAAAAAAA8LdiGT4AAAAAAAAAAAAAAAAARQJhKQAAAAAAAAAAAAAAAABFAmEpAAAAAAAAAAAAAAAAAEUCYSkAAAAAAAAAAAAAAAAARQJhKQAAAAAAAAAAAAAAAABFAmEpAAAAAAAAAAAAAAAAAEUCYSkAAAAAAAAAAAAAAAAARQJhKQAAAAAAAAAAAAAAAABFAmEpAAAAAAAAAAAAAAAAAEWCY2EXAAAAAAAAAAAAAOB/hykzU+nx8YVdRoErVq6c7BwcCrsM4L499thjkqTBgwdr5MiRhVzN3+ubb77RzJkzVblyZe3YsaOwy0EurF+/Xh988IEkKTAwUF5eXoVcEWCNsBQAAAAAAAAAAAAAQ3p8vPa+0qWwyyhwjdauUfHy5Qu7DAAAUMgISwEAAAAAAAAAAAAAACDfXF1dVaVKFUmSk5NTIVcD2EZYCgAAAAAAAAAAAAAAAPnWtm1btW3btrDLAO7JvrALAAAAAAAAAAAAAAAAAIC/A52lAAAAAAAAAAAAACCfLl26pNatW0uSAgMDVbJkSc2fP1+7du3S1atX5eLioqeeekq9evVS8+bNc5wnLS1N/v7++vnnn3Xu3DndunVL5cuXV+PGjfXaa6+pRo0axrEmk0nNmzdXbGysXn75ZU2aNMlqvm+//VZffPGFJGnevHlq2bKl1TEvvfSSjh8/rj59+uijjz6SJGVmZmr9+vXatGmTTp48qdTUVJUuXVre3t5q27atXnnlFRUrViw/t8ymI0eOyN/fX/v27VNsbIlt9bsAACAASURBVKyKFy+uSpUqqUmTJurdu7ceeeSRHMeeOXNGixcvVlhYmGJjY1WsWDFVrVpVbdu2Ve/eveXs7Jzj2N9++02rVq3Svn37dOXKFaWmpsrV1VVeXl5q2bKlevXqpTJlyliMCQ8PV9++fSVJR48e1bx587Rq1Spdv35dFStW1NChQ9WxY0fj+OjoaK1evVrBwcGKjo5WZmamPD095efnp/79+8vNzS3H+lJSUrRgwQIFBgbq0qVLKlGihLy9vdW1a1d16NDhfm+vlUOHDmn9+vU6cOCAYmNjdevWLZUqVUqPPvqo2rRpo27duqlEiRI5Xvfx48d14cIFfffdd9qzZ4/i4uJUpkwZ+fj4qH///nrqqadyPPdvv/2mhQsXav/+/YqJidFDDz2kNm3a6M0338zz9XzzzTeaOXOmfH19tXTpUgUFBWnp0qU6ceKEMjIyVK1aNXXr1k3dunWTJCUnJ2vu3LkKCgrS1atXVbp0aTVq1EgjRoyQp6enzXOYTCZt3rxZGzdu1PHjx3X9+nW5ubnpqaeeUteuXdWiRYsc6wsNDdWqVat06NAhJSYmytnZWZ6enmrevLn69Okjd3d3m+O2bt2qH374QUePHlVycrJcXV1VvXp1tWrVSj169JCrq6vNcSkpKVq7dq12796t06dPKykpSU5OTvLw8NAzzzyjXr16qXbt2jnWe+zYMS1cuFBHjhxRTEyMPDw81KpVKw0ZMkRhYWEaNWqUKleurB07dhhj1q9frw8++EDS3d9CLy8vSQX3G3nu3DktXLhQERERunLlisqUKaOmTZtqyJAhio+PN57tqVOncpwDICwFAAAAAAAAAAAAAAXo+PHj+uSTT5SUlGRsS09P186dO7Vz50795z//0dChQ63GnTp1SoMHD9bly5cttl+6dElr1qwxQgh9+vSRJNnZ2alFixZas2aN9u7da7OW7NsjIiKswlLx8fGKjIyUJLVq1cqoddCgQQoLC7M6NiwsTGFhYVqxYoUWL16cY7gjL9asWaOxY8cqKyvL2JaRkaHTp0/r9OnT8vf319dff23Umd2iRYs0depUZWZmGttu376to0eP6ujRo1q5cqXmz5+vRx991GrszJkzNXPmTJlMJovtSUlJSkpK0uHDh7VmzRr5+/urUqVKNmufPHmyli9fbvx9/vx5i7DN1q1bNWbMGKWlpVmMM1/b+vXrtXDhQnl7e1vNHRUVpY4dOyo6OtrYduvWLUVERCgiIkIHDhzQuHHjbNaVk8zMTI0bN06rV6+22peQkGDMvW7dOvn7+6tUqVI259m1a5dGjRqlW7duGdvi4uK0bds2/fTTT5o8ebI6d+5sNW7dunUaO3as7ty5Y2yLjo7W4sWLtW3bNjVq1ChX12PLpEmTtHjxYottx48f19ixYxUVFaUePXqob9++Ft+3uLg4bdq0SaGhoVq/fr3V875+/bqGDRum8PBwi+3Xrl1TUFCQgoKC1LFjR02cONEqTPj1119r9uzZFtuSk5OVnJysY8eOadmyZVqwYIHq1q1rccy7776rjRs3WmxLSkrSwYMHdfDgQfn7+2vJkiWqUqWKxTFHjx7V4MGDFRcXZ7E9IyNDUVFRioqK0g8//KBPP/1UXbp0sbp/S5cu1cSJEy2+F9HR0Vq6dKm2bt1qhJLyIq+/kYGBgRo1apQyMjKMbbGxsVq/fr22bdumIUOG5LkmFC0swwcAAAAAAAAAAAAABWjMmDHKysrS//3f/2nHjh365ZdfNHXqVKNz0KxZsxQVFWUxJjY2Vq+99pouX74sd3d3ffzxx9qxY4f27t2rpUuXqmnTpsrMzNSECRO0efNmY5w5/BQdHW01Z3p6ug4cOGD8HRERYVVrSEiITCaTXF1d1aBBA0l3g0dhYWFycHDQiBEjtGXLFu3du1dbtmxR//79Jd0N+Xz11Vf5v1n/X3R0tD755BNlZWWpWbNm8vf3V2hoqHbu3Klp06apfPnySk9P1wcffKCUlBSLsWvWrNFnn32mzMxM+fr6auHChQoLC9POnTs1YcIEeXh4KDo6WgMGDFBCQoLF2G3btumbb76RyWRSkyZNtHjxYu3evVu7d+/WkiVLjPt7+fLle17v8uXL1bZtW23btk07duzQuHHjVK9ePUnSwYMHNWrUKKWlpcnT01PTpk1TSEiIduzYoY8++kguLi66du2ahg4dqvT0dKu5t27dqqtXr2rAgAHavHmzQkJCNGvWLFWuXFmStGLFCqvwzp/5/vvvjaBU+/bttXLlSoWGhmrHjh2aP3++UfupU6e0cOHCHOcZPXq03Nzc9Nlnnxn3bezYsSpRooRMJpMmTJigGzduWIwJDw/XmDFjdOfOHXl7e+vbb79VWFiY8X7FxsZahYNy6/Dhw1q8eLF8fX3l7++vPXv2aMGCBapYsaIkafHixerfv79u3LihcePGaffu3dq1a5feeust2dvbKzExUXPnzrWYMysrS0OHDlV4eLgcHR31+uuva9OmTQoPD9fGjRvVp08f2dnZKSAgQBMnTrQYe+jQISMo1aFDB61du1ZhYWEKCgrSJ598IldXV12/fl3vvfeeRVhw06ZNxr3o16+fAgICtHfvXgUGBmr06NFydHTUlStX9Mknn1icLyUlRW+++abi4uL00EMPacKECQoMDNTevXu1ceNGvfXWWypZsqSysrI0adIkq+/U9u3bNWHCBJlMJtWqVUuLFi3S3r17FRAQoC5duiguLs4q+JUbefmNjIyM1IgRI5SRkSEvLy/NmjVLYWFh2rZtm15//XXdunVL06ZNy3NNKFroLAUAAAAAAAAAAAAABSgjI0PLly/Xk08+aWzr2LGjSpUqpcGDByszM1Pbt283gkeSNG3aNMXHx6tMmTJatWqVRZcYX19f+fj4aPjw4QoMDNTEiRPVpk0bFS9eXI0bN5aTk5MyMjIUFhZmLHkl3Q2M3Lx5U25ubkpKSlJkZKRSUlIsluzavXu3JKlp06ZycnKSdLd7iyR17tzZYkm0smXL6r333lNycrLWrVunrVu3aty4cbK3z3+Pjp07dyojI0POzs6aPXu2RVeeF198URUqVFCvXr2UlJSkPXv2qG3btpKkGzduaPLkyZKkNm3a6JtvvrGop0uXLmrSpIk6duyoq1evavbs2cZSg5L03XffSZJq1qypuXPnWpz34YcfVoMGDfTKK6/o+PHjCgkJybH+Rx55RNOnT5ej493/Bd+jRw9j37hx45SVlaVKlSpp9erVFt24+vTpYyzZd+HCBW3evNlmJ6ZJkyapU6dOxt9t2rSRp6ensczfzz//rIYNG97rFhuysrK0aNEiSVKTJk00bdo02dnZGfsrV64sX19ftW3bVrGxsQoJCdHw4cNtzuXk5KSVK1caISRJ6tWrl0wmk8aPH6+UlBTt2bNH7dq1M/abg0ReXl5avny50bXK3d1d7733nipUqGBzScncuH37tmrXrq2FCxca73XTpk313nvvacSIEbpz544uXLggf39/+fj4GOOGDRumM2fOaOvWrdqzZ4/FnBs2bDACh9OnTzfeQUlyc3PTRx99JE9PT02aNEkrV65U165dVatWLUn//U55eXlp6tSpxv12d3dX9+7dVaJECb333nv6/fffdeLECatxjRs31pgxY4zzlS1bVoMGDVJGRoZmzJih0NBQJSYmqmzZspKkjRs36tq1a5KkGTNm6JlnnrEY+/jjj6tUqVKaNGmS0tLSdODAAWP5wDt37uizzz6TJD355JPy9/dXyZIljbETJkyQu7u75s2bl8enk7ffyEmTJikzM1MVKlTQypUrje+Ru7u73n77bXl6emrs2LF5rglFC52lAAAAAAAAAAAAAKAANW3a1CIEYNakSRMjTHPp0iVj+/Xr141uUb1797ZaTkuS7O3t9c4770i6uxze9u3bJUkuLi7y9fWVJKtwh3kZvQ4dOqhUqVLKzMy06DSVlZWl0NBQSZKfn5+x3dzdKD4+3ub1DRkyRAsWLNDatWstQjb5cfv2bUl3gxqJiYlW+318fDRr1ixt2LBBTZs2NbYHBAQoNTVVkvT+++/bDG5VqlRJvXv3liStX7/eWPotKytLLVu2VKdOnTRkyBCrZdOku/fdHKaxVZdZ69atjWeb3W+//aZTp05JkoYOHWpz2cI2bdqoQYMGatiwoUVXIbMaNWpYBKXMHnvsMSMcd/HixRxr+6PU1FS9/PLLevHFF/XGG2/YfIYlS5Y0loO713V36NDBIihlZg7eSJbvevb7MWTIEJvL+/Xt21c1atS47+vJSf/+/Y2glFn2YFS9evUs/jZ7+umnJUkxMTEW21esWCFJatCggUVQKrvevXsbHb+yL3Fo/k6lpaVZLcUoSe3atdPs2bP1448/qmbNmlbjkpOTLZYsNOvRo4fmz5+vzZs3W9zLihUrqlevXurRo4dFUCq77OG67M/4wIEDunDhgiTpnXfeMYJS2b311lvy8PCwOe/9yO1vZHR0tPbt2yfpbqDN1veoW7duql27dp5rQtFCZykAAAAAAAAAAAAAKEDmkMkfFStWTKVKlVJiYqJu3rxpbD906JAyMjIkSY8//rgR/vmjcuXKycPDQ9euXdOBAwf0wgsvSLobTAkNDVV4eLhMJpMRfjGHpRo1aqRz587pl19+0b59+4wgy+HDh5WUlCR7e3s1b97cOI+Pj49Onz6t4OBg9e3bV506dVKzZs2McMQjjzyiRx55JD+3yIo5tJKenq5XXnlF3bt3V8uWLfXkk08a19OmTRurceZOP2XLlpW7u3uO965OnTqS7gaFTp48qdq1a8ve3l7Dhg3LsaasrCydOXPGCG2YTCZlZmbKwcHB6tgnnnjC5hzmZyBZBtL+aNmyZTnuM4d3bHnooYcUFRWV43XbUqpUKY0cOTLH/Xfu3NGJEyeMsJytkI5ZTu96uXLljM/Z3/W9e/can7O/c9nZ2dmpdevWOnv2bI7nvR+2astel7l70x+ZO69lXxIxJSVFkZGRku52W7rX/a5Tp46io6N18OBBY5uPj4+WLVuma9eu6aWXXlK3bt3UokULIxRWsmRJtW7d2mouHx8fBQcHKzIyUl27dtUrr7yi5s2bG98/d3d3i2CaWatWrdSqVasca4yLi9OhQ4eMv82/P5L0yy+/SJKcnZ3VqFEjm+OdnJzUqlUrrVq1Ksdz3EtufyOzd3W713W1a9dOx44dy1NNKFoISwEAAAAAAAAAAABAATIvhWWLuXuRyWQytpm7uEh3O7bcjytXrhif/fz8NGnSJGOpvVq1aik1NVVHjx6Vvb29GjRooFOnThlhKTPzEnxPP/20RaeWoUOHKiQkRBcvXlR4eLjCw8NlZ2enxx57TM2bN1fr1q3vGeDJi7p166pnz55avny5YmNjNWPGDM2YMUPlypVTkyZN5Ofnp5YtW8rZ2dlinLmjUmJiourXr39f57p69apVB5rk5GSFhITozJkzunDhgqKiovT7779bdQHK/tyyc3Nzs7nd3J3I1dXVIqiTGznNLckIbmVmZuZp7tjYWIWGhur3339XVFSUcd3Zg0I5XbOU87uevUtX9vHm99bV1dVmdyCz6tWr3/c15Ka27J3Hsi9HmdMxZtHR0cY9Xrx4sRYvXvyn58/+HW3btq38/PwUHBys8+fPa8qUKZoyZYoqVaqkpk2bys/PT02bNrXqbtarVy9t27ZNR48e1fHjx3X8+HFJUrVq1dS0aVO1bt1aDRs2zHEpTJPJpPDwcB07dkxRUVG6cOGCfv/9d8XGxuZYd3R0tCTJ09Pznkts5ucZ5fY30lzTn32PCuK9QdFAWAoAAAAAAAAAAAAACtAfl/76MykpKbk+R/YxVapUUbVq1XTu3DmFhYWpVq1a2r9/vzIyMvTEE0+oTJkyxpJbx44dU2pqqlxcXIyw1B87Hj300EP64Ycf9O233yogIECXL1+WyWTSyZMndfLkSc2fP1/e3t6aMGGCnnrqqVzXnpOPP/5YDRs21LJly3TgwAFlZWUpPj5eAQEBCggIkIuLi9588029/vrrNu/D/co+Jj09XdOnT9fKlSutglHFixc3lsbLHjKzpXjx4ja3JycnS5LNpczuV27fp/tx/fp1TZkyRRs3brToKiTdXdqxcePGunbtmtFNqaBqu3HjhqQ/vx+2lufLrRIlSuR7DrP8vmcODg6aPXu21q5dq5UrVxqhp8uXL2v16tVavXq13N3d9e6776pz587GuJIlS2r58uVasmSJ1q1bp99//12SdO7cOZ07d05Lly5V5cqV9fHHH1t1mAoKCtK0adOMMWZ2dnaqXr26nnrqKW3YsMGq7qSkJOPc9+Li4pKLu2Ept+/N/db0xzAlkBPCUgAAAAAAAAAAAABQiLIHALZu3Zqn7igtW7Y0wlIDBw40ln8zh6SeeuoplSxZUjdv3tTBgwdVq1YtI7Bha1krV1dXjRw5UiNHjtTJkycVGhqqPXv2aN++fbp9+7ZOnz6t1157TZs2bVKlSpXyctk2Pf/883r++eeVkJBgnDM0NFQxMTFKTU3VF198IScnJ7366quS/huIefrpp/O0JNjo0aMVGBgo6e5Sei1btpS3t7ceffRRVa9eXY6Ojpo+ffqfhqVyYq4v+5Jihe3OnTsaMGCAjhw5IunuUm/PPvusvL29VaNGDVWtWlX29vZ6++23/zQslVtlypSRJKtg2h9l72z1vyD7d/STTz5R9+7dcz2Hvb29unbtqq5du+rq1asKCQlRWFiYQkNDlZSUpISEBL3//vtydXXVc889Z4wrVqyYBg4cqIEDB+r8+fPG9yIsLEypqamKjo7W0KFDtXz5cmN5u6CgIA0bNkwmk0lubm5q27atateurRo1asjb21ulS5dWVFSUzbCU+Z39s2f0Z/sL0v9iTXiwEZYCAAAAAAAAAAAAgEJUsWJF4/OlS5fuGZYymUyys7Oz2t6iRQstWrRI+/fvV3p6uiIiIiRJvr6+ku52cqlfv75CQ0MVERGh+Ph4mUwmeXp66tFHH71nfY8//rgef/xxDRgwQKmpqZozZ46+/fZbpaamav369Ro2bFheLvue3N3d1aFDB3Xo0EEmk0mhoaEaNWqUkpOTtWTJEiMsValSJZ06dUqXLl2653y27tuhQ4eMoFSvXr00duxYm2MTExPzfB3mIFlKSooSEhJyXHpuy5YtOnfunGrWrKm2bdvm+Xz3Y9u2bUZQ6p133tHAgQNtHpef686J+V1PTU1VTEyMHn74YZvHmZdX/F9RoUIF43Ne3jVb83Xp0kVdunRRZmamtm7dqg8++EDp6elasmSJRVgqu6pVq6pq1arq1auX0tPTtWLFCk2ePFkZGRlasWKFEZaaOnWqTCaTKleurLVr19p873J6vl5eXpLuPoOsrKwcl+LLvnzoX81cU2pqquLi4vTQQw8Vek14sOW8wCQAAAAAAAAAAAAA4C9Xv359I1yxffv2HI+Ljo5WvXr11KZNGy1ZssRin4+Pj1xdXXXr1i3t3r1bJ0+elL29vRo0aGAcY+4yFRERoZCQEEl3O1L98Rx9+vTRs88+q127dlnV4OLiorfffttYJi0mJib3F2zDhAkT1K5dO40aNcpqn52dnZo2baqOHTtKkmJjY419zzzzjCQpLi5Ohw8fznH+efPmycfHRx07djQCFYcOHTL29+jRw+a4rKwshYeHW/ydG/Xr1zc+m++5LYsWLdKMGTO0fPnyXM2fF9mvu2fPnjaPuXnzpn799VdJub/me2nevLnxOSgoKMfjzEtE/q9wd3dXjRo1JEk7duyQyWSyeVxWVpbat2+vZs2a6Z133jG2Dx8+XK1bt9bUqVOtxjg4OOjFF19U06ZNJf33/b5586YGDBigFi1ayN/f32pcsWLF1K9fP3l7e0v673cxMTFR58+flyS1a9cux4CeufucuW4zc8Dy5s2bOXZUM5lMNn8f/irmmqR7vxvBwcF/Rzn4ByAsBQAAAAAAAAAAAACFyMPDQ35+fpKkdevW6cCBA1bHZGVlafLkybp586YuXryo2rVrW+x3cnJSkyZNJEmzZ89WZmamnnjiCZUuXdo4xhyWOnbsmBHc+eMSfB4eHjp16pTi4+O1dOlSm6GQixcvKiUlRZJUpUqVvF621fWdP39ewcHBioqKstpvMpl06tQpq3N26tRJxYoVkySNHz/e5nJ3Fy5c0KJFi3Tjxg2lp6fL09NTkiw65pw5c8ZmXTNnzjSCJ5KUkZGRq+t6+umnjc5ds2bN0o0bN6yO2bVrl9HpqX379rmaPy/+7LqzsrL06aefGs84t9d8L56enkbwZfbs2TbDdj/99JP2799fYOcsKF27dpUknT17VgsWLLB5zJIlS3T27FnFxsZadGy7deuWLl26pICAACUkJFiNS09P19mzZyX99/0uWbKkYmJidPXqVa1atUq3b9+2GpecnKzLly9bjHNwcDD2m+f8o5MnT2r+/PnG39mfcfPmzY0OYF9++aXNJRG///77v7X7l7e3t+rVqyfp7nuTnJxsdUxgYKDRUQ/4M4SlAAAAAAAAAAAAAKCQvf/++3J1dVVGRoYGDBigOXPm6Pz580pISND+/fv15ptv6ueff5YkvfjiixYdi8zMXaKOHz8u6b/hKLPatWvLxcVFd+7cUXJyslxdXS06T0l3u9X07dtX0t1OSG+++ab27dunuLg4RUdHa9u2bRo4cKBMJpOcnZ310ksvWYx/99139fzzz+v555/P1fX36dNHxYoVU1paml577TVt2LBBFy5cUEJCgg4fPqzRo0cbQYjevXsb4zw8PPSf//xHknT06FF17dpVgYGBiouL0+XLl/XDDz+oT58+SkpKkp2dnT788EOji1eTJk2Mz+PHj1dAQICuXr2qmJgYhYSEaPDgwZo1a5ZFnWlpabm6Lkn66KOPZG9vr6ioKHXv3l1BQUFKSEhQVFSUvv/+e6Oblre3tzp16pTr+XPL3MFIkkaPHq3t27crNjZWV65c0c8//6zevXtr/fr1xjF5ueZ7+fjjj+Xk5KS4uDh1795dW7ZsUUJCgi5evKjZs2dr9OjRFoGf/xU9e/bUk08+KenuMndjxozRsWPHlJSUpFOnTumzzz7TZ599Junucnl9+vQxxg4YMEDS3a5R/fr1U2BgoKKjoxUXF6eIiAgNHjzYCAlmf7/N406dOqXXXntNISEhiomJUUxMjHbt2qX+/fvrxo0bcnBwMLqjlS5dWnXq1JF0N4g3YcIEnT17VomJiTp58qS++uorde/e3eK5Zv/s4OCgDz74QJL066+/qm/fvgoLC1NiYqLOnj2ryZMna8qUKQV7c+/DmDFj5ODgoIsXL6pHjx7avn27xXtjqysdkBPHwi4AAAAAAAAAAAAAwP+OYuXKqdHaNYVdRoErVq5cYZdwT15eXvruu+80bNgwxcXF6auvvtJXX31ldZyfn58mTJhgc44WLVrIzs7O6AaVfekqSXJ0dNQzzzxjLGPVtGlTOTk5Wc3zxhtvKDIyUtu3b1dwcLDNpa1cXFz0zTffqNwf7uuVK1d07ty5+7vobKpVq6aJEydqzJgxio6O1vvvv2/zuO7du1stHTdw4EClpaVpzpw5On36tN566y2rcU5OTvr444/VrFkzY1vNmjX1+uuva/78+YqPj7dYNs2sVKlS6tKlixYuXChJioqKkoeHR66urXHjxpo8ebI++ugjnTlzRkOHDrU6pnr16po3b57N51HQWrRoofbt22vz5s26cOGChgwZYnVM+fLl1bp1a61YsULp6em6cuWK0W0ovx599FHNmzdPb731li5fvqyRI0da7Hdzc1Pv3r01c+bMAjlfQSlWrJjmz5+vIUOG6MiRI1q3bp3WrVtndVzVqlX17bffytnZ2djm6+ur0aNH68svv8zxHbW3t9fw4cPVokULY1vnzp11+PBhrVixQgcOHNDAgQOtxjk5OWn8+PF67LHHjG0ff/yx+vbtq7S0NC1dulRLly61GvfSSy8pIiJCly5dsuieJt1dvm/EiBH6+uuvdejQIb366qsW+ytXrqwaNWpo9+7dcnT8e2IndevW1cSJE/XRRx/p7NmzVu9tmTJl1KRJE23ZsuVvqwkPLt4QAAAAAAAAAAAAAAY7BwcVL1++sMsokurVq6dt27Zp+fLl2rFjh86dO6fU1FSjU0znzp31r3/9K8fx5cqVU+3atXX06FE5ODjIx8fH6piGDRsaYSnz0n9/5OTkpFmzZmnz5s3auHGjIiMjlZSUpBIlSqhSpUpq3ry5+vXrp/IF/J507NhRTzzxhJYuXaqIiAhduXJFmZmZeuihh1S/fn116dJFjRs3thpnZ2en4cOHq127dlq2bJkiIiIUExOjrKwsVapUSY0aNVLfvn1Vo0YNq7GjR49WrVq1tGLFCkVGRio1NVXOzs6qUqWKmjVrpp49e8rNzU2rV69WSkqKAgMDbd7XP9OpUyfVr19f33//vUJDQ3XlyhXZ29urevXq+te//qXevXurZMmSebpveTFt2jQ1bNhQGzZs0OnTp3X79m25urqqWrVq8vPzU/fu3XXz5k2tWrVKWVlZCgwMVL9+/Qrs/E2aNNHmzZu1aNEi7d69W5cvX1bp0qXVrFkzvfXWWzp06FCBnasgeXh4aOXKlfrxxx+1adMmRUZGKjk5WSVKlFDNmjXVrl079ejRQyVKlLAaO2jQIDVo0EDLly/XwYMHFRsbKzs7O5UvX16+vr7q2bOn1fKakjRu3Dj5+flp7dq1OnLkiOLj4+Xk5KSHH35Yzz77rPr27auqVatajKlTp442bNigefPmKSwsTNeuXZOjo6M8PDxUt25ddevWTQ0bNtT//d//afXq1dq1a5fS09ONJS0l6c0335SPj48WL16sQ4cOKTk5WeXLl1e7du00ePBgffHFF5Kk4sWLF+xNvofOnTvrbFgu2QAAIABJREFUiSee0HfffaeIiAglJCSobNmyatmypYYNG6YNGzZoy5YtFtcB2GJnsrXILPAn0tPTdfToUUl3f2j5sflzMUmpemGidbIYyI2IKb3lYG+vO9djdPmrNoVdDh5glUYEybH0w4VdBgAAAAAAAIC/2G+//aY7d+7I0dFRNWvWLOxyUEQsXLhQ33zzzf9s4AVA/o0cOVJbtmxRo0aNtHjx4sIuR5L0xRdf6Ntvv5Wnp6eCgoIKuxzYkJd/l/wV+RT7fM8AAAAAAAAAAAAAAMD/d+LECatONwAeDCdPntTo0aM1c+ZMpaSk2DzGZDIpMjJS0t0lJP9qcXFxxrKAMTExOR537Nixv60mPNhYhg8AAAAAAAAAAAAAUCC2bNmiLVu2aMyYMYVdCoA8cHV11aZNmyRJFSpU0CuvvGJ1zI8//qjz589Luruk4l+tVKlS2r59u9LT0yVJw4cPtzpm//792rt3799WEx5shKUAAAAAAAAAAAAAAAUiMjJSAwYMUK9evQq7FAB58Mgjj6hevXo6dOiQJk2apKSkJPn5+cnd3V2xsbHatm2bvvvuO0mSr6+vWrdu/ZfXVLx4cbVt21abNm3S/PnzlZWVpfbt28vDw0MJCQnatWuX5syZI5PJpGrVqql79+5/eU14sBGWAgAAAAAAAAAAAAAUiLfffruwSwCQT1OmTNGrr76qy5cva+rUqZo6darVMfXq1dOXX34pOzu7v6WmDz/8UGfOnNHJkyc1d+5czZ071+qY6tWra9asWSpevPjfUhMeXISlAAAAAAAAAAAAAAAAIEny8vLSjz/+qOXLlysoKEhnz57V7du3Va5cOdWoUUMdO3ZU+/bt5eTk9LfV5O7urjVr1mjNmjXaunWrTp8+rdTUVJUtW1ZeXl5q3769OnXqJGdn57+tJjy4CEsBAAAAAAAAAAAAAADA4OrqqkGDBmnQoEGFXYqhWLFi6tWrF8t8It/sC7sAAAAAAAAAAAAAAAAAAPg7EJYCAAAAAAAAAAAAAAAAUCQQlgIAAAAAAAAAAAAAAABQJBCWAgAAAAAAAAAAAAAAAFAkEJYCAAAAAAAAAAAA/qEcHBwkSZmZmTKZTIVcDQAAKKpMJpMyMzMlSfb2hRtXIiwFAAAAAAAAAAAA/EMVK1ZM0t3/QZmWllbI1QAAgKLq9u3bRnDb/O+TwkJYCgAAAAAAAAAAAPiHKl26tPE5ISGB7lIAAKBQXL9+3fjs4uJSiJUQlgIAAAAAAAAAAAD+sVxdXWVnZydJSklJ0aVLl5SamkpoCgAA/C0yMzMVHx+v+Ph4Y5urq2shViQ5FurZAQAAAAAAAAAAAPxl7O3tVblyZUVHR8tkMiklJUUpKSmys7OTg4NDYZcHAAD+wUwmkzIzMy22eXh4FPoyfISlAAAAAAAAAAAAgH+wUqVKWQSmpLv/8/LOnTuFXBkAAChKypQpo3LlyhV2GYSlAAAAAAAAAAAAgH+6UqVKydvbWykpKbp+/brS09OtOj0AAAAUNAcHBzk7O8vNzU0lSpQo7HIkEZYCAAAAAAAAAAAAigR7e3uVLl1apUuXLuxSAAAACo19YRcAAAAAAAAAAAAAAAAAAH8HwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEwlIAAAAAAAAAAAAAAAAAigTCUgAAAAAAAAAAAAAAAACKBMJSAAAAAAAAAAAAAAAAAIoEx8Iu4H/Jrl27tG7dOv36669KSEhQsWLF5OXlpRYtWqhv375yd3e3GpOQkKDGjRv/6dxubm4KDw+3ue/UqVP67rvvFB4eroSEBLm5ual27drq2bOnmjdvfs95MzIytGLFCgUEBOjs2bMymUyqXLmy2rRpo9dee01ubm73d/EAAAAAAAAAAAAAAADAPxxhKUl37tzR+++/rx9//NFie0ZGhiIjIxUZGanVq1dr1qxZqlevnsUxx44dy9e5g4KCNGLECGVkZBjbrl27puDgYAUHB6tPnz766KOPbI69ffu2BgwYoH379llsP3PmjM6cOaP169drwYIF8vb2zleNAAAAAAAAAAAAAAAAwD8BYSlJ06ZNM4JSrVu31sCBA1WtWjXFxsZq165dmj17tuLj4zV48GAFBATo4YcfNsZGRkZKkipXrmwVtsrOzs7Oatvx48c1atQoZWRkqE6dOnr33XdVs2ZNXbp0SXPmzNH27du1dOlSVatWTb169bIa/95772nfvn1ycnLSsGHD9OKLL6pYsWLauXOnpk6dqtjYWA0ePFibNm2Ss7Nzfm8TAAAAAAAAAAAAAAAA8ECzL+wCCltMTIyWLFkiSerQoYNmz56t+vXrq2zZsnrsscc0aNAgLVmyRI6OjkpKStK8efMsxh8/flySVKdOHbm4uOT4n62w0ldffaXbt2+rSpUqWrx4sXx9fVW2bFnVqVNHs2bN0nPPPSdJmjFjhlJSUizGHjlyRFu3bpUkffjhhxo8eLAeeeQRlS9fXl27dtXChQvl5OSk6OhoLV68uMDvGwAAAAAAAAAAAAAAAPCgKfJhqaCgIN25c0eSNHLkSJvH1K1bV35+fpKknTt3WuzLHpbKjbNnz2r37t2SpEGDBsnFxcViv52dnd5//33Z2dkpKSlJP/30k8X+hQsXSrrb0apr165W89epU0cdOnSQJK1ZsyZXtQEAAAAAAAAAAAAAAAD/REU+LBUbG6sSJUrooYceUuXKlXM8rkqVKsbxZsnJyYqOjpaU+7CUOShlZ2enVq1a2TzmkUce0WOPPSZJ2r59u7HdZDLpl19+kSS1aNFCDg4ONse3bt1akhQdHa0TJ07kqj4AAAAAAAAAAAAAAAD8P/buPdjK6r4f/3sfONwVAQUVUNAot4MxmjQSBkjQOBoxWlONaKM1xtzVEttGxpjWWk3SaE3SMSGJWCNJHTWCI2ptAgpe4vCVGpVzDpCAgcilgNxELnKA/fvDH7uecNPA2SjP6zWzh4e11udZn33+YJg571mLA03hw1JjxozJiy++uMPJTX9q4cKFSZLOnTtXxrafKlUqldK2bdt885vfzMiRI1NXV5dTTjkln//853c4iWq77eGlww8/PN26ddvlvgMHDmy2V5IsWrQo69atS5IMGjRol7UDBgyoPNfX1+/u6wEAAAAAAAAAwAGv9f5u4N2iU6dOu5xbunRppk+fniQ56aSTKuPbA0w1NTW5+OKLK9f5Jcnq1aszffr0TJ8+Peedd15uvPHGtG79fz/uJUuWJMluT7NKkiOPPDJJsmzZsjQ1NaW2trZymtWe6nv06JFWrVpl69atzWr2taamphZ794GgtrY2pVJpf7cBsFPlctm/4wAAAAAAAMC7Ukv8LlNYag+2bduW66+/vvLDv+iiiypz28NSW7duTd++ffPVr341J598cmpra/PCCy/k3//93zNnzpxMnDgxnTp1ynXXXVepXb16dZLk4IMP3u3+Bx10UJI3f5m9bt26dO3atVKbND/p6k+1bt067du3z+uvv57XXnvtHX7zt2/OnDkt9u4DweDBg9OmTZv93QbATjU1NWXWrFn7uw0AAAAAAACAqij8NXx7cvPNN+epp55Kkpx11lkZMmRIZW7z5s3p2LFjBg0alAceeCCjRo3KEUcckUMPPTSnnXZa7r333px44olJkgkTJmTu3LmV2jfeeCNJ0q5du93u37Zt2x1qtv/5p/M7s/39mzZt2uN3BQAAAAAAAACAA5mTpXahXC7n5ptvzoQJE5Ikxx9/fG688cZma374wx8mSbZs2dLsir3t2rVrl+uvvz6f+tSnUi6XM3HixIwdOzZJ0qpVqyTZ4/Vs5XK58lxTU9Os9p3Ub69tCf37909tbW2Lvf+9zs8GeDerra3N4MGD93cbAAAAAAAAADtoamra5zeeCUvtxObNmzN27Ng8/PDDSZJjjz02d955Zzp27LjT9TsLSm1XV1eXHj16ZNmyZXnxxRcr4x06dEiy5xOfNm/eXHnefpXb9tqk+SlTO7N9fk8nUO2N2tpa18wBvEeVSiX/hgMAAAAAAACF4Rq+P7Fq1apceumllaDUoEGD8vOf/zyHHXbYn/3OI488MkmyevXqylinTp2SJOvWrdtt7WuvvZbkzZOhOnfunCQ56KCDKvO7q9+yZUs2btyYJOnSpcuf0TkAAAAAAAAAABw4hKXeYsGCBbnwwgvz/PPPJ0mGDRuWCRMmpGvXrrute+tVeTuz/XSo9u3bV8b69u2bJFm6dOlua7fPH3HEEZWr9Pr06VOZX7JkyS5rly1blq1btyb5v8AWAAAAAAAAAAAUlbDU/2/OnDm58MILs3DhwiTJBRdckHHjxu3y6r3f/va3GTlyZN7//vfnoYce2uV7t27dmgULFiRpHnLq169fkmTx4sWV06N2pqGhIUnSv3//ylj37t0rJ0XNnj17l7WNjY2V5wEDBuxyHQAAAAAAAAAAFIGwVJKFCxfms5/9bOWavKuvvjo33nhjWrduvcuanj17ZsmSJdm0aVOefPLJXa57/PHHs379+iTJ8OHDK+Pbn7dt25Zp06bttPaVV17J7373uyRvnnL1ViNGjEiSTJs2Ldu2bdtp/dSpU5Mkhx12WLOwFQAAAAAAAAAAFFHhw1KbN2/OmDFjsnLlyiTJ2LFj8+Uvf3mPdd27d8+QIUOSJI8++mj+53/+Z4c1K1asyLe+9a0kyeGHH56zzjqrMte7d++cfPLJSZLbb78969ata1ZbLpfz7W9/O+VyOV26dMk555zTbP7cc89N8ubVgffcc88Oe7/00kuZPHlykuTSSy9NqVTa43cCAAAAAAAAAIADWeHDUvfdd1/lqrszzjgj559/ftavX7/bz3Zf//rX07Zt22zbti1XXHFF7rrrrixYsCArVqzI5MmT8+lPfzqLFy9O69atc9NNN6Vt27bN9h47dmxqamqyYMGCXHTRRXn66aezatWqNDQ05Ctf+UqmTJmSJLnyyivToUOHZrVDhgzJyJEjkyQ33XRTbrvttrzyyitZsWJF7r///nzuc5/Lli1b0qtXr4wePbolf4QAAAAAAAAAAPCeUCqXy+X93cT+dPrpp2fhwoXvqGbu3LmV5+nTp+drX/taXn/99Z2u7dChQ26++eaceeaZO52fOHFirr/++mzZsmWn85dddlmuvfbanc6tXbs2l19+eWbNmrXT+UMPPTS/+MUv0qdPn918mz/P5s2bK/sOHjw4bdq02ed7HGiWrVmfT9z0wP5ug/e4//edv06rmppseW1ZlnzvtP3dDu9hR/7tlLQ+uMf+bgMAAAAAAABgl1oin9J6r9/wHrZ69ep3HJT6UyNGjMijjz6au+66K0899VQWLVqUJDniiCMyfPjwXHrppTnyyCN3WX/eeedl0KBBGT9+fGbMmJGVK1emQ4cOqaury0UXXZTTTtt1GKJz58655557cs8992Ty5MmZP39+Nm/enJ49e+ZjH/tYrrjiinTr1m2vvh8AAAAAAAAAABwoCn+yFH8eJ0u9c06WYl9wshT7ipOlAAAAAAAAgHe7lsin1Oz1GwAAAAAAAAAAAN4DhKUAAAAAAAAAAIBCEJYCAAAAAAAAAAAKQVgKAAAAAAAAAAAoBGEpAAAAAAAAAACgEISlAAAAAAAAAACAQhCWAgAAAAAAAAAACkFYCgAAAAAAAAAAKARhKQAAAAAAAAAAoBCEpQAAAAAAAAAAgEIQlgIAAAAAAAAAAApBWAoAAAAAAAAAACgEYSkAAAAAAAAAAKAQhKUAAAAAAAAAAIBCEJYCAAAAAAAAAAAKQVgKAAAAAAAAAAAoBGEpAAAAAAAAAACgEISlAAAAAAAAAACAQhCWAgAAAAAAAAAACkFYCgAAAAAAAAAAKARhKQAAAAAAAAAAoBCEpQAAAAAAAAAAgEIQlgIAAAAAAAAAAApBWAoAAAAAAAAAACgEYSkAAAAAAAAAAKAQhKUAAAAAAAAAAIBCEJYCAAAAAAAAAAAKQVgKAAAAAAAAAAAoBGEpAAAAAAAAAACgEISlAAAAAAAAAACAQhCWAgAAAAAAAAAACkFYCgAAAAAAAAAAKARhKQAAAAAAAAAAoBCEpQAAAAAAAAAAgEIQlgIAAAAAAAAAAApBWAoAAAAAAAAAACgEYSkAAAAAAAAAAKAQhKUAAAAAAAAAAIBCEJYCAAAAAAAAAAAKQVgKAAAAAAAAAAAoBGEpAAAAAAAAAACgEISlAAAAAAAAAACAQhCWAgAAAAAAAAAACkFYCgAAAAAAAAAAKARhKQAAAAAAAAAAoBCEpQAAAAAAAAAAgEIQlgIAAAAAAAAAAApBWAoAAAAAAAAAACgEYSkAAAAAAAAAAKAQhKUAAAAAAAAAAIBCEJYCAAAAAAAAAAAKQVgKAAAAAAAAAAAoBGEpAAAAAAAAAACgEISlAAAAAAAAAACAQhCWAgAAAAAAAAAACkFYCgAKqFXHrvu7BQAAAAAAAICqE5YCgCIq+S8AAAAAAAAAUDyt93cDAMD+s3Xb1qzcsHJ/t8F7XLcO3dKqptX+bgMAAAAAAAD2SFgKAAps5YaV+cv/+Mv93QbvcZMum5Tunbrv7zYAAAAAAABgj9zBAwAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCEISwEAAAAAAAAAAIUgLAUAAAAAAAAAABSCsBQAAAAAAAAAAFAIwlIAAAAAAAAAAEAhCEsBAAAAAAAAAACFICwFAAAAAAAAAAAUgrAUAAAAAAAAAABQCMJSAAAAAAAAAABAIQhLAQAAAAAAAAAAhSAsBQAAAAAAAAAAFIKwFAAAAAAAAAAAUAjCUgAAAAAAAAAAQCG0bukNVq9enTlz5mTevHn5wx/+kLVr1+b111/Phg0b0rZt23Ts2DHdu3dPr1690r9//5xwwglp3759S7cFAAAAAAAAAAAUTIuEpebMmZNf/epXefLJJ9PY2Jhyufz2G2rdOieeeGLOOOOMnHnmmenatWtLtAgAAAAAAAAAABTMPg1LPfDAA7n33nsza9asytg7CUolSVNTU2bOnJmZM2fmO9/5Ts4666xcfvnled/73rcvWwUAAAAAAAAAAApmn4alrrvuuspzqVRKnz59Mnjw4Bx11FHp1atXjjjiiHTo0CEdOnTItm3bsnbt2qxduzZr1qzJnDlz0tDQkNmzZ2fjxo1Jks2bN+fBBx/MQw89lLPOOivXXHNNevTosS9bBgAAAAAAAAAACmKfhqV69uyZESNGZMSIETn55JPTqVOnd/yObdu2pb6+PlOnTs3DDz+cxYsXZ+vWrZk8eXKmTJmSq666Kn/zN3+zL9sGAAAAAAAAAAAKYJ+GpaZOnbrX76ipqckJJ5yQE044IWPGjMkzzzyTO++8M88880w2bNiQ73znO5k2bVpuvfXWdOvWbR90DQAAAAAAAAAAFEHN/m5gT4YOHZrx48fnl7/8ZU455ZSUy+XMmDEj5557bl544YX93R4AAAAAAAAAAPAe8a4PS21XV1eXu+66K+PGjUuvXr2yYsWKXHLJJXnwwQf3d2sAAAAAAAAAAMB7QFXCUkOGDMkXvvCFzJkzZ6/f9dGPfjSPPPJILr/88mzdujVjx47N97///X3QJQAAAAAAAAAAcCDbZ2GpV199Nc8++2zWrVu3w9zq1avz5JNP5o9//OM+2atNmzb5+7//+/z85z/P4YcfnnHjxu2T9wIAAAAAAAAAAAeu1vvqRd26dcuXvvSl1NfX56ijjkpdXV0GDx6curq6vXrvokWL0q5duxx66KE7zH3gAx/IpEmTctVVV+3VHgAAAAAAAAAAwIFvn4WlSqVSbrjhhpx//vn54x//mD/+8Y959NFHm6259957s2zZstTV1WXAgAFp167dHt/7+OOP51vf+lY+8pGPZPz48TvMH3LIITsdBwAAAAAAAAAAeKt9FpZKkoEDB+aSSy7Jf/zHf+wwVy6X85vf/Ca/+c1vkiQ1NTU55phjUldXl0GDBlUCVG3btm1W16VLl5TL5bzwwgu73Le2tnZffg0AAAAAAAAAAOAAtE/DUkny9a9/PV/4whcya9asyueJJ55IqVRKuVyurNu6dWvmzZuXefPm5cEHH0yStGrVKsccc0wGDRqU/v3758gjj8z999+fJNm2bdu+bhUAAAAAAAAAACiQfR6WSt68Gm/YsGEZNmxYkqR///5JkksuuSTt27dPfX196uvrs3bt2mZ1W7Zsye9///v8/ve/bzZeKpXSp0+flmgVAAAAAAAAAAAoiBYJS+3KBz/4wZx++umVv7/yyiuV4FR9fX0aGxuzbt26HepKpVJGjx5dzVYBAAAAAAAAAIADTFXCUv/5n/+ZWbNm5aijjmo23rt37/Tu3TtnnnlmZewPf/hDGhoa0tDQkPnz52fTpk0ZOXJkLrjggmq0CgAAAAAAAAAAHKCqEpY66aSTctJJJ72ttX379k3fvn0zatSoFu4KAAAAAAAAAAAokpr93QAAAAAAAAAAAEA1VOVkqe1WrFiR5557Lq+//noOPfTQHHPMMenTp081WwAAAAAAAAAAAAqqamGpH/3oR/nhD3+YLVu2NBtv3759Bg0alLq6ugwePDiDBw9O7969q9UWAAAAAAAAAABQEFUJSz3yyCP5/ve/v9O5DRs2ZObMmZk5c2Zl7OCDD64Ep7Z/DjvssGq0CgAAAAAAAAAAHKCqEpaaMGFCkqRUKmXo0KEZOHBgVqxYkblz5+b3v/99mpqamq1fu3ZtnnnmmTzzzDOVse7du2f69OnVaBcAAAAAAAAAADgAVSUsNXfu3JRKpZxxxhm57bbbms01NTVl7ty5qa+vT319fWbNmpX58+fvcF3f8uXLq9EqAAD8RdM8AAAgAElEQVQAAAAAAABwgKpKWKpVq1ZJkhEjRuwwV1tbm7q6utTV1VXG3njjjcyePbsSnqqvr8+CBQuq0SoAAAAAAAAAAHCAqkpY6uijj05jY+MO1+3tStu2bXPiiSfmxBNPrIxt2LChpdoDAAAAAAAAAAAKoKYam5x55pkpl8uZNWvWn/2ODh067MOOAAAAAAAAAACAoqlKWOqCCy5It27d8sgjj2TVqlXV2BIAAAAAAAAAAKCZqoSlDj744Nxwww1Zv359xo4dm3K5XI1tAQAAAAAAAAAAKqoSlvrf//3fnHbaabn66qszffr0fP7zn8/KlSursTUAAAAAAAAAAECSKoWlPvrRj2bIkCF57rnn0qFDhzz99NP5+Mc/nltuuSXz58+vRgsAAAAAAAAAAEDBta7WRqtXr86zzz5b+fuGDRsyfvz4jB8/Pj179sxf/MVf5MQTT0xdXV2OP/74tG5dtdYAAAAAAAAAAIACqEoi6dJLL01jY2MaGxuzfv36yni5XE6SLF68OJMmTcqkSZOSJLW1tenXr18GDx6curq61NXV5bjjjkupVKpGuwAAAAAAAAAAwAGoKmGpsWPHJnkzHPWHP/whDQ0Nqa+vT0NDQxobG7Nhw4Zm6zdv3pz6+vrU19dXxtq1a5ff/va31WgXAAAAAAAAAAA4AFX1rrtSqZRjjjkmxxxzTM4+++wkbwaoXn755TQ0NFRCVLNnz94hQLVp06ZqtgoAAAAAAAAAABxgqhqW2plSqZRjjz02xx57bD75yU8m+b8A1fbTpxoaGjJ79uz93CkAAAAAAAAAAPBett/DUjvz1gDVOeeck+TNABUAAAAAAAAAAMCfqyphqXPPPTeDBg1KXV1dBg0alP79+6dNmzbv6B2lUqmFugMAAAAAAAAAAIqgKmGpOXPmZO7cuZk4cWKSpFWrVjnuuOMq4am6urr069cvtbW11WgHAAAAAAAAAAAooKpdw/fWa/S2bNmSOXPmZM6cOfnlL3/5ZiOtW1cCVNs/xx9/fFq3flfeFAgAAAAAAAAAALzHVCWJ9Otf/zqzZs3KrFmzUl9fn8bGxqxfv77ZmqampsyePTuzZ8/O/fffnySpra3N8ccfXzl96vzzz69GuwAAAAAAAAAAwAGoKmGp3r17p3fv3vnEJz6R5M1Tpl5++eVKeOqll15KY2NjtmzZ0qxu8+bNaWhoSENDQ+6//35hKQAAAAAAAAAA4M+2X+64K5VKOfbYY3Psscfm3HPPTZJs3LgxTz31VH7xi19kxowZKZVKadWqVQ455JC8+uqr+6NNAAAAAAAAAADgAFKzvxvYrn379jn99NPzs5/9LD/5yU/SrVu3bN26NZdeemmmTZuWH/zgB/u7RQAAAAAAAAAA4D3sXROWeqvhw4fnF7/4Rbp165bbbrstc+fOzcc//vH93RYAAAAAAAAAAPAe9q4MSyXJ0UcfnRtvvDHbtm3LDTfckA0bNuzvlgAAAAAAAAAAgPewd21YKkk+9rGPpX///lm6dGkmTZq0v9sBAAAAAAAAAADew97VYakkGTJkSMrlch599NH93QoAAAAAAAAAAPAe1roam4wYMSKDBw9OXV1d5c/OnTu/rdpOnTolSebPn9+SLSZJpk+fngceeCAvvPBCVq1alTZt2uToo4/OiBEjcskll6Rr1647rWtqaso999yThx56KPPnz0+5XE7Pnj1z2mmn5bLLLsshhxyy233nzp2bO+64IzNmzMiqVatyyCGHpK6uLhdddFGGDx++29q93RsAAAAAAAAAAIqiKmGpZcuWZfny5Zk6dWplrFevXs3CU4MGDUrHjh13qH3++eeTJBs3bmyx/rZs2ZJrr702kydPbjbe1NSUxsbGNDY25r777svtt9+eD3zgA83WvPHGG7n88svz3HPPNRufN29e5s2bl4kTJ2b8+PE5/vjjd7r3lClT8rd/+7dpamqqjK1YsSJPPPFEnnjiiXzmM5/JN77xjZ3W7u3eAAAAAAAAAABQJFUJSyVJuVxu9vdFixZl0aJFeeyxx5IkpVIpffv2zcCBA3PcccelY8eOmTFjRp555pmUSqX06dOnxXq79dZbK0GpU089NZ/73OfSt2/fLF++PNOnT88Pf/jDrFy5Ml/84hfz0EMPpUePHpXar3/963nuuedSW1ubr371qxk1alTatGmTadOm5bvf/W6WL1+eL37xi3n44YfToUOHZvs2NDTka1/7WpqamjJ48OD8wz/8Q4477rgsWrQoP/rRjzJ16tRMmDAhffv2zcUXX7xD33uzNwAAAAAAAAAAFE1VwlIzZ87MrFmzUl9fn/r6+syaNStLlixptqZcLufll1/Oyy+/vNN3nH/++S3S27Jly3L33XcnSc4+++zccsstlbkuXbqkX79+OeWUUzJ69OisWbMmP/7xj/PNb34zSfLSSy/lv/7rv5Ik1113XUaPHl2pveCCCzJgwICMHj06ixcvzs9+9rN86Utfarb39773vbzxxhs56qij8rOf/axyslaXLl1y++2358orr8yvf/3r/OAHP8g555xTuZJwX+wNAAAAAAAAAABFU1ONTTp16pQhQ4bkiiuuyPe///08/vjjefbZZ/PTn/40V111VUaOHJnu3bunXC7v8EmSUaNG5a//+q9bpLcpU6Zky5YtSZIxY8bsdM0JJ5yQj33sY0mSadOmVcbvvPPOJEnPnj1zwQUX7FA3ePDgnH322UmS+++/v9nc/Pnz8+STTyZJPv/5z+9wBWGpVMq1116bUqmUNWvW5L//+7+bze/N3gAAAAAAAAAAUERVu4bvT3Xp0iXDhg3LsGHDKmOvvvpqGhsbM3/+/CxdujS1tbX5yEc+kqFDh7ZYH8uXL0+7du3SqVOn9OzZc5frjjrqqMr65M2TsJ5++ukkyYgRI9KqVaud1p166qmZOHFiFi9enNmzZ2fAgAFJUglKlUqljBw5cqe1vXr1Sr9+/TJnzpxMnTo1n/rUp/bJ3gAAAAAAAAAAUET7LSy1M4ceemiGDx+e4cOHV23PMWPGZMyYMXn99dd3u27hwoVJks6dOydJFi1alHXr1iVJBg0atMu6twaU6uvrK3+fPXt2kuTwww9Pt27ddlk/cODAzJkzJw0NDZWxvd0bAAAAAAAAAACK6F0VltqfOnXqtMu5pUuXZvr06UmSk046KUmyePHiyvzuTqTq0aNHWrVqla1btzarWbJkyR5rk+TII49MkixbtixNTU2pra3d6733taamphZ794GgtrY2pVJpf7cBAC2uXC77fwEAAAAAAAD7TEv87qnqYakNGzbk4YcfzowZM7J06dJs2rQpnTp1ylFHHZUTTjghI0aMSI8ePard1i5t27Yt119/feWHf9FFFyVJVq9eXVmz/bSpnWndunXat2+f119/Pa+99lplfHv9wQcfvNv9DzrooCRv/vJx3bp16dq1617vva/NmTOnxd59IBg8eHDatGmzv9sAgBbX1NSUWbNm7e82AAAAAAAAYJeqGpZ67LHH8o//+I87De4899xzeeCBB1JTU5Nhw4blqquuysCBA6vZ3k7dfPPNeeqpp5IkZ511VoYMGZIkeeONNypr2rZtu9t3tGvXLq+//no2bdpUGdte365du93WvvXd22v2dm8AAAAAAAAAACiiqoWlJk6cmOuuuy7Jm6ck7crWrVszffr0PPnkk7niiisyZsyYarXYTLlczs0335wJEyYkSY4//vjceOONlflWrVpVnvd0xdr271tTU7ND/dutfWv93u69r/Xv3z+1tbUt9v73Oj8bAIqitrY2gwcP3t9tAAAAAAAAcIBoamra5zeeVSUstXTp0vzTP/1TJbhz+umn51Of+lTe9773pXXr1lm5cmVefPHFSkhq27ZtKZfL+clPfpIVK1bk5ptvrkabFZs3b87YsWPz8MMPJ0mOPfbY3HnnnenYsWNlTYcOHSrPbz3paWe2z7/1FKjt9Xs68Wnz5s2V5+1Xue3t3vtabW2ta+YAgJRKJf8nAAAAAAAA4F2tKmGpu+++O5s3b06pVMo3vvGNXHzxxc3me/TokYEDB2b06NFZvHhxfvzjH+e+++5LuVzOpEmTUldXl4suuqgarWbVqlX5yle+kueffz5JMmjQoNxxxx3p2rVrs3UHHXRQ5XndunW7fN+WLVuycePGJEmXLl0q4506ddpjbZLKlYU1NTXp3LnzPtkbAAAAAAAAAACKqOXuZnuLZ555JqVSKUOHDt0hKPWnevbsmX/+53/Ov/3bv6V9+/Ypl8v53ve+VwkNtaQFCxbkwgsvrASlhg0blgkTJuwQlEqSPn36VJ6XLFmyy3cuW7YsW7duTZIceeSRlfG+ffsmefPUrd3ZPn/EEUdUrtLb270BAAAAAAAAAKCIqhKWWrx4cZLktNNOe9s1n/jEJ/Ktb30ryZunJ02cOLFFettuzpw5ufDCC7Nw4cIkyQUXXJBx48Y1u3rvrbp37145rWn27Nm7fG9jY2PlecCAAZXnfv36JXnzZ7O7IFhDQ0OSpH///vtsbwAAAAAAAAAAKKKqhKW2n27Uvn37d1R3xhln5OSTT065XM7jjz/eEq0lSRYuXJjPfvazWb16dZLk6quvzo033pjWrXd/S+GIESOSJNOmTcu2bdt2umbq1KlJksMOO6xZ4Gn48OFJkm3btmXatGk7rX3llVfyu9/9Lsmbp1ztq70BAAAAAAAAAKCIqhKW6tatW5I3r7l7pz7+8Y8nSSU0tK9t3rw5Y8aMycqVK5MkY8eOzZe//OW3VXvuuecmefN73XPPPTvMv/TSS5k8eXKS5NJLL02pVKrM9e7dOyeffHKS5Pbbb8+6deua1ZbL5Xz7299OuVxOly5dcs455+yzvQEAAAAAAAAAoIiqEpYaMGBAyuVyJk+evMtTkHblsMMOS5KsX7++JVrLfffdV7nq7owzzsj555+f9evX7/az3ZAhQzJy5MgkyU033ZTbbrstr7zySlasWJH7778/n/vc57Jly5b06tUro0eP3mHvsWPHpqamJgsWLMhFF12Up59+OqtWrUpDQ0O+8pWvZMqUKUmSK6+8Mh06dGhWu7d7AwAAAAAAAABA0ez+nrl95NRTT82UKVOyePHi3Hbbbbnmmmvedu2SJUuSJIceemiL9Hb33XdXnh977LE89thje6yZO3du5fnb3/52Lr/88syaNSvjxo3LuHHjmq099NBDM378+HTq1GmH9wwePDg33XRTrr/++vzud7/L5ZdfvsOayy67LBdffPFO+9ibvQEAAAAAAAAAoGiqcrLUqFGj0rt37yTJHXfckdtuuy3lcnmPdVu2bMmkSZNSKpVyzDHH7PO+Vq9enYULF+7VOzp37px77rkn1113XU444YR07NgxtbW16dOnTy677LI89NBD6dOnzy7rzzvvvEycODHnnHNODj/88NTW1qZz584ZOnRobr/99lx77bUttjcAAAAAAAAAABRJVU6Wqq2tzb/8y79Urob7yU9+kqeeeipXX311hg0blpqaHTNbmzZtytixY/Pyyy+nVCrlrLPO2ud9denSpdkpUX+u2traXHLJJbnkkkv+rPp+/frlX//1X/fL3gAAAAAAAAAAUBRVCUslyYc//OF897vfzbXXXptNmzZl9uzZ+eIXv5hDDjkkJ598cvr165euXbumqakp8+fPz5QpU7JmzZqUSqX06dOnRcJSAAAAAAAAAABAcVQtLJUkZ5xxRvr27Ztrrrkm8+bNS5KsWbMmU6dOzdSpU3dYXy6Xc/DBB+fWW29N27Ztq9kqAAAAAAAAAABwgNnx/rsW1q9fvzz00EO55ZZb8v73vz+lUinlcnmnnw9/+MN54IEHMnDgwGq3CQAAAAAAAAAAHGCqerLUdjU1NRk1alRGjRqVtWvXZubMmVm6dGlWr16dUqmUww8/PB/60Idy9NFH74/2AAAAAAAAAACAA9B+CUutWLEiGzduzFFHHZXOnTvn1FNP3R9tAAAAAAAAAAAABVLVa/imTJmSs88+O8OHD88NN9yw0zWbNm2qZksAAAAAAAAAAEBBVC0sNX78+Fx55ZWZN29eyuVyyuXyTtfdcccd+cu//Ms8+uij1WoNAAAAAAAAAAAogKqEperr63PrrbcmScrlcrp3756BAwfudO2yZcsye/bsXHPNNfnMZz6TFStWVKNFAAAAAAAAAADgAFeVsNTdd9+dbdu2JUnGjBmTadOm5e/+7u92unbjxo1J3gxVzZw5M6NHj87y5cur0SYAAAAAAAAAAHAAq0pYaubMmSmVShkxYkS+8IUvpKZm19vecsst+elPf5rjjjsu5XI5ixcvzjXXXFONNgEAAAAAAAAAgANYVcJSr776apJk2LBhb2v9sGHD8sADD+SjH/1o5YSpKVOmtGSLAAAAAAAAAADAAa4qYanWrVsnSQ4++OC3XdOmTZvceuut6dGjR5Jk0qRJLdIbAAAAAAAAAABQDFUJS/Xs2TNJ0tjY+I7qOnbsmPPPPz/lcjkvvfRSS7QGAAAAAAAAAAAURFXCUh/+8IdTLpfz4IMPZu3ate+otm/fvkmSNWvWtERrAAAAAAAAAABAQVQlLPVXf/VXKZVKWbNmTa6++ups2LDhbde+8sorSZJOnTq1VHsAAAAAAAAAAEABVCUs1b9//5x33nkpl8uZMWNGzjnnnPzqV79KuVzebd3y5cvz85//PKVSKQMGDKhGqwAAAAAAAAAAwAGqdbU2+uY3v5l58+blxRdfzKJFi3L11VfnsMMOy7Bhw/KhD30o/fr1S9euXdOxY8esXLkyzz77bMaNG5dXX301pVIp5557brVaBQAAAAAAAAAADkBVC0u1bds2d911V77xjW/kkUceSZKsWLEiEydOzMSJE3dbO2TIkHzyk5+sRpsAAAAAAAAAAMABqirX8G3Xvn373HrrrRk/fnw++MEPplwu7/EzcuTI/OAHP6hmmwAAAAAAAAAAwAFon54sde+99+bTn/70HtcNHTo0Q4cOzSuvvJInnngizz//fBYsWJDVq1enXC6na9euqaury6hRo3LKKaf8f+zde5SW1WEu8OcDBlBGQIwgCAKiKDBjqlGOd1u8ptFijregMYnHJE08SY+NicSVW6smTe05rUYxNVZrNGoiiSElamxjvEStaNTgfAPKRe4gEpCbIAzwnj9cfHUKXjO8JDO/31rf8pv97v3uZ+Yv1+JZe7dlRAAAAAAAAAAAoINq07LUN77xjcycOTPjx49PXV3d284fNGhQPvaxj+VjH/tYW8YAAAAAAAAAAADYRptew7f//vvn9ttvzznnnJMXXnihLV8NAAAAAAAAAADwe2nTstSdd96ZI488MtOmTcuZZ56Zf/qnf8qGDRvacgsAAAAAAAAAAID3pE3LUvX19bnxxhtz/vnnp6WlJd/73vfywQ9+MD/72c/achsAAAAAAAAAAIB3rU3LUknSqVOnfOUrX8lVV12VXXbZJYsXL86Xv/zlnHbaaZk8eXK2bNnS1lsCAAAAAAAAAAC8rTYvS231F3/xF/nJT36S97///SmKIrNmzcqll16aP/3TP83VV1+d2bNnt8k+zc3Nuf7669vkXQAAAAAAAAAAQPvVZUe+fOjQofnhD3+YO++8M9dcc01WrVqVZcuW5YYbbsgNN9yQwYMHZ/To0WloaMioUaMyfPjw1NXVbfddW7ZsyUsvvZR58+alqakp1Wo1zzzzTJYvX54kueiii3bkrwIAAAAAAAAAAPyR26FlqSSpVCo599xzc9ppp+Wmm27K7bffnjVr1iRJ5s2bl3nz5mXixIm1+T169EivXr3Sq1evVCqVvPbaa1m3bl2WLVuWzZs3t3p3URRJktGjR+/oXwMAAAAAAAAAAPgjt8PLUlvttttuufjii/PpT386P/3pTzNp0qQ0NTVtM2/t2rV59dVXs3jx4trY1lLUGw0cODAnnnhizj777AwdOnSHZgcAAAAAAAAAAP74lVaW2mrXXXfNeeedl/POOy+LFy/OY489lt/+9rd54YUXsnDhwqxcubJVOapSqaRnz54ZOnRohg0blpEjR+aoo45SkAIAAAAAAAAAAN6V0stSbzRgwICcddZZOeuss2pjGzduzLp169LS0pLu3bunvr4+lUplJ6YEAAAAAAAAAADag51altqerl27pmvXrjs7BgAAAAAAAAAA0M502tkBAAAAAAAAAAAAyqAsBQAAAAAAAAAAdAjKUgAAAAAAAAAAQIegLAUAAAAAAAAAAHQIylIAAAAAAAAAAECHoCwFAAAAAAAAAAB0CMpSAAAAAAAAAABAh6AsBQAAAAAAAAAAdAjKUgAAAAAAAAAAQIegLAUAAAAAAAAAAHQIpZal1q5dm+9+97v5yEc+ku985ztlbg0AAAAAAAAAAHRwXcraaNasWfnkJz+ZpUuXJkkaGhq2mbNixYr8+Mc/zowZM9K7d+/82Z/9WY466qiyIgIAAAAAAAAAAO1YKWWpdevW5bOf/WxeeumlN53z1FNP5XOf+1xWr15dG7v99tszZsyY/OM//mO6detWRlQAAAAAAAAAAKCdKuUavrvvvjsLFixIpVLJgQcemDvuuCNf/vKXa89XrFiRz33uc1m1alWKomj1+dWvfpWvfvWrZcQEAAAAAAAAAADasVLKUg888ECSpFevXvnBD36QQw45JF26/NehVt/5zneyatWqVCqV7Lnnnvn617+eK664IkOHDk1RFPn5z3+eqVOnlhEVAAAAAAAAAABop0q5hm/mzJmpVCo544wzUl9f3+rZ0qVL85Of/CRJUldXl1tuuSXDhg1Lkpxwwgk55ZRTsnr16kyePDnvf//7y4gLAAAAAAAAAAC0Q6WcLLVq1aokyZAhQ7Z59oMf/CAtLS2pVCo5++yza0WpJNl9991z+umnpyiKTJkypYyoAAAAAAAAAABAO1VKWaquri5JUqlUWo2vX78+d911V5Kkc+fO+V//639ts3bfffdNkixbtmwHpwQAAAAAAAAAANqzUspSAwYMSJI0NTW1Gv/hD3+YVatWpVKp5Pjjj6/Ne6Pu3bsnSdauXbvjgwIAAAAAAAAAAO1WKWWpww8/PEVRZNKkSfntb3+bJJk5c2a++93v1uZceOGF2127YMGCJEnPnj13fFAAAAAAAAAAAKDd6lLGJuedd17uuuuubNy4MR/96EczdOjQzJs3Lxs3bkylUsnRRx+dgw46aJt1RVHkF7/4RSqVSoYNG1ZGVAAAAAAAAAAAoJ0q5WSpoUOH5hvf+EYqlUo2bdqUWbNmZePGjUmSPfbYI1deeeV211111VWZNWtWkuTYY48tIyoAAAAAAAAAANBOlXKyVJKcccYZ2WefffLP//zPee6559KlS5cceeSRueSSS9KvX7/avGq1mh/96Ed58sknM3/+/CRJjx49csYZZ5QVFQAAAAAAAAAAaIdKK0slyWGHHZbDDjvsLefMnDkzEydOTKVSSVEUSZLx48enT58+ZUQEAAAAAAAAAADaqVLLUu9GURTp3bt3vvSlLzlVCgAAAAAAAAAA+L2VUpa66qqr0tjYmKOPPjq77bbbW849+OCD8+1vfzt77713/uRP/iR1dXVlRAQAAAAAAAAAANq5NitLFUWRSqWy3Wc333xzKpVKrrnmmpx00klv+Z4hQ4ZkyJAhbRULAAAAAAAAAAAgSRuWpZYvX54LLrgggwcPTmNjYxoaGtLQ0JBevXq11RYAAAAAAAAAAADvWZuVpd73vvdl3Lhxufzyy/PAAw/UxgcOHFj7PmfOnKxduzb19fXv+L233357HnzwwZx44ok555xz2iouAAAAAAAAAADQwXRqy5eNGzcujY2NKYqi9lm4cGHter6rr746o0ePzsknn5xLLrkkN998c5588smsXbv2Td9ZX1+fRx99NNddd11bRgUAAAAAAAAAADqYNjtZKkkqlUquueaa3H///WlqakpTU1MWLFiQSqWSoiiSJEVRZP78+Zk/f37uvffe2rp99tkno0aNSmNjY0aOHJkRI0Zkt912y9y5c5MkK1eubMuoAAAAAAAAAABAB9OmZakkGTBgQC644ILazytXrszhhx+eSqWSAw44IMuXL8+yZctarSmKIvPmzcu8efNqBaok2XXXXbNu3bokSd++fds6KgAAAAAAAAAA0IG0eVnqv+vdu3ft+0UXXZSTTjopy5YtS1NTU6rVaqrVapqbm7N8+fJt1r766qtJXj956uijj97RUQEAAAAAAAAAgHZsh5elkuTMM89MtVpNly6vb7fnnntmzJgxGTNmTG3OkiVLUq1W09TUlObm5kybNi2vvPJKkmT48OH5q7/6qzKiAgAAAAAAAAAA7VQpZakrr7zybef0798//fv3z4knnlgbW7FiRV577bUMGDBgR8YDAAAAAAAAAAA6gFLKUu9Vnz59dnYEAAAAAAAAAACgnei0MzZtaWnZGdsCAAAAAAAAAAAdWGknS82ePTv/8A//kCeeeCIbNmxIz549M3To0IwYMSKNjY1pbGzMfvvtl0qlUlYkAAAAAAAAAACgAymlLDVnzpx85CMfydq1a1MURZJk1apVmTp1aqZOnZof/vCHSZLu3btn1KhRtfJUY2NjBg0aVEZEAAAAAAAAAACgnSulLHXddddlzZo1SZJOnTqlT58+WblyZTZt2tRq3vr16/P000/n6aefro317NkzDQ0NaWxszMUXX1xGXAAAAAAAAAAAoB0qpSz1xBNPpFKpZJ999smtt96afv36ZdOmTXnxxRfT3NycarWaarWa559/Phs2bGi1dvXq1Xnsscfy+OOPK0sBAAAAAAAAAADvWSllqa2nSn30ox9Nv379Xt+4S5cMHz48w4cPz4c//OEkyebNmzNjxoxUq9U0NTWlWq1mxowZ25xABQAAAAAAAAAA8G6VUpbq169fFi5cmGG1rHkAACAASURBVN69e7/lvM6dO2fEiBEZMWJEzjrrrCTJxo0b8/zzz6darZYRFQAAAAAAAAAAaKc6lbHJ4YcfniSZN2/eu17btWvXHHTQQTn33HPbOhYAAAAAAAAAANCBlFKWOvfcc1OpVPKLX/yijO0AAAAAAAAAAAC2UUpZasSIETn33HMze/bs3HbbbWVsCQAAAAAAAAAA0EopZakk+dKXvpTGxsb8/d//fSZPnlzWtgAAAAAAAAAAAElKKkt94hOfyDXXXJM///M/T9euXXPppZfm4osvzuzZs8vYHgAAAAAAAAAAIF3K2OSJJ57IlClTaj8XRZH7778/999/fxobG3Pcccflf/yP/5GGhoZ07969jEgAAAAAAAAAAEAHU0pZqlevXlm1atU240VRpKmpKU1NTbnuuuvSuXPn7LvvvmloaEhDQ0MaGxtz4IEHpq6uroyYAAAAAAAAAABAO1ZKWWrKlClZuHBhqtVqmpuba59Vq1alKIravE2bNmXmzJmZOXNmfvrTn74esEuX7L///mlsbMzf/u3flhEXAAAAAAAAAABoh0opSyXJwIEDM3DgwJxyyim1sQULFqS5ublWopo2bdo2J1C1tLRk2rRpmT59urIUAAAAAAAAAADwnpVWltqeQYMGZdCgQdsUqLaWp6rVaqZNm5bVq1fvxJQAAAAAAAAAAEB7sFPLUtuztUD1wQ9+sDa2tUAFAAAAAAAAAADwXv3BlaW2Z2uBCgAAAAAAAAAA4L0qvSy1YcOGPPjgg5k6dWqWL1+ebt26pU+fPvnwhz+cIUOGlB0HAAAAAAAAAADoIEotS91zzz351re+lRUrVmzzbNSoURkyZEgWL16cp556Kqeeemo6d+5cZjwAAAAAAAAAAKAd61TWRnfccUe++MUvZsWKFSmKotXnjWbNmpXx48fn1FNPTVNTU1nxAAAAAAAAAACAdq6UstSCBQvyrW99K0VRpEePHvnCF76QSZMm5eGHH95m7ssvv5wkmTt3bs4777z88pe/LCMiAAAAAAAAAADQzpVSlvr+97+fTZs2pVu3brn11lvz6U9/OgceeGD69eu3zdz3v//9Oeigg1IURTZu3Ji//uu/zjPPPFNGTAAAAAAAAAAAoB0rpSz1xBNPpFKpZOzYsRk5cuRbzt1///1z1113Zfz48UmSlpaWfPGLX8zGjRvLiAoAAAAAAAAAALRTpZSlFi9enCQ55JBD3vGaCy64IJdcckmSZMmSJbn77rt3SDYAAAAAAAAAAKBjKKUstWnTptc36/TutvvUpz6VAw44IEny7//+722eCwAAAAAAAAAA6DhKKUv17ds3STJz5sx3vfa0005LURR5/vnn2zoWAAAAAAAAAADQgZRSljr44INTFEXuueee2ilT79Ree+2VJFmzZs2OiAYAAAAAAAAAAHQQpZSlTj/99CTJkiVLcu21176rtXPmzEmS7Lnnnm2eCwAAAAAAAAAA6DhKKUsdddRROeyww1IURb73ve/luuuuS1EUb7tu6dKlueOOO1KpVDJ8+PASkgIAAAAAAAAAAO1VKWWpJPn2t7+dPn36pCiKTJgwIWPHjs3dd9/9pvMfeeSRnHfeeXnllVeSJGPHji0rKgAAAAAAAAAA0A51KWujvffeO7fccks+85nPZPHixZk5c2a+8pWv1J7feOONuffee7N69eq88MILWbFiRZKkUqnk0EMPzcknn1xWVAAAAAAAAAAAoB0q7WSpJBk+fHgmTZqUs846K506dUpRFKlUKkmSarWa+++/P//5n/+ZFStWpCiKFEWRESNG5Oqrr06nTqVGBQAAAAAAAAAA2pnSG0g9e/bMFVdckV/84he54IILMmDAgFox6o2fwYMH55JLLsldd92VPfbYo+yYAAAAAAAAAABAO1PaNXz/3aBBgzJ+/PiMHz8+S5cuzbx587JmzZr07Nkze+21VwYNGrSzogEAAAAAAAAAAO3QTitLvVG/fv3Sr1+/nR0DAAAAAAAAAABox0otS61ZsyYzZsxIURQZNGiQghQAAAAAAAAAAFCa0spSV111VW677bZs2rSpNtazZ880NDS0+vTv37+sSAAAAAAAAAAAQAdSSlnq9ttvz80337zN+KpVq/L444/n8ccfr4316dNnmwLVnnvuWUZMAAAAAAAAAACgHSulLDVx4sTa92HDhmXQoEGZP39+5s6dmy1btrSau2LFijzyyCN55JFHamN77rlnGhsbM2HChDLiAgAAAAAAAAAA7VApZam5c+emUqnk+OOPz7XXXptKpZIkWb9+fZqbm1OtVlOtVtPc3Jy5c+emKIpW619++eX86le/KiMqAAAAAAAAAADQTpVSlurSpUs2bNiQE044oVaUSpJddtklhx56aA499NDa2KuvvlorUG397/z588uICQAAAAAAAAAAtGOllKWGDBmS5ubmrFy58m3n9ujRI6NHj87o0aNrY2vXrk21Wt2REQEAAAAAAAAAgHauUxmbfOhDH0pRFPn1r3/9ntbX19fn8MMPb+NUAAAAAAAAAABAR1JKWeqss87KgAED8vjjj+c3v/lNGVsCAAAAAAAAAAC0UkpZqr6+PldddVXq6upyySWXZPHixWVsCwAAAAAAAAAAUFNKWSpJDj300Nxwww1Zvnx5PvzhD2fy5MnZsmVLWdsDAAAAAAAAAAAdXGllqV//+te58sors2nTpqxatSqXXnppjj322Hz961/Pfffdl5deeqmsKAAAAAAAAAAAQAfUpYxNfvOb3+Szn/1sNm/enEqlUhv/3e9+l4kTJ2bixIlJkj322CMNDQ1paGhIY2NjGhsb06dPnzIiAgAAAAAAAAAA7VwpZanrr78+mzZtSpLU1dWlT58+WbZsWYqiSFEUtXnLly/Pww8/nIcffrg21r9//1qB6tOf/nQZcQEAAAAAAAAAgHaolLJUc3NzKpVKGhoacuONN6Z379557bXXMm3atFSr1dpn7ty52bJlS6u1S5YsyZIlS/If//EfylIAAAAAAAAAAMB7VkpZamsBaty4cendu3eSpHv37jnkkENyyCGH1OatW7cuzc3NrQpU8+fPT1EUra7vAwAAAAAAAAAAeLdKKUvtu+++ee6557J69eq3nLfrrrvmsMMOy2GHHVYbW7t2bZqamtLc3LyjYwIAAAAAAAAAAO1YpzI2OfXUU1MURR5++OF3vba+vj5HHHFEPvnJT+6AZAAAAAAAAAAAQEdRSlnqnHPOybBhw/LEE0/kiSeeKGNLAAAAAAAAAACAVkopS3Xt2jUTJkxI796988UvfjELFiwoY1sAAAAAAAAAAICaUspS559/fu64446cfvrpWbFiRc4444zce++9ZWwNAAAAAAAAAACQJOlSxiZPPfVUfvOb39R+Xr16dS655JJce+21Oe2003Lsscdm5MiR6dSplO4WAAAAAAAAAADQAZVSlkqSoihq3yuVSoqiyNy5c3Pttdfm2muvTffu3TNy5Mg0NDSksbExDQ0NGTJkSFnxAAAAAAAAAACAdq6UstTdd9+d5ubmNDU1pbm5OTNmzEhLS0urAtX69evzzDPP5JlnnqmN1dfXZ9SoUWloaEhDQ0NOOeWUMuICAAAAAAAAAADtUCllqZEjR2bkyJE566yzkiQtLS2ZMWNGmpubU61WU61WM3PmzLS0tLRat2bNmkyZMiVTpkxJpVJRlgIAAAAAAAAAAN6z0q7he6O6urqMGjUqo0aNytlnn53kvwpU1Wq1VqLaXoEKAAAAAAAAAADgvSilLHXVVVelsbExRx99dHbbbbftznljgWqrlpaWvPDCC2lubk5zc3MZUQEAAAAAAAAAgHaqzcpSRVGkUqls99nNN9+cSqWSa665JieddNI7fmddXV0aGhrS0NDQVjEBAAAAAAAAAIAOqs3KUsuXL88FF1yQwYMHp7GxsVZy6tWrV1ttAQAAAAAAAAAA8J61WVnqfe97X8aNG5fLL788DzzwQG184MCBte9z5szJ2rVrU19f/47fe/vtt+fBBx/MiSeemHPOOaet4gIAAAAAAAAAAB1Mp7Z82bhx49LY2JiiKGqfhQsX1q7nu/rqqzN69OicfPLJueSSS3LzzTfnySefzNq1a9/0nfX19Xn00Udz3XXXtWVUAAAAAAAAAACgg2mzk6WSpFKp5Jprrsn999+fpqamNDU1ZcGCBalUKimKIklSFEXmz5+f+fPn5957762t22effTJq1Kg0NjZm5MiRGTFiRHbbbbfMnTs3SbJy5cq2jAoAAAAAAAAAAHQwbVqWSpIBAwbkggsuqP28cuXKHH744alUKjnggAOyfPnyLFu2rNWaoigyb968zJs3r1agSpJdd90169atS5L07du3raMCAAAAAAAAAAAdSJuXpf673r17175fdNFFOemkk7Js2bI0NTWlWq2mWq2mubk5y5cv32btq6++muT1k6eOPvroHR0VAAAAAAAAAABox3Z4WSpJzjzzzFSr1XTp8vp2e+65Z8aMGZMxY8bU5ixZsiTVajVNTU1pbm7OtGnT8sorryRJhg8fnr/6q78qIyoAAAAAAAAAANBOlVKWuvLKK992Tv/+/dO/f/+ceOKJtbEVK1bktddey4ABA3ZkPAAAAAAAAAAAoAMopSz1XvXp02dnRwAAAAAAAAAAANqJTmVutmjRonz1q1/NMccc845OmwIAAAAAAAAAAGgrpZ0s9dRTT+Wzn/1sXn311TedM3v27Nx6662ZMWNGevfunTFjxuT0009PXV1dWTEBAAAAAAAAAIB2qpSy1MqVK/N//s//ydq1a2tjPXr0aDXnvvvuy/jx49PS0lIbe+ihhzJx4sTccMMN2X333cuICgAAAAAAAAAAtFOlXMP3ox/9KCtWrEilUskxxxyThx9+OH/9139de75gwYJcdtll2bhxY4qiaPVpamrKpZdeWkZMAAAAAAAAAACgHSulLPXrX/86SbLXXnvl+uuvT79+/Vo9v/rqq/Paa6+lUqlkv/32yw033JB//dd/zQc+8IEURZFHH300jz32WBlRAQAAAAAAAACAdqqUa/hmz56dSqWS008/PXV1da2ezZ07N/fdd18qlUp69OiRm266qVam+sAHPpATTjghy5Yty3333ZejjjqqjLgAAAAAAAAAAEA7VMrJUmvWrEmS7LPPPts8+/73v58tW7YkST72sY+1OnWqa9eu+fCHP5yiKPLMM8+UERUAAAAAAAAAAGinSilLde/ePUmycePGVuOrVq3KpEmTkiTdunXLRz/60W3WDho0KEnyu9/9bgenBAAAAAAAAAAA2rNSylJbT5R6+umnW43fcsstWb9+fSqVSk499dT06dNnm7Vdurx+U+D69et3fFAAAAAAAAAAAKDdKqUsdeSRR6Yoitxzzz257777kiSPP/54br755iRJpVLJBRdcsN21L774YpKkV69eZUQFAAAAAAAAAADaqVLKUueff37q6+uzZcuWfOELX8ghhxySCy+8MBs2bEilUsmHPvShDBs2bJt1GzduzM9//vNUKpUceOCBZUQFAAAAAAAAAADaqVLKUv369cv/+3//L926dUtRFFm3bl2KokiS7Lvvvvn617++zZrXXnstl112WRYvXpwk+bM/+7MyogIAAAAAAAAAAO1Ul7I2Ou644/Kzn/0sN910U5577rl06dIlRx55ZD71qU9lt912q8178skn88///M+ZOnVq1q1blyTZY489Mnbs2LKiAgAAAAAAAAAA7VBpZakkGTx4cC6//PK3nLNo0aI8/vjjqVQqKYoinTt3zhVXXJH6+vqSUgIAAAAAAAAAAO1RKdfwvRdFUWT48OH53ve+5wo+AAAAAAAAAADg91bqyVLvxFFHHZUf/OAHGTBgQAYMGLCz4wAAAAAAAAAAAO3EH1xZqm/fvunbt+/OjgEAAAAAAAAAALQzO6Us1dTUlKlTp2b58uXp1q1b9thjjxx77LHp16/fzogDAAAAAAAAAAB0AKWWpX7zm9/k8ssvz8yZM7d5ds011+Skk07K8uXLM2fOnBx66KFlRgMAAAAAAAAAANq5TmVt9Mtf/jKf+MQnMnPmzBRF0erzRs8//3zOP//8fPzjH8/ChQvLigcAAAAAAAAAALRzpZSlfve73+XSSy/Npk2b0rlz55xzzjmZMGFCbr/99m3mvvzyyymKIk8++WTOOOOMTJ06tYyIAAAAAAAAAABAO1fKNXy33XZb1q1bl86dO2fChAk57rjj3nTukCFD0r9//yxZsiSrVq3KhRdemB/96EcZNmxYGVEBAAAAAAAAAIB2qpSTpX7961+nUqnklFNOecuiVJIcfPDBuf/++/PRj340SbJ27dpcfPHF2bJlSxlRAQAAAAAAAACAdqqUstSCBQuSJEccccQ7mt+1a9d89atfrRWmZs2alXvuuWeH5QMAAAAAAAAAANq/UspS69evT5J07979Xa378pe/nEGDBiWJshQAAAAAAAAAAPB7KaUs9b73vS9JMnfu3He1rkuXLhk7dmyKokhzc/MOSAYAAAAAAAAAAHQUpZSlDjrooBRFkfvuu+9drx0yZEiSZOXKlW2cCgAAAAAAAAAA6EhKKUudeuqpSZIXX3wxt91227tau3jx4iTJ7rvv3ua5AAAAAAAAAACAjqOUstSJJ56Y4cOHpyiK/P3f/30mTZr0jtatXbs2P/zhD1OpVLLvvvvu4JQAAAAAAAAAAEB7VkpZqlKp5Kqrrsouu+ySzZs357LLLstf/uVfZsqUKW+6Zvbs2fnEJz5RO1nqtNNOKyMqAAAAAAAAAADQTnUpa6MDDzww3/3ud/P5z38+a9asySOPPJJHHnmk9vwnP/lJpk6dmtWrV2f69Olpbm5O8nrRar/99lOWAgAAAAAAAAAAfi+llaWS5PDDD89Pf/rTfOMb38hjjz2W5PUyVJJtylNFUSRJ9tprr1x33XXp2rVrmVEBAAAAAAAAAIB2ppRr+N5o4MCBuemmm3LrrbfmpJNOSrdu3VIUxTaf+vr6fOQjH8nkyZMzePDgsmMCAAAAAAAAAADtTCknS33rW9/KRRddlN69e9fGRo8endGjR2fDhg1pbm7OvHnzsmbNmvTs2TN77bVXDjnkEKdJAQAAAAAAAAAAbaaUstStt96aSZMm5S//8i9z/vnntypBdevWLYccckgOOeSQMqIAAAAAAAAAAAAdVGnX8K1Zsyb/9//+35xyyimZPHlyWdsCAAAAAAAAAAAkKaksdeGFF6Zbt24piiJLlizJpZdemjPOOCNTpkwpY3sAAAAAAAAAAIByylJf+tKX8u///u8566yz0rlz5xRFkWnTpuUTn/hEPvOZz2TWrFllxAAAAAAAAAAAADqw0q7h69u3b6644or8/Oc/z0knnZSiKFIURR5++OGMHTs2X/va1/Lyyy+XFQcAAAAAAAAAAOhgSitLbTVkyJB85zvfycSJE3P44YenKIps3rw5P/7xj3PyySfn2muvzbp168qOBQAAAAAAAAAAtHOll6W2amxszC233JKbbropI0eOTFEUWb9+fa6//vqcdNJJ+dGPfpQtW7bsrHgAAAAAAAAAAEA7s9PKUlsdddRRufvuu/OP//iPGTx4cIqiyO9+97v8zd/8TU477bQ8+OCDOzsiAAAAAAAAAADQDuz0stRWf/7nf5577703l19+eQYMGJCiKDJ79uxcdNFFOf/881OtVnd2RAAAAAAAAAAA4I/YH0xZKkk6d+6cs88+O/fff3/+5m/+JoMGDUpRFHnqqady9tln7+x4AAAAAAAAAADAH7EuOzvAVkuWLMm8efOycOHCvPTSS1m6dGn22WefLF68OJs3b05RFDs7IgAAAAAAAAAA8Ees1LLUqlWrMnfu3MyZMydz5sypfZ8/f342bNiw3TVKUgAAAAAAAAAAQFsopSw1bty4zJkzJ6tWrdru8+0VoiqVSvr375+hQ4dm3333zbBhw3Z0TAAAAAAAAAAAoB0rpSz17LPPplKpbLcU1a1bt+yzzz4ZNmxY9t1331af7t27lxEPAAAAAAAAAADoAEq7hq9Xr161EtQbi1EDBw5MpVIpKwYAAAAAAAAAANBBlVKW+s///M/svvvuZWwFAAAAAAAAAACwXZ3K2ERRCgAAAAAAAAAA2NlKKUsBAAAAAAAAAADsbKVcw7dVS0tLpk+fnjVr1uR973tfhgwZkm7dupUZAQAAAAAAAAAA6KBKK0tNmjQp3/72t7Nq1araWKdOnTJkyJA0NjbWPgceeGC6du1aViwAAAAAAAAAAKCDKKUs9dhjj+Wyyy5LkhRFURvfvHlzXnzxxbz44ov52c9+liTp3Llzhg8f3qpAtf/++6dTJzcGAgAAAAAAAAAA710pZakbb7wxRVGkUqlkv/32ywEHHJBly5ZlxowZWblyZau5mzZtyvTp0zN9+vTcddddSZLu3btnxIgRueOOO8qICwAAAAAAAAAAtEOllKWq1WoqlUqOPPLI3Hjjja1OiVq0aFGq1Wrt09zcnNWrV7dav379+jz77LNlRAUAAAAAAAAAANqpUspSmzdvTpJ88IMf3OY6vb333jt77713Tj755NrYvHnzauWppqamTJs2LevXry8jKgAAAAAAAAAA0E6VUpYaNGhQZs6cuU1R6s0MHjw4gwcPzoc+9KEkSVEUefHFF3dkRAAAAAAAAAAAoJ17Z+2l39Pxxx+foigyffr097S+Uqlk2LBhbZwKAAAAAAAAAADoSEopS40bNy719fW55557sm7dujK2BAAAAAAAAAAAaKWUslTfvn3zpS99KStWrMiVV15ZxpYAAAAAAAAAAACtdCljk82bN+ecc85Jc3NzJk6cmM6dO+drX/taunbtWsb2AAAAAAAAAAAA5ZwsdfDBB+fMM89MS0tLdtlll/z4xz/OySefnDvvvDNr1qwpIwIAAAAAAAAAANDBlXKy1MaNG9Pc3Jzm5uba2JIlS3L55Zfn7/7u73LwwQdn9OjR+ZM/+ZM0NDSkV69eZcQCAAAAAAAAAAA6kFLKUieccEKmTZuWxYsXtxoviiIbN27Mk08+mSeffLI2vvfee6ehoSGNjY1paGjIqFGjUl9fX0bUVq688srcdtttufLKK3PWWWe96bwVK1bkiCOOeNv39e7dO1OmTNnusxdeeCH/8i//kilTpmTFihXp3bt3Ghoacu655+bYY499y/e2tLTkzjvvzL/9279l9uzZKYoie++9d0444YRccMEF6d2799tmAwAAAAAAAACA9q6UstR1112X5PVS0dYTpqrVapqbm7NkyZIURdFq/qJFi7Jo0aLcf//9SZJKpZLBgwfnvvvuKyNukuSBBx7I7bff/o7mVqvV32uvX/7yl7n44ovT0tJSG1u2bFkefPDBPPjggzn//PPz1a9+dbtrN2zYkAsvvDBPPfVUq/FZs2Zl1qxZufvuu3PTTTdl+PDhv1dGAAAAAAAAAAD4Y1dKWWqrPn365JhjjskxxxxTG1uxYkWtOLW1RPXSSy+1WlcURebOnVtazgcffDAXX3xxtmzZ8o7mT5s2LcnrJ2JNnjz5TedVKpVtxpqbm/OFL3whLS0taWxszKWXXpr9998/CxcuzHe/+9088MADue222zJ06NCcd95526wfP358nnrqqdTV1eVzn/tcTj311HTt2jUPPfRQ/uEf/iEvv/xyPvOZz+TnP/95dt1113f4FwAAAAAAAAAAgPanTctSixYtyt577/2u1vTp0yfHHntsq6vmthao3liiWrp0aVtG3a4tW7ZkwoQJuf76699xUSp5vfCUJI2NjenRo8e72vPqq6/Ohg0bss8+++T73/9+bf3uu++eCRMm5POf/3z+4z/+I9/5zncyduzYVtcRPvfcc7XTtr7yla9k3LhxtWdnn312RowYkXHjxmXRokX5/ve/n89+9rPvKhsAAAAAAAAAALQnndryZccff3yOO+64XHLJJb/Xe7YWqC666KJMmDAhDz30UB577LE2Srl9jz76aE4//fRcd9112bJlS0aNGvWO176xLPVuzJ49O4888kiS5NOf/vQ2RatKpZIvf/nLqVQqWblyZe1awq1uvvnmJK+faHX22Wdv8/7GxsacdtppSZKJEye+q2wAAAAAAAAAANDetGlZKkmWLl2ae++9t9XYueeem29+85uZNGlSZs+enaIo3vV7+/Tp01YRt+vCCy/MCy+8kLq6unz+85/P1Vdf/Y7WrVq1KosWLUry7stSW4tSlUolY8aM2e6cgQMH5oADDkiSPPDAA7Xxoijy6KOPJkmOO+64dO7cebvrjz/++CSvn/o1ffr0d5UPAAAAAAAAAADakza9hm/EiBGZMWPGNlfYPfPMM3n22WdrP3fv3j0jRoxIQ0NDRo0alYaGhgwbNqwto7xrlUolxx9/fL7whS9k2LBhWbhw4Ttat/VUqUqlkm7duuXrX/96Hn300bz88supr6/PQQcdlHPPPTd/+qd/us3areWlvfbaK3vssceb7jFy5Mg8//zztb2SZOHChVmzZk2SvOUpWCNGjKh9r1arrX4GAAAAAAAAAICOpE3LUj/96U+zbt26TJ06dZtnbzxNav369Xn22WdbFah22WWXjBgxolaeGjVqVKkFqvvuuy9Dhw591+u2Fpg6deqU8847L5s2bao9e+WVV/Lwww/n4Ycfzv/8n/8zV1xxRbp0+a8/+eLFi5O8fo3eWxkwYECS10/tamlpSV1dXe00q7db369fv3Tu3DmbN29utaYttbS07JD3thd1dXWpVCo7OwYA7HBFUfj/AgAAAAAAANrMjvi3pzYtSyXJrrvumiOOOKLV2D333JNqtZpqtZrm5uZMnz4969evbzVn3bp1eeaZZ/LMM8/UxnbZZZeMHDkyo0aNyqhRo/IXf/EXbR235r0UpZL/Kktt3rw5Q4cOzec+97l8m5p/AAAAIABJREFU4AMfSF1dXX7729/m2muvzfPPP5+777479fX1+cpXvlJb+8orryRJevbs+ZZ77Lbbbkle/wfINWvWpE+fPrW1SdKrV683XdulS5fssssuWbt2bVavXv2efse38/zzz++Q97YXjY2N6dq1686OAQA7XEtLS5qamnZ2DAAAAAAAAHhTbV6W2p5hw4Zl2LBhGTt2bJLXSz+zZ8+ulaeq1Wqef/757Raonn766Tz99NOpVCo7tCz1Xm3cuDE9evTIkCFDctttt6VHjx61ZyeccEKOPvrofPzjH89vf/vb3HbbbTnzzDNzwAEHJEk2bNiQ5PVrCd9Kt27dat+3rtn63//+fHu6d++etWvX5rXXXnt3vxwAAAAAAAAAALQjpZSl/rtKpZL99tsv++23X04//fQkyZYtW/Liiy+2OoFqewWqPzTXX399kmTTpk2trtjbqnv37vna176WM844I0VR5O67785ll12WJOncuXOSvO0VbW+8wrBTp06t1r6b9VvXtrUDDzwwdXV1O+Td7YG/DQAdRV1dXRobG3d2DAAAAAAAANqJlpaWNr/xbKeUpbanU6dO2y1QzZ49u3b61B+y7RWltmpoaEi/fv2ydOnSTJ06tTa+6667Jsnbnvi0cePG2vet17ltXZu0PmVqe7Y+f7sTqN6ruro618wBAKlUKv6fAAAAAAAAgD9ofzBlqe3p1KlT9t9//+y///61AtUfqwEDBmTp0qV55ZVXamP19fVJkjVr1rzl2tWrVyd5/e/Rq1evJMluu+1We/5W6zdt2lQ7nWv33Xd/b+EBAAAAAAAAAKAd2DH3snVAb7wqb3u2ng61yy671MaGDh2aJFmyZMlbrt36vH///rWr9IYMGVJ7vnjx4jddu3Tp0mzevDnJ64UtAAAAAAAAAADoqJSlfg/PPvtsxowZk/e///35t3/7tzedt3nz5sydOzdJ65LTAQcckCRZtGhR7fSo7Wlubk6SHHjggbWxvn371k6Kmj59+puunTZtWu37iBEj3vyXAQAAAAAAAACAdk5Z6vew9957Z/HixXnttdfyyCOPvOm8X/3qV3n11VeTJMcee2xtfOv3LVu25KGHHtru2gULFmTGjBlJkmOOOabVs+OOOy5J8tBDD2XLli3bXf/AAw8kSfbcc89WZSsAAAAAAAAAAOholKV+D3379s0RRxyRJLn33nvz9NNPbzNn2bJl+bu/+7skyV577ZUPfehDtWeDBg3KBz7wgSTJhAkTsmbNmlZri6LIt7/97RRFkd133z1jx45t9fz0009PksydOzd33nnnNns/99xzmTx5cpLk4x//eCqVynv9VQEAAAAAAAAA4I9em5alnnvuucyZM6ctX/kHb/z48enWrVu2bNmST33qU7nlllsyd+7cLFu2LJMnT84555yTRYsWpUuXLvnmN7+Zbt26tVp/2WWXpVOnTpk7d27OPffcPProo1mxYkWam5vzv//3/84vf/nLJMnnP//57Lrrrq3WHnHEERkzZkyS5Jvf/Gb+6Z/+KQsWLMiyZcsyceLEfPKTn8ymTZsycODAjBs3rpw/CAAA/5+9u4/WsqzzBf59YIOAKIiaiGAIEi97b0rczlGXyik81ozpYCKO03EqNdc0jcdsOlNnZfOyUs+M5ozl2KKcSlIsh5aaTNqMZiOaL4Ga7ufhLRAk40UBN/IOm32fP1o80x4wX87mpvb+fNa6l5vrvn7P9fX/77puAAAAAAAAfks1dOWP3XjjjfnpT3+aj370o/nsZz/blT/9W2vcuHG5+eab8+lPfzqbN2/O//2//7d+k9QeAwYMyHXXXZfTTjttr/nm5uZce+21+cIXvpAlS5bk0ksv3WvPxz72sXz4wx/e5/l/93d/l0svvTStra2ZMWNGZsyY0en9EUcckW984xsZOHDg/8f/JQAAAAAAAAAA/O7r0rLUihUrkiTDhw/vtP7qq6/msMMO68qjfqtMnjw5999/f2677bY8+uijeemll5IkRx99dM4444x85CMfybBhw153/kMf+lAaGxvzjW98I0899VTWr1+fAQMGpKmpKX/8x3+cM88883VnBw0alO985zv5zne+kzlz5mTZsmXZuXNnjjnmmLz3ve/Nxz/+8Rx++OFd/v8MAAAAAAAAAAC/a7q0LLV+/fokyeDBgzutn3LKKRk2bFiuv/76tLS0dOWR+83w4cOzePHiN73/qKOOymc/+9m3faPW2LFjc/3117+t2T59+uRP/uRP8id/8idvax4AAAAAAAAAAHqCXl35YwcddFCSZPny5Xu9W716dTZs2NCVxwEAAAAAAAAAALxpXXqz1LHHHpuFCxdmxowZefnll/P+978/kyZN6sojAAAAAAAAAAAA3pYuvVnqnHPOSZLs3r07s2fPzmWXXZaTTjqp/v6nP/1pFixYkPb29q48FgAAAAAAAAAA4A116c1SH/nIR7Js2bLcc8896ejoSJJOxahZs2Zl1qxZ6dOnT971rnelqakpjY2NaW5uzpgxY9K7d++ujAMAAAAAAAAAAFDXpWWp3r1759prr82f/dmf5eGHH84TTzyRZ555Jm1tbUmSoiiSJDt37kytVkutVqvP9u3bN2PHjq0XqJqamjJmzJj06tWll18BAAAAAAAAAAA9VJeWpfY45phjcvHFF+fiiy9OURQZP358kuS0007Ljh07snDhwmzevLnTzI4dO9La2prW1tb62kEHHZRx48bVy1PnnXfe/ogLAAAAAAAAAAD0APulLPXrKpVK/e/p06fnrLPOSpIsX768frtUtVrNggULsmXLlk6z27dvz3PPPZfnnnsulUpFWQoAAAAAAAAAAHjb9ntZKkmampqyZMmSTmvHHXdcjjvuuHzwgx+sry1fvjzVarVeotpXgQoAAAAAAAAAAODtKKUs9b3vfS/t7e1pb2//jfv2FKjOOeecJElRFJ1uoAIAAAAAAAAAAHi7SilLJUlDQ0MaGt7acZVKJaNGjcqoUaPqBSoAAAAAAAAAAIC3o7Sy1H+1fPnyrFmzJtu2bcshhxySY489NkcdddSBigMAAAAAAAAAAHRzpZaldu7cmW9961u54447sm7dur3eH3HEEXnve9+bCy64IM3NzWVGAwAAAAAAAAAAurleZR20YcOGXHTRRbnpppuybt26FEWx17Nu3brMnj0706dPzyc/+cmsXbu2rHgAAAAAAAAAAEA3V8rNUkVR5IorrkitVquvNTU1ZcyYMWloaMi6devS2tra6baphx9+OD/72c/y9a9/PY2NjWXEBAAAAAAAAAAAurFSylIPPPBAnn766VQqlRx//PH50pe+lLFjx+61r1qt5s4778z999+f7du3Z/369bn00ktz9913Z9iwYWVEBQAAAAAAAAAAuqlSPsN33333JUkGDx6cmTNn7rMolfzqtqnrrrsuc+bMyfjx45MkbW1tufrqq8uICQAAAAAAAAAAdGOllKWq1WoqlUqmT5+eIUOGvOH+ESNG5K677sppp52WJHniiScyb968/R0TAAAAAAAAAADoxkopS23cuDFJMmbMmDc907dv39x444059NBDkyT33nvvfskGAAAAAAAAAAD0DKWUpQYOHJjkP0tTb9agQYNy7rnnpiiKzJ8/f39EAwAAAAAAAAAAeohSylJHH310kuSpp556y7NNTU1JkjVr1nRpJgAAAAAAAAAAoGcppSx16qmnpiiKPPzww1m0aNFbmu3o6Ejyq8/yAQAAAAAAAAAAvF2llKWmTp2a3r17Z/fu3bnyyiuzevXqNz07b968JMnQoUP3VzwAAAAAAAAAAKAHKKUsdfzxx2fatGkpiiIrV67MBRdckIceeugN55599tnMmTMnlUolJ598cglJAQAAAAAAAACA7qqUslSSfP7zn09LS0uKosj69etzxRVX5Pzzz8/MmTOzaNGi+uf2kmTt2rX553/+51xyySVpb29PpVLJtGnTyooKAAAAAAAAAAB0Qw1lHdS3b998/etfz1//9V9nzpw5SZIFCxZkwYIFSZJevXrlkEMOya5du7J169YkSVEUqVQqueSSSzJ27NiyogIAAAAAAAAAAN1QaTdLJcmAAQNyww035MYbb8zo0aNTFEX92b17dzZu3JgtW7bU15Lkwx/+cK666qoyYwIAAAAAAAAAAN1QaTdL/bqzzz47Z599dh599NHMnTs38+fPz6pVq7Jp06b06tUrRx55ZE466aRceOGFaWlpORARAQAAAAAAAACAbuaAlKX2OP3003P66afX/73nRqlevUq98AoAAAAAAAAAAOgBDmhZ6r+qVCqpVCoHOgYAAAAAAAAAANANucIJAAAAAAAAAADoEZSlAAAAAAAAAACAHkFZCgAAAAAAAAAA6BGUpQAAAAAAAAAAgB5BWQoAAAAAAAAAAOgRlKUAAAAAAAAAAIAeoZSy1Gc+85nMmTMnu3fvLuM4AAAAAAAAAACAvZRSlnr44Yfzl3/5l7nqqqvKOA4AAAAAAAAAAGAvpX6G77TTTivzOAAAAAAAAAAAgLpSylLjx49PkuzatauM4wAAAAAAAAAAAPZSSlnqkksuSVEUueeee1IURRlHAgAAAAAAAAAAdFJKWWrKlCm58MILU61Wc/3115dxJAAAAAAAAAAAQCcNZRyydOnSXH311eno6Mhtt92Wtra2fP7zn8/AgQPLOB4AAAAAAAAAAKCcm6U++MEP5uSTT86KFSsyePDg3HvvvZkyZUq+/OUv54UXXigjAgAAAAAAAAAA0MOVcrNUkmzZsiXz58+v/3vjxo2ZMWNGZsyYkeOOOy4tLS2ZNGlSJk6cmFGjRpUVCwAAAAAAAAAA6CFKKUt9/vOfT61WS7VazQsvvJCOjo4kSVEUSZLly5dn+fLlmT17dpKkf//+mTBhQpqamtLY2JimpqYcd9xxZUQFAAAAAAAAAAC6qVLKUhdffHH9723btmXhwoWpVqup1Wqp1WqdClRJsnXr1jz99NN5+umn62sHH3xwp5upAAAAAAAAAAAA3orSPsO3R//+/TNp0qRMmjSpvrZt27YsWLCgfvtUrVbL8uXLOxWotmzZUnZUAAAAAAAAAACgGym9LLUv/fv3z4knnpgTTzyxvrZt27b6zVN7HgAAAAAAAAAAgLfrt6IstS/9+/dPS0tLWlpaDnQUAAAAAAAAAACgGzhgZaktW7Zkw4YN6du3bw4//PA0NPzW9rYAAAAAAAAAAIBuoNSG0s6dO3PHHXfk7rvvzrJlyzq9u/nmm3PmmWemo6MjvXr1KjMWAAAAAAAAAADQA5TWSnr55Zdz4YUX5oYbbsiyZctSFEX9SZKOjo4kybPPPpuzzjor3//+98uKBgAAAAAAAAAA9ACllKU6OjryyU9+MgsXLkxRFDn00ENz5pln5oILLthr78svv5yVK1fmc5/7XP7X//pf2b59exkRAQAAAAAAAACAbq6UstR9992X1tbWVCqVnHfeeXnkkUfyT//0T/niF7+4196tW7cmSYqiyIMPPpjLL788O3fuLCMmAAAAAAAAAADQjZVSlrr//vuTJGPHjs21116bfv36ve7e888/P1/72tfyzne+M0VRZN68ebnuuuvKiAkAAAAAAAAAAHRjpZSlFixYkEqlknPPPTe9er3xkZMnT86dd96Z0aNHpyiK3HXXXVm0aFEJSQEAAAAAAAAAgO6qlLJUW1tbkmTo0KFveubwww/PjBkzctBBByVJvvOd7+yXbAAAAAAAAAAAQM9QSllqwIABSZJXX331Lc2NGDEiH/zgB1MURX7605/uj2gAAAAAAAAAAEAPUUpZavTo0UmSn/zkJ295tqWlJUmyZs2aLs0EAAAAAAAAAAD0LKWUpd773vemKIo88sgjWbRo0Vua3b17d5KkoaFhf0QDAAAAAAAAAAB6iFLKUtOmTcvBBx+cjo6OXHnllVm3bt2bnv33f//3JMmwYcP2VzwAAAAAAAAAAKAHKKUsNWTIkFx11VUpiiIrV67Meeedlx/+8IdvOPe1r30tc+fOTaVSyRlnnFFCUgAAAAAAAAAAoLsq7dt2//N//s8sXbo03/3ud7Nu3bpcddVVueGGG+rvX3rppSxbtiwbN27MwoULc9999+X5559Pkhx00EGZPn16WVEBAAAAAAAAAIBuqLSyVJL8zd/8TUaOHJl/+Id/yM6dO7Nq1apUKpUkyQ033NCpPJUkRVEkSb7whS9kxIgRZUYFAAAAAAAAAAC6mVI+w/frPvrRj+b+++/P1KlT06dPnxRF8brPYYcdlq985Ss5//zzy44JAAAAAAAAAAB0M6XeLLXH8OHD83d/93f53Oc+l//4j//I008/nRUrVmTz5s055JBDcvTRR+f000/PlClT0r9//wMREQAAAAAAAAAA6GYOSFlqj8GDB2fq1KmZOnXqgYwBAAAAAAAAAAD0AKV/hg8AAAAAAAAAAOBAKP1mqZ///Od58MEHs3Dhwrz66qupVCoZPHhwjjnmmLS0tOTkk0/OwIEDy44FAAAAAAAAAAB0c6WVpTZv3py/+qu/ygMPPPC6e2bOnJl+/frlnHPOySc+8YkcffTRZcUDAAAAAAAAAAC6uVI+w7dr165cdtlleeCBB1IUxW98tm3bltmzZ+cP/uAPMmvWrDLiAQAAAAAAAAAAPUApN0vNmjUrP/vZz1KpVNLQ0JDp06fnf/yP/5Hhw4dnx44deeWVV1Kr1fLoo4/m6aefTnt7e7Zt25Zrrrkmy5cvz9VXX11GTAAAAAAAAAAAoBsrpSz1/e9//1eHNTTk1ltvzcknn9zp/fHHH59TTjkll112WTZs2JDbb789t912W7Zt25ZZs2blHe94Ry6//PIyogIAAAAAAAAAAN1UKZ/hW758eSqVSqZNm7ZXUeq/GjJkSK688srcc889OfbYY1MURW6++ea8+OKLZUQFAAAAAAAAAAC6qVLKUgcddFCSZNKkSW96ZuTIkbn11lvTv3//tLe35/bbb99f8QAAAAAAAAAAgB6glLLUhAkTkiSvvfbaW5p75zvfmT/8wz9MURSZO3fu/ogGAAAAAAAAAAD0EKWUpS688MIURZEf/vCHb3n2hBNOSJKsWbOmq2MBAAAAAAAAAAA9SJeWpaZMmZKrrroqt912W55++uns2LEjSfKBD3wgp512WubPn59Zs2a9pd/cuXNnkuSII47oyqgAAAAAAAAAAEAP09CVP/bLX/4yq1atqt8g1bt374wZMyYTJ07MqaeemqeffjrXXHNNXnnllfz5n/95Ghre+Ph///d/T6VSyaRJk7oyKgAAAAAAAAAA0MN0aVkqSYqiqP/d3t6eRYsWZdGiRZ3ef+1rX8t9992Xj33sYzn33HMzaNCgff7WjBkz8uijj6Zv3775+Mc/3tVRAQAAAAAAAACAHqRLy1Lz5s1LtVpNa2tr/VmzZk2nPZVKJUmyevXqXHfddfn7v//7vPvd78573vOeDB06NL169crq1avzyCOPZOnSpenXr19uvPHGjB07tiujAgAAAAAAAAAAPUyXlqUOOeSQnHLKKTnllFPqa+vWrcvzzz9fL09Vq9W0tbXV37e3t+eZZ57JM8880+m3iqLIiBEjcv311+eEE07oypgAAAAAAAAAAEAP1OWf4fuvjjjiiLzvfe/L+973vvraL37xi063T9VqtWzbtq3TXKVSyUsvvZQ//uM/zqGHHprx48fntttu299xAQAAAAAAAACAbmq/l6X2ZcSIERkxYkT+4A/+IMmvbpFaunRpWltb67dQLVmyJLt27UqSbNy4MU899dSBiAoAAAAAAAAAAHQTB6Qs9V9VKpWMGTMmY8aMyYc+9KEkyc6dO7No0aL6p/taW1sPcEoAAAAAAAAAAOB32W9FWWpf+vbtm4kTJ2bixIkHOgoAAAAAAAAAANANlFqW2rRpU5YsWZKiKDJixIgcddRRZR4PAAAAAAAAAAD0YKWVpa6//vrcfvvtaW9vr68deuihaWpq6vQcffTRZUUCAAAAAAAAAAB6kFLKUrNmzco3v/nNvdY3btyYxx9/PI8//nh9bciQIXsVqI488sgyYgIAAAAAAAAAAN1YKWWp2bNn1/8ePXp0RowYkZUrV2bFihXp6OjotHfDhg2ZO3du5s6dW1878sgj09zcnFtuuaWMuAAAAAAAAAAAQDdUSllqxYoVqVQqmTJlSm6++eZUKpUkybZt21Kr1VKtVlOtVlOr1bJixYoURdFp/uWXX87DDz9cRlQAAAAAAAAAAKCbKqUs1dDQkB07duTMM8+sF6WSpH///mlpaUlLS0t9bcuWLfUC1Z7/rly5soyYAAAAAAAAAABAN1ZKWWrkyJGp1Wppa2t7w70HH3xwfu/3fi+/93u/V1/bvHlzqtXq/owIAAAAAAAAAAB0c73KOOTss89OURR59NFH39b8wIEDc/LJJ3dxKgAAAAAAAAAAoCcppSx1wQUXZNiwYXn88cczf/78Mo4EAAAAAAAAAADopJSy1MCBA3P99denT58++Yu/+IusWrWqjGMBAAAAAAAAAADqSilLJUlLS0u+9rWvZf369TnvvPMyZ86cdHR0lHU8AAAAAAAAAADQw5VWlnr00UdzzTXXpL29PRs3bsxf/uVf5owzzshf/dVf5YEHHsiaNWvKigIAAAAAAAAAAPRADWUcMn/+/HziE5/I7t27U6lU6uvr1q3L7NmzM3v27CTJ4YcfnqampjQ1NaW5uTnNzc0ZMmRIGREBAAAAAAAAAIBurpSy1Fe/+tW0t7cnSfr06ZMhQ4bklVdeSVEUKYqivm/9+vV55JFH8sgjj9TXjj766HqB6vLLLy8jLgAAAAAAAAAA0A2VUpaq1WqpVCppamrKrbfemsGDB2f79u1ZsGBBqtVq/VmxYkU6Ojo6za5evTqrV6/Ogw8+qCwFAAAAAAAAAAC8baWUpfYUoC666KIMHjw4SdKvX79MmjQpkyZNqu/bunVrarVapwLVypUrUxRFp8/3AQAAAAAAAAAAvFWllKVGjRqV559/Pq+99tpv3DdgwICcdNJJOemkk+prmzdvTmtra2q12v6OCQAAAAAAAAAAdGO9yjjkgx/8YIqiyCOPPPKWZwcOHJhTTjkll1122X5IBgAAAAAAAAAA9BSllKUuvPDCjB49Ok8++WSefPLJMo4EAAAAAAAAAADopJSyVN++fXPLLbdk8ODB+cxnPpNf/OIXZRwLAAAAAAAAAABQV0pZ6uKLL86dd96ZqVOnZsOGDTn//PNz//33l3E0AAAAAAAAAABAkqShjEPmzZuX+fPn1//92muv5S/+4i9y880355xzzskZZ5yRCRMmpFevUrpbAAAAAAAAAABAD1RKWSpJiqKo/12pVFIURVasWJGbb745N998c/r165cJEyakqakpzc3NaWpqysiRI8uKBwAAAAAAAAAAdHOllKXuvvvu1Gq1tLa2plarZcmSJdm1a1enAtW2bdvyzDPP5JlnnqmvDRw4MI2NjWlqakpTU1M+8IEPlBEXAAAAAAAAAADohkopS02YMCETJkzIBRdckCTZtWtXlixZklqtlmq1mmq1mp///OfZtWtXp7lNmzblqaeeylNPPZVKpaIsBQAAAAAAAAAAvG2lfYbv1/Xp0yeNjY1pbGzM9OnTk/xngapardZLVPsqUAEAAAAAAAAAALwdB6QstS+/XqDaY9euXVm8eHFqtVpqtdoBTAcAAAAAAAAAAPyuK6UstWjRoowZMya9e/d+S3N9+vRJU1NTmpqa9lMyAAAAAAAAAACgpyilLDV16tQcdNBBGTt2bBobG9PU1JTGxsa8613vSq9evcqIAAAAAAAAAAAA9HClfYZv586daW1tTWtra31tT4Fqz+1RjY2NOf744xWoAAAAAAAAAACALldKWWrSpElZuHBhtm3b1ml9+/btef755/P888/X1/r169epQNXU1JTRo0enUqmUERUAAAAAAAAAAOimSilL3XnnnSmKIkuXLk21Wk1ra2uq1WoWLVqUnTt3dtq7bdu2PPfcc3nuuefqa/369cuECRMya9asMuICAAAAAAAAAADdUGmf4atUKhkzZkzGjBmT8847L0nS3t6eJUuW5Lnnnsv8+fPzk5/8JG1tbUmSoijqs9u2bcszzzxTVlQAAAAAAAAAAKAbKq0stc/DGxoyYcKETJgwIRdddFHa29vzr//6r/nqV7+alStXpqGhIR/60Ify4osvZsGCBQcyKgAAAAAAAAAA8Duu14EO8OsaGhoyderUfP/738/v//7vp729PcuWLcutt96aefPmHeh4AAAAAAAAAADA77DfqrLUHv3798+NN96Y008/Pc8880xuuummAx0JAAAAAAAAAAD4HfdbWZZKkl69euWaa65JQ0NDvv3tb2fZsmUHOhIAAAAAAAAAAPA77Le2LJUkRx11VCZPnpzdu3dn9uzZBzoOAAAAAAAAAADwO6yUstQTTzyRTZs2va3ZcePGpSiKzJ07t4tTAQAAAAAAAAAAPUlDGYd87GMfS6VSybHHHpumpqY0NzenqakpjY2N6d+//2+c3b59e5Jk9erVZUQFAAAAAAAAAAC6qVLKUnusXLkyK1euzP33358k6dWrV0aNGpWmpqZ6iWrcuHHp27dvkmT9+vX5wQ9+kCTp06dPmVEBAAAAAAAAAIBuppSy1JQpU1Kr1bJmzZpO67t3787SpUuzdOnS3HvvvUmS3r17Z/jw4Rk4cGBeeOGFbN26NZVKJZMmTSojKgAAAAAAAAAA0E2VUpa65ZZbkiQbNmxIa2trWltbU61WU61Ws27duk5729vb8+KLL3Za6927d/7sz/6sjKgAAAAAAAAAAEA3Vepn+IYMGZLJkydn8uTJ9bU1a9akWq2mtbU1tVotCxcuzPr16+vvhw4dmr/927/NxIkTy4wKAAAAAAAAAAB0M6V53wdsAAAgAElEQVSWpfZl6NChGTp0aM4888z6WltbW9asWZM+ffpk1KhRqVQqBzAhAAAAAAAAAADQHRzwstS+DB48OIMHDz7QMQAAAAAAAAAAgG6k14EOAAAAAAAAAAAAUIbSbpbavn177rzzzjzxxBPZtGlTjjzyyIwaNSrjxo1Lc3Nzhg8fXlYUAAAAAAAAAACgByqlLLV58+Z8+MMfzpIlS153z6BBg9Lc3NzpOeKII8qIBwAAAAAAAAAA9ACllKVuueWWLF68+DfuaWtry2OPPZbHHnusvnbUUUfVi1NNTU059dRT93dUAAAAAAAAAACgmyqlLPXggw8mSQ455JD8zd/8TSZMmJB169Zl8eLFqdVqqVareeGFF7J79+5Oc2vXrs3atWvz0EMPpVKpZMGCBWXEBQAAAAAAAAAAuqFSylJr165NpVLJRz7ykZx99tlJkuOOOy4nnXRSfc/27dtTq9XS2tqaarWaarWaF198MUVRlBERAAAAAAAAAADo5kopSx122GF55ZVXMnLkyNfd069fv5x44ok58cQT62ubN2+uF6eq1WoJSQEAAAAAAAAAgO6qlLJUY2Nj/uM//iPr1q17S3MDBw7MySefnJNPPnk/JQMAAAAAAAAAAHqKXmUcMm3atBRFkUceeaSM4wAAAAAAAAAAAPZSSllqypQpOfXUU/Pkk0/mySefLONIAAAAAAAAAACATkopSyXJtddemyFDhuQzn/lMli1bVtaxAAAAAAAAAAAASUoqS331q1/N4sWL87d/+7fZsmVLLrzwwtx5553ZvXt3GccDAAAAAAAAAACkoYxDvvKVr6RSqSRJKpVKOjo68sUvfjEzZszIueeem//+3/973v3ud6dPnz5lxAEAAAAAAAAAAHqgUspSSVIURf2/e4pTL7/8cr7xjW/kG9/4Rg466KCMGzcuTU1NaWpqSnNzc0aPHl1WPAAAAAAAAAAAoJsrpSz1/e9/P7VaLdVqNbVaLYsWLcqOHTuS/GeJavv27Xnuuefy3HPP1ecGDBiQCRMm1AtUZ599dhlxAQAAAAAAAACAbqiUstTYsWMzduzYfOhDH0qS7N69Oz//+c9Tq9XqJarFixfXC1R7bNmyJfPnz8/8+fNTqVSUpQAAAAAAAAAAgLettM/w/brevXtn3LhxGTduXM4///wk/1mg2nP7VK1W22eBCgAAAAAAAAAA4O04IGWpffn1AtW0adOS7F2gAgAAAAAAAAAAeLt+a8pS+7KvAhUAAAAAAAAAAMDb0etABwAAAAAAAAAAACiDshQAAAAAAAAAANAjKEsBAAAAAAAAAAA9grIUAAAAAAAAAADQIyhLAQAAAAAAAAAAPYKyFAAAAAAAAAAA0CMoSwEAAAAAAAAAAD1Cl5alHnvssbS1tXXlTwIAAAAAAAAAAHSJhq78scsuuyyVSiXDhw9Pc3Nz/WlsbEz//v278igAAAAAAAAAAIC3pEvLUnu89NJLeemll/LAAw8kSXr16pVRo0Z1KlCNGzcuDQ375XgAAAAAAAAAAIC9dGlb6ayzzkq1Ws2qVas6re/evTtLly7N0qVLc8899yRJ+vTpk3HjxnUqUI0ePbor4wAAAAAAAAAAANR1aVnqK1/5SpJkw4YNaW1trT/VajXr16/vtHfnzp3193sMGDAgEyZMyMSJE+sFqmOOOaYrIwIAAAAAAAAAAD3UfvkO3pAhQzJ58uRMnjy5vrZq1apOBaparZbNmzd3mtuyZUvmz5+f+fPn19cGDx6c5ubmTJw4MX/+53++P+ICAAAAAAAAAAA9wH4pS+3LsGHDMmzYsLz//e+vry1btqx+81Rra2sWLVqUHTt2dJpra2vL3Llz8+ijjypLAQAAAAAAAAAAb1tpZal9GT16dEaPHp2pU6cmSdrb27N48eJON1AtW7Ysu3fvPpAxAQAAAAAAAACAbuCAlqX+q4aGhjQ2NqaxsTF/9Ed/lCTZvn17arVaWltbD3A6AAAAAAAAAADgd9lvVVlqX/r165cTTzwxJ5544oGOAgAAAAAAAAAA/A4rtSz16quv5p577smLL76Y/v37Z+jQoXnXu96VxsbGDBo0qMwoAAAAAAAAAABAD1NaWWrRokW55JJL8uqrr+7z/ciRI/Oe97wn73nPezJx4sSMHj06ffv2rb/fvHlzBg4cWFZcAAAAAAAAAACgmymlLLVjx45ceeWV2bBhw+vuWbFiRVasWJF77703SdK7d++84x3vyKBBg7Jx48asXbs2tVqtjLgAAAAAAAAAAEA3VEpZ6r777suLL76YSqWSlpaW/NEf/VGS5Nlnn80dd9yRJCmKotNMe3t7Vq1aldWrV6coilQqlTKiAgAAAAAAAAAA3VQpZakHH3wwSfKOd7wj3/zmN9OnT58kydlnn50f/vCHOfTQQ3Pttddm/vz5+dGPfpSf/exn9XLUnhLVYYcdVkZUAAAAAAAAAACgm+pVxiELFy5MpVLJ1KlT60WpPY477rgsX748I0eOzMc//vF897vfzb/8y79k5MiRKYoiffv2zezZs/P444+XERUAAAAAAAAAAOimSilLtbW1JUlGjBix17vBgwcnSVauXFlfmzhxYu6+++40NjZm165dufbaa8uICQAAAAAAAAAAdGOllKV69+6dJPVP6/26PTdNrVmzptN6//798+UvfzkNDQ157rnn8uMf/3j/BwUAAAAAAAAAALqtUspShx9+eJLkF7/4xV7v9hSp1q5du9e74cOH5wMf+ECKosicOXP2b0gAAAAAAAAAAKBbK6Us1djYWC88dXR0dHq352apVatW7XN20qRJSZJqtbp/QwIAAAAAAAAAAN1aKWWps846K8mvClF///d/3+ndoYcemqIo8vOf/3yfswMHDkySvPLKK/s3JAAAAAAAAAAA0K2VUpb6/d///YwcOTJJ8u1vfzuXXnppli1bliT19WeffTabN2/ea3bp0qVJsteNVAAAAAAAAAAAAG9FKWWp3r175x//8R/Tr1+/FEWRxx9/PD/5yU+SJKecckqSZNu2bfnSl77Uae6ll17KnXfemUqlkmHDhpURFQAAAAAAAAAA6KYayjpo/Pjx+fa3v51PfepTWbVqVY444ogkyTvf+c6ccsopeeKJJ3LXXXelVqvltNNOy4YNG3L//fdn06ZNqVQqmTJlSllRAQAAAAAAAACAbqi0slSSNDc35wc/+EG++93vZtSoUfX1q6++OtOnT8/WrVtTrVZTrVY7zQ0dOjSXXXZZmVEBAAAAAAAAAIBuppTP8P26fv365aMf/WjGjRtXXxs9enRmzpyZkSNHpiiKTk9TU1NmzpyZwYMHlx0VAAAAAAAAAADoRkq5WWrWrFlpbm7OhAkT0tCw7yObmprywAMPZP78+XnhhRfS0dGR8ePH593vfncZEQEAAAAAAAAAgG6ulLLUF7/4xVQqlXz5y1/OWWed9Rv3trS0pKWlpYxYAAAAAAAAAABAD9JlZam1a9fm//yf/5MxY8akubk5TU1NGTlyZFf9PAAAAAAAAAAAwP+XLitLHXXUUTnxxBNz8803p1KpJEkGDhyYxsbG+p7XXnvtLf/uvffem2effTaTJ0/O+973vq6KCwAAAAAAAAAA9DC9uvLHLr/88owcOTJFUaQoimzatClPPfVUKpVKiqLIF77whfy3//bfcumll+Yf/uEf8m//9m/55S9/+Rt/syiK3HXXXbnmmmu6MioAAAAAAAAAANDDdNnNUknSp0+f3HTTTfne976X1tbWLFq0KDt27KjfNFUURTZu3JjHH388jz/+eH1u0KBBaWxsTFNTU5qamjJhwoQcc8wxSZK2trYkySuvvNKVUQEAAAAAAAAAgB6mS8tSSTJu3LhcffXVSZL29vYsXrw4559/fiqVSo444oi8+uqraW9v7zTT1ta2V4HqkEMOydChQ/PCCy8kSQ4//PCujgoAAAAAAAAAAPQgXV6W6vTjDQ1pbGys//sLX/hCJk+enAULFqRardaf5cuXp6Ojo9Psa6+9lk2bNqUoilQqlZx44on7MyoAAAAAAAAAANDN7dey1B6nnHJKarVakuSggw7KCSeckBNOOKH+fuvWrfUCVWtra6rValauXJmiKJIkRx55ZD71qU+VERUAAAAAAAAAAOimSilLfetb30qS7Nq1a5/vBwwYkJaWlrS0tNTXtmzZkmXLlmXHjh1pbGzMgAEDyogKAAAAAAAAAAB0U6WUpfbo06fPm9578MEHZ+LEifsxDQAAAAAAAAAA0JOUWpb6dcuXL8+aNWuybdu2HHLIITn22GNz1FFHHag4AAAAAAAAAABAN1dqWWrnzp351re+lTvuuCPr1q3b6/0RRxyR9773vbngggvS3NxcZjQAAAAAAAAAAKCb61XWQRs2bMhFF12Um266KevWrUtRFHs969aty+zZszN9+vR88pOfzNq1a8uKBwAAAAAAAAAAdHOl3CxVFEWuuOKK1Gq1+lpTU1PGjBmThoaGrFu3Lq2trZ1um3r44Yfzs5/9LF//+tfT2NhYRkwAAAAAAAAAAKAbK6Us9cADD+Tpp59OpVLJ8ccfny996UsZO3bsXvuq1WruvPPO3H///dm+fXvWr1+fSy+9NHfffXeGDRtWRlQAAAAAAAAAAKCbKuUzfPfdd1+SZPDgwZk5c+Y+i1LJr26buu666zJnzpyMHz8+SdLW1parr766jJgAAAAAAAAAAEA3VkpZqlqtplKpZPr06RkyZMgb7h8xYkTuuuuunHbaaUmSJ554IvPmzdvfMQEAAAAAAAAAgG6slLLUxo0bkyRjxox50zN9+/bNjTfemEMPPTRJcu+99+6XbAAAAAAAAAAAQM9QSllq4MCBSf6zNPVmDRo0KOeee26Kosj8+fP3RzQAAAAAAAAAAKCHKKUsdfTRRydJnnrqqbc829TUlCRZs2ZNl2YCAAAAAAAAAAB6llLKUqeeemqKosjDDz+cRYsWvaXZjo6OJL/6LB8AAAAAAAAAAMDbVUpZaurUqendu3d2796dK6+8MqtXr37Ts/PmzUuSDB06dH/Fe13XXHNNxo4dm9mzZ7/h3l27duXb3/52pk2blhNOOCHvec97cvbZZ+cf//Ef09bW9obzixcvzv/+3/87Z5xxRpqamnLaaaflT//0TzN37tz9fjYAAAAAAAAAAPQEpZSljj/++EybNi1FUWTlypW54IIL8tBDD73h3LPPPps5c+akUqnk5JNPLiHpf/rRj36UWbNmvam9O3bsyMc+9rFce+21aW1tzdatW7Nt27YsXbo0M2bMyDnnnJMlS5a87vxDDz2U888/P/fdd1/Wrl2bXbt25ZVXXsmPf/zjfPzjH88111yz384GAAAAAAAAAICeopSyVJJ8/vOfT0tLS4qiyPr163PFFVfk/PPPz8yZM7No0aL65/aSZO3atfnnf/7nXHLJJWlvb0+lUsm0adPKipof//jH+dSnPtUp02/y2c9+NvPmzUufPn1y1VVX5Uc/+lEeffTRfPGLX8yhhx6al19+OX/6p3+arVu37jVbq9Xy6U9/Ort27Upzc3Nuv/32PPnkk/ne976XKVOmJEluv/321y1u/f+cDQAAAAAAAAAAPUlDWQf17ds3X//61/PXf/3XmTNnTpJkwYIFWbBgQZKkV69eOeSQQ7Jr1656sacoilQqlVxyySUZO3bsfs/Y0dGRW265JV/96lffdFHq+eefzwMPPJDkV4Wwiy66qP5u+vTpGT9+fC666KL88pe/zMyZM/OJT3yi0/xNN92UHTt25Nhjj83MmTNz8MEHJ0kOO+yw3HLLLbniiivy4IMP5itf+Ur+8A//MAMHDuyyswEAAAAAAAAAoCfp0pul7r333vziF7943fcDBgzIDTfckBtvvDGjR49OURT1Z/fu3dm4cWO2bNlSX0uSD3/4w7nqqqu6MuY+PfbYY5k6dWr+6Z/+KR0dHWlsbHxTc9/85jeTJMccc0ymT5++1/vm5uacc845SZLZs2d3erds2bLMnTs3SXL55ZfXi1J7VCqVfO5zn0ulUklbW1v+7d/+rcvOBgAAAAAAAACAnqZLy1Kf+9zn8v73vz8PPfTQb9x39tln5wc/+EFuvfXWXHzxxRk/fnwGDRqUSqWS3r17Z+jQoTnnnHNyxx135Oqrr07v3r27MuY+XXrppVm8eHH69OmTK664IjfddNMbzhRFkcceeyxJMnny5NfNuedzer/85S+zcOHC+vqeolSlUsn73ve+fc4OHz68fqvWj370oy47GwAAAAAAAAAAepou/wxfURRv+hN2p59+ek4//fROs0VRpFevLu1wvSmVSiVTpkzJpz/96YwePTovvfTSG8689NJL2bRpU5L8xpuoxo8fX/+7Wq3W/72nvDR06NAcfvjhrzs/YcKELFq0KLVarcvOBgAAAAAA4P+xd7+hXtb3H8dfRz1mJ/V0sn96dGSwtPSMgRA2yrLa2qitFhFlzRauiLZgLFiLTRYbhuRGY8MSNoPYjQOLxpgbscqdQ0QRbXdS+yPaBFPTpqYnKj3q+d2I813np6d/55ws348HHLi8rutzfT7fL944cJ58LgAAqhn2WGoompqa0tTUdFTmfuyxxzJ9+vSPNWbLli2N4/b29kHvO+200zJ69OgcPHhwwJitW7d+6NgkmTJlSpJk+/bt6e3tTXNz85DnHk69vb0j8txjRXNz81H7fw0An6a+vj6/FwAAAAAAADBsRuJvT5+pWOpo+rihVJLs3r27cdza2jrofWPGjMnxxx+ft956K3v37j1s/MSJEz9wngkTJiR57w+QPT09Oemkk4Y893B6+eWXR+S5x4qOjo6MHTv2aC8DAEZcb29v1qxZc7SXAQAAAAAAAIP69N93dwzZt29f4/i44477wHvHjRuXJHn33XcPG99/bTDvf3b/mKHODQAAAAAAAAAA1dhZaghGjx7dOP6w16z19fUlSUaN+l+f1j/+o459//ihzj2cZs6cmebm5hF59rHAdwNAFc3Nzeno6DjaywAAAAAAAOAY0dvbO+xvPBNLDUFLS0vj+P07PR1J//X37wLVP/7Ddnzav39/47j/dW5DnXs4NTc3e80cAJCmpia/EwAAAAAAAPCZNiJbDe3du3ckHvuZM2HChMZxT0/PoPcdOHAg77zzTpKkra2tcX78+PEfOjb53/c5atSotLa2DsvcAAAAAAAAAABQzYjEUosXL87cuXOzaNGi3H///XnyySfz+uuvj8RUR9UZZ5zRON66deug923fvj0HDx5MkkyZMqVxfvr06UmSbdu2feA8/dcnT57ceJXeUOcGAAAAAAAAAIBqRuQ1fH19fXnzzTfzzDPP5JlnnmmcP+mkkzJ79uwBP6eccspILOFTceqpp6atrS27d+/OSy+9lKuuuuqI97344ouN47PPPrtxPGPGjCTJli1bsnfv3kycOPGI49etW5ckmTlz5rDNDQAAAAAAAAAA1YzIzlL9+vr6Bvzs3LkzTz31VB544IHcfvvtmTdvXubNm5fbb789DzzwQJ566qns2rVrJJc07C688MIkSXd3dw4dOnTEe1avXp0kOeWUUwYET/PmzUuSHDp0KN3d3Uccu3nz5qxfvz5JcsEFFwzb3AAAAAAAAAAAUM2I7Cx17733ZsqUKVm7dm3j57XXXkvyXkD1fjt27EhXV1e6uroa5yZPnnzYDlSD7bp0tF111VX5y1/+kk2bNqWzszM33HDDgOsvvPBCVq1alSS56aab0tTU1Lg2bdq0zJkzJ//+97+zfPnyzJ8/PxMmTGhc7+vry9KlS9PX15e2trZceeWVwzY3AAAAAAAAAABUMyKx1Pjx4zN37tzMnTu3cW7Pnj1Zs2bNgIDq9ddfT3J4QLVt27Zs27YtTzzxROPctGnT8vjjj4/EcofkvPPOy8UXX5x//vOfWbJkSXbs2JFrrrkm48aNS3d3d5YtW5YDBw5k6tSpuf766w8bf/fdd+faa6/Npk2bsmDBgtx1110555xzsm3btixfvryxM9Qdd9yRlpaWYZ0bAAAAAAAAAAAqGZFY6khaW1tz/vnn5/zzz2+c27lz52EB1X//+9/D4qnkvdfRfVYtXbo0ixYtypo1a7JixYqsWLFiwPWTTz45K1euzPjx4w8b29HRkSVLlmTx4sVZv359Fi1adNg9N99882G7Rg3H3AAAAAAAAAAAUMmnFksdyaRJk3LRRRfloosuapx7/fXXs3bt2gER1Z49e47eIj+C1tbWdHZ2prOzM6tWrcrGjRuzf//+tLe3Z/78+bnlllsyadKkQcdfffXVmTVrVlauXJnnnnsuO3fuTEtLS2bPnp0FCxbk0ksvHbG5AQAAAAAAAACgiqMaSx3J6aefntNPP31AILR58+asXbv2U13H1KlT88orr3zk+5ubm7Nw4cIsXLjwE803Y8aM3HfffZ9o7FDnBgAAAAAAAACACj5zsdSRTJs2LdOmTTvaywAAAAAAAAAAAD7HRh3tBQAAAAAAAAAAAHwahnVnqXvuuSf/+te/MnHixOF8LAAAAAAAAAAAwJANayx13XXX5brrrhvORwIAAAAAAAAAAAwLr+EDAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAJaO2RsAACAASURBVAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWMOdoLOBbcc8896ezs/ND7Fi9enBtvvHHAud7e3nR2duavf/1rNm7cmL6+vrS3t+fSSy/NzTffnBNPPPEDn/nKK6/kD3/4Q5577rns2rUrJ554YmbPnp0FCxZk3rx5Q/pcAAAAAAAAAABwLBFLDYMXX3zxE43bt29fFi1alOeff37A+Q0bNmTDhg3585//nJUrV+ass8464vgnn3wyP/zhD9Pb29s498Ybb6SrqytdXV35zne+k5/97GefaG0AAAAAAAAAAHCsEUsN0cGDB/PKK68kSX7xi1/kiiuuGPTesWPHDvj3XXfdleeffz7Nzc35wQ9+kCuuuCJjx45Nd3d3li1blh07duS2227L3/72t7S0tAwYu27duvzoRz9Kb29vOjo68uMf/zhf/OIX89prr+XBBx/M6tWr88c//jHTp0/PDTfcMPwfHAAAAAAAAAAAPmdGHe0FfN5t2LAh7777bpJkzpw5OeGEEwb9aW5ubox74YUX8thjjyVJfvrTn+a2227L1KlTc+qpp+baa6/NQw89lObm5mzZsiUPP/zwYfP+5je/yb59+/KFL3whDz/8cM4999y0tbWlo6Mjy5cvz1e/+tUkyW9/+9u89dZbn8I3AQAAAAAAAAAAn21iqSHqfwVfS0tLzjzzzI887qGHHkqStLe359prrz3sekdHR775zW8mSR555JEB1zZu3JinnnoqSXLrrbfmhBNOGHC9qakpP/nJT9LU1JQ333wz//jHPz76BwIAAAAAAAAAgGOUWGqI1q1blySZPXt2Ro36aF9nX19fnn766STJhRdemNGjRx/xvksuuSRJsmXLlrz00kuN8/2hVFNTUy6++OIjjp06dWpmzJiRJFm9evVHWhcAAAAAAAAAABzLxFJD1B9LzZw5M3/6059y4403Zs6cOfnSl76Ub3zjG/nVr36V3bt3Dxjz2muvpaenJ0kya9asQZ999tlnN47Xrl3bOO4Pp04//fRMmjRp0PHnnHPOgDUCAAAAAAAAAEBlY472Aj7PDh06lJdffjlJ0tnZmd7e3gHXX3311bz66qt59NFH8+CDD+bLX/5ykvd2iurX3t4+6PNPO+20jB49OgcPHhwwZuvWrR86NkmmTJmSJNm+fXt6e3vT3Nz8MT7dR/f/PzcDNTc3p6mp6WgvAwBGXF9fn98LAAAAAAAAGDYj8bcnsdQQ/Oc//8nbb7+dJDlw4EAWLFiQa665JlOmTMkbb7yRVatW5aGHHsquXbty66235tFHH820adMG7DTV2to66PPHjBmT448/Pm+99Vb27t3bON8/fuLEiR+4vgkTJiR57w+XPT09Oemkkz7xZ/0g/cEYR9bR0ZGxY8ce7WUAwIjr7e3NmjVrjvYyAAAAAAAAYFBewzcEO3bsyOTJkzN69OgsW7YsP//5zzNr1qy0tbXlrLPOyp133pn7778/SbJnz54sW7YsSbJv377GM4477rgPnGPcuHFJknfffbdxrn98/7XBvP/Z758TAAAAAAAAAAAqsrPUEJx33nnp7u7OgQMHMmbMkb/Kr33ta5k/f366urryxBNPZM+ePRk9enTj+oe9nq2vry9JMmrU/7q2/vEfdez/Hz/cZs6cOWKv+DsW+G4AqKK5uTkdHR1HexkAAAAAAAAcI3p7e4f9jWdiqWEwWCjV75JLLklXV1cOHTqUtWvXpqWlpXHtw3Z86r/+/l2i+se/f7epI9m/f3/jeCRfA9fc3Ow1cwBAmpqa/E4AAAAAAADAZ5rX8H0KJk+e3DjetWtXJkyY0Ph3T0/PoOMOHDiQd955J0nS1tbWOD9+/PgPHZske/fuTfLerlKtra0ff+EAAAAAAAAAAHAMEUsNg/e/7u5Ient7G8fHH398zjjjjMa/t27dOui47du35+DBg0mSKVOmNM5Pnz49SbJt27YPnLf/+uTJk0f0NXwAAAAAAAAAAPB5oKAZgjvvvDNz587N17/+9Q+8b8OGDY3j6dOn59RTT23sFPXSSy8NOu7FF19sHJ999tmN4xkzZiRJtmzZ0tg96kjWrVuXJJk5c+YHrg8AAAAAAAAAACoQSw3B+PHjs3v37mzatCmbNm064j19fX35+9//niRpb2/PmWeemSS58MILkyTd3d05dOjQEceuXr06SXLKKacMCJ7mzZuXJDl06FC6u7uPOHbz5s1Zv359kuSCCy74eB8MAAAAAAAAAACOQWKpIfjWt77VOP7lL395xHt+//vfN3aPWrRoUZqampIkV111VZJk06ZN6ezsPGzcCy+8kFWrViVJbrrppsa4JJk2bVrmzJmTJFm+fHl6enoGjO3r68vSpUvT19eXtra2XHnllZ/0IwIAAAAAAAAAwDFDLDUEc+bMyeWXX54kefrpp/Pd7343zz//fHbt2pWXX345ixcvzq9//eskybnnnpvrr7++Mfa8887LxRdfnCRZsmRJ7r///mzevDlvvPFGHnnkkXzve9/LgQMHMnXq1AHj+t19990ZNWpUNm3alAULFuTpp5/Orl27sm7dunz/+9/Pk08+mSS544470tLSMtJfBQAAAAAAAAAAfOaNOdoL+Ly799578/bbb6erqyvPPvtsnn322cPu+cpXvpLf/e53GTVqYJu2dOnSLFq0KGvWrMmKFSuyYsWKAddPPvnkrFy5MuPHjz/smR0dHVmyZEkWL16c9evXZ9GiRYfdc/PNN+eGG24Y4icEAAAAAAAAAIBjg1hqiMaNG5cHH3wwjz/+eB599NGsWbMmPT09aW1tzcyZM/Ptb387l19++YDX6PVrbW1NZ2dnOjs7s2rVqmzcuDH79+9Pe3t75s+fn1tuuSWTJk0adO6rr746s2bNysqVK/Pcc89l586daWlpyezZs7NgwYJceumlI/nRAQAAAAAAAADgc0UsNQyamppy2WWX5bLLLvvYY5ubm7Nw4cIsXLjwE809Y8aM3HfffZ9oLAAAAAAAAAAAVDLqw28BAAAAAAAAAAD4/BNLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAACgBLEUAAAAAAAAAABQglgKAAAAAAAAAAAoQSwFAAAAAAAAAACUIJYCAAAAAAAAAABKEEsBAAAAAAAAAAAliKUAAAAAAAAAAIASxFIAAAAAAAAAAEAJYikAAAAAAAAAAKAEsRQAAAAAAAAAAFCCWAoAAAAAAAAAAChBLAUAAAAAAAAAAJQglgIAAAAAAAAAAEoQSwEAAAAAAAAAACWIpQAAAAAAAAAAgBLEUgAAAAAAAAAAQAliKQAAAAAAAAAAoASxFAAAAAAAAAAAUIJYCgAAAAAAAAAAKEEsBQAAAAAAAAAAlCCWAgAAAAAAAAAAShBLAQAAAAAAAAAAJYilAAAAAAAAAACAEsRSAAAAAAAAAABACWIpAAAAAAAAAID/a+++o6yszv2Bf2dgUIo0QUUE7KAi9naDYNTYYkHFxHK9QuwaE1vsmmau5ho1JjESNMYWr16NaC52FAvWZYkFRVREARtVOlN/f/DjXEaKoANn8Hw+a7HWnvPu99nPOWslnjXzffcGSoKwFAAAAAAAAAAAUBKEpQAAAAAAAAAAgJIgLAUAAAAAAAAAAJQEYSkAAAAAAAAAAKAkCEsBAAAAAAAAAAAlQVgKAAAAAAAAAAAoCcJSAAAAAAAAAABASRCWAgAAAAAAAAAASoKwFAAAAAAAAAAAUBKEpQAAAAAAAAAAgJIgLAUAAAAAAAAAAJQEYSkAAAAAAAAAAKAkCEsBAAAAAAAAAAAlQVgKAAAAAAAAAAAoCcJSAAAAAAAAAABASRCWAgAAAAAAAAAASoKwFAAAAAAAAAAAUBKEpQAAAAAAAAAAgJIgLAUAAAAAAAAAAJQEYSkAAAAAAAAAAKAkCEsBAAAAAAAAAAAlQVgKAAAAAAAAAAAoCcJSAAAAAAAAAABASRCWAgAAAAAAAAAASkLTYjcAAAAAK0pNbW0mTZ9T7DZYxXVo3TxNyj1vBgAAAADfBsJSAAAAfGtNmj4n+/3mH8Vug1XcAxcemrXbtix2GwAAAABAA/BYJAAAAAAAAAAAUBKEpQAAAAAAAAAAgJIgLAUAAAAAAAAAAJQEYSkAAACApWjfavVitwAAAAAANBBhKQAAAIClKC8vK3YLAAAAAEADaVrsBgAAAABWBXW1NamZOanYbbCKa7LGWikrE8ADAAAAKBZhKQAAAIBlUDNzUj7+/Z7FboNVXJeL/pWUNUlNbU0mz55c7HZYxa3ZYs00KW9S7DYAAABglSIsBQAAAAAr2eTZk3Pw3w4udhus4oYMHJK1Wq1V7DYAAABglVJe7AYAAAAAAAAAAABWBmEpAAAAAAAAAACgJAhLAQAAAAAAAAAAJUFYCgAAAAAAAAAAKAnCUgAAAAAAAAAAQEkQlgIAAAAAAAAAAEqCsBQAAAAAAAAAAFASmha7AQAAAAAAAL5damprM2n6nGK3wSquQ+vmaVJu7wcAoGEJSwEAAAAAANCgJk2fk/1+849it8Eq7oELD83abVsWuw0A4FtGFBsAAAAAAAAAACgJwlIAAAAAAAAAAEBJEJYCAAAAAAAAAABKgrAUAAAAAAAAAABQEoSlAAAAAAAAAACAkiAsBQAAAAAAAAAAlARhKQAAAAAAAAAAoCQISwEAAAAAAAAAACVBWAoAAAAAAAAAACgJwlIAAAAAAAAAAEBJEJYCAAAAAAAAAABKgrAUAAAAAAAAAABQEoSlAAAAAAAAAACAkiAsBQAAAAAAAAAAlARhKQAAAAAAAAAAoCQISwEAAAAAAAAAACVBWAoAAAAAAAAAACgJwlIAAAAAAAAAAEBJEJYCAAAAAAAAAABKgrAUAAAAAAAA0Oi0b7V6sVsAAL6FhKUAAAAAAACARqe8vKzYLQAA30JNi90AAAAAAAAAwJLU1dakZuakYrfBKq5Jqw4pK29S7DYAaASEpQAAAAAAAIBGq2bmpHz8+z2L3QaruHVPH5amrdcudhsANAKO4QMAAAAAAAAAAEqCnaUAAAAAAAAAAJZDXU1NKidPLnYbrOKarblmypo4InRlE5YCAAAAAAAAAFgOlZMn5/n+hxW7DVZxO999V1Zba61it1FyHMMHAAAAAAAAAACUBGEpAAAAAAAAAACgJAhLAQAAAAAAAAAAJUFYCgAAAAAAAAAAKAnCUgAAAAAAAAAAQEkQlgIAAAAAAAAAAEqCsBQAAAAAAAAAAFAShKUAAAAAAAAAAICSICwFAAAAAAAAAACUBGEpAAAAAAAAAACgJAhLAQAAAAAAAAAAJUFYCgAAAAAAAAAAKAlNi90A39w777yTG264IS+88EKmTJmStm3bpmfPnjnyyCPTp0+fYrcHAAAAAAAAAACNgrDUKm7YsGE5/fTTU1VVVXht4sSJGT58eIYPH56jjz46F110URE7BAAAAAAAAACAxsExfKuwkSNH5swzz0xVVVW23HLL3HrrrXn++edz9913Z4899kiS3Hrrrfn73/9e5E4BAAAAAAAAAKD4hKVWYb///e8zb968dO3aNTfffHN23HHHtGvXLltuuWWuvfbafO9730uS/OEPf8jMmTOL3C0AAAAAAAAAABSXsNQq6v33389TTz2VJDnhhBPSsmXLetfLyspy3nnnpaysLNOmTcvDDz9cjDYBAAAAAAAAAKDREJZaRS0ISpWVlWX33Xdf7Jz11lsv3bt3T5I89thjK603AAAAAAAAAABojISlVlFvv/12kmSdddbJmmuuucR5m2++eZJk5MiRK6UvAAAAAAAAAABorJoWuwG+no8//jhJ0rlz56XOW3fddZMkn332WaqqqlJRUdEg69fV1RXGs2fPTlVVVYPU/TZq2rRpysrKUl1dlbbN/U+Ob6aysjJNystTXVWdmtXbFbsdVmGVlZUpK2+S6qrqtG3WttjtsIqrrqpOZWVl6urqUl1dXex2AJL4Hk7D8j2chuS7OA3Jd3GgMfJdnIbkuzgNqbKqOrW+O9EAFvy3rqq6OmVt2hS7HVZxVdXVKfP/TUu1cB5l4azKN1FW11CVWKm+//3v57333svuu++e6667bonzbrrpplx22WVJkueeey7t27dvkPVnzZqVUaNGNUgtAAAAAAAAAABYmh49eqRly5bfuI5j+FZR8+bNS5KsvvrqS5232mqrLXIPAAAAAAAAAACUIvufrqKaNGmSJCkrK1vqvIU3Disvb7hsXPPmzdOjR48k/7fNIAAAAAAAAAAANJSFjyhs3rx5g9QUllpFtWjRIkkyd+7cpc6rrKwsjJs1a9Zg65eXlzfI1mYAAAAAAAAAALAkC5+q1hAcw7eKatWqVZJkxowZS503ffr0JPPDTW3atFnhfQEAAAAAAAAAQGMlLLWK2mCDDZIkn3zyyVLnLbjeqVOnBj2GDwAAAAAAAAAAVjXSM6uo7t27J0kmTJhQ2D1qcUaOHJkk6dGjx0rpCwAAAAAAAAAAGithqVVUnz59kiS1tbV54oknFjtn3LhxGT16dJJk1113XVmtAQAAAAAAAABAoyQstYrq0qVLtttuuyTJtddemxkzZtS7XldXl8svvzx1dXVp165dDjrooGK0CQAAAAAAAAAAjYaw1Crs/PPPT3l5ecaOHZsjjzwyI0aMyJQpUzJy5MiceuqpGTZsWJLktNNOS4sWLYrcLQAAAAAAAAAAFFdZXV1dXbGb4Ou75557cvHFF6e6unqx1wcOHJjzzjtvJXcFAAAAAAAAAACNj7DUt8A777yTv/71r3nhhRcyefLktGjRIj179syRRx6ZPffcs9jtAQAAAAAAAABAoyAsBQAAAAAAAAAAlITyYjcAAAAAAAAAAACwMghLAQAAAAAAAAAAJUFYCgAAAAAAAAAAKAnCUgAAAAAAAAAAQEkQlgIAAAAAAAAAAEqCsBQAAAAAAAAAAFAShKUAAAAAAAAAAICSICwFAAAAAAAAAACUhKbFbgAAqG/33XfPhAkTsuOOO+bWW29tkHtqamrywAMP5LHHHssbb7yRKVOmJEnatWuXTTfdNLvuumsOOuigtGrVapn7/Oyzz7LbbrultrY2ZWVlefjhh9OtW7dlvh8AABqrysrKPPzww3niiSfy1ltv5dNPP01lZWXatGmTddZZJzvttFP22WefbLXVVou9/5577sn555+/xPrNmjVLmzZt0qVLl3znO9/JUUcdlXbt2i127tFHH50XX3xxqf02adIkzZs3zzrrrJMtt9wyRx55ZHr16rXsbxgAABqZhb9T33LLLdlpp52SJOPHj88ee+yRJFl//fVz3333ZfXVV//Ket27d0+SnHTSSTnjjDMKry9cb3GaNGmSFi1apGPHjtlqq62y3377Zdddd01ZWdnXfm8AFJ+dpQDgW+7dd9/NQQcdlLPPPjsPPvhgxo8fn9mzZ2f27NmZMGFChg8fnl/96lfZe++98+ijjy5z3XvuuSe1tbVJkrq6utxxxx0r6i0AAMBKc++99+a73/1uzj777AwdOjRjxozJ7NmzU11dncmTJ2fkyJG58cYb84Mf/CDHH398Jk6cuNxrVFZWZuLEiXnllVfyxz/+Mfvss09eeumlr91zTU1NZs6cmffeey9DhgzJD37wgwwaNOhr1wMAgFXB2LFjc9VVV63QNWpqajJjxoyMGTMmQ4YMyfHHH59jjjkmn3766QpdF4AVy85SAPAt9vnnn+dHP/pRPv/883To0CHHH398dtlll6y11lopKyvLZ599lmeffTbXX399Jk2alNNPPz3XXXdd+vTp85W1hwwZkiTZaqut8tprr2XIkCE544wz0qxZsxX9tgAAYIX49a9/ndtuuy3J/N2fDj744Oyxxx7ZcMMNs8Yaa2T69Ol5/fXXc8899+SZZ57JU089lSOOOCL//d//nY4dOy625uDBg7P99tsXfq6rq0tlZWUmTZqUZ555Jn/6058ybdq0nHzyyXn44YfTvn37xdZZd911M3To0MVeq6qqyqeffprHH388gwcPzpw5c3L11Vdns802S9++fb/hpwIAAI3Xrbfemr333jvbbbfdN6514okn5sQTT6z3WnV1daZNm5Z33303d999d4YPH54XXnghAwYMyO23377E7+8ANG52lgKAb7Hrr78+n3/+edq0aZO77rorAwYMSPfu3dOuXbu0bds23bt3z8CBA3PHHXekdevWqa6uzm9+85vCjlFL8uKLL+bDDz9MkpxzzjlJkqlTp+ahhx5a4e8JAABWhOuvv74QlNpiiy3y0EMP5Ve/+lX69u2bLl26pG3btunatWv233//3Hjjjfntb3+bJk2aZNy4cfnNb36zxLqrr756WrZsWfjXqlWrtG/fPptuumkGDhyYyy+/PEkyffr03H777UusU1ZWVq/Owv/atm2bHj165JRTTsl1111XuGfw4MEN9OkAAEDjVFtbmwsuuCBz5879xrUqKioW+a7dpk2bdOvWLXvuuWcGDRpUOBrwgw8+yNlnn/2N1wSgOISlAOBb7PHHH0+S7Lvvvll33XWXOK9r16456aSTkszfunjkyJFLrfuPf/wjbBkZpQAAH21JREFUSbLhhhtm++23z9Zbb50kjuIDAGCVNG7cuPzxj39MknTr1i0333xzOnfuvNR7+vXrlwEDBiRJHnzwwcLDBMtrzz33TIsWLZLkK7+HL4tddtkl22yzTZLkX//6V6qrq79xTQAAaIw22WSTJPN/p/373/9+paw5YMCA9OvXL0nyzDPP5Lnnnlsp6wLQsISlAOBbbOLEiUnmH8vxVfr27Zvu3btn5513XuofVGbOnJlHHnkkSbLXXnslSQ488MAkycsvv5x33333m7YNAAAr1Y033ph58+YlSS655JKsscYay3Tfcccdl4qKilRUVOSVV175WmuXlZWladOmSVIITX1TnTp1SjL/yJApU6Y0SE0AAGhs/v3f/z29evVKktx888159dVXV8q6p59+euE7/ILdaQFYtQhLAcC32IKn4R988MGMGjVqqXM33njj/POf/8zNN99ceBJ9cR588MHMnj07SXLAAQckSfbbb79UVFQksbsUAACrlpqamtx///1Jki5duqR3797LfG/79u1z00035fnnn8/BBx/8tdZ//vnnM3369CTzd5lqCO+//36S+ceItG3btkFqAgBAY9OkSZNcdtlladasWWpra3P++ecXHoJYkTp16lQ4beHFF19MTU3NCl8TgIYlLAUA32IL/mAze/bs9O/fP2eddVYeffTRwh9jvo4FR/D17NkzG2+8cZKkXbt22W233ZIk9913X+bMmfPNGgcAgJXkzTffzBdffJEkhe+0y2P77bdPq1atluueuXPnZty4cfn73/+en/zkJ0nm79q69957L/f6X/bQQw/lnXfeSZL07t07zZo1+8Y1AQCgsdp4443z4x//OEnywQcfrLTj+LbYYoskyfTp0/Pee++tlDUBaDhNi90AALDiDBw4MC+88EJGjBiRqqqqDB06NEOHDk15eXk23XTTbL/99tlpp53yb//2b8v0B54xY8YUtjJecC77AgcffHAeffTRzJgxI0OHDs1hhx22Qt4TAAA0pNGjRxfG3bt3b/D6//Ef//GVc0499dScfPLJKS9f8nONdXV1mTVr1hJfHz9+fIYNG1Y4BqRFixY5++yzv37jAACwijjuuOPyyCOP5M0338xNN92Uvffeu7Dz04qy7rrrFsaTJ09eoWsB0PCEpQDgW6yioiKDBw/OjTfemMGDBxd2lKqtrc2oUaMyatSo3HbbbamoqMj3vve9nHHGGenatesS6919992Fut///vfrXevbt2/WXHPNTJ48OXfccYewFAAAq4SpU6cWxh07dlzivKqqqlRWVi7xenl5eZo3b/61ehgyZEi6du26yAMJC/v444+z7bbbLlO99dZbL7/73e8KO8ECAMC3WZMmTXL55Zfn4IMPTlVVVc4///zce++9WW211VbYmi1btiyMp02btsLWAWDFEJYCgG+5Jk2a5Pjjj8/RRx+dp556Kk888USef/75TJgwoTCnqqoqDzzwQIYNG5bf/OY3OfDAAxepU1NTk3/+859J5gej2rdvX+9606ZNc8ABB+Smm27Km2++mTfffDM9e/ZcsW8OAAC+odra2mWad/311+eaa65Z4vXOnTvn8ccfX+T1wYMHZ/vtt6/32ty5czNlypS88sorueGGG/LRRx/l3HPPzaxZs3LUUUct3xv4/9q3b5/ddtstffr0yR577OH4PQAASsomm2ySU089Nb///e8zZsyYXHPNNTnnnHNW2HpLe5ACgMZPWAoAGpmysrLlvqeuru4r71199dWz1157Za+99kqSfPLJJ3nhhRfy9NNP5/HHH8/s2bNTWVmZ8847L+utt94iT60/+eSTmThxYpKkV69eefvttxdZY/PNNy+M77jjjlx66aXL/V4AAGBlatOmTWG84PtuQ1p99dXrPXWezH8Kfc0118wmm2ySvffeO/3798+4ceNyxRVXZN99913kwYRk0TBWdXV13n///QwePDhDhw7N1KlTU1FRkd13311QCgCAknT88cfn0UcfzciRIwvH8W211VYrZK0ZM2YUxq1bt14hawCw4pQXuwEAoL4FWwNXVVUt8z1z5sypd++y6NSpU/r165crr7wyw4cPzyGHHJJk/g5SgwYNWmT+P/7xj8L4qquuSr9+/Rb5t/CTOvfff39mzpy5zP0AAEAxbLTRRoXx+PHjlzjvlFNOyTvvvLPIvwMOOOAbrd+2bdscf/zxSeZ/r3/wwQeX6b6mTZume/fuufLKK3PSSSelrq4ud955Z37605+murr6G/UEAACroqZNm+ayyy5LRUVFampqcv7556+wHaDGjRtXGK+33norZA0AVhxhKQBoZNq1a5dk2c85r6mpKYSSFn4C/cEHH8zvfve7XHfddV9Zo23btrnsssuy5ZZbJklee+21etenTJmSJ598cpn6WWD27Nm57777luseAABY2bbeeuu0aNEiSTJs2LCi9LDw0+5jx45d7vtPP/309O7dO0kyfPjwXHHFFQ3VGgAArFK6d++ek08+OUny/vvvL/Uo7W9iwe/Q27Rpk/XXX3+FrAHAiiMsBQCNTNeuXZMkEyZMWKbdpcaOHVuY161bt8LrDz74YK6//voMHjx4mZ+e2WGHHZIset76vffeW1jjlltuWewT9Qv+Pffcc6moqEgy/yg+AABozJo1a5Z99tknSfLee+/lpZdeWuk9LHyc9tc5lrusrCyXXXZZ4fiPm2++Oc8880yD9QcAAKuSE088MZtvvnmS5G9/+1tef/31Bq3/zjvv5N13302SfPe7323Q2gCsHMJSANDIbLfddknmB5aGDx/+lfMff/zxwnhB2ClJtt122yTzd3i65557lmntjz76KEn9o0iSFO7v1KlTdtxxx6XWaN++fXbfffckyejRo/Pyyy8v09oAAFAsJ554YiHwf+655y7XcdINceTdyJEjC+MNN9zwa9VYa621Csdi19XV5ZJLLikc1w0AAKVkccfxNaRBgwYVxj/4wQ8atDYAK4ewFAA0MnvttVfhKL7LL788n3766RLnjhs3LjfccEOSZIMNNigErZKkX79+adOmTaHOiBEjlrruk08+WQheHXHEEYXXX3/99cJTMgceeOAyPenev3//wtjuUgAANHbrr79+zjrrrCTJ+PHjc8QRR2T06NFLveeLL77IZZddloceeugbrT1nzpz89a9/TZJUVFRkzz33/Nq1DjvssMLDDePHj88f//jHb9QbAACsqnr06JGTTjopyfwdZBvKTTfdlAceeCDJ/N/lL/w7eQBWHU2L3QAAUF/r1q1zwQUX5Gc/+1kmTJiQQw45JAMGDEifPn2y9tprp6amJhMmTMjTTz+dW2+9NdOmTUuzZs1y6aWXprz8/3LQbdu2zVVXXZWTTjopc+bMybHHHps99tgj+++/fzbffPO0a9cus2bNyrvvvpv7778/Q4cOTW1tbXbbbbccdNBBhTp33313Ybzw60vTu3fvdOrUKZ988kkeeuihXHDBBYUAGAAANEYDBw7M9OnT8+c//zmjR4/OoYcemt133z377bdfNtpoo3To0CHTp0/P+++/n+HDh2fo0KGZNWtWkqRly5Y59thjF1t37ty5hXkL1NXVZdasWXn99ddz3XXXFf54M3DgwHTo0OEbvY9f/OIXOeigg1JVVZWbb745BxxwQDbbbLNvVBMAAFZFJ554Yh599NGMGjVqmeZXVVUt8t193rx5mTp1at56660MGTKkcNx1t27d8utf/7rBewZg5RCWAoBG6MADD0xtbW1+8YtfZPLkybnyyitz5ZVXLnZux44dc8UVV2T77bdf5Frv3r1z/fXX5+c//3k+/PDDPPbYY3nssccWW6esrCz9+/fPRRddlKZN539FmDt3buEpmZ49ey5yPN+SlJeX55BDDsm1116bysrKDBkyJD/60Y+W6V4AACiWn/70p9lmm21y2WWXZcyYMXnooYeWunNU8+bNc+CBB+bUU0/N2muvvdg5J5xwwjKtfcghh+T000//Wn0vbKONNspxxx2X6667LtXV1bn44ovzP//zP/UerAAAgFJQUVGRyy+/PIcddliqqqq+cv5f/vKX/OUvf/nKebvsskt+97vfpW3btg3RJgBFICwFAI1Uv379stNOO+Xuu+/Oc889l7Fjx2b69OlJ5u8atemmm2a33XbLoYcempYtWy6xzi677JKhQ4fm0UcfzdNPP5033ngjU6ZMyYwZM9KyZcuss8462WWXXXLAAQdkiy22qHfvI488khkzZiRZ9l2lFjjkkEPy5z//OXV1dbnzzjszcODAZTrCDwAAiqlPnz7ZddddM2LEiAwbNiyvv/56xo0bl9mzZ6dVq1Zp3759tthii+y8887Za6+9CkdfL48mTZqkZcuW6dSpU3r16pV+/fot9uGHr+vkk0/OAw88kA8//DBvvPFGbr311hxzzDENVh8AAFYVm222WU444YRce+21X+v+ioqKtGzZMp07d07Pnj2z3377Zeedd27gLgFY2crq6urqit0EAAAAAAAAAADAimb/bQAAAAAAAAAAoCQISwEAAAAAAAAAACVBWAoAAAAAAAAAACgJwlIAAAAAAAAAAEBJEJYCAAAAAAAAAABKgrAUAAAAAAAAAABQEoSlAAAAAAAAAACAkiAsBQAAAAAAAAAAlARhKQAAAAAAAAAAoCQISwEAAAAAAAAAACVBWAoAAAAAAAAAACgJwlIAAAAAAAAAAEBJEJYCAAAAAAAAAABKgrAUAAAAAAAAAABQEoSlAAAAAAAAAACAkiAsBQAAAAAAAAAAlARhKQAAAAD4lvvss8+K3QIAAABAo9C02A0AAAAAwPKYN29eRowYkccffzxvvvlmJk2alC+++CItW7bM2muvna233jr77bdfdt5552K3WnRTp07NFVdckVatWuWCCy4odjsAAAAARVdWV1dXV+wmAAAAAOCr1NXV5b777stVV121TDslbb311rnsssuy4YYbroTuGp9hw4blwgsvzLRp0/Jf//VfOeigg4rdEgAAAEDRCUsBAAAA0OjNnDkzp59+ep5++unCa2ussUa+973vZcMNN8zaa6+dOXPmZOTIkXnggQcyY8aMwpwbb7wxvXr1KlbrRfPDH/4w//rXv5IkDz74YMmGxgAAAAAWJiwFAAAAQKM2ZcqU/OhHP8rbb7+dJGnWrFnOOuusHH744Vl99dUXmf/FF1/k3HPPzfDhw5MkHTp0yP3335+2bduu1L6Lqbq6Ottuu23mzZuXVq1a5aWXXkpZWVmx2wIAAAAouvJiNwAAAAAAS1JTU5MzzzyzEJTq2LFj7rrrrgwYMGCxQakkadOmTf7whz9kyy23TJJMmjQpf/rTn1Zaz43B6NGjM2/evCTJFltsISgFAAAA8P8JSwEAAADQaP3hD3/Ic889lyRp3rx5Bg0alB49enzlfc2aNct5551X+Pnee+9NZWXlCuuzsXnjjTcK4549exaxEwAAAIDGxTF8AAAAADRK48aNyz777JPq6uokyaWXXprDDjtsuWrsscceGT9+fJLk7rvvLuw2tbDnn38+DzzwQF566aV8/vnnmTdvXjp27Jhtttkmhx9+eHbYYYelrnHkkUfm5ZdfTpIMGTIkm2+++RLn7rvvvhkzZkyS5IknnkinTp0K15577rkMGDAgSXLJJZfkqKOOSm1tbR566KHcc889GTVqVKZNm5bOnTtnn332ycknn7zI7lo77rhjvvjiiyWuf+edd2brrbdOklxzzTX585//nCQZMWJEOnbsmKFDh+aWW27J6NGjU15enn79+mXYsGH57LPPUlFRkddeey1NmjRZ6udx9tln53//93+TJBdddFGOPvropc4HAAAAWJmaFrsBAAAAAFicQYMGFYJSvXr1Sv/+/Ze7xiWXXJLp06enQ4cO6datW71rn3/+eS644II8/fTTi9w3YcKETJgwIUOHDs0hhxySX/7yl2nWrNki82prawtHBK622mrZZJNNltjLzJkzM3bs2CRJ+/bt6wWlkmTkyJGF8eabb54PPvgg559/fl599dV688aOHZtBgwblxRdfzC233JKKiookyYcffrjUoFTTpk2z2WabLbLeOuusk9atW+fMM8/M/fffX++evn37ZsyYMfnss89SVVWVjz/+OF26dFniGqNHjy7U6Nq1aw4//PAlzgUAAAAoBmEpAAAAABqdL774Ivfdd1/h5xNPPDFlZWXLXadv376LfX3cuHE55phjMmHChCRJixYtsv/++6dnz55p1qxZXn311QwZMiSVlZW55557Ultbm9/+9reL1Hn//fcze/bsJEmPHj0KwaXFGTlyZGpra5Ms/mi8N998M0lSXl6eadOm5fjjj8/MmTPTp0+ffOc730n79u0zevTo3HrrrZk7d25eeeWV/POf/8yhhx6aJGnZsmWuueaazJ07N+eee26SpE2bNvnVr36VZP4xhquttlq9fpJkyy23zFlnnZVHH3002267bQ466KDU1NTkpZdeSp8+ffLkk08WjkL88MMPlxqWuvrqqwvv8Ywzzljq5wEAAABQDMJSAAAAADQ6zz//fKqqqpIka665ZnbbbbcGq11ZWZkf//jHhaDUDjvskKuuuiprrbVWYc7BBx+cI444Isccc0y++OKL3Hvvvdl///2z66671qu1IOCUZLFH/C1p7hZbbLHI9QXhpSZNmuS0005Lx44dc8MNNxSOzVtg7bXXzqWXXppk/tF9C8JSHTp0yD777JNXXnml3jr77LPPImt9+umnmTRpUpLkpZdeytSpU/OTn/wkp5xySiGUdtRRRyVJNtxww8J9H374YXr37r3Y9/faa6/l8ccfTzJ/J7D99ttvaR8HAAAAQFGUF7sBAAAAAPiyZ599tjDeZZdd0rRpwz3zd/XVV2fUqFFJ5gecrr/++npBqQU222yzwg5NSfL3v/99kTlvvPFGYby43aKWde706dMzbty4JElVVVVat26d2267bZGgVDL/iL4FZs6cucj1hUNZS+pp4TlTp07NgAEDcuqppy52964vh6WW5MorryyMzznnnCXOAwAAACgmYSkAAAAAGp3Ro0cXxl+1Y9PymDJlSm6//fYkSUVFRa644oo0b958ifMPOOCANGvWLEny4osvpq6urt71r7uz1JdDTCNHjqxX+8ILL0znzp0XW2fBjlDJ/N2klrbO4naw+vKcTp065ayzzlpi3xtttFFh/NFHHy12zjPPPJMXXnghSfLd7343O+ywwxLrAQAAABSTsBQAAAAAjc6UKVMK4/XWW6/B6t5xxx2ZO3dukuSQQw7JBhtssNT5zZo1S7du3ZIks2bNyvTp0wvXqqurCztUtWjRot4OTF82bdq0ws5RHTp0yDrrrFPv+sLhpY022mipR9i98847hfHi1lyWsNSCI/+SZMCAAYVA2OKsvfbaadmyZZJk7Nixi51z9dVXJ5l/hODPfvazJdYCAAAAKDZhKQAAAAAanWnTphXGbdq0abC6Tz31VGF84IEHLtM9Cx8BuCBolczf/WrevHlJ5oeSysuX/Ku2rwowLRxeOvjggxd7HN7i5m622Wb1rs2aNSsffPBBkqRt27bp0qXLUmuUl5dn//33X+JaCywIlY0fPz41NTX1rj3yyCOFIwYPPfTQejtRAQAAADQ2wlIAAAAANDq1tbWF8cJhpW9i3rx5hdDSaqutlq233nqZ7lv42LtWrVoVxks7Vu/LXnvttaXOXbjWbrvtttRaC4JOZWVliwSv3nrrrcJnt6RdpT755JNMnjw5SbL11lsv9ii/L1uwg1VVVVU+/vjjwus1NTW55pprkiTNmzfPaaed9pW1AAAAAIpJWAoAAACARqd169aF8cJH3y2rurq63H777XnyySfzwQcfpLq6Op9//nmqqqqSzD/qbllCWJ9++mkmTpyYJFlzzTULx9El9QNOW2655VLrvPzyy4Xxl8NS06dPLxzR1759+2yyySbL1E+3bt3qfU5f7mlJAa6Fd6baYYcdltr3Agsf9zd+/PjC+L777st7772XJBk4cGDWWmutZaoHAAAAUCwN81geAAAAADSgTp06FUI57733Xvr27btc9z/33HP55S9/mSRp0aJFnnjiiUydOrVwvX379stUZ/jw4YVxr1696l1b1rDUzJkz89JLLxV+/vKOTwuHl7baaqul9vNVa37VcX9fnrPtttsudb0FFj5ab8KECUmSysrK/OlPf0oyP0h27LHHLlMtAAAAgGKysxQAAAAAjc7CIZ5nn312ue8fNGhQYXzooYemTZs2KS//v1+FVVZWLlOdO+64ozBe+Hi86urqjB49OkmyxhprpGvXrkuscdddd2XevHlJko4dO2bttdeud/2NN94ojL/qOL+F5y4uLLUstRYOS305ALYkC+8steAYvjvvvLMQnDr11FPrHVEIAAAA0FgJSwEAAADQ6Oy+++6F8TPPPFM46m1ZDBkyJC+88EKSpG3btjnllFOSJOuss05hzgcffPCVdYYPH55Ro0Ylmb871f7771+49tFHHxWO9Nt4442XWGPq1Km54YYbCj8vLsC08M5SXxWWWtrOUjNmzMhHH32UZP777ty582JrvPXWW0nm7961rDtsdevWrXBs4SeffJLZs2cXAmnrr79+fvjDHy5THQAAAIBiE5YCAAAAoNHZeuutC7tL1dXV5ZxzzsmcOXO+8r5nn302v/jFLwo/X3jhhYVAUIcOHbLBBhskSSZOnJjnnntuiXU+/vjjnHfeeYWfjz322Ho7J3366aeFccuWLRdbo7KyMmeffXYmTZpUeK2hwlJNmzbN5ptvXu/aW2+9lbq6uiRJjx49Fnv/J598ksmTJydZ8jF9i1NRUZH11lsvyfxj+G655ZbC+zrzzDMLQSoAAACAxk5YCgAAAIBG6ec//3lWX331JPMDRUcfffQSd4SqrKzM4MGDc8IJJ2Tu3LlJkpNOOikHHnhgvXkL74B08cUXF46UW9iYMWNy3HHHZdq0aUnmh4pOOOGEenMqKioK47feeiszZsyod/3DDz/MwIEDM2LEiHpBoi8HlL744ouMGzcuyfydnjp06LDY95ck48aNK/S08cYbFz6bBRbefWvdddddbI2Fd6b6qmDWly0Imo0ZMyZ//etfkyTbbLNN9t577+WqAwAAAFBMHvkCAAAAoFHq0aNHrrjiipx99tmZN29e3njjjey///7p27dvdtxxx6y55pqZO3du3n777TzyyCOZOHFikqS8vDxnnXVWjjvuuEVqHn300Rk2bFheeumljBs3LgceeGD69++fzTffPHPnzs0LL7yQhx9+uHDE3kYbbZRBgwalWbNm9er07NkzrVu3zvTp0zNlypT0798/e++9d5o0aZKRI0dmxIgRqampSZ8+fdKqVas88MADhfsWtjy7Sr3xxhuF8ZeP4EuSefPmFcZPPfVU7rrrrtTW1qZz587p3bt3kvphqeXZWSqZ/1kMHz688Dknyc9+9rPlqgEAAABQbMJSAAAAADRae+21V2677bZcfPHFGTVqVKqrq/PYY4/lscceW+z8LbfcMpdcckl69eq12OtNmzbNtddemwsvvDDDhg3LjBkz8re//W2ReeXl5enXr18uvPDCesfvLdC8efNcdNFFOffcc1NXV5exY8fmL3/5S+F6RUVFTj755Jx88sk55JBDkiRrrbVWOnbsWK/O8uz0tPDcxYWlevfunauuuipVVVWZNGlSLrrooiTJr3/968Kc5QlnfdmGG25Y7+c999wz22233XLVAAAAACg2YSkAAAAAGrVevXrl3nvvzRNPPJHHH388r7zySiZNmpSZM2emVatW6dy5c7bddtvsu+++yxTeadu2ba699to888wzGTJkSF5//fV89tlnKS8vT5cuXbLLLrukf//+2WSTTZZa56CDDspaa62VwYMH57XXXktdXV06d+6cvn375oc//GG6du2auXPnFo7HW1w4aXnCS18VrNp0001z00035dprr81bb72VWbNmZd11180OO+ywyHrrrrtu2rdvv9T1vmzhsFTTpk1z1llnLdf9AAAAAI1BWV1dXV2xmwAAAAAAGrdXX301hx9+eJLk8MMPzy9/+csidwQAAACw/MqL3QAAAAAA0Phde+21SZIWLVrktNNOK3I3AAAAAF+PsBQAAAAAsFQjRozI008/nSQ5/vjj06FDhyJ3BAAAAPD1OIYPAAAAAFiiqVOn5rDDDsu4cePSpUuX3H///VlttdWK3RYAAADA19K02A0AAAAAAI3LLbfcktatW2f27Nm57bbbMm7cuDRp0iT/+Z//KSgFAAAArNLsLAUAAAAA1NO7d+9MnDix8HNZWVkuueSSHHnkkUXsCgAAAOCbs7MUAAAAAFBPx44dM3369DRv3jw9e/bMKaecku22267YbQEAAAB8Y3aWAgAAAAAAAAAASkJ5sRsAAAAAAAAAAABYGYSlAAAAAAAAAACAkiAsBQAAAAAAAAAAlARhKQAAAAAAAAAAoCQISwEAAAAAAAAAACVBWAoAAAAAAAAAACgJwlIAAAAAAAAAAEBJEJYCAAAAAAAAAABKgrAUAAAAAAAAAABQEoSlAAAAAAAAAACAkiAsBQAAAAAAAAAAlARhKQAAAAAAAAAAoCQISwEAAAAAAAAAACVBWAoAAAAAAAAAACgJwlIAAAAAAAAAAEBJEJYCAAAAAAAAAABKgrAUAAAAAAAAAABQEoSlAAAAAAAAAACAkvD/AAp88zIscST6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set grid style\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "#set figure size and attributes\n",
    "plt.figure(num= 4, figsize=(12,8), dpi=200, facecolor = 'w' ,edgecolor = 'k')\n",
    "\n",
    "#boxplot using Seaborn\n",
    "sns.barplot(x= tic_all_3_countries['country'], y= tic_all_3_countries['total_investment_count'],\\\n",
    "            hue= tic_all_3_countries['main_sector'].rename('Main Sector'))\n",
    "plt.ylabel('Total number of investments (count)', fontstyle='italic', fontsize= 12)\n",
    "plt.xlabel('Country', fontstyle='italic', fontsize= 12)\n",
    "plt.title(\"Number of Investments in Top 3 main sectors of Top 3 countries for 'Venture' type funding\"\\\n",
    "          , fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'>Result of Data Analysis:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Based on the data analysis done, we conclude that *Spark Funds* should <br>\n",
    "(i) Invest in `Venture` type investment. <br>\n",
    "(ii) The best top English speaking countries to invest in, are `USA (United States)`, `GBR (Great Britain)` and `IND (India)`.<br>\n",
    "(iii) The best top two sectors within these countries for the venture type investment are `'Others'` and `'Social, Finance, Analytics, Advertising'`.<br>\n"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}