FinalProject_group079.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we will explore whether gender inequality truly exists in the film industry in various aspects. We will analyze how the spoke frequency of movie characters are influenced by their distinct gender. Then, we will explore the proportion of gender in films. Also, we will analyze how the leading character's gender in a movie will impact the movie rating and box office. By using data to explore and analyze these sub-questions, we will be able to find the answer to whether gender inequality exists in the film industry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group Members\n",
    "\n",
    " - Name: Heejin Kwon \n",
    "    \n",
    "    ID: XXXXXXXXX\n",
    "    \n",
    "    Contributions: Worked on Movie rating VS gender part's data wrangling, cleaning, visulization and analysis; overview; conclusion\n",
    "\n",
    " - Name: Yuanyuan Huang\n",
    "    \n",
    "    ID: XXXXXXXXX\n",
    "    \n",
    "    Contributions: Spoke frequency vs Gender part's data wrangling, cleaning, visulization and analysis; overview; conclusion\n",
    "\n",
    " - Name: Yining Chen\n",
    "    \n",
    "    ID: XXXXXXXXX\n",
    "    \n",
    "    Contributions: Gender vs Grossing part's data wrangling, cleaning, visulization and analysis; overview; conclusion\n",
    "\n",
    " - Name: Jiaxuan Zhang\n",
    "    \n",
    "    ID: XXXXXXXXX\n",
    "    \n",
    "    Contributions: Proportion of gender part's data wrangling, cleaning, visualization and analysis, overview, and conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- How’s the gender of movie characters influence their spoke frequency in movies over time?\n",
    "- How does the proportion between male and female characters in movies change over time?\n",
    "- Does leading actor's gender of movie has influence on this movie's rating and box office?\n",
    "- Does gender inequality exist in the film industry?\n",
    "- How does the gender of main character affect to the rate of movie? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   Gender Inequality has been a serious problem exists in almost every field of society. In the article, “Film Dialogue from 2,000 screenplays, Broken Down by Gender and Age”, written by Hanah Aanderson and Matt Daniels, it uncovers the gender issue that “white men dominate movie roles” by analyzing the film dialogue broken-down by gender [1]. It shows that male characters always have more amount of dialogue than female characters. In addition, The New York Film Academy releases the research, “Gender Inequality in Film Infographic Updated in 2018,” to further ensures the gender issues such that the women employed behind the scenes are roughly 10% compared to men [2].\n",
    "Inspired by these articles and researches, we decided to focus our topic of this research on “Explore the Gender Inequality in the Film Industry.” We wanted to figure out if gender inequality truly exists in the film industry and how it is reflected. Thus, we came up with the questions such as, are characters’ spoke frequency influenced by their distinct genders, and how the number of female and male characters distributed in movies.\n",
    "When we looked up on the Internet, we found some datasets that can be used to analyze in order to answer our questions. These datasets cover the information of the movies that were released from the ’90s to present, and each of the movies followed by its characters’ genders, names, and words that spoke in movies. By using the analyzing skills that we learn in the class, we will see the distribution of spoke frequency of the female and male as well as their appearing times in movies. Finally, we can conclude that if gender inequality exists in the film industry.\n",
    "\n",
    "References (include links):\n",
    "\n",
    "[1].Aanderson, H., & Daniels, M. (n.d.). The Largest Analysis of Film Dialogue by Gender, Ever. Retrieved from h​ ttps://pudding.cool/2017/03/film-dialogue/index.html\n",
    "\n",
    "[2].Perrone, J. J. (2018, March 08). Gender Inequality in Film Infographic for 2018. Retrieved from\n",
    "https://www.nyfa.edu/film-school-blog/gender-inequality-in-film-infographic-updated-in-2018/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that gender inequality has long existed in the film industry and it affect to various area. So, we came up with 4 sumed up hyphthesis:  \n",
    "- the male characters will speak and appear far more than female characters in movies generally.\n",
    "- the proportion of female characters will always be lower than male characters throughout the years.\n",
    "- the average rate of movie which is played by male main character will be more higher than female main character.\n",
    "- the male leading movies have a higher probability to get higher ratings and grossing.\n",
    " \n",
    "The reason for this hypothesis is that the problem of gender inequality has been exposed to our society for a long time and it exists in many fields until now, for example, the workplace, education, etc. Therefore, we assume that it is highly likely that the same issue exists in the film industry. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "- Dataset Name: Cornell Movie--Dialogs Corpus\n",
    " \n",
    "  Link to the dataset: https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html\n",
    " Number of observation:  It involves 9,035 characters, labeled with gender, from 617 different movies with 304,713 utterances.\n",
    " \n",
    "  On the website, Cristian Danescu-Niculescu-Mizil, from ​Department of Information Science,​ ​Cornell University​, we found the corpus, “Cornell Movie--Dialogs Corpus”, contains the dataset collection of fictional conversations extracted from movie scripts. \n",
    "\n",
    "\n",
    "- Dataset Name: IMDB Gender Data Analysis\n",
    "\n",
    "  Link to the dataset:  https://data.world/aydh/ellen/workspace/file?filename=title.basics.ellen.tsv\n",
    "\n",
    "  On data.world, we found a project, “IMDB Gender Data Analysis,” which discover the difference in IMDB ratings for movies with male or female leads. It includes the dataset of the number of actors and actresses that act in different movie from 1986 to 2017.\n",
    "\n",
    "- Dataset Name: Actor and actress number in Hollywood movies\n",
    " \n",
    "  Link to the dataset:  https://github.com/taubergm/HollywoodGenderData\n",
    " \n",
    "  On the github of HollywoodGenderData, it contains gender information of actors, directors, musics which are related to  Hollywood industry.\n",
    "\n",
    "- Dataset Name: Leading actor gender and movie analysis\n",
    "\n",
    "  Link to the dataset: https://github.com/minimaxir/movie-gender\n",
    "\n",
    "  From Max Woolf's open-sourced Github, it contains the dataset which includes box office of 2020 movies with male or female leads. The year of movies ranges from 2000 to 2016.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the numpy, pandas, scipy, matplotlib.pyplot,seaborn modules as np, pd, plt, sns, respectively. These libraries will help us loading data from files and plotting visual graphs in order to analyze from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest\n",
    "\n",
    "# Configure libraries\n",
    "# The seaborn library makes plots look nicer\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "\n",
    "# Don't display too many rows/cols of DataFrames\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 5\n",
    "\n",
    "# Round decimals when displaying DataFrames\n",
    "pd.set_option('precision', 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the datasets from Cornell Movie--Dialogs Corpus to get the number of dialogue that female and male speak on different movies throughout the years.  \n",
    "\"movie_titles_metadata.txt\" -- contains information about each movie title.  \n",
    "\"movie_characters_metadata.txt\" -- contains information about each movie character.  \n",
    "\"movie_lines.txt\" -- contains the actual text of each utterance.  \n",
    "\"movie_conversations.txt\" -- contains the structure of the conversations.  \n",
    "Since the columns of these files are seperated by special character `+++$+++` and the unknown value is written as \"?\", in order to successfully load the data, we use read_csv to load the data, set the sep to `+++$+++`, engine to 'python' and na_values to \"?\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset from Cornell Movie--Dialogs Corpus\n",
    "movie_titles_df = pd.read_csv(\"movie_titles_metadata.txt\",sep=\" \\+\\+\\+\\$\\+\\+\\+ \",engine='python',na_values=\"?\")\n",
    "movie_characters_df = pd.read_csv(\"movie_characters_metadata.txt\",sep=\" \\+\\+\\+\\$\\+\\+\\+ \",engine='python',na_values=\"?\")\n",
    "movie_lines_df = pd.read_csv(\"movie_lines.txt\",sep=\" \\+\\+\\+\\$\\+\\+\\+ \",engine='python',na_values=\"?\")\n",
    "movie_conversations_df = pd.read_csv(\"movie_conversations.txt\",sep=\" \\+\\+\\+\\$\\+\\+\\+ \",engine='python',na_values=\"?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the dataset from HollyWoodGenderData to get the proportion of gender\n",
    "\"all_actors_movies_gender_gold.csv\" -- contains information about the time and name of the movie characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>...</th>\n",
       "      <th>released</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Mission: Impossible 2</td>\n",
       "      <td>...</td>\n",
       "      <td>2000/05/24</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>Mission: Impossible 2</td>\n",
       "      <td>...</td>\n",
       "      <td>2000/05/24</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Mission: Impossible 2</td>\n",
       "      <td>...</td>\n",
       "      <td>2000/05/24</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20368</th>\n",
       "      <td>2018</td>\n",
       "      <td>Destroyer</td>\n",
       "      <td>...</td>\n",
       "      <td>2018/08/31</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20369</th>\n",
       "      <td>2018</td>\n",
       "      <td>Destroyer</td>\n",
       "      <td>...</td>\n",
       "      <td>2018/08/31</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20370</th>\n",
       "      <td>2018</td>\n",
       "      <td>Destroyer</td>\n",
       "      <td>...</td>\n",
       "      <td>2018/08/31</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20371 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year                   name   ...      released  gender\n",
       "0      2000  Mission: Impossible 2   ...    2000/05/24    male\n",
       "1      2000  Mission: Impossible 2   ...    2000/05/24    male\n",
       "2      2000  Mission: Impossible 2   ...    2000/05/24  female\n",
       "...     ...                    ...   ...           ...     ...\n",
       "20368  2018              Destroyer   ...    2018/08/31    male\n",
       "20369  2018              Destroyer   ...    2018/08/31    male\n",
       "20370  2018              Destroyer   ...    2018/08/31    male\n",
       "\n",
       "[20371 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read data file(Gender proportion)\n",
    "df_nofc = pd.read_csv('all_actors_movies_gender_gold.csv', encoding='cp1252')\n",
    "df_nofc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "drop unecessary columns 'year', 'budget','language','released','runtime','gross','country', 'starring'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>name</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mission: Impossible 2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mission: Impossible 2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mission: Impossible 2</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20368</th>\n",
       "      <td>Destroyer</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20369</th>\n",
       "      <td>Destroyer</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20370</th>\n",
       "      <td>Destroyer</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20371 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  gender\n",
       "0      Mission: Impossible 2    male\n",
       "1      Mission: Impossible 2    male\n",
       "2      Mission: Impossible 2  female\n",
       "...                      ...     ...\n",
       "20368              Destroyer    male\n",
       "20369              Destroyer    male\n",
       "20370              Destroyer    male\n",
       "\n",
       "[20371 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop unecessary columns\n",
    "df_nofc.drop(columns=['year', 'budget','language','released','runtime','gross','country', 'starring'],inplace= True)\n",
    "df_nofc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rating = pd.read_csv(\"title.ratings.ellen.tsv\",sep='\\t')\n",
    "#print(df_rating)\n",
    "df_principals = pd.read_csv(\"title.principals.ellen2.tsv\",sep='\\t')\n",
    "#print(df_principals)\n",
    "df_basics = pd.read_csv(\"title.basics.ellen.tsv\",sep='\\t')\n",
    "#print(df_basics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For gender and box office part here, we only need one dataset which contains all information we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>imdbID</th>\n",
       "      <th>Title</th>\n",
       "      <th>...</th>\n",
       "      <th>LeadActor</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2488496</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>...</td>\n",
       "      <td>Daisy Ridley</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0499549</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>...</td>\n",
       "      <td>Sam Worthington</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>...</td>\n",
       "      <td>Chris Pratt</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>tt0183659</td>\n",
       "      <td>Pollock</td>\n",
       "      <td>...</td>\n",
       "      <td>Ed Harris</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>tt0345074</td>\n",
       "      <td>Connie and Carla</td>\n",
       "      <td>...</td>\n",
       "      <td>Nia Vardalos</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>tt1742650</td>\n",
       "      <td>I Don't Know How She Does It</td>\n",
       "      <td>...</td>\n",
       "      <td>Sarah Jessica Parker</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2020 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         imdbID                                       Title  ...    \\\n",
       "0     tt2488496  Star Wars: Episode VII - The Force Awakens  ...     \n",
       "1     tt0499549                                      Avatar  ...     \n",
       "2     tt0369610                              Jurassic World  ...     \n",
       "...         ...                                         ...  ...     \n",
       "2017  tt0183659                                     Pollock  ...     \n",
       "2018  tt0345074                            Connie and Carla  ...     \n",
       "2019  tt1742650                I Don't Know How She Does It  ...     \n",
       "\n",
       "                 LeadActor Gender  \n",
       "0             Daisy Ridley      f  \n",
       "1          Sam Worthington      m  \n",
       "2              Chris Pratt      m  \n",
       "...                    ...    ...  \n",
       "2017             Ed Harris      m  \n",
       "2018          Nia Vardalos      f  \n",
       "2019  Sarah Jessica Parker      f  \n",
       "\n",
       "[2020 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read data file (Gender & box office)\n",
    "df_grossing = pd.read_csv('movie_gender_fixed.csv')\n",
    "#check data\n",
    "df_grossing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For data cleaning, We drop all the column that I don't need for my data. We dropped all the column variables except tconst(index for all variables), averageRating(average of rating on movie), numVotes(number of vote on movie), and startYear(year of released).\n",
    "\n",
    "We also removed the row that contains numVotes is less than 100 because It is too small value compare with most of values so it doesn't present an obvious graph.\n",
    "\n",
    "\n",
    "To clean up the data, we need to make the data more readable by adding column name or renaming it for easier understanding.  \n",
    "In the dataframe `movie_titles_df`, `movie_characters_df`, `movie_lines_df`, and `movie_conversations_df`, since they do not have column names when we load from file initially, we need to set the corresponded name for each columns so that we clearly see what each column contains when we print out the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------Average rate----------\n",
      " count    54299.00\n",
      "mean         5.84\n",
      "std          1.30\n",
      "           ...   \n",
      "50%          6.10\n",
      "75%          6.80\n",
      "max         10.00\n",
      "Name: averageRating, Length: 8, dtype: float64 \n",
      "\n",
      "----------Number of vote----------\n",
      " count    5.43e+04\n",
      "mean     1.12e+04\n",
      "std      5.63e+04\n",
      "           ...   \n",
      "50%      5.13e+02\n",
      "75%      2.14e+03\n",
      "max      2.01e+06\n",
      "Name: numVotes, Length: 8, dtype: float64 \n",
      "\n",
      "----------Year----------\n",
      " count     54299\n",
      "unique       34\n",
      "top        2016\n",
      "freq       2950\n",
      "Name: startYear, dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "combineDatas = pd.merge(df_rating, df_principals, on = 'tconst')\n",
    "combineDatas = pd.merge(combineDatas, df_basics, on = 'tconst')\n",
    "ratingVSsex = combineDatas.drop(['ordering', 'nconst', 'characters', 'job', 'endYear', 'runtimeMinutes', 'genres', \n",
    "                                'primaryTitle', 'originalTitle', 'isAdult', 'titleType'], 1)\n",
    "\n",
    "ratingVSsex = ratingVSsex[ratingVSsex['numVotes'] >= 100]\n",
    "#print(ratingVSsex)\n",
    "\n",
    "ratingVSsex = ratingVSsex.dropna()\n",
    "#print(ratingVSsex)\n",
    "\n",
    "rate_stats = ratingVSsex['averageRating'].describe()\n",
    "print(\"----------Average rate----------\\n\", rate_stats, \"\\n\")\n",
    "numVote_stats = ratingVSsex['numVotes'].describe()\n",
    "print(\"----------Number of vote----------\\n\", numVote_stats, \"\\n\")\n",
    "year_stats = ratingVSsex['startYear'].describe()\n",
    "print(\"----------Year----------\\n\", year_stats, \"\\n\")\n",
    "\n",
    "ratingVSsex.head()\n",
    "\n",
    "#Rename columns of each dataset\n",
    "movie_titles_df.columns = [\"movie_ID\", \"movie_title\", \"movie_year\", \"IMDB_rating\",\n",
    "                                    \"IMDB_votes\", \"genres_in_the_format\"]\n",
    "movie_characters_df.columns = [\"character_ID\", \"character_name\",\"movie_ID\", \"movie_title\",\n",
    "                                     \"gender\",\"position_in_credits\"]\n",
    "movie_lines_df.columns = [\"line_ID\", \"character_ID\", \"movie_ID\", \"character_name\", \"text_of_the_utterance\"]\n",
    "movie_conversations_df.columns = [\"character_ID1\", \"character_ID2\", \"movie_ID\", \"list_of_the_utterances\"]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further clean up, drop all rows from each dataframe that have missing data (NaN)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove all rows from dataset that have missing values. \n",
    "movie_characters_df = movie_characters_df.dropna(how='any')\n",
    "movie_lines_df = movie_lines_df.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some columns in the dataframe are useless for this project, so we drop these columns to make it cleaner when merging dataframe together later.  \n",
    "In dataframe `movie_characters_df`, drop the column 'position_in_credits'.\n",
    "In dataframe `movie_lines_df`, drop the column 'text_of_the_utterance'.\n",
    "In dataframe `movie_titles_df`, drop the column 'genres_in_the_format'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_characters_df = movie_characters_df.drop(['position_in_credits'], axis=1)\n",
    "movie_lines_df = movie_lines_df.drop(['text_of_the_utterance'], axis=1)\n",
    "movie_titles_df = movie_titles_df.drop(['genres_in_the_format'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some columns contain values that are in bad format, so we want to reformat the value.  \n",
    "In the dataframe `movie_titles_df`, some of the values in the columns 'movie_year' is weirdly ending with '/I', so we define a function standardize_year to eliminate '/I'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize_year(string):\n",
    "    string = string.lower()\n",
    "    string = string.strip()\n",
    "    string = string.replace('/i', '')\n",
    "    string = string.lower()\n",
    "    string = string.strip()\n",
    "    return string\n",
    "\n",
    "movie_titles_df['movie_year'] = movie_titles_df['movie_year'].apply(standardize_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For analyzing the spoke frequency of female and male characters on movies, we merge the `movie_lines_df`, which contains the dialogue line number of each character on different movies, and `movie_characters_df`, which contains the gender of each character to obtain the dataframe `dialogue_gender_df`.  \n",
    "Further more, we merge the obtaining `dialogue_gender_df` with `movie_titles_df` to get the 'movie_year' values and save the final dataframe back to `dialogue_gender_df`. Finally, drop the unnecessary columns 'IMDB_rating' and 'IMDB_votes' from `dialogue_gender_df` and drop any row have missing value again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dialogue_gender_df = pd.merge(movie_lines_df, movie_characters_df)\n",
    "dialogue_gender_df = pd.merge(dialogue_gender_df, movie_titles_df)\n",
    "dialogue_gender_df = dialogue_gender_df.drop(['IMDB_rating'], axis=1)\n",
    "dialogue_gender_df = dialogue_gender_df.drop(['IMDB_votes'], axis=1)\n",
    "dialogue_gender_df = dialogue_gender_df.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe dialogue_gender_df now looks like below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>line_ID</th>\n",
       "      <th>character_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>movie_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L2024</td>\n",
       "      <td>u16</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L2023</td>\n",
       "      <td>u16</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L2020</td>\n",
       "      <td>u16</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215147</th>\n",
       "      <td>L665885</td>\n",
       "      <td>u9020</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>1974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215148</th>\n",
       "      <td>L665695</td>\n",
       "      <td>u9020</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>1974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215149</th>\n",
       "      <td>L665652</td>\n",
       "      <td>u9020</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>1974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>215150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        line_ID character_ID    ...     gender movie_year\n",
       "0         L2024          u16    ...          m       1992\n",
       "1         L2023          u16    ...          m       1992\n",
       "2         L2020          u16    ...          m       1992\n",
       "...         ...          ...    ...        ...        ...\n",
       "215147  L665885        u9020    ...          f       1974\n",
       "215148  L665695        u9020    ...          f       1974\n",
       "215149  L665652        u9020    ...          f       1974\n",
       "\n",
       "[215150 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dialogue_gender_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since here we only check grossing's relationship with Leading actor's gender, we can drop some unnecessary columns here. Also chek columns with NaN values, if there is any, we need to drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>imdbID</th>\n",
       "      <th>Title</th>\n",
       "      <th>...</th>\n",
       "      <th>LeadActor</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2488496</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>...</td>\n",
       "      <td>Daisy Ridley</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0499549</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>...</td>\n",
       "      <td>Sam Worthington</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>...</td>\n",
       "      <td>Chris Pratt</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>tt0183659</td>\n",
       "      <td>Pollock</td>\n",
       "      <td>...</td>\n",
       "      <td>Ed Harris</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>tt0345074</td>\n",
       "      <td>Connie and Carla</td>\n",
       "      <td>...</td>\n",
       "      <td>Nia Vardalos</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>tt1742650</td>\n",
       "      <td>I Don't Know How She Does It</td>\n",
       "      <td>...</td>\n",
       "      <td>Sarah Jessica Parker</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2020 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         imdbID                                       Title   ...    \\\n",
       "0     tt2488496  Star Wars: Episode VII - The Force Awakens   ...     \n",
       "1     tt0499549                                      Avatar   ...     \n",
       "2     tt0369610                              Jurassic World   ...     \n",
       "...         ...                                         ...   ...     \n",
       "2017  tt0183659                                     Pollock   ...     \n",
       "2018  tt0345074                            Connie and Carla   ...     \n",
       "2019  tt1742650                I Don't Know How She Does It   ...     \n",
       "\n",
       "                 LeadActor  Gender  \n",
       "0             Daisy Ridley       f  \n",
       "1          Sam Worthington       m  \n",
       "2              Chris Pratt       m  \n",
       "...                    ...     ...  \n",
       "2017             Ed Harris       m  \n",
       "2018          Nia Vardalos       f  \n",
       "2019  Sarah Jessica Parker       f  \n",
       "\n",
       "[2020 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop Cast, Metacritic and Meter columns here since we are not going to use them.\n",
    "df_grossing = df_grossing.drop(['Cast', 'Metacritic', 'Meter'], axis=1)\n",
    "df_grossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#check null values in boxOffice column and Meter column\n",
    "n_nan_boxOffice = sum(df_grossing['AdjBoxOffice'].isnull())\n",
    "print(n_nan_boxOffice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there is no null data in this dataset, so we do not need to drop any rows and just use it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Spoke Frequency VS Gender\n",
    "We want to know how the spoke frequency of different characters on movies is vary based on their genders, so we use the data frame `dialogue_gender_df` to calculate the number of dialogue that female and male characters spoke on each movie. Store these two values to the list `f_distribution` and `m_distribution`. Also, we sum up the percentage of dialogue that female and male spoke on each movie, divide them by the length of the movie to get the average dialogue distribution for female and male. Store these two values in `f_average` and `m_average`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the number of dialogue that female and male have on each movie\n",
    "allmovie = dialogue_gender_df[\"movie_ID\"].unique()\n",
    "f_distribution = []\n",
    "m_distribution = []\n",
    "f_average = 0\n",
    "\n",
    "for i in range(len(allmovie)):\n",
    "    currmoive = dialogue_gender_df[dialogue_gender_df['movie_ID'] == allmovie[i]]\n",
    "    female = len(currmoive[currmoive['gender'] == 'f'])\n",
    "    male = len(currmoive[currmoive['gender'] == 'm'])\n",
    "    f_distribution.append(female)\n",
    "    m_distribution.append(male)\n",
    "    f_average = f_average + float(female)/float(female+male)\n",
    "    \n",
    "f_distribution = np.array(f_distribution)\n",
    "m_distribution = np.array(m_distribution)\n",
    "f_average = f_average / float(len(allmovie))\n",
    "m_average = 1 - f_average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "### -Boxplots "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make a boxplot on the list `f_distribution` and `m_distribution`. From this graph, we will be able to see the difference middle and distributions between the number of dialogue female and male spoke on movies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6MAAAJzCAYAAAD6L8WHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XlYlPX+//EXw6IoiIqChZVoiYkKuGB2CHIpS6Usdww0Oi2WpFaW5jnZYlliZGLa1woLE/clPdpxS7SxUnHL3Mo0S1wozYU02eb3hz/mOAIKA84tzvNxXV6Xc2/znoG57nnx2VwsFotFAAAAAAA4kMnoAgAAAAAAzocwCgAAAABwOMIoAAAAAMDhCKMAAAAAAIcjjAIAAAAAHI4wCgAAAABwOMIoAFSw5ORkBQUFXfbf7t27jS6ziKCgII0YMcLoMsqkNDXHxsYqKChI4eHhysvLK/aY/Px8tW3bVkFBQUpOTr4qdZRWYb0X/2vRooU6duyo0aNH68iRI0XO6dChg2JjY8v8XPaedy0p7vPWrFkzRUREaPDgwdqyZUuRc0aMGKGgoKAyP1dBQYEOHTp0xeM2bNigoKAgLViwoNjHFeW3336zeVwZP8MAnJub0QUAwPXqqaeeUsOGDYvdd+ONNzq4Gpw6dUoZGRm64447iuzbtGmTTp48aUBVJRs3bpz1/2fPntVPP/2k+fPn68svv9TMmTPVqFEj6/6XX35Znp6eRpR5zbj483b+/HkdPnxYixYtUv/+/TVu3DhFR0dbj+3Tp4/atWtXputnZ2dr4MCBioqKUkJCwmWPbdSokcaNG6eWLVuW/YWU0iuvvKIDBw5o+vTp1m3jxo3TzTfffNWeEwAqGmEUAK6SO++8U23btjW6DEiqU6eO/vrrL61evbrYMLpq1SrVrl1bJ06cMKC64j344INFtvXq1Ut9+/bVkCFDtHjxYplMFzo4derUydHlXXOK+7zFx8erb9++GjVqlFq1amX9I1BYWJjCwsLKdP2TJ09qx44dioqKuuKxderUKfbnV5HMZrMCAgJstl3t5wSAikY3XQDAda9KlSr6xz/+odWrVxe7f9WqVerQoYODqyq722+/XU8++aR++uknrVmzxuhyrnk1atTQ66+/rvPnz+uzzz4zuhwAwCUIowBgsK1bt+rRRx+1ttbEx8fr+++/tzmmQ4cOev311zV37lx17txZLVq0UI8ePfT999/r999/15AhQxQWFqa77rpL7733ngoKCqzn5ubm6v/+7//0wAMPKDQ0VC1atNADDzygefPmXbG2NWvWqG/fvgoJCVGbNm2UkJCgAwcOXPG80j5nUFCQpk6dqmnTpqlTp05q1qyZoqOj9eWXXxa55owZM6yvvWfPntq7d+8V67hYp06dlJmZWeS8HTt26MiRI7r33nvtfh3Fsfe9u5LC7qZff/21ddulYz8tFotmzpypnj17KiwsTM2bN9d9992nqVOnymKxXPb6GRkZGjhwoPX3MS4uTps2bSpy3Nq1a9WrVy+FhoaqY8eO+vzzzzVq1CibUB8bG1tsyC9u+759+/TMM8+odevWCgkJUd++fW1eo71at26tG2+80eZal44ZtVgsmjRpkjp37qzmzZvrzjvv1PDhw63jczds2KCOHTtKkiZNmqSgoCAdOnRICxYsUFBQkJYvX64OHTooJCREycnJJY4Rzc7O1siRI9WyZUuFh4frxRdf1O+//27dX9J5l24PCgpSZmamNm7cWGT7pWNGV61apb59+6pFixZq3bq1nnrqKe3Zs8fmmLJ8DgGgIhFGAeAqOXPmjE6cOFHkX25urvWY9evXKzY2VmfOnNGQIUM0aNAgHT58WP3791dGRobN9VavXq33339fPXv21ODBg7V//34lJCTo0Ucflclk0ogRI9S4cWN9+OGH+uKLL6znjRw5UhMnTlR4eLhGjRqlwYMH6+zZsxo1apQ2btxYYv0LFizQoEGD5OnpqeHDh2vgwIHaunWrevfufcVQVZbnnDlzpj799FP17t1bL774os6ePathw4bpxx9/tB6TnJys119/XTfddJNeeuklNWrUSP379y/Vz6FQ+/bt5erqWqR1dNWqVWrQoIHNGEx7XsfFyvPeXclNN90kT0/PIoHiYhMmTNCrr76qW2+9VSNHjtRzzz2nKlWq6N1339WiRYtKPG/16tWKjY3VkSNHNGjQIA0aNEhHjhzRwIEDbd63NWvWaNCgQcrNzdWwYcPUuXNnvfPOO1q1apVdr2nv3r3q06eP9u3bpyeffFLDhg1TXl6ennjiCS1btsyua17stttu088//6ycnJxi93/44Yf64IMPdNddd+mVV15Rr169tGrVKsXHxys/P1+NGjXSyJEjJUn33HOPxo0bp9q1a1vPHzlypKKjo5WQkHDZsajvvfeefvjhBz377LPq0aOHli1bpri4OP39999lej3jxo1TrVq11LBhQ40bN05t2rQp9rgZM2bomWeeUW5urp577jkNHDhQ33//vfr161fkD16l+RwCQEVjzCgAXCXPPPNMsdtTU1PVtm1bFRQUaPTo0WrevLk+//xzubq6SpIeeeQRde/eXWPGjLEJDseOHdMXX3xhbdE5efKkPvnkE7Vs2VLvvfeepAutZuHh4TKbzXrooYf0+++/6z//+Y8ef/xxPf/889ZrderUSffff79WrFih8PDwIjVmZ2frzTffVJcuXZSUlGTd3rt3b3Xt2lXjx4/XBx98UOzrK+tznjx5UitWrFDdunUlSSEhIerdu7eWLl2qxo0b68SJE5o6dao6deqkSZMmycXFRf3791dycrImTZp0+R/CRWrWrKlWrVpp9erVevrpp63bV65cWWyrqBHvXWnVqFGjxAmXcnNz9fnnn6tr1656++23rdt79eqldu3aafny5XrooYeKnJeXl6fXX39d/v7+mj9/vry8vCRJffv2Vbdu3fTaa68pMjJS7u7ueuutt3TTTTdp1qxZqlq1qiSpZcuWeuaZZ1S9evUyv54xY8aodu3aWrhwoapVqybpwudgwIABevPNN9WpUyd5eHiU+bqFatSoIenCJFaFv2cXW7JkiSIjI/Wvf/3Luu2GG27QzJkzlZmZqZtvvlmdOnXS2LFjFRQUVGRs5r333qthw4ZZH2/YsKHYOmrXrq3Zs2dbX2Pjxo01YsQIzZ07t0yzGj/44IN6//33Lzs29c8//1RiYqJatGihGTNmWN+/7t27q1u3bnrjjTc0d+5c6/FX+hwCwNVAGAWAq+Sll15SkyZNimwv3LZr1y799ttv6tevn06dOmVzTPv27fXpp5/q6NGjqlevniTp5ptvtulaGBgYKOlCS02hatWqydfX19r1r27dutq8ebN1ohvpQpfEwiVO/vrrr2JrX79+vbKzs9WpUyebSX1cXV11xx13aO3atcrLy5ObW9HbSFmfs1WrVjYB4fbbb5ck62vYsGGDcnJy1Lt3b7m4uFiPi42NLVMYlWQNFMeOHZO/v7/279+vn3/+2Sa02fs6CpXnvSutvLw8m/fiYu7u7vrmm29sWuClC+HEy8tLZ8+eLfa8Xbt26ejRo3rhhResQVS6EOQeeeQRvfvuu/rhhx/k6empX3/9VSNGjLAGUenCe9uoUaMyt/L9+eef2rhxo2JjY/X333/bnH/PPfdo7Nix2rFjh1q1alWm616s8GdW0ntWr149bdiwQZ999pm6du2qOnXqqG/fvurbt2+prh8REVGq42JiYqxBVJIeeOABjR07Vunp6RW+xM63336rc+fO6dFHH7UJ8vXr19cDDzyg2bNnKysrS35+fpKu/DkEgKuBMAoAV0lwcPBlZ9P99ddfJV3ocnfxMh4XO3LkiDWM+vr62uwrbEm9uLtg4faLxwV6eHho8eLFMpvN+uWXX3Tw4EFrkCpp/GBhbRe39lzqxIkT1i+ylyrLc15af+EX58Jxr5mZmZJUZMmKmjVrFnlPrqRjx45666239NVXX6lfv35atWqV6tWrp+bNm1ufx97XUai8792V5Ofn6/Tp09Y/RhTH3d1d6enpWr16tQ4cOKCDBw9a/+BRUt2F62cWd93CJVMOHz5sDdG33HJLkeMCAwPLvIZu4VqZ06dPt1mm5GLFra1aFidPnpSrq6u1hfRSL774ogYNGqS33npLY8eOVXBwsDp06KDevXsX25J6qdL+Hl661JOrq6sCAgKK/d0rr8KfZ3HLSxV2ST98+LD19/BKn0MAuBoIowBgkMIveUOGDFFoaGixx1z8RbKklrSSWnskKScnR4899pg2b96stm3bql27dho4cKDCw8N19913X7G2N954Q/Xr1y/2GB8fnwp5zotbHotT+PrOnz9fYp2lVb9+fTVp0kSrV69Wv379tHLlSt1zzz3FvodGvHelsW/fPuXm5hbb6i5dCJvDhw/Xf/7zH7Vq1UphYWHq06eP2rRpowEDBpR43ctNbFS4z93d3driWly32SpVqpTqNeTn5xf5f//+/UtcoubWW28t1XWLY7FYtGfPHjVq1KjErr5NmjTR8uXL9fXXX2vNmjX6+uuvNXHiRH366aeaNWtWseOJL3al3+FCxf2eFRQUWP+wVJKKDoQX/zwLlfY1AEBFIowCgEEK1wisVq2a7rzzTpt933//vU6dOmXTDdIey5Yt08aNG/Xmm2+qZ8+e1u3Hjh0rVW21a9cuUtuGDRtUUFBQ4hd7e5+zJDfddJMk6ZdffrEJYNnZ2frzzz/LfL1OnTpp6tSp2r9/v3bs2GEzHvRiRrx3pfHf//5Xkqyzu14qIyND//nPf/T0009ryJAh1u15eXk6efKk9f0sqe79+/cX2Vc46VJhK7104edxaffUX375xeaxyWQqdtKgP/74o8jzurq6Fnm/9u3bp0OHDsnT07PYmkvju+++059//qk+ffoUuz8/P1979uyRl5eXOnbsaH1fly1bpmHDhmnu3LlFZqi116UtoLm5ucrMzFTr1q0l/a+3w6XvmT1dZS/+eV76h4vCn/HFP08AMAJ/BgMAgzRr1kx169bV9OnTbcYfZmdna+jQoRo5cuQVW0yupHCSm0tbllJTUyX9byzdpe68805VqVJFH3/8sc3Yw2PHjunpp5/W+PHjS2yRtfc5S3LnnXeqWrVq+uyzz2zOnTFjRpmuU6hTp07KycnRm2++KR8fnxJnIjXivbuSffv26dNPP1VwcHCJs7aWVPecOXN07ty5EusODg5W3bp1NXPmTGVnZ1u3Z2dnKy0tTXXr1lWzZs3UrFkz3XDDDZo3b55NaNq2bZt27dplc806dero+PHjNgH+hx9+0MGDB62P/fz81KxZMy1cuNDmuNzcXL388st69tlny/w7c3Htb7/9tqpVq1bi7Mv5+fmKi4vTW2+9ZbM9JCRE0v9aDAs/i+VppVywYIFNq/DcuXN15swZa4twnTp1JKlIV+fiZhQ2mUyXraXw93DatGk2P6ejR49qyZIlatGiRZm7uQNARaNlFAAM4u7urn//+98aOnSoHn74YfXs2VNVqlTR3LlzdfjwYY0fP75ck9xIF76Qurm56cUXX1T//v3l5uamNWvWyGw2y93dvcRJeGrXrq3nnntOY8eOVZ8+ffTAAw8oLy9PaWlpOn/+vF566aUKf86SeHl5afjw4Xrttdc0YMAA3X///frpp5+0ePFiu1rMbr/9dgUEBMhsNuvhhx8uMfAb8d5d7OLlec6ePau9e/fqiy++kKenpxITE0sMtGFhYfLy8tLYsWN1+PBh1ahRQxs2bNCyZctUpUqVEuu++PexR48e1tbgefPmKSsrSxMnTrQGsxEjRmjo0KHq27evHnzwQZ04cUKpqalFWny7detmnZG4X79+On78uKZPn64GDRrYBPV//etfGjBggHr06KF+/fqpZs2aWrp0qbZv367nn39etWrVuuL79c033+jo0aOSLrQsHjp0SIsXL1ZWVpbGjx9/2fHNsbGxmjJlip555hnddddd+vvvvzV79mx5enqqR48eki6MUTaZTPrqq6904403FjsD85UcOnRIAwYMULdu3bRv3z6lpaUpLCxM3bt3lyQ1aNBAwcHBmjNnjqpVq6YGDRpo5cqV1nG1F6tdu7b27NmjtLQ0hYeHF/njQ61atay/h/369VN0dLT++usvzZw5UwUFBTYzBwOAUQijAGCgzp07KyUlRVOmTNHkyZNlMpl02223acqUKWrfvn25r9+4cWNNnDhRkyZNUlJSkqpXr67bbrtN06ZNU1pamjZu3Kjc3FybsWOFBg4cKH9/f02bNk3vvfeeqlatquDgYCUmJl52ZtPyPGdJYmJi5O3tralTp+qdd95RgwYNNHny5FIHu0t17NhRqamplw0URrx3F3vxxRet//fx8VG9evXUo0cPPf744/L39y/xvDp16mjq1KkaP368Jk+eLA8PDwUGBiopKUnff/+9UlNT9ccff1hb4S5W+Ps4efJkffDBB3Jzc1NISIjefPNNa1dSSbrvvvv03nvvacqUKUpMTJS/v79GjhypRYsW2cwg3L59e73yyitKTU3Vm2++qcDAQL366qvatGmT0tPTrceFhYVp5syZSk5O1rRp05SXl6fAwEC9/fbbxS5DU5wPP/zQ+n9PT0/5+/srNDRU8fHxat68+WXPffbZZ1WzZk3Nnz9f77zzjlxdXdWyZUslJiZax4t6enpq2LBh+uSTTzRmzJgiE2qVxujRo7VixQq9/fbbqlq1qvr27avnnnvO5o9OEydO1Ntvv61Zs2bJzc1NHTp00Msvv6z777/f5loJCQkaPXq03nrrLT3zzDPFjqsdOHCg/Pz8lJKSoqSkJHl6eio8PFyDBw+2mZkbAIziYrncjAUAAAAXyc/P16lTp4rMvipdWOe2Ro0adnehBgA4F8aMAgCAUsvPz1dkZKReeeUVm+0//vijfvrpJ7Vo0cKgygAAlQ3ddAEAQKl5eHjovvvu07x58+Ti4qJmzZopKytLM2fOVK1atfToo48aXSIAoJKgmy4AACiTv//+W5988okWL16sI0eOyNvbW+3atdPQoUNLXFsVAIBLEUYBAAAAAA7HmFEAAAAAgMMxZtROFotFtCkD9ilcHpHPEADAKNyLgPJxcVGJa16XFmHUThaLdPx4ttFlAJWSj4+nJOnUqXMGVwIAcFbci4Dy8fX1UjmzKN10AQAAAACORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAABOJz09XU2aBclsXmd0KYDTIowCAADAqZjN69Szbw95h/spLj6GQAoYhDAKAAAAp2E2r1NcfIxaDo5SUHSoWiVEEUgBgxBGAQAA4BQKg2irhCj5BQdIkvyCAwikgEGumTC6e/duBQcH6+jRozbbzWazevTooZCQEHXo0EEpKSlFzt2xY4diY2MVFhamiIgIJSUlKTc31+aYX375RU899ZRat26ttm3bavTo0crOzr6qrwkAAADXjueGJ6hh16bWIFrILzhADbs21XPDEwyqDHBO10QY3b9/v5588knl5eXZbN+yZYueeuopNWzYUMnJyYqOjta4ceP0ySefWI85ePCgBg4cqCpVqmjChAmKj4/XtGnTNHbsWOsxp06d0oABA/THH3/onXfe0fPPP69ly5bp+eefd9hrBAAAgLGSEpO1f+kuZe3MtNmetTNT+5fuUlJiskGVAc7Jzcgnz8vL0+zZs/Xuu+/K3d29yP6JEyeqadOmSkxMlCRFRkYqLy9PH374oWJjY+Xh4aGpU6fK29tbkydPloeHh6KiolS1alWNGTNGTz75pPz9/TVjxgydPn1aixYtUq1atSRJ/v7+euKJJ7R9+3aFhIQ49HUDAADA8SIiIpWakmbTVTdrZ6Y2J69VakqaIiIijS4RcCqGtoxu3rxZ48ePV3x8vF544QWbfefPn1dGRobuvfdem+2dO3fW6dOntWXLFknS+vXr1b59e3l4eFiPue+++5Sfny+z2Ww9pk2bNtYgKkkRERGqXr261q5de7VeHgAAAK4xhYF0y6S12rtkG0EUMJChLaONGjXSqlWr5OvrqwULFtjs++2335Sbm6vAwECb7bfccosk6cCBAwoJCdGRI0eKHFO7dm15eXnpwIEDki50A37ggQdsjnF1dVX9+vWtx5SVi4vk4+Np17mAs3Nzc5XEZwgAYIyuXTtr4dyFemLQE5o/e77uvvtuo0sCKh0Xl/Jfw9AwWqdOnRL3nTlzRpLk5eVls7169eqSpOzs7BKPKTyucIKiM2fOXPEYAAAAOI/27dvrpz0/KS8v3+hSAKdlaBi9HIvFIklyKSFym0ymyx5jsVhkMv2vF3JpjilbfdKpU+fsOhdwdoUtonyGAABG4V4ElI+vr1e5W0evidl0i+Pt7S1JRVouCx97e3tbWzuLa908e/as9RpeXl7FHvPXX38V22IKAAAAALi6rtkwevPNN8vV1VW//vqrzfbCx4GBgapevbr8/f118OBBm2OOHz+u7Oxs61jSwMDAIsfk5+fr0KFDRcabAgAA4PqXnp6uJs2CZDavM7oUwGlds2G0SpUqat26tVasWGHtjitJy5cvl7e3t5o1ayZJ+sc//qE1a9YoJyfH5hhXV1eFh4dbj9mwYYNOnjxpPcZsNuvs2bO68847HfSKAAAAcC0wm9epZ98e8g73U1x8DIEUMMg1G0YladCgQdqyZYuGDRumtWvXasKECfrkk0/05JNPytPzQj//f/7zn/r999/1xBNPaM2aNZo2bZrGjh2r3r1768Ybb5QkxcTEyMPDQwMHDtTKlSs1d+5cDR8+XJGRkWrZsqWRLxEAAAAOZDavU1x8jFoOjlJQdKhaJUQRSAGDuFgubnY00IIFCzRy5EitXbtW9erVs25fuXKlJk6cqAMHDsjf31/9+/dXfHy8zbkZGRkaN26cdu/erVq1aql79+5KSEiQu7u79Zgff/xRb731lrZu3arq1aurU6dOevHFF+0eM1pQYNHx48zEC9iDSSMAAEYoDKKtEqLkFxxg3Z61M5P1RoEy8vX1kslUvhmMrpkwWtkQRgH7EUYBAEYIbxeiGnf4Kyg6tMi+vUu26fR3x7Tx2+0GVAZUPhURRq/pbroAAABARUlKTNb+pbuUtTPTZnvWzkztX7pLSYnJBlUGOCfCKAAAAJxCRESkUlPStDl5rTWQ0kUXMA7ddO1EN13AfnTTBQAYyWxep/5xvWVxd5FyLEqbPocgCpQR3XQBAAAAO7iYXNS4S/Nyf5kGYD/CKAAAAJxG4Yy6bYZ0UFB0qNoM7cDSLoBBCKMAAABwCsUt7eIXHMBao4BBGDNqJ8aMAvZjzCgAwAgs7QJUHMaMAgAAAKXE0i7AtYUwCgAAAKfA0i7AtYVuunaimy5gP7rpAgCMZDav04DHYhTYpan2L91FEAXsQDddAAAAoIwiIiI1b9Z8ndmYRRAFDETLqJ1oGQXsR8soAMBo3IuA8qFlFAAAAABQKRFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAAAAAORxgFAAAAADgcYRQAAAAA4HCEUQAAAACAwxFGAQAA4HTS09PVpFmQzOZ1RpcCOC3CKAAAAJyK2bxOPfv2kHe4n+LiYwikgEEIowAAAHAaZvM6xcXHqOXgKAVFh6pVQhSBFDAIYRQAAABOoTCItkqIkl9wgCTJLziAQAoYxMVisViMLqIyKiiw6PjxbKPLAColHx9PSdKpU+cMrgQA4EzC24Woxh3+CooOLbJv75JtOv3dMW38drsBlQGVj6+vl0wml3Jdg5ZRAAAAOIWkxGTtX7pLWTszbbZn7czU/qW7lJSYbFBlgHMijAIAAMApREREKjUlTZuT11oDadbOTG1OXqvUlDRFREQaXCHgXOimaye66QL2o5suAMBIZvM6DXgsRoFdmmr/0l0EUcAOdNMFAAAAyigiIlLzZs3XmY1ZBFHAQLSM2omWUcB+tIwCAIzGvQgoH1pGAQAAAACVEmEUAAAAAOBwhFEAAAAAgMMRRgEAAAAADkcYBQAAAAA4HGEUAAAAAOBwhFEAAAAAgMMRRgEAAAAADkcYBQAAAAA4HGEUAAAAAOBwhFEAAAAAgMMRRgEAAAAADkcYBQAAAAA4HGEUAAAAAOBwhFEAAAAAgMMRRgEAAAAADkcYBQAAAAA4HGEUAAAAAOBwhFEAAAAAgMMRRgEAAAAADkcYBQAAAAA4HGEUAAAAAOBwhFEAAAAAgMMRRgEAAOB00tPT1aRZkMzmdUaXAjgtwigAAACcitm8Tj379pB3uJ/i4mMIpIBBCKMAAABwGmbzOsXFx6jl4CgFRYeqVUIUgRQwCGEUAAAATqEwiLZKiJJfcIAkyS84gEAKGMTFYrFYjC6iMioosOj48WyjywAqJR8fT0nSqVPnDK4EAOBMwtuFqMYd/gqKDi2yb++SbTr93TFt/Ha7AZUBlY+vr5dMJpdyXYOWUQAAADiFpMRk7V+6S1k7M222Z+3M1P6lu5SUmGxQZYBzIowCAADAKURERCo1JU2bk9daA2nWzkxtTl6r1JQ0RUREGlwh4FzopmsnuukC9qObLgDASGbzOg14LEaBXZpq/9JdBFHADnTTBQAAAMooIiJS82bN15mNWQRRwEC0jNqJllHAfrSMAgCMxr0IKB9aRgEAAAAAlRJhFAAAAADgcIRRAAAAAIDDEUYBAAAAAA5HGAUAAAAAOBxhFAAAAE4nPT1dTZoFyWxeZ3QpgNMijAJwOL4AAACMZDavU8++PeQd7qe4+BjuR4BBCKMAHIovAAAAI5nN6xQXH6OWg6MUFB2p6NKGAAAgAElEQVSqVglR3I8AgxBGATgMXwAAAEYqvA+1SoiSX3CAJMkvOID7EWCQShFGZ86cqfvvv1+hoaGKjo7W4sWLbfabzWb16NFDISEh6tChg1JSUopcY8eOHYqNjVVYWJgiIiKUlJSk3NxcR70EwOnxBQAAYLTnhieoYdem1vtQIb/gADXs2lTPDU8wqDLAObm++uqrrxpdxOXMnj1bo0eP1kMPPaSnn35aBQUFSkxM1K233qrbbrtNW7Zs0WOPPabw8HANHTpU3t7emjhxojw9PdWyZUtJ0sGDB9WvXz/Vr19fL730kho0aKApU6boxIkTioqKsqsui0U6dy6nIl8qcF3r2ecB1esQqFsiGttsr+5XQwWWfC2eNleP/3OQQdUBAJzB7U2ClTL+Q/kE1lZ1vxrW7Vk7M7Xr8wx9+MEnuvnmWwysEKg8qlXzkIuLS7mu4WKxWCwVVM9V0bdvX3l4eCg1NdW6rX///jKZTJo+fboGDhyos2fPas6cOdb9iYmJmjNnjtavXy8PDw+NGjVK69ev14oVK+Th4SFJSktL05gxY7RmzRr5+/uXua6CAouOH88u/wsEnERxLaPShS8Am5PXKjUlTRERkQZWCABwBpfej7gPAfbx9fWSyVS+MHrNd9M9f/68qlevbrOtZs2aOnnypM6fP6+MjAzde++9Nvs7d+6s06dPa8uWLZKk9evXq3379tYgKkn33Xef8vPzZTabr/6LAKCIiEilpqRpc/JaZe3MlEQQBQA4XuH9aMuktdq7ZBv3IcBAbkYXcCVxcXH697//rS+//FJ33XWXzGaz0tPTNWzYMP3222/Kzc1VYGCgzTm33HKhe8WBAwcUEhKiI0eOFDmmdu3a8vLy0oEDB+yqy8VF8vHxtO9FAU6qa9fOmj97vnr27aHALk11YNkuzZ89X3fffbfRpQEAnEjXrp21cO5CPTHoCe5DgJ3K2UNXUiUIo127dtV3332noUOHWrc99NBD+uc//6mtW7dKkry8vGzOKWxJzc7O1pkzZ4o9pvC47Gy62gKOdPfdd1u/AMybxRcAAIAx2rdvr5/2/KS8vHyjSwGc1jUfRgcNGqStW7dq5MiRatq0qbZv367JkyfLy8tLXbp0kaQSB86aTCYVDokt7hiLxSKTyb6eyhaLdOrUObvOBZzdXXdFavcPe3Tq1Dk+RwAAQxT2cOM+BNjH19er3K2j13QY3bJli8xms8aOHauHH35YkhQeHq4aNWrolVdeUc+ePSWpSOtm4WNvb29ri2hxLaBnz56Vt7f31XwJAAAAAIBiXNMTGB0+fFiSrEu0FGrdurUkaffu3XJ1ddWvv/5qs7/wcWBgoKpXry5/f38dPHjQ5pjjx48rOzu7yFhSAAAAXP/S09PVpFkQ61wDBrqmw2hhUNy0aZPN9m3btkmSGjZsqNatW2vFihW6eIWa5cuXy9vbW82aNZMk/eMf/9CaNWuUk5Njc4yrq6vCw8Ov9ssAAADANcRsXqeefXvIO9xPcfExBFLAIK6vvvrqq0YXURI/Pz/t2bNHaWlpqlq1qs6fP6/ly5fr3XffVbt27fT444+rXr16+vDDD/Xzzz/L09NTixYt0kcffaSEhAS1bdtW0oVQm5KSooyMDPn4+Cg9PV2JiYnq1auXoqOj7arNYpHOncu58oEAiqha1V2SdP58nsGVAACczcXrjN4S0Vg+gbU1eVSSWoW11s0332J0eUClUa2aR4lz95SWi+XiJsVrUE5OjiZNmqTFixfr+PHjCggIULdu3fTEE09Y1w1duXKlJk6cqAMHDsjf31/9+/dXfHy8zXUyMjI0btw47d69W7Vq1VL37t2VkJAgd3d3u+oqKLDo+HFm4gXswaQRAAAjXBxE/YIDrNtZ9xooO19fL5lM13kYvVYRRgH7EUYBAEYIbxeiGnf4Kyg6tMi+vUu26fR3x7Tx2+0GVAZUPhURRq/pMaMAAABARUlKTNb+pbuUtTPTZnvWzkztX7pLSYnJBlUGOCfCKAAAAJxCRESkUlPStDl5rTWQ0kUXMA7ddO1EN13AfnTTBQAYyWxepwGPxSiwS1PtX7qLIArYgW66AAAAQBlFRERq3qz5OrMxiyAKGIiWUTvRMgrYj5ZRAIDRuBcB5UPLKAAAAACgUiKMAgAAAAAcjjAKAAAAp5Oenq4mzYJkNq8zuhTAaRFGAQAA4FTM5nXq2beHvMP9FBcfQyAFDEIYBQAAgNMwm9cpLj5GLQdHKSg6VK0SogikgEEIowAAAHAKhUG0VUKU/IIDJEl+wQEEUsAgLO1iJ5Z2AezHdPoAACOEtwtRjTv8FRQdqqydmdqSYlbL+Aj5BQdo75JtOv3dMW38drvRZQKVAku7AKiUmDQCAGCEpMRk7V+6S3uXbtf68f9V7tkcrR//X+1dul37l+5SUmKy0SUCToUwCsChmDQCAGCUiIhIDR82Qj/M2iCTyaTGXZrLZDLph1kbNHzYCEVERBpdIuBU6KZrJ7rpAmV36VidrJ2Z2py8VqkpaXwBAABcdWbzOsXE9laB8tXuuXut96Jvk1bIJFelTZ/D/QgopYropksYtRNhFCib4iaNkEQgBQA4TPPQxjpx8oTufL5zkXvRN+8uV+2atbVj248GVghUHowZBVBpPDc8QQ27NrW5+UsXZjFs2LWpnhueYFBlAABn4WIy6faHWhZ7L7r9oZZyMfHVGHAkPnEAHKJw0oisnZk227N2ZjJpBADAISZPnKp9i3cUey/at3iHJk+calBlgHMijAJwiIiISKWmpGlz8lrrlwC66AIAHCkiIlKffzZbmyZ8ZXMv2jThK33+2WzuRYCDMWbUTowZBexjNq/TgMdiFNilqfYv3UUQBQA4nNm8To8M7KNbo5tr3+IdBFHADowZBVDpREREat6s+TqzMYsgCgAwREREpEaPelV7F23TSy+M4l4EGISWUTvRMgrYz8fHU5J06tQ5gysBADgjeukA5UfLKAAAAFAGhUuNtRwcpaDoULVKiFJcfIzM5nVGlwY4HcIoAAAAnEJxa177BQcQSAGD0E3XTnTTBexHN10AgBHC24Woxh3+CooOVdbOTG1JMatlfIT8ggO0d8k2nf7umDZ+u93oMoFKoSK66bpVUC0AAADANS0pMVlx8TGSyUW75mXIraqH1o//r5r2bG0dOwrAceimCwAAAKcQERGp4cNG6IdZG2QymdS4S3OZTCb9MGuDhg8bwSRGgIMRRgEAAOAUzOZ1GjtujNw83NXuuXsVFB2qds/dKzcPd40dN4Yxo4CDMWbUTowZBezHmFEAgBGahzbWiZMndOfzna0TGElS1s5MffPuctWuWVs7tv1oYIVA5cHSLgAAAEAp5eXl6faHWtoEUenCjLq3P9RSeXl5BlUGOCfCKAAAAJyCm5ubdi3YrKydmTbbs3ZmateCzXJzY25PwJEIowAAAHAKUyZ9LDeTu755d7k1kBZ20XUzuWvKpI8NrhBwLowZtRNjRgH7MWYUAGAUs3md+sT0UIElX8E9W2vnvAyZXFw1O20+s+kCZcCYUQAAAKCM3N3dFNy7jQ6k71Vw7zZyd6d7LmAEWkbtRMsoYD9aRgEARjCb1ykuPkatEqKKzKa7OXmtUlPSaB0FSqkiWkYJo3YijAL2I4wCAIwQ3i5ENe7wV1B0aJF9e5ds0+nvjmnjt9sNqAyofOimCwAAAJTSo3GPa+e8jGJn0905L0OPxj1uUGWAc6Jl1E60jAL2o2UUAGCE8HYhMjWoqkMbD6jd0HvkFxygrJ2Z+nbCStUPD1TBL3/TMgqUEi2jAAAAQCklJSYra3OmmnQP07cTVmrvkm36dsJKNekepqzNmUpKTDa6RMCp0DJqJ1pGAfvRMgoAMErhJEYNo4N14Ks9CuzQRPuX7GTyIqCMaBkFAAAAyiAiIlKpKWk68J+dCrw7iCAKGIgwCgAAAKcSERGpebPm68zGLIIoYCC66dqJbrqA/eimCwAwGvcioHzopgsAAAAAqJQIowAAAAAAhyOMAgAAAAAcjjAKAAAAAHA4wigAAAAAwOEIowAAAAAAhyOMAgAAAAAcjjAKAAAAAHA4wigAAACcTnp6upo0C5LZvM7oUgCnRRgFAACAUzGb1+mhXt3lHe6nRwb0IZACBiGMAgAAwGmYzev0yIA+ajOkg4KiQ9VmaAcCKWAQwigAAACcgjWIDu0gv+AASZJfcACBFDCIi8VisRhdRGVUUGDR8ePZRpcBVEo+Pp6SpFOnzhlcCQDAmbRo2UR+UTcrKDq0yL69S7Ypa+2v+n7LHgMqAyofX18vmUwu5boGLaMAAABwCpaCAu1euEVZOzNttmftzNTuhVtkKSgwqDLAORFGATgcMxgCAIwwZdLHcnVx07dJK6yBNGtnpr5NWiFXFzdNmfSxwRUCzqXM3XTz8vK0Y8cOHTlyROHh4apatary8/Pl4+NztWq8JtFNF7CP2bxOAx6LUWCXptq/dJdSU9IUERFpdFkAACdhNq9TTGxvFShfTbqHac+irTLJVWnT53A/AsrA4d10v/zyS919992KiYnR888/r59++kmbN29WVFSUPv6YvyQBuDyzeZ3i4mPUcnCUgqJD1SohSnHxMbSQAgAcJiIiUmnT58jVxVU/LttBEAUMVOowajab9fzzz6tBgwZ66aWXVNigWr9+fTVu3Fjvvvuuvvjii6tWKIDKrTCItkqIspnBkEAKAHC0iIhILZr/hW6o408QBQxU6m66/fr1U35+vmbNmqVTp06pXbt2mjZtmtq1a6e8vDzFxcXp77//1oIFC652zdcEuukCZRPeLkQ17vAvcQbD098d08ZvtxtQGQCgsoqMbKs9e3Yb9vxNmtyudes2GPb8gJEqopuuW2kP3L17t4YNGyaTqWhjqpubm7p166bExMRyFQPg+pWUmKy4+BjValjX2jIqXZg4onDsKAAAZVGeIOjj46kqVdyVlXW6AisCUBal7qbr7u6uvLy8EvefPHlS7u7uFVIUgOtPRESkUlPStDl5rc0MhpuT1zKJEQAAgBMqdRgNDw/XvHnzdP78+SL7srKylJaWplatWlVocQCuL4WBdMuktdq7ZBtBFAAAwImVeszozz//rD59+sjX11eRkZH6/PPP1b9/f7m6umrhwoXKycnRzJkzdfvtt1/tmq8JjBkF7Ld16wY9NfhJvfvORIIoAMAQdNMFyqcixoyWaZ3RvXv3asyYMdq0aZPN9mbNmulf//qXQkOLTkxyvSKMAvbz8fGUJJ06dc7gSgAAzoowCpSPw8NooZMnT+rXX39VQUGBAgICVLdu3XIVURkRRgH7EUYBAEYjjALl49DZdC9Ws2ZN1axZs1xPDAAAAABwXqUOox07dizVcatXr7a7GAAAAACAcyj1bLo33nhjkX/16tWTm5ubMjMz5e7urrvvvvsqlgrgepGenq4mzYJkNq8zuhQAAAAYpNQto9OnTy9x3w8//KB//vOfCg8Pr5CiAFy/zOZ1GvBYjAK7NFVcfAxLuwAAADipUreMXk6zZs30yCOP6IMPPqiIywG4TpnN6xQXH6OWg6MUFB2qVglRiouPoYUUAADACVVIGJWkOnXq6JdffqmoywG4zhQG0VYJUfILDpAk+QUHEEgBAACcVIWE0d9//10zZ87UjTfeWBGXA3Adem54ghp2bWoNooX8ggPUsGtTPTc8waDKAAAAYIRyz6abk5OjEydOKD8/X6NHj66wwgBcX5ISkxUXH6NaDevaBNKsnZnav3SXUlPSDKwOAAAAjlbqMFpSq6erq6vatm2rbt26MZsugBJFREQqNSXNpqtu1s5MbU5eyyRGAAAATsjFYrFYjC6iMioosOj48WyjywAqnYtn0y1sESWIAgAczcfHU1WquCsr67TRpQCVkq+vl0wml3Jdo8ImMAKA0oiIiNS8WfN1ZmMWQRQAAMCJlbpltEOHDnJxKTn5uri4yMPDQ76+vmrRooUeffRR1alTp8IKvdbQMgrYz8fHU5J06tQ5gysBADgrWkaB8nFoy2i7du2UnZ2tzMxMVa1aVbfffrtCQ0NVs2ZNHT58WH/88Ydq1aqlkydPKiUlRd27d9fhw4fLVRwAAAAA4PpU6gmMmjZtqiVLlmjy5Mnq0KGDzb5t27YpPj5e3bt3V69evfTjjz8qPj5e77//vt55550KLxoAAAAAULmVumV02rRpiouLKxJEJSk0NFSxsbGaOnWqJKlx48bq16+f1q9fX3GVAgAAAACuG6UOo8ePH5e/v3+J+319fXXs2DHrYz8/P2VnM6YSAAAAAFBUqcPorbfeqoULFyonJ6fIvpycHC1atEgNGza0btu5c6duuOGGiqkSAAAAAHBdKXUYHTx4sPbu3asHH3xQn332mdLT0/XNN99oxowZ6tu3r3bv3q1BgwZJkl599VXNmzdPDzzwQIUUuWnTJvXr108hISGKiIjQG2+8ob/++su632w2q0ePHgoJCVGHDh2UkpJS5Bo7duxQbGyswsLCFBERoaSkJOXm5lZIfQAAAACAsin1BEZRUVGaNGmS3nrrLY0dO9a6zIvFYtENN9ygCRMmqHPnzjpx4oTmzZunLl266LHHHit3gdu2bdOjjz6qDh06aMqUKTp48KCSkpJ04sQJvffee9qyZYueeuop3X///RoyZIg2b96scePGyWKxWJ//4MGDGjhwoMLCwjRhwgT9/PPPeu+995Sdna1XXnml3DUCAAAAAMqm1OuMXmzv3r06ePCg8vLyVL9+fTVv3twaTgsKCpSfny93d/cKKfCRRx6RJE2fPt36HDNmzNC0adO0ZMkSDRo0SGfPntWcOXOs5yQmJmrOnDlav369PDw8NGrUKK1fv14rVqyQh4eHJCktLU1jxozRmjVrLjsWtiSsMwrYj3VGAQBGY51RoHwcus5oofz8fP3999/Ky8uTh4eHTCaTNSRKkslkqrAgeuLECWVkZKhfv342z9G/f3+tWrVKJpNJGRkZuvfee23O69y5s06fPq0tW7ZIktavX6/27dtbg6gk3XfffcrPz5fZbK6QWgEAAAAApVfqbrqStGbNGr322ms2s+ZKF2bOHT16dLHLvpTHjz/+KIvFIh8fHw0dOlTp6elydXVVt27dNHLkSB06dEi5ubkKDAy0Oe+WW26RJB04cEAhISE6cuRIkWNq164tLy8vHThwwK7aXFz+17oDoGzc3Fwl8RkCABiHexFQPi7laxSVVIYwmpGRoYSEBPn6+mrYsGFq1KiRLBaL9u/fr7S0ND377LNKTU1Vy5Yty1/V/3fixAlJ0ogRI3TPPfdoypQp2rt3ryZMmKDz58+rT58+kiQvLy+b86pXry5Jys7O1pkzZ4o9pvA4lp8BAAAAAMcrdRhNTk5WQECA5s2bJ29vb5t9MTEx6tGjh6ZMmaKPPvqowoornO22ZcuWGj16tCSpXbt2slgseuedd9S7d29JsunCezGTyaTCIbHFHWOxWGQylbmn8v8/l/FugL0YMwoAMBr3IqB8fH29yt06Wuok9v3336tXr15Fgqh0odWxZ8+e2r59e/mquURhC2dkZKTN9oiICFksFu3YsUOSirRuFj729va2togW1wJ69uzZYl8PAAAAAODqsq9ZsBguLi4Vvm5ngwYNJEk5OTk22wufp379+nJ1ddWvv/5qs7/wcWBgoKpXry5/f38dPHjQ5pjjx48rOzu7yFhSAAAAAMDVV+owGhISonnz5uns2bNF9mVnZ2vu3Llq3rx5hRbXqFEjBQQEaNmyZTbb16xZIzc3N4WFhal169ZasWKFLl6hZvny5fL29lazZs0kSf/4xz+0Zs0am1C7fPlyubq6Kjw8vEJrBgAAAABcmeurr776amkOrF+/vj799FMtXrxY+fn5+vPPP3XgwAGtXr1aL7/8so4cOaIxY8aofv36FVaci4uL6tatq2nTpumXX36Rl5eXvvzyS33wwQeKjY3VPffco3r16unDDz/Uzz//LE9PTy1atEgfffSREhIS1LZtW0kXWkhTUlKUkZEhHx8fpaenKzExUb169VJ0dLRdtVks0rlzOVc+EEARVateWP7p/Pk8gysBADirqlXdNWbMGxo+fKTRpQCVUrVqHiXO3VNaLpaLmxSvYPXq1Xr99dd17Ngx6xNbLBbVrVtX//73v4us91lRVq1apQ8++ED79u2Tr6+v+vTpoyeffNI6+dDKlSs1ceJEHThwQP7+/urfv7/i4+NtrpGRkaFx48Zp9+7dqlWrlrp3766EhAS710QtKLDo+HFm4gXswaQRAACj+fh4qkoVd2VlnTa6FKBS8vX1ksnkwDAqSfn5+dq5c6cOHTokSQoICFBwcLDc3Mq0ZGmlRxgF7EcYBQAYjTAKlE9FhNEyJ0hXV1e1aNFCLVq0KNcTAwAAAACcV4lhNC4urswXc3Fx0WeffVauggAAAAAA178Sw2hhN1wAAAAAACpaiWH0q6++cmQdAAAAAAAnUup1RkvjxIkTFXk5AAAAAMB1qkwTGC1atEgrVqzQ2bNnVVBQYN2en5+vv/76S/v27dMPP/xQ4UUCAAAAAK4vpQ6jH330kZKSkuTu7i4vLy/9+eefqlevnk6ePKlz586patWqio2NvZq1AgAAAACuE6XuprtgwQI1adJE33zzjWbPni2LxaLU1FRlZGTolVde0fnz5xUSEnI1awUAAAAAXCdKHUYzMzP14IMPysvLSzfddJN8fHyUkZEhV1dXxcTEqEuXLizrAgAAAAAolVKHUTc3N1WvXt36+JZbbtHevXutj9u2batffvmlQosDAAAAAFyfSh1GGzVqpK1bt1ofBwYG2kxWdPr0aeXk5FRsdQAAAACA61Kpw+jDDz+sBQsW6IUXXtDZs2fVoUMHZWRkaNKkSVq2bJk+/fRTNWnS5GrWCgAAAAC4TpR6Nt1+/frp6NGjmjFjhtzc3HTvvfeqa9eumjRpkiTJy8tLL7zwwlUrFAAAAABw/XCxWCyWspyQl5cnN7f/ZdhNmzbp1KlTCgsLk6+vb4UXeK0qKLDo+PFso8sAKiUfH09J0qlT5wyuBADgrHx8PFWliruysk4bXQpQKfn6eslkcinXNUrdMmo9wc32lDZt2pSrAAAAAACA8ykxjMbFxWnQoEFq166d9fGVuLi4sLwLAAAAAOCKSgyjhw4d0tmzZ20eAwAAAABQEco8ZhQXMGYUsB9jRgEARmPMKFA+FTFmtNRLuwAAAAAAUFFKNYHRmjVr9PXXX2vPnj06efKkXFxcVLt2bQUFBaljx47WcaUAAAAAAJTGZbvp7t+/X0OGDNG+fftksVjk6empGjVqKC8vT6dPn1Zubq5cXFwUHBys8ePHq0GDBg4s3Vh00wXsRzddAIDR6KYLlM9VXdrl6NGj6tevn86fP69nn31W0dHRql+/vnV/QUGB9u3bpy+//FKfffaZHnnkES1cuFB169YtV0EAAAAAgOtfiWNGP/zwQ50/f16zZs3SoEGDbIKoJJlMJjVu3FhDhgzRnDlz9Ndff+njjz++6gUDAAAAACq/EsOo2WxWz5491aRJkyte5NZbb1X37t21YcOGCi0OAAAAAHB9KjGM/v7777rttttKfaGgoCAdOXKkQooCAAAAAFzfSgyj58+fV7Vq1Up9oWrVqun0aQaAAwAAAACujHVGAQAAAAAOd9kw6uJSvql6AQAAAAAoTonrjDZp0sSuMLp79+5yF1UZsM4oYD/WGQUAGI11RoHyuarrjLZp06ZcFwYAAAAAoCQlhtHp06c7sg4AAAAAgBNhAiMAAAAAgMMRRgEAAAAADkcYBQAAAAA4HGEUAAAAAOBwJYbR9PR0/fHHH46sBQAAAADgJEoMoy+88ILS09Otj+Pi4vTtt986oiYAAAAAwHWuxDBqsVi0efNmnTt3YVH6jRs36vjx4w4rDAAAAABw/XKxWCyW4naMHDlSCxculIuLi6QL4bTw/yVezMVFu3btqvgqr0EFBRYdP55tdBlApbR16wY9NfhJvfvOREVERBpdDgDACfn4eKpKFXdlZZ02uhSgUvL19ZLJdPl8eCVuJe147bXXFBwcrB9//FE5OTn64osv1KpVK910003lekIAzs1sXqcBj8UosEtTxcXHKDUljUAKAADghEpsGb1UkyZNlJiYqOjo6KtdU6VAyyhQdmbzOsXFx6hVQpT8ggOUtTNTm5PXEkgBAA5HyyhQPhXRMlrqMHqxP/74Q4cPH5a7u7v8/f1Vu3btchVRGRFGgbK5NIgWIpACAIxAGAXKx+Fh9IcfftAbb7yh77//3mZ7SEiIRo0apebNm5ermMqEMAqUTXi7ENW4w19B0aHK2pmpLSlmtYyPkF9wgPYu2abT3x3Txm+3G10mAMBJEEaB8rmqY0YvtXfvXsXGxkqSevfurUaNGqmgoED79+/XkiVLFBcXpzlz5ui2224rV0EArk9JicmKi4+RTC7as2irmjwQqm8nrFST7mHav3SXUlPSjC4RAAAADlTqltFBgwZpx44dmj17tgICAmz2HT16VD179lSrVq30/vvvX5VCrzW0jAJlN2XKJL3+1iuKePF+65hR87gv9crLr2vQoMFGlwcAcCK0jALlUxEtoyWuM3qpjIyM/9fevUdVVef/H38dLqICAiLgN7xBqSTeULNRUZPMvGXqWE2m4qVJW0qppWk/NXMaMygwtTSnLFNyJs0s817Z2HG0SbMyVBoDUcnEUFCQvHDO7w8XZ+YEFte9kfN8rOWa2Hufvd/7rMXa8+Lz+by3hg8fXiyISlLDhg314IMP6osvvqhQMQBqLqt1lxKSFjiCqCQFR4Yqeno/JSQtkNW6y+QKAQAAYKRSh9HLly/L29v7uvt9fHz0yy+/VEpRAGqeqdPiFD6glVPzIulaIA0f0EpTp8WZVBkAAADMUOoweuutt+qjjz7S1atXi+27cuWKNm7cqIxcaQEAACAASURBVBYtWlRqcQBqjsSExUrbdEhZKZlO27NSMpW26ZASExabVBkAAADMUOow+vDDD+vgwYMaMWKEtm3bptTUVKWmpmrLli0aMWKEUlJSNHbs2KqsFcANLDq6h95e8Y72L/6nI5DyWhcAAADXVaZXuyQnJ+vFF190mo5rt9vl5eWlKVOmaPTo0VVRY7VEAyOgfKzWXYodN1xh/Vs5uugSRAEARqOBEVAxhr9nVJJycnK0Z88enTx5Una7XY0aNVLXrl3l7+9foUJuNIRRoPxmzZqmFW+v0Ig/jVJ8fJLZ5QAAXBBhFKgYQ98zWsTf31/9+vWr0EUBuK6lS5doxco31GpYJ61as1JhYTfzWhcAAAAXVOYwCgDl9ev3jAaEB2ne/DmSRCAFAABwMaVuYAQAFfHrICr99z2j8+bP0dKlS0yuEAAAAEYq85pRXMOaUaBsGocFq+Xg9mp5T/ti+1I3fq3UDV/rRHqWCZUBAFwRa0aBiqmMNaOlHhm12WwVuhAA1/b09DlKWbevxPeMpqzbp6enzzGpMgAAAJih1GF00KBBWrlyZVXWAqAGe/TRSZrz9DxZ47c4vWfUGr9Fc56ex5pRAAAAF1PqBkYZGRmqU6dOVdYCoIYrCpzz5s9R5LBOSlm3jyAKAADgoko9MhodHa2tW7cqL491kgDKr02btnKzuOn7zQflZnFTmzZtzS4JAAAAJij1yGhERIRWrlypmJgYtW3bVoGBgXJzc86yFotF8+fPr/QiAdQMVusujYh9QLfe11Hpnx5Ri4FtNSL2Aa1e+Q9FR/cwuzwAAAAYqNTddCMiIn7/ZBaLDh8+XOGibgR00wXKpiiI3jK4rY5sOKCIQe115MOvFTE4Skc3fEsgBQAYim66QMVURjddXu1SToRRoGzadohQ3ZZ+OvnvdHWZfJeCI0OVlZKpPQt3qFHnMF1MzdW3Xx0xu0wAgIsgjAIVY+irXf6XzWbTzz//rMuXL1fo4gBcx6WCAmVY/+MIopIUHBmqLpPvUob1P7pUUGByhQAAADBSmcJoRkaG4uLi1LFjR3Xv3l379+/Xnj17dN9992nfvn1VVSOAGsCrTh1FDuvkCKJFgiNDFTmsk7zo1g0AAOBSSh1Gjx07pvvuu0///ve/1b17d8d2d3d3paWlaezYsfr666+rpEgAN75XFy3X0Q8POt4xWiQrJVNHPzyoVxctN6kyAAAAmKHUYTQxMVG1a9fW5s2bNXfuXBUtNe3cubM2b96sBg0aaMmSJVVWKIAbW3R0D61e+Q99ufBTRyDNSsnUlws/pXkRAACACyp1GN27d68efPBBBQYGymJxXqgaEhKi4cOH67vvvqv0AgHUHEWB9Iukj5W68Wt9kfQxQRQAAMBFlTqMXr58WfXq1bvufk9PT126dKlSigJQs1lk0febD0r2inVgAwAAwI2r1GE0IiJCn376aYn7rl69qg8//FAtW7astMIA1DxW6y6NGjtcnafcqXuWjtTtU+/UqLHDZbXuMrs0AAAAGKzUYXT8+PH617/+pSeffFJ79+6VJGVmZuqTTz7RqFGjdOjQIY0ZM6bKCgVwYysKoh3jejq92qVjXE8CKQAAgAuy2Is6EZXC+vXrNX/+fOXn58tut8tischut8vLy0tTpkzR6NGjq7DU6sVmsys7O8/sMoAbRucu7VTvDyFqeU/7YvtSN36t83tP6997vjGhMgCAK/LzqyMvL09lZZ03uxTghhQY6CM3t4otufIoy8FDhw5Vnz59tHv3bp04cUI2m02hoaHq2rWrAgICKlQIgJotMWGxRo0droDwIEnSVyus6jA2WpKUtumQ3l7xjpnlAQAAwGBlGhktYrfbdfbsWbm5ublsCGVkFCg7q3WXho+8XzYVKmJwlI5sOCA3ueudVe/SURcAYChGRoGKMXxk9IcfftDLL78sq9WqgoICSZKvr6/uvPNOPf7442rYsGGFigFQ87m5WXT75D4KjgxVQHiQvlxYcmM0AAAA1GylHhk9ePCgRo0apStXrqhHjx5q0qSJbDabjh07pt27d8vf319r1qxRkyZNqrrmaoGRUaBsSmpgJElZKZnav/ifenvFO4yOAgAMw8goUDGVMTJa6jAaGxurtLQ0JScnFwuc33//vUaNGqWOHTvqlVdeqVBBNwrCKFA2NDACAFQnhFGgYiojjJb61S7ffPONRo0aVeLIZ4sWLRQbG6s9e/ZUqBgANVdiwmKlbTqkrJRMp+1ZKZlK23RIiQmLTaoMAAAAZih1GK1Xr54KCwuvu79u3bqqXbt2pRQFoOaJju6haVNmyBq/xRFIs1IyZY3fomlTZjBFFwAAwMWUOow+9NBDeuutt3T06NFi+06fPq1Vq1bp/vvvr9TiANQcVusuJSQtUOT9t2nPwh1K3fi19izcocj7b1NC0gJZrbvMLhEAAAAGum433ZkzZxbbdunSJQ0ePFjdu3dXWFiYLBaLMjMztWvXLnl5eVVpoQBubFOnxSl8QCu1HNBOAc0a6KsVVnWZfNe1ZkY2u6ZOi2PNKAAAgAu5bgOjiIiIsp/MYtHhw4crXNSNgAZGQNnQTRcAUJ3QwAioGEO76cIZYRQou18HUoIoAMAshFGgYgijJiKMAuVjte5S7LjhCuvfSmmbDhFEAQCmIIwCFVMZYfS6a0ZLsmHDBu3evVtnzpyRzWYrtt9isWjlypUVKghAzRYd3UPr/v6eJkwaTxAFAABwYaUOo0lJSXrttdfk6empwMBAubmVuhFvpZo0aZJSU1O1Y8cOxzar1aqkpCQdPXpUgYGBGjFihMaOHev0uYMHDyo+Pl7fffedvL29NXToUMXFxcnT09PoWwBc3h133KEj36UqN7fA7FIAAABgklKH0ffff1/R0dFavHix6tSpU5U1XdcHH3ygHTt2qEmTJo5tX331lSZMmKB+/frp8ccf1/79+xUfHy+73a5x48ZJkjIyMjR69GhFRUVp4cKF+uGHH5SUlKS8vDzNmTPHlHsBXNnChQs1Z95sPT19jh59dJLZ5QAAAMAEpQ6jeXl5uvvuu00LoqdPn9Zf//pXNWzY0Gn7okWL1KpVKyUkJEiSevTooatXr2rZsmUaOXKkatWqpeXLl8vX11evvvqqatWqpZ49e6p27dp67rnnNH78eIWEhJhxS4BLWrp0if4yf45aDeukefOv/TGIQAoAAOB6Sj3Xtnv37tq7d29V1vKbZs2apW7duqlLly6ObZcuXdK+ffvUp08fp2PvvvtunT9/Xl999ZUkaffu3erVq5dq1arlOKZv374qLCyU1Wo15gYAaOnSJZo3f466Te+nlve0V/T0fpo3f46WLl1idmkAAAAwWKlHRmfPnq0xY8boiSeeUO/evRUYGCiLpXj3pNtuu61SC5SktWvXKiUlRR999JHi4+Md20+cOKErV64oLCzM6fimTZtKktLT09WuXTudOnWq2DH169eXj4+P0tPTK71eAMUVBdHo6f0c7xkNjgx1BFKJEVIAAABXUuow+uOPP+rChQvatGmTNm/eXGy/3W6XxWLR4cOHK7XAzMxMPf/883r++edVv359p30XLlyQJPn4+Dht9/b2lnRtavH1jik6Li+vfK9nsViutQQHUDrz4+cpclgnRxAtEhwZqshhnTQ/fp5mzJhmUnUAAFfj4eEuif8/B5RXCeOSZVbqMDpv3jydP39e48aNU7NmzeThUaa3wpSL3W7X008/rZ49e+ruu+8ucb+kEkdoJcnNze03j7Hb7aZ1BQZczbw5f9H/mzNTAeFBToE0KyVTh9bt01/nPW9idQAAADBaqRPlf/7zH02aNEl//vOfq7IeJ8nJyUpNTdXGjRt19epVSf8NoFevXpWvr68kFRvdLPrZ19fXMSJa0gjoxYsXHecoK7tdvJYCKIMxY8brl1+uOE3VzUrJlDV+i+Y8PU9jxozndwoAYJiiEVGePUD5BAb6VHh0tNRhtGHDhoaPIm7btk3nzp1TdHR0sX2RkZGaO3eu3N3ddfz4cad9RT+HhYXJ29tbISEhysjIcDomOztbeXl5xdaSAqg6bdq0lYebh/YkblfE4Cgd2XBAHm4eatOmrdmlAQAAwGClDqMPP/ywFi9erJ49e+qWW26pypocnn32WeXn5ztte+WVV3T48GEtWbJEjRo10pYtW7R9+3bFxsY6puJu27ZNvr6+at26tSSpW7du2rlzp6ZPn+7oqLtt2za5u7urc+fOhtwL4Oqs1l0aNXa4/vDkXZKkr1ZY1WXqtU7Yo8YO19sr3lF0dA8zSwQAAICBSh1Gjxw5Ijc3Nw0aNEiNGzdWgwYN5O7u7nSMxWLRypUrK6248PDwYtv8/f1Vq1YttWnTRpL06KOPasyYMZoyZYqGDBmiAwcO6I033tATTzzheCfqww8/rE2bNumRRx5RbGysjh07psTERN1///266aabKq1eANc3dVqcwge0cqwX7fvSA4594QNaaeq0OP17zzdmlQcAAACDlTqM7ty5U25ubmrYsKGuXLmiU6dOVWVdpdalSxctXrxYixYt0sSJExUSEqLp06dr7NixjmNuvvlmrVixQvHx8XrssccUEBCgMWPGKC4uzsTKAdeSmLBYI2IfKLGB0dEPD2r1yn+YWB0AAACMZrEXdQRCmdhsdmVnl++1MIArslp36YHhf5TNXqjI+29T+qdHFBYToZR3v5SbxV3/eOc9pukCAAzj51dHXl6eyso6b3YpwA0pMNBHbm4V62DEe00AGOLRSQ9LbnY17dFCKe9+qbA7Wirl3S/VtEcLyc1+bT8AAABcRqmn6Y4aNapUx7399tvlLgZAzWVxc1PjLjfr5N40x6tdAsKDtGfhDjXucrMupuaaXSIAAAAMVOowevLkyWLbbDabzp07p0uXLik0NFTNmzev1OIA1ByP/nmS0ztGJSk4MlRdJt/leNcoAAAAXEeF14wWFhbqk08+0axZs/TKK6/otttuq6zaqjXWjAJl07lLO9X7Q4ha3tO+2L7UjV/r/N7TdNMFABiGNaNAxVSLNaPu7u7q06eP7rvvPr344osVPR2AGioxYbGOfnhQWSmZTtuLuukmJiw2qTIAAACYodIaGDVr1kxHjhyprNMBqIFsNrv2JG53BNKslEztSdwum42m3gAAAK6m1GtGf8vly5f14YcfKjAwsDJOB6AGmjotTi0Gt1VAeJB2v7hVHrVr6eovl9Xtyb46l3ZGU6fFMU0XAADAhVS4m+7ly5eVnp6u8+fPKy4urtIKA1CzJCYs1qixwxV+T6Tc3NzUon8bHdlwQOeO/ay0TYf09op3zC4RAAAABqpQN13p2prR8PBwDRw4UMOHD6+0wgDULNHRPTRtygynjroB4UGOTrrR0T3MLhEAAAAGqnA3XVdFN12gbKzWXRo1drg6xvV0vNpFurZudP/if+rtFe8QSAEAhqGbLlAxldFNlzBaToRRoGx4tQsAoDohjAIVUxlh9LrTdJcsWVKuE06aNKncxQCoue7oHqNVa1YqIDxIkvTVCqs6jI2WJKWs26eRD8aaWR4AAAAMdt2R0YiIiNKdwOKchg8fPlzxqm4AjIwCZRPaJFA2u10Wi0Vu7m6KGBylIxsOyFZok91ul5vFoszj2WaXCQBwEYyMAhVTpSOjn3zyye9+OC8vT0lJSfrss8/k4eFx3Y67AFCvnp/82gTp5N40dZnax9HAaE/idjX6Q7hyD54xu0QAAAAY6LphNDQ09Hq7JEmbN2/WggULlJWVpQ4dOmju3Llq0aJFpRcIoGZ4bOJUp066khQcGaouU/s4OuoCAADAdZS5gdGJEyf07LPPavfu3fLz89OTTz6pYcOGVVV91RbTdIGyubV1uELvuuW6DYwydxzV4e/STKgMAOCKmKYLVEyVTtP9tStXrmj58uX629/+pkuXLmnIkCGaNm2aAgICKlQAANeQm5Or8+9/pYDwoGKvdjn8/leyX6WxNwC4ohYtmignJ8e06wcH1zPluv7+/vr+++OmXBuoLkoVRvfu3atnn31W6enpat68uZ555hl16tSpqmsDUIP41qun8xdytSdxu2PNaFZKpvYkbpet0KZ69fzMLhEAYIKcnBxTRif9/OpIknJzCwy/tmReCAaqk98Mo2fPntX8+fO1adMm1a5dW0888YTGjBkjD49SD6gCgCTpjeUr9eBDw1RYWKg9iduduul6uHnojeUrzS4RAAAABnK73o41a9aob9++2rRpk2JiYrR582b9+c9/JogCKJfo6B5ak7xO7hZ32Qpt+n7zQdkKbXK3uGtN8jpFR/cwu0QAAAAYqFTvGf31u0SvezKLRYcOHaqcyqo5GhgB5WO17tKDI4bJ4uUu+y+FBFEAcHHBwfVcdpouzZNwI6vSBkaDBw8udQgFgNKKju6hec/8RXPmzdbTM+YQRAEAAFxUmV/tgmsYGQXKx2rdpdhxwxXWv5XSNh3S2yveIZACgAtjZBS4MVXGyOh114wCQGWzWndp1Njh6jCpp1re014d43pq1Njhslp3mV0aAAAADEYYBWCIoiDaMa6n4z2jwZGhBFIAAAAXRRgFYIip0+IUPqCVI4gWCY4MVfiAVpo6Lc6kygAAAGAGwigAQyQmLFbapkPKSsl02p6Vkqm0TYeUmLDYpMoAAABgBhoYlRMNjACpR4/bdeTI4TJ9xr2Wh6Kf6qfgyFBlpWTK+sIWFV6+WuZrR0Tcql27vijz5wAA1QsNjIAbU5W+2gUAfk95wuCvu+mu/fsGuukCAAC4IKbpAjBUdHQPrfv7e0pZu4/XugAAALgwwigAw91xxx0qvHyVIAoAAODCCKMAAAAAAMMRRgEAAAAAhiOMAgAAAAAMRxgFAAAAABiOMAoAAAAAMBxhFAAAAABgOMIoAAAAAMBwhFEAAAAAgOEIowAAAAAAwxFGAQAAAACGI4wCAAAAAAxHGAUAAAAAGI4wCgAAAAAwHGEUAAAAAGA4wigAAAAAwHCEUQAAAACA4QijAAAAAADDEUYBAAAAAIYjjAIAAAAADEcYBQAAAAAYjjAKAAAAADAcYRQAAAAAYDjCKAAAAADAcIRRAAAAAIDhCKMAAAAAAMMRRgEAAAAAhiOMAgAAAAAMRxgFAAAAABiOMAoAAAAAMBxhFAAAAABgOMIoAAAAAMBwhFEAAAAAgOEIowAAAAAAwxFGAQAAAACGI4wCAAAAAAxHGAUAAAAAGI4wCgAAAAAwHGEUAAAAAGA4wigAAAAAwHCEUQAAAACA4TzMLgAAAACua0vcnbqwfLTh171g+BWdbYm70+QKAPNZ7Ha73ewibkQ2m13Z2XlmlwHckPz86sjLy1NZWefNLgUAYLLg4HqmPA/8/OpIknJzCwy/tmTefQOVJTDQR25ulgqdg2m6AAAAAADDEUYBAAAAAIYjjAIAAAAADEcYBQAAAAAYjjAKAAAAADAcYRQAAAAAYDjCKAAAAADAcIRRAAAAAIDhCKMAAAAAAMMRRgEAAAAAhiOMAgAAAAAMRxgFAAAAABiu2odRm82mNWvW6J577lFUVJR69+6t559/Xnl5eY5jDh48qJEjRyoqKkrR0dFKTEzUlStXnM5z7NgxTZgwQZ06ddLtt9+uZ555xukcAAAAAADjeJhdwO95/fXXtXDhQo0bN05dunRRenq6Fi1apKNHj+qNN95QRkaGRo8eraioKC1cuFA//PCDkpKSlJeXpzlz5kiScnNzFRsbq6CgIL3wwgvKzs5WQkKCfvrpJ7322msm3yEAAAAAuJ5qHUbtdrtef/11PfDAA3riiSckSV27dlVAQICmTJmiw4cPa/Xq1fL19dWrr76qWrVqqWfPnqpdu7aee+45jR8/XiEhIUpOTtb58+e1YcMGBQQESJJCQkL0yCOP6JtvvlG7du3MvE0AAAAAcDnVeppufn6+Bg0apIEDBzptDw8PlyQdP35cu3fvVq9evVSrVi3H/r59+6qwsFBWq1WStHv3bt12222OICpJ0dHR8vb21j//+U8D7gQAAAAA8L+q9cioj4+PZs2aVWz7xx9/LEm6+eabderUKYWFhTntr1+/vnx8fJSeni5JSktL06BBg5yOcXd3V6NGjRzHAAAAAACMU63DaEm++eYbLV++XL1791a9evUkXQutv+bt7e1oUHThwoXfPaasLBbJz69OuT4LuDoPD3dJ/A4BAK4x43lQHZ5FPAdxI7NYKn6Oaj1N99f279+vhx9+WI0aNdJzzz0nu90uSbKU8E3Y7Xa5uf339kpzDAAAAADAGDfMyOjmzZs1Y8YMNWvWTK+//roCAgKUn58vSSWObl68eFG+vr6Sro2clnRMfn6+QkNDy1WP3S7l5haU67OAqyv6SzC/QwAAyZznQXV4FvEcxI0sMNCnwqOjN8Sw4JtvvqmpU6eqffv2Sk5OVnBwsKRr02xDQkKUkZHhdHx2drby8vIca0nDwsKKHVNYWKiTJ08WW28KAAAAAKh61T6Mrl27VgsWLFC/fv30+uuvO0Y7i3Tr1k07d+7U5cuXHdu2bdsmd3d3de7c2XHMF198oZycHMcxVqtVFy9eVNeuXY25EQAAAACAg8VetPCyGsrOztadd96p+vXrKz4+Xh4ezrOKmzRponPnzmnIkCHq0KGDYmNjdezYMSUmJuqPf/yj5s6dK0k6e/as+vfvr4YNG2rixInKyclRQkKC2rVrp7/97W/lqs1msys7u3zNjwBX5+dXR15ensrKOm92KQAAkwUH1zPleWD2NF2z7huoLIGBPnJzq9g83WodRjds2KCnnnrquvvj4+N17733at++fYqPj9fhw4cVEBCgwYMHKy4uTp6eno5jv//+e82fP18HDhyQt7e3evfurenTp5fYZbc0CKNA+RFGAQBFCKPAjanGh9HqjDAKlB9hFABQhDAK3JgqI4xW+zWjAAAAAICahzAKAAAAADAcYRQAAAAAYDjCKAAAAADAcIRRAAAAAIDhCKMAAAAAAMMRRgEAAAAAhiOMAgAAAAAMRxgFAAAAABiOMAoAAAAAMBxhFAAAAABgOMIoAAAAAMBwhFEAAAAAgOEIowAAAAAAwxFGAQAAAACG8zC7AADmadGiiXJycky7fnBwPdOu7e/vr++/P27a9QEAAFwdYRRwYTk5OcrKOm/4df386kiScnMLDL92ETODMAAAAJimCwAAAAAwAWEUAAAAAGA4wigAAAAAwHCsGQUAAICpXHEdv7+/v9klAKYjjAIAAMA0ZjTSk6410/Py8jTt+gCYpgsAAAAAMAFhFAAAAABgOMIoAAAAAMBwhFEAAAAAgOEIowAAAAAAwxFGAQAAAACGI4wCAAAAAAxHGAUAAAAAGI4wCgAAAAAwHGEUAAAAAGA4wigAAAAAwHCEUQAAAACA4QijAAAAAADDEUYBAAAAAIYjjAIAAAAADEcYBQAAAAAYjjAKAAAAADAcYRQAAAAAYDjCKAAAAADAcIRRAAAAAIDhCKMAAAAAAMMRRgEAAAAAhiOMAgAAAAAMRxgFAAAAABiOMAoAAAAAMBxhFAAAAABgOMIoAAAAAMBwHmYXAMA8W+Lu1IXlow2/7gXDr1jclrg7zS4BAADApVnsdrvd7CJuRDabXdnZeWaXAVRIcHA9ZWWdN/y6fn51JEm5uQWGX7uIWfcOAKge/PzqyMvLk2cBUE6BgT5yc7NU6BxM0wUAAAAAGI4wCgAAAAAwHGEUAAAAAGA4wigAAAAAwHCEUQAAAACA4QijAAAAAADDEUYBAAAAAIYjjAIAAAAADEcYBQAAAAAYjjAKAAAAADAcYRQAAAAAYDjCKAAAAADAcIRRAAAAAIDhCKMAAAAAAMMRRgEAAAAAhiOMAgAAAAAMRxgFAAAAABiOMAoAAAAAMBxhFAAAAABgOMIoAAAAAMBwhFEAAAAAgOEIowAAAAAAw3mYXQAAcwUH1zO7BFP4+/ubXQIAAIBLI4wCLiwr67wp1/XzqyMvL0/Trg8AAADzMU0XAAAAAGA4wigAAAAAwHCEUQAAAACA4QijAAAAAADDEUYBAAAAAIYjjAIAAAAADEcYBQAAAAAYjjAKAAAAADAcYRQAAAAAYDjCKAAAAADAcC4VRj/66CMNGDBAbdu2Vb9+/bRhwwazSwIAAAAAl+QyYXTLli168skn1a1bN73yyivq3LmznnrqKW3dutXs0gAAAADA5Vjsdrvd7CKMcNddd6l169ZKSkpybJs8ebJSU1O1ZcuWMp/PZrMrOzuvMksEXIafXx15eXkqK+u82aUAAFwUzyKgYgIDfeTmZqnQOVxiZPTEiRM6fvy4+vTp47T97rvvVlpamk6cOGFSZQAAAADgmlwijKalpUmSwsLCnLY3bdpUkpSenm54TQAAAADgyjzMLsAIFy5ckCT5+Pg4bff29pYk5eWVfbqtxXJtegeAsvPwcJfE7xAAwDw8i4CKsVRshq4kFwmjRctiLb/6xoq2u7m5xAAxUOmiotrr0KGUcn/ey8uz3J9t1SpSBw58Xe7PAwBufBV9Dkk8iwAzuUQY9fX1lVR8BDQ/P99pf1nY7VJubkHFiwNuYJ99tqdcnyv6K3RFf4f4HQQA11be55DEswioqMBAnwqPjrrEkGDRWtHjx487bc/IyHDaDwAAAAAwhkuE0aZNm6pRo0bF3im6fft2NWvWTDfddJNJlQEAAACAa3KJabqSNHHiRM2cOVN+fn6644479Omnn2rLli1O7x0FAAAAABjDZcLo0KFDdfnyZa1YsUJr165V48aN9cILL6h///5mlwYAAAAALsdiL2opizKx2ezKzi77K2EAVF7TCAAAyotnEVAxgYE+cnOrWAcjl1gzCgAAAACoXgijAAAAAADDEUYBAAAAAIYjjAIAAAAADEcYBQAAAAAYjjAKAAAAADAcYRQAAAAAYDjCKAAAAADAcIRRAAAAAIDhCKMAAAAAAMMRRgEAAAAAXmui4wAADi1JREFUhiOMAgAAAAAMRxgFAAAAABiOMAoAAAAAMBxhFAAAAABgOMIoAAAAAMBwFrvdbje7iBuR3W4X3xxQPhbLtf/ldwgAYBaeRUDFWCySpegXqbznIIwCAAAAAIzGNF0AAAAAgOEIowAAAAAAwxFGAQAAAACGI4wCAAAAAAxHGAUAAAAAGI4wCgAAAAAwHGEUAAAAAGA4wigAAAAAwHCEUQAAAACA4QijAAAAAADDEUYBAAAAAIYjjAIAAAAADEcYBeBkxowZatmy5XX//etf/zK7RI0cOVKjR482uwwAgIlGjhypli1basSIEdc95sEHH1TLli21ePHiUp+3ZcuWevXVVyujRAC/w8PsAgBUPw0bNtTLL79c4r5bbrnF4GoAACiZxWLR/v37debMGQUFBTnt++mnn3TgwAGTKgNQGoRRAMXUqlVL7du3N7sMAAB+U+vWrZWamqrt27froYcectq3detWNW/eXD/88INJ1QH4PUzTBVAu7777rvr376/WrVsrJiZGy5cvl91ud+yfMWOGxo8fr+TkZMXExKhdu3YaN26czpw5o3Xr1ql3796KiorS6NGjdfLkScfnLl68qISEBPXp00etW7dWhw4dNG7cOB05cuS6tdhsNi1btky9e/dW69at1bdvX61du7ZK7x8AYD4fHx9FR0dr69atxfZt3rxZ/fr1c9p24sQJTZs2TdHR0YqMjFTXrl01Y8YM5ebmXvca586d06xZs9SlSxe1bdtWDz74oPbv31/p9wK4IkZGAZTo6tWrxba5u7vLYrHotddeU1JSkmJjY9W9e3cdPHhQixYt0tmzZzVjxgzH8V9++aXOnDmj2bNn6+zZs5o7d65GjhwpLy8vzZgxQzk5OfrrX/+q5557TsuWLZMkTZ8+XQcOHNDUqVPVuHFjZWRk6OWXX9aTTz6pjRs3ymKxFKtr7ty5Wr9+vR599FG1a9dOu3fv1uzZs/XLL79o5MiRVfclAQBM169fP82YMUPZ2dkKDAyUJGVmZurbb79VQkKClixZIkkqKCjQiBEjFBwcrLlz58rHx0cHDhzQkiVLVLt2bc2dO7fYuS9duqTRo0crOztbU6dOVVBQkP7+979r9OjRSk5OVtu2bY28VaDGIYwCKOb48eOKjIwstn3u3LkaOHCgli5dqoceekgzZ86UJEVHR6tu3bp64YUXNGrUKN10002SpPz8fL388stq3LixJGnHjh3auXOnPv74Y8e2w4cP66OPPpJ07aFfUFCg2bNnq2/fvpKkzp07Ky8vTwsWLNC5c+dUv359p5rS09P17rvvavr06Ro7dqyjnsLCQr388ssaNmyY6tSpUwXfEgCgOoiJiZGHh4d27NihP/3pT5KkLVu2qFWrVmratKnjuLS0NIWGhio+Pl6NGjWSJP3hD3/QN998oy+//LLEc3/wwQdKTU3V2rVr1aZNG0lSjx49NGzYMCUlJenNN9+s4rsDajbCKIBiGjZs6PhL8v8KDQ3VgQMHVFBQoJiYGKfR05iYGM2fP1979+7V0KFDJUmBgYGO0Fn0c/369Z22+fv768KFC5IkLy8vvfHGG5Kk06dPKz09XceOHdPOnTslSVeuXClW0969e2W329WrV69i9axcuVLffvutbr/99op8HQCAaux/p+oWhdHNmzerf//+TsdFRkbqnXfekc1m07Fjx5SRkaGjR48qLS3tuufes2ePQkJCdOuttzo9Y3r16qXXXntNly9fVq1atarmxgAXQBgFUEytWrUcfwH+tZycHElyjEL+WlZWluO/vb29i+3/vVHKzz//XPPnz1daWpq8vb0VERGhunXrSpLTmtRf11M0kvpb9QAAaqZ+/frpqaee0tmzZ5WXl6dDhw6V+EfVN998U8uWLVNOTo4aNGig1q1bq06dOrp48WKJ583JydFPP/1U4mwh6dp60pCQkEq9F8CVEEYBlImvr68kKSkpyWmEs0hwcHC5z338+HFNnDhRd911l5YvX+44f3Jysj7//PPfrGf16tWqXbt2sf1FU7EAADVX0VTdTz75RGfPnlX79u0dS0aKbNy4UQsWLND06dM1ZMgQx7KPxx9/XIcOHSrxvL6+vrr55pv1wgsvlLg/ICCgcm8EcDF00wVQJu3atZOnp6eysrLUpk0bx7+rV68qKSlJZ86cKfe5v/vuO126dEkTJkxwCrpFQdRmsxX7TKdOnSRJubm5TvWcOnVKixYtUkFBQbnrAQDcGLy9vdW9e3dt27ZN27ZtKzZFV5L279+vgIAAjRs3zhFE8/PztX///hKfL5J022236ccff1RwcLDTM+aTTz7RqlWr5OnpWaX3BdR0jIwCKJP69etr7NixSkpKUl5enjp27Kgff/xRSUlJ8vX1VfPmzct97sjISHl4eCghIUGjR4/WpUuXtH79en322WeSVGKwjIiI0MCBA/X000/rxIkTuvXWW3X06FElJiYqMjKy2F/GAQA1U1FX3cLCQkeH9v/Vtm1brVmzRvHx8brjjjv0008/acWKFfr555+LNccrMnToUK1evVpjxozR+PHjFRISos8++0xvvvmmJk2aVGKHdwClRxgFUGZTpkxRUFCQ3nnnHS1btkz+/v7q3r27pk6dKi8vr3Kft2nTpnrppZe0ZMkSTZgwQX5+fmrfvr1WrVqlkSNHat++fbr55puLfW7BggVatmyZVq9erdOnT6tBgwYaNmyYHnvssYrcJgDgBtKrVy+5u7srKiqqxCUjQ4YM0cmTJ/Xee+9p9erVCgkJUc+ePTV8+HDNnj1b6enpCgsLc/qMt7e3kpOT9dJLL2nBggXKz89X48aNNXv2bI0YMcKoWwNqLIu9pI4gAAAAAABUIdaMAgAAAAAMRxgFAAAAABiOMAoAAAAAMBxhFAAAAABgOMIoAAAAAMBwhFEAAAAAgOF4zygAAJXIZrNp69ateu+993T06FFlZ2fL399fnTp1UmxsrKKiokypKyYmRqGhoVq1apUp1wcA4NcIowAAVJILFy5oypQp+vzzz9W5c2eNHDlS/v7+yszM1AcffKA//elPmj17tkaMGGF2qQAAmI4wCgBAJXnmmWdktVq1YMECDRkyxGnfhAkTNGHCBC1YsEDdunVTWFiYSVUCAFA9sGYUAIBKsH//fm3atEn33ntvsSAqSV5eXpo7d66uXLmi9evXm1AhAADVCyOjAABUgo0bN0qSHnnkkese07RpU7311ltq166d0/YDBw5o0aJF+vrrryVJUVFRmjx5stq2bes4JiYmRtHR0erYsaOWL1+u48eP6//+7/8UGxurhx56yOl8mzdv1muvvab09HQ1adJEs2bNKrGe0l63a9eustls2rhxowICArRhwwbVr1+/DN8OAADFWex2u93sIgAAuNH1799f58+fl9VqLdPndu/erfHjxysiIkIDBw7U5cuXtX79emVmZurNN99Up06dJF0LhXa7Xfn5+RoxYoQaNGigf/zjHzpy5IiWL1+unj17SpLWr1+vmTNnKioqSgMGDFBGRobWrVsnm82mdu3aORoYleW6586dU1hYmO6//379/PPPmjRpUiV+cwAAV0UYBQCgEnTo0EHh4eFat26d0/aCggIVFBQ4bXN3d5efn59sNpv69OmjoKAgrV69Wu7u7pKkixcvavDgwapbt642bNgg6Voo/PHHH7VhwwZFRERIks6cOaPu3btrwIABeumll1RYWKju3bvrpptu0po1a+Tp6SnpvwG1c+fOWrVqVZmve+rUKW3btk1NmjSpui8QAOByWDMKAEAlsNvtKunvu4sWLVKXLl2c/hWtKT106JBOnDih3r17Kzc3V2fPntXZs2f1yy+/qFevXjp8+LB++uknx7nCwsIcQVSSgoKC1KBBA/3888+SpJSUFGVnZ2vo0KGOICpJ9957r/z8/Bw/l/W6TZo0IYgCACoda0YBAKgEwcHBjlD4vx544AF1797d8fOCBQuUl5cnSTp+/LgkKT4+XvHx8SWe99SpU2rYsKEklbhOs1atWrLZbJKkzMxMSSoWHN3d3dW0aVPHz2W9bmBgYInHAABQEYRRAAAqQVRUlN5//31lZGQ4Bb9mzZqpWbNmjp/9/PwcYbQoRD7++ONq3759iecNDw93/Leb229PaLJYLJKkS5cuFdtXdK3yXLdoGi8AAJWJMAoAQCUYNGiQ3n//fb311lt65plnSvWZ0NBQSVLdunXVtWtXp33ffvutcnNzVbt27VLX0LhxY0nSsWPHnLbb7XZlZmaqefPmVXJdAADKgzWjAABUgq5du6p///5as2aNkpOTSzzmww8/1Hfffef4uXXr1goKCtKqVauUn5/v2J6Xl6fJkydr5syZZRqVbNWqlUJDQ7VmzRqnpkmbNm3SuXPnquy6AACUByOjAABUkr/85S8qLCzUvHnz9N5776l3794KCgrS6dOntX37dqWmpqpBgwaaMWOGJMnT01OzZ8/W5MmTNXToUA0bNkxeXl5au3atfvzxR7344ovy8Cj9o9pisWj27NmaOHGiHnjgAf3xj3/U6dOnlZycLH9/f8dxlX1dAADKg1e7AABQyXbu3Kn33ntPKSkpOnPmjHx8fHTrrbeqd+/eGjp0qOrUqeN0/J49e7R06VIdPHhQbm5uat68ucaPH69evXo5jomJiVFoaKjjPaG/tf3zzz/X4sWLlZqaqpCQED322GNKTk6Wh4eH03EVuS4AABVFGAUAAAAAGI41owAAAAAAwxFGAQAAAACGI4wCAAAAAAxHGAUAAAAAGI4wCgAAAAAwHGEUAAAAAGA4wigAAAAAwHCEUQAAAACA4QijAAAAAADDEUYBAAAAAIb7/01qwNu0nORQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_title('Female and Male Dialogue Distribution')\n",
    "ax.set_xlabel('Gender')\n",
    "ax.set_ylabel('Number of Dialogue')\n",
    "ax.boxplot([f_distribution,m_distribution],flierprops= dict(markerfacecolor='g', marker='D'))\n",
    "plt.xticks([1,2], ['Female', 'Male'])\n",
    "fig.set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analysis:  \n",
    "According to the boxplot above, the green line inside the left box is around 100, which means that the median of the number of dialogues that female spoke is around 100 lines. The green line inside the right box is around 250, which means that the median of the number of dialogues that male spoke is around 250 lines. In addition, the range of quartiles of female's data is between 50 to 170, which means 50% of the female has between 50 to 170 lines of dialogues. The range of quartiles of male's data is between 170 to 350, which means 50% of the male has between 170 to 350 lines of dialogues. There are some outliers in female's data that are between 600 to 800 and the outliers in female's data are between 850 to 1000.\n",
    "Based on this graph, we know that the number of dialogue in general movie is distributed unevenly. Since the median of the number of dialogue that female spoke on each movie is far less than male and the dialogue distribution for the female is less than male, we can see that the spoke frequency of female characters is less than male characters on the movie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Barplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make a barplot on the `f_average` and `m_average`. From this graph, we will be able to see the difference in dialogue average distribution based on gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(('Female', 'Male'),[f_average, m_average],align='center')\n",
    "plt.title('Dialogue Average Distribution Based on Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Dialogue Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analysis:  \n",
    "According to the barplot above, female characters have an average of 30% of the dialogue in a movie while male characters have an average of 70%. This simply shows the huge difference between the proportion of dialogue distribution based on gender in the movie industry. Female obviously has less than half of the dialogue than male on the average movie, which further ensures the gender issue that exists in the movie industry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will see how the spoke frequency change throughout the years. Thus, we store each movie with its year to the list `movie_year_list`, and sort the list by ascending order of years. Then, we iterate through each movie with ascending order of movie years, and store the number of dialogue that female and male spoke on that movie to the list `f_year_distribution` and `m_year_distribution`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_year_list = []\n",
    "for i in range(len(movie_titles_df)):\n",
    "    movie_year_list.append([movie_titles_df.iloc[i]['movie_year'],movie_titles_df.iloc[i]['movie_ID']])\n",
    "\n",
    "#sort the movie list by ascanding order of year\n",
    "movie_year_list = np.array(sorted(movie_year_list))\n",
    "year_list = movie_year_list[:,0]\n",
    "f_year_distribution = []\n",
    "m_year_distribution = []\n",
    "for i in range(len(movie_year_list)):\n",
    "    currmoive = dialogue_gender_df[dialogue_gender_df['movie_ID'] == movie_year_list[i][1]]\n",
    "    female = len(currmoive[currmoive['gender'] == 'f'])\n",
    "    male = len(currmoive[currmoive['gender'] == 'm'])\n",
    "    f_year_distribution.append(female)\n",
    "    m_year_distribution.append(male)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to see how the number of dialogue that female and male spoke change throughout the years. So, we use polyfit to fit a 1-degree linear model, and use the model to get the linear regression with the given movie years from dataframe `dialogue_gender_df`. We save the result in `female_predict` and `male_predict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction\n",
    "a1, b1 = np.polyfit(range(0,len(f_year_distribution)), f_year_distribution,1)\n",
    "female_predict = a1 * np.array(range(0,len(f_year_distribution))) + b1\n",
    "a2, b2 = np.polyfit(range(0,len(m_year_distribution)), m_year_distribution,1)\n",
    "male_predict = a2 * np.array(range(0,len(m_year_distribution))) + b2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Scatterplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make a scatterplot on the `f_year_distribution` and `m_year_distribution`. Also, we will draw the prediction line, `female_predict` and `male_predict`, with the given movie year range from `dialogue_gender_df`. From this graph, we will be able to see the change in the number of dialogue for female and male throughout the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = year_list\n",
    "f, ax = plt.subplots()\n",
    "ax.scatter(X,np.array(f_year_distribution), color='salmon', label = 'female')\n",
    "ax.scatter(X,np.array(m_year_distribution), color='royalblue',label = 'male', alpha = 0.55)\n",
    "plt.legend(loc='upper left',bbox_to_anchor=(1, 1))\n",
    "plt.plot(X, female_predict, color='m',label = 'female',linewidth=5)\n",
    "plt.plot(X, male_predict, color='navy',label = 'male',linewidth=5)\n",
    "plt.legend(loc='upper left',bbox_to_anchor=(1, 1))\n",
    "plt.title('Spoke Frequency Prediction')\n",
    "plt.xlabel('Movie years')\n",
    "plt.ylabel('Number of Dialogue')\n",
    "plt.xticks(rotation='vertical')\n",
    "f.set_size_inches(18, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analysis:  \n",
    "According to the scatterplot above, we can see that the blue line which predicts the number of dialogue that male looks like a horizontal line throughout the years at about 270. The pink line which predicts the number of dialogue that female also looks like a horizontal line at about 100. The blue line that predicts from male's data always lies above the pink line that predicts from female's data, and the blue dots which represent the number of dialogue that male appear generally above the pink dots which represent the number of dialogue that female.  \n",
    "Based on these plots, we can conclude that the number of dialogue that female and male have in movies is distributed unequally. Male characters always have more dialogue than female characters in the movie. Thus, the spoken frequency of male will be higher than the female characters. This phenomena has existed in the movie industry for many years and do not have any changed since the blue and pink line grow horizontally throughout the years. It gives further evidence that gender inequality does exist in the movie industry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Proportion of gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know what is the proportion of male and female characters in the movies overall. We take the data from the HollyWoodGender dataset, the movies are across 2000 to 2018. This section gives visualization and analysis of the proportion of female and male characters in movies. The dataframe we used in this section is `df_nofc`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print the value counts out, so we know the basic outline of the numbers of characters in different movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movie 43               27\n",
       "Kill Bill: Volume 1    22\n",
       "You Got Served         22\n",
       "                       ..\n",
       "Voyage of Time          1\n",
       "Countdown to Zero       1\n",
       "Stonewall Uprising      1\n",
       "Name: name, Length: 3322, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the value counts out\n",
    "counts = df_nofc['name'].value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a function called `female proportion` to caluculate the proportion of female in a movie. This function takes a string (movie name) as input, and out puts a number (proportion of female characters in this movie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a function that take movie as an input and output the proportion of female characters\n",
    "def female_proportion (string):\n",
    "    \n",
    "        # get the value counts of gender in this movie\n",
    "        df_string = df_nofc.loc[df_nofc['name'] == string]\n",
    "        g_counts = df_string['gender'].value_counts()\n",
    "        \n",
    "        # if there are female characters, store the number of female characters in value_female\n",
    "        # else, let value_female be 0\n",
    "        if 'female' in g_counts.index.values:\n",
    "            value_female = g_counts.loc['female']\n",
    "        else: \n",
    "            value_female = 0\n",
    "        \n",
    "        # if there are male characters, store the number of female characters in value_male\n",
    "        # else, let value_male be 0\n",
    "        if 'male' in g_counts.index.values:\n",
    "            value_male = g_counts.loc['male']\n",
    "        else:\n",
    "            value_male = 0\n",
    "            \n",
    "        # let value_total store the number of female and male characters\n",
    "        value_total = value_female + value_male\n",
    "        \n",
    "        # if value_total is 0 (which means all characters in the movie is unknown gender), return 0\n",
    "        if value_total == 0:\n",
    "            return 0\n",
    "        \n",
    "        # calculate female proportion by deviding it, and return the result.\n",
    "        proportion = value_female/value_total\n",
    "        return proportion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a function called `male proportion` to caluculate the proportion of female in a movie. This function takes a string (movie name) as input, and out puts a number (proportion of female characters in this movie). We create two functions to avoid other unknown genders as confounding variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a function that take movie as an input and output the proportion of male characters\n",
    "def male_proportion (string):\n",
    "    \n",
    "        # get the value counts of gender in this movie\n",
    "        df_string = df_nofc.loc[df_nofc['name'] == string]\n",
    "        g_counts = df_string['gender'].value_counts()\n",
    "        \n",
    "        # if there are female characters, store the number of female characters in value_female\n",
    "        # else, let value_female be 0\n",
    "        if 'female' in g_counts.index.values:\n",
    "            value_female = g_counts.loc['female']\n",
    "        else: \n",
    "            value_female = 0\n",
    "        \n",
    "        # if there are male characters, store the number of female characters in value_male\n",
    "        # else, let value_male be 0\n",
    "        if 'male' in g_counts.index.values:\n",
    "            value_male = g_counts.loc['male']\n",
    "        else:\n",
    "            value_male = 0\n",
    "            \n",
    "        # let value_total store the number of female and male characters\n",
    "        value_total = value_female + value_male\n",
    "        \n",
    "        # if value_total is 0 (which means all characters in the movie is unknown gender), return 0\n",
    "        if value_total == 0:\n",
    "            return 0\n",
    "        \n",
    "        # calculate male proportion by deviding it, and return the result.\n",
    "        proportion = value_male/value_total\n",
    "        return proportion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following 2 code cells are to test if the function is operating correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.125"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test the function female_proportion\n",
    "female_proportion ('Mission: Impossible 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.875"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_proportion ('Mission: Impossible 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Female proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, apply `female proportion` to all values to the movies, and save the output female proportions to a variable called  `real_femalep`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.41\n",
       "1       0.26\n",
       "2       0.22\n",
       "        ... \n",
       "3319    0.00\n",
       "3320    0.00\n",
       "3321    0.00\n",
       "Name: index, Length: 3322, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save all calculated female proportions to real_femalep\n",
    "df_counts = counts.to_frame()\n",
    "df_counts.reset_index(level=0, inplace=True)\n",
    "real_femalep = df_counts[\"index\"].apply(female_proportion)\n",
    "real_femalep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot the histogram of the proportions of female characters in a movie. The x-axis is the proportion of female characters, and the y axis is the counts of movies with this proportion. The more positively skewed, the less female characters in movies overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'proportion of female characters')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the proportion of female proportions. \n",
    "real_femalep.hist(bins = 20)\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('proportion of female characters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Male proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, apply `male proportion` to all values to the movies, and save the output female proportions to a variable called  `real_malep`. We do this again to avoid the `unknown` gender as a confounding variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.59\n",
       "1       0.74\n",
       "2       0.78\n",
       "        ... \n",
       "3319    0.00\n",
       "3320    0.00\n",
       "3321    0.00\n",
       "Name: index, Length: 3322, dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save all calculated female proportions to real_malep\n",
    "df_counts = counts.to_frame()\n",
    "df_counts.reset_index(level=0, inplace=True)\n",
    "real_malep = df_counts[\"index\"].apply(male_proportion)\n",
    "real_malep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot the histogram of the proportions of male characters in a movie. The x-axis is the proportion of male characters, and the y axis is the counts of movies with this proportion. The more positively skewed, the less male characters in movies overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'proportion of male characters')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the proportion of female proportions. The more positively skewed, the less female characters in movies.\n",
    "real_malep.hist(bins = 20)\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('proportion of male characters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analysis:  \n",
    "As the above histograms shows, the data of proportion of female characters is positively skewed, and the proportion of male characters is negatively skewed. It indicates that there are less female characters compared to other genders of characters overall. \n",
    "I also showed the histogram of male proportion, because there's also a number of \"unknown\" gender in the movies which could be a confounding variable, and movies that contain only unknown genders will be counted as proportion 0 in both male and female proporiton calculation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Movie Rating VS Gender\n",
    "In this part, we are going to find out how gender affect to rate of movie. There is 4 graphs that we can simply check how gender and rate of movies are related.\n",
    "### Visualization\n",
    "### -Barplots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratingVSsex[\"startYear\"].value_counts()\n",
    "\n",
    "ratingVSsex.head()\n",
    "\n",
    "ratingVSsex[\"category\"].value_counts().plot(\"bar\")\n",
    "\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: This graphe is showing relationship between gender and number of votes. As you can see, There is more people who vote to movie that male is main character. From this data, I can interpret movie that male is main character is more popular to populace and I also can say male character more plays a main character than female in the movie industry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Two plotted graph\n",
    "\n",
    "we made two-plotted graph to compare how rate of movie(y-axis) have been changed by gender on 1986 through 2018(x-axis). `p` takes group of `category`. The variables `actor` and `actress` take actor and actress values seperately and we draw the each of datas on same graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1986\n",
      "1     1987\n",
      "2     1988\n",
      "      ... \n",
      "31    2017\n",
      "32    2018\n",
      "33      \\N\n",
      "Name: startYear, Length: 34, dtype: object\n",
      "0     6.03\n",
      "1     5.99\n",
      "2     5.98\n",
      "      ... \n",
      "31    5.96\n",
      "32    6.20\n",
      "33    5.30\n",
      "Name: averageRating, Length: 34, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABFkAAAKZCAYAAABjpfSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8XHd97//XmU0z2hdrs2xrsbzb8hbHSewEAoGSEKAxpWBoCgHawC+lv5Sb9gKFJoHeC4FcCj+WQNM2aaBNWEpuEpoQCBBiZ7Xj2JJ3W9Ziy5IsS7LWGc1yzu+PMzOy41XyjEYzej8fjzwsj+ac7/dI0VjnM5/FsCzLQkRERERERERELosj1RsQEREREREREckECrKIiIiIiIiIiCSAgiwiIiIiIiIiIgmgIIuIiIiIiIiISAIoyCIiIiIiIiIikgAKsoiIiIiIiIiIJICCLCIiIiIiIiIiCaAgi4iIiIiIiIhIAijIIiIiIiIiIiKSAAqyiIiIiIiIiIgkgIIsIiIiIiIiIiIJoCCLiIiIiIiIiEgCKMgiIiIiIiIiIpIACrKIiIiIiIiIiCSAgiwiIiIiIiIiIgngSvUGpgPLsrCsVO9iYgzD/nMq9z3Va+oaM2NNXWP6r5eKNXWNmbGmrjH910vFmrrGzFgz09dLxZq6xsxYU9c4vRkGGLELmCQFWbC/+b29w6nexoQUFPgAGBjwZ+yausbMWFPXmP7rpWJNXWNmrKlrTP/1UrGmrjEz1sz09VKxpq4xM9bUNU5vJSW5XGaMReVCIiIiIiIiIiKJoCCLiIiIiIiIiEgCKMgiIiIiIiIiIpIACrKIiIiIiIiIiCSAgiwiIiIiIiIiIgmgIIuIiIiIiIiISAIoyCIiIiIiIiIikgAKsoiIiIiIiIiIJICCLCIiIiIiIiIiCaAgi4iIiIiIiIhIAijIIiIiIiIiIiKSAAqyiIiIiIiIiIgkgIIsIiIiIiIiIiIJoCCLiIiIiIiIiEgCKMgiIiIiIiIiIpIACrKIiIiIiIiIiCSAgiwiIiIiIiIiIgmgIIuIiIiIiIiISAIoyCIiIiIiIiIikgAKsoiIiIiIiIiIJICCLCIiIiIiIiIiCZDyIMu2bdvYvHkzK1euZOPGjXzlK19hZGTkvM/v6enhi1/8Itdffz2rV69m06ZNPPPMM1O4YxERERERERGRs7lSufjOnTu57bbbeNvb3sYDDzxAW1sb3/zmN+nr6+Of/umfznp+MBjkk5/8JENDQ/z1X/81ZWVlPPvss9x5551EIhFuvvnmFFyFiIiIiIiIyPQTCIbZt7uTlQtKU72VGSOlQZb777+fVatW8e1vfxvDMLjmmmswTZOHHnoIv9+Pz+c74/kvvPAC+/fv52c/+xkNDQ0AbNiwgePHj/Pggw8qyCIiIiIiIiIS9eSLrfzq1Xbee20tf7yhNtXbmRFSVi7U19fH9u3b2bx5M4ZhxB//yEc+wnPPPXdWgAUgJyeHD37wg6xYseKMx+vq6mhvb0/6nkVERERERETSxb62fgC6+0ZTvJOZI2WZLAcPHsSyLAoKCrjzzjt5/vnncTqd3HzzzXz+85/H6/WedczVV1/N1VdffcZjoVCIP/zhDyxYsGDSezEMKCg4O6gznblcTmBq9z3Va+oaM2NNXWP6r5eKNXWNmbGmrjH910vFmrrGzFgz09dLxZq6xsxYcyrXC4UjdPQMA+Afi2TkNSbaafkfk5bSTBaAz33ucxQVFfHAAw/wmc98hieeeIJ77rnnks9z//3309rayl/+5V8maaciIiIiIiIi6aWtc4hwxALAPxZO8W5mjpRlsoRCIQDWrFnD3XffDdiZKpZlcd9993HHHXcwd+7c8x5vWRbf+MY3ePjhh/nEJz7BDTfcMOm9WBYMDPgnfXwqxKKCU7nvqV5T15gZa+oa03+9VKypa8yMNXWN6b9eKtbUNWbGmpm+XirW1DVmxppTuV7T4Z74xyOBUEZeY6KVlORedjZLyjJZcnJyALjuuuvOeHzjxo1YlsWBAwfOe2wwGOR//I//wb/+67/yiU98gr/7u79L6l5FRERERERE0klL52D849GAMlmmSsoyWWpqagA7YHK6WIaLcZ7w0fDwMLfffjs7duzgC1/4Ah/96EeTuk8RERERERGRdNPaNRT/2K8gy5RJWSbL/Pnzqaqq4umnnz7j8d///ve4XC5Wr1591jGRSIRPf/rT7Nq1i29+85sKsIiIiIiIiIi8yVgwwvGTI+N/D0WImGYKdzRzpCyTxTAM7rrrLj772c9y1113sWnTJnbv3s0DDzzArbfeSnFxMX19fbS3t1NfX09ubi6PPfYYr732Gh/84AeprKxk586dZ5xv5cqVqbocERERERERkWmhrXsIyzrzsUAwQo43ZXkWM0bKgiwAN910Ex6Ph+9973vcfvvtlJSUcMcdd3D77bcD8Pzzz/P5z3+eRx55hPXr1/Pss88C8JOf/ISf/OQnZ5zL6XSyd+/eKb8GERERERERkemkNdqPxe1yEArbGSz+sTA5XncqtzUjpDTIAnDDDTecdzLQpk2b2LRpU/zvjzzyyFRtS0RERERERCQttUT7sSyYU8De1n4AAmORVG5pxlCukIiIiIiIiEgGiWWyLKkuij/mD6r57VRQkEVEREREREQkQ4wGQnT3+wFYMKcQt8u+7fcrk2VKKMgiIiIiIiIikiFipUKGAfPKc/Fl2V1CAspkmRIKsoiIiIiIiIhkiFip0OySHLweF9nRIIt/TEGWqaAgi4iIiIiIiEiGaO20M1lqKvIA8HljQRaVC00FBVlEREREREREMkRrl53JUlOZDxDPZFG50NRQkEVEREREREQkAwyOBOkdHAOgNhpkUSbL1FKQRURERERERCQDtET7sTgdBnPLcoDxTBaNcJ4aCrKIiIiIiIiIZIDW6GShOaW5uF1OgPHpQmp8OyUUZBERERERERHJALFMltrKvPhjPq8bAH9Q5UJTQUEWERERERERkTRnWVZ8fHOs6S2c1vhWmSxTQkEWERERERERkTTXPzTG4GgIGB/fDJAda3yrTJYpoSCLiIiIiIiISJqLlQq5XQ5mz8qJPx7ryeJXJsuUUJBFREREREREJM3Fmt7OK8/F5Ry/1Y9lsgQ0XWhKKMgiIiIiIiIikubiTW8r8s94fHy6UATTsqZ8XzONgiwiIiIiIiIiacxuemtnstScNlkIIDs6XcgCxtSXJekUZBERERERERFJYydO+RmN9lyprTx3JgtAQEGWpFOQRURERERERCSNxUqFvB4n5cXZZ3wu+7Qgi5rfJp+CLCIiIiIiIiJpLF4qVJGHwzDO+JzPe1qQRc1vk05BFhEREREREZE01hrNZKl5U6kQgMflwOmwAy+BMZULJZuCLCIiIiIiIiJpyjQt2rqHATuT5c0Mw4j3ZVG5UPIpyCIiIiIiIiKSpjp7RxgL2Rkqb256G5MdLRlSuVDyKcgiIiIiIiIikqZaov1Ycn1uZhV4z/mcWCaLyoWST0EWERERERERkTTV0hXtx1KRh/GmprcxymSZOgqyiIiIiIiIiKSp+GSh85QKgTJZppKCLCIiIiIiIiJpKBwxOXrCDrLUnqPpbYwvyw0ok2UqKMgiIiIiIiIikoaO9QwTjljAhTNZ4uVCmi6UdAqyiIiIiIiIiKShWKlQYa6Horys8z5vfISzyoWSTUEWERERERERkTTU0hlrenv+LBaA7Cw1vp0qCrKIiIiIiIiIpKHWrmg/lsrz92MB8HljjW8VZEk2BVlERERERERE0sxYKEJHzwgAtRfoxwKnZbKoXCjpFGQRERERERERSTNHu4cxLbvpbfUFJgvBaZksKhdKOgVZRERERERERNJMS5fdj2VWgZe8bM8Fn3t6JosVDcxIcijIIiIiIiIiIpJmWmNNby9SKgTj04VMyyIYNpO6r5lOQRYRERERERGRNNPSeWlNbwGyo+VCoOa3yaYgi4iIiIiIiEga8Y+F6eobBaD2IuObAXxZ7vFjg2p+m0wKsoiIiIiIiIikkdjoZoOLN72FMzNZ/MpkSSoFWURERERERETSSGu06W1FSXa838qFZLmdGNGPVS6UXAqyiIiIiIiIiKSRWD+WmksoFQJwOAy8WU5A5ULJpiCLiIiIiIiISBoZnyx08VKhGK8nNsZZmSzJpCCLiIiIiIiISJoYGg1yciAAQO0ljG+OiZUVBZTJklQKsoiIiIiIiIikiVjTW4dhMLcs95KP83mi5ULKZEkqBVlERERERERE0kRLtFSoqjSHLLfzko/zRjNZ/EEFWZJJQRYRERERERGRNNEabXpbO4F+LDCeyRIYU7lQMinIIiIiIiIiIpImWqLjmy91slCMMlmmhoIsIiIiIiIiImmgf2iMgeEgMLGmtwDZsca3ymRJKgVZRERERERERNJAbHSzy+mgqjRnQsd61fh2SijIIiIiIiIiIpIGYqVCc8tycTkndjvvU7nQlFCQRURERERERCQNTLbpLYwHWVQulFwKsoiIiIiIiIhMc5Zlxcc3T7TpLZxWLqRMlqRypXoDIpJ5IqbJ8GiIodEQ7SdHKMrLIi9LLzciIiIiIpPVMxBgJGAHSC4nk8WvTJak0l2PiFxUKBxhKBo0GRoNjv/pD5318fBoMP7if7p3XDGXD1w/f8K1oyIiIiIiMt70NsvtpLJkYk1vAXwe+/Y/HDEJhU3cLv1engwKsojMMJZlEQhGOHHKf0bAZHg0FA2UBM8MqPhDjAUvP9r9m+1HOXTsFJ963zLKirITcCUiIiIiIjNHrB9LdXkuDocx4eO9Wc74x4FgGLfLk7C9yTgFWURmkLauIf7pO1sZHAlO+hwOwyA3201etps8n5u8bI/98el/+tzxjyvKcvmv3zfz898dorVriHse2sbHblzMlUvKE3hlIiIiIiKZrTU6WaimcuL9WGA8kwXAH4yQp/c9k0JBFpEZ5JlX284KsLicjmhA5LRAic9z9mPRP7OzXBjGpUfO3S4nH3rHQmrKc/nnp/YwMBzkB0/sYV9bP5vfvgCP23nxk4iIiIiIzGCmZdHaFZssNMkgy+mZLGNqfpssCrKIzBDD/hA7Dp4E4CN/tIgVtcXk+dx4Pc4JBU0ma0l1EffediX/8su97G7p4w87j3O4Y4BPvW85VbMmXlMqIiIiIjJTdPWOEoiW8NdMouktgPf0TBYFWZJGnW5EZohX93YTjph4PU5uvLqGskIfvglmpVyu/BwPd/7pSj5w/XycDoOOnhG+8vA2tuw6jmVZU7YPEREREZF0EisVys5yUVbom9Q5HA6DLHdsjLMmDCWLgiwiM8TWxk4ANjTMjo9vSwWHYXDj+mo+95E1lOR7CYZNHnpmPw/+cq8i6iIiIiIi59ASbXpbU5l3WW+SxprfqlwoeRRkEZkB2ruHaOu2X5jffsXcFO/GNr+qgHs+vo41C0sBeGVPN19+eBtt0VpTERERERGxxcY3T7YfS0ys+a0yWZJHQRaRGWBLNIulsiSbhfMKU7ybcTleN3fcspyPvGMhLqdBd7+f//Wj7fz29WMqHxIRERERAcIRk/YTwwDUVFxmkEWZLEmnIItIhguFI7yypwuAjQ2VU9qD5VIYhsHb187h72+9gvIiH+GIxX/85iDfe3w3I4FQqrcnIiIiIpJSx0+OEAqbANROsultTKz57aiCLEmjIItIhnvj0ElGAmEchsE1RSc58R9fYnjXc6ne1lmqK/L4h4+t46pl5QDsONjDPf/2Goc7BlK8MxERERGR1ImNbs7P8VCUl3VZ54r1ZgyMqVwoWRRkEclwWxs7cRPmLyt24nj+OwSPH2TwhUexzOn3wurLcvEXNy/ltpsW43E76B0c42s/3sHTr7RhqnxIRERERGagllg/lorLa3oL4PPEpgspkyVZFGQRyWB9gwEG2g9xV8F/s2RsV/xxMzBMpPtwCnd2foZhcG3DbL700XVUleZgWhY/f76Zb/10F4MjwVRvT0RERERkSsWCLDWX2fQWwBvNZNFUz+RRkEUkQ1mmydHf/ZS/yX+aCucAGE48V/4J7tJqAMKtO1K8wwurmpXDl/78Ct6yajYAu1v6uPvfXmNfa1+KdyYiIiIiMjVC4QgdPSPA5fdjgdMa32q6UNIoyCKSgcyhHkaf+ip1J36H07AYdpeQfcuXyFp1M94F6wAIt70x7Sf4eNxOPvquxXzqfcvwepwMjAS5/7GdPP7CESKmmertiYiIiIgkVfuJYSKm/Tv75U4WgtNGOCuTJWkUZBHJIJZlETr4IiM//xJm9yEAXggswrzxizhn1QDgq7/Cfu7gCcz+46na6oRcuaSce25bR3VFHhbw1EutfOM/36BvMJDqrYmIiIiIJE1rp930tiQ/i/wcz2WfL14upEyWpFGQRSRDWIFhAr/9PoHnH4RQAL8jhweG3s7OwndQVVEUf567rAZn/izAzmZJF2VF2fz9rWt557q5ABw8NsA9D21j1+GTKd6ZiIiIiEhytCawHwuMN74NKJMlaRRkEckA4WO7Gfn5Fwkf2WY/MHc1Xxt4D/tDVWxsqDzjuYZh4J2/1j6ubXr3ZXkzl9PBh96+gL9+fwM5XhfD/hDf/nkjj/32EOGIyodEREREJLO0RMc311Rcfj8WOD2TRUGWZFGQRSSNWeEggZf+E//T92ONngK3F+91H+f1ivdzKuTB43Jw5ZLys46LlQyZJ45gjp6a6m1ftlULZnHvx69kwZwCAH697Shf/fHrnDjlT/HOREREREQSIxAM03ky1vQ2sZkswZCpHodJoiCLSJqK9LYz+vi9hHb/GgBHeT057/8y7sXXsbWpC4ArFpfhi0arT5c1dym4fQCE23ZO3aYTqDjfy999eDU3X1ODAbR0DnHvQ6+xbf+JVG9NREREROSytXUNERtTkahMltPvDTRhKDkUZBFJM5ZlEtz1DKOPfxmzvwMMB54rbiH7PZ/HkV9GR88wR47btZvXvqlUKMZwunDNawCm/yjnC3E6HGy6ro7PfmgV+Tke/GMRHvi/u/nh402MhfSPhoiIiIikr5Zo09vyIh/ZXndCzuk9LciiCUPJoSCLSBoxh3vx//LrjL36EzDDGAXlZL/vi2SteR+Gw07929rUCUBZoY+FcwvPey5X9WoAIsf3YoXSe0rPsppi7v34lSyrsRv8/vq1dn70zL4U70pEREREZPJau+w3ThNVKgTj5UIAgTG9KZkMCrKIpInQ4VcY+fkXiXTuB8C95HpyNn0ZZ1ld/DnhiMlLu+1SoQ0NlRiGcd7zueY1gMMJkTDhY7uTu/kpUJDj4W8+uIq3r50DwIuNnZiWdZGjRERERESmp9j45kSVCgF4Padlsqj5bVIoyCIyzVljI/h/+wMCv/sBBP0Y3jx8f/T/4r32oxjurDOe29jcy9BoCAPYsLziguc1PNk4KxcDEG5Nn1HOF+IwDN4RHfE8OBKkJTryTkREREQknQz7Q/GhDoka3wzgdjlwOe0wgF+ZLEmhIIvINBY+vo+Rn3+JcPMrADjnrST7T/4xXurzZlsb7VKhZXXFFOd7L3r+2HnC7TuxzMx4kS0r9DG7NAeApubeFO9GRERERGTi2qKjmw0DqssTl8kC4MuyS4YCymRJCgVZRKYhKxIi8Mpj+H/5dayRPnB5yLr2Y/j+6E4c2QXnPObU8BiN0aDCtQ2zL2kdV000WDM2QqTrUEL2Ph2sXVQGwC4FWUREREQkDcUysmfPyiHrtD4qieCLlgyp8W1yKMgiMs1E+o4x+viXCTX+CrBwlNaRs+nLeJa89YI9Vl7e3YVpWeR4Xayqn3VJazlyS3CUVAMQbsuMkiGAtYvtIEtb1xADw2Mp3o2IiIiIyMS0RjNZaisSVyoU441msqhcKDkUZBGZJizLJNj0LKOP34PZdxQMA8+a95H9vi/gKLxwfxXLstgSLRW6elkFbtel/2jHslnCbW9gZUij2MXVxfii4+kajyibRURERETSSyyTpbYysaVCMJ7JonKh5FCQRWQaMEf68T/9fxh7+VGIhDHySsl+79+TdcUtGA7XRY9v7hikq28UgI0NlRNaO9aXxRo8gdl/fOKbn4bcLgcN0WyeRpUMiYiIiEgaGRgeo3/IzsZOZNPbmNibkcpkSY6L372JSFKNHniZkWcfhLERANyLriPr6s0YHt8ln2NLox0cqS7PY94EG2M5SuZh5JZgDfcSbtuBs7hqQsdPV2sWlfLqni72tPQRjpjxLurJMHb8oP1BztykrSEiIiIiM0NLtFTI6TCYU5qb8PPHGt9qhHNyKJNFJIUGtjxG35PfgrERjKxcvO/8DN63fHxCAZZAMMxr+08AE89iATAMA1f1KiBzRjkDrIk2vw0EIxw6NpC0dczBE/Q8eg89j92LOaysGRERERG5PK3RUqE5ZbkTagNwqbzRTJaAGt8mhYIsIiliBYYZeu1JAJxzV5D9gX/EXbN2wufZvr+HsWAEl9PB+qXlk9qLq3oNAGbPEczRU5M6x3RTnO9lXrkd+U/mKOfQoZfBjEAkTPjY7qStIyIiIiIzQ0tntOltEkqF4LTpQkGVCyWDgiwiKRI6sg3MCIY7C987/gpHduGkzrM1Wiq0ZuEscn3uSZ3DOXsRRLNnwm07J3WO6ahhvt2XZVfzyaSc37IsQodfjv89cmxPUtYRERERkZnBsixau6JNbysS3/QWxsuFlMmSHAqyiKRIOHpz7ltwJYYra1Ln6Oob5WC0FObahtmT3ovhcOGa22Dvq3XHpM8z3TTMLwGgs3eUnlP+hJ/fPNmKNdAV/3ukYy+WZSZ8HRERERGZGXoHAwyNhoDkNL0F8CqTJakUZBFJAXPoJJEuu1lq9tKNkz7Pi0322OaS/CyWVBdd1p5iU4Yix/dihQKXda7poq4yP57dk4wpQ6FDdqDMkV0AgDU2jHmyPeHriIiIiMjM0BotFfK4HMyelZ2UNeKNb5XJkhQKsoikQKj5VQAc2flkVa+Y1DkiphkPsmxYUYnDYVzWnlzzGsDhzKjeIg6HwYq6YgCajiQ2yGKZEcLNrwCQu/ZGnAV2o91wR2Z87URERERk6rVES4XmVeThdCTndj3WkyWg6UJJoSCLSArES4UWXY3hcE7qHHta+jg1HATsIMvlMjzZOCsX2/vLoClDK6IlQ/va+hkLJS4lMnJ8H5bf/kcwe8kGvNFgmfqyiIiIiMhkxTJZapLUjwVOny4UwbSspK0zUynIIjLFIn1HMfuOAZdXKrSl0c5iWVJdRGnhpY98vhBXjV0yFG7fiWVmRo3m8toSDANCYZP9bf0JO2+sVMhZsRBXQRlZNdEgS9chrPBYwtYRERERkZnBtCxau5I7WQjGy4UsYEx9WRJOQZY01dwxwH0/2s7OQ8mZmiLJEz5sl5gY+WV4KhdM6hyDo8H4935jw+VnscTE+rIwNkKk61DCzptKuT4386vsnimNCSoZssJjhFtfB8BVfxUAWfOWAwaY4Yz52omIiIjI1DnR74/3SUlmJkusXAggoCBLwinIkqZe29PFa3u7+f/+q5Gf/f4wEVMTTdKBZZmEokEWd/1VGMbk+qi8sqebiGnhy3KxdmFpwvbnyC3BUVINQLgtc0qGVkZLhhoP92IlICUy3PoGhAJgOHHXXQmA05eHo7TG/nyG9LQRERERkanT0mmXovuynJQXJ6fpLYyXC4Ga3yaDgixp6uYNtSyttRt6PvNqO/c/upOBYZUoTHeRrkNYw3Y2RSwDYqIsy2Jr43EA1i8tx+OeXE+X84mXDLW9kZCAxHSwos4OsvQOBjjeO3rZ5wtFe+q45jVgeHPjj7uqlgEQ6VBfFhERERGZmPF+LPk4Jvlm7KXwecbvH/xqfptwCrKkqbwcD/d8Yj03rp8HwIGjp7jnoW0caE9czwlJvFjDW8esapyFsyd1jtauIY71jABwbQJLhWJiJUPW4AnM/uMJP38qzC3LpSgvC4DG5ssrsTMDQ0SO2pkqrvqrz/icc44dZDF7j2KODlzWOiIiIiIys8QmCyWzVAjA7XLgjE4mDYypXCjRFGRJY06ngw9cX89nNq3Al+ViYCTINx7dyTOvtGVMBkImsSJhQke2AeB+0835RGyNNrytKs1Jyguwo2QeRq6d+RFu25Hw86eCYRg0REuGmpovry9LuPk1sCLg9uKqXnXG55zl9eDyABA5vvey1hERERGRmSNimrR3J7/pLdi/G3uj2SwqF0o8BVkywOqFpdz9sSuYV5aLaVn87PlmvvuLJkYDoVRvTU4TOdYEYyOAgWv++kmdIxiK8MrebgCubZg96Z4uF2IYRjx4kEmjnBuiJUOHjg0wGpj8PybxUqHaKzCiAZUYw+nGWbkIgLBGOYuIiIjIJeo8OUowZPfZrKlMbiYLgC/al0VBlsRTkCVDlBVl84Vb18bLR944dJIvP7w9Hg2V1IuP/J29GEdO0aTO8frBHvxjYZwOg6uWlSdye2dwVa8BwOw5gjmSGSVoS2qKcDkNIqbF3ta+SZ3DHOzB7D4MnD8b6fS+LMooExEREZFLEWt6m+tzU5LvTfp63uiEIb+mCyWcgiwZxON2cttNS7jtpsW4XQ5OnPLzj4+8zgu7MqOvRjqzgn7CbTuBxJQKrVowi/xsz0WePXnO2YvA4wMg3L4raetMJa/HxaK5hQDsmmRfllgWi5FdiHP2knM+xxkNslgj/ZgDnZNaR0RERERmltau8VKhZGSrv5kvyy4XCiiTJeEUZMlA1zbM5u9vXUtZoY9wxOThZ/bzb0/vIxhSlDJVwq07IBIEpwtX3RWTOkfPKT/72uyskmQ0vD2d4XDhmrsSiO49QzTMnwXYfVnMCWaZWJYVb1zsmr8ew3Hul09H8RwMn11HG1HJkIiIiIhcglgmS7Kb3sbEy4U0XSjhFGTJUPPK8/iHj13B6gX2TeXWxk7+949e50T/5Y+vlYmL9/GYuxLDM7mZ9y822VkRhbkelkXHdydTrC9L5PherFAg6etNhVjz28HREG1dEyulM3vbME/Z3wP3gvNnIxmGEc9mUV8WEREREbmYUNjk6IlhIPlNb2PGG9/qjfhEU5Alg2V73fzVphV84Pr5OAyD9hPD3PvwdnaNi5hsAAAgAElEQVQc7En11mYUc3SASId9s+26wM35Bc9hWmyNBlk2rKjEeZ4sikRyzWsAhxMiYcJHm5K+3lQoL86mvMgug2qc4JShWE8dR2EljpLqCz7XFR3lHOncj2Xq3QEREREROb9jPcNETDvLeiqa3sJ4JktAmSwJpyBLhjMMgxvXV/O3m1dRkOPBPxbmu79o4qe/P0zENFO9vRkhfOQ1sCzw+HDNbZjUOfa19dM3OAbAxhXJLRWKMTzZOCsXAxBuy5wpQyui2SwTCbJYpkm4+VUAXPVXX7RONpbJQihA5MSRyW1URERERGaE1mipUFFeFoW5WVOypi/W+FaZLAmX8iDLtm3b2Lx5MytXrmTjxo185StfYWRk5LzPHxkZ4d5772XDhg2sXr2av/iLv6C1tXXqNpymFs0r4p7b1rEw2vjzV6+2841Hd3JqeCzFO8t8sVIh9zlG/l6qLY128+KFcwooL55cudFkuGpWA3bzW8vMjBfgldG+LK2dgwyOBC/pmMjxfVijpwBw11910ec7copwFM22j1XJkIiIiIhcQEu0jH2q+rEAeKONb9WTJfFSGmTZuXMnt912G6WlpTzwwAPccccdPPnkk3zxi1887zF/8zd/w69+9Svuuusu7rvvPrq7u/nzP/9zhoY0qvhiCnKz+NvNq7hx/TwADh49xT0PbeNAe2aM6J2OzIFuzGgmg2uSU4WG/SF2HLSn4WxsmJ2wvV0KV7UdZGFshEjXoSldO1kWzi0ky+3EApqOXFo2SyxQ5iivx5FfdknHxPuydCjIIiIiIiLnF8tkqZmifiwwnsmi6UKJl9Igy/3338+qVav49re/zTXXXMPmzZu58847aWpqwu/3n/X87du384c//IH77ruPW265hXe+8508/PDDDA0N8eijj6bgCtKP0+HgA9fX85lNK/BluRgcCfL1R9/g6VfasCY4bUUuLnT4FSA68jdaejNRr+7tJhwxyfI4uWJxaSK3d1GO3JJ4/5FMKRlyuxwsrSkCLq1kyAoHCbdst4+dQKAs1pfFPHEEK6iG0yIiIiJytrFghI6TdiVH7RT1Y4HTMllULpRwKQuy9PX1sX37djZv3nxGf4OPfOQjPPfcc/h8vrOOefHFF8nJyWHDhg3xx4qLi1m3bh0vvPDClOw7U6xeWMrdH7uCeWW5WBb8/PlmvvNfTYwGQqneWsY4Y+Rv/VXnHfl7MVsb7Ya3Vy4uwxuNOE+leMlQ646MCcTFpgztbukjHLlwb6Jw204IBcBw4pp/5SWv4axYBIYTLJPw8f2XtV8RERERyUztJ4aI/YpdU5GCTBaVCyVcyoIsBw8exLIsCgoKuPPOO1m1ahVr167l7rvvJhA497jYI0eOUF1djdPpPOPxefPm0dLSMhXbzihlRdl84da1XNtgN1Ldefgk9z68jfZulV4lgnmyDXOgC7i0Ph7n0t49RFv0+3HtFJcKxcRKhqyhHsz+4ynZQ6KtqLODLP6xMM0dAxd8bixQ5py7HIf30t9dMDw+nOXzAfVlEREREZFza+m0f9cvLfSS63NP2bqx6UL+sUjGvJE6XUz92+JRfX19AHzuc5/jHe94Bw888AAHDhzgW9/6FmNjY3zta18765jh4WFyc3PPejwnJ4fh4eFJ78UwoKDg7MyZ6czlsgNNidj3nZvX0LDgKA8+sZueUwH+149e55PvXcYN6+Ylbc1LMdXrRSImLZ1DlBX5ErLmqde3AeAqnk1R3eJzTqS52DW+9oLdz2V2aQ5rlpZfdKrNpZjo19XKX8RYfimRwR5c3U3k19Yndb1EuNiaBQU+airzae0c5MCxAa5cce4AlukfZuhoo31Mw1vIPs/5zreeMX8Vg10HsTr3JvT6p+PXNN3XS8WausbMWDPT10vFmrrGzFgz09dLxZq6xsxY883rxUqFFs4rStoeznWNs6LDNEzLwpedRZbHec5jE7VeukjA7VbqgiyhkF2WsmbNGu6++24Arr76aizL4r777uOOO+5g7ty5ZxxzoQibY5KlGGJ72xVzqZ2dz/3/sYOuvlEe+EUTB9r6+eT7lpPlTtwP3HTTNxjgjYM9vHGwh8ZDPYwEwuT43Hx60wquXj75UcmWaTK6/yUAspdunFRwJBSO8MIbHQC8fe3chARYJsMwDHz1axne8Sv8h7eTf9UtKdlHoq1ZVEpr5yA7DvRw641Lzvmc0QMvgxnBcHvx1l8x4TWyalbAiz8l3N9JePAkrvxZl7ttSYKuvlF+v/0o2/Z1s3ZJOR9+x8KU/byJiIjIzBLLqq6fUzil62Z7x0MBo2OhhAZZZrqUBVlycnIAuO666854fOPGjXzta1/jwIEDZwVZcnNzOXbs2FnnGhkZOWeGy6WyLBgYOLvR7nRWUODDsiwGB89dWjUZxTkevvjna/nX/97HG4dO8rvXj3Ho6Cn+n1uWU16UHY9ETtXXKhnrhSMmzR0DNB7pZfeRPo6eODsDasQf4v7/2MF1K2ez+YYFkwoyhTv2Yo7YU5sic6447zVc6Bq37T/BsD+EwzBYXV+SsK/DZL6ukYoVwK8IdR6m//hxHDlFSV3vcl3KmovmFAB2SdaR9n5KCrxnPWe0ye715KxZw9CoCUzs+2j5ZoPHB0E//fu241n8lglfy0TWS6ZM+Pk/XTAU4fWDPWzZdZz97afij7d1DZGb5eStq6qSsu7p9H3MjDUzfb1UrKlrzIw1M329VKypa8yMNU9fbzQQ4ng0k6Wi0Ju0PZzrGsPB8Ya3J06O4DATVzKUiu9jopSU5F52NkvKgiw1NTUABIPBMx6PZbic613E2tpaXn75ZSzLOuPzbW1t1NbWJm+z09Do/pfpf/aHZG386KT7fZxLttfNX21awa9ea+e/nj/C0RPDfPnhbXzi3Uu5/k3lQ+mibzBAUzSosret76wO2r4sJ0uri1kxv4Rl9bP40TP7aTx8khd2HefQsVPc/t5lzCufWKfv0KHoyN+y+Zc88vfNtjTa/U8a5pdQmJs1qXMkinP2oniwINy2E8/S61O6n0Som51PjtfFSCBM45Ferl995k21OXSSSNdBYGJThU5nOJy4Zi8l3Pq63ZclQUEWmRzLsmjrHmLLrk5e2duN/7SRhYW5HkoKfDR3DPCfvzlIdXketVM4RlFERERmnrYuux+LARO+37hc3tMyV/wa45xQKQuyzJ8/n6qqKp5++mk+/OEPxx///e9/j8vlYvXq1Wcds3HjRn7wgx/w0ksvxScMxaYU3X777VO29+kg2HkYK+gntP8PCQ2ygB3gunF9NXWV+fzgiT0MjAT57i+aaO8Z4c/+aFFC10qGcMTk0NFTNB3po6mll46ekbOeM7cslxV1JayoK2Z+VQEup11uVlDg40u3XclPf3OAX7xwhM7eUf7xke184Pp6blg755JKCCY78vd0fYMB9hyx+xZtbJh82VKiGA4XrrkrCTe/QrjtjYwIsjgdDpbXlfDq3m4aD588K8gSH7/ty8dZtXTy68xZZgdZju/DskwMQ6WNU23YH+KVPV1saew8I3vN6TBYVT+La1dWsqy2GK/Pw//8/osc7xnh+4838Q8fW0detieFOxcREZFM1hINslSUZMcb0U6VLI8TA7CAgIIsCZWyIIthGNx111189rOf5a677mLTpk3s3r2bBx54gFtvvZXi4mL6+vpob2+nvr6e3Nxc1q1bx5VXXhk/prCwkO985zvk5eWxefPmVF1KSnjK7cydSE8LlmlOejzwhSyaV8Q9t63jgSf2cPDoKZ7ccoSXmjqpKPZRWuBjVqGX0gIfpYU+Sgu9ZHunrhv2m50c8NtBleZe9rX3MxY8M1slO8vF0tpiVtQVs7y2hKK882eGOBwGN15VzeLqIn74xB5OnPLz6HOH2NPSx8ffvYT8i9x0hdt3QcgPhmNCI39P9+LuLiwgP9sdHzecaq6a1YSbXyFyfC9WKIDhPru8Jt00zLeDLPva+gmFI7ijTbrs8dt2Tx3X/PUYjsnXqLqqljEGWIEhzN52nLNqErBzuRjTstjX2s+WxuPsONhDODKeAjt7Vg7XNlRy9bIK8nPGf56zvW7+9iNr+Z/f20rv4Bj//NRe/uYDK3E41J9FREREEq+lcxAgJdmzDsPAm+XEPxbB/6Z7J7k8KQuyANx00014PB6+973vcfvtt1NSUsIdd9wRz0p5/vnn+fznP88jjzzC+vXrAfjud7/L1772Nb7+9a9jmiZr167lW9/6FgUFBam8lCnnmb3A/iAUwDx1HGfxnKSsU5Cbxd9uXsUv/nCEZ15t5+QpPydPnbu2LjvLZQdeCn3R4IuXWYU+ZhV4mVXgw+1KXCAoFI5w8OgATUd6aTrSS2fv6FnPqa7IY0VdMSvqSqibnY9zgoGo2sp87r5tHf/xm4O8tLuLxuZe7v7X1/jkzUtZVlt83uPC0QwI55xlOHwTf8E0LYut0VKhq5dXxLNsUs01dwU4nBAJEz7ahLtuXaq3dNmW1xZjAMGwyf72U/HRzmZve3xc9WSzkWKM/DKM3BKs4V7Cx/YqyJJkJwf8bG3s5MWmTnoHx+KPZ3mcrF9SzrUNldTNzj9vVtq88jxuu3EJP3xyD3ta+nhiawu3XFc3VdsXERGRGaQ1Or45VSXKXo/LDrIokyWhUhpkAbjhhhu44YYbzvm5TZs2sWnTpjMeKygo4Ktf/Spf/epXp2J705azoAyHLw/TP0TkRHPSgixgl1V84Pp63nrFXA609XO0a4iTA356TvnpGQjEs0ZGx8K0dw/T3n12M1kDKMzLorRgPPBiZ8DY/xXkenBcpBTnxCk/Tc12UGV/ez/BkHnG53O8LpbXlbC8tpjldSUU5Fx+mr8vy8Unb17K8tpiHnn2AAMjQf7PT3byrvXz2HRd3VkBEGtsxM5kYfI354eOnqLnlN3QeOOK1JcKxRiebJyVi4l07CHc9kZGBFnysj3UVeXT3DFI4+HeeJAldNjuqWMUVOAovbx+T4Zh4JqzjND+F4h07IFVN132vuVMoXCEHQdPsrXxOHtb+zm9bduCOQVc2zCbdYvLLrlr/vql5TR3DPDc68d46qVW6mbns7Jek6FEREQkcQZHg/RGh5jUVExtP5aY7CwX/UNjBJTJklApD7LI5BiGgadyAYEjOzBPHJmShpr1cwqpn1N4Rpdoy7IY9ofoORUYD7yc9nHf4BgR08IC+ofG6B8a4+CxgbPO7XI6KCnwUhotQZpV6KV6dgFup4Nte7poOtJLd/+ZGTQGUFOZH89Wqa3MT1pa/1XLKqirKuCHT+yhpXOQX73azv62fm5/3zLKi7Ljzwu1bAczDC4Prpo1k1prS2MnYDdmrSqd/NSsZHDVrLaDLO27sMzIZZXRTBcNdSV2kOXIST5sLQDLItz8KmAHyhIxytdZtdwOsnQdwAoHMVzq85EI7d1DbGns5JU9XYwExt+BKcjxcM2KCq5tmE1FcfYFznB+f/q2elq7hjjcMcCDT+3lH25bR1mhL1FbFxERkRmuNVoq5HQYzCtPze/83iz7d3llsiSWgixpzDO7nsCRHURONKdsD4ZhkJftsTMCZp+d5hYxTfqHxuzAyyk/PQN+Tp4K0DNgB2MGR+zpUuGISXffKN19Z5f9nC4v2x3PVFlWW3zR/iiJVFbo4/N/toYntrbw9MtttHYNcc9D27j1nQu5ZrmdcRIrFXJVr5lUzxL/WJjt+08A06Ph7Zu5qlcz9uKPYWyESNchXLMXp3pLl61h/iwe39JCz6kAXX2jlI61Y0XHbyeqqbSrailgQCRMpOsgrjnLE3LemWg0EOKVvd1saeyMd+QHu654ZX0J1zbMZsX84gmXB76Zy+ng03+8nHsfeo3B0RDf/0UTX7h1LZ5JjHQXERERebNYqVBVaU68L+BU83nscIA/qCBLIinIksY8lXZfFrO/Ayvox/BMv3dZnQ4Hswp8zCrwQXXRWZ8fC0U4ORCgJ9rrZTwLxv4zGIpQOzs/OgmohOqKvIuWFSWTy+ng/W+Zz9LqIh785V5ODQf5l1/uY3dLH3+2oYzI8f3A5G/OX9vXTTBs4nE5uHJxeSK3nhCO3BIcs6oxT7YRbnsjI4Is88pzKcj1MDAcpLG5l7f47Ya3jrI6HAWJ+R4Y3tzo162V8LE9CrJMkGlZHGjrZ0tTJ68f6CEUHi8VLC/O5rqGSq5ZXkFBgkedF+Vl8an3Lef+x3bSfmKYH//6ILfdtDgh2U0iIiIys8Wa3tZUpKYfC4A3OtEoMKZyoURSkCWNeSrm2x9YFpGTrbhmL0nthiYhy+2kalYOVbNyzvpcfr6XiGkxMjx2jiNTa0lNMfd+/Eoeeno/Ow+f5JU93ZR2vMANDgsjKxfn3MndRG+NlgqtXVRGtnd6/ni6qlcTPNlGuHUH1lUfSvsbTsMwaKgrYUtjJ3sOd3PNWGz89jUJXcc1ZxnBk612Xxa5JL0Dfp55sYWtTZ3xPkVgv26sW1zGtSsrqa8qSOr/g4uri3j/W+v42e+b2drUyfyqfN6yquriB4qIiIich2VZtHbFmt6mph8LgC/ar06ZLIk1PcaWyKQ4vDk4CmcDpLRkKFkMw5g2k3XOJS/bw2fev4I/e+dCXE4Hi8xDAHTkLMZi4il/HSdHaD5uR7SvnYalQjGu6tUAWEM9mP0dKd5NYjTMt5uaurv3XPb47fNxVi0DopOL/IMJPXcm+ulvD/Gp+34XL+UCmF+Vz8duXMw3/2oDH3/3EhbMKZySIN+7rpzH2oWlAPzHbw7G33kSERERmYy+wQAD0bYJqcxk8SmTJSmm51vlcskcZXWYp47bzW9lyhmGwdvWzGFxQYC83/YB8LPWEjyPvcFfvGcZRXmXXr7wYjSLpbTQy8J5hUnZbyI4SuaNjyRu3ZHUyVZTZWlNEU6HwRq3Hax0zlk+qfHbF+IsrwenByJBIh17cSSo30smOtDez0+eOwhAfraba5ZXsqGh8pwZb1PBMAw+/u4lHOsZprvfz/cf383dt60j1+dOyX5EREQkvR2ODgJxuxxUlabm9xsAr0eNb5Nh+qYJyCVxltUBEOluxrKsizxbkqWkzx7bPOosoCVcxv72U9z9b6/xxqGeSzo+HDF5abcdZNm4ojKlfWcuxjCMeDZLuG1nineTGL4sF8vneFnqtjNzEtXw9nSGy4OzciGASoYuYCwU4aFn7N5G9XMKuP+ODfzp2+pTFmCJ8WW5uGPTCjxuB72DAf75yT2Ypl5zRUREZOIOHzsFwLyy3JRm7scyWVQulFgKsqQ5Z5ndl8XyD2CN9KV4NzOTZVmEolOFCldcyx2bGsjxuhj2h/jOfzXx418fIBi6cApeU3Mvg6MhDGDDiulbKhQTG09t9hzBjE7iSXfXFXXiMkzGLBfO6smN374YV7RkKHxsj4Ki5/HElhZO9PtxOQ3ueP/KaVUyOKc0l4+9y272vLuljydfbEnxjkRERCQdNUczWVJZKgQqF0qW6fPbq0yKo3gOuOwxxpnYlyUdmCeasYbsjBVX/dWsWVjKvR+/ksXRkp/f7ejgK49sp6Nn+Lzn2BItFVpWW0xx/sRHP081Z+VCiE6zypRsllr/PgCagnM52hdKyhrOOXaQxRrpwxroSsoa6aylc5Bnt7UD8P631jOvInWN4M7nqmUVvH2NXSL35IutNDafTPGOREREJJ1YlkVzRzTIksKmt3BauZAyWRJKQZY0ZzicOEtrAbtkSKZe6NDLADiK5+IstqeOFOd7uetDq9l0XR0Ow6CjZ4Qv//t2fr/j2FkZDP1DARqbewHYOI0b3p7OcLhwzV0JQLjtjRTv5vKZw704T9qNi7cH65J24+wonoMR7fUSPqaSodOFIyb/9vQ+LAvmlOZwy1vrU72l8/rg2+uZX2V/Hx98ai89p/wp3pGIiIiki+6+UYb99ht6tZXTI5PFr0yWhFKQJQM4Su2+LGp+O/UsM0z4yGuAncVyOofD4OZravj8n61hVoGXUNjkR78+yHd/0RR/YQX4wxsdmJZFjtfF6gWlU7r/y+GqsfuyRDr2YoUCF3n29BYr9xpzZHMgVBkPeiWaYThwVi0F1JflzX75UisdPSMYBtx20xLcrun7z5PL6eDT71tOXrabkUCY7z3edNGSQBEREREYb3qb5XFSUZyd0r34PHaQJRwxCYXNlO4lk0zf32LlksWb355sxTKV6jWVIsf2YgXsGffu+vXnfM78qgLuue1KrlpaDsAbh05y97+9xv62fizL4nfbjwJ2GcJ0vrF8M9fcBnA4wQwTPtqU6u1clvBhOxtprGoNJg6OHB9kaDSYlLXifVmO79PPa9SxE8P898ttAPzRlfNS/q7OpSjO9/Kp9y7DMKC9e5gf/+ZgqrckIiIiaaC5w256W1Oeh8OR2mEX3ixn/OOASoYSJn3u6OS8nOXRtPpICLP3WGo3M8OEojfnzspFOHJLzvu8bK+Lv3jPUj7x7iVkeZz0D43xjUff4Ns/2UlHzwgA16ZJqVCM4fHhnL0ESO+SoUjvUcw+++dm1sq34HE7sIDdR5LTSNo5Z7n9QShA5IQap0ZMu0woYlqUF/n44421qd7SJVtSU8z732I3H9/a2MkLu46neEciIiIy3cUyWVLdjwXGM1kA/EFl5SaKgiwZwJFThJFTBKj57VSyQmOEW3cAZ5cKnYthGGxYUck9t62jpiIPC9gSvSmbV57LvPLUv9BOlKt6FQDh9l1YZnq+MMeyWIz8MjyV9SytLgag8UhySoYcOUU4CmcDEDm2OylrpJPfbDtGa5edDfaxGxfjcTsvcsT0cuP6eaxeMAuAH//6IK1dgynekYiIiExX4YjJkWjT2+mQues7LZPFH1AmS6IoyJIhnNG+LBH1ZZky4bYdEB4DhxN37RWXfFx5UTZfuHUtN66fF3/s2obZydhi0rmq7b4sjI0Q6Uq/cgnLMuP9WNz1V2MYBivm2xlJu4/0EjGTU5s63pdlb1LOny66+0Z5fIv9mnX9mioWzStK8Y4mzjAMPvHupZQX+QhHTL73i91n9FwSERERidm+/wSBYASHAQvmFKZ6O3hPy2RRuVDiKMiSIZzldsq6MlmmTuzm3DW3AcObO6FjXU4HH7i+nn+8/Wpue/dS3ro6PYMsjtwSHLOqgfQc5RzpPIg1YpcFuRfY2UgNdXaQZSQQprkjOVkJrugo58iJZqzgzJxMY1oWDz2zn1DYpCQ/iz+Jlt2ko2yviztuWYHH5aB3MMA/P7UH07QufqAknDl4gt5nHyTcrxHpIiIyvViWxa+32b0Y1y+roCgvK8U7sgd1ZEWziDVhKHEUZMkQjjL7BsUa6MIaG0nxbjKfGRgictQu9biUUqHzWVJTzM0ba3E60vdH0VW9BoBw646zxlNPd+HDLwHgKK3FUVABQEmBl6rSHACaklQy5KxcDIYTLJPI8f1JWWO6+8MbHRw8ajd+++i7FsdHCKarOWW5fPTGxYDdz+fJF9VvZ6oFgmGO/OK7+BufY+/Pf5Dq7YiIiJzh0LGBeIn0ezbWpXg342LNb/3KZEmY9L2zkzM4Z9WAYX87VTKUfOHm18CKgNsb70syU8VGOVtDPZj9HSnezaWzIiFCR7YDdqnQ6RqiJUO7DidplLPHF88+C3fMvL4svQMBfvq8nXW3YXkFy+vO3zQ6nVy9rIK3rakC4KkXW5M2ClzO1nFyhO89/BvKg+0A+PoOs7v5RIp3JSIiMi6WxbJgbiGLqqdPiXSs+W1gTEGWRFGQJUMY7iwcxXMAlQxNhXCsVKh2LYbLk+LdpJajeC5GdLJSrBFwOgi3N0JwFAwHrvlnjt+OlQwd6xmmbzCQlPWd0VHOkWN7knL+6cqyLP792f2MBSPk53j44NsXpHpLCfWhty9g/ux8LODBp/bQc2pmloNNpZf3dPGVf9/GwkBj/DGfI8SzT7/AwPBYCncmIiJiO3HKzxsHewB4zzSbpOiLZ7KoXChRFGTJIM4yNb+dCuZQD5HuQ8DZGRAzkWEY8Qa46TTKOTZVyFm1FEd2wRmfq59TEC9fSdaUoVhfFnOgC3N45mQ8vLS7Kz4e+9Z3LiTX507xjhLL5XTw6T9eTl62m5FAmO8/vptQWL+0JEMoHOGRX+3nwaf2YoWCrM868w2Gqsgx/uWXezHTrIxRREQyz3Pbj2IBxflZXLWsItXbOUOs+a1fmSwJoyBLBnGWjTe/TbfeGOkk1vDW8OXjnL0kxbuZHlw1dl8Ws6cFc6Q/xbu5OGtsJN6o91yBMqfDwfJae5RzU5JKPhylteDxATMnm2VgeIzHfmsHKK9YVMraRWUp3lFyFOd7+dR7l2EY0NY9xI9/nX6Tt6a7E6f8/O8f7eD5nccBeG9VDz4jCA4XvkX2z/Qidyd7Wvt55pW2VG5VRERmuNFAmC2NnQC8fe0cnM7pdQsee2MxoMa3CTO9vsNyWWLNbxkbwRrsTu1mMpRlWfEMCNf89RgO50WOmBmclQvjAYN0mDIUatkOZhicnniA6M1ifVn2tvYTCid+lLPhcOKKBunCHTMjyPLj3xxkJBAmx+viI+9clOrtJNWSmmI2XWdnF25p7OSFXcdTvKPMseNgD/c+tI227iEMA265ro635tjBO1fdOrKXbACgzt2DhxCPv9DC4Y6BVG5ZRERmsC2NxxkLRshyO3nLyuk3UdTnUePbRFOQJYM4CivG3xlXyVBSmH1HMfvtmyWVCo0zHC5cc1cC6VEyFO+pU7MGI/oz82Yr6kowgLFQJD4FJ9GcVUsBiHTsxbISH8iZTrbvP8HrB+xa5M03LKAgJ/N7Gd10VTWrF8wC4Me/PkhrV3JGgs8U4YjJT353iO/+ogn/WJj8HA93fWg1N9WbmCdbAfAsfRtZ85aBYeDAZP2sAUzL4odP7GEkEErtBYiIyIwTMU2e2243vN24opJs7/Qrk/ZmqVwo0RRkySCG4cBZGuvLoua3yRA6ZGexGPnldkmFefsAACAASURBVLmHxMWmDEU69mIFp2+zT3O4Lz422b3gqvM+Lz/HQ01lPgC7mk8mZS+uquUAWIEhzN6jSVljOhj2h/jxb+ySmRV1JVw9zWqRk8UwDD7x7qWUFfkIR0y+//huhv260Z+M/qExvv7oGzz7mv1zsnBuIffcto4l1UUE9/4OsJtwO8rrcWRl46moB+A9tX48Lge9gwEefma/SmlFRGRK7Th4kt7BMQzghnVzUr2dc4o1vg2o8W3CKMiSYdT8NnksyyTc/CoA7vqrMAwjxTuaXlxzG8DhBDNM+Nj0HUscbn4FsDC8eTjnLL/gc1dGS4aSNYrXKCiPT2aKZHDJ0GO/PcTgSBCvx8lH37VoRv3sZHtd3HHLCjwuBycHAjz4lBqxTtSe1j7ueeg1Dh+zS35uuqqav928isLcLKzA8Pjr8tK3xf/fyqppAMDXd4gPv2MhAK8f6In3cBEREZkKv97WDsCqBbMoL8pO8W7OzafGtwmnIEuGiTW/NXvbscLBFO8ms0Q6D2KN2FNR3AtUKvRmhscXbwQ8nUuGQrGeOnXrMByuCz53RTTIcqLfT3ffaML3YhgGrugo53CGNr9tbO7lpd1dAHzg+nqK870p3tHUm1uWy0fftRiApiO9PPVia2o3lCZM0+KJrS1887GdDI2GyPG6+Os/aeBP3jofp8P+9SV08EWIhMDtPeN12VttB1DN/mNsqPdx5RK7yfKjzx3i6Inhqb+YBFNGjojI9Nd8fIDmDrtU+J3r5qZ4N+cXLxdSJkvCKMiSYRzRTBbMCGZve2o3k2FiDW8dpbU4CmZGucNExUc5t+/CMqffC3WkryNeluNecM1Fn19dkUd+tHfIriRlszijo5wjXQcvOTDa1TfK/32hmb7BQFL2lCj+sTCPPGuXZi2aW8hbVk2/Zm9T5erlFVy/pgqAJ7e20JSk0eCZYnA0yD/9bBdPbG3BAmor87j7Y+tYVT8r/hzLMgnus0uF3As2YLjHA3ie2QvBlQWA+f+zd9/hcZTn3se/M7NVvXdbsmVLltwrxgYbMDa9F1MCoSSk15NDyAlJIDnJSTtJDqlvQoDg0DHVgCmmGmzci4ply5KLZPVets7M+8fsrm3cVHa1u9Lzua5cELTavQ22duee+/ndRyq5/aIppCfZ8Koaf3u5DFeUfpDUdJ1/vlLOXT9/h02VIuBeEAQhkr292fjMOT4zjqJxSWGu5tT8wbdOMckSNKLJMsrI9gSk+HQA1CaRyxIsuurBU7MZMI4KCSdnyp9l/I2rD7Ux8tbW+htlUnz60W1cpyFLEtMn+lc5hyaXxQi/lUD1oDbuO+Pjj7T28V9//ZhVb+zhJ49sYkd1aOoKhuff3097twuzSeaOS6Ygj6FjQidz0wWTmZiTgA78/ZVymjuCPx01Guyr6+TBRzdTXmtMDi6bk8d9t84lLen4kGq1vhK9y2g0mEsvOO5rkmJCyTY2WHnryomxmfjyVdNQZImGtn6eeCfyfj6diabrPPbGHl7fcIDuPjcPr6lk/xGxNUkQBCEStXU52bLHCPxfMX9cRB+VPjrJIposwSKaLKOQ/8iQCL8NHu/hXeDuB0nCVHhWuMuJWHJcKnJaPgDeA5F1ZEjXtcBRocFk6swsNO6cVx3uxBmCNx/ZFo+cNh44cy5LQ1sfv3lqO129xsRLn9PLQ8/v4pl39+FVI2s7UdWhDt7bXg/ANedOJDMlMs8hjySzSearV08jzm6mz+nlt09sw+2JzomKUNB1nTc3HeLXT26no8eF1aLw5aumcuuKIsymEz+ueHyBt0pWEUpK7glfN/mnxOrL0XWdCdkJXLfUeH9cv6uBjeWNIfzVBJem6zy+dg/rdzUAYLMoeFWNP63eHfETbYIgCGPRum11aLpOYpyFBSWZ4S7ntPyTLG6PhqpF1ufJaCWaLKNQIPy2RYTfBot/5a+SU4ocE7njfpHAlD8HMHJZIik3QG3ch95rHNEwDSJTp7QgBUWW8Ko6lQc6QlLbQHJZmjr6jQZLn5tYm4kf3D6PyXmJALy56TC/fGIbrV2RsdXJ5VF59A3jmNCE7HiWR2iafjikJNj48lVTkSTYX9/Fw6+UiyBcoN/p4c8vlvHMu9Womk5ueiw//vy8U34w1fo6AtlPn51i8VN8f670/k60TiPwdsWCcUyfaGQtPf5mFU1RME1kNFiq+HCn0WC5dFEBv/raOditJrr63Pzxhd24RLNOEAQhYjjdXj7wBa0vm5OHSYnsS2679WhGodgwFByR/V9cGBIl07hTp/e0ovWLUeLh0t0OvAd3AOKo0ED4VznrPS1oHfVhruaoQKZOWgFK0sCzQWJspkAzI2S5LL6LQa3tIJqj+4SvN3c6+PWT2+nsdWO3Kvz4rrOYV5LJvbfM5rKz85GAmiPdPPDIZrbtbQlJjYPx8ke1NHc4UGSJOy8tCYSUCobSghSuXWI0w9dtOcx//X0jb20+TL9zbI7pHmzs4cHHjv7eXTw9i/tvn0d2auwpv8dT+T7oGpI9AdOEeSd9jJyci2Q3/uyq9RXGP5Mk7r68hMQ4C063yt9eLo+4KbBjabrOqjer+HDn0Q/rd11eSl5GHF/xNesONvbw6OuVEdXUFgRBGMvW72rA4fJiMcmcN/vESctIYzumySI2DAWH+OQ7Csmp48G3NUUTq5yHzVu7xdheoZhP+WFeOEpOGRdYS+w9sC3M1Rh01XtMps7gN0PN8B0Z2l3TFpILGSVrMihmANQjlcd9rbXTwW+e3EZHjwubReG7N85iki88TZFlrltayHdWziQ+xky/y8ufXtjNk+/sDduFY21DN2/61hVevqiAvPS4sNQR6S5ZmM8F84wJn+YOB0+v28d//PljVr1ZRX1rX5irGxm6rvP+jnp+vmorLZ1OzCaZOy+Zwt2XlWI1K6f+Ps2LZ88HAJiLlyApJ98SJkmSL/OI49bKJ8RYuOeKqUgYDYrn34/Mo7W6rvPvt/YG7oaePyeXW5ZPDhx1nDYxlZUXTAZgU2Uzaz45EK5SBUEQBB9N03lnSx0Ai6ZlEWc3h7miM/MfFwJwusQkSzCIJssoJClmo9GCyGUJBo/vqJApfxaSxX6GRwuSJB3dMhQhq5y9h3eBq8/I1Jk0+Ewd/yrnjh5XSNa/SiYLSlYRAOoxR4baupz8+qnttHUb+RTfvXEWhbmJJ3z/tAmpPHDnAqaMN5ov72yp4xerttLcObLHh7yqxiOvV6LrkJcey2Vn54/o60cTWZL42nUz+cO3l3D+7FysZgWXR+W97fX86OFP+c1T29m+twVNG53TCS63ysNrKnh8bRVeVSMj2c4Pb5vLuTPPPGXmPbAdvb8TkDCXnHfax5ryjFXOakMVunb07lxJfjKXLyoA4K3Nh9kZYQHS/gbL+75co/Nn5/K55UUnZEktn5fHOTOyAXjxo1q2VoV/kk0QBGEs21ndGvj8tTyC1zYf69jjQiL8NjhEk2WU8h8ZEk2W4dH6O1GPGGPmpiFMQIxVpgIjl0VrqUXtbQ9zNUePCg01UycnNYa0RGM97K4QHRnyh3R6fSGdHT0ufvPUdlq7nFjMMt+5YSaT8k5ssPglx1v53k2zuXJxARJwoLGHBx/dxJY9zSGp92Re23CQ+pY+JAnuvLQk4s8gR4JxmfHcdlEx//u1Rdx0wSQyfBt0Kg928McXdnPf/9vA2k8P0ef0hLnS4DnS2sfPHt/ChnJjM9C84nR+csd8xmfGD+j7A4G342cgx6ed9rH+SRY8TtTPTHZeeU5B4CjgP1+rpKPHNZhfRsjous4Tb+8NBEefNzuXW1ec2GABo6l924riwK/j4TUVHGrqGdF6BUEQhKPe8q1tnj4x9bTHXiOJSZEDAfMOMckSFOIT8Ch1NPy2Fl2kRA+Zd/+noOtgicE0bnq4y4kaSnYRWIxtMo7qrWGtRXf3H83UGUTg7bEkSQpMs+yqCW0ui97bRlfDYX795DaaOx1YTDLfvn4mRePO3BySZYmrz53I926aRUKsBYdL5S8vlfHvt6rweEP7plnX0hs4rnDRgvFMyE4I6euNNjE2MysWjOcXX1rIt66fwbQJxurw1i4nz75XzX/86WMee2MPdSGYpBpJG8sb+dm/tnCktQ9Flrj5wsl85eppx91FOx2tsyFwpM5SuuyMj5djk5GTjekY9TPB0oos86UrpxJrM9Hr8PD3V8rDPjmk6zpPvr2Pd7cZDZals3L43Iqi064/N5tkvnbNdFITrLg8Kn9cvYvuPvdIlSwIgiD4HGzsoepwJ2CsbY4m/iNDodikORaJJsso5V/jjMeJ1tkQ3mKimP+okHnifCQl8s9URgpJNmEaPwMAZ/WWsNbird16NFOnYO6Qn2emr8myv76LXkfwpwrk1HFINuNO/ro1b9HU4cBskvnm9TOYkp88qOcqKUjhwTvnU+L7vne3GbkXodqkomoaj75eiarpZCbbufqcCSF5nbFAliRmTkrjuytn8fMvnsWyuXnYLApur8aHO4/w40c28asntrG1qjmq1ix6vCqPv1nF31+twOVRSUmwct+tc1g+b9yA16kDuCveA0CKT0cZN21A3+NvYHpPsiI9JcHGXZeVAMaa9nDmmui6zlPv7GPdNuMs/5KZOdx2UfFpGyx+CbEWvnHdDKxmhbZuF39+cTceb/T8/hAEQRgN3vJl0uWmx1JaMLjPbuHmD78VwbfBIZoso5QUnx64YFObq8NcTXTSOhvRWmoBcVRoKPyrnJ2HytDc4Vst7PEdFTLlzx5Wpk7x+GTMJhldh7La4E+zSJKMlmlc7GW5D2JSJL5x7XRKC1KG9HyJcVb+Y+Usrjl3ApIEh5p6efDRzWyqbApm2QC8vbmO2gbjiMIdl0zBcprQUmHgslNjuXV5Ef/7tcXccuFkMlOM6bCqw538+cUyvv+3Dby24QA9/ZE9tdDU3s8vVm0L5ItMm5jCT+6Yf9J8odPRvS48e9cDYC45H0ka2EcY/4p0rbkG/SQ/i2ZPTufCuUYI8csf11J1KDSr2k9H13WeWrePd7b6GyzZ3H7xwBosfuMz4/nC5cbxqH11Xax6q0psHBIEQRghHT0uNlUaR7RXDPIGQiSwW/xNFnFcKBhEk2WUkiQJ2XdkSGwYGhr/xbkUm2IcfxEGxTRuOsgKqF6ctTvDUoPW14FabxwtGOpRIT+rWQlMhoQil6Wn383aQ8bZ3cnmRr5+dSnTJqYO6zllWeKKxRO49+bZJB2zsvbxtXtwe4LzJtrU3s+LHxk/Y86fk0vx+Oi6cxMN7FYTF84bx8+/eBbfvXEmMwpTkYD2bherP6jhe3/5hEdeq+RgY+RkcfQ6PFTXd/HGhgP85x8/4mBTD5IE1yyZyLdvmEl8jGXQz+mt/hTc/SCbMBefM+DvU7KLQVJA11CP7DnpY244fxLjM+PQdfj7qxUhmVY7FV3Xeebd6sA2inNmZHP7xVMG1WDxm1uczjXnGpNk63c18LbvOQVBEITQendbHaqmkxBjZuHUzHCXM2h2qzguFEwDOwQtRCUlYyLqoZ0nhP0JZ6br+tGtQoVnDfiOqXCUZLGj5JSg1pXhrN6CkjVzxGvw7v8U0MEai5I3/EydGYWp7NrfRllNO5qmI8vBuUvR6/Dw26d30NORwiVJYJc8TI3vBoLzJl08PpkH7lrAw69WUFbbzvs7jlBd381Xrp46rFA2Tdd59I09eLwaqQlWrl9aGJR6hZOTJYlpE1OZNjGVpo5+3ttWz0e7GnC4vKzf3cD63Q1Mzktk2dw85hSlhzx42O1Rae5w0NjeT2N7P03t/TR29NPY1k+f8/gPaQkxZr505VRKhjiZBeD2Bd6aJs5Htg8880ey2FEyC1Eb9+KtL8dUMPuEx5hNMl++ahoPPrqZjh4X/1xTwTevnxHyO5H+Bos/KPGc6dncccnQGix+ly8qoL61j02VzTzz7j5yUmOG3bAVBEEQTs3lUQPTmufNzsVsir6JXpuYZAkq0WQZxfy5LFpHHbrHiWS2hbmi6KG11KJ3G8cqhjsBMZaZ8mej1pXhqNmGferFyEk5Izo+6dlnTCOZJy5AUob/426G70Kl1+GhpqGbSYM87nAyfU4P//v0Dg4396LIcbjt6VgcLXjrK1CyJg/7+f0SYix8+8aZvLHxIC9+WEtdSy8/fWwLt19czNlTs4b0nB9sr2evL+Dt8xdPGXB4qTB8mckx3LRsMlefO4EN5U2s21rHkdY+9tV1sa+ui+R4K+fNymHprFwSYgc/NeKnaTqt3U6jgXJMM6WpvZ+27jNv40mKs1JSkMz1SwtJjrcOuQ61uQat9QAAltILBv39Sm4pauNe1JPksvhlpcRw20VFPLymkp3723hnS11I12/qus5z7+0PNFgWT8/ijkuH12ABY5L1zktLaOpwcLCxh7++XM79t8+Nmi0XgiAI0WZDWSN9Ti8mReL8OXnhLmdI/JMsYoVzcIhPxKOYku4Ln9R11JZaTDkl4S0oiviPCsnJucgp0ZUOHklM+bNxfbwK3dlH/3M/RLLGoWRNRskuQskqRk4bjySH5seQp60Ore2gUUeQGmVpSXZy0mI50trHrv2tw26y9Du9/O6ZHRxs6kGWJGPTSVsdnvJ1xsXg3KuCUrefLElcdnYBk/OS+H+vlNPR4+Ifr1aw52AHtywvwjqILJW2LifPvm+siF88LUvcKQ8Tm8XE+bNzOW9WDpUHO1i3tY4d+1rp6HHx4ke1vPrJAeZPyeTCeXmn3Pik6zo9/Z7jJ1La+2nqcNDc0Y9XPX2uh9WikJUcQ2aKnayUGLJSYshMiSEzOYZs31rmrq7h5TL5A2/llHHImZMG/f2m3Km4t76E1tmA1tuOHHfyiZpF07KpONDBJ2WNPPteNZPHJVKQFfxNWbqu89z7+1m76ZDvdbO485KSYTdY/KxmhW9eN4OfPraZrj43Dz2/i/s/P49YmwhwFwRBCCZN13l7i9EsX1iaReIwbmyEkz/41imCb4NCNFlGMckai5yUbay8bK4RTZYB0jXVd8wETJMWRl1wVSSR41KIP/s6+na+jdbfje7qxXtwO96D240HmCwomZNQMiejZBejZBQimYd+t/tY/RVGQKYUn4YyhIuyU5kxMdXXZGnj2iVDPx7jcHn5/bM7qG0wsiq+eEUp86Zk4D0wzWiyNO1HdzuGFdZ7KkXjknjgzvn887VKdu1v46NdDdQc6ebLV08jN+3Md7t1Xedfb+7B5VZJiLWwclnwJm6EoZEkidKCFEoLUmjtdPDu9no+2nmEPqeXDeWNbChvpDAngUsXT8Bskqmp6zymoeI44zYBRZZIT7L7Gih2MlNiyEqOISs1hsRYS0h/TurOXrz7fZveSs8f0mvJGRPAbAOPE7W+HLn43FM+9nMrith/pJum9n7+9nI5P7ljflCntHRd5/kP9rP2U6PBcvbULO66tCRoxw/9kuOtfP266fzqie00dTj420tlfPvGmSiyOP4qCIIQLGU1bTS0Gdsbo21t87ECwbducVwoGESTZZSTMwqNO3fN+8NdStRwHSxDd3QDYJ60MMzVRL/Ec24kYfENdB6qxdu4F7VhL2rjXvSeFvC6UesrUOsrjAdLCnJavm/SpQhTVhGSLW7Qr6nrOv2Vvi0khQuDmqkzozCVtZsOcaipl44e15COQDjdXn7/3E72H+lGAr5weSlnlRr5K0rOFJBk0FXUhj2Y8k/MjwiG+BgL37x+Bm9tOszqD/ZT39rHz/61mc8tL+acGdmn/d5Pyhopq2kH4LYVRcTZxd3xSJKWZOfG8ydx1TkT2FjeyLqtddS19LH/SDd/fO70IdTJ8VYyk4+fSMlKiSE10RbyjJdT8ez92FjDbrZhHuKmN0k2YcopMZq89RWYT9NksVlMfOWqqfz341to7nCw6q0qvnh5aVAaSbqus/qDGt7YaDRYFk7N5O7Lgt9g8SvMSeTOS6fwj1crKD/QwTPrqrlluQhyFwRBCBb/kc/SgmTyMgb/mTVSBIJvxSRLUIgmyyinZEzEu3c9anMNuq6LqYwB6K/8GAAlczJyfHqYqxkdJElCTsrGkpQNU5YCvs0/DVVGTkLjXrT2etBVtJYatJYaPLvWAiAn56BkGU0XJbsYOe7Mx1Lc9VWoXS1A8I4K+U3KS8RuVXC4VHbXtLFkZs6gvt/lVvnDc7uorutCAu66rOS4TBTJYkfJKERt2oe3rjxkTRYwjg9dfNZ4Jucl8reXy2jrdvHI65XsOdTBbSuKsVpOPD7U1evi6XX7AJhXnM7c4oyQ1ScMj9WssHRWLktm5rD3cCfvbK1j+75WbBaFzOQYsvwTKb6jPZkp9kDwXaTQdQ13pRF4a568aFiTXUpuKd6D21Hry8/4fjg+M56VF0zmibf3srG8iakFKSyefvrm45nous4LH9bw+kbjGOPC0ky+cFlpyBosfmdPzaK+pY/XNx7kna115GXEDfrnliAIgnCiuuZeKg50ANE9xQLHBN+KSZagiKxPU0LQ+cNv9f5O9L52pAFcoI5luseNY98mwDgqJISOHJuMPGlhYFpId/WhNu4zNoA07kVrqQVNRes4gtZxBE/l+wBIcanHNF2KThqm659ikVPHoyTnBrVukyIztSCFLVUt7No/uCaLy6Pyf8/vPBoWe8mUk164KXlTUZv2nTakM5gKcxP5yZ0LeOS1SnZUt/JJWSO1Dd185epp5KUff1fm32/vpc/pJdZm4tYVxSNSnzA8kiRRPD6Z4vHJxMVZkWWJ7m5nuMsaELW+Er3LF0I+hMDbYyl5UwHQHd1oHXUoZ8jbumBOLhUH2tm+r5VVb1UxMSdhyOGxuq7z4ke1vLbBaLCcVZrJ3ZeHboLls65dOpEjrX3sqG5l1ZtVZCbbxbp1QRCEYXrLl8WSlRL9W9wCwbdikiUoxMHcUU5OyQPFCGBSxZGhM3Ls34rudoCkYCpcEO5yxhTJGospfxbWs24k9qr7ibvjr9gvvw/LvGtR8qYZeQqA3tuGt3oDrvX/ov+5H9L3+DdwvPl/uHe+gdq8H93rwlHly28I0Wao6YXGG2n5gXa8qjag73F7VP64ehd7DhkNltsvLj5lg8aUa1wM+kM6R0Kc3cw3rpvOzcsmo8gSDW39/OxfW/hw5xF03Qg+3VDWwNYqY0Lo5gsnR22421imKHJUTTR6fGublawilJThbWyQE7ORYo3GglpXccbH+7f0pCRYcXs0/vpSOR7v0O7wvby+ljWfHABgQUkGX7i8ZESzUWRJ4otXlJKbFouq6fz5xTJaO4cXRiwIgjCWdfe52Vhu3ARYPn9c0ILLw8WfyeIU24WCQjRZRjlJVlDSCwBjBaZwev4JCGXcNGRbfJirGdskkwVTzhSsc64k5tLvEff5PxNzzQNYz74ZU8FcJN9/H3+YruvTZ+h/6Wf0PvZVNEcPIGEqDM00kn+Vs8utBqZSTsfjVfnTC7sDI6WfW1HEebNOPWETCOmEEZtmAeOicvn8cfzXbXNJS7Th8Wo89sYe/rGmguaOfh5+2ahl2sSUIa99FoSB0vo6AiHZw51iAeP3t+JrYHoH+Ocqzm7mS1dORZYk6lp6eebd6kG/7ksf1fDKxwcAmD8lgy9eURqW8Fm71cQ3rp9BnN1Mr8PDQ6t3iTuWgiAIQ/Te9nq8qkaszcSiadH/mejodiEVTT/9VkHhzESTZQyQMyYCoIkmy2npzl6cNb4P9EMMVxRCx98wtEy/CPuKbxB720PE3vhLrEvuxFS0GMmfn6MZd5qt+dOQY0MzDp8YZ6Ugy2jy7NrfdtrHerwaf36xjLJaYyLl5gsnc8Gc09+R94d0wsAvBoNpQnYCD9w5n7nFxr/TjeVNfON/P6Cz14XVovD5i6ZE1TSEEJ08le+DriHZEzBNmBuU5zTllgKgNuxBVwfWYJicl8RV504A4N1t9YFproF4eX1toMEyb0oG91wZngaLX0aSna9dMw1Flqhr6ePhNRXiw7QgCMIgebwq726rA+C82blYzSdm2EUb/3EhHeMmojA8oskyBvhzWdSWWnRN3LU6FW9dGWgqktka0rBRITiMMN0sLFOWYj/vi8Td/Btib/09tmVfIWHxjSRfdE9IX3+G78jQ6ZosXlXjry+VBR6z8oJJLJ83sGA0/x13tb4CXR/YkaRgirGZ+erV07h1eREmRQoci7rxvEJSE20jXo8wtuiaF8+eDwAwFy9BUoKzwcr/5wqvG7Vp4FMply3MpyTfaNo++nolrV1nPmrzyse1vLy+FjBCou8J0wTLZxWPT+bWFcaGoe37WnnxQ3EDRhAEYTA2ljfR0+9BkaUz3jiLFvZjgu/FlOPwhf/dXgg5f5MF1YPWXhfeYiKYP7PGkj0JyTz4tbxC+MmxyZgLzyJh0XWYEkO79WZGYRoAje39NLb1nfB1r6rxt5fL2VHdCsAN5xVy0YLxA35+07EhnWH6cytJEsvm5vHD2+YxdUIKyxeMZ+ns4AYJC8LJeA9sR+/vBCTMJecF7XnlmEQjq4zBHcWTZYkvXF5KfIyZfpeXv79Sgaqduvn56icHeOkjo8Eytzide66cGrYV2Cdz3qxclvkuDF7bcJCNFY1hrkgQBCE66LrO277A2wUlGSTHj45rBv9xIRAbhoIhct7xhZCR41KQYpIAkctyOmqL8YHYkjUpzJUI0aAgO574GOPu+raq5uO+pmoaf3+1gm17jWMF1yyZyCUL8wf1/FJiFlJsivF8dSN/ZOhY+Vnx/PSes/nyNdOjPthNiA6eyvcAUMbPQI5PC+pzDzaXxS853srdlxnHjarruwJTKp+15pMDgemQOUXpfCnCGix+N1046ZjpnD3UNnSHuSJBEITIV3Gwg7oW4+baivkDv3kW6eyWo0eenGKSZdgi711fCInAkaEmsWHoZHTVi9Z6ADAmWQThTGRJYrovAHfrnqMZDaqm8Y9XK9iyx2i8XHXOBK5Y/Q1rtAAAIABJREFUVDDo55ckKTDNEo5cFkEIF62zAbXe2P5jCULg7WcFtne11KK7TpxCO50Zhalc7JtIe+2Tg5QfOH7712sbDvCCr8Eye3IaX74qMhssAIos85Wrp5GRbMfj1Xho9S46elzhLksQBCGivb3ZmGIpGpdEftboWZJhNskosnEjzSE2DA1bZL7zC0En+5osmljjfFJaex34QhBFk0UYKH8uS3ltG063F03TeeS1SjZVGg2WyxcVcOXigiE/fyCXpaEK3esedr2CEA3cFcYUixSfjpI3PejPr2QXg6yAruM9smfQ33/t0olMyE5ABx5+tYJOX2Pi9Y0HWf3B0QbLV66eFrENFr84u5lvXT8Du1Whq9fNn17YhdsjxsQFQRBOpqGtL5Czt2L+wDL2ooUkSdh80yxOl3gfGK7IfvcXgkbxbxjqahz0nbuxwJ/HosSloMSnhLkaIVpMm5CCLEl4vBq7qlt59I1KNpQ3AXDpwnyuOXfCsLbwKL5NKKieQYV0CkK00r0uPHvXA2AuOQ8pBEGxktmKkmk004eyIt2kyHzpqqlGY6LPzR+f28lLH+zn+feN95FZk6KjweKXnRrLl66chiRBbUMPj72xB11sHBIEQTiBf4olPcnGrEnBPcoaSmpTNc1PP4ir7vQ3Fuy+XBYRfDt80fEJQBg2JX0C+C72RC7LidQW49+JObswzJUI0STGZmZSXiIAf35+Fx/vNsIjL1owjuuWThz2mmPZnoCcahxNUOvKhlesIEQBb/Wn4O4H2YS5+NyQvc5Qc1n8MpLsfP7iKQDs2NfCqrXGB9eZhalR1WDxm1GYyo3nG42njRVNvL7xYJgrEgRBiCy9Dg+flBmf8y6cNw5Zjp6MOte2V3AfrsCx99PTPs7m2zAkgm+HL7o+BQhDJpmtRzcqiCbLCbRmX+itOCokDJL/yFCvwwPAhfPyuPH8ScNusPgdvRisCMrzCUIkc/sCb00T5yPbE0L2OoHtXV1NaD2tQ3qOBSWZLJmZE/j/MwpT+eo10zGbovOj1Yr541g8PQuAFz6oYfu+ljN8hyAIwtjx/vZ63F4Nu9XEOdOzw13OgOmaitq4FwBzRsFpH2u3+o8LiUmW4YrOTwLCkCjpvvBbkctyHN3dj9bZAIgmizB4M31NFoAL5uRy87LJQWuwwNGLQa31ILqzN2jPKwiRRm2uQfNteTOHIPD2WHJaAVjswPCCpW++cDIXzM3jskUFfC2KGyxgnMe//aIpTMpNRAf+/moFdc3iZ44gCIJX1Vi3rQ6ApTNzAsdqooHWehA8TgCs46ee9rGB40Ii+HbYovfTgDBoSqY//LZGnLc+htpyANABCUvmxDBXI0Sb3PQ4vnLtdO6+Yiq3Li8KaoMFQMkqAsUE6GKaRRjV/IG3csq4QGZKqEiyginHyDxSh/HnympW+Nr1M7nriqlR3WDxM5tkvnbtdFISrLjcKg+t3kV3vwjdFgRhbNtc2UxXrxtZklg2Ny/c5QyK90glAEpSJqaE0+fI+INvHSL4dtii/xOBMGCyL/xWd/WidzeHuZrI4Z/skZNzkK0xYa5GiEYXzh/PpYsKgt5gAZBMFpSsYgDUepHLIoxOurMX7/6NAJhLzw/Jn6XP8gdLq/UV6LoW8teLFomxFr553QwsZpnWLid/ebEMj3d0//vZe7iT//rrx7z+yYFwlyIIQoTRdZ23fIG3c4vTSU20hbmiwVF9TRbbGaZY4Ogki1NMsgybaLKMIXJSNpiN8WhxZOgozZdRI6eLKRYhMgVyWerKxRSaMCp59n4MqgfMNsyTzh6R1wzksjh70NoOj8hrRovxmfF84TKjCbX3cCcPv1I2an/2lB9o53fP7KDqUCePrCmntqE73CUJghBB9h7u5GBTDxB9a5t1zYvauA8A67jSMz7e7g++FZMswyaaLGOIJMmBVc6iyWLQdT0QBOz/dyMIkSZwMdjbht7dFOZqBCG4dF3DXfkuAObJi5B8WSmhJiVkIsUZmUpDWeU82s2bksHV50wA4J3Nh3nt4wPhLSgEdlS38n/P7cLtm9TRdXh8bRWqNrondwRBGDj/FEthTgKFuYlhrmZwtOZa8LqAM+exANh8wbcik2X4RJNljDnaZBEbhgD0vnZ0RxcgmixC5JJTxyHZ4gFjmkUQRhO1vhK9y2gehjrw9liSJGES27tO64rFBcybkgHAo69V8Mr62lEz0bJ5TzN/fmE3XlUjKyWG790yB4CDTT28u60+zNUJghAJmjr62bHP2EC3YsH4MFczeP48FikxCyUu5YyP90+yiO1CwyeaLGOMv5GgtR1C94owu0CzSTEjp+SGtxhBOAVJklFySgBxx10YfTwVxhSLklWEkjKygYKBXJaGKvGeeBKSJHH3ZSXMnGSEJb60vpbH3tiDV43uSY9Pyhr428tlqJpOXnos3791DmdPz2a57yLqxQ9r6OhxhblKQRDC7Z0tdehAaoKNOUWnD42NRGpDFQCmnCkDenxgkkUcFxo20WQZY+QMY8MQmorWdii8xUSAwFGhtAIkOXrWsQljj+I7MuQ9UomuiTc/YXTQ+jrwHtwOjOwUi5+/yYLqQW2qHvHXjwZWs8J/3TGf8+YYDbCPdjXw0OpdOKL0Tuf7O+r555pKdB0KsuK595Y5JMZaALj1omLiY8w43SpPvrM3zJUKghBO/U4P63c1ALBsbh6KHF2XzbrqCeSx+G/UnUlgkkUcFxq26PrdIgybbE9Aik8HRC4LgNbiC70VR4WECGfKm2b8jduBu1H82RVGB0/l+6BrSLZ4TBPmjvjry/YE5FRjekFMiZ2aSZH5+vUzuHJxAQBlNe386sltdPZG17THW5sP8/jaKnRgUl4i37tpNnF2c+Dr8TEWbrpgMgBbq1rYWd0apkoFQQi3D3c24PKoWC0KS2bmhLucQVOba0A1JjSV7IFNsvi3Czlc6qg5Ghou4tb9GKRkTMTb0zLmc1l0TUNtOQCIPBYh8slxqUiJWehdjbgO7MaaUxTukoRh0jUNb2cjalc/6CpoGugquqaBpoLu/+vJ/plmTDT5vufU33v843SrBXvxQojPD/cvH13z4tnzAQDmKUuRFPMZviM0lNypaG2H8NZXYA1LBdFBkiSuPnciKQk2Hl9bxaGmXn7++Ba+feMsctNiw13eGb36yQFe/ND43FOSn8w3r5uB1aKc8LiFUzNZv7uByoMdPPH2XqbkJ2M1n/g4QRBGL1XTeGerEXh77vRsYmzRd8msHtkDgJyUgxwzsMBe/3EhTddxezXxs28You93jDBsSkYh3v2fjvlJFq2zPpC4rYj1zUIUMOVOxdPViPPgLhIWXRfucoRh0LqbaVr9EN62uhF9XQ/Qu+0NzFOXYV1wI5I5fG0F74Ht6P2dgIS5ZGnY6jDlTcWz6w20lgPozl4kW1zYaokGS2bmkBJv5c8vldHW7eIXq7byjWunMyU/OdylnZSu67zwYQ2vbTgIwIzCVL52zTTMppNfPEiSxOdWFPGTRzbR2uXklY9rueG8SSNZsiAIYba1qoX2bhcScOG8kc0KCxbVF3qrDDCPBY4eFwIj/FY0WYZOHBcag/xTG3pPK5qjO8zVhI9/kkeyxSPFR1+YlTD2+HNZ3Ef2obkdYa5GGCq1qZr+l3525gaLJIFsApMFzHawxiLZE5BikpDiUpHi05ESs5CTc5BTxiGn5SOnT0TOnISSXYySU4KSNw1l3AxM+bMxFczFnGYcjfGUr6Pv+fvx+u50hYOn8j0AlPEzkH3HWMNBySoCxQTogU0MwulNm5jKfbfMITHOgsPl5XfP7mBjRWO4yzqBrus8va460GCZV5zO16+dfsoGi192aiyXLjSmvd7adJi6lt6Q1yoIQuTwr22eNTmNjOSYMFczeLrXjdps5IwNNI8Fjh4XAnC4Rf7fcIhJljFITh0PsmKE3zbvR86fHe6SwkJrPprHIklSmKsRhDMz5UwBSQZNxXW4AtJKw12SMEiems043/s7qB5kezwpV3wLhzUDSVaM/7ayArIMkowkBf8+SEK8hZ7Nr9K9/jn0nhYca36JeeqFWBfcMKJTLVpnA6pvbbIlDIG3x5JMFpTMyahHKlHryzFPnB/WeqJFflY8P7xtLn94bhdHWvv4+ysVtHe7uOSs8RHxnqrpOv9+s4r3dxwB4Oypmdx1WcmAwysvOzufTyuaaOpw8PibVdx36xzkCPh1CYIQWtX1XdQcMW5CXxSFa5vBl7upGuG1g5pksR5tQEdruHmkEJMsY5BksiCnGndo1Kaxe2RI9YXeijwWIVpIlphASLPrwO4wVyMMhq7ruHe+gfOdv4DqQUrIJP3Wn2HLn24EkltjkSx2JJMFSTaFpMECIMkKCWddTcy1DyKnTwDAU/6OMdXiW/U4EtwVxhSLFJ+Gkjd9xF73VALbu+pE+O1gpCXa+cHn5lA8LgmA59/fz7/f2ouqhXfFs6pp/HNNZaDBsmRmDndfXjqo7SBmk8LnLioGoLquK7BlRBCE0c0/xZKfFc/kvIFlmUSaQB5LSh6yLX7A32c1K/hbyU7RZBkW0WQZo/yNBX+jYazRPS60dmNUX+SxCNHElGtcDDoP7gpzJcJA6ZqK6+NVuD59BtBRMicTc/X9mJOzw1aTkpJLzFX3Y1lwPcgmY6rl1f/B+ckT6J7QbozRvS48e9cDYC45HykC1mL6/1zpPS1o3c1hria6xNrMfHflLM4qzQTgve31/PmFMlxhGjX3qhr/7+VyNpQbx5cunJfH5y8uHtIUytSCFBZONX5dz71XTXe/O6i1CoIQWVq7HGytMt4DVswfFxFTeUNxNI9l4EeFwMiksvk3DInjQsMS/k82QlgEmizNNcY2ijFGbT0AvtVkiu9uriBEg8Ad97Z6tL6OMFcjnInuceJ48//wVLwLgKnwLOyX/eeg7iyFiiQrWGddfvxUS9nb9K3+UUinWrzVn4K7H2QT5uJzQ/Y6gyGn5oPV2JDj9R1jEgbObJL54hWlXLLQGK3fUd3Kr5/aRlffyDYlPF6VP72wmy1VLYBx5OfmZZOHdaG08oLJxFhN9Dm9PPtudbBKFQQhAq3bWoeuQ1KchflTMsJdzpDoXldguclAVzcfy39kSBwXGh7RZBmjlExfUr7HidY59kZg/XksUmKm2CQhRBUlYyKSxQ6A88NHcW1fg6dmM2rbYXRvaCcQhMHR+jrof+UXqIeNqSPLrMuxXfAlJJMlzJUdLzDVMt831dLdjOPVX4ZsqsXtC7w1TZyPbE8I+vMPhSTLmHx3/NR6cWRoKGRJ4obzJnHbiiIkCWobevj541tobO8fkdd3uVX+7/ld7NrfBsA1SyZy3dLCYd+JToy1cP15hQB8UtZI5UHR3BaE0cjh8vLhTuOI4bK5eZiU6LxMVhurQVMBCVN28aC/379hyCkmWYZFBN+OUVJ8OpItHt3Zg9a8HyUlN9wljahAHos4KiREGUk2YcufjmPfJtTDuwIX8IGvxyYjJ2YhJ2QiJ2YiJRp/lRMykBRzmKoee9S2wzjW/h69rx0kGeu5n8cyJXxris9EkhWssy/HlD8L5wf/RGupxVP2Nt5DO7EtvXtIH9RORm2uQWupBcAc5sDbz1LypuGt3YK3vgJd0yLiGFM0On9OHsnxNv72ShmtXU5+/vgWvnn9DCbnJYXsNR0uL394bif76roAuGnZZFbMHxe0518yK4ePyxrYX9/NqjerePCuBZhN4veHIIwm67YcxuFSsZhkls6K3usi/1EhOXXckG4k28QkS1CIJssYJUkScvoE4yKteT/mKUvCXdKI8q9vFqG3QjRKuvBOTOnjcTQeQutqQutuAo8TAL2vA7WvI/AmGyBJSHGpgeaLnJjl+2smUnwakhxZbwe6pgaO9EUb7+HdON75s/HfxGzDvvzrmPKmhbusAVFS8oi56n7cO9/AvfWlwFSLedqFWBdcj2Qa3gYif+CtnJJ3dKIyQphyS3EBuPrQ2g6hpBeEuaLoNWtyGt+/ZQ5/eG4nPf0efvPUDu65opR5IRi/73V4+P2zO6ht6EECbruomPNmB/cCSZYkbr9oCg8+upnG9n7e+PQgVy4WR40FYbRQNZ3XPjZuACyenk2cPXpvSnkbjNDbweax+PknWRxu0WQZjsj6VC2MKCWz0NdkGVvht1p/F3qvMU4smixCNFLiUkhcfAN0OQBjc43u6DIaLl2N6F1NgeaL1tUEqgd0Hb2nFbWn9cTjEJKCFJ8WaLoc24SRYlMHdEdf1zTwOtHdTnSPEzzGX3WPA9z+v3eBx/GZrzuP+frRf96jekAxYy45D8vsKyLmWMmZuCvfx7X+cdA1pNgU7Jd8ByUleHfUR8JxUy3vP4zWeiAoUy26qw/v/o2AMcUSaYGCckIGUnw6ek8L3voy0WQZpgnZCfzw9nn8/tmdNLX389eXylgZ5AmT7j43v316B3UtvUgS3H1ZCYumhSZQelxGHCvmj2PtpkOs+eQgZ5VkkpkSE5LXEgRhZG2pbKK5w/hMdeG8vDBXM3S6x4nWbDSLTINY3Xwsf/Ct0yWOCw2HaLKMYUqGccZY66hD9ziRzLYwVzQyNP9GJVlBTh0f3mIEIQgkSUKKSUKOSYLPXADruobe1+FrwBhNGK2rCb27ydiioqmgq+jdTajdTaiHP/Pksgk5IQNPWg6SyYy7r+/4BonHaKyghiDgUvXgKXsbT9VHWGZdhmX6imFPUoSKrmu4Nz2Pe+frgBGkar/428ixyWGubOiUlDxirv4R7p2vf2aqZTnWBdcN+r+Fp2q90fAz2zBPOjtEVQ+PKXcqnj3vo9ZXwKzLw13OSbmbaunf8wl68YUR33zMSLLzw9vm8tDzu6iu7+Lpdfto63KyctmkIW37OVZHj4vfPr2dhrZ+FFniniunhjyo8qpzJrB5TxNt3S7+/VYV3105K+KahYIgDN6r643GxIzCVLJTY8NczdCpjftAV0GSULKKhvQcdovvuJCYZBkW0WQZwwJbdXQdtaU2EPo32vknd+TU8SKjQhj1JEk2jgnFpUJu6XFf0zUVvbftaAOm+5gmTE8r6BpoXrTOIzg7jwzl1cFsRTLbjCauxX707wN/tSKZ7b6v2wJfj0tOxH1kH10bXgR3P+7Nq/GUr8M671pMRedEVF6G7nXjfP9hvDWbAFDGz8S+7CujonFtTLVcgSl/9jFTLW8ZUy3n3Y1pgB/idF0PBN6aJy8KhDdHGiXP12Rp3IvudUdcSLHu7qf1hV+h9XagHKnBfsl/RPxFfpzdzPdumsU/1lSwtaqFt7ccpr3HyRcvL8ViVob0nK2dDn7z9HZaOp2YFImvXj2dWZPTglz5iawWhVuXF/PQ6l2UH+jg08omFpZmhfx1BUEInZoj3VQeaAcI6qRdOBzNY8lHsg6tWWQXkyxBIZosY5hkjUVOykbrbEBtrhlzTRYReiuMdZKsICVkICdkwLjpx31NV73oPa1o3Y1onU2YnG2gqbh1k9EIsdjA/JmmieUzDRSTBUkaWjPEmmjHmleCmn82rh1r8JS9g97fifPDR5B3v4n1rBtQxs0M+wWm5uzB+eZDqE37AOMYjHXRrUjy0C4eI5Ux1XI/7h2v4972Mnp3E45X/mfAUy3qkUr0rkYg8gJvj2W8D0qgelEb90Zclo5r82q0XmO7jVpXhnffJ5iLFoe5qjOzmBW+cvU0nn23mrc2H2ZrVQtdvTv4xnXTiY8ZXCOrqb2f3zy9nfZuFxaTzDeum8HUCSkhqvxEsyanMXtyGtv3tfL0umpmTEwlxiZu2AhCNOrocfHXl3YDkJ8VT0l+9E6fAniP+PNYhnZUCMBmEcG3wTCoJsuRI6e/kylJEhaLhaSkJBRldH3AHK3kjIlonQ2Blcajna5rRzcLiTwWQTglSTEhJWUhJ2XBeEhMNCYPunw5MCNWhy0O28KbsExdhmvzC3irN6B11ONY+weU7GKsZ60M259lrauR/jd+j97dBEhYF67EPP2isDd+QkWSTVjnXImpYM7xUy2HfVktp5lq8ZSvA0DJKkJJidzz7pItDjm9AK2lFm9deUQ1WdTm/XjK3wVAjktG6+3AueFJlLxpyDGJYa7uzGRJ4qZlk0lJsPHMun1U13fxi1Vb+c6NM8lIHli2SX1LL799egddfW5sFoVv3zCTonGh21p0KrcuL6LiYAfdfW5Wf1DDbRcFZ/uWIAgjp99phGa3dbuwWRS+dn34b9wMh+52oLUeABjWjXP/JIs4LjQ8g2qyXHDBwILqFEWhpKSE73znOyxatGjIxQmhp2QU4t37MWrzfnRdj+ofLgOhdzWB27hIFE0WQYgecnw69gu+hDrjYlyfPotaX47aUEX/Sz/FNHEB1gXXGxM5I8TbuBfnmw+hu3pBMWO74EuYJ8wbsdcPpxOmWrpOP9Wi9rTjPbgdiOwpFj9TbinullojlyVC6JoX54ePATrm9HzSrruPhke/B64+XJ88gf3Cr4a7xAFbMX8cKfFW/rGmgqYOBz9ftZVvXT+TiTmnz5c52NjD/z6zg16Hhxirie+unHXG7wmVlAQb15wzgaffreb97fUsmp5FYU7kN7oEQTB4vCp/XL2bupY+FFnie7fOpTA3ccRvJAWT2rjXOOYtyUPOY4FjmixikmVYBjXH/dOf/pSEhATsdjsrV67kBz/4AQ888AB33HEHaWlp2Gw27rrrLlauXElbWxv33HMPW7ZsCVXtQhD4w2/1/k70vvYwVxN6gU1KlhikxMzwFiMIwqApafnEXPaf2C/9HnKqcXbaW7OJvmd/gPOTJ9CcPSGvwVO9EceaX6O7epFs8cRccd+YabD4+adaYq59ADktH9DxlL1F3+of423cd9xje3etM7Yt2eIxTZgbnoIHQcmdCoDWdhDN0R3magzuXW+htR8GJJIvugclPgXb2bcAxu9/z4Gt4S1wkOZNyeA/b5pNnN1MT7+HXz+5je37Wk75+P31Xfz6qe30OjzEx5i595bZYWuw+C2bl8f4jDh04PG1VaiaFtZ6BEEYGE3T+cerFVQd7gTgzkunMLsoPcxVDZ/Xn8eSXjCs3LOjx4VEJstwDKrJsnfvXmJiYli7di0PPPAAt99+OytXruT73/8+a9asIT09HU3T+NGPfsRrr71GUVERf/nLX0JVuxAEckoeKMZ56LGwyvloHsuEIWdFCIIQfqa8acRc+yC2876IFJsCmoqn7G36nroX1/Y16F5X0F9T13Vc29fgfPdvoHmRE7OIufpHgWb1WKSkjCPm6h9hmXctyIpvquUXODc8he51oate+nYZR4XMU5ZGRdi4kjkJfHX6QwTDSetuwb31JQDMU5dhyZ4EgGnyIhTfcSbX+lXorr6w1TgUk/IS+a/b5pKeZMPt1fjTC7t5d1vdCY+rOtTBb5/ZgcPlJSnOwvdvmcP4zPgwVHw8RZa5/eIpSMDh5l7e2XJi7YIgRBZd13nqnX1sqTKaujecXxiyte8jTfXlsQw3YzMQfCuOCw3LoK4y16xZwy233EJGxokj2YmJiaxcuZKXXjI+CNjtdq666irKysqCU6kQEpKsoKQXAMZ579FO5LEIwughSTLmosXErvwl1rNWgiUGPA7cm5+n75n78Oz5ED1Id5d1zYvro0dxb34eACW7mJir7h/RI0qRKjDVcs0xUy2736Rv9Y/p/uR5X1CrhLlkabhLHRDJZAmMWqt15WGtRdd1nOv/BaobKTYZ6/zrAl+TJAnbuXeAyYre34nr02fCV+gQZaXE8MPb5jEhOx5dh3+/tZfn3qtG03QAymra+N2zO3G5VVITrNx36xxy0iJnverEnATOm5MLwEsf1dLW5QxzRYIgnM7rGw+yztfMXT5vHBcvGB/mioJDd/WhtR0EQMkeeugtgN1iNFm8qo7HKyb0hmpQTRZVVdFO84HV4/HgdB59g7Farad9vBAZZF/DYbSH3+peN1rbIUA0WQRhNJFMFiwzLyHupl9jnnExyCb0vg6cHz5C/+of4z20E13Xh/z8utuBY+0f8Oz5EADTpLOxX/o9JFtcsH4Jo4KSeuJUS8/GF42vjZ+BHB8949imPOPIkLe+fFi/d4bLu/9T1DrjZpV10edOGAGX49OwLrgeAM+eD/FGUI7MQCXEWrj35jnMmmSsYH7j00P84ZkdfLzrCA+t3oXHq5GRbOe+W+cOOCB3JF23pJDEWAsuj8qT7+wNdzmCIJzC+l0NrP7AuNZZUJLBymWTRk0WpdqwF3QdJAUla/KwnstmPbq8RoTfDt2gmizz5s3jX//6F7W1tSd8rb6+nlWrVjFnzpzAP1u3bh0TJ4qL2UjnH3VXWw6ga6P3D5PWfhg043yhLNY3C8Ko499EFLvyfzBNOhsAraMOx9rf41jzqyEdidR62+h/5eeBC13LnKuwnX9PVBx7CYfjplpS8wP/3FJ6fhirGjx/Love2+bbHjXydGcvrg1PAmDKn435FHk25tJlyJnGESLnh4+ie4J/VC7UrBaFr187nfN9UyEf7zrC757ajlfVyUmL5b5b55CaaAtzlScXYzNx0zLjomb7vla27z11towgCOGxa38bj71hHKcpyU/m7stKkUdJgwWOyWPJmIBkHt7PSv8kC4BThN8O2aC2C917773cfPPNXHHFFSxZsoT8/HwsFgsHDhzgww8/xGQyce+99wJw2WWXUVNTw29+85uQFC4ETyBPQHWjtdehpBWEtZ5Q8V9gSXGpUbHuUhCEoTn5JqI9g95EpLYewLH2D+j9nSAp2JbeibnonBH4FUQ/JXUcMdf8CNPBjaBreMfNDHdJgyKnjkOyxaM7e/DWV2BJzBrxGlybnkV3dIPZhnXx5075OEmWsS25i/7VP0bvacG15QVsZ988gpUGhyxLfG55EWkJNp573zi+PD4jju/eNIuEGEuYqzu9BSUZrN/dQHltO0+8s5eSgmRslkF9xBYEIUSDj/b3AAAgAElEQVRqjnTzl5d2o+k64zPi+Pq10zGbRlcuo9oQnDwWAPuxkywi/HbIBvUOMGHCBF588UX++Mc/sm7dOt59910AYmJiWLZsGd/61rcYN24c7e3tpKSkcMcdd3D55ZeHpHAheOS4FKSYJPT+TtTmmlHfZBFHhQRhbPBvIvLWleH69Bm0tsN4azbhPbAVc+kFWOZciWw7eYCm99AOHO/8FbwusNixL/8GptzSEf4VRDdJNhE3azlA1K3FlCQZJacEb80mI5dlhFdPexuqAsfTrPOvQ45LPe3jleQcLHOuxL3lBTxlb2EuXBCVgcySJHHJwnyKClKoOtjB0pnZxNoif2pMkiRuW1HE/Q9vor3bxcvra1l5wfBG9gVBGL7G9n7+8NxO3B6NtEQb37lxZiDYdbTQnb1obYcBUILQZDm2QSzCb4du0L/LsrOz+cUvfgFAZ2cnXq+X1NTU4860paSksGrVquBVKYScklGI98BWI/x2hD9MjhQReisIY5MpbxpKTine6g24Nq9G72vHU/Y2nqr1WGZdhmX6ciSTNfB4d/k6XJ/8G3QdKS4V+yXfRUnODeOvQAgHJW+q0ZQ7UoGuaUjyyNz51FUPro8eA0BOn4C5dNmAvs8y61K8NZvR2g/j/OARYq59EEmJzouJOcUZzCnOiKrmXEZyDFcsyufFj2p5e3MdZ0/NiogtSIIwVnX1uvjdMzvodXiIs5v57spZJMZZz/yNUcbbUAXoIJuM7XjDJMsSVrOCy6OKSZZhGNYnhqSkJNLS0kZNaNBYJvvueGlNo3PDkO7sRe8yztWLPBZBGHsk+dhNRDeeuImo6iN01Uvne6twfbwKdB05fYKxolk0WMYkky+XBbcDrfXELLpQce94Ha2zASQZ27l3DLi5I8kmbEvvBklC66jHvWNNiCsVPuvis/LJTo1B03VWvVmFFsbQZEEYyxwuL79/dietXU4sZplv3zCTrJTIC84OBtWXx6JkFiKZgnO00h9+K4Jvh25QtzjcbjcPPfQQr776Km1tbajqid0tSZKoqIi+dPuxzj/doXU1orv6kKyRsyIxGNQW3wdkSR61x6EEQTgzYxPRpZiLl+DasQZP2TvGJqIP/knDp8+gOXsBI2jUdsGXkcyj766XMDByfBpSYiZ6VxPeuvIROX6jdTbi3v4qAObpK1DS8s/wHcdT0guwzLgE987XcW9/FdOEeSgpeaEoVTgJs0nm9ouK+dWT29l/pJsPdhzh/NmiSSsII8nj1fjTC7s51NyLLEl89erpTMxJCHdZIaMeMfJYhru6+Vh2i4ku3CL4dhgG1WT59a9/zb///W8KCwuZN28eFktkB5EJA6ekF4Akga6jttRiypsW7pKCyp/HIqfkiosmQRACm4gsU5fh2vwC3uoNgQaLedoKrAtvGrHjIULkMuVOxdPVhFpfAXOuDOlr6bqOc/2/QPMixaVinXvNkJ7HMvdqPLVb0bubjGNDV90vfi+PoOLxySyelsXHZY08//5+5hSlkxgrPi8LwkjQdJ1/vlZB5cEOAO68dAozCk+faRXNNEc3WkcdAEpOEJssgUkWcVxoqAbVZHnjjTdYsWIFDz30UKjqEcJEMtuQU/LQ2g6jNu0ffU0Wfx5LevQFAQqCEDpHNxFdBHvfw5pXgnf8wnCXJUQIJXcqnop3UZv2oXtcIW3Se/euD4x92875/JBfSzJZsC25E8eaX6K11OApexvLjIuCWapwBjdeMIkd1a30Ob08s24f91w5NdwlCcKop+s6z6yrZlNlMwDXLZ3I4unZYa4qtNSGKuNvFFNQpy394bcOMckyZIO6tdHX18e5554bqlqEMPM3INTm0ZXLous6mn+SJWNCmKsRBCESKWkFpFzyVWKnnx/uUoQIYsqZYkx5aipqY1XIXkdzdOPc+LTxmhMXYBo/Y1jPZ8qZgrnE+L3s2rwarbt52DUKAxcfY+HG840Ayo0VTZTXtoe5IkEY/d7cdJi3txhbdpbNzePShYM7bhmNjuaxTA5aHgsQ2MDkFMG3QzaoJsu0adMoKysLVS1CmAVyWZpr0EdRWJve04ru7AHEZiFBEARh4CRrLHK60Zz31pWH7HVcG58GVx9Y7FgX3RKU57SedSNSbAqobpwfPTaq3tejweIZ2UzOSwRg1VtVeLziYkUQQmVDWSPPvlcNwLwpGdy8bPKYWMwSyGMJ4lEhALtFBN8O16CaLN///vdZu3YtTzzxBO3tois/2siZxiSL7upFH0V3vfxHhTBZkZNEAJ0gCIIwcP4tQ2p9aEL9vXXlePd9AoB1wY3IMUlBeV7JYsd27u2AUbu36qOgPK8wMLIkcftFxSiyRHOHg9c2HAx3SYIwKpXVtPHI68ZEx5TxSXzx8hJkefQ3WLT+TrTOIwAoOSVBfW6bVRwXGq5BNVnuvfdeAP77v/+bxYsXU1JScsL/SktLQ1KoEHpyUjaY7cDoOjLkD71V0gtE+J8gCIIwKIqvyaK1H0br7wrqc+tetxF2izHubS5ZGtTnN42fhWmSkTHk3PgUWl9HUJ9fOL3c9DguPmv8/2fvvuOkqu/9j7/Omb69w7JLXXpfelERVFRAmiViiwaTG3OjiSUxJrnpRWPUeIP+ouaqoGiwINhjQUR6W3pdYIHty/Y27Zzz+2N2BwhtZ3dmZ8vn+XjwAGZnzvnM7g7sfM7n+/4C8PHG4xSU1oa5IiE6lmMFVTz33h403SA9OYofzh+OxWwKd1mtonGKBZMVU3Jw4xAag2+dEnzbbAEF344YMaJTjF51VoqiYkrpjZa3D634KJZ+k8JdUlA05rG0xvabQgghOhZTlwwwW8HrRsvfh9p3YtCO7d7+vm9yVDVhu/xuFCX4FwJsE29DO7kHw1WDa91r2K+5X36Wa0WzJvVi074iTlU6ee3fB/nJgkz5/AsRBEXldfzt7Z24PBqJMTYevGUEEfaA3tq2a/6lQl37oZgsQT22Q4JvWyyg78THH388VHWINsKUktHQZOkYkyyG7kU7lQPgX1cvhBBCNJVismBKHYh2chfe3L1YgtRk0cpyce/8BADriBmYEkKznFV1xGCbfDvOVS/gzdmO99hWLH3GhuRc4lw2i4k7pg/gb2/v5MCJCjbsLWTS0I6944kQoVZZ6+bpZTuorvMQaTfz0LdGEh8dut3f2iJvQUPobZDzWOCM5UIyydJssnZCnMUfflt6AsPrDnM1LaeX5YHmAWSSRQghRPOY03xLobW8fUEJkDUMHec3r4KhocR0wZp5Q4uPeTHmjAmYeowAwLXuNQxnTUjPJ842PCORMQNTAFi2Kpuaek+YKxKi/ap3efnbWzspqXBiNav86OYRpCZGhrusVqXXlmNUFgFgDnIeC5wOvnXKJEuzXbTJMmjQID744AP/3wcOHHjeHBbJZOk41MZGhK6hl54IbzFB0JjHokTEoUTGh7kaIYQQ7ZEp3ZfLYtSWoVcWtPh4nv2r0Yt8O2HYL/92ULfePB9FUbBfdhdY7Bj1VTg3vhnS84lzLbiqH3arieo6D++s7hjTwkK0Nq+m8/x7uzleVI2qKHx/7lD6psWGu6xW17h1M2YbanKvoB//9CSLNFma66LLhebOnUuPHj3O+rusI+3YVEcMSnQyRnUJWvERTF36hrukFjkdettbvneFEEI0ixqfjuKIwaivQsvdhymuW7OPpddV4Nr8NgDmfpP9UzKhpkYlYht/C661S/AeWoc3YwLm7sNa5dwC4qNtzL+iD298cZg1O/O5dkJPBvZKCHdZQrQbumHw8sf72ZvjC/C+67oBjOybFOaqwsOfx5LaH0UNfg5NREOTxe3R0XQdk2wcErCLflX+/Oc/n/X3YGeyeL1eRo0ahcvlOuv2iIgIsrKyzvuYsrIynnzySb755hvcbjeZmZk89thj9OrVK6i1dWamlD54q0v8DYr2TG/YvlmVpUJCCCGaSVEUTGlD8GZvQMvbC0OvbvaxXOuXgrsexRaFbeKtQazy0iyDrsR7ZBNawUGc37xK5E1/QLE6WrWGzmzaqHTW7SnkeGE1L6zYw5P3XxbukoRoN9756ggb9/qWyMy7vDdXjGh+s7u98zZMsphSg5/HAmC3nt6hyenWiLRLkyVQAX3G7rrrLjZs2HDBj69atYqZM2c2+XjHjh3D5XLxxBNPsGzZMv+vxYsXn/f+hmHw3//936xZs4ZHHnmEv/zlL5SUlHDXXXdRWRncbRU7s8ZclvbeZDHc9ejlDfvHNzwnIYQQojkaJ068+fsx9OaFAXpP7MB7dAsAtom3otqjg1ZfUyiKiv3ye8BkwagpxbXl3VY9f2enqgrfvm4AigIniqr5cO2xcJckRLvw780n+HSzL8Zg6qg0Zk3qFd6CwkivKcWoLgFCk8cC4LCdnsOQHYaa56KTLPX19ZSXl/v/vnnzZq655hp69ux5zn11XWfNmjXk5uY2+eQHDhxAVVWuvfZaHI5LX0nJyclh+/btPPHEE8ydOxeAjIwMrr76alatWsW8efOafG5xYY0BsUZ1CXp9FaojJswVNY9vVyEDUDCFYL2iEEKIzsOU5stlweNELz6KqWu/gB5veFw4177mO1a3QZj7TQ52iU2ixnXFOnoe7s1v4dn7JeaM8ZgDfC6i+Xp1jeGqUel8sS2Xf31xiJOFVUwalkpGtxhZ1izEeWzcV8iyVb4Mq9H9k7n96v6d+rXiz2Ox2FGTzn1PHgxnNlmcLtlhqDku2WSZO3cu1dXVgG9c9k9/+hN/+tOfznt/wzCYPLnpPzTs37+fHj16NKnBAviXFUVGnk6Qjo31hR1VVFQ0+bzi4tTEHqCafOG3xUdQe2aGu6RmadyGWo1LRbFGhLkaIYQQ7ZkalYAal4peUYA3b1/ATRbXtvcwakrBZMZ+2bfD+ibBOvxavEc3oZ86jmvNy5jm/zbk4bvitHlX9GHX0VKKy+tZvSOf1Tvy6RLvYNLQrkwc2pWkWFnCJQTA3pwy/u9DX1Ohf/c4vjd7MKraeRssAF5/HssAFNV0iXs3z5nLhST8tnku2mRJSEjgySefZPfu3RiGwXPPPcc111zDgAEDzrmvqqokJCQEtFzo4MGDWK1WFi5cyPbt2zGbzVx//fX89Kc/JSoq6pz7Dxw4kPHjx/Pcc8/Rp08f4uPjefzxx4mIiODqq5u/PlqcTTFbURN7oJccQys+irmdNln0Yt8YripLhYQQQgSBKW0IekWBL5dl9JwmP047dRzP7s8AsGbegBrXNVQlNomimrBPWUjd8t+iVxTgzvoA29gbw1pTZ+KwmfnLDy/j6+15fLnlBCeKaygqr+e9b47x3jfHGNgjjolDuzJmQMpZV5SFCAevphOOtsbxwmoWLd+NphukJUfywI3DsJhD01RoTxonWczdQpPHAmA2qVjMKh6vTr1MsjTLJf/lnjJlClOmTAEgPz+fW2+9lREjRgTl5AcOHKCmpoabb76Z73//++zZs4e///3vHDt2jCVLlpz3Ks9vfvMb7r33XmbMmAGA1Wrlueeeo3v37s2uQ1Egtp1dNTA3/CMTqrqN7gOoKTmGUnrMf45Qn/M/tfR8dad8TZaoHgOIauIx2ttzbA/nlOfY/s8XjnPKc+wY5+xoz9HaP5PSvV+gFR8h2gGq1XHJ8xm6TvH7i8HQMSemkXzFTSim5r9xDtrzix2Aafxsqje+h3vnx8QNvwxrSq/QnrOJOtr3zYXON/fKvsy6rDc5BVV8nZXLmh35VFS7OHCiggMnKlj6+SEmDOnKlaPSGZqRhKmFV/A7+texs3zftOb59ueU8Ydnvsbp0oiwmYl0WIiKsBDlsBAVYfX9fuZt57ndZjEFNLlnNpsoLK3l2Xd24XJrJMXa+fXC8SSG8Dm3l6+jt6KY6ppS32P7j8QawOMDPWeE3UxljRvVrDbr8xKO12OwBGPQNKD/5f9zt6HzKSsrIyGhaVvSPfPMM8TGxvonY8aOHUtiYiI/+clPWL9+/TlLj44cOcKtt95Kjx49+PnPf47dbuett97igQce4J///CdjxowJ5OmIi7Cm+rZudhcewTB0FKV9pUpr1WVoNWXA6ecihBBCtISt+2BQVNA1XCf34cgYfcnH1GR9iqfQFyQfP/17LWqwBFvMxPnUH9qEtyyf8k9fIOWOP4Rs/FxcWK/UGHqlDuaOaweyM/sUq7fnsWVfIW6Pzpod+azZkU9CjJ0rMtO4MjON7l1aNzBZdE6VNS6efnO7P5OjzuWlzuWlpKI+oOOYTerZjZgIC1H2082YSIeF6DMaNzarmb+8vpWKGhdRDgu/vGdcSBss7Ynr5F4AFFsklhDnTTpsviZLnVOWCzVHwP/Tr1ixgs8++4y6ujp0XfffrmkatbW1ZGdns2fPniYda9y4cefcduWVVwK+KZf/bLK8+uqrALz88sv+LJbJkydz22238ac//Ynly5cH+nQAMAyorAzsH4xwa+wKhqpuPco3GWS46yk/fhRTfFrIz/mfWnI+z7F9vj+YzNTZkqlv4jHa03NsL+eU59j+zxeOc8pz7Bjn7HjPUUFN6YNelE3VoSzcSYMvej69ppTab5YBYBk4BWd0T5wtrCvYz89y2T143/8TnqKjnFq7AuuIGSE/56V0vO+bpp8vo2s0GTMGsmBaBlsPlrBudwGHcyspq3Ky4usjrPj6CL26RjNpaFfGD+5CdETTs3TaynPsKOcLxzlb63y6YfC3t3ZSVuXCYTPzo1tGUlProrbeQ63TS62z4fd6z3/82XvObjReTaeixkVFjSugGixmlftvHEaM3Rzy59tevo712TsBMHXtT1V1YJ/PQM9pa5hEKauob9bnJRyvx2BJTIxq8TRLQE2Wl156iaeffhqLxUJUVBTl5eV07dqViooK6uvrsdvt3HnnnU06VmlpKatWrWLChAlnLfVxOp0AxMfHn/OY/Px8MjIy/A0W8IXxjh49miVLlgTyVMQlKDEpKLYoDFcNetERTPFp4S4pIHpj6G1SLxS17Vw1FEII0b6Z04bgLsr25bJchGEYuNa9Dh4niiMG2/hbWqnCwJi79sMyZBqevV/i2voe5l6jUGPDmxkjIMJu4YoR3bhiRDeKy+tYv6eQ9XsKOVXpJKewmpzCapatymZ4RiKThqYyom8iZlP7mjoWbdfHG46z55hvIvy++cMYO7hLk98sa7ovx6O23kON00NtvZe6MxoxNU4PdWc0Zc5s0mi6AYDZpPD92UPolx4XsufY3hiGgVbQEHobwjyWRg6br8nilODbZgno3efy5csZOHAgr732GuXl5VxzzTUsWbKEbt26sWzZMn7/+983Oa9FURR+9atfcdddd/HYY4/5b//4448xmUyMHn3uCG7v3r157733qKysPKvRsnPnTtLS2lcToK1TFN/VOu3kLrTio1gGXhHukgKilfjyWEzJEnorhBAieEzpQ2D7SvTyfPTacrjAGLs3Zxve41kA2CbehmKLPO/92gLb2JvwHt+BUVOKc80rOGY92u6WCXdkKfERzL28D7Mv6012biXrdhew5UAxTrdG1uFTZB0+RaTdzPjBXZg0NJXeqdGdeotb0TIHT5Tz3je+JY5TM9OYPLxbQI83qSpRDt8SoS4BPM4wDFweDdVixmpWQdMv/aBOxKgqxqgtB8DUbVDIz2e3+toEEnzbPAH9D5qXl8ecOXOIioqie/fuxMbGsnXrVkwmE7fddhszZsxg8eLFTTpWQkICt99+O6+99hp///vf2bBhA4sWLeKvf/0rt99+Oz179qSsrIwdO3ZQU1MDwN13343JZGLhwoV89tlnrFmzhocffpjNmzdz//33B/7sxUWZUjIA0EqOhLmSwBi6frrJIjsLCSGECCJTSh+w2AHQ8vad9z6Gu843xQKYug/DnDG+1eprDsXqwH75twHQCg7i2f91mCsS56MqCv27x3HPjEE8c/9lfG/2YIb2SUBRoNbpZdX2PP6wZCu//OcmPtqQQ1mVM9wli3amqtbNC+/vxTCgR0oUt17VermGiqJgt5pJjnMQG2VrtfO2F96GXYUUWxRqQnrIz9c4ySJbODdPQE0Ws9lMZOTpKzE9e/bk4MGD/r+PHz+enJycJh/v0Ucf5cEHH+Sjjz7ie9/7HitWrOCBBx7wT7asXr2ab33rW+zd6xvJTU9P58033yQpKYmf/exnPPTQQxQUFPDKK6/4dxsSwWPq4muy6GW5GJ728x+1XlEADfVKk0UIIUQwKaoZU6ovsN97gSVDrs3vYtRVgMmKffJd7WKqwNx9OOZ+viw816Zl6A3h8aJtsllMTBjclYduGclffzCZW6b2JS3Z9zN6QWkd7359lJ88v54n38xi/Z4CGfkXl6QbBi99uI+KGjd2q4n75g2VLZPbEC2/YalQ6oBWmTS02xonWeTfjuYIaLlQRkYGWVlZ3HzzzYBv+c6ZIbdVVVW43e4mH89isfDd736X7373u+f9+Pz585k/f/45NfzjH/8IpGzRTKbk3r4/GAZaSQ4knZuT0xbpJb4RR8UejRKdHOZqhBBCdDTmtCFoJ3ai5e3DMIyzmihaUTaefasAsI6eixrTfv4fsk9cQG3uboz6KpxrF+O49sftokHU2cVH27hufA+uHdedE0U1rNtTwKZ9RVTXedh/vJz9x8uxWQ4xcWhXBvdJJDHKSrfESGxWeQMtTvtow3H2NuSw3H39QLrER4S5ItHIMAy0hkmW1shjAXA0LBdySpOlWQJqssyfP5/f/va3uN1ufve73zFt2jR+9KMfsWjRIvr06cOrr77KwIGt84UXoafYIlFju6JXFqIVH4VBmeEuqUm0xtDb5N7yw6EQQoigM6UPAcCoq8BbmoslqWFHPt2L85vFgIGa0B3r8OlhrDJwij0K2+Q7cH7xPNqJnXiPbMLSd0K4yxJNpCgKPbtG07NrNLdM7cueo2Ws31PAjuxTuDwaq7PyWJ2V57svkBznIC05krTkKNIbfu8S75AA3U7owPFyVjTmsIxKY9ygQNJURKjplQUY9ZVA6+SxwJnLhSSTpTkCarIsWLCAwsJCli5ditlsZvr06cycOZNFixYBEBUVxSOPPBKSQkV4qF0y0CsL/bv1tAdaseSxCCGECB01rhtKRBxGXQXO47v9TRb3rs/Qy04CCvYr7mmXu9uZe4/F3GsU3pztuNYvxZQ2+ILhvqLtMptURvZLYmS/JGrqPWw5UMyuI6UcL6ymosaFARRX1FNcUU/W4VNnPE6ha0Ik6SmRpCVFkp4cRVpyJIkxdrlw1UFVnpnD0iWKW6e1Xg6LaJrGpUKKPRq1lXZ8PR18K5MszRHw//4PPvgg999/P2az76FPPfUUCxYsoKKigszMTBITE4NepAgfU0oG3kPr/NMhbZ3hdTX8gCs7CwkhhAgNRVEwpQ3Be3gdrpzdRI+egV5VgnvbCgAsQ6a120a/oijYJt+JN38/hrMa14Y3YN6D4S5LtECUw8LUzDTmXul783yyoJK8klrySmrILakl71QNeSW1ON0aXs0gt6SG3JKas45ht5p8Uy9Jp6de0pMjiY6whuMptRl6w5bD7ZWuG7z0wV4qa904bCZ+MFdyWNoifx5Lt4Gt1uw8vYWzTLI0R7MusTQ2WBqNGTMGAE3TeP755/nBD37Q8spEm9D4Q6JRV4G3uhRzdNtuommnToDh2/Ktvf6AK4QQou0zpw32NVlO7sXQvDjXLgbNjRIZj23sTeEur0XUyHhsE27FteYVvNkbqT+yDUfG6HCXJYIkJsJKTE8rg3qeztozDIPSKqev6VLia7rkltRSUFqLphs43RpH8qo4kld19rEiraQnN0y8JEWSnhLVKfJeyqtdfLH1JF/vzKdrQgQ/nD+MuHa4I86HG3LYl+PbFvju6weRIjksbY5hGGgFjU2W1lkqBKczWWSSpXku2WSpqqri7bffZufOnRiGweDBg7n99tuJiYk56347d+7kl7/8JdnZ2dJk6UDUhHQwWUFz484/jHlA226yNC5rUmJSUOxRYa5GCCFER+XPZfG4qFi1GC3XtxGAbdIdKNb2v7zGMuAKvNkb0fL3U/HZP7F9ZxC+JA/RESmKQlKsg6RYByP7Jvlv92o6ReX1p6deGhowJRX1GPi2/N1X6/a/UYfTeS89u8UwsGc8o/smERPZMSZeCkpr+XTTCTbsLcSr+aZYjuZX8fjr23nk1pEkxbWf1/7+4+WsXOtbYj9tVBpjB6aEuSJxPnp5Pka9r7nZWqG3cHp3IadbQzcMVFkuGJCLNllOnDjBnXfeSXFxMYbh+4fk888/Z+nSpbz11lt069YNt9vNX//6V5YuXYqmadxwww2tUrhoHYpqxpTcC63wEO6CbCIGtO0APK3YF9olUyxCCCFCSY2IQ41PQy/Po3bHZwCYe2Zi6d0xJj4UxZcrU/v2L9Fqyqj4/P9QJ92NYmp/OTOi+cwmlbQkXz7LuDMuorvcGvmlteQW15B3qpbchuZLZa37rLyXLfuKeENVGD0gmamZafTvHtcus12ycyv5ZNPxs/JroiMsXD4yjS+2nKC4op4/L/U1WlITI8NYadNU1rp5sSGHpWeXaL41rV+4SxIX0LirkOKIRY1NbbXzNi4XAt/r3WGTf/sDcdHP1tNPP01xcTEPP/ww8+bNw+FwsGbNGn73u9/xu9/9jr/85S/ce++97Nq1i+7du/Ob3/yGyZMnt1btopWoKX0amiyHw13KJWklDaG3kscihBAixExpQ9DLfbu1YLFjm3xHeAsKMjUmBdvY+bg2/ou6/WtRi09gn/o9TAnp4S5NhJnNaqJ3agy9U8+ebK+uczcsNaqhsKKezXuLqKn3sHl/MZv3F9MtKZIrR3Zj0tBUIuxt+02bbhjszD7FJ5tOkJ1b6b89Jc7BteN7MHloV5KTopg4NJU/vrqZ8moXjy/dzkO3jKRn1+gwVn5xum7w4vunc1jumzsEi1l2lGqrzgypIa0AACAASURBVNy6uTUblI3LhcC3ZEiaLIG56Gdr27ZtzJs3j3vvvdd/23XXXYfL5eIXv/gFP/nJT9i9ezd33XUXDz/8MDZb+1uLKC7NlJKBB/AUHsXQ2u66PL2+CqO6BJBJFiGEEKFnTh+MZ49visU29kbUqLa9pLY5LMOmY8VF9aYV6KUnqFv+G2xjb8Qy7FoUVd6YibNFR1gZ2NPKwJ7xxMY6uHe2xpebjvNVVh5H86vIP1XLG18c5p2vjzBhcBemZqa3uYaEx6uzcV8hn246QUFpnf/2nl2jmTGhJ6P7J6Oqp9/sDu6dwE9vy+TpZTuprvPwlzezePDmEfRNjw1H+Zf04foc9h/3Le+6R3JY2jTD0PGGIY8FTi8XAtnGuTku2mQpLy8nMzPznNvHjBmD1+tl06ZNPPfcc0ybNi1kBYrw84ffet14Tp0Ee9cwV3R+eolvqRCKCTWxR3iLEUII0eGZ0ofiGDgRxWJHHXxVuMsJCUVRib38Vux9Mjn14SKMqmJcm5bhPbED+5R7UWOSw12iaMNsFhOTh6UyeVgqxwur+Sorj437CnF7dNbsLGDNzgJ6p8YwNTONsYNSsFnCF5hb5/Ty9c48Pt9ykooat//2oX0SuH58Twb2uPBSp15dY3j09lH89V9ZVNa4+euyLO6/cThDeiW0VvlNsj+nzJ/DctXodMZIDkubppflgasWAHMr5rEAOM4Ir3ZK+G3ALtpk8Xq92O32c26PiPB1PBcuXCgNlk5AiUxAiYjDqKvAXZANvdtmk6Uxj0VN7I5i7hgBa0IIIdouRTWTeMOPAaisrA9zNaFlSxtA5I2/w7VxGZ79X6EVHKT23f/BPvE2zAMub5c5G6J19ewazd3XD+SWqX3ZsLeQr7LyyD9Vy7GCKo4VVPGvLw8zeVgqV2Z2a9Vck4oaF59vOcnqHXnUu3xX7FVFYfzgFK4d14MeXZo2aZOWFMljd4zmr29mcarSybNv7+S+OUPJ7N82GpGVNS5e+GAfBr6vxS1T+4a7JHEJ/jyWyHiUmC6tem6LWcWkKmi6Qb1bmiyBatHiqlGjRgWrDtGGKYqCKSUDb8423PmHMfW+LNwlnZeE3gohhBCho1js2C//NuaemTjXvIxRV4FzzcuYj2dhu/xu1Ii2uTxCtC0RdjNXjU5n2qg0DudW8lVWHlsPFFPn8vL51pN8vvUkg3rGMzUzjZH9kjCbQrMs7Xw7BdksJq4Y0Y1rxqaTFBv4TkEpcQ5fo+VfWRSU1vHce3tYOHMQE4eG9wKlrhu8+ME+qmrdOGxm7ps7VHJY2gF/Hktq6+axQEP4udVErdOL0yXLhQLVoiaLKmtxOw01pQ/kbMNdcJi2uDmdYRinQ2+lySKEEEKEjLnHcCJv+gPOtUvwHt2M93gWWlE2tsvv7jC7K4nQUxSF/t3j6N89jgVX9eObXfl8vSOfU5VO9h8vZ//xcmKjrFwxvBtTRnYjIebc6frmuNBOQVePTmfqqHSiHJYWHT8+2sajt4/i6WU7OFFUwz8/3IfTozE1M62lpTfbB2fksHxnxkBS2tFW052Voet4Cw4Crbt185kcNjO1Ti/1slwoYJdsshw9epQtW7acdVt1dTUABw8exGw+9xBjx44NUnmirTClZADgLctHryhEjWtbS4aMqiL/mkVVmixCCCFESCn2KOxX3Ye31yica5dgOKtxfv53vP0nY590O4pVwjRF08VEWpk5sRfXj+/JnmNlrM7KY+eRU1TWuPlgfQ4fbshhREYSU0elMaR3AmqAV/V1w2BXdikfbzp+wZ2CrEHMg4mJsPLTBZn87Z1dZOdW8tq/D+J0ebl+Qs+gnaOp9uWU8X5DDsvVo9MZPUByWNoDvewEuH3By+ZWDr1tZG/YYUiCbwN3ySbLP/7xD/7xj3+c92NPPPHEeW/fv39/y6oSbY6pa19MsSlolcW4tr2H46r7wl3SWRqXCmF1oMa2rQaQEEII0REpioKl7wRMqQNwfv1/aLl78B5aR23+AexX3hu2Nwai/VJVheEZiQzPSKS00snXO/NYs7OAqlo3O7JPsSP7FMlxdq4cmcZlw1OJjrh4Bt+Fdgrq1bBT0Kj/2CkomCLsFh6+ZSSL3tvN3mNlvL36CPVuL/Mu79NqSz8qa1y82JDD0js1mpslh6Xd8OexRCWiRIcn18dh8zUeJfg2cBdtsvzwhz9srTpEG6eoZmIm3UT5J8/jPbIJbeRMTG1oBx9/HktyHxRFlrEJIYQQrUWNjMdx/cN49n2Ja+NbGDWl1H/4BJah07GNu0nC6EWzJMbamX9FBrMn92b7oRJWZ+Vx4EQFJRVO3l59hPe+OcqYgSlMzUyjb9rZeUD1Li+rdzRvp6BgsllNPHDjcF54fy/bD5Xw4frj1Ls0FlzdL+BpnEDpusEL7+/157B8f47ksLQn3vzGrZtbP4+lkcPWOMkiTZZASZNFNFnE4Mup3rwSb2keri3Libjux+EuyU8raWyy9A5zJUIIIUTnoygK1iFXY04bSv3qF9GLj+LZ8xla7h7sU7+HKblXuEsU7ZTZpDJuUBfGDepCQWktX2XlsW53IfUuLxv3FrFxbxHpyZFcP6k3wzMS+Xj9MVZnnbtT0HXje9I9JarV67eYVe6bO4SXPzrAhr2FfLktF6fby93XD8QUwnzL99cd48CJCgC+M2MQyZLD0m4YuoZWcAgI31IhAHvDNs71EnwbsGYF33q9Xnbv3k1BQQHjxo3DbrejaRqxsZIq35EpqkrM5G9R9v7TaCd2oBVlY+oS/rFDQ/OgnzoBSB6LEEIIEU5qXFciZv8C946PcG9biV6RT92K32MdPRvryFkoavByL0Tnk5oYyW1X9+fGKRls3lfEV1l55BRWk1tSy0sr95x135buFBRMJlVl4axB2G0mvtruaxK53Brfmz0kJLsn7c0p44N1OQBcPSad0QOav9zEW1mC6mjaNtYiOPRTx8FTD/h2FgqXxkkWp0yyBCzgV/Unn3zClVdeyW233cbDDz/M4cOH2bZtG1OmTOGf//xnKGoUbYij/zjUJF9ol2vLu2GuxkcvPQm678UvOwsJIYQQ4aWoJmyjZhMx939Q47qBoeHe+h51K/+IXlEQ7vJEB2CzmLh8RDd+dfdY/ufbY7h8eCpWi+9tTXSEhXlX9OHJH0xiwdX9wt5gaaQqCndc058ZDeG3Ww+W8L/v7sLlCe6UQEWNi5fe3+vPYbmlBTks3tw9FL50P6fe+gOGoQevSHFRjUuFlOhk1OiksNXhaAy+lUmWgAXUZFm7di0PP/wwvXr14tFHH8UwfHvKp6en079/f5566ilWrlwZkkJF26AoCraxNwK+QCZv3r4wV3R6qZASmYAaERfmaoQQQggBYEruRcT832AZdi0AeslRat/9Ne49X8gbNhE0vVNjuGfGIP752NX86fuTePK+SdwwqVeLt2IOBUVRuOnKDG6c4rsouOdoGc8s20GdMziTApqu88LKvVTVeYhoyGFp7qSMYRi4tiwHw8BdcBjvsW1BqVFcmlbgC701h2nr5kb2huBbyWQJXECvuueee46hQ4eyZMkS5syZ4789IyODN954g8zMTBYvXhz0IkXbYkofhqlrf8A3zdLYbAsXf+itTLEIIYQQbYpitmKfuADHrEdRohJBc+Na/zr1Hz+FXlMW7vJEBxLpsDCgZ3xQt2IOlZkTe3H7Nb6fpQ/lVvLkv7KornNf4lGX9v7aHA6ebMhhmdmyHBYtfz96w4VMAPe2ldIcbQWG7vXnsZjCvENb4ySL7C4UuICaLPv372fmzJmo5wlpMpvNzJo1i2PHjgWtONE2KYqCtWGaRS8+gnZiR1jr0aXJIoQQQrRp5m6DiLzp95j7XwaAlreX2nd+iSd7Q9gv1ggRDleNTmfhzEEoChwvrOaJN7Ior3Y1+3h7jpXy4focAKaP7c6o/i3b9ted9QEA5riuAOjluXiPbW3RMcWl6SU54PV9H4S9yWKT5ULNFVCTxWKx4PVeuJNVUVGBxdL2RvNE8JlTB2BKHwqAa8vysHW2DVctemUhAGpKRlhqEEIIIcSlKdYIHFfei/2a+1Hs0eCuw7nqBZxfPo/hrAl3eUK0usnDUvnB3KGYVIX8U7U8vnQbJRX1AR+nvNrFSx/sa8hhieGmK1v2M7FWlI2W71uyEnf1Pdj7jgFkmqU1+PNYYrugRsaHtRZHw3IhCb4NXEBNlnHjxvHOO+/gcp3bZS0uLuaNN95g9OjRQStOtG2N2Sx62Um8R7eEpQatpGFySlEwNQTyCiGEEKLtsvQeTcRNf8DcMxMA79Et1L79C7wndoW5MiFa3+gBKfzopuFYzSolFU4eX7qdgtLaJj9e03VefH8v1Q05LPfNafmORa6sDwFQk3ph6zWCmEk3AaCX58k0S4g1NrfMqeGdYgGwnzHJIhOHgQnoFfjQQw9RUlLC7Nmzef7551EUhS+//JI///nPzJo1i5qaGh544IFQ1SraGFNyb8y9fE0119b3MPTWHyVrzGNR49NRLPZWP78QQgghAqdGxGKf/gD2KQvBYseor6T+06dxfvMqutsZ7vKEaFVD+yTy0LdG4rCZKK928efXt3O8sLpJj1259pg/h2XhrEEktSCHBUArPemPArBmzvLFBHTp7W+KyjRL6BiaF63oMACmMIfewulMFt0wcHvlax6IgJosGRkZLF26lJSUFF577TUMw+D1119n8eLF9OjRg1dffZVBg8LfdROtxzpmPqBgVBbiPbSu1c9/OvS2d6ufWwghhBDNpygKlgGXE3nT7zGlDgDAs381RYt/iuvkvrBcvBEiXPp3j+OnC0YR5bBQU+/hL29mcTi34qKP2XO0lI/WHwd8OSyZ/VqWwwLg3tEwxRLXDXOvUf7braPnAg3TLEdlmiUUtJKj4PUFILeJJovtdIi0hN8GxhzoAwYMGMBrr71GRUUFJ06cQNd10tLSSE5u+YtatD+mhDTMfSfgzd6Aa/tKzP0mophaJ5fHMAx/6rmaLKG3QgghRHukRifjmPUont3/xrX5XbSKIkr+9VtQFJSIeNToJJSoBNSoM39PRI1KQLG27Kq9EG1Jz67RPHr7KJ76VxYVNW6eWraD++cPZ0jvhHPuW17t4sWGHJY+3VqewwKgVxbiPboZAOvImSjK6evxpqSemHuNwpuzHff2FZj7jDnr46LlGpcKqXGpqBFxYa4G7NbTrYJ6t0ZsGGtpbwJusjSKi4sjLi78X3wRfrYx8/Ae2YxRU4pn/2qsQ69plfMaNaUY9VUAmCT0VgghhGi3FEXFOvx6TOnD8Hzzf3iKjoFhYNSWodVeZKtnWyTq+Row0YkoUYkojhh5IyjalbSkSB67YzRPvpnFqUonz76zk/+aPZTRA05f0NZ0nRdW7qGm3kOk3cz3g5DDAuDe8TEYBkp0Eua+48/5uHXUHLw529HL8/Ee3YIl49z7iObTGkJvw72rUKMzJ1nqZZIlIAE1WaZNm4aiKBf8uKIoWK1WEhMTGT58OPfccw9JSUktLlK0bWpMCpaBl+PZvxp31gdYBlyBYrGF/LxawxQLZitqfLeQn08IIYQQoWVKSCf+zj/jrSikKj8Po6YUveGX4f+9DPSGH/hdteiuWvTSk+c/oGpuaL4kNky/JJ7+c3QiSmQCitnaek9QiCZIjnPw2B2j+eu/sigoreP/rdjDwpmDuG6yb3n8im+OcSi3EoCFMweTFNvyiS69phTPYd/Sf+uIGSjquW8TfdMso/HmbMO9fSXm3mNRVGliBoOhedCKsoG2sVQIwGYxoQAGslwoUAE1WSZOnMgXX3xBZWUlGRkZ9O7dG5vNRk5ODvv27cNmszFkyBAqKip4+eWXWblyJW+99Rbduskb4I7Omjkbz6G1GPVVuPd+jm3krJCf05/HktwbRTVd4t5CCCGEaA8URcESn4pZPf/EtGHoGPVVGNWn0GvKMGpO+Rox1aUYtaXoNWXgatidRfdiVBWjVRVf+HyOGFxxKURmXgfpY0LxlIQIWHy0jZ/dPoqnl+3keFE1L324D0wqXRIi+GiDL4fl2nHdGdkvOBe03bs+BV1DiYjD0v+yC97POmo23pxtvmmWYzLNEixa0RHQPACYUttGk0VRFOw2M/UuL/VuycgKREBNlsGDB/PBBx/w/PPPM23atLM+tmPHDr7zne8wd+5cbr75Zg4dOsR3vvMdnn32WZ544omgFi3aHjUqAcvgq/Ds/jfunZ9gHTQVxRYZ0nPqjTsLJUvorRBCCNFZKIqKEhEHEXGYupz/Poa7/qwGjH8Kprrh97pyaNiS1Kivwl1fhbtgEbbJd2AdcnUrPhshLiw6wspPFmTy7Ds7OZxbyUsr92C3+i4sZnSL4cYpwVkur9dX4dn/NQDW4ddedLrrrGmWbTLNEiz+PJb4NFRHTJirOc1hM/maLDLJEpCAmiyvvPIKd9111zkNFoCRI0dy55138uKLL3LzzTfTv39/FixYwJtvvhm0YkXbZh05E8+Br8FVi3v3v7GNmR+ycxm6hnYqB5A8FiGEEEKcTbE6MCWkQULaeT9u6F6M2orTDZjstbhP7sO17nVQVKyDz/1ZV4hwiLCbeehbI1m0fDd7j5XhdGsNOSxDg5LDAuDZ/RlobrBFYhk09ZL3t46e45tmqcjHe3Qzlr4TglJHZ6YVNOaxtI0plka+bZxdOGWSJSABvTJLS0vp0uUClwyAxMREioqK/H9PSUmhpqam+dWJdkV1xPhDb927P0NvCKUNBb087/QWZymys5AQQgghmk5RzajRSZhTB2DpN4mkG3+GrccQAFxrl+Devzq8BQpxBpvFxAM3DufKzDRiIq18b/YQEmPtQTm24arFvfdLAKxDp6NYLn1cU2IPzL1GA+De/j6Grgells7K8Lp9y4VoO6G3jewN4bcyyRKYgJosffv25b333sPtdp/zMbfbzYoVK+jT5/Qb3r1795KamtryKkW7YR1+HdgiwePEveOjkJ2nMY9FccSgRJ67rZ0QQgghRFOpFhuJ836KKXUAAK5vXsV94OswVyXEaRazyv23jOTlX1zNsD6JQTuue++X4KkHix3rkKua/Djr6LkA/mkW0XxaUXZDmLeCuY3ksTRyNGzjLE2WwATUZPnhD3/IwYMHmTNnDosXL2b16tWsX7+epUuXcuutt7J//37uu+8+AH7zm9/wzjvvMHv27JAULtomxRaJdcT1AHj2fekLnwuBxjwWU0rGRXe8EkIIIYRoCtVqx3HdQ6cbLWtewXNgTZirEuJswfy51/C4fEuFAOvgaSj2qCY/1pTYHXNvX1C0e/tKmWZpAX8eS2J6QF+D1mC3NTRZZLlQQAJqskyZMoVFixbh9Xr585//zH333cfChQv5/e9/T3l5OX/729+49tprKSsr45133mHGjBksXLgwVLWLNso65BoURwxoXtxZ74fkHI3bN0vorRBCCCGCRbHYcFz3IKau/QFwrnkFz8FvwlyVEKHh2b8aw1UDJjOWYdMDfrx11BwA9IoCmWZpAS2/IY8ltW0tFQJwNAQtyxbOgQko+BZg6tSpTJ06lQMHDnDixAm8Xi/p6ekMGzbM31mNi4sjKysLi8US9IJF26dYbFgzb8C1fimeA99gHTEDNSYlaMc3PE5fJgsSeiuEEEKI4FIsdhzXPUj9J0+jFR3G+fXLoCgX3dZWiPbG0Dy4d30CgGXAFNSI82+ZfjGN0yzeY1txb1+Juc842WkoQIbH5b943NZCbwEcNlku1BzNfhUMHDiQ6dOnM2PGDIYPH+5vsJSVlaGqqjRYOjnLoCtRohLB0HBtfS+ox9ZKcvzbLpqSewX12EIIIYQQitWB4/qHULv0BQycq/8Pz6F14S5LiKDxHFqHUVcBism/1L85rKPPnGbZFKzyOg2t6DDoGr48lgHhLuccjVuGy3KhwAQ8ybJixQo+++wz6urq0M9Ye6dpGrW1tWRnZ7Nnz56gFinaH8VkwTZqDs41L+PN3og2cpZvK8UgaAy9VeNSUWyRQTmmEEIIIcSZFKuDiOsfpu7jv6IXH8G5+p++iZZ+k8JdmhAtYuiaf4MKc7+JqNFJzT6WKeGMaZZtKzH3GS/TLAHw57Ek9WiT72saJ1lkuVBgAnoFvPTSSzz22GOsXbuWQ4cOsWXLFk6ePMnu3bvZtm0bOTk53HnnnaGqVbQz5v6TUWK7AgburcuDdlzdn8ciWzcLIYQQInQUq4OIGQ+jpvTBN9HyEp7sDeEuS4gW8R7ZhFFdAijYRs5s8fH80yyVhTLNEiBvYx5LG9u6uZF/uZBbmiyBCKjJsnz5cgYOHMj69etZtmwZhmGwZMkStm7dyq9+9StcLhcjRowIVa2inVFUE7aG7d28OdvQSo4F5biaf2chCb0VQgghRGgp1ggiZjziu7hjGDi/ehFP9sZwlyVEsxiGfnqKpc8Y1LjUFh/TlNAdc5+xALi3yU5DTWW469Eb3h+Z22AeC5yxXMgly4UCEVCTJS8vjzlz5hAVFUX37t2JjY1l69atmEwmbrvtNmbMmMHixYtDVatoh8wZ41ATugPg2vJui4+n11Vg1Pq2hZbQWyGEEEK0Bl+j5WHfroaNjZYjspuKaH+8x7P8G0hYR84K2nH9Ow1VFuI9Ik3IptAKD4Ohg6Jg6tr28ljgjOVCMskSkICaLGazmcjI02vFevbsycGDB/1/Hz9+PDk5OUErTrR/iqJiGzsfAC13D96Cg5d4xMU1TrFgMvubN0IIIYQQoabYIn0TLUk9wdBxrvoHnqNbwl2WEE1mGAburA8BMHUfjimpZ9CObUpI90+zuLa/L9MsTeD157H0QrE6wlzN+TmsviaLVzPweOVr2lQBNVkyMjLIysry/713795nhdxWVVXhdruDV53oEEw9RjasZQb3lncxGnYGag69MfQ2sSeKKeDcZiGEEEKIZvM1Wn6CmtjQaPny/0mjRbQbWt5e//IUa+YNQT++b5pFwZBplibRCnx5LOY2mscCYLeZ/H+WXJamC6jJMn/+fJYvX84jjzxCXV0d06ZNY+vWrSxatIiPP/6YV199lYED2+Z6MhE+iqJgG3sTAFrhIbTc3c0+ln8f+RQJvRVCCCFE61PsUUTM/AlqYo+GRss/8BzbFu6yhLgk/xRL6gDMXfsF/fjnTrNIjseF6K469FM5AJjaaB4LnJ5kAdlhKBABNVkWLFjAf/3Xf7F69WrMZjPTp09n5syZLFq0iIceegin08kjjzwSqlpFO2ZOG+xPzXY1c5rFMHS0Yl/3XZosQgghhAgXX6Plp6iJ3cHQcH7xPJ6c7eEuS4gL0goP+ycnQjHF0ujsaRbZaehCXLn7wTBAMWHq2j/c5VyQ48xJFgm/bbKAmizl5eU8+OCDbNy4EavViqIoPPXUU7z++ussWrSIf//732RmZoaqVtHO2cbeCIB+6jjeY1sDfrxeUQieegBMsn2zEEIIIcJIsUfhmPlT1IT0hkbLc3hzsi79QCHCwLXDN8WiJvfGlDYkZOcxJaSdMc2yUqZZLsB1Yi8AakpvFIs9zNVcmP3MSRZZLtRkATVZ5s2bx3PPPYfZfHYWxpgxY7j66qtJTEwManGiYzF16Yupx0gA3FvfCzgQS29YKoQtEiUmJdjlCSGEEEIERLVH+xot8emga9R/sQjv8R3hLkuIs2injqOd2AmANXMWiqKE9Hynp1mK8Mp25+fV2GQxp7bdpUIAqqpgs8g2zoEKqMlSVlZGcnJyqGoRnUDjTkN6RT7e7A0BPbZxZyFTSp+Q/+cghBBCCNEUqiMGx6yfosan+Rotny/Ce0IaLaLtcO/4CAA1Pg1zz9CvOjhrmiVLsln+k15fg6f4ONC281gaNYbfSvBt0wXUZLnhhhtYtmwZubm5oapHdHCmxB6YM8YD4Nq2AkNr+ovV32SRpUJCCCGEaEN8jZZHUeO7ge6l/rNFeE/sCndZQqBXFOBt2AHLOnImihLQ279ms46WaZYLceXuBwxQTZhCEEAcbI3htxJ823QB7YGrqipHjx7l2muvpUePHiQmJqKqZ79QFUVh8eLFQS1SdCy20fPwHt2CUV2C5+AarIOnXfIxhteNXnoSkNBbIYQQQrQ9qiMGx8xHqf/wCfSKfOo//18c03+EufuwcJcmOjHXjo8BAyU62X+hszWY4tMwZ4zDe2QTru3vY+47AUU1XfqBnUDjUiFTSgaK2Rbmai7N4Z9kkYmkpgqolblu3Tri4+Pp0qULLpeL/Px8cnNzz/p18uTJUNUqOgg1riuW/pMBcG9/H8PrvuRj9NITYPhe2Gpy75DWJ4QQQgjRHGpErG/pUFwqaF7qP3sWb+6ecJclOim9+hTew+uBhimWVm5y+LNZqmSaxTB09KoSvMezqD/q24msPSwVgtPht/UyydJkAU2yrFq1KlR1iE7GOmoOnsMbMOoq8Oz7Euvw6y96/8alQkp0MqojpjVKFEIIIYQImBoRh2PWo9R98DhGZSH1/34Wx7U/xpweuh1dhDgf965PwNBQIuL8Fzhbkym+G+aM8XiPbOw00yyGYWDUV6KX5aGX56KX5aKV5aGX54HXddZ9Td0GhanKwDhsjcuFZJKlqQJqspypqKiIwsJC+vTpg81mw2w2n7N0SIgLUaOTsAy6Es/eL3BnfYRl4JUoVscF739m6K0QQgghRFumRsQRMetR6j58HKOyiPp//w3HdQ9iThsc7tJEJ6HXVeI5sAYA6/DrUUyWsNRhHTUb75FNDdMsG7D0vywsdYSC4apFK89HL8v1N1T0sjwMV82FH2QyY0lMx9ZrOKQOaL1iW8BhleDbQAXcZNm2bRt//OMf2b9/PwAvv/wymqbx85//nJ/97GfMmDEj6EWKjsmaOQvPgTUYrhrcuz/DNnrOBe+rlUiTRQghhBDthxoZT8Ssn/kmWqqKqP/0bziufxBzO7l6Ldo3z+5/g+ZBsUVh/P5NmgAAIABJREFUGXRl2OowxXfD3Hc83uzGaZaJ7W6axfC60Svy0cvy0Mpy0cvz0MtyMWrLLvwgRUGN6YKakO77FZ+GKSEdJSaFuPgoACor61vpGbSM3SbLhQIVUJNl165d3HPPPaSmpvLtb3/bH3AbGxuL2WzmkUceITIykilTpoSkWNGxqBFxWIddg3vHR7h3fYJ1yFUo9qhz7qc7qzGqigHZWUgIIYQQ7Yev0dIw0VJVTP0nz+C4/kGIHRXu0kQHZrhqce/zxTxYhk1HsYQ3XNU6ajbe7E0YVcV4D6/HMuDysNZzIYauoVcWNUyl+BopWnkeRlURGMYFH6dEJfqbKGp8mq+pEpeKYra2YvWh418uJMG3TRZQk+XZZ58lPT2d5cuXU1dXx6uvvgrAsGHDeP/991mwYAEvvPCCNFlEk1mHX4977yrw1OPe+TG28beccx+9+JjvD4oJNalnK1cohBBCCNF8alSCr9HyweMY1SXUf/oMrqjHsHWXpUMiNNx7vwCPEyx2rEOuCnc5mOLOmGbJ+gBzv0ltZppFK8+nbP1neIqO4SnNA/3C0xqKPdo/laImpGOKT0NNSEOxRrRixa3Pv7uQTLI0WUBNlqysLH7wgx9gt9uprz97vCkqKopbbrmF//3f/w1qgaJjU+xRWEdch3vre7j3fIFl2HTUiLiz7qMVHwFATUzvMB1hIYQQQnQealQiETf8zN9oOfXu4yTd9BhE9wp3aaKDMTxO3Ls/A/BNidsiw1yRz+lslrYxzWJoHt80fdaH5zZWzDbUhDRM8elnLfdRHDEoihKegsPI0bi7kEyyNFnAmSxW64Xf5LpcLnRdb1FBovOxDp2OZ88XGM5q3Ns/wH7ZnWd9XCvxTbLIUiEhhBBCtFdqVOLppUPVpyh5649Y+k7EMuQqTDKpK4LEs381uGrBZMEydHq4y/EzxXXDnDEBb/YGXzZLv4koarP3YGkRrfAwzm9eQS/P99UW24XI4VPxRPgyVJSoRBRFNnRpZG+YZHHKJEuTBfTdM2LECD788MPzfqyuro63336bYcOGBaUw0XkoVgfWkTMB8BxYjV5d4v+YYRjosrOQEEIIIToANTqJiFmPYopNBs2D5+Aa6pb/mtqVf8BzeD2G5gl3iaIdM7xu3Ls+BcAycApqRGyYKzqbbdRsUBSM6hK8hze0+vkNdz3OtUuoe/9PvgaLomIZfj1d7nmSmAnzMPfMRI1OlgbLfzg9ySJNlqYK6DvogQceYN++fdxxxx2sWLECRVHYtWsXS5YsYc6cOeTm5vL9738/VLWKDswyeBpKRBzoGq5t7/tv1yqK/NugqdJkEUIIIUQ7p0Yn0+XuJ4m7+juo8d0A0IuycX71IrVLH8K1+R30mtIwVynaI8+hdRh1FaCYsI64PtzlnEONS8WcMQEA1/b3MS6SfxJs3pwsat/+OZ59qwADNaknEfN+jX3Ct1DDHAzc1jUG37o9OpqsWmmSgGa0MjMzeeGFF/j1r3/NE088AcAzzzwDQHJyMs888wwTJkwIfpWiw1PMVqyj5uBauxjv4bVoI6+H2Azchb48Fix21LjU8BYphBBCCBEEqtVBVOa1eHtfjlZwAM/eL/HmbPctnd7xIe6dH2HuMdK3lChtsFxZF5dk6BrunR8DYOk/CTUqMcwVnZ9t1Gy8Rzb6plkOrccy8IqQnk+vq8C17nW8x7b6bjBZsY2dh2Xo9DYTvtvW2a2nP09Ot0akXf49upSAF8JNnjyZzz//nH379nHixAl0XSctLY2hQ4diNodnXZ3oGCwDL8e982OM6hLcW1dAz4dxFxwGwJTcW37AEEIIIUSHoigK5m6DMHcbhF5bjmf/ajwHvsaoq8B7PAvv8SyU2C5YB0/D0v+yNhNiKtoe75FNGNUloChYR8wMdzkX1DjN4s3e4NtpqP+kkGSzGIaB5+AaXBuXgbsOAFPaEOyXfxs1JiXo5+vIGidZwLfDUKTdEsZq2oeAvqN/8YtfMHv2bMaPH8+QIUMYMmRIqOoSnZCimrGNmYfzqxfxHt2Mu+gY7gLfJIvksQghhBCiI1Mj47GNmYd11A14j23Hs+9LtIKDGJVFuDa8iWvzu1j6TcAyWIJyxdkMQ8e9w5ebae49FjWua5grujjbqDkhnWbRKwpxfvMKWsFBABRbFLaJC3xbR3fC3YFa6swmi9MlOww1RUBNlo8++ojly5eTnJzMrFmzmD17NgMHDgxVbaITMmdMQN3xIXp5PpVr3sBd5NtZSPJYhBBCCNEZKKoZS8Y4LBnj0MpO4tn3FZ5D68DrwnNgDZ4Da1C79MU65CrMvcegmOSqcmfnzcny75RjzZwV5mouTY3rirnvRLyH1wd1msXQvbh3foJ7+0rQfHkv5r4TsU1cgOqIafHxO6szlwtJ+G3TBPTdvHHjRlatWsXHH3/M0qVLeeWVV+jduzezZ89m5syZdO/ePVR1ik5CUVWsY+bj/HwRrpxd/ttl+2YhhBBCdDamhO6YLrsL27ib8Rxah2ffKvSKfF9QblE2iuNNLAOuwDJ4apvN4BChZRgG7qwPADD1GIEpsUeYK2oaW+ZsvNkbMKpL8Bxah3XglBYdTys+gnPNK+hluQAoUYnYL/825u7Dg1Fup2Y2qVjMKh6vTr1MsjRJQE0Wu93OjBkzmDFjBjU1NXzxxRd88sknPPfcczz77LOMGDGCG264gdtvvz1U9YpOwNxrNGpSL/RTOQAokQmokfHhLUoIIYQQIkwUqwPr0KuxDLnq7KDc+qrTQbk9M31LidIGy5KITkTL3eP/mdmWeUN4iwmAb5plEt7D63BnfYCl/+RmTbMYHieuLe/i2fMFYICiYBk6HduYeSgWe/AL76QcVlNDk0UmWZqi2XNZUVFRzJ07l7lz53Ly5En++Mc/snr1anbu3ClNFtEiiqJgG3sj9Z88BfhCb4UQQgghOruzgnJryvAcWI1n/2qM+iq8Odvx5mxHje2KZfA035tWCcrt8BqzWEzdBmHq0jfM1QTGNuoGvNnrMapPNWuaxXtiJ861SzAatjxXE7tjv/weyXIMAbvNTFWdR5YLNVGzmyxlZWV8/vnnfPLJJ2zduhVN0xg7diyzZ88OZn2ikzKlD8XWcxiu47sx9ZAxPyGEEEKIM6lRCdjGzMeaORtvzjY8e79EKzyEXlmIa8MbuLa8g6XvJN820ImypL8j8hYe8oe7WtvRFEsjNfY/pln6TW7S4/T6Klzrl+I9ssl3g8mCdfQcrMOvC8lORQIcVt/nVYJvmyag78Ly8nI+++wzPv30U7Zs2YLX62XAgAH8+Mc/ZtasWXTt2raTrEX7oSgKifN+grsgG2dMr3CXI4QQQgjRJikmM5aM8VgyxqOVnsSz70s8hzc0BOWuxnNgNaau/bGMuQ5H//HhLlcEkTvLN8WiJvfB1G1QmKtpHtuoxmyWU3gOr4Px113wvoZh4D20FufGf4GrFvBN8Ngvvxs1tktrldwpOWy+8FtZLtQ0ATVZLrvsMjRNo1u3bixcuJAbbriBvn3b11iaaD9Uiw17j//P3p2HN1mlfRz/PUmaJhQoO4yyiKCtFNkpuyzDIjgqoii4gOziNaKAiqCCyswor6KoCIiMgrgvyLiOooIygzIKKKKAIGVRFtmhdE1y3j9KArWFLknz0PL9XBcXbfr0ue9T2kNy9z7nJCnzcLrdqQAAAJzxnFXryNnpZsW2ufakjXJ3yb/7Zx14/2e5Kv9JMcnXylm3Gfu2lHL+fVvl35FzSERs88tL7b+nI76mXBe0k+/n/ypr9bsyrbrLcuZ9iRo48rsyls+X/7efch6IjZOn7QC5LuxYasdemniOd7KwXKhwilRk6d+/vy6//HK1bNnylNccOHBAVapUCTsxAAAAAEVnucvJ3biHYpK6y79zfc5GudtWy3dwl3wfPynnORcptu0AOavVsztVFFOoi6VybTnrNbU5m/DENr9Cvk1fyaTu17F1X6h80z+HPmYCfmWt/VhZqxZL/ixJkuv8ZMW2v0GOcvF2pXzWCXaysFyocIpUZHnggQe0ePFi/fOf/1RaWpoCgUDoY36/X8eOHdPmzZu1bt26iCcKAAAAoPAsy5Lr3EZyndtI5bL26tCyhcrculb+neuVtugBxSR0krt1PznKVbI7VRSB/+BO+VJWSZLczf8iy3LYnFF4crpZ2sv383909Ot3FNc4ZwNc/96tyvjyeQX2b5eUc+Kop+Mgueo1szPds5Inlk6WoihSkeW5557T448/rpiYGJUvX14HDx5UrVq1dOjQIaWnp8vj8eimm24qqVwBAAAAFENM9bqq3v9e7V+3Uplfv6bAwZ3K3vilsn9ZKXezy+Ru0kuWK9buNFEIWd9/IMnIqlhDrvNb251ORMQ2v1y+TSvkP7JXqWs+Vsb+35X9w8eSMZIsxST9WbGtr5bl9tqd6lnpxMa3FFkKo0hlz0WLFikxMVErVqzQ66+/LmOMXnzxRX377beaPHmyMjMz1bRp6W5XAwAAAMoqV50mKnf1VMV2HCTLU0HyZSrr20U69vpEZW9aIWMCBd8EtvEd/l2+TV9JktzNLpPlcNqcUWQEu1kk6fDSF5W99t+SMXJUPlflrrxXng43UmCxUWjj2yyWCxVGkYosv/32m6688kqVL19ederUUXx8vL799ls5nU5df/316tOnjxYsWFBSuQIAAAAIk+Vwyt2om+IGTJO7aR/J4ZI5dkAZS+cqbfFU+Y4fC4wzz9H/vSeZgKy4yoo5XpQoK2JbXCEFlz45XHK36qdy/R6UsyYHrdgttPEtnSyFUqQii8vlUlxcXOj9evXqaePGE5NwmzZttHXr1oglBwAAAKBkWO5yim1zreKufViu85MlSYG9KUp/72GlL5mpwJHfbc4QJ/OnHtSxH5ZKktxNestyxticUWQ5KtZQ5d6jFdekm8pd85BiW1yR70lDiL7Qxrd0shRKkYosDRo00Jo1a0Lv169fP9cmt0eOHFFWVlbksgMAAABQohwVq8vb/VZ5r7hXjurnS5J8Kd/q2BuTlPH1azKZx2zOEJJ09NsPJH+2LE8FxSR2tjudEhGXdIkq9xolZ6Vz7E4FJ/HSyVIkRSqy9OvXT4sWLdKdd96ptLQ0devWTd9++61mzpypDz/8UPPnz1diYmJJ5QoAAACghLhqXaByfe+Tp9soWXFVpIBP2Wv/rWOvTVDWj5/KBPgttl0Ch3YrdfVHkqSYi3vKimGTYkRP8HShjCy/AsbYnM2Zr0j9VwMHDtTu3bv18ssvy+VyqWfPnrrssss0c+ZMSVL58uV15513lkiiAAAAAEqWZTkU07CdXOe1VNYPHyvruw9kMlOV+d+XlP3j54pte52cdZrIsiy7Uz1rGGOU8d+Fkt8nZ3x1uS/uaXdKOMsElwtJUmaWX95YlnGdTpG/OmPHjtVtt90mlyvnU6dPn66BAwfq0KFDat68uapWrRrxJAEAAABEj+VyK7b55YpJ6KSsbxcpe8NyBQ7tVPq/n5Dz3CTFthsgZ5U6dqd5VvD9slL+336UJFX681BlcdQ2oiy4XEjKWTJEkeX0ivXVCRZYglq1ahWRZAAAAACcORzlKslzyVDFJHVX5tevyf/bT/L/9qPS3p6smIRL5G51lRzlKtmdZpllstKU+dWrkiTPBa3lbdBCWYfTbc4KZxvPSUUVjnEuWJH2ZAEAAABw9nFWrStvn7vkvfQOOeJrScYoe8MXOvb6Pcpc876Mj8MvSkLmN2/LpB+WXLGq1O1mu9PBWcrrPrFcKIPNbwtEnw8AAACAAlmWJVfdZnLWbqzsn5Ypa9VimcxUZX3zlrLXL1Vscn+5GrRhv5YI8e/dquyfPpckxbbsK1fFajZnhLNVjMshp8OSP2CUnkWRpSB0sgAAAAAoNMvhkrtxd8UNmKaYJpdKDqdM6n5lfD5Haf+aKv/uTXanWOqZQEAZ/1kgGSNH5dqKubiH3SnhLGZZljzHu1kyMlkuVBCKLAAAAACKzIqNk6ftAMVd+7Bc9XP2aAz8vkVp7/5d+9+dId+h323OsPTKXr9Ugb0pkqTYToNlOViAAHsFN7tNZ7lQgSiyAAAAACg2R8Ua8vb4q7yXT5Sj2nmSpPSNX2nPgrvl37vV1txKo0DaIWV+85YkKSahk1y1LrA5I0DyHD9hiI1vC0aRBQAAAEDYXH9KULmrJsvTZYQc5eJlstKV/u/HFTi8x+7USpXMr1+XstKl2Di521xrdzqAJMkbG1wuRCdLQSiyAAAAAIgIy3Io5sIOqj5gshye8jLpR5T24WMKpB2yO7VSwffbT/Jt/kqSFNvmWjk8FWzOCMgRWi7ExrcFosgCAAAAIKJiqtZW1asnSE63zNG9Sv/ocZmsdLvTOqMZf7Yy//OiJMlRs6FiEjrZnBFwwok9WVguVBCKLAAAAAAiLvacC+XtcatkORTYv13pnzwl48+2O60zVtb3HylweLdkOeTpOFiWxUs1nDm8wdOF6GQpED+5AAAAAEqEq24zeS4ZIkny71yvjKXPyZiAzVmdeQJHflfWmvckSTEX95Szah2bMwJy89DJUmgUWQAAAACUmJiETnIn95ck+bb8T5krXpYxxuaszhzGGGX89yXJny0rropiW/a1OyUgj2AnC3uyFIwiCwAAAIAS5W7aRzGNe0qSsn/8TFnfvW9zRmcO39ZV8u9YK0mKbX+DrBiPzRkBeQU7WThdqGAUWQAAAACUKMuyFNtugFwN2kqSsr55W1kbvrA5K/uZ7AxlrnhFkuSs21Su81rYnBGQP6+b5UKFRZEFAAAAQImzLIc8XYbLeW6SJClz+Xz5tq6xOSt7Za5aLHPsgOR0y9P+RlmWZXdKQL68sWx8W1gUWQAAAABEheV0ydvjr3JUO08yRumfzZJv9ya707KFf/8OZf/wiSTJ3eJyOSpWtzkj4NRO3viWPZVOjyILAAAAgKix3F55e4+TVbGm5M9W+r+fkP/Ab3anFVXGBJTxnwWSCchR6U9yN+ltd0rAaQWXCwWMUZaPE8JOhyILAAAAgKhyeCuqXJ/xsrwVpaw0pX80XYHU/XanFTXZG5crsGezJCm24yBZTpfNGQGnF1wuJLH5bUEosgAAAACIOkfFGvL2Hi/FeGSOHVD6h9NlMlLtTqvEBTKOKnPlG5IkV8N2cp1zkc0ZAQXzuE8UAtOz2Pz2dCiyAAAAALCFs1o9eXuOkRwuBQ7tVNq/n5DxZdqdVonK/PoNKfOY5C6n2LYD7E4HKJSTO1nS6WQ5LYosAAAAAGzjOreRPN1GSrIU+P0XpX86SyZQNl/E+Xb/LN/PyyVJscnXyFEu3uaMgMKJjXEqePYVRZbTo8gCAAAAwFYx5ycrtsMNkiT/9u+V8eX8MneCiQn4lLn8RUmSo3p9xSR2sTchoAgsy8p1whBOzdYdlnw+n1q0aKHMzNwtgeXKldOaNWvy/ZxAIKBnn31Wb731lvbu3at69erplltu0WWXXRaNlAEAAACUAHdSd5m0w8pa8558P/9HWeXiFZvc3+60Iib7h08UOPirZFnydBosy8Hvu1G6eGOdSs/0KSOLTpbTsbXIkpKSoszMTE2bNk3nnXde6HHHaSacf/zjH3r99dc1btw4JSYm6oMPPtD48eNVvnx5de7cOQpZAwAAACgJ7lb9ZNIPK3vDl8r67gNZ3ni5L+5pd1phC6TuV+aqxZKkmEZ/lrPaefYmBBRDzjHOmSwXKoCtRZYNGzbI4XCoV69e8nq9BV6/fft2vfzyy3rooYfUv39OVbtdu3baunWrli9fTpEFAAAAKMUsy1Jsx8Ey6Ufl27ZGmV+9IstbUTEN29qdWlgyV7ws+bJkeeMV27qf3ekAxeI5vvktpwudnq1FlvXr16tu3bqFKrBI0qeffiqPx6O+ffvmevyll14qifQAAAAARJnlcMrz59FK//Ax+Xf/rIxlz8nyVJCrdpLdqRWLb9t38m1dLUmKbTdQlruczRkBxeM9foxzBp0sp2UZG3eUGjZsmH7//XfVqFFDq1evlsvlUu/evXX33XerfPnyea6fMGGCNm3apNtvv13Tp0/X5s2bVbt2bd1xxx3q06dPsfMwxig7u3RV41yunCqizxe9vKMdkzGWjZiMsfTHsyMmYywbMRlj6Y9nR0zGWDZiRiJeICNVv786Rb59v8qK8aj6gCly1zq/xOIVVWFiBrIzteeF8fIf3qvYeherWv97ZVnWKa8PN16klcbvmzM9Zmke42OvrNZXP+xSzzZ1NarvxSUezw4xMc5i/4wG2brb0oYNG7R9+3Z17txZc+fO1a233qr3339fo0ePznc38QMHDmjXrl2aNGmSbrzxRs2bN09JSUkaO3asvv76axtGAAAAAKAkODzlVe2aSXJWqCqTnaF9bz8s38HddqdVJEe/WiT/4b2S06VK3YeG/eINsFO54OlCGXSynI6ty4WeeOIJxcfHKyEhQZLUunVrVa1aVXfddZdWrFihDh065Lo+OztbBw4c0Jw5c9S1a1dJOXuybNmyRTNnzlTbtsVbq2mMdPhweniDibL4+JwlVtHMO9oxGWPZiMkYS388O2IyxrIRkzGW/nh2xGSMZSNm5OKVU+yl45X27t8VSDuiPa//TeWuvFeOcpVKKF7hFRTTf3Cn0r55T5LkbnqZ0pyVpTDyOxPHWNrj2RGzNI/RebxGeCQ187T3smOMkVK1anmFWwu1tZMlOTk5VGAJ6tKli6ScLpc/iouLk9PpzFV8sSxL7du318aNG0s0VwAAAADR56x8jspdOlZyumWO7lX6R4/LZJ3ZL96MMcr8zwIp4JdVsabczS6zOyUgbB43G98Whm1Flv379+vNN9/Ujh07cj2ekZEhSapcuXKez6lXr54CgYB8vtztSdnZ2bTeAQAAAGWUs2ZDeXvcKlkOBfZvV/onT8n4s+1O65R8m1bIvyvnl8CejjfJcrltzggInzeWjW8Lw7Yii2VZmjx5cp6TgT788EM5nU61bNkyz+d06tRJxhh99NFHocd8Pp+WL1+e7/UAAAAAygZX3WbyXDJEkuTfuV4ZS+fKBAI2Z5WXyUhV5tevSZJc5yfLVbuxzRkBkREssqRnUWQ5Hdv2ZKlSpYpuuOEGLVy4UOXLl1erVq20atUqzZkzRzfccIPq1aunAwcOaPv27WrYsKHKly+vdu3aqXPnzvrb3/6mtLQ0nXfeeXrllVf022+/afr06XYNBQAAAEAUxCR0UiD9iLL+96Z8W75RpqeiYjvcaHdauWR+85ZMxlEpxqPYdgPtTgeImNByoUyWC52OrRvfTpgwQTVr1tTbb7+tuXPnqmbNmhozZoyGDx8uSVq2bJkmTpyoF198UW3atJEkPfXUU3ryySc1d+5cHT58WI0aNdLzzz+vxo2pEAMAAABlnbtpH5m0w8pe94myf/pMVrl4qet1dqclSfL//ouy138hSYpt1U+OuLxbIAClVWi5EJ0sp2VrkSUmJkYjRozQiBEj8v14v3791K9fv1yPeTweTZgwQRMmTIhGigAAAADOIJZlKbbdAJn0I/L98rWyvl2kY1WrKa7Jn23NywT8yli+QJKRo2pdxSTZmw8QaV53TvnA5zfK9gUU47L1HJ0zFl8VAAAAAKWKZTnk6TJcznOTJEkHP3lO6Zu/tTWn7B8/U2D/dkmWPJ0Gy3I4bc0HiDRP7InvafZlOTWKLAAAAABKHcvpkrfHX+Wodp5kjPa/N0MZ/31J2Zu/VuDoXhljopZL4NhBZX67SJIUc1FnOWs0iFpsIFqCnSwSJwydjq3LhQAAAACguCy3V97e45T53j/kO7Rb2T9+quwfP835mDdezpoN5KjRUM6aDeSsfp4sV2yJ5JH51atSdoYsTwXFJvcvkRiA3bwnd7Kw+e0pUWQBAAAAUGo5vBVVfeCDSv3uY6VtWy//3hQpO0Mm/bB8W1dLW1fnXGg55ahaJ6fgUqOBnDUbyqpQXZZlhRXf9+s6+bb8T5IU23aArNi4cIcEnJE8J3eysFzolCiyAAAAACjVnOUrKb7jddLF6TKBgAIHf5V/zy/y/75Z/j2/yBzeLRm/Avu2KrBvq7J//EySZHkrylmjgRw1G8hZo6Gc1evLiil8t4vxZSnjPwtzcvhTglwXtC+R8QFnAofDUmyMU5nZfjpZToMiCwAAAIAyw3I45KxaV86qdaVGXSVJJiNV/t9/kX/P5py/f99yvNvliHzb1kjb1gQ/Oafb5XinS0HdLkdW/kvmyB7Jciq246Cwu2KAM50n9niRhU6WU6LIAgAAAKBMszzl5arbVK66TSUpp9vl0G853S57flHg980KHNolmYAC+7YpsG+bsn/6/PjnVpAjVHRpcLzbxaPsg7t0dOViSZK76aVyVj7XtvEB0eJ1u3RYWWx8exoUWQAAAACcVSyHQ84qdeSsUke6qIuk490ue7ccL7xsPt7tki6TcVT+7d/Jv/274CfLUaW20uWX/D5Z5avK3eIK+wYDRJE3NqeEkJ7FcqFTocgCAAAA4KxnecrLVaeJXHWaSJKMCShwcJf8v29W4Pj+LoGDO3O6XfZvV+D453k63FhipxYBZ5rgCUPpdLKcEkUWAAAAAPgDy3LIWeVcOaucKyV2liSZzGPy702Rf89mOQ5tl7tWQ5l6zW3OFIge7/EThjLY+PaUKLIAAAAAQCFYsXFy1W4sV+3Gio/3SpIOH063OSsgejzBThY2vj0lh90JAAAAAACAM1+wk4XlQqdGkQUAAAAAABTIc3zj2ww2vj0liiwAAAAAAKBAbHxbMIosAAAAAACgQKHlQnSynBJFFgAAAAAAUKDgxrcZdLKcEkUWAAAAAABQoBOdLBRZToUiCwAAAAAAKJD3+Ma3WdkB+fwBm7M5M1FkAQAAAAAABfK4naG3OWEofxRZAAAAAABAgYKdLBL7spwKRRYAAAAAAFCgk4ssnDCUP4osAABBdeV3AAAgAElEQVQAAACgQCcvF0qnkyVfFFkAAAAAAECBXE6HYlw5ZYQMThjKF0UWAAAAAABQKN7j3SzpmSwXyg9FFgAAAAAAUCie4/uypNPJki+KLAAAAAAAoFC87pwiSwadLPmiyAIAAAAAAArFGxtcLkQnS34osgAAAAAAgELxuFkudDoUWQAAAAAAQKEEO1lYLpQ/iiwAAAAAAKBQ2Pj29CiyAAAAAACAQjmx8S1FlvxQZAEAAAAAAIUS2vg2i+VC+aHIAgAAAAAACiW08S2dLPmiyAIAAAAAAAoltPEtnSz5osgCAAAAAAAKxUsny2lRZAEAAAAAAIUSPF0oI8uvgDE2Z3PmocgCAAAAAAAKJbhcSJIyWTKUB0UWAAAAAABQKMHlQhJLhvJDkQUAAAAAABRKcLmQxDHO+aHIAgAAAAAACsXrPrFcKINOljwosgAAAAAAgEKJcTnkdFiSpPQsiix/RJEFAAAAAAAUimVZ8gZPGMpkudAfUWQBAAAAAACF5jm+ZIiNb/OiyAIAAAAAAAot2MnCxrd5UWQBAAAAAACFFtz8lo1v86LIAgAAAAAACs0T6mShyPJHFFkAAAAAAEChhZYLsfFtHhRZAAAAAABAoYWWC9HJkgdFFgAAAAAAUGgeOllOiSILAAAAAAAotGAnC3uy5EWRBQAAAAAAFFqwk4XThfKiyAIAAAAAAArN6w4uF6LI8kcUWQAAAAAAQKF5Y48vF2JPljwosgAAAAAAgEILbXyb5ZMxxuZsziwUWQAAAAAAQKEFlwsZI2VlB2zO5sxCkQUAAAAAABRacLmQxAlDf0SRBQAAAAAAFJrneCeLxOa3f0SRBQAAAAAAFNrJnSwZWWx+ezKKLAAAAAAAoNBiY5yyjr9NJ0tuFFkAAAAAAEChWZZ14oQhjnHOhSILAAAAAAAokuCSoQw2vs2FIgsAAAAAACiS4DHOLBfKjSILAAAAAAAoEs/xTpZ0Nr7NhSILAAAAAAAokmAnSwadLLlQZAEAAAAAAEUS2viWTpZcKLIAAAAAAIAi8bqPb3xLJ0suFFkAAAAAAECReGPZ+DY/FFkAAAAAAECReNxsfJsfiiwAAAAAAKBIgp0sLBfKjSILAAAAAAAoktByoSyKLCejyAIAAAAAAIoktFwok+VCJ6PIAgAAAAAAiiS0XIhOllwosgAAAAAAgCLxunOKLD6/UbYvYHM2Zw6KLAAAAAAAoEg8sc7Q2+zLcgJFFgAAAAAAUCTBThaJE4ZORpEFAAAAAAAUSXBPFonNb09GkQUAAAAAABRJ8HQhic1vT0aRBQAAAAAAFInDYSmWY5zzoMgCAAAAAACKzBssstDJEkKRBQAAAAAAFFlwXxY2vj2BIgsAAAAAACgyz/EThtKzWC4URJEFAAAAAAAUmTc2uCcLnSxBFFkAAAAAAECRed3B5UJ0sgRRZAEAAAAAAEXmiWXj2z+iyAIAAAAAAIos2MnCcqETKLIAAAAAAIAi88RSZPkjiiwAAAAAAKDIQhvfcrpQCEUWAAAAAABQZCc2vqWTJYgiCwAAAAAAKDIPnSx5UGQBAAAAAABFRidLXhRZAAAAAABAkXmPb3yb5QvI5w/YnM2ZgSILAAAAAAAoMo/bGXo7gyVDkiiyAAAAAACAYgh2skgsGQqiyAIAAAAAAIrs5CILm9/moMgCAAAAAACK7OTlQul0skiiyAIAAAAAAIrB5XQoxpVTVsjIosgiUWQBAAAAAADF5D3ezZKeyXIhiSILAAAAAAAoJs/xfVnS6WSRRJEFAAAAAAAUk9edU2TJoJNFEkUWAAAAAABQTN7Y4HIhOlkkiiwAAAAAAKCYPG6WC52MIgsAAAAAACiWYCcLy4VyuOwM7vP51KJFC2VmZuZ6vFy5clqzZk2Bn79r1y795S9/0bBhw3TrrbeWVJoAAAAAACAfbHybm61FlpSUFGVmZmratGk677zzQo87HAU32BhjNGnSJKWmppZghgAAAAAA4FRObHxLkUWyuciyYcMGORwO9erVS16vt0if+8orr2jLli0llBkAAAAAAChIaOPbLJYLSTbvybJ+/XrVrVu3yAWWHTt26LHHHtPUqVNLKDMAAAAAAFCQ0Ma3dLJIsrmTZePGjXK73Ro2bJhWr14tl8ul3r176+6771b58uXz/ZxAIKB77rlHvXv31iWXXBKRPCxLio8vWqHHbi5XTrUwmnlHOyZjLBsxGWPpj2dHTMZYNmIyxtIfz46YjLFsxCzr8eyIyRjLRsyyOMYqlXLum5kdUHy815YxRoplhX8P25cLpaamqn///rrlllu0bt06Pf3000pJSdGLL74oK58RLliwQDt27NCcOXNsyBgAAAAAAASV89DJcjJbiyxPPPGE4uPjlZCQIElq3bq1qlatqrvuuksrVqxQhw4dcl2/ZcsWzZgxQ0899ZQqVKgQsTyMkQ4fTo/Y/aIhWBWMZt7RjskYy0ZMxlj649kRkzGWjZiMsfTHsyMmYywbMct6PDtiMsayEbMsjjHgC0jKKbIcPJSmypXKlWi8klS1avmwu1ls3ZMlOTk5VGAJ6tKli6ScLpeT+f1+3XPPPbr00kvVoUMH+Xw++Xw5lbJAIBB6GwAAAAAAREe52BO9G5lsfmtfkWX//v168803tWPHjlyPZ2RkSJIqV66c6/Fdu3bp+++/1+LFi5WUlBT6I0lPP/106G0AAAAAABAdnuOnC0ksGZJsXC5kWZYmT56sQYMGaeLEiaHHP/zwQzmdTrVs2TLX9TVq1NBbb72V5z7XXHONBg4cqKuvvrrEcwYAAAAAACd43SfKChzjbGORpUqVKrrhhhu0cOFClS9fXq1atdKqVas0Z84c3XDDDapXr54OHDig7du3q2HDhipfvrwuvvjifO9Vo0aNU34MAAAAAACUDO9JnSwZdLLYu/HthAkTVLNmTb399tuaO3euatasqTFjxmj48OGSpGXLlmnixIl68cUX1aZNGztTBQAAAAAAf+ByOuR0WPIHjNKzKLLYWmSJiYnRiBEjNGLEiHw/3q9fP/Xr1++099i4cWNJpAYAAAAAAApgWZa8sS6lpmcrI5PlQraeLgQAAAAAAEo3jztnyRAb31JkAQAAAAAAYfAeP8aZjW8psgAAAAAAgDB46WQJocgCAAAAAACKzRPsZKHIQpEFAAAAAAAUX3C5UAanC1FkAQAAAAAAxXdiuRB7slBkAQAAAAAAxRZaLkQnC0UWAAAAAABQfMFOlgw6WSiyAAAAAACA4qOT5QSKLAAAAAAAoNi87uMb33K6EEUWAAAAAABQfN5YNr4NosgCAAAAAACK7eTlQsYYm7OxF0UWAAAAAABQbMHlQsZImdlndzcLRRYAAAAAAFBsweVCkpSecXbvy0KRBQAAAAAAFJvneCeLJKWd5ZvfUmQBAAAAAADFRifLCRRZAAAAAABAscXGOGUdf5tOFgAAAAAAgGKyLOvECUMUWQAAAAAAAIovuGQojeVCAAAAAAAAxRc8xjk9M9vmTOxFkQUAAAAAAITFE+xkYbkQAAAAAABA8YU6WVguBAAAAAAAUHzBjW/pZAEAAAAAAAiD152zXIhOFgAAAAAAgDB46WSRRJEFAAAAAACEyRPsZKHIAgAAAAAAUHzBThaWCwEAAAAAAISB5UI5KLIAAAAAAICwhDpZKLIAAAAAAAAUX/B0oTSWCwEAAAAAABSf53gni88fULbPb3M29qHIAgAAAAAAwhLsZJHO7m4WiiwAAAAAACAswT1ZpLN7XxaKLAAAAAAAICwe94kiy9l8whBFFgAAAAAAEBbPScuF0lkuBAAAAAAAUDwOh6XY4AlDdLIAAAAAAAAU34ljnLNtzsQ+FFkAAAAAAEDYgpvfslwIAAAAAAAgDMHNb1kuBAAAAAAAEAZvbM5yIY5wBgAAAAAACIM32MnCciEAAAAAAIDi89DJQpEFAAAAAACEz8ueLBRZAAAAAABA+DycLkSRBQAAAAAAhI+NbymyAAAAAACACGDjW4osAAAAAAAgAtj4liILAAAAAACIgBMb32bbnIl9KLIAAAAAAICweY9vfJuVHZDPH7A5G3tQZAEAAAAAAGEr742RJFmW5Pcbm7Oxh8vuBAAAAAAAQOn3p6rldE3XhirncSnW7bQ7HVtQZAEAAAAAAGGzLEsDeyZIkg4fTrc5G3uwXAgAAAAAACACKLIAAAAAAABEAEUWAAAAAACACKDIAgAAAAAAEAEUWQAAAAAAACKAIgsAAAAAAEAEUGQBAAAAAACIAIosAAAAAAAAEUCRBQAAAAAAIAIosgAAAAAAAEQARRYAAAAAAIAIoMgCAAAAAAAQARRZAAAAAAAAIoAiCwAAAAAAQARQZAEAAAAAAIgAiiwAAAAAAAARQJEFAAAAAAAgAiiyAAAAAAAARABFFgAAAAAAgAigyAIAAAAAABABFFkAAAAAAAAigCILAAAAAABABFBkAQAAAAAAiADLGGPsTsJuxhiVtq+CZeX8Hc28ox2TMZaNmIyx9MezIyZjLBsxGWPpj2dHTMZYNmKW9Xh2xGSMZSMmYzyzWZZkBQdQ3HtQZAEAAAAAAAgfy4UAAAAAAAAigCILAAAAAABABFBkAQAAAAAAiACKLAAAAAAAABFAkQUAAAAAACACKLIAAAAAAABEAEUWAAAAAACACKDIAgAAAAAAEAEUWQAAAAAAACKAIgsAAAAAAEAEUGQBAAAAAACIAIosAAAAAAAAEUCRBQAAAAAAIAJcdicAAAAAAABKh507dxbp+nPOOaeEMjkzWcYYY3cSAAAgcgKBgD7++GN9+eWXSklJUWpqqizLUoUKFVS/fn116NBBl156qRyO0tvQunr1aj3//PPavn27LrjgAg0fPlwXXXRRrms2bNig22+/XR9//HHY8YwxWrlypTIzM9WuXTu53W599dVX+uc//6k9e/YoISFBt956q84///ywY51OIBBQjx49NGfOHF1wwQURu+8LL7ygyy+/XNWqVQs99tVXX+npp5/Whg0bVLlyZbVp00Z//etfI/ZkeePGjfrvf/+roUOHSpK2bt2qZ599Vhs3bpQktW7dWkOHDlXNmjUjEq979+4aOHCghg0bFpH7FYbf79eaNWt06NAhtWvXTnFxcUpJSdGMGTO0efNm1a5dW6NHj1azZs0iFjMQCOjtt9/WF198oR07dig9PV0ej0fx8fFKSkpS9+7d1apVq4jGY74pm/ONVDJzjh3zjRTdOceO+UaK3pyTmJgoy7IKda1lWfrpp5/CilfaUGQpJfbu3atvvvlGffr0kSQdPnxYCxcu1MaNG2VZllq1aqVrrrlG5cqVCzvW4MGDNWDAAPXu3TvsexXVrl27dOjQodAP7oEDBzRv3rzQpDBkyBDVqVMnYvFWrFiR50lIpUqVlJSUpM6dO0c0liStXbv2lE9C2rdvH9EnWXbYsWOHXnrpJW3fvl0XXnihrr/++jz/Uf3yyy/6+9//rueffz6icbOystSgQQNJ0ubNm7Vw4cLQE5HBgwerSpUqEYuXn0AgoJtvvlkPPfSQzjvvvIjd95NPPlHbtm1VsWLF0GObN2/Ws88+m+uJyM0336y4uLiIxIzmfCPZN+eU1flm3759GjFihDZs2KAGDRqobt26iouLkzFGx44d0/bt27VlyxYlJCRo3rx5uZ7klhYrV67UkCFD1KBBA9WrV0/ffPONjh07pnvvvVcDBw4MXff9999rwIABWr9+fVjx9u/fr6FDh4aeiNerV09TpkzRyJEjVbt2bV144YX67rvvlJaWptdee00NGzYMK9577713yo8FAgFNmDBBd9xxh84991xJ0uWXXx5WPEm66KKL9Prrr6tJkyaSpOXLl2vUqFGhF8kZGRn64osvlJGRoZdffjnsMa5YsUK33HKL6tSpow8++EBff/21Ro4cqYoVK6ply5bKzs7WqlWrZIzRK6+8EnY86cSLgi5dumjKlCmqVatW2Pc8nb1792rUqFFav369jDGqWbOmnn32WQ0bNkxer1dNmjTRjz/+qJ07d+qNN97I86K9OFJTUzV48GClpKTo/PPP1+7du3XkyBH16NFD+/bt08aNG3X48GH16tVLjz32mFyu8JramW9K/3wjRX/OifZ8I0V/zon2fCNFd8559913C8xl9uzZSk1NVZMmTfTGG28UO1ZpRJGlFPjxxx81dOhQeb1eLVu2TBs2bNCQIUOUlpamCy64QNnZ2frll19Uq1YtvfTSS2H/EAcnhRtuuEFjx46N2Au300lNTdWdd96pL774QpKUkJCg2bNn6+abb9b+/fvVoEEDpaSkyBijRYsWhf1iJDs7W3fccYc+++wzVa5cWUeOHJHL5VKTJk20a9cu7dq1S5ZlafDgwbrrrrvCHt+xY8c0fvx4LVu2THFxcapdu3auJyG//vqr0tPT1blzZz3++OMRe/EaTRs2bNDAgQPl8XhUq1Ytbdq0SR6PR48++qi6du0aui5ST0KknK/ruHHj9OWXX0qSWrZsqfvuu0833HCDnE6n6tSpo19++UWVK1fWa6+9FvZvJlavXn3KjwUCAd144436xz/+ESqytGjRIqx4Ut4nImvXrtXgwYPl8XjUokULZWRkaPXq1apSpYpefvnlsH/+oz3fSNGfc8r6fDN+/HitXr1a8+bNCxUe/2jz5s0aOXKkWrRoocceeyysePfff3+hr7UsSw899FBY8STp+uuvV7Vq1TRjxgw5HA4dOXJE9913n5YsWaL7779f119/vaTIzTf33HOP1qxZo6lTp8rlcmnq1KnaunWr2rZtq5kzZ8rpdOrYsWMaMmSIqlatqtmzZ4cVr3HjxvL7/ZJyfqP9R5ZlhR63LCsi82liYqLeeOON0Fxz1VVXqVq1apo9e3bohXhaWpoGDx6sqlWras6cOWHFu/baa1W9enXNmDFDMTExuvLKK1W1alXNmjVLHo9HknT06FGNGDFCcXFx+uc//xneAJUzxgkTJmjhwoU6ePCghg8frkGDBqlChQph3zs/48eP1w8//KCHHnpIlSpV0qOPPqp169YpMTFRzz33nNxut7KzszVq1Cg5HA7Nmzcv7JhTp07VmjVrNG/ePFWpUkU+n0+TJ09WTEyMHnzwQQUCAb3zzjt68MEHNXz4cI0ZMybsMUZzvnnggQcKfa1lWZoyZUpY8aSyP99I0Z9zoj3fSNGfc6I930j2zDn5eeedd/TII48oMzNTt99+uwYPHlyqO9mKxeCMN2jQIDNw4ECzf/9+Y4wxAwcONH379jV79uwJXbN9+3bTp08fc9ttt4UdLyEhwTzyyCOmVatWplOnTuadd94xfr8/7PuezgMPPGDatWtn3nrrLbNkyRJz1VVXma5du5q+ffuaAwcOGGOMOXTokOnXr58ZM2ZM2PFmzJhhevToYTZv3myMMebw4cNm5MiR5sknnzTGGHPw4EHz5JNPmsTERDN//vyw402ZMsW0bt3afPHFF/l+Lf1+v1m2bJlJTk42DzzwQNjxjDFm9uzZhf4zZ86csOMNHTrU3HjjjSYtLc0YY8zWrVvNddddZ5KSksySJUtC13333XcmMTEx7HjGGPO3v/3NdOjQwSxatMi8++67pmvXrqZt27ZmwIAB5ujRo8YYY3bt2mUuvfRSM378+LDjJSQkmMTERJOYmGgSEhJyvR987OS/IyEhIcF8//33ofevv/56c80115gjR46EHtu9e7fp1auXGTduXNjxoj3fGBP9OaeszzctW7Y077//foHXvffee6ZNmzZhx7vttttMQkKCadasmenatetp/3Tr1i3seMbkjHH58uW5HgsEAmbcuHGmUaNG5uOPPzbGRG6+SU5ODt3TGGPWrFljEhISzH//+99c13388cemZcuWYcf74YcfzGWXXWa6dOliPv/8c/Prr7+G/mzbts0kJCSYpUuXhh6LhD/ONUlJSWbZsmV5rvvkk09MixYtwo7XrFmzXF+/Ro0a5fk3NcaYpUuXmmbNmoUdz5gTYzx69Kh55JFHTOPGjU3Lli3NI488YtavXx+RGCdr3759ru+bnTt3moSEhDxf188++8y0bds2IjE7dOiQ6/9cY3L+H0xKSjKHDx8OPbZw4cKI/DxGe7659dZbTUJCgmnatKm55JJLTvunc+fOYcczpuzPN8ZEf86J9nxjTPTnnGjPN8bYM+ecbNeuXWbYsGEmMTHR3HjjjWbbtm0Rj1FasPFtKbBu3To98cQToeUOa9eu1dNPP60aNWqErqlTp45uv/12TZo0KSIxe/furUGDBunhhx/WPffco5kzZ2rIkCHq06ePKleuHJEYJ/v888919913q2/fvpKk888/X3369NHEiRND8eLj4zVy5Ej97W9/Czvev/71L40fPz70W5eKFStq0qRJuvLKKzV8+HBVqlRJY8aMkdPp1KuvvqrBgweHFe/f//637rrrLl1yySX5ftzhcKhz586688479eSTT0bkNy9Lly7V2rVrJeX/W4mTWZalUaNGhRVv3bp1mjZtmrxer6Sc9tYFCxZo5MiRGj9+vJ5//nm1bNkyrBh/9NFHH+nOO+8Mfd9Uq1ZNQ4YM0cMPP6zy5ctLkmrVqqVbb701It83M2fO1JQpU+R2u3XXXXflanv2+/0aMmSIHnjgAdWvXz/sWKfy/fffa8aMGbl+E1KzZk399a9/1dSpU8O+vx3zjRTdOaeszzcul0sxMTEFXudwOJSdnR1WLEl66qmn9H//93965ZVXNGvWLCUmJoZ9z4J4vV4dO3Ys12OWZWnatGnau3dv6OfT6XRGJJ4xRrGxsaH3GzVqpAsuuCDP/d1ud0RiNm7cWIsWLdKMGTM0duxY3XLLLRo5cqQcDkfot83Vq1cPte6XhBo1auS7lMSyLLnd7rDvX6FCBe3atSv0fu3atXXkyJE81x08eDD0/0qklC9fXhMmTNCgQYM0f/58vfnmm5o/f77q1q2rli1bqmHDhoqPj9fVV18dVhy/35/vb2//+D3idDqVkZERVqygo0eP5nnM5XLJ5/Np3759oaWnDRo00MGDB8OOF+355plnntHDDz+sN954Q88995wuvPDCsO9ZkLI+30j2zzklPd9I9s050ZpvJHvmnKBXX31V06dPlzEmV4fX2eos69spndxut1JTU0PvV69ePd8Xzaf6wSquP/3pT3rqqaf02muvKTExUX//+9/VqVMnDRs2TM8884w+/vhjffXVVxGJlZaWlmvPjEqVKklS6IVyUIUKFfKdEItq3759ee4dHx+vjIyMXBNwy5YttWfPnrDj+f1+Va9evcDrKleunOc/8uJ65ZVX1KdPH8XHx+uzzz7Thg0bTvknEq3m+X3vxcbGatasWapXr55uvfVWbdmyJew4J0tPT8/1dW3WrJnKlSuX5z/kypUrh54khKN79+764IMP1LRpUz344IPas2ePkpOTQ3+knCcqJ78faZUrV861P0tQxYoVFQgEwr6/XfONFL05p6zPNx06dND06dOVkpJyymu2bt2q6dOnq0OHDmHHk6S7775bLVu2LFIrfzhatGihWbNmae/evbked7lceuaZZ1S7dm2NGjVK//nPfyIW75lnntGBAwck5fycvPfee2rTpk3omtTUVM2dO1fNmzePSEy32627775bL7zwgt555x1dc8012rRpU0TufSqPPvqoHn/8cX3wwQdKTk7W888/n2vuPHDggGbNmqWmTZuGHeuyyy7T//3f/2nlypWSpNGjR+vxxx/X5s2bQ9f873//0/Tp09W9e/ew4+XnT3/6kyZOnKgVK1Zo5syZatOmjdatW6fHH39c9913X9j3b9WqlR577DGtWrVKGzZs0H333af4+HgtWLBAPp9PkuTz+fTiiy9GbE+2pKQkzZ07V2lpaaHH5s+fL4/Ho7p160rKmQPnz58fkT1g7Jhv7rnnHjVr1oz5JoLzTTBONOecaM43kv1zTknPN5I9c86OHTt000036cEHH1SzZs30/vvvn/UFFkksFyoNJk2aZLp162ZSUlKMMcbMmzfP9O3bN9TOb4wx27ZtM7169TJjx44NO94fW/iCdu/ebRYsWGCGDh1qWrVqFdElETfffLPp37+/2b17tzly5IiZMGGCadKkibnrrrtyXTdu3Dhz3XXXhR3vqquuMiNGjMi1JGH+/PkmKSnJZGZmGmNyWkHvuecec9VVV4Udb9SoUaZ///65WnX/6PDhw6Zv375myJAhYccLysrKMldffbUZOXJkxO55KiNGjDCDBg0yGRkZeT62c+dO07FjR9O5c2fz+uuvR+z75qabbjIjRowI/ZsFBQKB0Ns+n8+MHj3a3HjjjRGJGfTBBx+YNm3amNGjR5u9e/can89nEhISzLp16yIaJyEhwYwdO9a8/vrrZs2aNeb+++/PsywoMzPTDBkyxNx0001hx4v2fGNM9Oecsj7f7Nu3z1xxxRUmMTHR/OUvfzG33Xabueeee8zEiRPNbbfdZq688krTqFEjc+mll5rdu3eHHS9oy5Yt5sYbbzQ///xzxO55Ktu2bTOdOnUyjRs3NtOnT8/z8T179pjevXtH7HsmJSXFdOrUySQlJZm1a9fm+fjnn39uWrZsaVq3bl0i48/IyDBTp041TZo0MU8//bRJTEyM+FzzzDPPmDFjxpiePXuaiy66KPS1C7aZf/jhh6ZJkyYRG2NGRoYZNWqUSUhIML179zZjxowxHTt2NBdddJHp2rWr6dChg0lMTDR9+/Y97f+dRXGqueaP/H5/rjmvuHbs2GF69OgRWlJ62WWXmdWrV5uLL77Y9OrVy9x5552mR48e5uKLLzbfffdd2PGMMWblypUmKSnJdOzY0YwdO9Zcd911JjEx0cydO9cYY8ySJUtM8+bNTXJycr7fy0Vl13yzefNmM3DgQLNp06aI3fNUzrb5xpiSn3OiPd8YE/05J9rzjTHRn8dkyl8AABmFSURBVHNeeOEF06xZM5OcnGzeeeedCIyg7GDj21Lg0KFDGjp0qDZt2qS2bduqYcOGev/993Xs2DElJSUpKytLP/74o6pXr65XX301IhvfnrwZ1ans3LlT+/btK/C6wtiwYYMGDx4c+q1x9erVNXnyZN12221KTk5W8+bNtWrVKq1atUqzZ89W586dw4q3ZMkS3XbbbbrooovUsWNH7dy5Ux999JEGDx6sCRMmaMWKFZoyZYp2796t2bNnq2PHjmHF27ZtmwYNGqSjR48qOTlZ9evXV1xcnCzLUmpqqrZv366VK1cqJiZGCxcujMgu6kFr167V2LFj9eyzz0b0vn/0448/6qabblK5cuU0dOjQ0PF4QZs2bdLNN98c+s1MJLpn1q5dqyFDhsjr9WrevHl5limsXLlSkydP1s6dOzV//vyIL1fat2+f7r//fq1evVoTJkzQvffeq7feektJSUkRi3Hvvfdq48aN2rx5c6i107IszZ07V506ddIXX3yhyZMn68CBAxEZY7TnGyn6c05Zn2+knE6j999/XytWrNCWLVt09OhRGWNyHanap0+fiLVh2+Hw4cNavHixatWqpV69euX5eFpamp5++mktWbJEn376adjxDhw4oMWLF+vqq69WfHx8ro+tWrUq9G8a6VPpTrZy5UpNmjRJv/32m95+++2IzjUnS09P14YNG7Rx40Z17dpVNWvW1IoVK/T5559HfIxff/21Pv30U23cuFEHDhxQdna24uLiVK9ePXXq1ElXXHFFoZajFMZNN92kBx544JQbtJaEjIwMrVq1SpmZmerYsaPcbrdWrlypZ599Vjt37lT9+vU1evToiDyXClq9erXmzp2rrVu36pxzztFVV10VOg1m3bp1+vrrr3XFFVfkWgYaDuabsjnfSCfmnJ07d0b8+U1Qenq6Nm7cqA0bNqhbt26qUaNGic03UvTmHDvmGyl6c86AAQP0/fffq0KFCho9enSBJ4dF4hS80oQiSynh9/u1ePFiLVmyRD///HO+k8LgwYPzTMTF0a1bt6itqz/Zvn379NlnnykrK0u9e/dWtWrV9N5772nmzJmhSWHMmDERa+H76KOPNHv2bKWkpISehIwYMUJOp1P/+9//9NFHH2nAgAFKSEiISLwjR47opZdeOu2TkOuvv75E9ryJlh07dmjhwoVq0KCBrrvuujwf37Nnj/7+979r6dKl+uGHHyISc8uWLXr55ZfzneCXL1+ul19+WbfcckuJHo/9zjvv6OGHH9bRo0dL7ElIIBBQSkqK1q9fr40bN+raa69VnTp1tGTJEi1atCiiT9KjOd9I9sw5dsw3s2bNyvWipyTnG5RdaWlpWrdunRo3blwqT6IDULow5yA/RXnOFqlT8EoTiiwAos7v90dss7YzxZ49e7RixQp169YtYsUHIBxr167Vl19+qZSUFKWmpsqyLFWoUEHnn3++2rdvH7F17gXFq1+/vtq3b18ihc5oxzxdvA4dOpT5r2k0x1hS36enixnt7xu7xlgS/44Azi6//fZbka4vyY3az0QUWUoRn8+ntLS00KaXmZmZeuutt7R582bVrl1b/fv3z3dDzNISz46Y0Y6XkpKi5cuXa8eOHUpPT5fH41GlSpWUlJSk1q1b59kcsyRjNmrUSMnJyRGPGe14p4tZUl/Xs3mMZ8P3amn/mh47dkzjx4/XsmXLFBcXp9q1aysuLk7GGB07dky//vqr0tPT1blzZz3++ONh/2Yy2vHOhjHyNWWMpSkmAJzKtm3b9OWXX+qmm26yO5WooshSCmRlZWnatGl68803lZ2drc6dO+uxxx7TsGHDtHbtWlWtWlX79+9XrVq19NZbb6lq1aqlKt7ZMsaHH35YCxcuDJ0AExsbq6pVq+r333+Xz+dTxYoVdccdd0R0R+5ox2SMjLG0xCzL8R544AF9+OGHeuyxx9SxY8c8p0AFAgEtX75cd999t/r06RP2kfHRjmdHzLIez46YjLFsjHHOnDmFvtayLI0aNapUxbMjJmOMfDw7Yp4NY8xPRkaGVq5cqS+//FLLly/Xnj171Lp1a82bNy/isc5oUdxkF8X0xBNPmGbNmpknn3zSzJ8/33Tt2tX07dvXdO/e3WzZssUYk7Pzec+ePc2kSZNKXTw7YkY73vz580379u3NF198YdLS0syGDRtM//79zauvvmoCgYD56aefzIQJE0xiYqL517/+FXY8O2IyRsZYWmKW9Xht2rQxb7zxRoHXvfHGG6ZDhw6lLp4dMct6PDtiMsbIx7Mj5rXXXhs6ySQhIeG0fyJx8k6049kRkzEyxtIU0xhjfvnlF/PCCy+YoUOHmiZNmpgePXqYhx56yCxbtsykp6dHLE5pQpGlFPjzn/9sXnrppdD7P/zwg0lISMjzZHzx4sWma9eupS6eHTGjHa9nz555nvSsX7/etGrVKtfxw1OnTjVXXHFF2PHsiMkYGWNpiVnW47Vq1cosXbq0wOuWLFlimjVrVuri2RGzrMezIyZjjHw8O2L6fD4zbtw406ZNG/Prr7+Gfb8zLZ4dMRlj2YhZ1sf42WefmSlTppiuXbuapk2bmmHDhpkFCxaYlJSUEo1bWjgK7nWB3Q4cOKD69euH3g8eZfbHo1Nr1qypffv2lbp4dsSMdrxdu3bpnHPOyfXYueeeq6NHj2rHjh2hx7p3767t27eHHc+OmIyRMZaWmGU9XsuWLTVr1qzQEdX5OXLkiJ555hk1b9681MWzI2ZZj2dHTMYY+Xh2xHQ6nXrkkUdUu3ZtPfTQQ2Hf70yLZ0dMxlg2Ypb1MT7yyCNyuVyaMmWKVq5cqXnz5mnQoEE677zzSjRuaeGyOwEU7MILL9SLL76oVq1aye12a968eXI4HPr888+VnJwcuu7TTz/NVTgoLfHsiBnteOeee67effdddejQIfTY0qVL5XA4chV2li9fnufFWGmJyRgZY2mJWdbjTZw4UYMGDVKXLl2UnJys+vXrKy4uTpZlKTU1Vdu3b9fKlSsVExOjRx99tNTFsyNmWY9nR0zGWDbGKEkxMTGaPHmyxo4dq82bN6thw4YRue+ZEs+OmIyxbMQsy2P85JNPSuS+ZQUb35YCK1eu1PDhwxUTE6OYmBj5/X6NGzdODz30kPr166fmzZvr22+/1bvvvquHH35Yffv2LVXxzoYxvv7665oyZYq6dOmiLl26aOfOnVq4cKF69uypadOm6bvvvtP06dP17bffatq0abriiivCimdHTMbIGEtLzLIeT8r5TfVLL72kFStWaMuWLTp69Oj/t3fvMVWXDxzHP4BS5lFUZJiuxmV68AgCJRAokKiYNMpKPYq3mVBqSG6UaXlB/QNXQ4fG0px4BXEoOqkmKiKlhpeSeYkJOq2TkowDXs7xggee3x+u04+AX5rnx+Pz9fPa/OecA+/vB+fmnnG+B0KIZh/hmpCQgO7duz92S0ZPRlPrPRlNbtTGRiIi+hs571KiR1VZWSkyMjJEenq6qKysFEIIsXbtWhEUFCT0er0IDQ0VmzZtUrYno9nevXXr1omQkBCh1+vFwIEDxYIFC4TFYhFCCFFSUiKSkpJEaWmpw3oymtzIjao0td4jIiIiIjn4myyKa2xsRF1dHdzd3Vt8TJ8WejKa/89eU1MTzGZzu/782rvJjdpocqP6vZqaGhw5cgSXLl2CxWKBs7MzdDodfHx8EBoa2uIeVKr1ZDS13pPR5EZtb/T29kZYWJjyPRlNbuRGlZrUHO/JohCLxYJjx47BZDLhzp076NSpE7p16waDwQAPDw/lezKa7d27ffs2Tp8+3azn5uYGg8EAvV7v8J6MJjdyoypNrfZsNhuWL1+OvLw82Gw26HQ6dO7cGUII3L59GxaLBR06dIDRaMSnn34KFxcXpXpPw0b+TLlRlabWe9zIjar0ZDWpDe36ezP0r23atMn+Nha9Xi8MBoMwGAz2zzkfMWKEOHjwoLI9GU2t92Q0uZEbVWlqubdixQoREBAgcnNzhdlsbvG82WwWOTk5IiAgQKxcuVK5noym1nsymtzIjSr0ZDS5kRtValLreMiigN27d4vg4GCRm5srqqqqRHFxsYiLixN79+4VV69eFfv37xeTJ08WBoNBfP/998r1ZDS13pPR5EZuVKWp9d6QIUPEhg0b/vF12dnZIjo6WrmejKbWezKa3Oj4noym1nsymtzo+J6M5tOwkdrGQxYFvPHGGy3+wfz0009i8ODBwmazCSGEaGpqEnPmzBFGo1G5noym1nsymtzIjao0td4LCgp6qJvolpSUiIEDByrXk9HUek9Gkxsd35PR1HpPRpMbHd+T0XwaNlLb2uduhvRYLl261OL9+v369UNtbS0uX74MAHBycoLRaERlZaVyPRlNrfdkNLnR8T0ZTW5Uv+fv74+cnBzYbLY2X2Oz2bBp0yb0799fuZ6MptZ7Mprc6PiejKbWezKa3Oj4nozm07CR2sYb3yrAw8MDhw4dQnh4uP2xkydPwsnJCT179rQ/dvbsWbi7uyvXk9HUek9Gkxu5UZWm1ntz587FtGnTEBsbi6FDh8Lb2xs6nQ4AYLVa8euvv6KkpAQ1NTXIzs5WriejqfWejCY3cqMKPRlNbuRGlZrUOh6yKGDs2LHIzMxEY2MjoqOjcfXqVaxatQqRkZFwc3NDVVUVsrOzUVhYiI8++ki5noym1nsymtzIjao0td4LCAjAzp078fXXX+PgwYOorq5u9rynpycGDx6MxMRE+Pj4KNeT0dR6T0aTG7lRhZ6MJjdyo0pNaoPs9yvRP2tsbBRpaWmif//+9k+mmDZtmqitrRVCCPHtt9+KuLg4kZubq2RPRlPrPRlNbuRGVZpa7/2poqJC7Nu3T1y4cEHU1NSIa9euCavVan++rq5O7NmzR9mejKbWezKa3MiNKvRkNLmRG1VqUnM8ZFGI2WwWp06dEn/88YcmezKaWu/JaHKjNprcqG7PYrGIxMRE4efnZ/8ze/Zscf369WavKy8vF35+fsr1ZDS13pPR5EbH92Q0td6T0eRGx/dkNJ+GjdQ23vhWIT169EBQUBA8PT012ZPR1HpPRpMbtdHkRnV7q1evxunTp5GRkYFdu3bhgw8+QGlpKSZNmoTa2lrlezKaWu/JaHIjN6rQk9HkRm5UqUmt4z1ZFHDt2rVHev3j/ge+vXsymlrvyWhyo+N7MprcqH6vuLgYc+bMQVxcHADAz88PkZGRSEpKQlJSErZs2WK/EZ4jtHdPRlPrPRlNbuRGFXoymtzIjSo1qXU8ZFHAsGHD0NjY+NCvr6ioUKono6n1nowmNzq+J6PJjer3amtr4eXl1eyxwMBAfPXVV3j33Xcxe/ZsrFu37rEaMnsymlrvyWhyIzeq0JPR5EZuVKlJreMhiwLy8/Px/vvvo6GhAampqejQ4f/719bePRlNrfdkNLlRG01uVL/3wgsvoKysrNlHRgPAyy+/jPT0dKSmpuKTTz5BQkKCkj0ZTa33ZDS5kRtV6MlociM3qtSkNsi+KQw9nIsXL4qXXnpJZGVlabIno6n1nowmN2qjyY1q93Jzc4XBYBDLli0TP//8c4vns7OzhV6vFzExMQ658V1792Q0td6T0eRGblShJ6PJjdyoUpNa55KWlpYm+6CH/ln37t2h0+mQlZWFsWPHolOnTprqyWhqvSejyY3aaHKj2j1/f3907NgReXl5cHZ2RlRUVLPng4OD4enpiaKiIjQ2NiI5OVmpnoym1nsymtzIjSr0ZDS5kRtValIbZJ/y0MOz2Wzi2LFjwmw2a7Ino6n1nowmN2qjyY3q94QQ4tatW20+Zzabxa5du5TuyWhqvSejyY3cqEJPRpMbuVGlJv3FSQghZB/0EBERERERERGpzln2BRARERERERERaQEPWYiIiIiIiIiIHICHLERERPREs1gsqKurc+j3bGhowLVr1wAApaWl0Ov1MBqNaOtd1GazGSEhIYiKioLFYnHotRAREZF28JCFiIiInlhnz57FqFGjUFVV5bDveeXKFcTHx+PIkSMAgOjoaIwcORLl5eXYsWNHq1+zfPly3Lx5E4sWLYJOp3PYtRAREZG28JCFiIiInliVlZWoqalx6Pf8/fffcfny5WaPffbZZ+jcuTMyMjJw/fr1Zs8dO3YMe/bswciRIzF8+HCHXgsRERFpCw9ZiIiI6Knn6emJlJQU1NfXY8WKFfbH79+/jyVLlqBr165YuHChxCskIiIiFfCQhYiIiKS5ceMG5s2bh1dffRX+/v4YPnw4MjIycO/ePaxevRrz588HAEyZMgUxMTH2r/vxxx+RmJiIsLAwDBgwAJGRkVi0aBFu3rxpf828efPw2muvIScnByEhIQgJCcG2bdswZcoUAMD8+fOh1+vtr588eTL69++P/Px8nD17FgCwceNGXLx4ER9//DE8PDyaXfv169exZMkSDBkyBP7+/oiLi8OWLVta3NflzJkzSE5ORnh4OAYMGICIiAikpqba7wkDACtXrkRwcDD27t2LiIgIBAcHo6CgwEE/ZSIiImovLmlpaWmyL4KIiIieTjNnzsSJEyeQkJCA119/HS4uLti6dSvMZjNGjx4NIQTOnTuHGTNmYMyYMfDx8cHhw4eRmJgIDw8PTJo0CVFRUWhoaEBhYSHMZrP9LT0HDhzAqVOnUF5ejlmzZsHLywuDBw9Gjx49cPLkSRiNRkydOhV+fn4AAGdnZxgMBuzYsQOXLl1CVFQUUlJSEBQUhAULFsDJycl+3RaLBePHj0d5eTmMRiNGjRoFq9WKDRs24MaNG4iKigIAVFRUYMKECXBxccGkSZMwbNgwdOzYEd988w3Onz+P0aNHAwDKyspw/PhxHD16FNOnT0dAQACGDh2Kbt26tfPfCBERET2ODrIvgIiIiJ5OZrMZR48exdy5czF9+nQAwNixYyGEgMlkgp+fH4KCgrB9+3ZEREQgLCwMwIPfLnn++eexYcMGuLq6AgASEhJgNBpRVFSE9PR0e+Pu3btYvHgx3n77bftjVqsVa9asQVBQEN58881m1zRw4ECMGzcOeXl5SEpKgs1mw7Jly5odsADAunXrYDKZUFBQgL59+9qv4fPPP8f69esxbtw49OvXDzk5OXBxccHmzZvRtWtXAMD48eNx9+5dFBUV4datW+jSpQsAoLGxEVOnTkVSUpIjf8xERETUjvh2ISIiIpKiS5cueO6555Cbm4uioiLcvn0bAJCeno6NGze2+XVr167Fzp077QcsAFBfXw+dTmf/Hv9tyJAhj3Rdqamp6NmzJ3755RfMmjUL3t7eLV6zb98++Pn5wd3dHXV1dfY/I0aMAAAcOnQIALB06VIUFxfbD1iAB78F8+yzzwJAi+t91GslIiKiJwt/k4WIiIikcHV1xdKlS7Fw4UKkpKTA1dUVoaGhiI2NxejRo/HMM8+0+nUuLi4wmUzIzMzEhQsX8NtvvzW7v8nfubu7P9J1de3aFZGRkdi1axfi4+NbfY3JZML9+/cRHh7e6vPV1dUAHrwFqb6+HmvWrMH58+dhMplw9epV+31b/n7/lke9ViIiInqy8JCFiIiIpImPj0dkZCQOHDiA0tJSHD16FIcPH0Zubi7y8/Nb/Zq8vDwsXrwY3t7eGDRoEGJjYxEYGIgtW7agsLCwxetdXFwcft1NTU0IDQ3FzJkzW33e09MTAFBcXIzk5GT06tULr7zyCqKjoxEQEICSkhKsX7++Xa6ViIiI2g8PWYiIiEgKq9WKiooK9O3bF2PGjMGYMWPQ0NCAL774Aps3b8bhw4dbfM29e/ewfPlyhIWFITs7Gx06/PVfmczMzHa79t69e8NqtSIiIqLZ4/X19Th+/Di8vLwAAMuWLYOvry/y8/PRqVMn++v4yUFERETaxHuyEBERkRRVVVWYOHEiduzYYX/M1dUVBoMBwIPf6nB2fvBflaamJgAPbmR7584deHl5NTtgqaiowPHjxwEANpvtf3b//G2RP7/nvxETE4Nz587hhx9+aPZ4VlYWUlJScPHiRQAPPua5T58+zQ5Yrly5guLi4oe6ViIiIlILf5OFiIiIpAgMDMSgQYOwcuVKVFdXQ6/Xo7q6Glu3boWPjw/Cw8NRVlYGANi2bRtqa2sRHx+PwMBAFBQUQKfTwdvbG1VVVcjPz7cfyFitVri5ubXZ7d69OwBgz549EELgrbfeanZg8zBmzJiBAwcOYNasWZgwYQJ8fX1x4sQJFBYWYujQofYb2EZFRaGoqAhpaWkYMGAATCYTtm/fjrt379qvlYiIiLSDhyxEREQkhZOTE7KysvDll1+ipKQE27dvh5ubG2JjY/Hhhx/C1dUV4eHhGDVqFEpKSlBWVobY2FhkZmYiPT0dO3fuRENDA/r06YP33nsPvr6+mD17NsrKyjBy5Mg2u76+vpg8eTIKCgpw5swZhIWF4cUXX3yka+/Rowfy8vKwatUqfPfdd7hx4wZ69+6N5ORkJCUl2T/yeenSpejcuTP279+P3bt3o1evXnjnnXcQExODiRMnoqysDHq9/rF+jkRERPTkcBJ/v609ERERERERERE9Mt6ThYiIiIiIiIjIAXjIQkRERERERETkADxkISIiIiIiIiJyAB6yEBERERERERE5AA9ZiIiIiIiIiIgcgIcsREREREREREQOwEMWIiIiIiIiIiIH4CELEREREREREZED8JCFiIiIiIiIiMgBeMhCREREREREROQA/wHZbMFkTSLM8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratingVSsex = ratingVSsex.groupby([\"category\", \"startYear\"])[\"averageRating\"].apply(np.mean).reset_index()\n",
    "\n",
    "\n",
    "p = ratingVSsex.groupby(\"category\")\n",
    "actor = p.get_group(\"actor\")\n",
    "actress = p.get_group(\"actress\")\n",
    "\n",
    "print(actor[\"startYear\"])\n",
    "print(actor[\"averageRating\"])\n",
    "\n",
    "\n",
    "plt.plot(actor[\"startYear\"], actor[\"averageRating\"])\n",
    "plt.plot(actress[\"startYear\"], actress[\"averageRating\"])\n",
    "plt.xlabel(\"startYear\")\n",
    "plt.ylabel(\"averageRating\")\n",
    "\n",
    "plt.gcf().set_size_inches(18.5, 10.5)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: This graph showing each gender's averagRating through startYear. Movies that have male main character are generally having higher rate than movies that have female main chracter. Only in 1990 and 1994, female main character movie had higher average rating. However, we also can see that both gender of movie rating moving same. When actor's movie rating goes lower, then actress's movie rating also goes lower which means when something happen like economic depression things, movie ratings are affected together no matter what gender plays main character. \n",
    "\n",
    "There are some outlier for each gender. On 1988, actress's average rating is really low comparing to other actress's averag rating and actor's average rating is really high on 2018. Otherwise, movie that plays by male character have been rated higher than female."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Testing distribution\n",
    "\n",
    "To run some statistical tests on our data, we should check the distributions. `h_ac` and `h_acss` takes each actor's averageRating and actress's averageRating and we are going to use `h_ac` and `h_acss` for `normaltest` function to test for mormality of the distribution. `normaltest` return two values: (1) a test statistic and (2) a p-value.\n",
    "Save these values as `st_ac`, `p_ac`, `st_acss`, `p_acss`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.296868903599446\n",
      "3.2125856333588847e-06\n",
      "1.1065519571072222\n",
      "0.57506282772013\n"
     ]
    }
   ],
   "source": [
    "# TEST DISTRIBUTION\n",
    "h_ac = ratingVSsex[ratingVSsex['category'] == 'actor']['averageRating'].values\n",
    "h_acss = ratingVSsex[ratingVSsex['category'] == 'actress']['averageRating'].values\n",
    "\n",
    "# TESTING NOMALITY, (1) a test statistic and (2) a p-value\n",
    "st_ac, p_ac = normaltest(h_ac)\n",
    "st_acss, p_acss = normaltest(h_acss)\n",
    "\n",
    "print(st_ac)\n",
    "print(p_ac)\n",
    "print(st_acss)\n",
    "print(p_acss)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these results, and using an alpha significance value of 0.01(this sig. value is what we used in the class):\n",
    "\n",
    "Set boolean values (`True`, `False`) of whether each distribution can be considered to be normally distributed (set as `True` if the test supports it is normally distributed (or, more formally, we have not rejected the null hypothesis), and `False` if the test suggests the data is not normally distributed (we should reject the null hypothesis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reject p_ac\n"
     ]
    }
   ],
   "source": [
    "# Set boolean values\n",
    "if p_ac < 0.01:\n",
    "    print(\"reject p_ac\")\n",
    "if p_acss < 0.01:\n",
    "    print(\"reject p_acss\")\n",
    "    \n",
    "is_n_ac = True\n",
    "is_n_acss = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `p_ac` variable is rejected. our normaltest for `p_ac` isn't behaving as expected but `p_acss` is passed, so our normaltest method is supposed to return p_acss value more than 0.01. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Comparing each gender mean on averageRating\n",
    "\n",
    "actor's data: plot the comparison of the data and a normal distribution. This plots a histogram, with the hypothetical normal distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph for actor - averageRating\n",
    "xs = np.arange(h_ac.min(), h_ac.max(), 0.1)\n",
    "fit = stats.norm.pdf(xs, np.mean(h_ac), np.std(h_ac))\n",
    "plt.plot(xs, fit, label='Normal Dist.', lw=4)\n",
    "plt.hist(h_ac, density=True, label='Actual Data');\n",
    "plt.title('actor - averageRating Data')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "actress's data: plot the comparison of the data and a normal distribution. This plots a histogram, with the hypothetical normal distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph for actress - averageRating\n",
    "xs = np.arange(h_acss.min(), h_acss.max(), 0.1)\n",
    "fit = stats.norm.pdf(xs, np.mean(h_acss), np.std(h_acss))\n",
    "plt.plot(xs, fit, label='Normal Dist.', lw=4)\n",
    "plt.hist(h_acss, density=True, label='Actual Data');\n",
    "plt.title('actress - averageRating Data')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: Our `nomaltest` data was rejected and now you can see why it is rejected. threre is no value between 5.4 to 5.6 so it's `normaltest` return value so low. \n",
    "\n",
    "But you can still see mean value of actor's average rate.\n",
    "By comparing mean of 'actor - averageRating' and 'actress - averageRating', we can see actor's mean is higher than actress's mean on averageRating. actor's mean is around at 5.9 and actress's mean is btween 5.7 and 5.8.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Data Analysis & Results\n",
    "\n",
    "Now we are going to analysis the data to address relation between movie rating VS gender. \n",
    "\n",
    "Save the averageRating for actor to a variable called `h_ac`.\n",
    "Save the averageRating for actress to a variable called `h_acss`.\n",
    "\n",
    "Save these value to avg_`h_ac` for actor, and `avg_h_acss` for actress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average rating of actor is \t 5.90\n",
      "Average rating of actress is \t\t 5.81\n"
     ]
    }
   ],
   "source": [
    "h_ac = ratingVSsex.loc[ratingVSsex['category'] == 'actor', ['averageRating']]\n",
    "h_acss = ratingVSsex.loc[ratingVSsex['category'] == 'actress', ['averageRating']]\n",
    "\n",
    "df1 = pd.DataFrame(data=h_ac.index, columns = ['averageRating'])\n",
    "df2 = pd.DataFrame(data=h_acss.index, columns = ['averageRating'])\n",
    "\n",
    "avg_h_ac = (ratingVSsex.loc[ratingVSsex['category'] == 'actor', ['averageRating']])['averageRating'].mean()\n",
    "avg_h_acss = (ratingVSsex.loc[ratingVSsex['category'] == 'actress', ['averageRating']])['averageRating'].mean()\n",
    "\n",
    "# Print out the average rating \n",
    "print('Average rating of actor is \\t {:2.2f}'.format(avg_h_ac))\n",
    "print('Average rating of actress is \\t\\t {:2.2f}'.format(avg_h_acss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the cell above, it looks like there might indeed be a difference in the average rating actor vs actress.\n",
    "\n",
    "Now we want to statistically test this difference. To do so, we will use a t-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Science accomplished, there is a significant difference!\n"
     ]
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(h_ac, h_acss, equal_var = False)\n",
    "\n",
    "\n",
    "if p_val < 0.01:\n",
    "    print('Data Science accomplished, there is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this test passed significance, our code and data is correct so far. \n",
    "\n",
    "We created a new dataframe, called `df2`, which only includes data from 'actor' and 'actress' categories. \n",
    "And by using `pivot_table` method, we created pivot table, and assigned it to a variable `pv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          averageRating   R-squared:                       0.100\n",
      "Model:                            OLS   Adj. R-squared:                  0.086\n",
      "Method:                 Least Squares   F-statistic:                     7.241\n",
      "Date:                Mon, 10 Jun 2019   Prob (F-statistic):            0.00905\n",
      "Time:                        22:06:33   Log-Likelihood:                 36.205\n",
      "No. Observations:                  67   AIC:                            -68.41\n",
      "Df Residuals:                      65   BIC:                            -64.00\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept               5.9010      0.025    240.435      0.000       5.852       5.950\n",
      "category[T.actress]    -0.0941      0.035     -2.691      0.009      -0.164      -0.024\n",
      "==============================================================================\n",
      "Omnibus:                       19.110   Durbin-Watson:                   1.475\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               40.011\n",
      "Skew:                          -0.886   Prob(JB):                     2.05e-09\n",
      "Kurtosis:                       6.346   Cond. No.                         2.60\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "df2 = (ratingVSsex.loc[(ratingVSsex['category'] == 'actor') | (ratingVSsex['category'] == 'actress')])\n",
    "pv = df2.pivot_table(index=['category'], values = 'averageRating')\n",
    "pv\n",
    "\n",
    "outcome_1, predictors_1 = patsy.dmatrices('averageRating ~ category', df2)\n",
    "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we printed out ordinary least squares(OLS) to see if we can recover the coefficient we used to simulate the data. For this we used the libarary `sm`(statsmodels).\n",
    "\n",
    "The actress variable is significant according to the t-test and our P-value is less than our confident level which is 0.01. It means there is a statistically significant relationship between the `averageRating` and `actress`(`averageRating` significantly predict `actress`).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Grossing VS Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "### -Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2587d400>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_grossing['AdjBoxOffice'], bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram above, we have noticed that the values in AdjBoxOffice are not normally distributed which can hurt prediction ability in some scenarios. To address this, we will perform a log transformation on the AdjBoxOffice values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grossing = df_grossing.assign(AdjBoxOffice10 = df_grossing['AdjBoxOffice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we add a AdjBoxOffice10 column transform the AdjBoxOffice column using a log-base-10 transform. That is, replace each value in AdjBoxOffice10 with the  𝑙𝑜𝑔10  of that value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>imdbID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>AdjBoxOffice</th>\n",
       "      <th>LeadActor</th>\n",
       "      <th>Gender</th>\n",
       "      <th>AdjBoxOffice10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2488496</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>2015</td>\n",
       "      <td>934381231</td>\n",
       "      <td>Daisy Ridley</td>\n",
       "      <td>f</td>\n",
       "      <td>8.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0499549</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009</td>\n",
       "      <td>843234239</td>\n",
       "      <td>Sam Worthington</td>\n",
       "      <td>m</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>2015</td>\n",
       "      <td>654709324</td>\n",
       "      <td>Chris Pratt</td>\n",
       "      <td>m</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>tt0183659</td>\n",
       "      <td>Pollock</td>\n",
       "      <td>2000</td>\n",
       "      <td>10085976</td>\n",
       "      <td>Ed Harris</td>\n",
       "      <td>m</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>tt0345074</td>\n",
       "      <td>Connie and Carla</td>\n",
       "      <td>2004</td>\n",
       "      <td>10075024</td>\n",
       "      <td>Nia Vardalos</td>\n",
       "      <td>f</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>tt1742650</td>\n",
       "      <td>I Don't Know How She Does It</td>\n",
       "      <td>2011</td>\n",
       "      <td>10048401</td>\n",
       "      <td>Sarah Jessica Parker</td>\n",
       "      <td>f</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2020 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         imdbID                                       Title  Year  \\\n",
       "0     tt2488496  Star Wars: Episode VII - The Force Awakens  2015   \n",
       "1     tt0499549                                      Avatar  2009   \n",
       "2     tt0369610                              Jurassic World  2015   \n",
       "...         ...                                         ...   ...   \n",
       "2017  tt0183659                                     Pollock  2000   \n",
       "2018  tt0345074                            Connie and Carla  2004   \n",
       "2019  tt1742650                I Don't Know How She Does It  2011   \n",
       "\n",
       "      AdjBoxOffice             LeadActor Gender  AdjBoxOffice10  \n",
       "0        934381231          Daisy Ridley      f            8.97  \n",
       "1        843234239       Sam Worthington      m            8.93  \n",
       "2        654709324           Chris Pratt      m            8.82  \n",
       "...            ...                   ...    ...             ...  \n",
       "2017      10085976             Ed Harris      m            7.00  \n",
       "2018      10075024          Nia Vardalos      f            7.00  \n",
       "2019      10048401  Sarah Jessica Parker      f            7.00  \n",
       "\n",
       "[2020 rows x 7 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grossing['AdjBoxOffice10'] = np.log10(df_grossing['AdjBoxOffice10'] )\n",
    "df_grossing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can draw the histogram again to check whether its distribution is enough for us to do further exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c24e8a2b0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_grossing['AdjBoxOffice10'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the histogram of AdjBoxOffice10, it is ditributed normally now, which is easier for us to further our exploration. Then, we classify the data frame to different gender, and explore their different movie number distribution. Also, check the mean gross of both genders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c258f2710>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gross_f = df_grossing[df_grossing['Gender'] == 'f']\n",
    "df_gross_f['AdjBoxOffice10'].plot.hist(bins=12, alpha=0.5)\n",
    "f3 = plt.gcf()\n",
    "gross_f_mean_10 = df_gross_f['AdjBoxOffice10'].mean()\n",
    "plt.xlabel(\"Movie Gross of Female leading actor\")\n",
    "plt.title('Female leading actor gross distribution')\n",
    "plt.axvline(gross_f_mean_10, color='black', linestyle='dashed', linewidth=2)\n",
    "plt.legend({'Mean':gross_f_mean_10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: From the histogram above, we can see that most female leading movies' grossings are in range 7.5~7.8 of log-base-10 of original box office, which is not very high when we transform to original box office, and it also leads to the mean of movie grossing becoming low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c25919588>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gross_m = df_grossing[df_grossing['Gender'] == 'm']\n",
    "df_gross_m['AdjBoxOffice10'].plot.hist(bins=12, alpha=0.5)\n",
    "f4 = plt.gcf()\n",
    "plt.xlabel(\"Movie Gross of Male leading actor\")\n",
    "plt.title('Male leading actor gross distribution')\n",
    "gross_m_mean_10 = df_gross_m['AdjBoxOffice10'].mean()\n",
    "plt.axvline(gross_m_mean_10, color='black', linestyle='dashed', linewidth=2)\n",
    "plt.legend({'Mean':gross_m_mean_10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: From the histogram above, we can see that the distribution of male leading movies' grossing is flatter then female's and obviously, there are more movies have high grossings compared with female leading movies, which let the mean become higher than female's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it may seem not so clear how big difference is when we separate gender in two histograms. In order to deal with this problem, we use a density plot and a grouped bar plot to put female and male leading movie gross distribution together, to explore the huge movie number difference between gender."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Density Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of different genders\n",
    "gender = ['f', 'm']\n",
    "\n",
    "# Iterate through two gender\n",
    "for gender in gender:\n",
    "    subset = df_grossing[df_grossing['Gender'] == gender]\n",
    "    \n",
    "    # Draw the density plot\n",
    "    sns.distplot(subset['AdjBoxOffice10'], hist = False, kde = True,\n",
    "                 kde_kws = {'linewidth': 3},\n",
    "                 label = gender)\n",
    "    \n",
    "# Plot formatting\n",
    "legend = ['Female', 'Male']\n",
    "plt.legend(legend, prop={'size': 16}, title = 'Gender')\n",
    "plt.title('Density Plot with Different Gender')\n",
    "plt.xlabel('Movie Gross under log-base-10')\n",
    "plt.ylabel('Density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: From above density plot, we can clearly observe the defferent distribution of female and male leading movie grossing. Blue line represents female leading movie grossing, orange line represents male leading movie grossing. What we can find is that female leading movies have a an obvious mode which is near its mean. However, male leading movies has a flatter distribution which cause them have a higher mean value. We will explore the difference more using grouped bar plot and t-test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Grouped Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAEtCAYAAACcfsP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XeYFFXWx/HvMEMeAcUA6qooelgTiLgosiquYngxu6bVVV9zXhVd4ysGjGBOYBbzmgO6iiJmXcUBXfWou2YFFGHI6jDz/nGroafpng7TPd0z8/s8D08z1bfqnq6uqj5169atsrq6OkRERERi2hQ7ABERESktSg5ERESkHiUHIiIiUo+SAxEREalHyYGIiIjUo+RARERE6qlIV8DM7gQOBmqBHu7+Y4pyU4CNgbvc/ZA8xhhb/svAWu6+Vp6XuxNwBNAf6AlUA28DN7n7+HzWVQxmNgI4D+jl7l+a2SHAHcAQd385KtMPuBXYEJgG9AL6Jk5z91Zz32uy9ZSn5a7t7v/N1/JaukJ9D4WUaczJ9rtS2ccSjxulsqzG1Nsct6VkmuoYkjY5iNMGGEZYufWYWS9CYlBII4HO+VqYmbUDxgCHAJOBW4AfgB7A/sAzZjba3Yfnq84S8QpwEPBx3LRbgT7AWcA0d68zs2WmNXmkLYyZ/ZOwjR1S5FCkNGgfa1rJjn3NipmNAdYDhhS6rmySgy+A3UiSHAB7AD8CK+UjqGTc/YU8L/J8wkH6XGBk/I5pZpcBtwGnmtkz7j4xz3UXTZRxJmadGwNPufuVaaZJ4wwF7ip2EFIytI81oRTHvuZmB+DLpqgomz4HTwDbm1nHJO/tATyZn5AKz8zWAoYDk9z9osSM3d0XAycC84Ejmz7CJtcWmJvBNBHJH+1jUrKyaTl4HPgbsB3wVGyima0MDAIuAQ5LnMnM/ki47rN5NOkdYIS7vxK9fxPhmn/P+P4MZtaJ0BrxoLv/b7I+B2a2PuFywxCgHfA+cIG7/zPNZ9mH8NkvTVXA3eeY2Y7Ah3H1vQwsAt6N1sUC4E/u/oGZbQRcCGwDtAemAJe6++Nx87cHLgN2BVYDZhCSqnPcfVZUpozQmvEXYE1CH4jngbPc/ZuGPpSZrQNcDmwLLCZcNqlJKHMI0XU3YC2WtgQdbGYHA4cmm+bud5pZB+CcKLbVgG+Be4CL3P3XhOXvDYwCVgEud/cRWc7fD/g7sBPhu5oAnBx/zTK6NHRWtLzVgW8ILT6jogSPTOpMY1Uze4yQsc8FHgLOdvc5cXE0WEeUjH6RsE5PBq6KPtPVcct6D9gEWMndZ0bT+hG27X3d/SEzaxPNfwShf8hPwMPAuQlxpS1nZtsAEwmtGrsDfwYqgTeBU9x9SqoVEzfv9sB+wF6EE45HgeOBrYGLCc2gn0bLeylu/t6Ebf1PwMrAPOB14Ax3/3cD9eb8nWZSZ5bb4MqEfXoY4Rj0AOEyZUMxxJYPjdvH+kWfZQfCcelO4AzgQMJ+sTpQBRwb/z2aWX/gbGAwsAIwK/psp7v7tw3EvTxwAbAnsCLhLPxm4NpsL4lkuqxMY8322OfuL2f5PXch/MbtCXQBXiR8768RfXcNfNYehO90Z8J3ugh4j7Afvh5Xrgw4gXBCug6hH8qDhN+0BWYWWy9rRv9fUq+ZHRbN24dwnHqecJz6Mnp/LcIx6GTCsXkz4FV33y5V3Nm0HLxGOLjsljB9N8IZ9ouJM5jZrsDLwBqEH84Lo/+/GL0HcC9QTjiwxNsF6BS9v4zox/hNYH3CAehsQiY+3sz2TfNZtiF0sJzUUCF3f83dZydMHkzok3AaYWf8yMw2A94CBgKjCTtmO+AxMzsubt7rCQfqB4BjCQfqIwkbQMxZhGTqOeA4Ql+I3YHnzaw8VaxmtgrwBmHnuIqw4R4AnNTAR4xdgwN4Nfp/0mlR3U8DpxISmhOBlwjr/ZFow453B+G7Ow+YkMP8TwLLR+tjDGF7eCihzOPR8t8mbPSvExK+K6J1km2dyYwlHLz+Tmg9Ow54IjZvhnX8yLLr9H7ge8L3RbSsboQDVRlhO4vZgXCgez76+zbCgfD1qL5/AEcDL0U/LmRZDsL17/6EffQyQjI/3swyOYG4k7BfnwGMJ1yuexwYBzwGnEnoy/Nw9Blj2+tbwB+B6wj7w32EJOWJKLFZRmO+0xzqbHAbjNbhJMLx4HbCtjiQsP4akq997BnCdnEq4cdmeDTtEsJ3fyHh0sXDse8xOm6+BvSOyh0HPEtI7salCtjMOsfFfRfh5OhD4GrCcS1jmS4r01hzPPbFS/c9lxOOx0cR9qEzCMeEtK3lUUv7q4QT0jsJ29zNwADC/rVcXPEbgGsIP+KxbWA4ITmEsL5+Aj5h6bEaM7uCsP/+RPhdupXwu/xOlBTEuwj4jrDOk/62xmTccuDui83saWAXM2vj7rXRW3sAz7j7L2a2pHy0Md4QBTIg7kxlDGFDuNHMniUcuL4inLHcHFflvoTOW6mu919HOOj2d/f50bKvI+xM15jZYw2cRawOzHT3hfETo529Msln/ynuz87AYfG9XaN6a4HNYtls1CLyOnCFmT0YLeMvwO3uflbcvPOAHc2s0t3nRWWedfeT4sp8AxxDONP/T4rPdBqhz8cAd58czXcXcS0fST7Xf4H/mtk44L/uHtsIl5kWZdl/AnaMb5kxs3cIO9SuhB/PmEfd/Zy4ctnO/6677xVXrjNwtJmt6+6fWbjLZCdCdnxxVOxmM2sLHGdmFxJ2kGzqTGYK4UyjJpr3O2AE4UzxKcJO2mAd7v4EcE+SdfocsJeZlUctHVsRtqMfo//HYtsBeN3dZ0dn64cAR7v7mLj6xgP/JBzArsm0XNznnA4MjmtxWURItIYA6fr7fB99/lozu4WQfG8H7OTuz0XLm09IdDeLlncI0D2q85O4+OYSDr79SH4GnnZ9k/o7zbbOBrdB4HDCmdoesRbC6PO/A3RNEUPK/S6HfeQtd98vKvMgYbvZHtg4rhWkkpBc9AI+I/w41RG26Z+j5YyNWuH2M7MV4qbHO43QAjTA3T+Ipt1kZhcDZ5rZ2IZamXJcVqaxZn3sS5Duez4A2AI4wt1vjcrcTEhc/pBm2bsSkpvE7zTWUrI98KiFVvCjgVvc/ci4cnOBs81sfXe/x8wuAqbHbTO/JyQSjwF7xVpdzOxxwsnzZYTf0pgfgAPd/bd0KyXbcQ4eJzTFbR4F0IWwMT+WpGx/wo/w9fFNndGZ+PWE5pUB0Ye5D9jaQhNdbLk7AffHJSFLmFl3QpPleKCjma1oZisC3aJYViEchFJpQzg7S3Q4YQdL/BdvIVHGFsWyCuFsYVx8M5e7LyKcwXYkbAAQmgj3NbNDYmdQ7n6uu28WJQaxMkPM7KRo2bj7GHfv5+6pEgMI6+tfsZ0jmm8G4Qw1H/YirIv3Yus7WufjCc14wxLKJ17ayXb+xFaCqui1R/Q6jPBDmnjWMpxwkJ+TQ53JXBVLDCLXRq//k+Pnivcs4Udk0+jvIYQzwFcJZ7exg/uWhDPCWH11hLOO+PomE5ohh2VZLuaRWGIQSVzfDXkitp9Gr/8BFsYSg0jsskrPqNxlwCoJP9IdCesMkiTpcZ8rp/WdQ53ptsGdCAfqJZcOoxOVW1PFkEa2n23JcdfdqwmXKT9NuCRTb70TfnDXik8AouPtoujPhtb7h8APCbHFPnsm+1K2y8o01sYe+9J9z3sQLmcs6Ywf/bim7Ujq7g8SfjNjrX6xy6Exsc/wP4TfpGupbxSh9efzFFXsEs13afzlGHd/O6pzmNVv/Xspk8QAsutzQFTZAkI29AbhGkotYeNN1CsWZ5L3YreSrEnIbu4lND3uScimdgM6kLrZY53o9YToXzJrEM7ck/ke+L2ZtU1YUY8TmmxiTiM0OcabmZCwrBW9pvucEM7+HyJsZLeY2ZuEHfz2aOeG8OP2FKGJ7SoL16CfJGSU01J8nlgcyc6YPkkyLRfrELLzpONcENZ3vBmNnD+x3C/Ra+zSylrAjPjEEyBaR9NgyXXIbOpMpt76c/dZZjaLpd97Y+p4gdAsvC3hbHMIIamaBuweJQax/jSx5GAdwsHg6xTLnJNluZh067sh0xP+rkmyvNgPcPwJSbvoTGhTwtlVr7j6Up24NPY7zabOTLbBZL3fc93nsv1sydZ74n5Xb717uE25u5mdSfjRWYdwfCqLL5cito5ZxNaQjJaVRaxr0bhjX7rveV3gi4TkOZvl1wJnmNkgwmfoTbgEDvU/A4TWnSWik+nES9vx0v3O7kC4BBKTuH2klFVy4O4LzewFwo/3GYSM6oW4s954DV3Pja2QX6Pl/tvMphKuy9xMaAbx+EwwQexLu4Gl2WailB2aCInN9oTWhwmxidGZf3wnlwOTzJu4gWTzOV80szUI2d4wQuJxJXCymW3q7j+6+1QzWxfYMSq3I6HjzilmtkX8WU+COkJClSqGxipnabNkMrMS/k5cT9nOv0yLUZLlZVImmzqTSdbRqg1LP1/Odbh7dZQgbmtmYwkHwLMITX8VhKbMHYCv3P2juPrmEhLpZBZmWS4m3bpsSE2SaQ12UDOzTQnX6xcQ9sHbCa0a6xD261RyXt851JluneR7n8v2s+Wy3v+H8EP6PeES7LOEDtY7EE7QGortNcIt4Ml831C9uSwri1gb+z2k+55T3VWyKMm0esxsTUI/l86Ek+sHCC0TZdT/7Yr9pmU71kWmvz+x9ZN4XE4p25YDCB/oDjPbkNCck6rTx5fRax+WzepinRPie9/fC1xsZmsTfrgvaiCG2LJr3H1C/BvRtZtehANAKvcTevmeTFxykKNYLH2SvLfkc1q4U6Ef8K27PwA8YKED1CmEyw/7mdmNhJEJ57j7k0QdXsxsH0KnxSMI15eS+S/hOl6itbP+RMl9SehE81J8y4mFa/x7Uv+7LMT8ib4m3FpbGZ+cWujdfCrhLpZ81LkWcYmmma1EuBQQu8TT2DrGE7bF7QgHqdcJB6LZhH4HO7C01SBW31DCddJ6ZxRmthcwM8tyxXIF4QxtA69/l9JZqWcBGre+c60zlf8CW5lZRcKlp1z3uS/J7z6SzHWEBGRAdAkkVsdfMohtuSTH2+UJl5Y/SzZTI5eVaayFPvb9F9jMzMq8/l0Z62Yw73mEywp9ov4LAJjZAQnlYi186xA3SJOZrUY4gbzO3V9Lsvwvo9c+hI7Z8Yxws8Aswh0WWcklw32KkH2MItxNkKrH5nuEM6Bjo+tEwJJrRsdG770XV/7+KJ5rCM2o96UKwN1/IGSQh5jZqnHLbks4G3iYBhIfd3fCgWJnM7vYkvSMNrOhhOSnQVEz9rvAgWa2etz87Qg//L8Qmo9XIFxCOTNu3lrgX9GfiwnZ40TCJYV4b8eVSeVRYAMLt1/GYujK0l7RjfUk4TMckzD9aEI2nPKWmDzNn2g8YXs5ImH6MYSWp2l5qvPwhL9jI2bGsv5s6qhl2X3uWcJ+NByocvfqaLt4jdARqjf1k4PY/nZ2/ELMbBfCdn9AluWKpTvhslD8j3RXlo4emWr/bcx3mmudqTxKSBSXbCPRMSjXsVHyvY8k053QEhX/Y/s7lrYwNbTe+0Zn8/HOIfTg3zCLGDJdVqaxFvrY9xihaX6fuOW3IXwv6XQn/EB/FTdvu7h5Y58hdmk+cZmHRPXGWi4WU/8YEhtW4O8WdzdLdJK0PeFmgZxG3sy65cDdZ5rZa4Qzmoke3YudpNxvZnYC4Rr7uxaG44WwI60K7B2fHbv7N2b2CqG5/a00ne9g6W0+70Vn3DMJtxQNBM5MFVeccwmdQc4E9jSzfxCysJUIzfmDCNdlUzXxJYvlX1Escwn3Gm8KnBi7bmRm9xKSpc6ESxvdCfeDTwce8nBP/LXAORburX+O8MNxJKEl5PYGYhgd1fmomV1NuLZ0FA03O2XjVsIzNq6LNrx3gI2iOiaTfOTMfM6f6ElC0jXazDYgJFmDgL8S7gv+OdrmGlvnHy30/B1P6Bj4V8J3FbtfP5s6fgS2MbMjgH+6+9fuPsXMvid0oI3v4PQyYV9YSP07dsYTWuKGR61sLxBaN44nnH2MyrJcsTxLOKA9RGhu7UE4NqwSvb9civka853mWmcq4wj75vVRi+WnhH0wk06cyeR7H0nmWUKn6JsJ+8zahAQ7NjR9qnVwCaEj4aPRvP8m3G57ULTMZ7OIIdNlZRproY99dxJ+tMeZ2RaE1oy9WDp2T0M/vs8S+ug9E/3GdCV8x7F+c8sBuHtVdLw6MTrhfRHYIKr37rg7QX4kJFbHEAbx+3f0m3Ei8EJ0rOpJ6Is3i3D5Pye5XhuLnTU92lAhd3+E0LT5PaF55SxC79kh8T1848Q6IKZsNYhb9puEg/W7hGbkKwgbzSHunnJwo7j5f3P32EAt/yKcSd0AnE44w/s7oafsTVnEErvX+CLC9ajd3f26uKJHEu49HkTolTqc0Iw82JfeLnkeocWhN2GjP4+o+bKB/ga4+1zCDvYwYccYQbir4oJ08WfC3X8hNPmNjl6vJfx43QQMdfeGLuM0ev4ky6sl9H25lJAhX0Nokj2O6Fpmnurcj9B56ppoGRcRDkS5fK6/E65fXkfY7mJivfpfiZsWG4NjosfdchudBfyZcJa1YRTXgcAjwB/dfXo25YpoBCFB2YKwPg4lJDD9CPvftslmauR3mlOdqUQd1HaI6t6HsC1+TUjAspbvfSSFYwhjIOxGWAd7A3dH9UHq9f4zYb3dSdiuriX8OF5IwoleOlksK6NYm+DY9xvhe76XkMBcThicLtbC80uKWSHcgnoWIbG5lrBtfEjoX/QT9df3UYTfn76E/XVHwrEsvnX0PMKP/tWEPn8Qxiw4jpDkjiYMRvgYsKm7f0GOyurq9KwPERGRZMxsBWBu4i2AUd+dhwmj5L6UdOZmLF892UVERFqik4AF8X3KIvsR7hh5v+lDKjy1HIiIiKQQ9WmaTLhD6RZC/6+hhI6RF7n7uUUMr2CUHIiIiDTAzAYSrvcPIPRt+xS40d1vKWpgBaTkQEREROrJZRAkyV0NoZ9H4tC1IiKSWhfCHSX6zWoiajloWrV1dXVlma7ysuguXX1F2dO6y53WXe607nLX0LorK4OysrI61Im+ySgLa1pz6uroOnNmskdRLKtr144AVFcnDoMv6TTVupsy5X369t2k0WVKiba73Gnd5a6hdde9eyVlZWpxbUrKwkQaYfvtt85LGRGRUqLkQEREROpRciAiIiL1KDkQaYThw9M/1ySTMiIipUR3KzSt2bW1deqQ2AS07nKndZc7rbvcpeuQ2KZNWTXQrYnDarV0t4JII2y00Xp88MGnjS4jufv110UsXDif2trF1NYW92Rn7tzyKKbFRY2jOWjTpow2bcrp2LEz7dp1KHY4kkDJgUgjTJ8+LS9lJHt1dbVUV89k0aIFlJW1oby8grKy4l4pralRUpCpxYsX8+uvv7Bw4Tw6dOjEcsutTps2utJdKpQciORR587tqahY9gAXazJNpqamlvnzG3okvCSzcOF8Fi1aQOfOXams7FL0xACgvDyM5LN4sS7XZqKurpZ58+Ywf341c+fOoWtXXTUoFUoORBph44371fu7oqINC2rnUTWtasm0dTdclze+fy3p/P169KNTRWVBY2ypFi1aSHl5Wyoru1IWG15PmpWysjZUVnZl0aIFzJ8/X8lBCVFyINIIEya8ssy0qmlVDLlryNIJe1P/7zgTD57IoFUHFyq8Fq2uro42bcqVGDRzZWWh70FtbW2xQ5E4xW+HE2nGTj31xPSFnix8HCIi+aTkQKQRxo27M32hyQUPQ0Qkr3RZQURanFQdQwstdokjNn6MOptKc6XkQERanIqKNrRjHsyqSl+4UJbvBzl2Nh05cgTPPvt0yvevuuoGNttsYK6R5cXxxx9JeXkF11xzY1HjkMJQciDSCFOnevpCpxQ+DkliVhW8mLwjaJP400RYPvfOpiuvvAoXXHBp0vd69eqV83JFMqHkQKQRpkypokePng0X+gHo0iThSAvStm1bNtxwo2KHIa2UkgORRjjooH2ZMWNOw4XuB0YUPpZcrrPrmnjz9uSTj/HQQ/fx3XffsuKKK7Hrrnty4IEHL+n7MHLkCKqrZzNw4CDuv38cs2b9TN++m3D22SN4883Xufvu25k162fWX38jzjjjHHr2XBWAhQsXcscdt/DKKxOZPn0abdu2Y8MNN+LYY0+id+91k8ZSW1vLPffcydNPP8GPP86gZ89VOeCAgxg2bPcmWx+SP0oORFqIZAMwNUQDMJW+mpqaZaaVl4exHcaNu4OxY29kn332Z+DAQXz88b+57babmT17FieccPKS8lVV7zNz5kxOPvl0Zs+exejRl3LCCUfRrl17jj/+ZObMqeaaa0Zx1VVXcPnlVwFw4YX/x4cfTuWoo45j1VVX49tvv+HWW2/m/PPP5u67H0w6tsSoUZfw7LNPc/DBh7H++hvyzjtvcdllI1m0aBF7771f4VaSFISSA5EWZJkBmBqgAZhK23fffcs222y+zPThw89gu+125K67bmPPPf/MCSeETi1/+MPmdOzYiRtuuJo//3l/evToAcCCBfO58MJLWXXV1QCYNGkib7zxKg8++DirrbY6AJ995kyY8E8AfvnlFxYtWsTJJ5/GkCHbAbDJJpsyf/48rr/+ambPns3yyy9fL6avv/6Kp556nGOPPYn99z9wSTy1tYu59dabGTZsdzp00MOVmhMlByKNMGrUNekLDSt8HNLyrLzyKlx88RXLTF9llZ58+OFUFi1axODBW9VrXdhyyz9y7bWjmTz5X+y88y4ALL/8CksSA4AVVliBbt2WX5IYAHTp0pV588Kj5Nu3b8+VV14HwI8/zuCbb77m66+/4o03whDgNTW/LRPT5Mn/oq6uji23/GO9eAYP3pqHHrqfjz76kP79BzRmdUgTU3Ig0gh//euh6QvpmCg5aNu2LX36rJ/0vTlzqgE4+eTjk77/008/Lvl/p06dlnk/3Vn822+/ybXXjuarr76kU6fO9O69Lh07huXExnCIV10d4jnggL1SxPNTg/VJ6WmxyYGZtQGOBI4F1gamA08A57n73KjMBOBPSWbfzN3fjcqsC1wJ/BGoAf4BnB5bhrRuK6/cJX2HxBE0SYdEaT06dw59Rc4//+J6LQAxK664Us7L/u67bznzzOFsvfUQLr/86iXLf/TRf/D2228knaeyMsRz/fVjkyYesY6O0ny05OGTTweuB54BdgdGAwcTftxj+gLXAFsk/PsYwMyWB14CVgH+CpwJ7Efofy4iUhQbbLARbdu2ZebMn+jTZ/0l/2pqahgz5oZGnal/8snH/PrrLxx00KH1Eo+33gqJQW3tsi0Hffv2B2DOnDn14pk+fRq33nozCxcuyjkeKY4W2XJgZmWE5GCMu58ZTZ5gZjOBB8ysHzADWBF4zt3fSrGo44DlgX7uPjNa9rfAeDMb6O5vF/SDiIgk0a1bN/bb70DGjLmBefPm0bfvJkyb9gNjx95IZWUlvXqtnfOyzfpQXl7OTTddyz77HMCvv/7K+PFP8uaboc/BokULl5mnd+912W67Hbjkkgv4/vtvWW+9PnzxxX8YM+ZGzPos6RwpzUeLTA6A5YB7gAcTpn8Sva4DxNq5pjawnKHApFhiEHkemAvsDCg5aOWGDt0xfaH1Ch+HJLF8vzBKYTHrL6AjjzyW7t2789hjDzNu3B106dKVgQO34KijjqN9+/Y5L3f11X/HiBEjuf32sfz976fQpUsXNthgQ667bgwnnHAUU6a8z1prLTtC4znnnM9dd93GI4/8gx9/nM4KK3Rn2LBdOfzwoxvzMaVIypJ1LmmpzOw8wtXf3wN7AOcRkojdgUrCJYS/ufunUfkZwD3ufkrCcqYAH7t7tjfvzq6rq+v622+LMypcUVEOQE1NZuVlqWKtu4qKcl75elJWtxNutcbWeYkzX3U3l+3u22+/oaZmMSuuuOxZaadO7SgvLy9CVPUtXryYBQt+LXYYJe+nn6bRrl0Fa6yxZtLtrm3bcsrKyqqBbk0fXevUUlsOlmFmA4EzgMfd/RMz6wu0BxYCewK/IyQLr5pZX3efBnQFkvU2m4sGxBVgjz1257HHHm+40H3AAU0SjkSK94McGxyo9Zx0ScvUKpIDM9sSeBr4Ajg8mnwRoU/CxLhybxI6I54AnE3Y05Pt5WVAbS6x1NVBdfWy1+yS6dq1I5B5eVmqqdbd+PHP1KsjVm89nza8jJqaxXmJM2ndaSSru7lsd7/+Gs4wFy8unR/iWGNFKcXUHNTWho6OqfaF7t0rSTIooxRQi08OzGxf4E7CIXrHWP8Bd/8wsay7/9fMPibcxQBQTfIWguWALwsRr4iISLG15FsZMbNTCLcdvgls5e4/RNPLzOwgM/tjktk6ArH7gBzonbDMcqBX9J6IiEiL02KTAzM7jDC2wUOEFoPq2HvuXgecBlwVDZYUm6c/IRl4OZr0PDDEzFaIW/RQQufFCQX9ANIspB0ACTQAkog0Oy3ysoKZrQxcC3xFGAipv5nFF/mccMh+BLjXzG4H1iD0Q3gfGBeVu5HQ/+BFM7sA6A5cDjzr7smHCpNW5e6770g/hPK7aAhlEWlWWmrLwY5AJ2BN4FXCZYX4fzu6+6OEWxjXAR4DLgGeBIa6+2IAd/8JGALMBO4FRhJaIvZtyg8jpWv48JPSF3q68HGIiORTi2w5cPe7gbszKPcE4XkLDZX5ENguT6GJiIiUvJbaciAiIiI5apEtB9K6de7cfskof5ne+19TU8v8+b9kXde4cYkjdCexf9aLlUYK20DTn/uURTfjx0aezXW7Eik2JQfS4lRUtGHOr9VUTavKqHy/Hv3oVFGZU119+2Ywfn7PnBYtjVBR0YYFtfMy3gYKoTHb1fHHH0lV1WT69evP9dePTVrmmGMO44MPpnDooUdw2GFHZbTcwYMHcPjhR3PIIYenLyytmpIDaZGqplVl9YyBQasOzqmejTe29LczXoluZyyCbLaBQmjMdgWhFWLq1CpmzvyJ7t1XrPfejBnT+fDDhp4ZJ9I46nMgIlKC+vT5PRUVFUyatOyTJSdOnECvXmuXxMPWgYMKAAAgAElEQVSlpGVSciAiUoI6darkD3/YnIkTlx1v7cUXX2DbbbevN+27777lwgvPZbfddmDrrQeyyy5DGTlyBHPmpG7Zqq6ezWWXXcSwYduz7bZbcswxhzF1avEuxUjpUHIg0ggHHXRI+kL9Cx6GtFDbbrs9U6dWMWvWz0umTZv2Ax9//G+2226HJdMWLVrECSccxddff82pp57JVVfdwN5778vzzz/L2LE3Jl32L7/8wkknHcsbb7zG0Ucfx0UXXcZyy3Xhb387lo8//nfBP5uUNvU5EGmE0aOvTV9o18LHIS3T4MFbUV4eLi3svvteALz00gusu66x+uq/W1Luq6++pEePnpx77gX07LkqAP37D+Cjjz6kqmpy0mX/85/j+c9/PuOWW+6iT5/1Adh880EcccTBjBlzA1dfnTypkNZBLQcijbDddlulLzSm8HFIy9SpU2cGDtyciRNfXDLtxRdfYLvthtYrZ9aHG2+8lVVW6cE333zNm2++zn33jeOrr76kpua3pMt+7713WGmllendez1qamqoqamhtraWQYMGU1U1md9+Sz6ftA5qORBphIyuz/5Q+Dik5RoyZHtGjjyP2bNnM3/+PD799BNGjrximXIPPHAP48bdQXV1NSus0J0+fX5Phw4dWbhwQdLlVldXM2PGdLbZZvMU789mxRVXyutnkeZDyYGISAmLXVp49dWXmT17FhtssBE9evSoV+b555/j+uuv5thjT2LnnXehW7duAJx77hl8+uknSZdbWVnJWmv14pxzzk/6fteu3fL7QaRZUXIg0girrNIjfaHcxsERAaBTp04MHLgFL7/8ErNm/cxOOw1bpszUqVV069aNAw44aMm0BQsWMHVqFe3atU+63H79+vPWW2+w4oor1WshuOWWm5g27YeUSYO0DkoOpCByGb62OQ41+8EHn6YvNLzwcUjLtu222zFy5Ahqa2sZMmTZ58Ctv/4GPP74w9x44zVsscVgfvxxBvffP46ff55Jt27LJ13mzjvvysMPP8Tf/nYsBx10KCuttDKvv/4qDz54L4ceesSSoaCldVJyIAWR7fC1jRlqtpguv/xiTj/9rIYLTSQ8+FuaVL8e/Zh48LIDCDVl/fmy5ZZbUV5ezkYb9WXFFVdc5v2ddhrGDz98zzPPPMnDDz/ESiutxBZbDGaPPf7M5ZeP5Ouvv2KNNdasN0+nTp248cZbuPnm67nuuqtYsGABq666GieffBp77aWn0rd2Sg6kYJpqCONiGjXq0vTJwSSUHDSxmppaOlVUNvk2lezBS7lIfJ5Cp06dePHF15cpN2nS20v+f9hhRyV9xsKuu+6x5P+vvfZuvfdWWKE7Z511Xk4xSsum5EBEWpxiXZ4qLw/JweLFdUWpXyRfNM6BiIiI1KPkQKQRXnhhUvpCRxY+DhGRfFJyICIiIvUoORBphO233zp9obHpi4iIlBIlByLSLJWVQV1dbncDSGmpq6ulTRuNq1BKlByISLNUUdGOmprfqK1dXOxQpBFqaxdTU/NbypEcpTiUHIg0wvDhZ6QvlMGVB8lex46dgDqqq39WgtBM1dYuprr6Z6COysrlih2OxNE4ByKNkHYAJNAASAXStm17lltueebOncWMGQupqGhLWVlxz3faRNXX6mpHWnV1tdHjpOtYbrnl6dChQ7FDkjgtNjkwszaEm8iOBdYGpgNPAOe5+9yozABgFDAAmAPcGb3/W9xy1gWuBP4I1AD/AE6PLUNat402Wi/98xVGoecrFEjnzl1o1649ixYt4LffflsyMmGxVFSUA/Drr2rJSKdNm3I6d+5Ahw6daNtWlxRKTYtNDoDTgYuAK4AXgfWAC4H1gR3NrHc0/Q1gH+D3wEigC3A8gJktD7wE/AD8FVgFuBz4HbDso9Gk1Zk+fVr6QvMKH0dr1rZt+5L5cenatSMA1dULixyJSOO0yOTAzMoIycEYdz8zmjzBzGYCD5hZP0ICUA3s5u6/AuPNbAFwnZld4u7fAccBywP93H1mtOxvo7ID3f1tREREWpiW2iFxOeAe4L6E6Z9Er+sAQ4GnosQg5mGgPHqP6HVSLDGIPA/MBXbOd9DS/Gy8cQZP3utZ+DhERPKpRbYcuPsc4MQkb+0evX5MuDTgCfP9aGZzAIsm9SEkGfFlFpvZF3FlpBWbMOGV9IWWfVCeiEhJa5HJQTJmNhA4A3gcmBVNnpOk6FxCvwOArhmUyUpZ2dLrkunEOjdlWr6UxGLPdp58fNamrPuYY47mpptubrjuJ4Fd8193suXkMk9i3c15uys2rbvcNbTuyjQ+UpNrqZcV6jGzLYHngC+Aw4HYppasa3MZUBv3/3RlpBW7/fbb0heaXPg4RETyqcW3HJjZvoRbFD8FdnT3mWZWGb2d7Oy/ktBRkeg1WZnlgC9ziaeuLvOezM2553MuZ041NYvz8lmbuu74+VrC527O212xad3lrqF11717pVoPmliLbjkws1OA+4E3ga3c/QcAd58HfAf0Tii/MiEZiPVF8CRlyoFeJPRXEBERaSlKKjkwswfMbFcza5uHZR0GjAYeIrQYVCcUeR7YxczaxU3bC1gMvBxXZoiZrRBXZiihdWFCY2OU5m/q1AxyxFMKH4eISD6V2mWFrYA/A9Vm9gjhVsSX3T2rYc+iFoBrga+A64H+ZvVuLvicMJjR/oQxC64mDJJ0MTDW3b+Oyt0InAC8aGYXAN2j+Z519zdy+4jSkkyZUkWPHmnuVfyBHLuviogUR0m1HACrAdsRhijejXB2/p2ZXWlmf8hiOTsCnYA1gVcJlxXi/+3o7p+wtBXgYcL53ZXASbGFuPtPhJHxZwL3EkZQfAjYN/ePKC3JQQdlsCncX/g4RETyqaRaDqIWgonARDM7lvDjvS9wEHBSNL7AfcC97p6yPdfd7wbuzqC+V4HN05T5kJCwiIiItAql1nKwhLsvdvdn3f0QYEvCGfvawDnAR2b2hpntVswYRUREWqKSajmIZ2brE/of7EMYqbAGeIbQvF9HGHfuUTMb4e4XFi1QadVGjbomfSE9oktEmpmSSg7MrA8hGYg9JRHgdcIDkB5y95/jij9oZm8R+gooOZCi+OtfD01faEDh4xARyaeSSg6Aj6LXD4CzgPvc/ZsGyn8DlMazWqVVWnnlLsyYkWyE7Tgjon8tWOfO7amoyP4qZU1NLfPn/1KAiESkMUotObiU0Nnw3xmW38/dFxcyIBFJr6KiDQtq51E1rSrjefr16Eenisr0BUWkyZVUcuDuZ5nZGmZ2KXCZu88CMLO/AytH02bElVdiIFIiqqZVMeSuIRmXn3jwRAatOriAEYlIrkrqbgUz25DwmJpTgTXi3loeOBZ438x6FSM2kWSGDt0xfaH1Ch+HiEg+lVRyQLisMBdY392nxCa6+xnA+sCvwGVFik1kGffc81D6QgcUPg4RkXwqteRgc+Bqd/8s8Q13/4IwFPLWTR6VSAoHHrhP+kL3FT4OEZF8KrXkoA3QoYH3y4Dsn0srUiDPP/9c+kKfFj4OEZF8KrXk4C3gKDPrlviGmVUChwNvN3lUIiIirUhJ3a0AnA9MAj40s3sJT0+sBXoTnqDYA8hg1BkRERHJVUklB+7+tpltD4wChhMuI8RMAQ5x9zeLEpxIEmkHQIIWPwCSiLQ8pXZZAXd/1d0HEloJ/gBsAazm7pu4+8tFDU4kwd1335G+0LuFj0NEJJ9KquUgnrv/CPxY7DhEGjJ8+Enpn6/wNHq+gog0KyWXHJjZjsBfCC0H5UmK1Ln7n5o2KhERkdajpJIDMzsWuC76czqgJ7KIiIg0sZJKDoC/EToe7uTu04sdjEg648Y9mL7Q/oWPQ0Qkn0qtQ+LvgDFKDKS56Nu3X/pCPQsfh4hIPpVacvAfYJViByGSqY03tvSFrix8HCIi+VRqycElwIlmtkGxAxEREWmtSq3PwWBgHjDFzJxwK2NtQhndrZChzp3bU1GRXf5XU1PL/PnqByoi0pqVWnKwI1AHfAN0AtYsbjjNW0VFGxbUzqNqWlVG5fv16EenisoCR9WyHHTQIekL9S94GCIieVVSyYG79yp2DC1N1bQqhtw1JKOyEw+eyKBVBxc4opZl9Ohr0xfatfBxiIjkU0klB/HMbFXC3QufAAuBGndPvMQgUlTbbbcVEya80nChMcBRTRKOiEhelFxyYGZbAtcCsXvEtifEebuZneLuD+WwzH7Av4Be7v5t3PTPgXWSzLKSu/8UlRlAeBDUAGAOcCdwnrv/lm0c0vJMnZrBJZsfCh+HiEg+ldTdCma2GTABWA64Ou6tn4HfgPvMbKcsl2mE0e0rEqZXAmsDZxAe7hT/b3ZUpjfwIqHlYh9gNHAKcFWWH01ERKTZKLWWg4uAL4BNgc7AyQDu/q6Z9QVeB84Cnk23IDOrAI4ELiUkFok2JjwS+gl3/yTFYs4AqoHd3P1XYLyZLQCuM7NL3P27bD6ctDyrrNIjfSH18RSRZqakWg4IZ+13uPtCwl0LS7j7HGAssGGGyxoMXE442/97kvf7AYuAzxpYxlDgqSgxiHmY8ECooRnGIS3YBx98mr7Q8MLHISKST6WWHEDDD1vqQOYxfwys7e7nAzVJ3u8LzATuN7PZZjbPzB4wsx4AZtaJ0CHS42eKHiU9B8hgaDxp6S6//OL0hSYWPg4RkXwqtcsKbwMHEDok1mNmnYHDCR0L08rg+Qx9CY+F/jfhSZB9gAuAiWbWH+galZuTZN65QJdM4khUVgZdu3bMqGxFRXhidablU82f7Ty51tca6x416lJGjryw4bonAQ3cTVpqnzuX7S6XulPV35w1dp9tzRpad2VlTR2NlFpy8H/Ay2Y2CXiCcGlhoJltCJxIGBTp6DzVdSJQ5u5vR3+/amYfAa8BBwLPRNPrksxbxrIjN4qIiLQIJZUcuPubZjYMuJlw+yDAyOj1B2Bfd89LI627v5Nk2utmVk1oVbg/mpyshaCS0FExa3V1UF29MKOysQw60/Kp5s9GTc3inOtrrXXHz9cSPncu212uZ8r5+uylorH7bGvW0Lrr3r1SrQdNrKSSAwB3fyG6hbA/4VbDcuBL4F13T9Z3IGvRJYp9gMnuPiVuehnQDvjJ3eeZ2XdA74R5VyYkDPX6Ikjr9MILk9IXOrLwcYiI5FPJJQcA7l4HvBf9K4RFhLsYXgb2jJu+G9Axmg7wPLCLmZ0Wd8fCXsDiuDIiIiItSkklB2b2Uibl3H3bxtTj7ovN7CJgtJldCzxJuEXyfMK4By9HRS8H9ieMb3A1sB5wMTDW3b9uTAzSMmy//dbMmJGsz2qcscCIpohGRCQ/Sio5IFxGSOwAWA6sSLiN8Uvgw3xU5O5XRv0LTiLcBfEzoa/DiLgyn5jZUOAKwvgGPwFXAuflIwYREZFSVFLJgbuvlWy6mZUTmvxvZWlHxWyWeyfhmQiJ028Dbksz76vA5tnWKSIi0lyV4iBIy3D3xe7+KHALcFmx4xGJGT78jPSFti58HCIi+dQskoM4nxFuMxQpCaefflb6Qg0MgCQiUoqaTXJgZu0JgxPNKHYsIjEbbbRe+kJZXwgTESmukupz0MDdCu0JzzJYHnUGlBIyffq09IXmFT4OEZF8KqnkgOR3K0AYV+ATwqiFNzZpRCIiIq1MSSUHqe5WEClVG2/cL32hnoWPQ0Qkn5pNnwORUjRhwivpCx1V+DhERPKppFoOMh0hMUGdu/8p78GIZODUU09k9OhlnjBe35PArk0SjohIXpRay8EvhAcdbUO4ZbGSkMCsF03bAuiV8G/tIsQpAsC4cXemLzS54GGIiORVSbUcANcThik+gfD8gt9ib5jZ3sBdwOnu/o8ixSciItLilVrLwSXAre5+Q3xiAODuDwPXARcWJTIREZFWotSSg96EWxZT+QFYo4liEUlr6lRPX+iUwschIpJPpZYcfAIcaGZtE98ws47A/wLvN3lUIilMmVKVvtAPhY9DRCSfSq3PwSXAg8D7ZjYG+CKabsDRwO+AoUWKTWQZBx20LzNmzGm40P3EPQhcRKT0lVRy4O7/iFoILgeuYeloiWXAf4Bh7p7BjeUiIiKSq5JKDgDc/W4zuwfYjKX9Cz4Dprh7sqGVRUREJI9Krc8BAO5eC3wDfA08D3xEaD0QKSmjRl2TvtCwwschIpJPJddyYGZbAtcCsUHrtyfEebuZneLuDxUtOJEEf/3roekLDSh8HK1Z587tqajI/jynpqaW+fN/KUBEIs1fSSUHZrYZMIHQanA1cHL01s/Ab8B9ZjbX3Z8tUojSyqT74Wnfvi2//LJ0SI6KivJlC41AHRILqKKiDQtq51E1LYM7RyL9evSjU0VlAaMSad5KKjkALiLcobAp0JkoOXD3d82sL/A6cBag5ECaREVFG9oxD2al/uFpN+u1pX+sNLgJopJEVdOqGHLXkIzLTzx4IoNW1XclkkqpJQdbABe6+0Iz6xT/hrvPMbOxwAXFCU1arVlV8GIDPzzx7+09q/DxiIgUWCl2SGzoImAHSjNmaaWGbZJBofUKHoaISF6V2g/t28AByd4ws87A4cC/mjQikQY8NTyDQkm3aBGR0lVqycH/AZuY2STgYMIgSAPN7ERgCuHxzCOLGJ9IPbuMyqDQfQUPQ0Qkr0oqOXD3Nwl3ha8OjCKMbTCScOdCR2Bfd59YvAhF6ns6kyd9fFrwMERE8qqkOiSaWXd3f8HMegObAOsA5cCXwLvuXpPjcvsRLkf0cvdv46YPJSQfGwDTgevdfXTCvAMIicoAYA5wJ3Be4iOlRUREWoqSSg4ID1y6xd0vBCZH/xrFzAx4moTPamaDoukPAucCg4ErzKzM3UdFZXoDLwJvAPsAvyckE12A4xsbm4iISCkqteRgJWBaPhZkZhXAkcClhAGUEl0ATHb3g6K/n4seFX22mV3n7r8AZwDVwG7u/isw3swWANeZ2SXu/l0+YpXmq+7eDAqNKHQUIiL5VVJ9DoB7gSPNbK08LGsw4emOo4G/x79hZh2ArYBHEuZ5GOgGDIr+Hgo8FSUG8WXK0aOjBRj7UgaF3i14GCIieVVqLQe1QB/gMzP7HJgBLE4oU+fuf8pgWR8Da7v7DDM7JOG9tYG2gCdM/zx6NTN7G/hdYhl3/9HM5gCWQQzSwh11Gxy5bZpCT6PnK4hIs1JqycH2wE/R/zuw9JHNWXP36Q283TV6nZMwfW702qWBMrFyXXKJq6wMunbtmFHZ2Dj9mZZPNX+28+RaX0usO5dl5avuXJaTj7pz2e5yXU/5+OzFrDtVLPlebmvQ0Lor0zN5m1xRkwMz2xj4yt2rAdy9VxNVHdvU6lK8X5umTFlURkREpMUpdsvB+8BBxA0TY2blwJbAlFjSUACx5Sae/XeJe39OijIAlXHLyEpdHVRXL8yobCyDzrR8qvmzUVOzOOf6WmLdXbt2pF0D8z15agYL3z+3urOVr8+dy3aX65lyPj57MetOFUu+l9saNLTuunevVOtBEyt2h8RkX3c3YCLhyYyF8h9CX4beCdNjf7u7zwO+SyxjZisTEobE/grSCm2aSVtXz4KHISKSV8VODlIpaI7o7ouAV4A9zSy+rr0ILQKx/uXPA7uYWbuEMouBlwsZozQPq2Uy2sWVBQ9DRCSvin1ZoZguAiYAD5jZnYTbF08DznD3BVGZywmNwuPN7GrC8/UuBsa6+9dNH7KIiEjhlWrLQcG5+0uEVoDfA48DfwFOc/fL48p8QhjPoJIwvsEphPPAk5o8YBERkSbSKloO3P1OwjMREqc/BjyWZt5Xgc0LEpg0e0cMyaBQ/4KHISKSV6WQHPQxs63i/o6NL7CxmSV90JK7v1L4sETSG3t4BoV2LXgYIiJ5VQrJwdnRv0Sjk0yLKfzINCIZ2PRseG9kmkJjgKOaIhoRkfwodnJwfpHrF2mUyV9mUOiHQkchIpJfRU0O3F3JgYiISIlptXcriORDz24ZFKoseBgiInml5ECkEb6/IYNCwwsehohIXhW7z4FIWp07t6eiIvM8timepBgz4hEYsVeaQhOBTG55FBEpEUoOpORVVLShHfNgVlVmM6w0uLABxTn/0QySg0koORCRZkXJgTQPs6rgxQx/YfeeVdhYRERaOPU5EBERkXqUHIg0wrsXZVDoyIKHISKSV0oOREREpB4lByKNMOCcDAqNLXgYIiJ5peRARERE6tHdCiIlqpTHdxCRlk3JgUgjnLdnBoW2zm3ZpTy+gwSJCVwsQevatWOD89XU1DJ//i8FjU2kMZQciDRC2gGQoHEDIGl8h5JWUdGGBbXzqJqWYQIH9OvRj04VeuCGlDYlB5JWts3b0HqauFc9LoPnK4xCz1dowaqmVTHkrswzwIkHT2TQqmrlkdKm5EDSyrp5G1pNE/cPszMoNK/gYYiI5JWSA8lMNs3boCZuEZFmTLcyijRC/7UyKNSz0FGIiOSXkgORRnhvZAaFjip4GCIieaXkQKQRjrw1g0JPFjwMEZG8UnIg0gi3TMyg0OSChyEikldKDkRERKSeVn23gplVAHOBDglvzXf3yqjMUGAksAEwHbje3Uc3aaAiIiJNqFUnB4AREoODgU/jpi8GMLNBwNPAg8C5wGDgCjMrc/dRTRyrlKDvrs+g0CkFD0NEJK9ae3LQF6gFHnb3BUnevwCY7O4HRX8/Z2ZtgbPN7Dp31+Dordx7X8Cqy6cp9APQpSmiERHJj9be56Af8J9kiYGZdQC2Ah5JeOthoBswqPDhSanbNZMLTPcXPAwRkbxSywH8YmbPES4Z/AY8RBgJ/3dAW8AT5vk8ejUgk77qIiIizYqSg9DgewtwMTAAGEH44T8zKjMnYZ650WtODcVlZekf5xoT//jX8vI2lJWVZV1XtioqypeJr6keopSs7qaqX3UvW2+m22n8PPmoP5dltMa6W5qGtrtcjmXSOK09OdgX+NndP4j+fsXMpgP3AEOjaXUp5q0tdHDxysrKaFNTrYcflZgxh2VQaFjBw8i7srLsfvh08BZpWVp1cuDuk5JMfibh78QWgtjf1bnUWVcH1dULMyoby6CrqxfStWtH2jXBw49qahYvE1/Xrh1pl/WSspes7qaqP9e6j9w2g4UPKEzd+ZDq+66oKM8uGc0xEU312bOR6xl4c6+7pYk/3iXq3r1SCWgTa7XJgZmtDOwKvOTu/417K7a3Tyfc0tg7YdbY34l9EaQVKvsL1N2bptCI6F9zk00yqqdwirQorfluhVpgDHB8wvR9CUnBBOAVYE8zi89Z9yK0GrzbFEGKiIg0tVbbcuDuP5nZDcCJZjYHeBXYEjibMAri52Z2ESFJeMDM7iTcvngacEaKcRFERESavdbccgBwKnAWsB+hr8HBwHlEY9q5+0uEloLfA48DfwFOc/fLixKtlJxhm2RQaL2ChyEiklettuUAwN1/Ay6P/qUq8xjwWJMFJc3KU8MzKHRAwcMQEcmr1t5yINIou2TyhI37Ch6GiEheKTkQaYSn38+g0Kfpi4iIlBIlByIiIlKPkgMRERGpR8mBSCOkHQAJmucASCLSqik5EGmEsS9lUEjDZYlIM6PkQKQRjrotg0JPFzwMEZG8UnIgIiIi9Sg5EBERkXqUHIg0wpOnZlBo/4KHISKSV0oORBph014ZFOpZ8DBERPJKyYFII6yW+MDvZK4seBgiInnVqh+8JCKlp3Pn9lRUZH7eUlFRXsBoRFonJQciUlIqKtrQjnkwqyqzGVYaXNiARFohJQcijXDEkAwK9S94GC3PrCp4MZOVC+w9q7CxlKhsW1hiampqmT//lwJEJC2JkgORRhh7eAaFdi14GNIKVVS0YUHtPKqmZdjCAvTr0Y9OFZUFjEpaCiUHIo2w6dnw3sg0hcYARzVFNNLaVE2rYshdGbawABMPnsigVXUZRtLT3QoijTD5ywwK/VDoKERE8kvJgYiIiNSj5ECkEXp2y6CQLvGKSDOj5ECkEb6/IYNCwwsehohIXqlDokgjjHgERuyVptBEIPM+Y1JEGoBJJFByINII5z+aQXIwCSUHzYQGYBIJlByIiMTTAEwi6nMgIiIi9anlIANmtj9wDrA28CVwibvfXdSgpCS8e1EGhY4seBgiInmlloM0zOzPwL3A88DuwMvAXWa2dzHjEhERKRS1HKR3CfCQu58c/f1PM1sBuBB4uHhhSSkYcA7U3Zum0FhgRBMEI9JE9NCnlk/JQQPMbG1gHeDMhLceBvYxs17u/kXTRyYiLU1zuo1SD31q+crq6uqKHUPJMrOdgWeAvu4+NW76JsBkYCd3fy6LRdbW1dWV5RpPGUDd4ixmKKcOWJzhPOVlqQ82xaw76/qbsO7Zc+bRrUvcAS9J3fPmzKOyS/KDYnP93I2tO139rbbusuwOD3V1dXmrOxdNWXdZWVkduhTeZNRy0LCu0euchOlzo9cuWS6vtqysrE2S5WWuLLuvrAyoyHKekqw7y/qbqu5u3ZYdPzmx7mRl8lF30qK0/HXe4uvOQllZWdHqhib93F2A2qaqTJQcpBNL4xObV2LTs91Ytb5FRKTkqYmmYdXRa2ILwXIJ74uIiLQYSg4a5tFr74TpvRPeFxERaTGUHDTA3T8HvgASxzTYC/jM3b9u+qhEREQKS9fA07sAuMPMZgFPA7sC+wD7FTUqERGRAtGtjBkws6OA4cDvgP8Shk8eV9yoRERECkPJgYiIiNSjPgciIiJSj5IDERERqUfJgYiIiNSj5EBERETqUXIgIiIi9WicgyIws22AiQ0UOcTd70oyXyVwGWEQpkrgFeAkd/+sEHGWokasuwOBZLef3uDux+cpvGbBzI4GTgLWAP4DXObu9zZQvtVvdzE5rDttd4CZdQQuBA4AugFTgAvc/dkG5tF2V0RqOSiOycAWCf8GAR8D3wDjU8z3IPBn4O/AX4HVgIlm1jVF+ZYo13XXF/g8ybyjChxvSTGzI4GbCI8i32h5xZ4AAAstSURBVA2YANxjZn9uYDZtd+S87rTdBf8ATgBuIQwk9zzwmJnt1cA82u6KSC0HReDuc4C34qeZ2UmAAYPc/cfEecxsMLAzsJO7PxdNe5UwvPPRhAy7xctl3UX6Au+5+1sp3m8tDgEmuvvw6O8JZjYAOJZwAK9H2109h5DFuou0+u3OzPoD/wOc7u5XRJMnmFkH4Goze8zdaxPm0XZXZGo5KAFmtgqhye0md387RbGhwFzghdiE6IdwEmEnapUyXHcQDtJTmyaqktaBsB3Fmwl0T1Fe291S2a470HYHIXGHMPx8vFeA1YGNk8yj7a7I1HJQGs4HaoFzGijTB/jc3RcnTP8c2LdQgTUDadedmfUEVgY2MbNPgHUIw2Bf1AqHwb4GuCVqCv8n4SA8DDgrRXltd0tlte603S0Re0DdmoTLfzFrx71WJcyj7a7IlBwUmZmtBBwMjHL32Q0U7QrMSTJ9LtClELGVuizWXd/odW3gdGAR4Rrm3WZW4e53FDbSknI/sC3wUNy0u+KaexNpu1sq23Wn7S74F/ARcL2Z/S8hERgEnBa93znJPNruikzJQfEdQbi8c02acmVAsgdhlBHOnFujTNfdu8AuwCR3jzULPx93SaK1HKQBniQcmE8hdO4cCPyfmc1x9xOTlNd2t1S2607bHeDuv5rZHsDdhMsCEPoOnAPcBSxIMpu2uyJTclB8ewPPuftPacpVs7QZLt5y0XutUUbrLno/8XonhF7n25nZihms/2bPzAYBOwCHuvud0eRJZjYbGGNmt7j7Bwmzabsjt3Wn7W4pd/8U2NzMehBaBT4DBkdv/5xkFm13RaYOiUVkZqsBm1C/mTIVB9Y2s7KE6b2j91qVbNadmW1hZocleasjUEPrOdisGb2+njD9leh1/STzaLsLsl532u4CM+toZgea2ZruPs2DWqA/oXUgsb8BaLsrOiUHxTUwen0tg7LPEwYP2S42IbrmvhXhfuvWJpt1twVwq5kt6RVtZm0ILQ+vu/tvBYivFMUOqlslTN8iev0yyTza7oJc1p22u+BX4Abgf2MTokGRjiJccpmVZB5td0VWVleX7LKONAUzO49w7+8yHXKiHWEd4KPo3n7MbCLhtp/TCU1xIwi3UW2UYgdrsbJZd2a2PPA+sJhwnXMu4d70PwFbpbkFskUxs8cIB9z/I6yTAdH/X3P3nbXdpZbtutN2t5SZXQYcB5xK6G9wOqH/xjbu/o62u9KjloPiWgVItZH/D/AmoektZk9Cp6hRwJ3At8CfWumOkvG6i9bP1sA7wFWESxGVhHXXag7Qkf2A64CTgeeAwwnb0x7R+9ruUstq3Wm7q+dcwro7F3iY0LHwT+7+TvS+trsSo5YDERERqUctByIiIlKPkgMRERGpR8mBiIiI1KPkQEREROpRciAiIiL1KDkQERGRevRsBZE8MLM7CU+IrAV6RM+eT1ZuCmFgl7vc/ZACxPEysJa7r5Xn5e5EeNBVf6AnYejft4Gb3H18PusSkeJTy4FIfrUBhiV7w8x6ERKDQhoJ/C1fCzOzdmZ2BzCe8HyBW4BjgKuBtYBnzGxUvuoTkdKglgOR/PoC2I3kj+PdA/gRWKlQlbv7C3le5PnAIYSR7Ua6+5JR06IhcW8DTjWzZ9x9Yp7rFpEiUcuBSH49AWwfPVgm0R6E4WCbBTNbCxhOeDjORfGJAYC7LwZOBOYDRzZ9hCJSKGo5EMmvxwnN+tsBT8UmmtnKhAfNXAIs8xhfM/sjcB6weTTpHWCEu78SvX8T4Zp/z/j+DGbWidAa8aC7/2+yPgdmtj7hcsMQoB3hYUAXuPs/03yWfQjHiEtTFYgeMLQj8GFcfS8D/9/enYVaVcVxHP9mWZZR0UxFc/wkiqKSoKKBaISy2UofhAZolGa0RKiQooFKm2jwIRwalWZMy8QmzKCi4f/QhJZNF5oHy+zhv7atc7rn3Bten/p94LA9e6+171oXruu/17R/A94sv4tfyD3x35W0J3AdcCiwHvA2cENEzK7yrwfcCBwPbAt8TQZV1zT76pdX+U4ARpHDHd+Tb/IbHxFL+qiXmfXBPQdmA2sh8C05tFAbQT5hz2vPIOl4YD6wPdlwXlf+Pa9cA5gGrA2c3Jb9OGCDcv1fSmP8GrA7MAm4GhgMPCtpZB91OZScYPlyt0QRsTAivms7fRBwBnAF+dKc9yUNB14nX7d9CzCeDFZmSbqgyjuFDIRmkm8xfIzsmXi4SjOeDKaeJ9/2dx9wAjBH0tp91MvM+uCeA7MBFBErJD0NHCdpUET8VS6dCDwTEb9LWpVe0jrku+4/B/arXld7L/k0fpek54BXgM+AU4F7qh85ElgGdBrvn0z2LOwTET+Xe08GXgRulzQrIpZ3yLsd0BMRv9YnJQ0h3y7YXvdvq69DgbMiYn6VbzIZbAyPiKXl3N2lbjdJerjcYxTwYESMr/L+BBwtacOI+KmkeS4ixlZplpCTJXcEPupQJzPrB/ccmA282cCWlCECSRsBhwOzekm7D9kIT2kCA4DyJD6F7Fbfr4z3TwcOKUMUzX2PAWZUQcgqkjYjXxn8LLC+pM0lbQ5sUsqyFTC8Sz0Gka/WbXc2GXC0f2q/AguqsmxF9hg81AQGpZ6/ATcB6wNHlNNLgZGSxkjapKSbEBHDS2DQpDlM0thybyLi3ojYOyIcGJitJgcHZgNvDjnO3gwJHEs+Mfe2H8BO5Ri9XPugHHcox2Zo4aTyfQQwhA5DCsAu5XgR/27Iby3Xtu9Sjy+ATSUNbjs/m2zIm8+cXvL2tAUsO5Zjf+p5Hvl/01TgG0kLJF0iaeMqz+Xk8M1twDJJiyRNkLR1l/qYWT85ODAbYKUb/gX+mXdwIvBC9dRb6+3JvNH8fS4v930PeIecKAg5pBAR8VaH/M3Y+520Nub1Z36Xn/9qKcMh9cmIWBoRc5sPOazRbkXb9/9Sz3lk0HIGOc9gGBnMvCtpi5LmHWA38nf8ALA1cC3wgaRhXX6WmfWD5xyYrRmzgamS9iC7/sd2SPdpOQ4jl0HWmskJ9ez7acAkSTuTjfv1XcrQ3PvP0oj/c+NcwbAT2cPRyQxyRcAlwNwu6fqjKUtvDfeqepaVCnsDSyNiJjBT0iDgUnL44XRJdwF7AT9ExJOU5aGSTiODiXOAy1azvGb/a+45MFszniKfnm8mVxN02t9gMfnkfX6ZQwCsmk9wfrm2uEo/g/y7vZ2c6T+9UwEiYhm5nHCMpG2qew8GHiRXAXR8QIiIIBvkYyVNKo10C0lHksFPVxHxZSnLaEnbVfnXJRv+38nelk3J1RXjqrx/AYvK1xVkj8hL5JBC7Y0qjZmtBvccmK0BEdEjaSFwFPBSRPR0SPeHpIuAR4A3Jd1fLp0NbAOcUo/dR8QSSQvILZpf78fku4vJlQmLyxN3D9ldvz8wrlO5KhPIlQnjgJMkPUr2AmxBLqM8APiBDGT60pRlUSnLj8BoYF/g4jIJ8ztJ08hgaSg5tLEZcCHwFfBIRCyXdAdwjaRZ5HLGDcjljr+QgY+ZrQb3HJitOc3GPk90SxQRjwNHkhMAJ5Jr+D8BDqs3B6o0ExA79hpU934NOJB8ar+M7AkYCoyJiI6bG1X5/4iIC8l5B4uAM8k5DFeSkyyvIjdduvs/lGUxOaHwenKzpBMiYnKV9Fxyr4cDgDtK2leAg6rlkhPJHoddyT0TJgIfAwdHxId9lcXMultr5cqVfacyMzOz/w33HJiZmVkLBwdmZmbWwsGBmZmZtXBwYGZmZi0cHJiZmVkLBwdmZmbWwsGBmZmZtXBwYGZmZi0cHJiZmVkLBwdmZmbW4m+JMeKX4MsmfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gross_f = df_gross_f['AdjBoxOffice10']\n",
    "legend = ['Female', 'Male']\n",
    "gross_m = df_gross_m['AdjBoxOffice10']\n",
    "plt.hist([gross_f, gross_m], color=['orange', 'green'])\n",
    "plt.xlabel(\"Movie Gross\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(legend)\n",
    "plt.xticks()\n",
    "plt.yticks()\n",
    "plt.axvline(gross_f_mean_10, color='black', linestyle='dashed', linewidth=1)\n",
    "plt.axvline(gross_m_mean_10, color='black', linestyle='dashed', linewidth=1)\n",
    "plt.title('Movie Gross difference between male and female leading actor')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: From above bar plot, through comparison with two genders, we can see that green bars, which represents male leading movies, are always at least 3 times higher than orange bars which represents female leading movies. Thus, we can conclude that there are much more male leading movies than female leading movies in each grossing. \n",
    "Then, I put two mean line in the plot, left is the mean of female leading movie grossings, right one the mean of male leading movie grossings. We can observe that even though under log-base-10 grossing,  average grossing of female and male leading movies still have an obvious difference. How big the difference is in real grossings?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To solve this question, I use the data in real box office to calculate mean and median values and percentage to show the big difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female gross mean is:  65586882.4\n",
      "Male gross mean is:  79786060.37\n",
      "Actual difference of mean is:  14199177.97 dollars.\n",
      "Percentage difference of mean is:  21.65 %.\n",
      "Female gross median is:  44144648.0\n",
      "Male gross median is:  49841069.0\n",
      "Actual difference of median is:  14199177.97 dollars.\n",
      "Percentage difference of median is:  12.9 %.\n"
     ]
    }
   ],
   "source": [
    "gross_f_mean = df_gross_f['AdjBoxOffice'].mean()\n",
    "gross_m_mean = df_gross_m['AdjBoxOffice'].mean()\n",
    "print(\"Female gross mean is: \", round(gross_f_mean, 2))\n",
    "print(\"Male gross mean is: \", round(gross_m_mean, 2))\n",
    "actual_difference = gross_m_mean - gross_f_mean\n",
    "print(\"Actual difference of mean is: \", round(actual_difference, 2), \"dollars.\")\n",
    "percentage_difference = actual_difference/gross_f_mean\n",
    "print(\"Percentage difference of mean is: \", round(percentage_difference*100, 2), \"%.\")\n",
    "\n",
    "gross_f_median = df_gross_f['AdjBoxOffice'].median()\n",
    "gross_m_median = df_gross_m['AdjBoxOffice'].median()\n",
    "print(\"Female gross median is: \", round(gross_f_median, 2))\n",
    "print(\"Male gross median is: \", round(gross_m_median, 2))\n",
    "actual_difference_median = gross_m_median - gross_f_median\n",
    "print(\"Actual difference of median is: \", round(actual_difference, 2), \"dollars.\")\n",
    "percentage_difference_median = actual_difference_median/gross_f_median\n",
    "print(\"Percentage difference of median is: \", round(percentage_difference_median*100, 2), \"%.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: From above data, we can conclude that there really exists big difference in both mean and median, since the percentage differences are 21.65% and 12.9%, which are big numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then in order to verify my conclusion above, we use t-test to test to test mean value difference statistically to get an accurate answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0077614146355086365\n"
     ]
    }
   ],
   "source": [
    "t_val, p_val = stats.ttest_ind(df_gross_f['AdjBoxOffice10'], df_gross_m['AdjBoxOffice10'])\n",
    "print(p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we use an alpha value of 0.01, we can see that p_val is smaller than this value, which means there is really a significant difference between female and male leading movie's box office."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2015, 2009, 2012, 2008, 2004, 2002, 2006, 2003, 2010, 2013, 2001,\n",
       "       2011, 2007, 2000, 2005, 2014, 2016])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check year range of movies\n",
    "df_grossing['Year'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After exploring mean and median value of male and female leading movie grossings, we change an angle to continue exploring. Which is, what is the proportion of female in past years' highest grossing movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function which find given year's highest grossing\n",
    "def calculate_years_highest_gross(year):\n",
    "    highest_grossing = 0\n",
    "    for i in range(0, len(df_grossing)):\n",
    "        this_year = df_grossing.loc[i, 'Year']\n",
    "        this_grossing = df_grossing.loc[i,'AdjBoxOffice']\n",
    "        if this_year == year:\n",
    "            if highest_grossing < this_grossing:\n",
    "                highest_grossing = this_grossing\n",
    "    return highest_grossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# form pairs of year and that year's highest grossing\n",
    "pair = {}\n",
    "for year in range(2000, 2017):\n",
    "    average_this_year = calculate_years_highest_gross(year)\n",
    "    pair.update({year: average_this_year})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "During 17 years, there are 3 highest grossing movies of the year whose leading actor is female.  The proportion of female leading highest grossing films is: 17.65 %\n"
     ]
    }
   ],
   "source": [
    "#get the gender of the movie with highest grossing of each year, and count proportion of female and male\n",
    "female_highest = 0\n",
    "male_highest = 0\n",
    "for year in range(2000, 2017):\n",
    "    df_highest = df_grossing[df_grossing['AdjBoxOffice'] == pair.get(year)]\n",
    "    if df_highest['Gender'].all() == 'f':\n",
    "        female_highest += 1\n",
    "    else:\n",
    "        male_highest += 1\n",
    "print(\"During 17 years, there are\", female_highest, \"highest grossing movies of the year whose leading actor is female.\", \" The proportion of female leading highest grossing films is:\", round(100*female_highest/(male_highest+female_highest), 2), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: From above data, we can see that from 2000 to 2016, only 3 out of 17 female leading movies gain highest grossing of the year. We can conclude that generally male leading movies are more poplular and appealing among audience. And, audience prefer to watch male leading films."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our dataset rank movies from highest grossing to lowest one, we will next extract 200 highest grossing films to analyze gender proportion in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get top 200 movies' data as a new data frame\n",
    "df_top200 = df_grossing.iloc[0:200, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Gender columns of original and top200 datafram\n",
    "df_top200_leadingGender_portion = df_top200['Gender'].value_counts().to_frame()\n",
    "df_top200_leadingGender_portion.rename(columns={'Gender':'Gender_top200grossing'}, inplace=True)\n",
    "df_grossing_leadingGender_portion = df_grossing['Gender'].value_counts().to_frame()\n",
    "df_grossing_leadingGender_portion.rename(columns={'Gender':'Gender_grossing'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Gender_top200grossing</th>\n",
       "      <th>Gender_grossing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>173</td>\n",
       "      <td>1553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>27</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender_top200grossing  Gender_grossing\n",
       "m                    173             1553\n",
       "f                     27              467"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge them together to see more clearly\n",
    "df_leadingGender_portion = pd.merge(df_top200_leadingGender_portion, df_grossing_leadingGender_portion, left_index=True, right_index=True)\n",
    "df_leadingGender_portion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 11.14 % male leading movies whose grossings are in top 10% of total male leading movies.\n",
      "But, there are only 5.78 % female leading movies whose grossings are in top 10% of total female leading movies, which is about half of male's proportion.\n"
     ]
    }
   ],
   "source": [
    "print(\"There are\", round(100*173/1553, 2), \"% male leading movies whose grossings are in top 10% of total male leading movies.\")\n",
    "print(\"But, there are only\", round(100*27/467, 2),  \"% female leading movies whose grossings are in top 10% of total female leading movies, which is about half of male's proportion.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: From the data above, we can see that there are the proportion of top 10% male leading movies out of total male leading movies is twice as big as female's proportion, which is a big difference in proportion, we can conlude that compared to female leading movies, male leading movies is actually more likely to gain higher grossings, and is more popular than female leading movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df_leadingGender_portion.plot.pie( subplots=True, labels = ['male', 'female'], figsize=(10, 5), autopct='%1.1f%%', textprops={'fontsize': 15})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis: This pie chart compares gender proportion in top 10% grossing films and all films. From the pie chart, we can easily see the result only 13.5% of movies in top 10% grossing films are female leading films. Which is near half of female proportion in all films. We can conclude that for higher grossing films, female leading movies occupy fewer percentage than in all films, which means among high grossing films, they are less attracting than male leading movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the datasets we collect are free from online and they all grant permission for everyone to use, which state on their readme files or data description files. Also, since some of these datasets are collected for analysis purpose, there might be similar analysis appeared before.\n",
    "\n",
    "Since the datasets do not contain any personal information and all the data are public, so using these data won't consider an infringement of privacy.  \n",
    "\n",
    "The potential ethical issue can be if there is more action movie or hero movie in our dataset, it could make a\n",
    "big difference between male and female about taking more scenes in movies, but we are going to try not to\n",
    "focus on only one side. We will try to collect as many different genres of movies data as we can to avoid biases.  \n",
    "Although gender inequality seems to be a worldwide problem, the degree of inequality may differ among\n",
    "different countries and at different times. Since our movie data are mostly from English-spoken countries\n",
    "and European countries, also, the release date of moves are mainly from the 1980s to now. Thus, our main\n",
    "research scope of gender inequality problem is among English-spoken countries and European countries\n",
    "from the 1980s to now.  \n",
    "\n",
    "The result of our analysis may be used by someone who is interested in the topic of the gender inequality if the film industry, and since our dataset focuses on the film industry among English-spoken countries, other NonEnglish-spoken countries may find that our result may inapplicable. Thus, we will clearly state here that our analysis and result is just specific for the film industry in English-spoken countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, gender inequality does exist in the film industry in most of the English-Spoken countries for many years. In order to explore the gender inequality in the film industry, we bring up some sub-questions to compare the spoke frequency, rating, grossing verse gender.  \n",
    "From the dataset, Cornell Movie--Dialogs Corpus, that we find on the website of Department of Information Science, Cornell University, we collect 304,713 utterances from 617 different movies. Then, after we separate these dialogues based on gender,  we find out that the number of dialogue distributed to female and male characters is uneven. Male characters always tend to have more dialogue than female characters in the movies throughout the years. Therefore, the spoke frequency of movie characters is influenced by their gender, which male characters speak more frequent than female characters.  \n",
    "In addition, from the analyze gender and grossing dataset, we collect 2000 movies data. We use two angles to explore the gender problem. First, we compare distribution, mean and median value of movie grossing based on gender difference, and we found that female leading movies' number, mean grossing, and median grossing are all far fewer than male leading movies, which indicates that female leading movies' box office is bleaker than male leading movies' box office. Second, we explore the gender proportion of top-grossing movies in 17 years and the top 10% grossing movies. And we found that female's proportion is far less than male's proportion in each of exploration, which means male leading movies are more likely to gain higher box office. In general, male leading movies obviously earn more than female leading movies, which means that female leading movies don't have enough strength or charm to appeal audience to watch and directors to make such kinds of movie, and also means female are not valued enough and gender inequality still exists until now.  \n",
    "Then, we analyze the proportion of gender in movies overall. We found that there are overall more male gendered characters than female gendered characters. First we wrote functions to calculate both male and female characters, secondly, we plotted both proportions of female and male characters in movies to avoid the unknown gender as a confounding variable. We found that the female proportion in movies is positively skewed, and the male proportion in movies is negatively skewed, which means that there are less female characters in movies overall. This indicates that in the movie industry, the male characters are still dominating.   \n",
    "Also, from the movie rating VS gender dataset, we can easily see that there is a difference between actor and actress on the rate of movie. Our hypothesis was the movie that male main character plays will make a higher rate of the movie than the female main character. And it turns out just as we expected. When we compare that through the year, the result is also the same. However, we had expected there is a lot more average rating difference between the male characters and the female characters, but there was only a little difference which is not expected. Since we didn't research in the same genre of movie, we don't know exactly which genre of movie we collected. If we have more action genre of movie, it will lead to the more male main character. It is kind of our limitation on this dataset.   \n",
    "Overall, our analysis points out the fact that gender inequality does exist in the film industry in most of the English-Spoken countries, and our society should pay more attention and work to eliminate this issue. Everyone should be treated in any field of lives. Only after people get the euqal chances to speak and represent themselves, our society can finally achieve harmony."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}