gentle_introduction_to_machine_learning.ipynb

{
 "cells": [
  {
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   "source": [
    "# GENTLE INTRODUCTION TO MACHINE LEARNING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "1. [Introduction](#Introduction)\n",
    "\n",
    "2. [Required libraries](#Required-libraries)\n",
    "\n",
    "3. [The problem domain](#The-problem-domain)\n",
    "\n",
    "4. [Step 1: Answering the question](#Step-1:-Answering-the-question)\n",
    "\n",
    "5. [Step 2: Checking the data](#Step-2:-Checking-the-data)\n",
    "\n",
    "6. [Step 3: Tidying the data](#Step-3:-Tidying-the-data)\n",
    "\n",
    "    - [Bonus: Testing our data](#Bonus:-Testing-our-data)\n",
    "\n",
    "7. [Step 4: Exploratory analysis](#Step-4:-Exploratory-analysis)\n",
    "\n",
    "8. [Step 5: Classification](#Step-5:-Classification)\n",
    "\n",
    "    - [Cross-validation](#Cross-validation)\n",
    "\n",
    "    - [Parameter tuning](#Parameter-tuning)\n",
    "\n",
    "9. [Further reading](#Further-reading)\n",
    "\n",
    "10. [Acknowledgements](#Acknowledgements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "In the time it took you to read this sentence, terabytes of data have been collectively generated across the world — more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.\n",
    "\n",
    "In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields — statistics, physics, computer science, design, and many more — the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.\n",
    "\n",
    "In this notebook, we're going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like.\n",
    "\n",
    "<img src='./images/datascience-pipeline.PNG'/>\n",
    "\n",
    "In addition to providing code examples, the goal is to imbue in you a sense of good practices so you can be a more effective — and more collaborative — data scientist.\n",
    "\n",
    "We will be following along with the data analysis checklist from [The Elements of Data Analytic Style](https://leanpub.com/datastyle), which we strongly recommend reading as a free and quick guidebook to performing outstanding data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required libraries\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This notebook uses several Python packages that come standard with the Anaconda Python distribution. The primary libraries that we'll be using are:\n",
    "\n",
    "* **NumPy**: Provides a fast numerical array structure and helper functions.\n",
    "* **pandas**: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.\n",
    "* **scikit-learn**: The essential Machine Learning package in Python.\n",
    "* **matplotlib**: Basic plotting library in Python; most other Python plotting libraries are built on top of it.\n",
    "* **Seaborn**: Advanced statistical plotting library.\n",
    "\n",
    "To make sure you have all of the packages you need, install them with `conda`:\n",
    "\n",
    "    conda install numpy pandas scikit-learn matplotlib seaborn\n",
    "\n",
    "`conda` may ask you to update some of them if you don't have the most recent version. Allow it to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The problem domain\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "For the purposes of this exercise, let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\n",
    "\n",
    "We've been tasked by our head of data science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\n",
    "\n",
    "<img src=\"images/petal_sepal.jpg\" />\n",
    "\n",
    "We've been given a [data set](iris-data.csv) from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\n",
    "\n",
    "### *Iris setosa*\n",
    "\n",
    "<img src=\"images/iris_setosa.jpg\" />\n",
    "\n",
    "### *Iris versicolor*\n",
    "<img src=\"images/iris_versicolor.jpg\" />\n",
    "\n",
    "### *Iris virginica*\n",
    "<img src=\"images/iris_virginica.jpg\" />\n",
    "\n",
    "The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future.\n",
    "\n",
    "**Note:** The data set we're working with is the famous [*Iris* data set](https://archive.ics.uci.edu/ml/datasets/Iris) — included with this notebook — which we have modified slightly for demonstration purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Answering the question\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The first step to any data analysis project is to define the question or problem we're looking to solve, and to define a measure (or set of measures) for our success at solving that task. The data analysis checklist has us answer a handful of questions to accomplish that, so let's work through those questions.\n",
    "\n",
    ">Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?\n",
    "\n",
    "We're trying to classify the species (i.e., class) of the flower based on four measurements that we're provided: sepal length, sepal width, petal length, and petal width.\n",
    "\n",
    ">Did you define the metric for success before beginning?\n",
    "\n",
    "Let's do that now. Since we're performing classification, we can use [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision) — the fraction of correctly classified flowers — to quantify how well our model is performing. Our head of data has told us that we should achieve at least 90% accuracy.\n",
    "\n",
    ">Did you understand the context for the question and the scientific or business application?\n",
    "\n",
    "We're building part of a data analysis pipeline for a smartphone app that will be able to classify the species of flowers from pictures taken on the smartphone. In the future, this pipeline will be connected to another pipeline that automatically measures from pictures the traits we're using to perform this classification.\n",
    "\n",
    ">Did you record the experimental design?\n",
    "\n",
    "Our head of data has told us that the field researchers are hand-measuring 50 randomly-sampled flowers of each species using a standardized methodology. The field researchers take pictures of each flower they sample from pre-defined angles so the measurements and species can be confirmed by the other field researchers at a later point. At the end of each day, the data is compiled and stored on a private company's Hadoop or GitHub repository.\n",
    "\n",
    ">Did you consider whether the question could be answered with the available data?\n",
    "\n",
    "The data set we currently have is only for three types of *Iris* flowers. The model built off of this data set will only work for those *Iris* flowers, so we will need more data to create a general flower classifier.\n",
    "\n",
    "<hr />\n",
    "\n",
    "Notice that we've spent a fair amount of time working on the problem without writing a line of code or even looking at the data.\n",
    "\n",
    "**Thinking about and documenting the problem we're working on is an important step to performing effective data analysis that often goes overlooked.** Don't skip it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Checking the data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The next step is to look at the data we're working with. Even curated data sets from the government can have errors in them, and it's vital that we spot these errors before investing too much time in our analysis.\n",
    "\n",
    "Generally, we're looking to answer the following questions:\n",
    "\n",
    "* Is there anything wrong with the data?\n",
    "* Are there any quirks with the data?\n",
    "* Do I need to fix or remove any of the data?\n",
    "\n",
    "Let's start by reading the data into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "0              5.1             3.5              1.4             0.2   \n",
       "1              4.9             3.0              1.4             0.2   \n",
       "2              4.7             3.2              1.3             0.2   \n",
       "3              4.6             3.1              1.5             0.2   \n",
       "4              5.0             3.6              1.4             0.2   \n",
       "\n",
       "         class  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "iris_data = pd.read_csv('iris-data.csv')\n",
    "iris_data.head()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're in luck! The data seems to be in a usable format.\n",
    "\n",
    "The first row in the data file defines the column headers, and the headers are descriptive enough for us to understand what each column represents. The headers even give us the units that the measurements were recorded in, just in case we needed to know at a later point in the project.\n",
    "\n",
    "Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower.\n",
    "\n",
    "**One of the first things we should look for is missing data.** Thankfully, the field researchers already told us that they put a 'NA' into the spreadsheet when they were missing a measurement.\n",
    "\n",
    "We can tell pandas to automatically identify missing values if it knows our missing value marker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris_data = pd.read_csv('iris-data.csv', na_values=['NA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voilà! Now pandas knows to treat rows with 'NA' as missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, it's always a good idea to look at the distribution of our data — especially the outliers.\n",
    "\n",
    "Let's start by printing out some summary statistics about the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.644627</td>\n",
       "      <td>3.054667</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.236552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.312781</td>\n",
       "      <td>0.433123</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.755058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.055000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
       "count       150.000000      150.000000       150.000000      145.000000\n",
       "mean          5.644627        3.054667         3.758667        1.236552\n",
       "std           1.312781        0.433123         1.764420        0.755058\n",
       "min           0.055000        2.000000         1.000000        0.100000\n",
       "25%           5.100000        2.800000         1.600000        0.400000\n",
       "50%           5.700000        3.000000         4.350000        1.300000\n",
       "75%           6.400000        3.300000         5.100000        1.800000\n",
       "max           7.900000        4.400000         6.900000        2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "iris_data.describe()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see several useful values from this table. For example, we see that five `petal_width_cm` entries are missing.\n",
    "\n",
    "If you ask Divergence, though, tables like this are rarely useful unless we know that our data should fall in a particular range. It's usually better to visualize the data in some way. Visualization makes outliers and errors immediately stand out, whereas they might go unnoticed in a large table of numbers.\n",
    "\n",
    "Since we know we're going to be plotting in this section, let's set up the notebook so we can plot inside of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This line tells the notebook to show plots inside of the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\n",
    "\n",
    "We can even have the plotting package color each entry by its class to look for trends within the classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f3f2422ad30>"
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     "execution_count": 7,
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3oLf1FeF8IS/ZXAzJWguDfS3TvWheCRx5G+NDYgqNuaQYABAc9aDnwB9RuefiabMpeUcm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J6igQohhBBCCCFBMldVwVyphzQtDfKszJCUyTAMHn30UYhEIjQ1NWHy5Mk4e/YsHnroITz88MMw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f26611630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We have to temporarily drop the rows with 'NA' values\n",
    "# because the Seaborn plotting function does not know\n",
    "# what to do with them\n",
    "sb.pairplot(iris_data.dropna(), hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatterplot matrix, we can already see some issues with the data set:\n",
    "\n",
    "1. There are five classes when there should only be three, meaning there were some coding errors.\n",
    "\n",
    "2. There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
    "\n",
    "3. We had to drop those rows with missing values.\n",
    "\n",
    "In all of these cases, we need to figure out what to do with the erroneous data. Which takes us to the next step..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Tidying the data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Now that we've identified several errors in the data set, we need to fix them before we proceed with the analysis.\n",
    "\n",
    "Let's walk through the issues one-by-one.\n",
    "\n",
    ">There are five classes when there should only be three, meaning there were some coding errors.\n",
    "\n",
    "After talking with the field researchers, it sounds like one of them forgot to add `Iris-` before their `Iris-versicolor` entries. The other extraneous class, `Iris-setossa`, was simply a typo that they forgot to fix.\n",
    "\n",
    "Let's use the DataFrame to fix these errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'\n",
    "iris_data.loc[iris_data['class'] == 'Iris-setossa', 'class'] = 'Iris-setosa'\n",
    "\n",
    "iris_data['class'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! Now we only have three class types. Imagine how embarrassing it would've been to create a model that used the wrong classes.\n",
    "\n",
    ">There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
    "\n",
    "Fixing outliers can be tricky business. It's rarely clear whether the outlier was caused by measurement error, recording the data in improper units, or if the outlier is a real anomaly. For that reason, we should be judicious when working with outliers: if we decide to exclude any data, we need to make sure to document what data we excluded and provide solid reasoning for excluding that data. (i.e., \"This data didn't fit my hypothesis\" will not stand peer review.)\n",
    "\n",
    "In the case of the one anomalous entry for `Iris-setosa`, let's say field researchers know that it's impossible for `Iris-setosa` to have a sepal width below 2.5 cm. Clearly this entry was made in error, and we're better off just scrapping the entry than spending hours finding out what happened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f1897a080>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IpCWhHw/hcHavzxeJtPTTNPGXyPsy0bw8dskkFfbzgn/HDACAIZ6eMd9syZIl8ZgDAACI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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f189827b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This line drops any 'Iris-setosa' rows with a separal width less than 2.5 cm\n",
    "iris_data = iris_data.loc[(iris_data['class'] != 'Iris-setosa') | (iris_data['sepal_width_cm'] >= 2.5)]\n",
    "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'sepal_width_cm'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Now all of our `Iris-setosa` rows have a sepal width greater than 2.5.\n",
    "\n",
    "The next data issue to address is the several near-zero sepal lengths for the `Iris-versicolor` rows. Let's take a look at those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.067</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.060</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.057</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.055</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.055</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "77            0.067             3.0              5.0             1.7   \n",
       "78            0.060             2.9              4.5             1.5   \n",
       "79            0.057             2.6              3.5             1.0   \n",
       "80            0.055             2.4              3.8             1.1   \n",
       "81            0.055             2.4              3.7             1.0   \n",
       "\n",
       "              class  \n",
       "77  Iris-versicolor  \n",
       "78  Iris-versicolor  \n",
       "79  Iris-versicolor  \n",
       "80  Iris-versicolor  \n",
       "81  Iris-versicolor  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') & (iris_data['sepal_length_cm'] < 1.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about that? All of these near-zero `sepal_length_cm` entries seem to be off by two orders of magnitude, as if they had been recorded in meters instead of centimeters.\n",
    "\n",
    "After some brief correspondence with the field researchers, we find that one of them forgot to convert those measurements to centimeters. Let's do that for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f18e6eeb8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pGV/koS57ixd+MhYv/GStxwAgiUKnabNnz45t27ZFRMS+ffti6tSp0djYWOpgAFCPCp0x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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f18bfb5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') & (iris_data['sepal_length_cm'] < 1.0), 'sepal_length_cm'] *= 100.0\n",
    "\n",
    "iris_data.loc[iris_data['class'] == 'Iris-versicolor', 'sepal_length_cm'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phew! Good thing we fixed those outliers. They could've really thrown our analysis off.\n",
    "\n",
    ">We had to drop those rows with missing values.\n",
    "\n",
    "Let's take a look at the rows with missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "7               5.0             3.4              1.5             NaN   \n",
       "8               4.4             2.9              1.4             NaN   \n",
       "9               4.9             3.1              1.5             NaN   \n",
       "10              5.4             3.7              1.5             NaN   \n",
       "11              4.8             3.4              1.6             NaN   \n",
       "\n",
       "          class  \n",
       "7   Iris-setosa  \n",
       "8   Iris-setosa  \n",
       "9   Iris-setosa  \n",
       "10  Iris-setosa  \n",
       "11  Iris-setosa  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
    "              (iris_data['sepal_width_cm'].isnull()) |\n",
    "              (iris_data['petal_length_cm'].isnull()) |\n",
    "              (iris_data['petal_width_cm'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not ideal that we had to drop those rows, especially considering they're all `Iris-setosa` entries. Since it seems like the missing data is systematic — all of the missing values are in the same column for the same *Iris* type — this error could potentially bias our analysis.\n",
    "\n",
    "One way to deal with missing data is **mean imputation**: If we know that the values for a measurement fall in a certain range, we can fill in empty values with the average of that measurement.\n",
    "\n",
    "Let's see if we can do that here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f188f96a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9GpKTv4qiGIqbqXvHjBoZDXu3Rv+Bd8oe5aCirze6uo6O7dv/VPYoB+3Y8Wrs29tV9hgD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\nmAEgEWEGgESEGQASEWYASESYASCR/w+EKNDBbirE3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f18962c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the petal widths for `Iris-setosa` fall within the 0.2-0.3 range, so let's fill in these entries with the average measured petal width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "7               5.0             3.4              1.5            0.25   \n",
       "8               4.4             2.9              1.4            0.25   \n",
       "9               4.9             3.1              1.5            0.25   \n",
       "10              5.4             3.7              1.5            0.25   \n",
       "11              4.8             3.4              1.6            0.25   \n",
       "\n",
       "          class  \n",
       "7   Iris-setosa  \n",
       "8   Iris-setosa  \n",
       "9   Iris-setosa  \n",
       "10  Iris-setosa  \n",
       "11  Iris-setosa  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_petal_width = iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].mean()\n",
    "\n",
    "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
    "              (iris_data['petal_width_cm'].isnull()),\n",
    "              'petal_width_cm'] = average_petal_width\n",
    "\n",
    "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
    "              (iris_data['petal_width_cm'] == average_petal_width)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sepal_length_cm, sepal_width_cm, petal_length_cm, petal_width_cm, class]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
    "              (iris_data['sepal_width_cm'].isnull()) |\n",
    "              (iris_data['petal_length_cm'].isnull()) |\n",
    "              (iris_data['petal_width_cm'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now we've recovered those rows and no longer have missing data in our data set.\n",
    "\n",
    "**Note:** If you don't feel comfortable imputing your data, you can drop all rows with missing data with the `dropna()` call:\n",
    "\n",
    "    iris_data.dropna(inplace=True)\n",
    "\n",
    "After all this hard work, we don't want to repeat this process every time we work with the data set. Let's save the tidied data file *as a separate file* and work directly with that data file from now on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_data.to_csv('iris-data-clean.csv', index=False)\n",
    "\n",
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at the scatterplot matrix now that we've tidied the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f3f18885240>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XB5vNBr1ej+xszyusE1Fs+QoBsNutaD+6Hw3GkUW2VBddCsmYX9cUxcLL9Wkl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oocA7QTpgAY+LprIiLyzZ+4Qn/G3/jCi5G3bh0sNVWQZWRDXnhxOJpPQ8BgY1vFPD1HEEULTlRi\nmEQiQ2rxdOjmqgUxLgNdd01ERL75E1fo1/grkUI+bgJ0s6YHFJ9Iw9dgY1td6vPwHEEULSI2UXny\nySfx+eefw2Kx4Ac/+AGuvPJKx7n9+/dj06ZNkMlkmDlzJlavXh2pZhIRERERUQREZKJy8OBBnDp1\nCjt27MD58+dxww03CCYqjz/+OF577TXo9XosX74c8+bNQ35+fiSaSkREREREERCRiUppaSmKi4sB\nABqNBp2dnbDZbJBIJKioqEBKSgrS09MBALNmzcKBAwc4USEiIiIiGkYiMlGRSCRQKpUAgD/84Q+Y\nNWsWJBIJAKChoQFardZxrVarRYVoK8doYU+suKeuP6jNJbEXURSwWCz4+OOPvF4zc+blYWoNUfRx\nTpRrD4LneE7Rhs8dNNxENJj+gw8+wJ/+9Ce8+uqrHq+x2aI3L4K7xIoMYKdodObMaTz5j2eRqE1y\ne76jqR1GY26YW0UUPTieUyxgP6XhJmITlb179+Lll1/Gq6++CpVK5Xhdr9ejvr7ecVxbWwu9Xu+z\nPp1OPaj2BFJ+T12t4Li2qxYz8kvC9v4sH7zy4RTstvpT37lzKugKMqHOcp8ssdV0Hlqtyu05sWBe\nF4n3tF8n/rmFog/FUr8UC1bbY6WegY7nsfK5IlVPOMXC726w6gvmc4fYcPo5UuyIyESlra0NTz31\nFLZu3Qq1WtjpsrOz0d7eDpPJBL1ej927d2Pjxo0+6xzMtno6XWDb8qUr012OA6kn0Pdn+eCVD6dg\nbgHp72dvamoLyjXBvi4S72m/zvnnNtg+5E6w64zFfhqsn0E46hnIeB5LnytS9YRTLPzuBqu+YD13\niMXKGBiKNlJ0i8hE5d1338X58+fxox/9yBFEP2XKFIwZMwZz587Fhg0bsG7dOgDAggULkJsbnUtS\ngp14iYiIIoOJcikW8LmDhpuITFQWLVqERYsWeTxfUlKCHTt2hLFFgQl24iUiIooMJsqlWMDnDhpu\nuFUEERERERFFnYju+kVEg9fb2+v1vEwmC1NLiIiIiIKHExWiGPfLW5ZilNT9l6Ndvb0Y+4M7kZGb\nF95GEREREQ0SJypEMS4vMRElkLg919Hbg9ZeS5hbRERERDR4jFEhIiIiIqKow29UiGLcl3U1aFco\n3Z7rtlgwpqsrzC0iIiIiGjxOVIhiXOu80fiqMMntuZ5OMwqSEsLcIiIiIqLB49IvIiIiIiKKOpyo\nEBERERFR1OHSLyIaliwWC3bseAMAoFYr0drqGsuzZMkyAHBc58mSJcuYr4aIiCjIOFEhomHpzJnT\n+M0fPoUiKcXt+e7285gyZSoA+HVdfv5FIWsrERHRcMSJChENW1ljp0E1ItvtubZzVQO+joiIiIIn\nYhOVsrIy3HXXXVi5ciWWLVsmODdnzhxkZWVBIpFAIpHg6aefhl6vj1BLiUjMYrGivb7V4/n2+lZY\nLFbIZAyDIyIiosBEZKLS2dmJxx57DFOnTnV7XiKRYMuWLVAq3eeGIKJIs+H8kZHoVmvdnu1sbQLm\n28LcJiIiIhpKIjJRUSgU2LJlC15++WW35202G2w2PuQQRSuZTIbUnEKvy6EYXE5ERESDEZF1GVKp\nFHK53Os1GzZswM0334xnnnkmTK0iIiIiIop+K1asQG1tbaSbEXJRGUy/du1azJgxAykpKVi9ejXe\nf/99XHXVVZFuFlFUaiqvhyLOdWtdAOjt6oHlYgsA+Iwpcfdvb9d1NNd5vM75nKfr/Lkm1q4jIiIK\nB4lEEukmhIXEFsE1Vs8//zxGjBjhEkzvbPv27WhqasLdd98dxpYREREREUWHlpYWPPjgg5BKpbBa\nraiursYLL7yA2tpavPDCC9BoNACAp556Cp9++im2b98OlUoFAHjiiSfw6quv4uTJk7BYLBg/fjxu\nu+22SH4cv0XdNyptbW1Yu3YtXnrpJcTHx+Pw4cO4+uqrI90sIiIiIqKI2L59O6644gpcf/31+Pjj\nj/HQQw8B6Htu/tnPfoacnBzceeedOHPmDPbs2YNrr70W11xzDU6fPo2enh588MEHeOaZZ5CZmYkT\nJ05E+NP4LyITlaNHj+KXv/wlTCYT4uLi8N5772HOnDnIycnB3LlzMXv2bCxevBhKpRLjxo3DvHnz\nItFMIiIiIqKIM5lMuPTSSwEAM2fOhMFgAADI5XK8+OKLUKlUqKioQFdXF/7zP/8T//3f/43t27fj\n0ksvxb333ouf//zn+PWvf43q6mosWrQIBQUFkfw4fovo0i8iIiIiIvJuy5YtUKvVWLx4MT744AM8\n+OCD2LVrF+68805s2bIFqampWLZsGR588EF0dnaiuLgYcXFxuP/++3HbbbehtbUVkydPRk9PD268\n8Ubs2rUr0h/JL5yoEBERERFFsZaWFvz0pz+FVCpFb28v6urqsHnzZmzfvh3ffPMNMjMzER8fj9ra\nWlxxxRX485//jBEjRqC3txePPfYYnnnmGdTX1yMuLg5GoxF33XVXpD+SXzhRISIiIiKiqBORPCpE\nRERERETecKJCRERERERRhxMVIiIiIiKKOpyoEBERERFR1OFEhYiIiIiIog4nKkREREREFHU4USEi\nIiIiihJVVVW48cYbBa/t3bsXO3bsCPp7vffee0GvM5jiIt0AIiIiIiLqJ5FIBMczZswIyfu8/PLL\nmDdvXkjqDgZOVIiIiIiIAlTd0IbK+nZkaBNhSFcHpU6bzYYHHngA8fHxOH/+PC6//HKUlZXhvvvu\nw49//GM0NDTAbDZjzZo1uOyyywRl33rrLbzxxhuQy+UoKCjAz372M5w6dQqPPPIIpFIpkpKS8MQT\nT+DNN9/EyZMncc899+C5557Dk08+iS+++AJWqxU333wzFi5c6LauTz/9FM8++yzi4+ORnJyMX//6\n14iLC82Ugku/iIiIiIgCcLrqPNa/uA+PbDmAH2/+GMe+bQxq/SkpKXjuuecA9H3LUlZWhnPnzmHb\ntm149dVX0dzc7FLmtddew/PPP4833ngDF198Mbq7u/Hoo4/i0Ucfxf/8z/9g2rRp2L59O1atWgW1\nWo3nnnsOR44cwalTp/C73/0OW7duxQsvvID29naXusxmM1paWrBx40Zs27YNSUlJ+OSTT4L6mZ3x\nGxUiIiIiogD8s6wBDc1dAID2zl58dqIO40amBq3+CRMmCI7z8/PR0dGB//qv/8LcuXMxf/58lzIL\nFizA6tWrcd111+G73/0uFAoFvvrqK/z0pz+FzWZDT0+PS71ff/01Jk2aBABISEhAfn4+ysvLBXUt\nWLAAcrkcI0aMwIMPPgiLxYLKykpMnTo1aJ9XLConKvb/Ac3Nzejp6cFdd93l8rUWEREREVEkJavk\ngmNNktzDlYGJj48XHCsUCrz55pv4/PPPsXPnTnz00UdYtGgRNm7cCIlEgqeffho/+MEPcN111+Fv\nf/sbbr31Vmzbtg2JiYl4/fXXvb6XzWZz/NtsNkMikQjqWrlyJbZt24b169fjlVdewciRI/Hoo48G\n9fOKReXSr507d2LUqFF4/fXX8eyzz+Lxxx+PdJOIiIiIiASmFGXi5nljkZuhxg2z8zFrYnZI3+/4\n8ePYtWsXLr30UmzYsAGnT5/GJZdcgm3btuH111+HTqfDpk2bkJaWhpUrV+KSSy6ByWTC2LFj8fHH\nHwMA3n33XRw4cAAAYLVaAQBFRUU4dOgQAKC9vR2VlZXIy8tzW1dbWxsyMzPR0tKCgwcPoqenJ2Sf\nNyq/URkxYgROnjwJAGhuboZWq41wi4iIiIiIhJKU8Vh6VQGWXlUQ1HrFu37Z5eTkYOPGjfj9738P\nmUyGVatWuZRLSkrC4sWLodFoYDAYMG7cOKxfvx4PPfQQXnnlFSiVSmzcuBEAUFhYiEWLFuHNN9/E\nuHHjsHz5cvT29uL++++HUql0W9fNN9+MJUuWYOTIkbj99tvx/PPP4/LLL0daWlpQfwYAILE5f88T\nRW6//XaUl5ejpaUFL7/8sstaOiIiIiIiGrqicunXrl27kJWVhffffx9bt27Fww8/HOkmERERERFR\nGEXlROXzzz93JLYpKChAXV0dvH3xE6VfChG5YF+lWMB+SrGCfZVoaIvKGJXc3Fz885//xJVXXomq\nqiokJSV5XKsH9K3Hq69vDfj9dDo1yw/z8uEy2L4qNtjPHo462cbg1RcuweqnwfoZsJ7Yqydcgj2m\nAsN3fBmubaToFpUTlcWLF2P9+vVYsWIFLBYLHnnkkUg3iYiIiIiIwigqJyqJiYn49a9/HelmEBER\nERFRhERljAoREREREQ1vnKgQEREREUWJqqoq3HjjjYLX9u7dix07doS9Lffddx/MZrPf17tr+2BE\n5dIvIiIiIqLhSryJlH033HCzJ4YcCG8bYA0UJypERERERAGqaa2DqbUO6UmpyE7ODEqdNpsNDzzw\nAOLj4/8/e3ce3mSV6A/8m6Rp0yVtaZvuTcGyFCpgESqgUPG6i+N4RxkQgRlFf+OCjqjjiDM443Id\nHZFxvV4X7ijqII7DuOE2ioKKiKNXkVWW0r1N93RJkyb5/VHyNufN2jZr+/08D8/Dm/ec856k532T\ns6OtrQ0LFizAoUOHcOutt+L2229HU1MTzGYzVq1ahTPOOEOKd+DAATzwwAN44YUXAABPPPEEUlNT\nMWfOHNxzzz1QKpVITEzEn/70J7S3t+O2225DUlISrrzyShw6dAgffvghlEolzjrrLFx77bU466yz\n8M4776C1tRV33HEH7HY7cnNz8eCDD6KxsRFr1qyB2WyGSqXC/fffL7yHXbt2Yf369VCr1cjOzsb9\n99+Pd955B9u3b4fBYMAjjzyCzMxMr58Dh34REREREQ1BRWsV/rBtPf6040nc9dGfccBwJKDpp6am\n4rHHHgPQ31Nx6NAhtLa2YuPGjXj++efR3t4uhC8uLobBYEBnZycA4OOPP8a5556Le++9F/feey/+\n93//F3PnzsVLL70EoL9is27dOpSXl2PDhg3YtGkTNm3ahOTkZOmaALB+/XpcffXVeOmll5CZmYk9\ne/bg0UcfxWWXXYaNGzdiyZIlePzxx4W8/OEPf8Cjjz6KjRs3IiUlBW+//TYAoK6uTkrHF1ZUiIiI\niIiGYE/DAbT0tAEAui09+L+6vQFNf9q0acJxUVERuru7cccdd2Dnzp246KKLXOIsWLAA27dvR11d\nHTQaDTIzM/H999/jd7/7HZYtW4Y333wTzc3NAAC9Xi9VSs4//3ysWLECmzdvxsUXXyylZ7fbsW/f\nPpSWlgIAbrvtNkybNg0//PADTjvtNADAaaedhv3790tx2tvboVQqkZWVBQAoKyvDvn37AABTp071\n+/1z6BcRERER0RBo45Jkx4kBTV+tVgvHcXFx2Lx5M7755hts2bIF27Ztw6JFi7Bu3TooFAo8/PDD\nOOecc/DSSy+htbUV5557LoD+rT9efPFFIa2amhoh/bvvvhvHjh3D1q1bsWzZMrz22mvSOaVSCZvN\nJsRXKpWw2+0AAIvFAqVyoP9DoVAI4S0WC1Qqldv35A17VIiIiIiIhqAs7xQsOvki6FNycfGks3FG\nYVlQr7d//368+eabmDFjBu6++24cPXoUp5xyCjZu3IgXX3wRmZmZOOWUU3DkyBF8+umnOP/88wEA\nkyZNwvbt2wEAW7duxZdffgkAUkWjs7MTTz75JMaNG4cbbrgBqamp6OzshN1uh0KhwLRp06Q4jz32\nGHbu3ImpU6dKr3311Vc4+eSTpXwmJydDqVSivr7e7Xl/sUeFiIiIiGgIEmLjcVnJQlxWsjCg6Xpa\nOSs/Px/r1q3Dq6++CpVKhauvvtptuNLSUuzfvx/Z2dkAgDVr1mDt2rV49tlnodFosG7dOhiNRuk6\nSUlJaG1txeWXX47ExETMmDEDKSkp0vlVq1bhzjvvxCuvvILc3FysWrUKRUVFuOuuu7B582bExsbi\n/vvvh8VikfJwzz33YPXq1YiJiYFer8dFF12EN954Y3Cfg91RlYpyBoNxyHF1Osbh3foAACAASURB\nVC3jj/L4oTScvMoN972HIk3mMXDphVIg8h6oz4DpRF86oRQN9y7zGHnpOdKkyBaRPSp///vf8cYb\nb0ChUMBut2Pv3r345ptvwp0tIiIapDt/93scPnLY43m1Wo2XX3ghoOvuExHRyBCRFZXLLrsMl112\nGQBg9+7deO+998KcIyIiGgproh7a6Qs8njc1fg+bzSZNsiQiInKI+Mn0Tz75JK6//vpwZ4OIiIiI\niEIooisqe/bsQU5ODtLT08OdFSIiIiIiCqGInky/du1aXHzxxZg1a1a4s0IhZLda0bL7a3QdP47E\nwrFIK5sJhTKi69QjEv8OFAj/747HUNtX6PG8pekHvLPhtxz6RTRK8buGvInIOSoOX331FdauXetX\n2HCvGsX4gYtv3vc9Kh55RDoeu3o1YqdMcxc1YNcPpWhYBcVgMA767+ArvUCKltVkuOqXFmZLH+Bl\nnrzVZoPBYPRaUYnEVa2Yjvd0Qika7l3m0TN/v2u46tfoFLFV1sbGRiQmJiImJqLrUhQEvVVVXo8p\nNPh3ICKiYON3jauamhr87Gc/E17bsWMHNm3aNOQ0H3jgAdTU1AQk7A033DDkfAxWxNYCDAYD56aM\nUpqCAuE4TnZMocG/AxERBRu/a9yTL9k+b968YaV35513Bizsk08+Oay8DEbEVlRKSkrwzDPPhDsb\noWO3wbz/B/RWVUFTUAD15JMBRcR2eAWVurgE+pVXwVRZhXh9AWKLS8KdpVFJPflkjF29Gr1VVYgr\nKEDs5JPFANFQZqMhj0REI8mJ525lfQ1isvNcn7vy53JxiffvmijQU1eHnppaaLKykFCQH5A07XY7\n7rzzTqjVarS1tWHBggU4dOgQbr31Vtx+++1oamqC2WzGqlWrcMYZZ0jxDhw4gAceeAAvvPACAOCJ\nJ55ASkoKPvjgA9x999147733UFVVhZqaGmzYsAG/+c1vUF9fj1NOOQXvvvsuPv30UyxbtkwK29HR\ngWPHjqG6uhpr1qzBvHnzMHv2bHz55ZfYt28f7rnnHiiVSpSWluL222/Hzp078eijj0KtViMlJQV/\n+ctfhjU6KmIrKqONef8PAZkPMBKYD+xF5XMbpOOxyamj9rMIK4USsVOmefzso6HMRkMeiYhGEl/P\nXU/no/XZ3Hn0GPbf/wDMTc1QJSZiyu/vRPLkyQFLPzU1Fffccw+2bNkChUKBQ4cOobW1FRs3bkRn\nZyc+/fRTIXxxcTEMBgM6OzuRlJSEjz/+GE8//TQ+/PBDKUxfXx9eeuklbNu2DRaLBZs2bcInn3yC\nF1980eX6DQ0NePbZZ7Fjxw68+uqrmDdvntTbc//99+Pee+/FhAkT8Nvf/hZ1dXXo6OjAunXrkJeX\nhzvuuAOfffYZzjzzzCG/f1ZUIoS7MZrRetMOiVMLi93ULZwadZ9FlPBZZn21qkVCHomIKKA8zTlx\n9KCMtOdy23ffw9zUDACwdnWh9Zv/C2hFZdo08bMpKipCd3c37rjjDpx99tm46KKLXOIsWLAA27dv\nR2lpKTQaDTIzM+G8yO/UqVMBAEeOHMGMGTMAAOXl5W4XNTn11FMBANnZ2TAaxcUMjh07hgkTJgAA\n/vSnPwEAqqqqcNddd8FqtaK6uhpz5swZ6lsHwIpKxBjtYzSdW1gy5p8hnBttn0W08FVmI6E3Y7Tf\nV0REoSZ/7qpTtMJ3QeHKq4Xz0f5cVicnC8cx2sCuJKZWq4XjuLg4bN68Gd988w22bNmCbdu2YdGi\nRVi3bh0UCgUefvhhnHPOOXjppZfQ2tqKc88912OadrtdqJzI58UAEM7LdzRRullGes2aNXj22Wcx\nbtw43HvvvYN7s26wohIhfM4HGOGcW1ha//0NCq5YjD6LdVR+FtHCV5mNhFaz0X5fERGFmuO5a62v\ngSo7D+a6OuG8pat7RD2X02efhl6DAc1f7ERqaSl084c36d2X/fv348cff8RPfvITTJs2DVdeeSVO\nOeUUbNy4UQqTmZmJP/7xj+jo6MA999zjMS29Xo8PPvgAAPDZZ5/BarX6lQdHhWX8+PH4/vvvMW3a\nNNx111246qqr0NnZiZycHHR0dGDXrl0oLi4exrtlRSVy+JgPMNI5t8BYu7qhys5FvPyz4MToyOKj\nzEZab4aXrTyIiCgIFADi8vOE12JzckbU752YxAToFy+CfvGigKbrrncDAPLz87Fu3Tq8+uqrUKlU\nuPrqq92GKy0txf79+5Gdne0xvQULFuD111/H0qVLUVZWhtTUVK/XludtzZo1+MMf/gCFQoFTTjkF\nRUVFuOKKK7B48WKMGzcOK1euxBNPPIEFCxYgIyPD7/cuXCuSd6YfjEjasJDxhxC/sV2qhEgtLLJK\niKdNobjhY4RuJHaiYuloVXP3Nx2qaNlIjBs+avHL1Y+gSVHkMYyp8Xts/PMqbvg4wtIJpWi4d0dT\nHl2eu7fcAigUXr/fQ51H5zRHs/b2duzatQvnnnsuGhoa8Mtf/hJbt24Nd7YE7FGh4PK3F8SPHqVI\nGEpEXrj5W8dOmQZd+ekB/3LxF8sMEVEA+fGd7vLcra6G9ryL+OyNQImJiXj33Xfx/PPPw263Y82a\nNeHOkgtWVCioAjmhOtKGEpEoEibPy7HMEBEFjj/PeT53o0dMTAzWr18f7mx4xYoKBVUgW7Q5MTqy\nRWLvBcsMEVHg+POcl0+m53OXhiNiKypvvvkmnn/+ecTExOCmm25CeXl5uLNEQxDQlpVRvuBApIvI\nVjSWGSKigPHrOX/iuRvOYb80ckRkRaWtrQ1PPvkk/vnPf6KrqwuPPfYYKyrRRDaGdeztt6G34nh/\ni3ZxCcz7vhfHt/qIz9W9IpT871RcIvZenPhbB3XDR5YVIqKQcemldvedPthnsK/neARsHkzhE5EV\nlS+++AKnn3464uPjER8f73UNaIo87sawas/r3znV3SpMyDzdZ3y2iEceT38nx9/K3xW3gpEHIiIK\nAlkvdSCe876e43zOj24RWSWtqalBT08PrrvuOlx55ZXYuXNnuLMUvew2mPd9D+P778Cy73vAbgv6\nJc11dciYdwbGzJqJjPlnCJs9uRvfKudPGAo/X38nn39Hd2VzkOWVZYWIKIRkz+hAPIOH/V1CI1pQ\ne1QaGhrw/vvvw2g0wnm7lhtvvNFrPLvdjra2Njz11FOorq7G8uXLsW3btmBmdcQKR0uEOjEetTs+\nk471K6+S/u/P+NaInOtALnz9nXydd1c2AQyqvLKsEBGFjvy5XbhS3GxwKM9gX8/x2JRk4VidMrr3\nPhltglpRueaaa1BSUoKsrKxBxcvIyEBpaSkUCgUKCgqQmJiIlpYWpKWleYwz3E17Rmr8yvoa4dha\nXwNd+eku4TzFt1utaNn9NbqOH0di4Viklc2EQunaEZeRliCF6+vsFtPo6pLSt8+bg7i435xIrxBp\nZbNcru8ujLtr+pP/SBTovAbjvfuTpq+/k21uGdC5Et3HK5FQqEf26adBGTPwyKk01CNj3hmwmkxQ\nxWtgbWoA+vqEa3gqr448DqWsDOc9R0KaoRKovMeqY4A+z+dVSiV0Oq3XDR8DmR+mE5p0Qika7t2R\nkkf5bwqb2YTiOz0/g3U6rc/fEcJ3xVg33xVm08B3hUYDu7k3KsspDU1QKyqpqal44IEHBh3v9NNP\nx5o1a3DNNdegra0N3d3dXispAHem9xQ/JjtPOFZl57mE9Rbfn/GnOp0WtZ99KYXLmH+GeM0c2TWL\npiC+aApsAJqau9xfXxbGG+5MH8Ydj738ncz7vkfFM89Jx/akZKHsKOM0aHLueZs4EaoMsVHDXXl1\nyeMgyoonYf8c/UwvlAK1Q7nZ0gcoPIex2mwwGIzcmX6EpRNK0XDvjpQ8uvymyMiCzcMz2JGmr98R\nLt8VieJ3RUxGFppefHkg/qyygL13VngiX1ArKueccw7efPNNlJaWCl9Cubm5XuNlZWXhvPPOw6JF\ni6BQKLB27dpgZnNE87qPhKeVNJxW4LCbxN4RT3tjOI8Zbd+3H/orl8DU2IR4vR6xxSVBe38UQAFe\nWcXS2Ii8S38Kc0sLYjPSYTEYEOt03twuftFY2o3QnHYG9z0hIgoXHytwqYtLoF95FUyVVYjXF/j1\n/S6fU+KYt+q4hq+9Wbgvy+gW1IrKwYMH8dZbbyE1NVV6TaFQ4JNPPvEZd9GiRVi0aFEQczdKeNlH\nwtP8FfOhfbAcO4q+lhZo8nKhSkyAtau/whKXn+92eWHnMaYpkyej8qW/DaSbnMIVOqJAoOczqVQK\nVG75p3SsX7FMOO8yLjk/H7DbYetog7W9DfaUZMBu99oaTxTJrFYrKiqOejzf2pqE5ORMn8PeiELF\n5wpcB/ai8rkNA+eTU31+T2gK9cIw35jkJHGeyzWyeS75+R7T4tfB6BPUisp3332H3bt3IzY21ndg\nCjlPrRi2uhrUOP/AvPIKWHot/RPcVEpU/Plh6ZxjeWHnnht/e2EosgR6Z3lTXb3Lscbp2K5SCuOO\nEaOC6avPhS9BPezQzJ4/5DwQhVNFxVF8cctNyElIcHv+i+5uzF3/GIqKJoQ4Z0Tu+foeGMr3hN1q\nE4b5FsgqIrbeXpfvAmdcnnh0C2pF5eSTT0Zvby8rKqHgqbvWZoXpq89PdNPqETdzNkxf7+w/zs2G\nOiMdlqZmAAMrbZgaDULSpsZGpC66EgBgfP8d4Zz00HLqubHs+x7AQDiuxBQdXHo4CvXeN/KSl61Z\nc2A+uE8KH6/Xi+mPLRTSM9fVCV9e6uwcWNvbhDimqmook4e5mRhRGOUkJECfxHHwFCF8DO0a9GqO\nXno/HMz1soVTrH3CcV9np/BdEFc4FrbWFum7xdzeIaTHxs/RJejLE5911lkoKiqCSqWC3W6HQqHA\nyy+/7DsyDYqnFgeXFupeEyo3Dnz++hXLYLeYhXGf8bk5QtqanIFjf5aD9TovhiKWfBwwrDZUrF8v\nnZe3YrmULYsZlS9sHAh/x2+gX3kVequqEVeQD2WiVras5cCy1UB/WbLLlqHUZGexJY2IKEB89U74\n+v521xPui8uWBSuWCcfy7wKVSiF+t/xCHDbMxs/RJagVlV/96lfBTJ6ceOqONVWKr5sMBqElo9dg\nQGxqijDu09LbJzyILEYjLO+/09/6UlziuxLiZV4MRbATfzdd+ekwGIxue8+EioqsbPVUi8tWWmpq\nodL096YqoHCdUNludC1Ldjv0sMNUWQWNvgB9XT1e80BERP7zOXTLx/e3pa4ecRkZA4uk1DcgdpL3\nxkj5winyYcHy74LuH/aIeTQ0czL9KBbUisrYsWPx3nvvYfny5QCA9evX44orrgjmJUctTz0dLsNv\ndBmofO8V6bhgyc9R9cqrAAZaVuJyclD7t4HJ8BnzzpC6ZR1h+GNx5PPVeyYvW/H5smUr5a1iy68U\nzqtTtK5lSQFoZs+HZnb/Yf8wQs95ICIi/w13k1xfi6S4I9+wUZOb7ZIH5+8Ce0e7eD4nW2hEo9El\nqBWVO++8Ez/72c+k4wkTJuDOO+/Ehg0bvMSioRC6awv1gNUG44leEP3/uwamYxXQ6AtgaRPHenYd\nq5D+72hZcU4rRq1C7RtvSmHkywqq2bIxYvkaAhBXNlfo/dDMmoux6TrPrWKtbQPLFaenwdpr8Z0H\nYSlMLnVNRDQcwx2a7WuRFHf6TL1Oz/502BQxYh6KS4T5i3Gz5ojfLWXuN/2l0SGoFRWz2YwLL7xQ\nOl64cCFeffXVYF5y9HLqrjXv+95lbkHqoqUAAJWshVqpVkv/l1pWZBPjHUsTA0BMYrzL+FZk8iEy\nIvkawqdUCb0fALy3io1JQaXTpl162bhkd1yXwuRS10REQzbModkuozT0vntkYjSxOP6SUy/MyquE\nPLjbEFL+3UKjV1ArKgCwfft2lJWVwWazYceOHVAouAp2sHkbg6qeNAX6FcvQU12D+IJ8KDN0iC/I\n8zjuU9764i5tGiV8rBYj5+hxcUyml/fmWdqNUHpbVQyBXzKZiIiGzl1PutfVIeFhc1+nYz7nyZug\nVlTuu+8+3H333bj55puhVCpRWlqKe++9N5iXJHgfg2ra/YWwMpN+5VXQ/3yR53GfstYXeTWTcwZG\nj0GvZX+ix6XgYi0MBqNLb546ReszveGOpyYiogCS9aS76w0Z7HNcPodFncLlvGlAUCsqhYWF+Otf\n/+r23LPPPotrrrkmmJcftbyNQXVeqUmVmABrezsqX92MmOy8gZYQLy3nXHp49Bp0q9eJfVYOV1VL\n446dy45jvpO39FjeiIgil9vvhcknw7z/B1TW1/T/tvCxWmhfr3nQ8xdp9Aj60C9PduzY4bGi8tVX\nX+Hmm2/GhAkTYLfbMWnSJPzud78LcQ6jmJcxqM7jS8fMmIGa116Xjh0tIV5bzrn08Kg12N4NT7vM\nD6p3juWNIozVasWhQ4fQ0tLpNdzYsSeFKEdE4ePue8HTbwhPz/GYODWObxTnsBA5hK2iYrfbvZ4v\nKyvDo48+GqLcRDBrH0xffIqe6hokFOQhbvZ8mA/tl3o7KvLj8WljHbI0WZiUPAEKyOYMyHpHnFfT\nUKjEjZocLdpeW87d9bbQqOA8vymhIA/qCZPQ+cXH/XNQ9PlIPG0+FMqBR0pvnbgbcW9dvTgnZdIU\npxW9CriiF0WFioqj+OKWm5CTkOAxTF13N7D+sRDmimiITnynO3o/YiaX4KDxMGqMdcjT5rj+rpD/\nBpg4WfheiJ00BcZ/vS9cwlfvu8sclrYOn/MXafQIW0XF16R6XxWZEc3pQaDWxIo7yVttwnHylT9D\nuqEG7bpKHJ6hxITkCUJS5oN70bn7K1hNJlga6pCkUkrjS3u/3CGEdYwLdW0hyR9Iz01LCVf9ikCD\nnPgOwHWoVtlcQDlQmXWZ32S1odapLObagaS5Z0nHcRnpqHxn60D4ZUuFsqNfeZVsRa9U9pxQVMhJ\nSIA+iePoKfqZ9+0RVgnNWHUNHm9+QzpeNfMqAAqp4nJStQkVjwyE169YJn4vxKgRmyqbcyI7losd\nkyo0avkzf5FGj7BVVHw5cuQIrr/+erS3t+OGG27A3Llzw52lkHGuDOjK5wvnemprhWPVweOI3f01\nYgHEa7OBU8WKirW2RtqsEQDi8/OBE7vIWrq6hR3oHbuAV+THo+XK/0CSoQudukQo8hPgSJWrfkWH\nQU98h+ehWtJ5+U70srLYW1WNJKdjo1FcnrjH0Cher5IrvVD0sVpt/T0mXtR1d0NvtUGlYiswRTbT\n4UPCsf1oJZAycHy47RjePbxNOv6D6TQhfE91jZheZRXisrOE3xb23l6vebB1dQm/U/JzcoTz/G4Y\n3SKyolJYWIgbb7wRF1xwAaqqqrB8+XJ8+OGHiInxnF2dbnitW+GOn5GWgJbdX6Pr+HEolKr+ie5d\n3YjNSBfCJeTJdv/W9C/yp0pMQGqbGT0fv4vEwrFIK5sJhVKJo0ZxHHWfsVPKq3KsHgecdqAvvvM3\nSNdp8WljHTbb9gDpAGzAIlMh5hbNAAAoxo+D8xTo5KJxAXn/4Y4fSoHOq7v0KuvFLw9rfQ105d57\nvg5XVQvHvVXVKLh4IG3LuLHC+XhZWdQUFgh5qctJFc6rcrKE48SxhcJxyvhxSB/EZxOKzzES0wyV\nQOU9Vh0D9Hk+r1IqodNpoZINRQ1WfoabTlNTIl6ZFoOENLXHMN0tMbggLREqlQrHfKSXlpYUkPcW\nKZ9POETDvRupeexJSRGO45LF42SNeA1rTppwnOA04gLof673NjcPvKAArF1dXvN61CguXd9nFIeC\nOX83RGP5pOEJW0Vl7NixHs9lZWXhggsuAAAUFBQgIyMDDQ0NyJP9MHLmcXldP+h02rDHr/3sS6EF\nPGPeGWja8RkaP90O/bKl6KmtQ3x+HuLmzMfYjMz+FoYULar/tglA/8T42r9tluI7WtCVmWJFR5mZ\nPpDXkyZh7OrVsNbXQJWdB9tJxTAYjMjSiD8oszRZUpwfdWqht+VQphoZiP7PP5SGk1c5T+89JltW\noc3O83ldVX62eJyXLcSxa5OFVjIkJQnH6tQMIXycOgFxTudNqQlC2VEV5wgrwTjK33De91AFOr1g\npBmN5VSn08Js6XNdOcGJ1WbrX77aS0UlUJ9lINJpb++BrjgH2txUj2GMtW1ob+/xK72Wls5h5ymS\nPh9HOqEUDfdupOaxIz1eeI73ZCZjVeHV0lAvpWzIsNaiQqxzb0lCgjTXUKMvgHrmHNi++hy1r2+R\n4uhXXuU1r5rxE8XjSZMxtniKy3dDsD5HimxBragcOnQImzdvhtFoFOacPPTQQ7jnnns8xnvrrbdg\nMBhw1VVXwWAwoLm5GVlZWR7DjwTyIVQxqSnIufxyaSk/zYmHhR02HM2PQ01KIsZqc5G3ZDF6q6qh\nHpMq9cIAgOnHg/1pxqmgO7McfV1dUGk06GhvRqLjIidWVNKVny7c/JOSJ2DVTMeDKhtKhRIf1XyC\nPG0OGroahd6W/zQWYC5mhOATosEYyrK+e8ZqMO7Kn0FZ3wRbdgb2jNNgjtP53uOVQvd8VrLTuGMF\nYK6pRWzxVOml2IZ2mJ3O26rrsFHt1FPXNRaW/EzUpCQiTxuHSQqvv2eJiGiY7LDhYMePUkWkMrUH\nyYVxSDL0oVMXh+bkDlyUfAaKkydJ4Qd+D+Sg72On/bAUQG9lFVJ+ermwi7yvDR7l3H5fnfh9QhTU\nisrq1atx0UUXoaRkcKv5nHXWWbj11lvx0Ucfoa+vD3/84x+9DvsaCeQT2DUTJrm9SQ92/IjHv34e\nALBMORXWlz6Szjl6YQCgr60dTTvekV5v3f01ACB9le+9axRQojh5EoqTJ+FAx0E8uvtZ6dyK6ZcL\nYfO0OfLoFAmGsKzvmMR03Gv7J5AJwAasSrxaOC8vo7Hpaahymiyfe9Vy4bw6MR61ThWb3KuWA6aB\n8/GxGqksA8CqmVdLX45ERBR4zr8hAGDptEux0fYvqQFpRZL4He/8ewAATGMqUfn2O9J5/bKlLtcY\n9Ea9XIaevAjqr/+0tDRcd911g46XmJiIp59+Ogg5ilz+toDXGAdmiCQZusSTyYnQ/ORsjEkcg8Y3\n33F5PbYgH2OmiRPh3HFucVHHqJCgjke3pX8Yg9HU6dLb8ve973heHpmihqMnrcHUIP09ncnLaPPR\n/cL5juZGVHcclFre8mStajZjN1bMvBw1HXXIT8lFd684IbnGWItJyROE1j6WKSIi/8l7TOTP0Bqj\nuAiK2WLB0qmXorazHrnabMxIPwUHnJ7j8vjdDQ1C/O6GBpfeEsd3hWNYOTfqpeEISkXFZrMB6O8Z\n+fzzzzFr1iyhR0Sp5A8PF362KDj3YHTpkhDrdO7QmD5stO3BcuU0jOnqdnkdrd9jlTHHZ6u1vMXl\ndP1MfF759Ynr53rsbWGLeHRztJzNK5rpfhywrIwqLa3i6fxModzck3OJcN6SPQYvfPeadLx02qXC\nea0myaXssUwREfnP1zM0Pk6sVsTEqPDynoH5JDHTY4TntDx+bEGuED82XzwG4HFYOdFQBKWiMmXK\nFCgUCmFeiuNYoVBg//79XmKTN87zR9K0eSjMnAprfS0akxWoTuvBqeapqIpNQvavFiOt2Yr2jDg8\n27Ud6K87osZYJ4w9PdjxIz5tbEB2fDbsdhtqjPXosYqTQJNjtfjPSRdKrSsOzr078rRp5DuSFYPM\nay+Hqq4Z1px07MnoA5zqLgczgUmrrkFvdRXi8gtwMEsBOC0G09DZhNP1M2Hq64UmJg49ZhOMJrGX\nkGWKiMh/rt/LtdLredocGE2d+EnxuWjtaUNafCqau8UGp5oO79/riafNR669f1XIuIJ8JM4Wt1Ag\nCrSgVFQOHDgAAGhvb0eKbOm7Ku67MSzy8aKYAujKz8C3+z/Atu8/lsLlTr0UhTPnoKbjILq/HpgY\n4Nwj49zy4txrcrp+lnDN8aknuf2xKJ+fwvkqo4s6Ng7rOz8FtAA6gSUasQcFKiXWNm8B4gE0f4MV\n+fL5TdkuLXdyLFNERP6TPzO1miShh2XJ1Evwtz1vCMfOsrQ6r+kplDFImnuWsGcWUTAFbY6KzWbD\njTfeiBdffFHqSbFYLLj++uvx1ltvBeuywedux+/BRHczftTd60qFElUdNV7H6dtgxdfN36DmeB2U\nCnFpz9aeNmmlrptnXSOlNTF5vDT+1LnnxNQ3sCHTt3U/YFHJxejrs7r0ojjzNaeBwsfXOGX3kfrL\ndmV9DWKy83zuZm82m6WWuTHxqei1mIWWOkN3ixC+Q5jflIMJyUXAdEhzViYmj4cCCiGMc3nlnBUi\nIu/EVTtz0NAlzikxdDULxy3d7Vh88k9Q19mInKRMJCsSsWL6wFzCidoimPd9L/7m8fK9QBRoQamo\nvP3223j88cdx/PhxTJ48WXpdoVBg3rx5wbhkyLjb8RuZ3jfSc+bci5GgjsflUxaip7EHCTEJ2Lz3\nLWnSunMPh6dx+l83fyO1SMt7QdrNRmyt3CZdw+Gw8ahTL8pAnORYrTQMJ0mdgCR1Itr6xE2Y5HzO\naaCwcTdO2ddEddeyfQuO5ms8hler1Xjzuw+k4yVTL8E/vLTUJcSKY6OPGI8JPSramVqpt9BR3g90\nHOScFSKiIUqJT3EaYqtBrlbcLystIcWlh8X5+KT0XjQ/PjAX1bFHG1GoBKWisnDhQixcuBCPP/44\nVq1aFYxLhI18vxP5sS/O40dLc0qEH2rOlRPnHg5P4/Sdx5J+W/cD/nPy+YBNiZgYFd4+9C+311hU\ncrEQZ1HJxYDCjgRVAv763cCGkX12q8+KEkUud/OHAHj90S8vy+0Vh/F4/S6P4Y2mTiG8vKWuubtV\nmIPS0tMqTNp0LouOPMrLGedBERH5T95Itfjkn0jf5QAwLiVfeC43y3q+64yNwrGpslI47q2qYkWF\nQiqoyxNnZ2fj73//u3jBmBiMGzcO06dPD+alg2bQ64PLOI/3dK6MyI81xC5VZgAAIABJREFUMXFu\n4zjLTxlYbaPb0gNtrBZl6bNw0HgIpTklMPX1Ykx8qri8cG+n0LqSk5CN00+agU3/Jw7H86eiRJHL\n3fwhXz/65WW7NysFaPccPk8rrvaiF74ANdAlpOGDPdul8/85+XwhvLFXrOi4K+ecB0VE5D/5c76l\np014Ljd2NQkVl5+fLDYY5WgzhWNNoR7OS5wM9jcP0XAFtaLyxRdf4PPPP8eMGTOgUqnw73//G7Nm\nzUJVVRXKy8txyy23BPPyQTGUHb+dOY8fTY5Pwr9r90jnTtYVo1BbcGJ/EhWy4jO9zhE5Na0U9ul2\n1HTWIS8pBzPT+3eIt9ttTg+iPUJPTWZiBt49vE1KY0Zm/07i8h+A/lSUKHLJxym7K0Pyv6t87fvj\n+fHA157Dy69htBiFL8CTpuuFlrsxcWOE+ONTx2HVzJO85tGf90FERP3kz+n0hDFCg5F8SG5qbAqW\nTL0EdcZG5GgzUaabiYyZGdIzN007HtrV2iH/5iEarqBWVKxWK7Zu3YqMjAwAQHNzMx544AFs2bIF\nixcvDualg2eYO6g6r9plhw3amVo0mAaWBzaauqBUqtDW24YOcweS+5Jghx2KE/Hlk6RnpZ+Ki4pT\nhDkiNcZ64ZrOyws3dInduo6w4g9C/ypKFLlcVoeDHz/6ZWvfj4fN58R252tsqRB75RqMBoxNKZA2\nEpuWdrLL9R35HMz7ICIi9yYmjxcmw9cZxcn0Ld1t0vm85ByUpE7Bjx1HYLVbkRGXATXULs9c7hpP\n4RTUikpDQ4NUSQGA9PR0VFdXQ6FQSJtCetPb24uFCxfihhtuwE9/+tNgZjUsnCej7zjyNR7/egMA\n4CfF5+LNAwOTlO3T7ShL75/47m6SdKZuppCuvEXF0/LCzmHd/SCcqGUFZSQZ7I9+eXhfE9uzkjKE\n+OmJY1w2EitLn8VKBxFRkBzqOCzMS5X3oKQlpPb/nkjvP+aCJRTpglpRyc3NxU033YSysjIoFAp8\n++23SExMxHvvvYecHN/DiZ566imkpqYGM4sRw7EpEwB09nYJY0rrOhvwkekTDz0idfKkvLaccygN\nDZVzGQUgLXvpKEv2PptskqZsIzFjvfTlSEREg+e8UbNjWwDn1Rjlvwk6ejqEZeO73GyqKz9mRYUi\nSVArKg8++CDeeOMNHDhwADabDdOnT8ell16Krq4ulJeXe4179OhRHD161Ge4kSI5Xiv9Pz1hDF7b\n+7Z0fHnJQul4xXT5pnmuFT5vLeccSkND5VxGAUAdoxZa4lZMv1yYo+KykVii2ONCRESD425UhbjI\nifibIDk+2esGj1ywhCJdUCsqsbGxOP/88zF79mzptdbWVhT4sWrEgw8+iLVr12LLli0+w0Yr5w0b\nMzRjML/wNHRZutEkWy6wqbsFp+ZOhSZGgx5zj9Qjkp+cC7vdhr/vfUea41JjrOfGeORxY1Fv+6jI\nW+omJo/HoY7DUniz2Sz0mDR2NQnXNMo2dGzubhLCW/osIf0MiIhGGnnPdo2xVhyy7TRHJS85B/Wd\n4iiM5q5WwGnzeW7cTJEuqBWV++67D6+//jrS0tIAQNqh/qOPPvIa75///CdKS0uRl5cnxRuJnDds\nBPr3Ufl37R6XzRvNVrO0OtiK6ZdLPSL9Y0s3SHGdW7M5znR0c9fqBnjfR0UeZ8X0y4XyKe8xWTrt\nUuGaWk2SOKcFwCv7BlryHHkgIqKhkfdsazVJwrGvOSrpieLqi9y4mSJdUCsqu3btwpdffom4uDjf\ngZ18+umnqK6uxrZt21BfX4+4uDhkZ2djzpw5HuPodFqP5/wRyPg2mw1f136PyvYa6FPyMDNvGpQK\n196NmuPi2NAEdTzOLjoD41L0mJ1fiqqOWiigxFsHP5TCdPf1SNf6tHFgNQ/5niwNpgbMKxIn2fub\n/6GI9vihFOi8ukvPuWwA/eVBTl5GPm0Qw8jHLnf3dePKaZdKvXYKO8QeE5tZyEt6xgzExcX4vA+G\nKhSfYySmGSqBynusOgbo83xepVRCp9NCpVKFJD/DTae1Ncl3IABpaf3hjvkRLhDvLVI+n3CIhns3\nUOmZDb3Cc9dmt+JY7xHpOVvfIz7Hm7taxI13u1uF8M7P5dH0OVL0CGpFpbCwcNCVFABYv3699P8n\nnngC+fn5XispAIbVEqDTaQMa399VNPJlG+Z1W3qE3eDLM+djd8vX0maNAJAQEy9dK0uTJb2uidEI\naWVpsvx+T4F+/9EYP5QC2Wrl6b07lw13x47XnOMmqBPE80k64ThWGYuXvh8Yirl02qVCD8uMmdNd\n8jIurghlJafAYDCiuUmcxDkcw/2bBzu9YKQZjeVUp9PCbOmDtL66G1abDQaD0WtFJVCfZSDSaWnp\n9B1okOGGm6dI+nwc6YRSNNy7gUovPS4Dr1QO9FSPHzMOD3/+P9KxvKc7PTENH8jmqDiHd/w+iZZn\nYDDySJEt6DvTL126FKeeeqrwJXTzzTcH87Jh5+8qGjPSToFlqgW1nfXITMzAv47scInT3dsjtIb0\nmE1SGOexpTnxOZiROVWYo0Kjl6fV3bztiyKfg2LvswvhD7ceEa7R0NnEFeSIiEJIPqekoatBWCW0\npbtNeI4rrMDSqZei1ti/n5XdKm4NwVW+KNIFtaKSmprqsyfElxtvvDFAuQkdf1fROGw8ior2Kpj6\nemG29mFa1mRsq9gpxMlKzMTm/QMb6TmP83c3trQ4uTig74Wik6fV3ZxfO2g8hG8av4OprxcNPQZM\nSity6SFxDm/sE1uy8rTZXEGOiCiMkuKS8Pl+ce7gu4e3SceO57hjAv2BjoNCfK7yRZEuqBWVG2+8\nEa2traiursbUqVNhs9mgVI78laj83aukvrtB+GF42ZQLpR3kHXG47wkFi7z85Wmzva7+cmpaKezT\n7dJqMjPTZ4Q6y0REo5p80ZNFJRcL5y0Wi9ffDPxNQdEmqBWVd955B48++ihiY2Px9ttv495770VJ\nSQkuu+yyYF427Pzdq8TYK45h7jB3IUmVOKS0iAZLXv6MvZ0oz5rncfUXJVTCjsZy7pZE5hLZRESB\nIx9aLn+OZyVmCb8Z7LAJQ3wnJU/gbwqKKkGtqGzYsAFvvPEGrr32WgDAHXfcgWXLlo34ioq/xqee\nBGCgi9ZkMeGDw58C4PLCFHzy8jc+ddyw0vO1ERkRuWe1WlFRcdRnuLFjT/K5OhqNbPKhWuNTx2HV\nzJM89pDwuUzRLqgVFa1Wi/j4eOlYo9FArVYH85IhJd8gb7AtyM6T4mBX4O1D/wLQv0xxQ08jW6bJ\no+GWPcD3EIDB9pD4u4gEEYkqKo7ii1tuQk5Cgscwdd3dwPrHUFTEoTqj2YTkov7J8Z39k+MnJI+H\nCjEen7V8LlO0C2pFZcyYMdiyZQt6e3uxd+9ebN26Vdr8cSQYbkuF82T4HUcGliEuzSnB5r3iBHo+\nWMhZIFrJfA0rHOw1/F1Egohc5SQkQJ/EpVLJu383f4uX9wwsEx8zPaZ/SK4HfC5TtAtqReWPf/wj\n/vKXv6Crqwu/+93vcOqpp+K+++4L5iVDKpAtFc6t2z3WHuEcW0BILhStZIO9BidpEhEFV01Hneux\nh3mDAJ/LFP2CWlFJTk7G2rVrg3mJsApkS4Vz63b/8oEDcwfYAkJyoWglG+w1uPADEVFw5aeIG0Xn\nJfO5TCNbUCoq5eXlUCg8b0X8ySefBOOyIeE8bl+fko8V0y9HTWcd8rW5mJg8PiDXYAsI+SLf9Muf\nMiKfczIxeTwOdRz2OAeF5ZCIKLKUpk1H79Re1BkbkavNwoz0U8KdJaKgCkpF5ZVXXvEZ5sCBAygu\njr7NCZ3H7Z+unynsQ6GdqQ1IqwVbQMgXd5t9+iKfc7Ji+uV44bvXpGP5HBSWQyKiyPJN8//hb3ve\nkI7V09Ve56gQRbugVFTy8vJ8hvmv//ovvPjii8G4fFA5j9s39fW6nOOPOopULnNO5GOdWX6JiCLa\nYOeoEEW7oM5R8cZut3s8ZzKZ8Nvf/hbNzc0wm8247rrrcOaZZ4Yuc144j9PXxGg8niOKNC5zTpK5\nGgwRUTQZ7BwVomgXtoqKtzksH3/8MaZOnYqrr74atbW1+OUvfxkxFRXncfsFyXmYkTkVDaZGv+cJ\nEIWLfM7JxOTxSJ6ZzDkoRERR4tS0Utin21HTWYe8pBzMTJ8R7iwRBVXYKireXHjhhdL/a2trkZMT\nOS0G7sbtzyuahUZD+6A2xyMKNXdl1/nYDhsOdBwc1iaSREQUPEqoUJY+C7piLQwGo/Tc5m8PGqki\nsqLisHjxYjQ2NuLpp58Od1Z8CsQGfEThxDJMRBRd+NymkS4i56g4bNq0CQcOHMBtt92GN99802tY\nnW54O/oON36DqcHleF7RzJBdn/GjZ0fnQOc1UOl92ji8MuxNMP4+kfo5BjvNUAlU3mPVMUCf5/Mq\npRI6nRYqlSok+RluOq2tSX6FS0vrD3csQOEcYT3lP1I+n3CIhns3WHkM5HN7tH6OFNmCUlHZuXOn\n1/Nz5szBAw884PH83r17kZ6ejuzsbBQXF8NqtaKlpQVpaWke4/i7RKs7Op122PGzNFnCa1maLL/T\nDMT1GX948UNpOHmVG+57dzacMuxNIPMYrDSjJY+hFKi/vdnSB3iekgirzQaDwei1ohKozzIQ6bS0\ndIYlnCOsu/xH0ufjSCeUouHeDVYeA/XcjpZnYDDySJEtKBWVp556yuM5hUKBOXPmID8/32OY3bt3\no7a2FmvWrEFTUxN6enq8VlIiATfHo2g3lE0kiYgofPjbg0a6oFRUNm7c6PHc+++/7zP+kiVLsGbN\nGixduhS9vb24++67A5m9oODmeBTthrKJJBF5ZrXaUNfd7fF8XXc39FYbVCpOfqah4W8PGumCOkel\ntrYWL730ElpbWwEAZrMZu3btwnnnnec1XlxcHNatWxfMrBEREQWZHa9Mi0FCmtrt2e6WGJwG3/M1\niYhGq6BWVH7zm99g/vz52LZtG6688kp89NFHeOihh4J5SSIiooigUqmgK86BNjfV7XljbZvPRQSI\niEazoPY3q1QqXHvttcjIyMDSpUvx3//933j55ZeDeUkiIiIiIhoBgtqj0tvbi/r6eigUClRVVSE3\nNxc1NTXBvCQREUUhq9WKTZvcN2RptRoYjSYsXryUPRBERKNIUCsqK1euxM6dO3H11VfjkksugUql\nwsKFC4N5SSIiikIVFUfx36/tRFyi+2FSvV1tmD17DoqKuKoREdFoEdSKyrhx41BUVAQA+Oqrr9DV\n1YVjx/zZ1oqIiEab3ElzkTQmz+25zlb2xhMRjTZBmaPS0dGByspKrFmzBlVVVaiqqkJdXR2am5tx\nxx13BOOSREREREQ0ggSlR+Xbb7/FCy+8gP3792PFihXS60qlEmeccUYwLklERERERCNIUCoq5eXl\nKC8vx9/+9jcsWbIkGJcgIiIiIqIRLKjLE19wwQV48MEHcfvttwMAPv74Y7S0tATzkkRERERENAIE\ndTL973//e8yaNQvffvstgP6d6e+44w48++yzPuM+9NBD+Oabb2C1WnHttdfinHPOCWZWiYiIIp7V\nakVFxVHhtdbWJLS0dAqvjR17EpdyJqKoF9SKSktLC5YvX44PP/wQAHD++ef7teHjrl27cOTIEWza\ntAltbW249NJLWVEhIqJRr6LiKL645SbkJCRIr8nX0qzr7gbWP8alnIko6gW1ogIAFosFCoUCANDU\n1ITu7m6fccrKyjB9+nQAQHJyMnp6emC326V0iIiIRquchATok7ThzgYRUdAFtaKydOlSXHbZZWhq\nasKvfvUr7NmzB3fddZfPeAqFAhqNBgDw2muvoby8nJUUIiIiIqJRJKiT6cvLy3HOOecgPj4eP/74\nI5YvX46zzjrL7/j/+te/8I9//AO///3vg5hLIiIiIiKKNEHtUVm9ejVSU1OxcuVK2O12/Pvf/8bq\n1avx1FNP+Yy7Y8cOPPPMM3j++eeRlJTkM7xON7xucG/xrTY7vtpbj+N17Ribk4KykmwolWIPT1p6\nks8wQ70+4wc/figFOq/BeO+Rkkdv954/afpz7w43j95EU7mUC1TeY9UxQJ/n8yqlEjqdFh0dvp/z\naWlJYX9WtLb6zifQn9fBhJPPMwlE2KG812gss6G6d309T4b7vApEHiMpvWCkGY3lk4YnqBWV9vZ2\n/M///I90vGTJElxxxRU+43V2duLPf/4z/vrXv0Kr9a9QGgzGIedTp9N6jb/3eCvW/e1b6fjWJaUo\nKRwjxN/xTZXXMMO5PuMHP34oDSevcsN976FIczjpebr3/E3T170biDx6EozPMZQCkXedTguzpQ/w\n0mZjtdlgMBhdVq1yp6WlM6zPCkcewhFuKGEH+14DVWajsaw68/Q5+HqeDPd5FYg8Rkp6wUgzWHmk\nyBbUoV/5+fkwGAzScVNTEwoLC33G27p1K9ra2vDrX/8ay5Ytw/Lly1FfXx/MrHpV1dDp9djfMEQ0\nOMO9r3hfElGg+Hqe8HlDFHhB7VGpra3FOeecg/Hjx8Nms+HYsWMoKirC0qVLAcDjUsWLFi3CokWL\ngpm1QdFniV34BVmuXfr+hAEAm82GXQcNqKzvhD5bi9MmZ7iEsdvt2FfZhqqGTuizkjC5MBUKb02S\nRCOEvOzL7yNP95Un7u7LvcdbeW8R0aD5ep64e17tPd6K+m9rkJOWwOcN0RAEtaLy61//OpjJh8zk\nwlTcuqQUVQ2dKMhKwpTCVJcwSiUwvzQPPb19iI+LgcpDX9WugwY8+8Zep1dK8BNdihBmX2XbkIeR\nEUUzedn/1aUn+3VfeSK/L7tMFjy95QfpPO8tIvKXr+eJ/HnF5w3R8AW1olJWVhbM5ING3qo7qSAF\nHd1mtHeZkdJtgR12KKCA1WrD5/saUG04jIKsRCTEqtDT2wcFgOrGLhQXuD6QKus7vR4D7ruP+XCj\naOO4jxyticX6FOyvbJfuK/nx5MJUl7Jf3dgl/V8BoK65G1Yb/G6hrJLFd04P4L1FRP5rMppQmKVF\nbXMXcjMS0W40Ceflzyv586a2qf+YPbpE/gv6ho/RSN6q+4uLJuOv7+x3ClGCOZOz8Pm+Bun1+aV5\n2P5tjRTimktK3Katz9bKjoc+jIwoksnvo2suKRF6E+XHty4pdSn7aSkavP35wBpHyy+cPKjexqQE\ntXBf/uKiycJ53lvkYLVasX37Nq9h5s9fEKLcUCTqswAb3zsgHS+7oFg4n5EqPq/kz5ukBDVHSxAN\nEisqJzj3ovSYxbU0nVtFMlLi0G3qwysfHUZMzEBLSE+vGKe2qQuvbjsCfbYWZcUZOHCi5Tg/KwlX\nXTwFx+uNyNclYdZknUteivUpuOaSkhPzWJIwuTDFJQxRpJP3jjS19eBnC8ajud2E9BQNmtp6XMKf\nV5YvDLOsqG8X4jS2eO8RkXo5G7uQn5UEs+y+7O7p8zqMcyjzwzinbGSoqDiKhz56FAlpiW7Pd7d0\nQa/3vRgMjRzy50mrsUca2pUQF4PmdhN+tmA82jvNGJebjM5us3DebLbi1iWlqG/pRnZaAkdLEA0B\nKyonOLf+lpfmCefynVpdy2cU4OX3D7qES4gTP0pNbAxe//wwAMDSJ/bIOPe+xKqVLnNU9le2Cy3N\nyQlsdaHoI+8d0SbE4cV3B+6D5Re49m4ooEBJ4RipvBvaTGKcC8U4KdpY4di5l9PdNVK0sUL6ckOZ\nH8Y5ZSOHrjgH2lzXOYgAYKxtC3FuKNzcPU/e+qxCOl52QTE2vntAOO/cg7v8wskoKRyDM2fqYTAY\nXZov2KNL5Nuoq6i4a/0EgIbWbqnlNnNMPH55UTG6eqwoyErCxIIU2Gx21Bg6oVQqkKiJQZepD3uP\nNmHpeZNQ39KN3IxEnHeaHi3GXsTHxaDeqeVXPk7V0fuSqIlBe6cZmz44IIy3Z6sLRSP5vTUhPwXL\nL5yMGkMn8nVJMLR2C+ENbd1OPYdaTC5McUmjqV3sdTG0douTVbstwqo78nutrrnLJbw38nvPnzHl\nvF+JRib586S5Q+xRaTOahGP586quSYzvz8I8RCQadRUVd62fmbpkxKiUeH3bQen1X1w0GeeXFQAA\ndu5vwItbXXtEppyUIfWuOF7fva9B+r9Dnk4cShB/ovfl1MlZ2PzRj0JeSgrHcI4KRSX5vbX8wsnC\nfSPv3dClJrj0HAIQ03AT511ZD4tz+BWyHpecjEQhD7cuKfX6HuT3nj9jynm/Eo1M8u/u9OR4vPN5\nhXS87IJioYdF/rySx5f3GBORb6OuouLcc5KeopFaeeuaxNbe2qYuvPdVFfRZSS4rcyVoYvAfMwsQ\nq1YJryeeeF03Jh5NbT2YNSUL8XExsNtsUitKflYSOrp6ERujQoImRmiNqWvqQknhGLa6UFSS9yzI\nWxMbW7ukHpY8XRJ6TOL8EXebo9W3dAn3q6XPgivOnYSGlm5kpSegu8cshFcp7PjFRZP7x5RnJmLu\n1CzoUjTSvTRZn+J1HxX5vedPbwnvV6KRSaWwDzxv0hKEkRIAUN8s/m5obBOfcXOnZoUyu0Qj0qir\nqLjrOQFcV+PSJsRi88f9vR2/WDhFOJeoUeP1nYdxxXmThNcTNGq8u/Mwykvz8KnTONVZJ1phSwrH\nYO/xVjz35j4AwM8WjHe7UhhbXSgayXsWcjLE1sTMtEScOS1HOt7+Q71wPiE+BhnJGuG13IxElzHi\n3ua5jEmOd7lvnO+lvcdbvfaQyO89f8aU834lGpnSkuPxsKyX2FlOeoJwnJkq9uDqUjR8LhAN06ir\nqMh7ThzH/TvE94+Xz0yLx7tfDCwxaOuzCqtwpSfHYtFZE9Brtgjj3602G847rRDjcrWYWZzptoXV\nuYW2plFsrW03iq3DAFcUough71moaxLnh/RZrEL45rYe4XxzuwnzpmYLaRQXpkAdo5TuPXnvZkNL\n96B6M9z1kACe92VhbwnR6CW//3+sbhN6eLtMZuEZ1irbV4Xz1YiGL2IrKocOHcINN9yAX/ziF1i6\ndGnA0vW0j4kSSsyZnIU5k7Ow73grmtp7pTC6MQkoKRyDOZMHunEn5o3Bzv2NeH3bUem1ay4pEcK4\ne0A5tzrHxYpDx9y11nJFIYoW7nojXvnwkHRePj8kPTUeb30m7jngrnfCcV86ruJMn500qN4Mea9P\nijZ2UD0sRDR6uKxC2G5ymXf3+rfibwBnnK9GNHwRWVHp6enBfffdhzlz5gQ8beeeE312Ek5zs4+J\nv62oXd3mQa0oJE97bE4SZhZnSmusO67jbU8XttBQtHDsB1TV2ImCTNf9gMy9fcL9YzZbPaQ0wHH/\nOtJ0d/96467XxxnvLyJykI9oMJnMwpyVvj6L8DyZXJiC5AT2wBIFUkRWVOLi4vDcc8/hmWeeCXja\nzj0nzuQPpCmFqT5/sORmJHptMXbHXQutY411B297urCFhqKFr/2AhnL/OO7fn8wfD4PBCLvdjr2V\nnifHyw1lDgoRjU7yEQ1XLZyCDW/vk46vvaTE5fucPbBEgRWRFRWlUonY2FjfAQNoKEOsHK2z8h6R\n4XIeR//1/gYsPW8SLBYbW2goqvhaMSsQ8z+GOzQyWPcwUSSwWq2oqDjqNczYsSdBpVJ5DTNayZ9h\ndrtN6AVOTQrt7xSi0SgiKypDodNpfQfyor6l2+X4zJl6n/EydcnDuq6Dc/4n6Ad+aHWZ+lCYk4I5\nU3PcRXMbf7jXH43xQynQeQ3Gew9Ems7lGADG68e4pDuc+0en06LeadU8wP/7NlB58CWayqVcoPIe\nq44B+jyfVymV0Om06Ojw3ZuVlpYUtGdFa6t/1/eXv2Ed4Y75CDeUsB0djfjilpuQk5DgNkxddzfS\nXtiAiRMnSq9FY5kN1jNQ/gxrMVqElToLMpMwb4Z/z5tIfU4HM71gpBmN5ZOGZ8RUVJyHTg2WTqdF\nTpr4IM9OS/A7TZ1OO+zrO8c/KTtRaGkuyk70mn6grz8a44fScPIqN9z3Hsw0HeXY0VvhqxwPhiOP\nw7lv3aUXSIFOMxrLqU6nhdnS5zrGzonVZoPBYERLi+s+OnItLZ1Be1b4e31/+Rs2GGk6h81JSIA+\nyXPZcf5MA1Vmo7GsOnN8DvLvYnkx9vd5Ey3Pl9GaR4psI6aiMlyRtAwpVxqikcBRjuVzsAIpku5b\nIhpZ5N/Fdtj5vCEKsYisqOzduxd/+tOfUFtbi5iYGLz//vt44oknkJwcvCEarBwQRR/et0QUKnze\nEIVeRFZUSkpKsHHjxnBng4iIiIiIwkQZ7gwQERERERHJsaJCREREREQRJyKHfhEREQ2X1WrF9u3b\npOOUlAS0t3e7hJs/f0Eos0VERH5iRYWIiEakioqjeOijR5GQlugxTHdLF/T6whDmioiI/MWKChER\njVi64hxocz0vI2usbQthboiIaDA4R4WIiIiIiCIOKypERERERBRxWFEhIiIiIqKIw4oKERERERFF\nnIidTP/AAw/gu+++g0KhwJo1azB16tRwZ4mIiIiIiEIkIisqu3fvxvHjx7Fp0yYcOXIEd911FzZt\n2hTubBERERERUYhE5NCvnTt34uyzzwYAFBUVoaOjA11dXWHOFRERERERhUpEVlSampqQlpYmHY8Z\nMwZNTU1hzBEREREREYVSRA79krPb7eHOAhERDUFPeyN6zd2ez7dUSP/vbm/0GE5+7uWXX/R63aVL\nlwMAugxGr+Gcz3sL62+4oaZZ1+35M3KcHxfgsM7hiIgikcIegbWAJ554ApmZmVi0aBEA4Oyzz8ab\nb76JhISEMOeMiIiIiIhCISKHfp1++ul4//33AQB79+5FVlYWKylERERERKNIRA79Ki0tRUlJCRYv\nXgyVSoW1a9eGO0tERERERBRCETn0i4iIiIiIRreIHPpFRERERESjGysqREREREQUcVhRISIiIiKi\niMOKChERERERRRxWVIiIiIiIKOKwokJERERERBGHFRUiIiIiIooq411+AAAgAElEQVQ4rKgQERER\nEVHEYUWFiIiIiIgiDisqREREREQUcVhRISIiIiKiiMOKChERERERRRxWVIiIiIiIKOLEhPqCJpMJ\nv/3tb9Hc3Ayz2YzrrrsOZ555pnT+iy++wPr166FSqTB//nxcf/31oc4iERERERGFWcgrKh9//DGm\nTp2Kq6++GrW1tfjlL38pVFTuv/9+bNiwAZmZmbjyyitx3nnnoaioKNTZJCIiIiKiMAp5ReXCCy+U\n/l9bW4ucnBzpuKqqCqmpqcjKygIAlJeX48svv2RFhYiIiIholAl5RcVh8eLFaGxsxNNPPy291tTU\nhLS0NOk4LS0NVVVV4cgeERERERGFUdgm02/atAlPPfUUbrvtNo9h7HZ7CHNERERERESRIuQ9Knv3\n7kV6ejqys7NRXFwMq9WKlpYWpKWlITMzEwaDQQrb0NCAzMxMn2na7XYoFIpgZpsoIFhWKRqwnFK0\nCEVZ/a/1G7CzOt3j+QnaGjzyBy78QxQMIa+o7N69G7W1tVizZg2amprQ09MjDffKy8tDV1cXamtr\nkZmZiU8++QTr1q3zmaZCoYDBYBxynnQ6LeOP8vihMtyyKjfc9x6KNJnHwKUXKoEqp4H6DJhO9KUT\nKoF+pgKun0N3j8Vr+N5ei9c8RMvzZbTmkSJbyCsqS5YswZo1a7B06VL09vZi7dq12LJlC7RaLc4+\n+2zcfffdWL16NQBg4cKFKCwsDHUWiYiIiIgozEJeUYmLi/PaSzJz5kxs2rQphDkiIiIiIqJIw53p\niYiIiIgo4rCiQkREREREEYcVFSIiIiIiijisqBARERERUcRhRYWIiIiIiCIOKypERERERBRxWFEh\nIiIiIqKIw4oKERERERFFHFZUiIiIiIgo4rCiQkREREREEYcVFSIiIiIiijisqBARERERUcSJCdeF\nH3roIXzzzTewWq249tprcc4550jnzjrrLOTm5kKhUEChUODhhx9GZmZmuLJKREREREQhFpaKyq5d\nu3DkyBFs2rQJbW1tuPTSS4WKikKhwHPPPQeNRhOO7BERERERUZiFpaJSVlaG6dOnAwCSk5PR09MD\nu90OhUIBALDb7bDb7eHIGhERERERRYCwVFQUCoXUW/Laa6+hvLxcqqQ43H333aiursbMmTOxevXq\ncGRzZLDbYN7/A3qrqqApKIB68smAwsfUpKHECWR8Gr1sVpi++hymyirE6/WIK5sLKFWew7Os0Wjh\nrqwDMO//AZWGeijjNDC3G3kfENGIErY5KgDwr3/9C//4xz/w/PPPC6/ffPPNmDdvHlJTU3H99dfj\ngw8+wLnnnhumXEY38/4fUPHII9Lx2NWrETtlWsDjBDI+jV6mrz5H5XMbpGM97NDMnu8xPMsajRbu\nyjoAVDzyCDLmnYGmHZ8J53gfENFIELaKyo4dO/DMM8/g+eefR1JSknDukksukf4/f/58HDp0yGdF\nRafTDis/IzV+ZX2NcGytr4Gu/HSv8f2NE6z4QzHc+KEU6LwG472HK4+Hq6qF496qahRc7D6uTqcd\nUlkbbh7DnWaoBCrvTCcw6bgr69L/TSaXc5F0HwRbsO/dhHi117BxcWqfeYiG58tozSNFtrBUVDo7\nO/HnP/8Zf/3rX6HVal3O3XzzzXj66aehVquxe/dunH/++T7TNBiMQ86PTqcdsfFjsvOEY1V2nktY\neXx/4gQz/mAFIn4oDSevcsN976FIczDpaQoKhOO4gny3cR1pDrasBSKP4UozGstpoD4DpuP+ueoY\nMK2K17icC+d9EI1l1Zn8c+jusXgN39trCep3XCjSHM15pMgWlorK1q1b0dbWhl//+tfSJPrZs2dj\n4sSJOPvss3HmmWfi5z//OTQaDaZMmYLzzjsvHNmMDMMcg68uLoF+5VXSmP/Y4pJBxinwK44Qf/LJ\nGLt6NXqrqhBXUIDYE2OpaYQZbNn0I3xc2VzoYYepsgoafQE0s+bCvO97j3FY1iia2a1Wr+UbgDRv\nq7euHvoVy2DpNgllfezq1bA2NUA/cSIs7UbeB0Q0ooSlorJo0SIsWrTI4/lly5Zh2bJlIcxR5Br2\nfJEDe4Ux/2OTU3zPUXGJkzq48c4KJWKnTOMY6RFusGXTr/BKFTSz50Mz+0Scfd97j8OyRlGsZffX\nPu8Jl3lbK68SwsROmSa1NHNBfyIaabgsSITrraryehyM+MO9Jo0Ogy0nLItEoq7jx4Vjd+XbVFnl\n9ZjIH1arFUeO/Ojx36FDh2C1WsOdTSIXYV31i3xzHbNf4CFk4OIP95o0Ogy2nLAsEokSC8cKx+7K\nd7xeLxxr9LwHaPAqKo7ii1tuQk5CgtvzX3R3Y+76x1BUNCHEOSPyjhWVCDekMfiyuQD6/3cNTMcq\n+ueoTJriOiZafk3nOSqFekCpgPH9d/rDT5oC0+4vxH0uAm2we2lQYJwoN5X1NYjJzoO6uATmA3uH\nPj/kxN/xcFU1NAUFiJs5W5wvNaEYph0foae6BgkFeYibUw6oxEfSUOZYEUW8E/eata4G+l8sg7mt\nA7EpyTD9eBBWQyOUmjiY2zr675tZcwbmbeVkw9prgeXAHtitNvRW999btrllvue6eMmHI5593pzg\nv3cKm5yEBOiTOHmcogsrKpFuCGPw5XMBnNfY18Muzj9ZvRrIFJexlM9REeKvWIbKFzZK5/SwAxdf\nNLj35MNg99KgwJCXG/3Kq1zKymDmh7j8HS1msez0mlC58eWBYzugmfcfYp6GMMeKKNLJ77W8S38q\n3Rvu9kTRzJ4PZfLAfC15GHSuRMUzzwlx/LlP5PmIi/sNUDRlyO+LiCjQOEdlBJKPc3ZeY18+vtnd\nmGhv8XuqxbX8gzFemmOyw0P+d/enrHgjjy8vOz21tV7Pu7sm56jQSCAvx+aWFun/8j1RHGGd48jD\ndB+v9Jq+v/mQz5khIgo3VlRGIPm4fpVmYC2YeL3vMf9e4+eLa/kHY7w0x2SHh/zv7k9Z8Ub+d5SX\nnfi8PK/n3eWJc1RoJJCX69j0NOn/8j1RHGXeOY48TEKh3m2cweYjsbDQr3hERKHCoV8jkDB3ID8f\niFFBnZ3TP4+guARjk1O9znnxGn/SFOjV6oF9LsqGtvuxNy57aQThGuTK8Xe31tdAlZ3nV1nxxvF3\n7K2qRlxBPjSz5mJsum4gvYmToVco0FNdg/j8PGjmlnvME/dJoZFEutfqaqBITIS11/L/2bvzwKaq\nvH/875u0TVuatqRtuqQL0kpbaq1AWUqFKiII8hP5KoLsjg4zggiiCOKCM86IC4s+8jiK6AiiwyOO\nuI/iDIIL0II4gEWsbN1butK0adM2Pb8/Qi65N9vN0jRtP69/6M2995yT5JwTbnI+94PE+36H9upq\n+A8ciMTUVHQ0Ngn6vGAsDEpCyIiR0JeVQZGQgJjc0WAhoU6PE/H4Uo0aidq6lu586oQQ4hS6UOmL\nrMQOBKRe/uBiXVcOA9BedAol35Qag6dNAZj2zgcEeS66p/0cZKHhkIdpIQ8NBzjO8TnEfZff96i8\nXD77r934KEcJHC+/jwERLcb3UWbZrwLH3WQ/94O7eVLcTJhKSLewMtYACMYC/zfrEgTKKwYlQX+h\nGIEJCVBOmgJwMsj8/KSPE9GYCEi/hj+Pk9HYIIT4FrpQ6WfsBdo7m0yyu7ib5JJ4h6P3yRfeR19o\nAyHu8PScTWOCENKb0Ncn/Yy9QHlfCVSmAOrewdH75Avvoy+0gRB3eHrOpjFBCOlN6EKln7EXKO8r\ngcoUQN07OHqffOF99IU2EOIOT8/ZNCYIIb0JLf3qZ4QJ9BLARUQiKEFjDJ5OvxLHIkgCJpfxa6Kt\nJgG0co7TsQDm5w9KwqCHHuIDRSmA2jeJ+5I4GaP/kHQkzp+L1ooKBGs0CLg6zXFSOg/HlFAwPul1\nzMZAQFgoDIYuJC6cj7bKKgTGxqCzrRUJc+4GpwxBe2UlOEBaosbL5bZXViLpvt+h/ZKWxkQ/YjB0\noVKns7m/UqdDoqHL5n5CeorkC5Xm5mZotVowxvjH4uLiXK74hRdewLFjx2AwGLB48WLcfPPN/L6D\nBw9i8+bNkMvlGD9+PJYsWeJyPUTIIoHeypVInHWXIKDTbsJIK0kAoc51e92ztfOVkz2bSJJ4lmUy\nxnDBe9526FtRQkcm2LbWRzy+ft7dYHxCvMxqMsg9H/HbpvnYfF6WkqiRYlP6O4b3rvVDsMrf6l5d\nvR9Gg1ndR0hPknSh8vTTT2PPnj0YOHAgf6HCcRz279/vUqX5+fk4e/Ysdu3ahcbGRsyYMUNwofLX\nv/4Vb731FtRqNebNm4fJkycjOTnZpbqIkJT1ya4kjLRWrjMfgu6eT7zP0XvmKMGjtfeY+gHp7+wl\ngwSuzMfm83JLcTGCHFyo0Njq3+RyOaLSYqGMC7e6X1vRCLlc7uVWEeKYpAuVH3/8EQUFBVAoFB6p\ndNSoUcjKygIAhIaGorW1FYwxcByH0tJShIeHIzo6GgCQl5eHw4cP04WKh0hZn2w/YaT1xGLurnum\nddO9j6P3LDjBfoJHKX2P+gHpb+wlgwSuzMfm8/KApCQ4WrRDY4sQ0htJulBJTU1FR0eHxy5UOI5D\n4OVJdvfu3cjLywN3OVdGbW0tVKorE7NKpUJpf7grSZcBbQU/XF7vnwguIgL6c+c9nvtBsGY/MQFd\nTZdw5tXXjffnHzUWkMklJIwMs1jz724sgKTzKSdG97r8+pZUlQvz6tjgnzoUiQvn8wkbA1KF3+gq\nxoxHoqELrRUVCNJoEJgzHoMi1Vfe47QMi5gVj8eUUJ8hvkQUf1Kib4NMEYjONj38AgPQfkmLwIQE\nDFr1CPQXiuEfpuSTQXZc0sI/TInOllYMeughwbxsNVGjuO+nZWDQypVor6yE34Ag6EtLwQE0Jggh\nPk3ShcqECRMwceJEJCcnC34a3LFjh1uV//vf/8aHH36IN9980+Yx5jExfVlbwQ+C9f6aGbej8vK6\nZI+uJTZbs992+FtBnYlgCBwz3mHCR6tr/t2NBZBwPq2x7l7Ovr5tRw6iZPs7/Haiv7+x/5jKK/pF\nGJMSqRa8x+2nTlitz5MxJdRniC+xFf+nmXE7indeiUOxFqNnLTGqaV62lqjRXt+nMUEI6S0kXahs\n3LgRq1evRkxMjMcq/u6777B161a8+eabCAkJ4R9Xq9Woqanht6urq6FWqx2WFxWldKs9PX2+vrRM\nsG2+LtlQVY6ovFyP139GVKe+tAwJ/59rz8Mbr19JlTDmwfx1cbd+b/J0Wz1Vnr3X1xpH/cdRec7W\nJ+Zun3GlPGf1pn4p5qm2UzlXWPTHy3EmFnEoTo4Fa+2x1fcdjYne2Ge7e+wGB1kPQDdRKPwdtqGn\n55eGhhCHx6hUIT77+dRd5RHfJ+lCJSUlBTNmzPBYpc3NzXjxxRfx9ttvQ6kUdjqNRoOWlhZUVFRA\nrVZj//792Lhxo8Myze9a5ayoKGWPn2+xLtls+Zs8RmO3fFfrt1yzHO9SOd56/fxihDEOptfFE/V7\nkzttFXP3uZuz9fra4qj/OCrP2frMudtnXC3PGZ4uszf2U0+9Bn2lHIv+eHkJdEBEhPBxJ8aCrfbY\n6vv2xoQnXx9v6u6xq2vtsHu8Xt/RLZ/R9jhbZn19s6RjfPXzqTvKM5VJfJukC5XBgwdj9erVGD58\nuGDp15133ulSpV988QUaGxuxYsUKPoh+zJgxGDJkCCZOnIh169Zh5cqVAIBp06YhKSnJpXp6E8Wo\nsUgEQ1tJKQITEyCLiETszJl8HEnj++8iKDGRjyNxmShfSeLv74W+pBSKhHgEjnLuGzy36zfPwyIB\n5cToXqbX11BVLsyrY2LoRNvBA2gtK0dwggaKUdcjcWG7MUYlQYPAkWOdKs8b7yf1GeJLzPujf5gS\nrLMTiYOS0FZbh8QF89De0AB/pRKcXAawLsmxI8xgkBzvRWOCENKbSLpQaWxshEwmw3//+1/B465e\nqNx111246667bO7Pzs7Grl27XCq715LJEThmPALHXHko4OqhtuNIXGRt3XLKkqke/5bCmfqhlniB\nRDkxutfl1zcqL9dqf2g7eEAYk2LoEsagqKKE742D8rzyflKfIb5E1B87Cr7H+a3b+N2R465H1aef\nA3AudqT+yFHp8V40JgghvYikC5X169fjwoULGDRoEADg1KlTGDrU/j3biWeI85a0lZQKLmacJSWP\nSnfq6fqJ61zJi0IIsU1XXCLYNs+N4sx4aikuFmzTWCSE9BWSflfevHkzXn/9dX779ddflxQ3Qtwn\nzlsSmOjeve97+l76PV0/cZ1lXpQ4wTa9l4Q4J3iQcFmzeW4UZ8bTgKRBgm0ai4SQvkLSLyr5+fmC\npVgvv/wyZs+e3W2NIleIY1fcjSPp6fXJPV0/cZ0iJw+JDHzeFGNelGh6LwlxUcykiTAYDMb5PSkR\nsrDwKzmrnBhPqlHZNK8SQvokSRcqHR0daG9vR0BAAACgpaUFBoOhWxtGLhPFrjBmQP3xH9BWUoKg\npESEKEJRsq8MfjEaMLkM+gvFCExIEPxtaNGi7fwFYzD+yBy+aA5Ae9EplHxTKkzw526SPNH55m3x\nT7+G1kf7KkcJH2UyyCKi4KdrgzwiCjDL3cABQFcX2o58zyctVQwfhbZD36KoogLBGg0UOePR/ttp\nvl/4pQ1F/cl8tJWUIDApEarMMeA4N24UQYgPY12d0B3+Fh3llfAPC4OfSgU24UbIQsMhD9NCrgyD\nf2oGAtIyjSd0GdCW/61xPMXFoNMABESrwQxd0JeV8Ukc208XovTymFVOmgIAlOSUENJnSLpQmT17\nNqZOnYprrrkGXV1dOHnyJB544IHubhuxov7EYdS98gYAoAUALicMA64kD7P3d2JHuyAg2nyfKQDT\n3SR5tpKauVIW8R5H77t4f+J9vxPe6GHhfGGw/bxWlOx878p2V5dgO+53C1D3ljFpbAsALAMisrxw\n5zlCekDL4W9R8daVJMmR465HZVsbit++8pj5mLOWBFh74Tw/lwKWY3DQ5btlUkJHQkhfIelrlpkz\nZ2LHjh2YMmUKpk2bhvfeew933HEHAOD06dPd2kAi1FZiO/hSyt/igGhx8Kb5v+LHpRIfb60O4nsc\nve/ibfGNHiyC7Ssr7W6Lk5yK+zYhfYm4vxva2qArEydNvTKmxOOrvb5eMJdaO0ZfWko3LCGE9CmS\nflEBgLi4OMTFxVk8/uyzz2LHjh1WziDdITAp0fjt82XmwZdS/g5KsJ5wDLgSgOluwLv4fFcDRIl3\nOXrfxfvFN3oIihcH24u242JF5ccLyxeVR0hfokgU9nd5YCCC44WPmY858fgKUKnAWJfgMfExioQE\n4zJMG2USQkhvI/lCxRbGmCfa0Xu4kbDQE1SZY4Blxm+fgxITERIYiqAEDeTRcYCf3BiIGR8v+LtL\n1wx1UJAxGH/kWAxSRRmDLi8fF5SgESTk80vPQMSy3xtjBxIT4Z+e4VQbBQHz5m2hIE+f5p+WgcT7\nfgd9aRkCE+IRkCZ83y1uhJCWgUGhYVe2U4ci0d+fv/GDYvgoJDKG1ooKBMXFQTF2PAZFxfDH+6cN\nRcQABd/PVNe6cd9tQnyFjRi/AaPHQwMOHWUV8A8NBTcwDIWDgpC07PcIqGq0mB8FN1KJjYGhCwhJ\nSUHIiJHQl5UJxqA4qSoF1hNC+gq3L1Q4Tvz9Td/mVsJCD+A4uXEdv9la/qjrx/IJ9QJSr3womf8d\nOPLK8eJg9qjrcwQJ+X7VnsErdR8DAwDU/YRl2kikhaY60UjLhGLmbSG+qf10oXC9e2i41QSOgvdV\ntG1+44fTTb/iFcPXQDQAQyGWtSYiTXS8uC8T0tvZivXiZH4YkHMjgMtj4+ibwM/GY5bl3Gs5x1pJ\nAmwSkJF15W8rSVXphiWEkL6CbgXipP6w/rdcW2l3m/RNnu7b1I9IfyRlHNHYIIQQaehCxUn9IWGh\nRhlrd5v0TZ7u29SPSH8kZRzR2CCEEGkoRsVJvpSwsAsGHK07hvLiSsQr4zBCNQwyCPNQMHTh16bf\nUK6thEYZi9TQq8E5uD5NDb0ay7LvFZzjFHfzsJAeYerb4vXutjjqW6nKFPw5Yjr0ZcaxolKmOGyD\nuMwhoSkoajrjVP8lpCeJY/z80ofidNOvgj5smmNr9bUIkAWgXFsBAI77N82thJB+RvKFyqlTp9DU\n1CS4MMnJycH69etdqrioqAhLly7FokWLMHfuXMG+CRMmIC4uDhzHgeM4bNiwAWq12qV6PM7KOv2e\ncrTuGLYf381vsyyGUREjBcf82vSbcS30ZcuyrayFFuEgQ1poqnNxKWbczcNCesjlvi1e726Lo77V\n8Ushn/OnGYBypdJhPxCXuTBrpqCPS+m/hPQkcYzfwvoAq304LTQV5/V+2PDD6xb7bKG5lRDS30i6\nUHnggQfw22+/CS4WOI5DTk4O4kW3V5SitbUVf/nLX5CTk2N1P8dx2LZtGwLNbmtLLJU3VVpuR4iO\nsbIWurv/o2dtjTZ9mPY9jvqWK/3AokxxH/dC/yXEHc704ZJL5Tb3WUNzKyGkv5F0oVJRUYGvvvrK\nY5UqFAps27YNW7dutbqfMdbvlpS5Ij5MmNdGE2q5zrkn1kL3hzge4rhvudIPxGVY9HFay098nMW4\nCLU9ThLDNDb3WUNzKyGkv5F0oZKcnIz29nYEBAR4pFKZTOawrHXr1qGsrAzZ2dlYuXKlR+r1BdbW\n9dt63NpaZfPjBocn4YmBt6CjtAL+CRqoVddZHJMUqsFTA6dCX1oGRVICIpWD+fXS8aFxYKwLBy5e\nRHRgNF+nvbbYar85X4rj6a8YM6D+xGHjOvmkRKgyx4Dj5PbPufzeHrhYLegPtgxRJuPJgVPRXlqG\ngMR4RJn1LY0yFinpaVAuXYCO0goEJMSBS09FQd0RlDdVIj4sDsNV1+G3prNW1+6bx6gos5Wux0sR\n0o2szYeDQwfh7szpqNReRKxSjWAoMDdzBqqbaxAfFgddpw6fX/gC2Y3BiGlsx/OBt6Cp7iIUSY7j\nuGhuJYT0N3YvVFatWgWO49Dc3Ixp06YhMzMTcvmV/+y88MIL3dKo5cuXY9y4cQgPD8eSJUuwd+9e\nTJo0qVvq8jZr6/rVUdmSY0nMj3soJA/arca1z20AApYFICIr1+KYS2bHsKUMrzR8AQDITczGDyVH\nLeq01xZb7RfwoTie/qr+xGE+PqQFAJZdzllih7PxTA0n8tH0v28DMPYtLAXftwDg7szp+EfDl0AI\ngIYTuLtWgX+c/Jjf35HZgXdP7rGoTxwf5U68FCHdydqYqdXXCvr57Gtuw66fP+G3cxOzMbhMj0s7\n34f/uOtR+933ACTGcdHcSgjpZ+xeqIwdO9bmvu5M9Dh9+nT+7/Hjx6OoqMjhhUpUlNKtOr11/oGL\n1YLt6rZqwb/mj49LFl0AiM6XV9ahy2yfvqwUUROVdo9pLyszBnkCaOvUW63TWhtNbbHV/t7y+vsC\nT7fVWnkVZaK17Jf7hj323ndrxHWY9y0AqNReFOwXb1c0VzlVn5g3XkdfLNNbPNX2vlyOtTFzsblO\n8Fhls7Dft3XqEVLTAgAwtLUJ9hmqyhGV51oCVF97fbypu8ducJC/3WMVCn+Hbejp+aWhIcThMSpV\niM/Pq72xfxL32L1QmTFjBgBgw4YNeOSRRwT7Hn/8cdx+++0eb1BzczOWL1+O1157Df7+/jhy5Ahu\nueUWh+dJuUuRLVFRSq+dHx0YbXXb2uPWyjQ/zhAbKdiniE9ATY3W7jEB8fFAwwkAQKCf8GYFpjrt\ntcVW+3vL62/rfG9yp61itp67IiEBzebbl/uGPVL7oM06EuKB+hP8dlyosLxYpfDOfXEhMU7VZ87d\n97y7y+uOMntjP/XUa+Cr5VgbM34y4RLLuBDhMYF+CrREcQgAIA8SzsHyGI1L7fPF18ebunvs6lo7\n7B6v13fYbYMvzC/19c2SjvHlebW7Xkfi2+xeqHz99dfYu3cvDh06hIsXr3wr1NnZiSNHjrhcaWFh\nIZ577jlUVFTAz88PX331FSZMmID4+HhMnDgRN9xwA2bNmoXAwEAMHToUkydPdrmunmAvxsNWjhKp\nuUvMj+NCExCx7D7oy8oQmJiEzs52lHyyEwOTErBy5GJcaCrjj2krKUVgYiIGXjsay7SxKNdWIiFU\ng+HqTFS3XURMUAwY68J/yvcjIVSDRVl3oaypAvGhcZBxHP5Tvp+PGbjSzhjIOBk+KPxcUkwD8R5V\n5hhgGfhcDqprxzg8x9S3qtsux6goU9B+6gSfs8EvPQO/aq/kNLk6cxQ6lnagvbQMisR4RGSNxcL6\nAShvqoQmNBZZEZlgmYxfq58dNRyyTBkqtFWIC43ByMgR8Mvy448fEuo4zwohPUE8p0dEDgcgnI8T\nwjRo1DeipU2HuzOno7alHpEDVGjQXcLdmdNRp2tAZPBAXNI3Q58WhPAH7kHQpXYk3jcEHZe0tmNO\nKHcK8UEGgwEXLpyzuX/QoMFebA3py+xeqIwbNw4qlQo///yz4FbCHMfhgQcecLnSjIwMvPPOOzb3\nz58/H/Pnz3e5/J5mb62/rRwlUnOXWByXlYyoiUqc3PupIF4gdOki3DTsBv4YZF0pQ1zPuOSR+O7s\nUbxy9C3+MVP8iq04lrTQVJxu+hUvH3nD6vMkPYvj5MaYFAdxKYJzLvetccnZqKnRov3UCUHOhohl\nvzfmh7hsYdZMbG/4whiDUn8CC+sHCPJFzM3sFKzVl2XKBDEpfll+guNDs0Op/xCfJJ7TFQo/XKVI\nFszHBXVHRP1/hqC/35Y2Ce+ZjYdl2fci4/JYs3cjfsqdQnzRhQvncPChBxEbHGyxr1KnAzb/D2Ji\nhvdAy0hfY/dCJTAwECNGjMDHH3/ssTt+9Qc9kbukvbTMcnuY9PPFbTbFr4jjWMyfS088T+I94pwN\nbSUlghgUq3l8zFRoq+xuU44U0luI57qSS+W4Sp0sPMZB/8MxJqUAACAASURBVG9obbRbpi2UO4X4\nqtjgYCSG0NIp0r3sXqikpaXZDJqXy+X4+eefu6VRvV1P5C5RJCXAPCwzIMG5RJwWOTD8FJf/DbR5\nXE88T+I94pwNgYmJQN1P/LajPD6aUGEMSpwyRrSf+g/pHcR9U5z/BLAcD3Gi/j8wKNxumbZQ7hRC\nSH9m90KlsLAQjDG89tprSE1NxZgxY9DZ2YlDhw7h/Pnz3mpjryM13sSToq7NBVvKjDktEuKhvu56\np84XtjkGMk6O6CA1H8dSrq2yeC4WMQ2U46JPEeds8E/PwDJtpM0cJ0NCUxCaHXolhiU0GfIsP5Q3\nV0ITEosREcOgylbZPJ76D/FV4jk9W3Mt6mpbBMeMUA0Dy2J8zNWV/l6BoIBAaNu0mJs5A63tbdAo\n4yT3d8qdQgjpz+xeqJhypuTn5wtiUqZOnYr77ruve1vWi0mNN+mCAUfrjqG8uBKJofFoN+hR3lSF\nQeGJ6OhqR3lTFTShMRgVORJyB7k5OU6GxuRYlKuB+NA4NGrP8BcXMk6G0qZy/j+HRU1nHPznkMPV\nymQMUV7ZlxaaZvN5mmIaiG/j+9vlhIsjVMMgw5U7FFlL+Gies4EJbnQNdKEL9fp6NOgbEKxQoB3t\nqNXXol7fgECFPwZjEEL9Q9Ea1IpQ/1DIrIwLypFCfI2tm6Gkhabi6tBk5NccwVs//h8iggeirqUB\nkQNUaG5rRkjgANTqGhAeFAo/mR8OVxXA3994W1uVQoWcqNGu3WyEcqcQQvoxSZnpW1tbsWvXLowY\nMQIymQzHjh1DfX19d7etzztad4wPvjQPWr8tTYlPTu/lj2OZwNioHKtlmJgHe4oD4M23F2bNFAR8\nOptwkvRe5v0NAFgWw6iIkfy2oz4g3n935nRBsLwhs0uwzTIh2KY+RXoDe+Mgv+YI3j25B7mJ2dh7\n8lv+GHGgfG5iNiKCVfjk+F6r5RBCCJFG0tc7L774Io4cOYKVK1dixYoV+OGHH/D88893d9v6PPPg\nS/OgdXHQpTgo02pZWutlibetBTCLz7e2TXo/R8HvjvqAeNtRQkfxNvUp0hvY6/emuVg8x4rn7LZO\nvcvB84QQQq6Q9IvKVVddhY0bN3Z3W/od8+BL86B1lSjoUhyEbI15YKY4AN4UGA/YDmCmwPi+z2Hw\nu4M+IN52lNBRvE19ivQG9vq96QYR4jlWHCgf6KdwOXieEELIFXYvVFasWIGXXnoJeXl5Vu/+tX//\n/u5qV78wXHUdOjI7UNFchcTQeAwOT0RZUwWiAiMxJ3M6Ki4nylP6h2DP+U+sxhWYDAlNwcKsmShv\nqkRSeAKGqTNRoa0SBMbbC2DuiRsAEO8SB/tmRwjvcS++OcLVockoqDvCx7QMU2XxfUwTGovrIq4V\nJHQcETUMyAS/PTJqBAKyAlDeXIl4ZRwldCS9grW50BS3YujsMiZvbKnH3ZnTUa9rhCo4HE2tTcbt\n1kYE+QchLCAErJNhYdZMtLa3IiggCOXaCr58SoxLCCHS2L1QeeKJJwAA77zzDh9YTzznt6azgoRg\ny7LvxZjBo3G66Vds++97/OPmMSbiuAKToqYzFrEnN2lu4LeFgfGuJ5wkvZcMcmPfibC+X3xzBHEC\nu47MDkF/RZYwBiUyOxLXR+UCUcbt002/Cs5XZiupfxGfZ20uPN30K145+qZF/J845i83MRtfnTkA\n4EpMiulcE4pVIYQQ6ex+rRMZGQnAmCn++eefxw8//ACO46DRaKDRWN5HnjjH1lpoW8kXAcu4Akdl\nEeIqRwns3I1xIaS3MPVdiwS4TXbmaor/I4QQt0mKUdm3bx9OnjyJ/Px8PPnkk2hoaMCIESPw+OOP\nd3f7+jRba6FtJV8ELOMKHJVFiKssEtg5mbCR+iTpK0x9VxybIh4jgrma4v8IIcRtki5U5HI5MjIy\n0NnZia6uLhQUFODYsWPd3bZexda99+3h40ouJ8Tzk/nhP+X7kRCqwbLs36FcW4U4ZQx0nToEyQOt\nxhWYUIwJcZa4z14dmowf635CebExpmSYKstGAjtpCRspISjxFdZyBEmNE2HogozjcOfQqdC1t2Ju\n5gy0d3UgJihalPRUGA9I8X+EEOI+SRcqixYtgkwmQ0ZGBrKzszFv3jwolcrubluv4koeEnFcifn6\nZ3GMyQiV9QsUE4oxIc4S99m5mTMEMSh8PJRZTIszCRspISjxFe7kiRKfm5uYjZyEEbhKkQzAcgyY\nxwMCNDcTQog7JH2llJ6ejo6ODpw+fRq//vorzp49C4PB4FbFRUVFuPnmm/Huu+9a7Dt48CBmzpyJ\n2bNn49VXX3WrHm9xZR2y3VgUWsdMupm4jzmKQSGkt3InTsTaPF1yqdwj7SKEEGKfpF9UVq9eDQDQ\narUoKCjAa6+95tbyr9bWVvzlL39BTo71bOt//etf8dZbb0GtVmPevHmYPHkykpOTXarLW1xZh2w3\nFoXWMZNuZpkXxX4MCiG9lTtxItbm6cQwupkMIYR4g6QLlaKiIhw5cgRHjx5FUVERkpOT+YsXVygU\nCmzbtg1bt2612FdaWorw8HBERxuTyeXl5eHw4cM+c6Fivq4/IUyDRn0jyosrkRSagAeyf3c5d4m0\ndcjmMSrxyjgMDBiI6CA14kPjwFgX/lO+HxplLGScDKVN5RaxL+6suya9m6P33lrMFADBYymhgzE3\ncwYqtFWIC43ByMgR8Mvy42OmRkQMw+mmXwUxKUVNZ5yKwyLEF9iKl+qCAUfrjvG5gmSQoeRSGeLD\n4jBcdR1+azqLcm0FFl13F+p1DQj0D0SI/wBUa2tQ01wPbVszNMo4GguEENJNJF2oPPPMM8jNzcU9\n99yDzMxMQfLH06dPIy0tzalKZTIZAgICrO6rra2FSqXit1UqFUpLS50qvzuZr1e+LW0SPjm9l9+3\nMGumIK7EEVu5T4z33X+Lf1wcu2Ja6+zOumvSuzl6763tB2A3JsUvyw+jIkYiKk2JmhqtRf4Hcc4I\n6m+kt7AVL3W07pjNOEFx3iDzMSTOp0JjgRBCuoekC5V33nnH5r5nn30WO3bs8FiDxBhjko6LinIv\nuF/q+QcuVvN/N7Q2CvaVN1ciKk16O8zLAoDqtmqMS862eNw8dsV0jL3zXeGt189Xz/cmT7TV0Xtv\nbb9YRbMoJsWs/0ZFKS3KKG8WrtV3pr91x/vj6TJ7Qxu9yVNt9+VyyottxwmKx4f5GBLnU+nJuddX\ny/Gm7h67wUH+do9VKPwdtqGn55eGhhCHx6hUIZLLbGgIwXkHZQG9Y54mvk3ShYo9Ui8kpFKr1aip\nqeG3q6uroVarHZ7nzl2FoqKUks+PDozm/1YFhQv2aUJinWqHeVmm7ZoarcXj5rErpmPsne8sZ55/\nXz3fmzxxByxH7721/WIWeVEu91/T6yk+RxMiXKsvtb+5+/54o8ze0kZv8kTbPfUadFc58UrbeVDE\n48N8PIjzqfTU3OvL5XhTd49dXWuH3eP1+g67bfCF+aW+vlnSMVLLdFSeaX9vmKeJb3P7QsV8GZgn\naDQatLS0oKKiAmq1Gvv378fGjRs9Woc7zO+JnxSaIMiDYivHiaOyxOumhffdt35vfnvnk77P0Xtv\nK3eD+WNXhyYbY1Iu50kR919xGY7yphDS24xQDeNzBSWEacCB43NWifMGmY+hOn0tUrJmCmJUCCGE\neJ7bFyquKCwsxHPPPYeKigr4+fnhq6++woQJExAfH4+JEydi3bp1WLlyJQBg2rRpSEpK6olmWmVx\nT/yQZH5Nv6tliddNW7vvvvje/PbOJ32fo/feVu4G8WPiPCmOyqB8EKQvkUFuMQbMc1bZGkNRUTTn\nEkKIN/TIhUpGRobduJfs7Gzs2rXLiy0ihBBCCCGE+BK376fo6RgVQgghhBBCCLH7i8qhQ4fsnpyT\nk4P169d7tEGEEEIIIYQQYvdC5dVXX7W5j+M45OTkID4+3uONIoQQQgghfZ/BYMCFC+fsHjNo0GAv\ntYb4GrsXKvbiSL766iuPN4YQQgghhPQfFy6cw8GHHkRscLDV/ZU6HbD5fxAT49ydVUnfICmYvqKi\nAjt37kRDQwMAoL29Hfn5+Zg8eXK3No4QQgghhPRtscHBSAyhnCbEkqRg+kcffRTh4eH473//i2uu\nuQYNDQ144YUXurtthBBCCCGEkH5K0oWKXC7H4sWLERkZiblz5+Jvf/sb3n333e5uGyGEEEIIIaSf\nknShotfrUVVVBY7jUFpaCj8/P5SXl3d32wghhBBCCCH9lKQYlfvuuw+HDh3Cvffei+nTp0Mul2Pa\ntGnd3TZCCCGEEEJIPyXpQuWqq65CcnIyAKCgoAAtLS04f/58tzaMEEIIIYQQ0n/ZXfrV1NSEkpIS\nrF27FqWlpSgtLUVlZSXq6uqwevVqb7XRZzHGUFjcgF17T+NUcQMYWE83iZA+jcZc32N6T78sKKX3\nlBBCiIDdX1R++uknbN++Hb/88gsWLlzIPy6TyXD99dd3e+N83amSRmz8x0/89sN3D0NG0sAebBEh\nfRuNub6H3lNCCCG22L1QycvLQ15eHv7xj3/g7rvv9lil69evx/Hjx8FxHNauXYvMzEx+34QJExAX\nFweO48BxHDZs2AC1Wu2xuj2ptLrZYps+YAnpPjTm+h56TwkhhNgiKUZlypQpeP7551FbW4sXX3wR\n+/btw3XXXQeVSuV0hUeOHEFxcTF27dqFs2fP4vHHH8euXbv4/RzHYdu2bQgMDHS6bG9LjA4RbCeI\ntgkhnkVjru+h95QQQogtki5UnnzySYwcORI//WT8eb69vR2rV6/GG2+84XSFhw4dwsSJEwEAycnJ\naGpqQktLCwYMGADAuF6Zsd6xRjk9KRwP3z0MVfU6xKiCMTQpvKebREifRmOu7zG9p6XVzUiIDqH3\nlBBCCE9SHpX6+nosWLAA/v7+AIBbbrkFbW1tLlVYW1sr+CVm4MCBqK2tFRyzbt06zJkzB5s2b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XK87W2R3vNfFtXr9QGTZsGDIyMjB79mzI5XI89dRT2LNnD5RKJSZOnIh169Zh5cqVAIBp06Yh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\n5wWDDdtofD+jAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f187f9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean, hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, we purposely inserted numerous errors into this data set to demonstrate some of the many possible scenarios you may face while tidying your data.\n",
    "\n",
    "The general takeaways here should be:\n",
    "\n",
    "* Make sure your data is encoded properly\n",
    "\n",
    "* Make sure your data falls within the expected range, and use domain knowledge whenever possible to define that expected range\n",
    "\n",
    "* Deal with missing data in one way or another: replace it if you can or drop it\n",
    "\n",
    "* Never tidy your data manually because that is not easily reproducible\n",
    "\n",
    "* Use code as a record of how you tidied your data\n",
    "\n",
    "* Plot everything you can about the data at this stage of the analysis so you can *visually* confirm everything looks correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Testing our data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "We should test our data. Just how we use unit tests to verify our expectations from code, we can similarly set up unit tests to verify our expectations about a data set.\n",
    "\n",
    "We can quickly test our data using `assert` statements: We assert that something must be true, and if it is, then nothing happens and the notebook continues running. However, if our assertion is wrong, then the notebook stops running and brings it to our attention. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert 1 == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test a few things that we know about our data set now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We know that we should only have three classes\n",
    "assert len(iris_data_clean['class'].unique()) == 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\n",
    "assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We know that our data set should have no missing measurements\n",
    "assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['sepal_width_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_width_cm'].isnull())]) == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And so on. If any of these expectations are violated, then our analysis immediately stops and we have to return to the tidying stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Exploratory analysis\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Now after spending entirely too much time tidying our data, we can start analyzing it!\n",
    "\n",
    "Exploratory analysis is the step where we start delving deeper into the data set beyond the outliers and errors. We'll be looking to answer questions such as:\n",
    "\n",
    "* How is my data distributed?\n",
    "\n",
    "* Are there any correlations in my data?\n",
    "\n",
    "* Are there any confounding factors that explain these correlations?\n",
    "\n",
    "This is the stage where we plot all the data in as many ways as possible. Create many charts, but don't bother making them pretty — these charts are for internal use.\n",
    "\n",
    "Let's return to that scatterplot matrix that we used earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f3f16306080>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+eHM1Wrsd7lX1bps3Bf0Dp/Gv8yrR+/kZ5GSlwZqvx+TSXK/ySgtycMd1F+Oz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\n2hulpP38azEagTNttHCAIIjkY4QRi10luHiuHR+e7oHXNwz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j/H0Q64t/HrFrbOe1jfWe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\nQtEMNUhWKBQKhUKhUCia8X/Z6FnGBajxUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f163064e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is normally distributed for the most part, which is great news if we plan on using any modeling methods that assume the data is normally distributed.\n",
    "\n",
    "There's something strange going with the petal measurements. Maybe it's something to do with the different `Iris` types. Let's color code the data by the class again to see if that clears things up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f3f15b05080>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XB5vNBr1ej+xszyusE1Fs+QoBsNutaD+6Hw3GkUW2VBddCsmYX9cUxcLL9Wkl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oocA7QTpgAY+LprIiLyzZ+4Qn/G3/jCi5G3bh0sNVWQZWRDXnhxOJpPQ8BgY1vFPD1HEEULTlRi\nmEQiQ2rxdOjmqgUxLgNdd01ERL75E1fo1/grkUI+bgJ0s6YHFJ9Iw9dgY1td6vPwHEEULSI2UXny\nySfx+eefw2Kx4Ac/+AGuvPJKx7n9+/dj06ZNkMlkmDlzJlavXh2pZhIRERERUQREZKJy8OBBnDp1\nCjt27MD58+dxww03CCYqjz/+OF577TXo9XosX74c8+bNQ35+fiSaSkREREREERCRiUppaSmKi4sB\nABqNBp2dnbDZbJBIJKioqEBKSgrS09MBALNmzcKBAwc4USEiIiIiGkYiMlGRSCRQKpUAgD/84Q+Y\nNWsWJBIJAKChoQFardZxrVarRYVoK8doYU+suKeuP6jNJbEXURSwWCz4+OOPvF4zc+blYWoNUfRx\nTpRrD4LneE7Rhs8dNNxENJj+gw8+wJ/+9Ce8+uqrHq+x2aI3L4K7xIoMYKdodObMaTz5j2eRqE1y\ne76jqR1GY26YW0UUPTieUyxgP6XhJmITlb179+Lll1/Gq6++CpVK5Xhdr9ejvr7ecVxbWwu9Xu+z\nPp1OPaj2BFJ+T12t4Li2qxYz8kvC9v4sH7zy4RTstvpT37lzKugKMqHOcp8ssdV0Hlqtyu05sWBe\nF4n3tF8n/rmFog/FUr8UC1bbY6WegY7nsfK5IlVPOMXC726w6gvmc4fYcPo5UuyIyESlra0NTz31\nFLZu3Qq1WtjpsrOz0d7eDpPJBL1ej927d2Pjxo0+6xzMtno6XWDb8qUr012OA6kn0Pdn+eCVD6dg\nbgHp72dvamoLyjXBvi4S72m/zvnnNtg+5E6w64zFfhqsn0E46hnIeB5LnytS9YRTLPzuBqu+YD13\niMXKGBiKNlJ0i8hE5d1338X58+fxox/9yBFEP2XKFIwZMwZz587Fhg0bsG7dOgDAggULkJsbnUtS\ngp14iYiIIoOJcikW8LmDhpuITFQWLVqERYsWeTxfUlKCHTt2hLFFgQl24iUiIooMJsqlWMDnDhpu\nuFUEERERERFFnYju+kVEg9fb2+v1vEwmC1NLiIiIiIKHExWiGPfLW5ZilNT9l6Ndvb0Y+4M7kZGb\nF95GEREREQ0SJypEMS4vMRElkLg919Hbg9ZeS5hbRERERDR4jFEhIiIiIqKow29UiGLcl3U1aFco\n3Z7rtlgwpqsrzC0iIiIiGjxOVIhiXOu80fiqMMntuZ5OMwqSEsLcIiIiIqLB49IvIiIiIiKKOpyo\nEBERERFR1OHSLyIaliwWC3bseAMAoFYr0drqGsuzZMkyAHBc58mSJcuYr4aIiCjIOFEhomHpzJnT\n+M0fPoUiKcXt+e7285gyZSoA+HVdfv5FIWsrERHRcMSJChENW1ljp0E1ItvtubZzVQO+joiIiIIn\nYhOVsrIy3HXXXVi5ciWWLVsmODdnzhxkZWVBIpFAIpHg6aefhl6vj1BLiUjMYrGivb7V4/n2+lZY\nLFbIZAyDIyIiosBEZKLS2dmJxx57DFOnTnV7XiKRYMuWLVAq3eeGIKJIs+H8kZHoVmvdnu1sbQLm\n28LcJiIiIhpKIjJRUSgU2LJlC15++WW35202G2w2PuQQRSuZTIbUnEKvy6EYXE5ERESDEZF1GVKp\nFHK53Os1GzZswM0334xnnnkmTK0iIiIiIop+K1asQG1tbaSbEXJRGUy/du1azJgxAykpKVi9ejXe\nf/99XHXVVZFuFlFUaiqvhyLOdWtdAOjt6oHlYgsA+Iwpcfdvb9d1NNd5vM75nKfr/Lkm1q4jIiIK\nB4lEEukmhIXEFsE1Vs8//zxGjBjhEkzvbPv27WhqasLdd98dxpYREREREUWHlpYWPPjgg5BKpbBa\nraiursYLL7yA2tpavPDCC9BoNACAp556Cp9++im2b98OlUoFAHjiiSfw6quv4uTJk7BYLBg/fjxu\nu+22SH4cv0XdNyptbW1Yu3YtXnrpJcTHx+Pw4cO4+uqrI90sIiIiIqKI2L59O6644gpcf/31+Pjj\nj/HQQw8B6Htu/tnPfoacnBzceeedOHPmDPbs2YNrr70W11xzDU6fPo2enh588MEHeOaZZ5CZmYkT\nJ05E+NP4LyITlaNHj+KXv/wlTCYT4uLi8N5772HOnDnIycnB3LlzMXv2bCxevBhKpRLjxo3DvHnz\nItFMIiIiIqKIM5lMuPTSSwEAM2fOhMFgAADI5XK8+OKLUKlUqKioQFdXF/7zP/8T//3f/43t27fj\n0ksvxb333ouf//zn+PWvf43q6mosWrQIBQUFkfw4fovo0i8iIiIiIvJuy5YtUKvVWLx4MT744AM8\n+OCD2LVrF+68805s2bIFqampWLZsGR588EF0dnaiuLgYcXFxuP/++3HbbbehtbUVkydPRk9PD268\n8Ubs2rUr0h/JL5yoEBERERFFsZaWFvz0pz+FVCpFb28v6urqsHnzZmzfvh3ffPMNMjMzER8fj9ra\nWlxxxRX485//jBEjRqC3txePPfYYnnnmGdTX1yMuLg5GoxF33XVXpD+SXzhRISIiIiKiqBORPCpE\nRERERETecKJCRERERERRhxMVIiIiIiKKOpyoEBERERFR1OFEhYiIiIiIog4nKkREREREFHU4USEi\nIiIiihJVVVW48cYbBa/t3bsXO3bsCPp7vffee0GvM5jiIt0AIiIiIiLqJ5FIBMczZswIyfu8/PLL\nmDdvXkjqDgZOVIiIiIiIAlTd0IbK+nZkaBNhSFcHpU6bzYYHHngA8fHxOH/+PC6//HKUlZXhvvvu\nw49//GM0NDTAbDZjzZo1uOyyywRl33rrLbzxxhuQy+UoKCjAz372M5w6dQqPPPIIpFIpkpKS8MQT\nT+DNN9/EyZMncc899+C5557Dk08+iS+++AJWqxU333wzFi5c6LauTz/9FM8++yzi4+ORnJyMX//6\n14iLC82Ugku/iIiIiIgCcLrqPNa/uA+PbDmAH2/+GMe+bQxq/SkpKXjuuecA9H3LUlZWhnPnzmHb\ntm149dVX0dzc7FLmtddew/PPP4833ngDF198Mbq7u/Hoo4/i0Ucfxf/8z/9g2rRp2L59O1atWgW1\nWo3nnnsOR44cwalTp/C73/0OW7duxQsvvID29naXusxmM1paWrBx40Zs27YNSUlJ+OSTT4L6mZ3x\nGxUiIiIiogD8s6wBDc1dAID2zl58dqIO40amBq3+CRMmCI7z8/PR0dGB//qv/8LcuXMxf/58lzIL\nFizA6tWrcd111+G73/0uFAoFvvrqK/z0pz+FzWZDT0+PS71ff/01Jk2aBABISEhAfn4+ysvLBXUt\nWLAAcrkcI0aMwIMPPgiLxYLKykpMnTo1aJ9XLConKvb/Ac3Nzejp6cFdd93l8rUWEREREVEkJavk\ngmNNktzDlYGJj48XHCsUCrz55pv4/PPPsXPnTnz00UdYtGgRNm7cCIlEgqeffho/+MEPcN111+Fv\nf/sbbr31Vmzbtg2JiYl4/fXXvb6XzWZz/NtsNkMikQjqWrlyJbZt24b169fjlVdewciRI/Hoo48G\n9fOKReXSr507d2LUqFF4/fXX8eyzz+Lxxx+PdJOIiIiIiASmFGXi5nljkZuhxg2z8zFrYnZI3+/4\n8ePYtWsXLr30UmzYsAGnT5/GJZdcgm3btuH111+HTqfDpk2bkJaWhpUrV+KSSy6ByWTC2LFj8fHH\nHwMA3n33XRw4cAAAYLVaAQBFRUU4dOgQAKC9vR2VlZXIy8tzW1dbWxsyMzPR0tKCgwcPoqenJ2Sf\nNyq/URkxYgROnjwJAGhuboZWq41wi4iIiIiIhJKU8Vh6VQGWXlUQ1HrFu37Z5eTkYOPGjfj9738P\nmUyGVatWuZRLSkrC4sWLodFoYDAYMG7cOKxfvx4PPfQQXnnlFSiVSmzcuBEAUFhYiEWLFuHNN9/E\nuHHjsHz5cvT29uL++++HUql0W9fNN9+MJUuWYOTIkbj99tvx/PPP4/LLL0daWlpQfwYAILE5f88T\nRW6//XaUl5ejpaUFL7/8sstaOiIiIiIiGrqicunXrl27kJWVhffffx9bt27Fww8/HOkmERERERFR\nGEXlROXzzz93JLYpKChAXV0dvH3xE6VfChG5YF+lWMB+SrGCfZVoaIvKGJXc3Fz885//xJVXXomq\nqiokJSV5XKsH9K3Hq69vDfj9dDo1yw/z8uEy2L4qNtjPHo462cbg1RcuweqnwfoZsJ7Yqydcgj2m\nAsN3fBmubaToFpUTlcWLF2P9+vVYsWIFLBYLHnnkkUg3iYiIiIiIwigqJyqJiYn49a9/HelmEBER\nERFRhERljAoREREREQ1vnKgQEREREUWJqqoq3HjjjYLX9u7dix07doS9Lffddx/MZrPf17tr+2BE\n5dIvIiIiIqLhSryJlH033HCzJ4YcCG8bYA0UJypERERERAGqaa2DqbUO6UmpyE7ODEqdNpsNDzzw\nAOLj4/8/e3ce3mSV6A/8m6Rp0yVtaZvuTcGyFCpgESqgUPG6i+N4RxkQgRlFf+OCjqjjiDM443Id\nHZFxvV4X7ijqII7DuOE2ioKKiKNXkVWW0r1N93RJkyb5/VHyNufN2jZr+/08D8/Dm/ec856k532T\ns6OtrQ0LFizAoUOHcOutt+L2229HU1MTzGYzVq1ahTPOOEOKd+DAATzwwAN44YUXAABPPPEEUlNT\nMWfOHNxzzz1QKpVITEzEn/70J7S3t+O2225DUlISrrzyShw6dAgffvghlEolzjrrLFx77bU466yz\n8M4776C1tRV33HEH7HY7cnNz8eCDD6KxsRFr1qyB2WyGSqXC/fffL7yHXbt2Yf369VCr1cjOzsb9\n99+Pd955B9u3b4fBYMAjjzyCzMxMr58Dh34REREREQ1BRWsV/rBtPf6040nc9dGfccBwJKDpp6am\n4rHHHgPQ31Nx6NAhtLa2YuPGjXj++efR3t4uhC8uLobBYEBnZycA4OOPP8a5556Le++9F/feey/+\n93//F3PnzsVLL70EoL9is27dOpSXl2PDhg3YtGkTNm3ahOTkZOmaALB+/XpcffXVeOmll5CZmYk9\ne/bg0UcfxWWXXYaNGzdiyZIlePzxx4W8/OEPf8Cjjz6KjRs3IiUlBW+//TYAoK6uTkrHF1ZUiIiI\niIiGYE/DAbT0tAEAui09+L+6vQFNf9q0acJxUVERuru7cccdd2Dnzp246KKLXOIsWLAA27dvR11d\nHTQaDTIzM/H999/jd7/7HZYtW4Y333wTzc3NAAC9Xi9VSs4//3ysWLECmzdvxsUXXyylZ7fbsW/f\nPpSWlgIAbrvtNkybNg0//PADTjvtNADAaaedhv3790tx2tvboVQqkZWVBQAoKyvDvn37AABTp071\n+/1z6BcRERER0RBo45Jkx4kBTV+tVgvHcXFx2Lx5M7755hts2bIF27Ztw6JFi7Bu3TooFAo8/PDD\nOOecc/DSSy+htbUV5557LoD+rT9efPFFIa2amhoh/bvvvhvHjh3D1q1bsWzZMrz22mvSOaVSCZvN\nJsRXKpWw2+0AAIvFAqVyoP9DoVAI4S0WC1Qqldv35A17VIiIiIiIhqAs7xQsOvki6FNycfGks3FG\nYVlQr7d//368+eabmDFjBu6++24cPXoUp5xyCjZu3IgXX3wRmZmZOOWUU3DkyBF8+umnOP/88wEA\nkyZNwvbt2wEAW7duxZdffgkAUkWjs7MTTz75JMaNG4cbbrgBqamp6OzshN1uh0KhwLRp06Q4jz32\nGHbu3ImpU6dKr3311Vc4+eSTpXwmJydDqVSivr7e7Xl/sUeFiIiIiGgIEmLjcVnJQlxWsjCg6Xpa\nOSs/Px/r1q3Dq6++CpVKhauvvtptuNLSUuzfvx/Z2dkAgDVr1mDt2rV49tlnodFosG7dOhiNRuk6\nSUlJaG1txeWXX47ExETMmDEDKSkp0vlVq1bhzjvvxCuvvILc3FysWrUKRUVFuOuuu7B582bExsbi\n/vvvh8VikfJwzz33YPXq1YiJiYFer8dFF12EN954Y3Cfg91RlYpyBoNxyHF1Osbh3foAACAASURB\nVC3jj/L4oTScvMoN972HIk3mMXDphVIg8h6oz4DpRF86oRQN9y7zGHnpOdKkyBaRPSp///vf8cYb\nb0ChUMBut2Pv3r345ptvwp0tIiIapDt/93scPnLY43m1Wo2XX3ghoOvuExHRyBCRFZXLLrsMl112\nGQBg9+7deO+998KcIyIiGgproh7a6Qs8njc1fg+bzSZNsiQiInKI+Mn0Tz75JK6//vpwZ4OIiIiI\niEIooisqe/bsQU5ODtLT08OdFSIiIiIiCqGInky/du1aXHzxxZg1a1a4s0IhZLda0bL7a3QdP47E\nwrFIK5sJhTKi69QjEv8OFAj/747HUNtX6PG8pekHvLPhtxz6RTRK8buGvInIOSoOX331FdauXetX\n2HCvGsX4gYtv3vc9Kh55RDoeu3o1YqdMcxc1YNcPpWhYBcVgMA767+ArvUCKltVkuOqXFmZLH+Bl\nnrzVZoPBYPRaUYnEVa2Yjvd0Qika7l3m0TN/v2u46tfoFLFV1sbGRiQmJiImJqLrUhQEvVVVXo8p\nNPh3ICKiYON3jauamhr87Gc/E17bsWMHNm3aNOQ0H3jgAdTU1AQk7A033DDkfAxWxNYCDAYD56aM\nUpqCAuE4TnZMocG/AxERBRu/a9yTL9k+b968YaV35513Bizsk08+Oay8DEbEVlRKSkrwzDPPhDsb\noWO3wbz/B/RWVUFTUAD15JMBRcR2eAWVurgE+pVXwVRZhXh9AWKLS8KdpVFJPflkjF29Gr1VVYgr\nKEDs5JPFANFQZqMhj0REI8mJ525lfQ1isvNcn7vy53JxiffvmijQU1eHnppaaLKykFCQH5A07XY7\n7rzzTqjVarS1tWHBggU4dOgQbr31Vtx+++1oamqC2WzGqlWrcMYZZ0jxDhw4gAceeAAvvPACAOCJ\nJ55ASkoKPvjgA9x999147733UFVVhZqaGmzYsAG/+c1vUF9fj1NOOQXvvvsuPv30UyxbtkwK29HR\ngWPHjqG6uhpr1qzBvHnzMHv2bHz55ZfYt28f7rnnHiiVSpSWluL222/Hzp078eijj0KtViMlJQV/\n+ctfhjU6KmIrKqONef8PAZkPMBKYD+xF5XMbpOOxyamj9rMIK4USsVOmefzso6HMRkMeiYhGEl/P\nXU/no/XZ3Hn0GPbf/wDMTc1QJSZiyu/vRPLkyQFLPzU1Fffccw+2bNkChUKBQ4cOobW1FRs3bkRn\nZyc+/fRTIXxxcTEMBgM6OzuRlJSEjz/+GE8//TQ+/PBDKUxfXx9eeuklbNu2DRaLBZs2bcInn3yC\nF1980eX6DQ0NePbZZ7Fjxw68+uqrmDdvntTbc//99+Pee+/FhAkT8Nvf/hZ1dXXo6OjAunXrkJeX\nhzvuuAOfffYZzjzzzCG/f1ZUIoS7MZrRetMOiVMLi93ULZwadZ9FlPBZZn21qkVCHomIKKA8zTlx\n9KCMtOdy23ffw9zUDACwdnWh9Zv/C2hFZdo08bMpKipCd3c37rjjDpx99tm46KKLXOIsWLAA27dv\nR2lpKTQaDTIzM+G8yO/UqVMBAEeOHMGMGTMAAOXl5W4XNTn11FMBANnZ2TAaxcUMjh07hgkTJgAA\n/vSnPwEAqqqqcNddd8FqtaK6uhpz5swZ6lsHwIpKxBjtYzSdW1gy5p8hnBttn0W08FVmI6E3Y7Tf\nV0REoSZ/7qpTtMJ3QeHKq4Xz0f5cVicnC8cx2sCuJKZWq4XjuLg4bN68Gd988w22bNmCbdu2YdGi\nRVi3bh0UCgUefvhhnHPOOXjppZfQ2tqKc88912OadrtdqJzI58UAEM7LdzRRullGes2aNXj22Wcx\nbtw43HvvvYN7s26wohIhfM4HGOGcW1ha//0NCq5YjD6LdVR+FtHCV5mNhFaz0X5fERGFmuO5a62v\ngSo7D+a6OuG8pat7RD2X02efhl6DAc1f7ERqaSl084c36d2X/fv348cff8RPfvITTJs2DVdeeSVO\nOeUUbNy4UQqTmZmJP/7xj+jo6MA999zjMS29Xo8PPvgAAPDZZ5/BarX6lQdHhWX8+PH4/vvvMW3a\nNNx111246qqr0NnZiZycHHR0dGDXrl0oLi4exrtlRSVy+JgPMNI5t8BYu7qhys5FvPyz4MToyOKj\nzEZab4aXrTyIiCgIFADi8vOE12JzckbU752YxAToFy+CfvGigKbrrncDAPLz87Fu3Tq8+uqrUKlU\nuPrqq92GKy0txf79+5Gdne0xvQULFuD111/H0qVLUVZWhtTUVK/XludtzZo1+MMf/gCFQoFTTjkF\nRUVFuOKKK7B48WKMGzcOK1euxBNPPIEFCxYgIyPD7/cuXCuSd6YfjEjasJDxhxC/sV2qhEgtLLJK\niKdNobjhY4RuJHaiYuloVXP3Nx2qaNlIjBs+avHL1Y+gSVHkMYyp8Xts/PMqbvg4wtIJpWi4d0dT\nHl2eu7fcAigUXr/fQ51H5zRHs/b2duzatQvnnnsuGhoa8Mtf/hJbt24Nd7YE7FGh4PK3F8SPHqVI\nGEpEXrj5W8dOmQZd+ekB/3LxF8sMEVEA+fGd7vLcra6G9ryL+OyNQImJiXj33Xfx/PPPw263Y82a\nNeHOkgtWVCioAjmhOtKGEpEoEibPy7HMEBEFjj/PeT53o0dMTAzWr18f7mx4xYoKBVUgW7Q5MTqy\nRWLvBcsMEVHg+POcl0+m53OXhiNiKypvvvkmnn/+ecTExOCmm25CeXl5uLNEQxDQlpVRvuBApIvI\nVjSWGSKigPHrOX/iuRvOYb80ckRkRaWtrQ1PPvkk/vnPf6KrqwuPPfYYKyrRRDaGdeztt6G34nh/\ni3ZxCcz7vhfHt/qIz9W9IpT871RcIvZenPhbB3XDR5YVIqKQcemldvedPthnsK/neARsHkzhE5EV\nlS+++AKnn3464uPjER8f73UNaIo87sawas/r3znV3SpMyDzdZ3y2iEceT38nx9/K3xW3gpEHIiIK\nAlkvdSCe876e43zOj24RWSWtqalBT08PrrvuOlx55ZXYuXNnuLMUvew2mPd9D+P778Cy73vAbgv6\nJc11dciYdwbGzJqJjPlnCJs9uRvfKudPGAo/X38nn39Hd2VzkOWVZYWIKIRkz+hAPIOH/V1CI1pQ\ne1QaGhrw/vvvw2g0wnm7lhtvvNFrPLvdjra2Njz11FOorq7G8uXLsW3btmBmdcQKR0uEOjEetTs+\nk471K6+S/u/P+NaInOtALnz9nXydd1c2AQyqvLKsEBGFjvy5XbhS3GxwKM9gX8/x2JRk4VidMrr3\nPhltglpRueaaa1BSUoKsrKxBxcvIyEBpaSkUCgUKCgqQmJiIlpYWpKWleYwz3E17Rmr8yvoa4dha\nXwNd+eku4TzFt1utaNn9NbqOH0di4Viklc2EQunaEZeRliCF6+vsFtPo6pLSt8+bg7i435xIrxBp\nZbNcru8ujLtr+pP/SBTovAbjvfuTpq+/k21uGdC5Et3HK5FQqEf26adBGTPwyKk01CNj3hmwmkxQ\nxWtgbWoA+vqEa3gqr448DqWsDOc9R0KaoRKovMeqY4A+z+dVSiV0Oq3XDR8DmR+mE5p0Qika7t2R\nkkf5bwqb2YTiOz0/g3U6rc/fEcJ3xVg33xVm08B3hUYDu7k3KsspDU1QKyqpqal44IEHBh3v9NNP\nx5o1a3DNNdegra0N3d3dXispAHem9xQ/JjtPOFZl57mE9Rbfn/GnOp0WtZ99KYXLmH+GeM0c2TWL\npiC+aApsAJqau9xfXxbGG+5MH8Ydj738ncz7vkfFM89Jx/akZKHsKOM0aHLueZs4EaoMsVHDXXl1\nyeMgyoonYf8c/UwvlAK1Q7nZ0gcoPIex2mwwGIzcmX6EpRNK0XDvjpQ8uvymyMiCzcMz2JGmr98R\nLt8VieJ3RUxGFppefHkg/qyygL13VngiX1ArKueccw7efPNNlJaWCl9Cubm5XuNlZWXhvPPOw6JF\ni6BQKLB27dpgZnNE87qPhKeVNJxW4LCbxN4RT3tjOI8Zbd+3H/orl8DU2IR4vR6xxSVBe38UQAFe\nWcXS2Ii8S38Kc0sLYjPSYTEYEOt03twuftFY2o3QnHYG9z0hIgoXHytwqYtLoF95FUyVVYjXF/j1\n/S6fU+KYt+q4hq+9Wbgvy+gW1IrKwYMH8dZbbyE1NVV6TaFQ4JNPPvEZd9GiRVi0aFEQczdKeNlH\nwtP8FfOhfbAcO4q+lhZo8nKhSkyAtau/whKXn+92eWHnMaYpkyej8qW/DaSbnMIVOqJAoOczqVQK\nVG75p3SsX7FMOO8yLjk/H7DbYetog7W9DfaUZMBu99oaTxTJrFYrKiqOejzf2pqE5ORMn8PeiELF\n5wpcB/ai8rkNA+eTU31+T2gK9cIw35jkJHGeyzWyeS75+R7T4tfB6BPUisp3332H3bt3IzY21ndg\nCjlPrRi2uhrUOP/AvPIKWHot/RPcVEpU/Plh6ZxjeWHnnht/e2EosgR6Z3lTXb3Lscbp2K5SCuOO\nEaOC6avPhS9BPezQzJ4/5DwQhVNFxVF8cctNyElIcHv+i+5uzF3/GIqKJoQ4Z0Tu+foeGMr3hN1q\nE4b5FsgqIrbeXpfvAmdcnnh0C2pF5eSTT0Zvby8rKqHgqbvWZoXpq89PdNPqETdzNkxf7+w/zs2G\nOiMdlqZmAAMrbZgaDULSpsZGpC66EgBgfP8d4Zz00HLqubHs+x7AQDiuxBQdXHo4CvXeN/KSl61Z\nc2A+uE8KH6/Xi+mPLRTSM9fVCV9e6uwcWNvbhDimqmook4e5mRhRGOUkJECfxHHwFCF8DO0a9GqO\nXno/HMz1soVTrH3CcV9np/BdEFc4FrbWFum7xdzeIaTHxs/RJejLE5911lkoKiqCSqWC3W6HQqHA\nyy+/7DsyDYqnFgeXFupeEyo3Dnz++hXLYLeYhXGf8bk5QtqanIFjf5aD9TovhiKWfBwwrDZUrF8v\nnZe3YrmULYsZlS9sHAh/x2+gX3kVequqEVeQD2WiVras5cCy1UB/WbLLlqHUZGexJY2IKEB89U74\n+v521xPui8uWBSuWCcfy7wKVSiF+t/xCHDbMxs/RJagVlV/96lfBTJ6ceOqONVWKr5sMBqElo9dg\nQGxqijDu09LbJzyILEYjLO+/09/6UlziuxLiZV4MRbATfzdd+ekwGIxue8+EioqsbPVUi8tWWmpq\nodL096YqoHCdUNludC1Ldjv0sMNUWQWNvgB9XT1e80BERP7zOXTLx/e3pa4ecRkZA4uk1DcgdpL3\nxkj5winyYcHy74LuH/aIeTQ0czL9KBbUisrYsWPx3nvvYfny5QCA9evX44orrgjmJUctTz0dLsNv\ndBmofO8V6bhgyc9R9cqrAAZaVuJyclD7t4HJ8BnzzpC6ZR1h+GNx5PPVeyYvW/H5smUr5a1iy68U\nzqtTtK5lSQFoZs+HZnb/Yf8wQs95ICIi/w13k1xfi6S4I9+wUZOb7ZIH5+8Ce0e7eD4nW2hEo9El\nqBWVO++8Ez/72c+k4wkTJuDOO+/Ehg0bvMSioRC6awv1gNUG44leEP3/uwamYxXQ6AtgaRPHenYd\nq5D+72hZcU4rRq1C7RtvSmHkywqq2bIxYvkaAhBXNlfo/dDMmoux6TrPrWKtbQPLFaenwdpr8Z0H\nYSlMLnVNRDQcwx2a7WuRFHf6TL1Oz/502BQxYh6KS4T5i3Gz5ojfLWXuN/2l0SGoFRWz2YwLL7xQ\nOl64cCFeffXVYF5y9HLqrjXv+95lbkHqoqUAAJWshVqpVkv/l1pWZBPjHUsTA0BMYrzL+FZk8iEy\nIvkawqdUCb0fALy3io1JQaXTpl162bhkd1yXwuRS10REQzbModkuozT0vntkYjSxOP6SUy/MyquE\nPLjbEFL+3UKjV1ArKgCwfft2lJWVwWazYceOHVAouAp2sHkbg6qeNAX6FcvQU12D+IJ8KDN0iC/I\n8zjuU9764i5tGiV8rBYj5+hxcUyml/fmWdqNUHpbVQyBXzKZiIiGzl1PutfVIeFhc1+nYz7nyZug\nVlTuu+8+3H333bj55puhVCpRWlqKe++9N5iXJHgfg2ra/YWwMpN+5VXQ/3yR53GfstYXeTWTcwZG\nj0GvZX+ix6XgYi0MBqNLb546ReszveGOpyYiogCS9aS76w0Z7HNcPodFncLlvGlAUCsqhYWF+Otf\n/+r23LPPPotrrrkmmJcftbyNQXVeqUmVmABrezsqX92MmOy8gZYQLy3nXHp49Bp0q9eJfVYOV1VL\n446dy45jvpO39FjeiIgil9vvhcknw7z/B1TW1/T/tvCxWmhfr3nQ8xdp9Aj60C9PduzY4bGi8tVX\nX+Hmm2/GhAkTYLfbMWnSJPzud78LcQ6jmJcxqM7jS8fMmIGa116Xjh0tIV5bzrn08Kg12N4NT7vM\nD6p3juWNIozVasWhQ4fQ0tLpNdzYsSeFKEdE4ePue8HTbwhPz/GYODWObxTnsBA5hK2iYrfbvZ4v\nKyvDo48+GqLcRDBrH0xffIqe6hokFOQhbvZ8mA/tl3o7KvLj8WljHbI0WZiUPAEKyOYMyHpHnFfT\nUKjEjZocLdpeW87d9bbQqOA8vymhIA/qCZPQ+cXH/XNQ9PlIPG0+FMqBR0pvnbgbcW9dvTgnZdIU\npxW9CriiF0WFioqj+OKWm5CTkOAxTF13N7D+sRDmimiITnynO3o/YiaX4KDxMGqMdcjT5rj+rpD/\nBpg4WfheiJ00BcZ/vS9cwlfvu8sclrYOn/MXafQIW0XF16R6XxWZEc3pQaDWxIo7yVttwnHylT9D\nuqEG7bpKHJ6hxITkCUJS5oN70bn7K1hNJlga6pCkUkrjS3u/3CGEdYwLdW0hyR9Iz01LCVf9ikCD\nnPgOwHWoVtlcQDlQmXWZ32S1odapLObagaS5Z0nHcRnpqHxn60D4ZUuFsqNfeZVsRa9U9pxQVMhJ\nSIA+iePoKfqZ9+0RVgnNWHUNHm9+QzpeNfMqAAqp4nJStQkVjwyE169YJn4vxKgRmyqbcyI7losd\nkyo0avkzf5FGj7BVVHw5cuQIrr/+erS3t+OGG27A3Llzw52lkHGuDOjK5wvnemprhWPVweOI3f01\nYgHEa7OBU8WKirW2RtqsEQDi8/OBE7vIWrq6hR3oHbuAV+THo+XK/0CSoQudukQo8hPgSJWrfkWH\nQU98h+ehWtJ5+U70srLYW1WNJKdjo1FcnrjH0Cher5IrvVD0sVpt/T0mXtR1d0NvtUGlYiswRTbT\n4UPCsf1oJZAycHy47RjePbxNOv6D6TQhfE91jZheZRXisrOE3xb23l6vebB1dQm/U/JzcoTz/G4Y\n3SKyolJYWIgbb7wRF1xwAaqqqrB8+XJ8+OGHiInxnF2dbnitW+GOn5GWgJbdX6Pr+HEolKr+ie5d\n3YjNSBfCJeTJdv/W9C/yp0pMQGqbGT0fv4vEwrFIK5sJhVKJo0ZxHHWfsVPKq3KsHgecdqAvvvM3\nSNdp8WljHTbb9gDpAGzAIlMh5hbNAAAoxo+D8xTo5KJxAXn/4Y4fSoHOq7v0KuvFLw9rfQ105d57\nvg5XVQvHvVXVKLh4IG3LuLHC+XhZWdQUFgh5qctJFc6rcrKE48SxhcJxyvhxSB/EZxOKzzES0wyV\nQOU9Vh0D9Hk+r1IqodNpoZINRQ1WfoabTlNTIl6ZFoOENLXHMN0tMbggLREqlQrHfKSXlpYUkPcW\nKZ9POETDvRupeexJSRGO45LF42SNeA1rTppwnOA04gLof673NjcPvKAArF1dXvN61CguXd9nFIeC\nOX83RGP5pOEJW0Vl7NixHs9lZWXhggsuAAAUFBQgIyMDDQ0NyJP9MHLmcXldP+h02rDHr/3sS6EF\nPGPeGWja8RkaP90O/bKl6KmtQ3x+HuLmzMfYjMz+FoYULar/tglA/8T42r9tluI7WtCVmWJFR5mZ\nPpDXkyZh7OrVsNbXQJWdB9tJxTAYjMjSiD8oszRZUpwfdWqht+VQphoZiP7PP5SGk1c5T+89JltW\noc3O83ldVX62eJyXLcSxa5OFVjIkJQnH6tQMIXycOgFxTudNqQlC2VEV5wgrwTjK33De91AFOr1g\npBmN5VSn08Js6XNdOcGJ1WbrX77aS0UlUJ9lINJpb++BrjgH2txUj2GMtW1ob+/xK72Wls5h5ymS\nPh9HOqEUDfdupOaxIz1eeI73ZCZjVeHV0lAvpWzIsNaiQqxzb0lCgjTXUKMvgHrmHNi++hy1r2+R\n4uhXXuU1r5rxE8XjSZMxtniKy3dDsD5HimxBragcOnQImzdvhtFoFOacPPTQQ7jnnns8xnvrrbdg\nMBhw1VVXwWAwoLm5GVlZWR7DjwTyIVQxqSnIufxyaSk/zYmHhR02HM2PQ01KIsZqc5G3ZDF6q6qh\nHpMq9cIAgOnHg/1pxqmgO7McfV1dUGk06GhvRqLjIidWVNKVny7c/JOSJ2DVTMeDKhtKhRIf1XyC\nPG0OGroahd6W/zQWYC5mhOATosEYyrK+e8ZqMO7Kn0FZ3wRbdgb2jNNgjtP53uOVQvd8VrLTuGMF\nYK6pRWzxVOml2IZ2mJ3O26rrsFHt1FPXNRaW/EzUpCQiTxuHSQqvv2eJiGiY7LDhYMePUkWkMrUH\nyYVxSDL0oVMXh+bkDlyUfAaKkydJ4Qd+D+Sg72On/bAUQG9lFVJ+ermwi7yvDR7l3H5fnfh9QhTU\nisrq1atx0UUXoaRkcKv5nHXWWbj11lvx0Ucfoa+vD3/84x+9DvsaCeQT2DUTJrm9SQ92/IjHv34e\nALBMORXWlz6Szjl6YQCgr60dTTvekV5v3f01ACB9le+9axRQojh5EoqTJ+FAx0E8uvtZ6dyK6ZcL\nYfO0OfLoFAmGsKzvmMR03Gv7J5AJwAasSrxaOC8vo7Hpaahymiyfe9Vy4bw6MR61ThWb3KuWA6aB\n8/GxGqksA8CqmVdLX45ERBR4zr8hAGDptEux0fYvqQFpRZL4He/8ewAATGMqUfn2O9J5/bKlLtcY\n9Ea9XIaevAjqr/+0tDRcd911g46XmJiIp59+Ogg5ilz+toDXGAdmiCQZusSTyYnQ/ORsjEkcg8Y3\n33F5PbYgH2OmiRPh3HFucVHHqJCgjke3pX8Yg9HU6dLb8ve973heHpmihqMnrcHUIP09ncnLaPPR\n/cL5juZGVHcclFre8mStajZjN1bMvBw1HXXIT8lFd684IbnGWItJyROE1j6WKSIi/8l7TOTP0Bqj\nuAiK2WLB0qmXorazHrnabMxIPwUHnJ7j8vjdDQ1C/O6GBpfeEsd3hWNYOTfqpeEISkXFZrMB6O8Z\n+fzzzzFr1iyhR0Sp5A8PF362KDj3YHTpkhDrdO7QmD5stO3BcuU0jOnqdnkdrd9jlTHHZ6u1vMXl\ndP1MfF759Ynr53rsbWGLeHRztJzNK5rpfhywrIwqLa3i6fxModzck3OJcN6SPQYvfPeadLx02qXC\nea0myaXssUwREfnP1zM0Pk6sVsTEqPDynoH5JDHTY4TntDx+bEGuED82XzwG4HFYOdFQBKWiMmXK\nFCgUCmFeiuNYoVBg//79XmKTN87zR9K0eSjMnAprfS0akxWoTuvBqeapqIpNQvavFiOt2Yr2jDg8\n27Ud6K87osZYJ4w9PdjxIz5tbEB2fDbsdhtqjPXosYqTQJNjtfjPSRdKrSsOzr078rRp5DuSFYPM\nay+Hqq4Z1px07MnoA5zqLgczgUmrrkFvdRXi8gtwMEsBOC0G09DZhNP1M2Hq64UmJg49ZhOMJrGX\nkGWKiMh/rt/LtdLredocGE2d+EnxuWjtaUNafCqau8UGp5oO79/riafNR669f1XIuIJ8JM4Wt1Ag\nCrSgVFQOHDgAAGhvb0eKbOm7Ku67MSzy8aKYAujKz8C3+z/Atu8/lsLlTr0UhTPnoKbjILq/HpgY\n4Nwj49zy4txrcrp+lnDN8aknuf2xKJ+fwvkqo4s6Ng7rOz8FtAA6gSUasQcFKiXWNm8B4gE0f4MV\n+fL5TdkuLXdyLFNERP6TPzO1miShh2XJ1Evwtz1vCMfOsrQ6r+kplDFImnuWsGcWUTAFbY6KzWbD\njTfeiBdffFHqSbFYLLj++uvx1ltvBeuywedux+/BRHczftTd60qFElUdNV7H6dtgxdfN36DmeB2U\nCnFpz9aeNmmlrptnXSOlNTF5vDT+1LnnxNQ3sCHTt3U/YFHJxejrs7r0ojjzNaeBwsfXOGX3kfrL\ndmV9DWKy83zuZm82m6WWuTHxqei1mIWWOkN3ixC+Q5jflIMJyUXAdEhzViYmj4cCCiGMc3nlnBUi\nIu/EVTtz0NAlzikxdDULxy3d7Vh88k9Q19mInKRMJCsSsWL6wFzCidoimPd9L/7m8fK9QBRoQamo\nvP3223j88cdx/PhxTJ48WXpdoVBg3rx5wbhkyLjb8RuZ3jfSc+bci5GgjsflUxaip7EHCTEJ2Lz3\nLWnSunMPh6dx+l83fyO1SMt7QdrNRmyt3CZdw+Gw8ahTL8pAnORYrTQMJ0mdgCR1Itr6xE2Y5HzO\naaCwcTdO2ddEddeyfQuO5ms8hler1Xjzuw+k4yVTL8E/vLTUJcSKY6OPGI8JPSramVqpt9BR3g90\nHOScFSKiIUqJT3EaYqtBrlbcLystIcWlh8X5+KT0XjQ/PjAX1bFHG1GoBKWisnDhQixcuBCPP/44\nVq1aFYxLhI18vxP5sS/O40dLc0qEH2rOlRPnHg5P4/Sdx5J+W/cD/nPy+YBNiZgYFd4+9C+311hU\ncrEQZ1HJxYDCjgRVAv763cCGkX12q8+KEkUud/OHAHj90S8vy+0Vh/F4/S6P4Y2mTiG8vKWuubtV\nmIPS0tMqTNp0LouOPMrLGedBERH5T95Itfjkn0jf5QAwLiVfeC43y3q+64yNwrGpslI47q2qYkWF\nQiqoyxNnZ2fj73//u3jBmBiMGzcO06dPD+alg2bQ64PLOI/3dK6MyI81xC5VZgAAIABJREFUMXFu\n4zjLTxlYbaPb0gNtrBZl6bNw0HgIpTklMPX1Ykx8qri8cG+n0LqSk5CN00+agU3/Jw7H86eiRJHL\n3fwhXz/65WW7NysFaPccPk8rrvaiF74ANdAlpOGDPdul8/85+XwhvLFXrOi4K+ecB0VE5D/5c76l\np014Ljd2NQkVl5+fLDYY5WgzhWNNoR7OS5wM9jcP0XAFtaLyxRdf4PPPP8eMGTOgUqnw73//G7Nm\nzUJVVRXKy8txyy23BPPyQTGUHb+dOY8fTY5Pwr9r90jnTtYVo1BbcGJ/EhWy4jO9zhE5Na0U9ul2\n1HTWIS8pBzPT+3eIt9ttTg+iPUJPTWZiBt49vE1KY0Zm/07i8h+A/lSUKHLJxym7K0Pyv6t87fvj\n+fHA157Dy69htBiFL8CTpuuFlrsxcWOE+ONTx2HVzJO85tGf90FERP3kz+n0hDFCg5F8SG5qbAqW\nTL0EdcZG5GgzUaabiYyZGdIzN007HtrV2iH/5iEarqBWVKxWK7Zu3YqMjAwAQHNzMx544AFs2bIF\nixcvDualg2eYO6g6r9plhw3amVo0mAaWBzaauqBUqtDW24YOcweS+5Jghx2KE/Hlk6RnpZ+Ki4pT\nhDkiNcZ64ZrOyws3dInduo6w4g9C/ypKFLlcVoeDHz/6ZWvfj4fN58R252tsqRB75RqMBoxNKZA2\nEpuWdrLL9R35HMz7ICIi9yYmjxcmw9cZxcn0Ld1t0vm85ByUpE7Bjx1HYLVbkRGXATXULs9c7hpP\n4RTUikpDQ4NUSQGA9PR0VFdXQ6FQSJtCetPb24uFCxfihhtuwE9/+tNgZjUsnCej7zjyNR7/egMA\n4CfF5+LNAwOTlO3T7ShL75/47m6SdKZuppCuvEXF0/LCzmHd/SCcqGUFZSQZ7I9+eXhfE9uzkjKE\n+OmJY1w2EitLn8VKBxFRkBzqOCzMS5X3oKQlpPb/nkjvP+aCJRTpglpRyc3NxU033YSysjIoFAp8\n++23SExMxHvvvYecHN/DiZ566imkpqYGM4sRw7EpEwB09nYJY0rrOhvwkekTDz0idfKkvLaccygN\nDZVzGQUgLXvpKEv2PptskqZsIzFjvfTlSEREg+e8UbNjWwDn1Rjlvwk6ejqEZeO73GyqKz9mRYUi\nSVArKg8++CDeeOMNHDhwADabDdOnT8ell16Krq4ulJeXe4179OhRHD161Ge4kSI5Xiv9Pz1hDF7b\n+7Z0fHnJQul4xXT5pnmuFT5vLeccSkND5VxGAUAdoxZa4lZMv1yYo+KykVii2ONCRESD425UhbjI\nifibIDk+2esGj1ywhCJdUCsqsbGxOP/88zF79mzptdbWVhT4sWrEgw8+iLVr12LLli0+w0Yr5w0b\nMzRjML/wNHRZutEkWy6wqbsFp+ZOhSZGgx5zj9Qjkp+cC7vdhr/vfUea41JjrOfGeORxY1Fv+6jI\nW+omJo/HoY7DUniz2Sz0mDR2NQnXNMo2dGzubhLCW/osIf0MiIhGGnnPdo2xVhyy7TRHJS85B/Wd\n4iiM5q5WwGnzeW7cTJEuqBWV++67D6+//jrS0tIAQNqh/qOPPvIa75///CdKS0uRl5cnxRuJnDds\nBPr3Ufl37R6XzRvNVrO0OtiK6ZdLPSL9Y0s3SHGdW7M5znR0c9fqBnjfR0UeZ8X0y4XyKe8xWTrt\nUuGaWk2SOKcFwCv7BlryHHkgIqKhkfdsazVJwrGvOSrpieLqi9y4mSJdUCsqu3btwpdffom4uDjf\ngZ18+umnqK6uxrZt21BfX4+4uDhkZ2djzpw5HuPodFqP5/wRyPg2mw1f136PyvYa6FPyMDNvGpQK\n196NmuPi2NAEdTzOLjoD41L0mJ1fiqqOWiigxFsHP5TCdPf1SNf6tHFgNQ/5niwNpgbMKxIn2fub\n/6GI9vihFOi8ukvPuWwA/eVBTl5GPm0Qw8jHLnf3dePKaZdKvXYKO8QeE5tZyEt6xgzExcX4vA+G\nKhSfYySmGSqBynusOgbo83xepVRCp9NCpVKFJD/DTae1Ncl3IABpaf3hjvkRLhDvLVI+n3CIhns3\nUOmZDb3Cc9dmt+JY7xHpOVvfIz7Hm7taxI13u1uF8M7P5dH0OVL0CGpFpbCwcNCVFABYv3699P8n\nnngC+fn5XispAIbVEqDTaQMa399VNPJlG+Z1W3qE3eDLM+djd8vX0maNAJAQEy9dK0uTJb2uidEI\naWVpsvx+T4F+/9EYP5QC2Wrl6b07lw13x47XnOMmqBPE80k64ThWGYuXvh8Yirl02qVCD8uMmdNd\n8jIurghlJafAYDCiuUmcxDkcw/2bBzu9YKQZjeVUp9PCbOmDtL66G1abDQaD0WtFJVCfZSDSaWnp\n9B1okOGGm6dI+nwc6YRSNNy7gUovPS4Dr1QO9FSPHzMOD3/+P9KxvKc7PTENH8jmqDiHd/w+iZZn\nYDDySJEt6DvTL126FKeeeqrwJXTzzTcH87Jh5+8qGjPSToFlqgW1nfXITMzAv47scInT3dsjtIb0\nmE1SGOexpTnxOZiROVWYo0Kjl6fV3bztiyKfg2LvswvhD7ceEa7R0NnEFeSIiEJIPqekoatBWCW0\npbtNeI4rrMDSqZei1ti/n5XdKm4NwVW+KNIFtaKSmprqsyfElxtvvDFAuQkdf1fROGw8ior2Kpj6\nemG29mFa1mRsq9gpxMlKzMTm/QMb6TmP83c3trQ4uTig74Wik6fV3ZxfO2g8hG8av4OprxcNPQZM\nSity6SFxDm/sE1uy8rTZXEGOiCiMkuKS8Pl+ce7gu4e3SceO57hjAv2BjoNCfK7yRZEuqBWVG2+8\nEa2traiursbUqVNhs9mgVI78laj83aukvrtB+GF42ZQLpR3kHXG47wkFi7z85Wmzva7+cmpaKezT\n7dJqMjPTZ4Q6y0REo5p80ZNFJRcL5y0Wi9ffDPxNQdEmqBWVd955B48++ihiY2Px9ttv495770VJ\nSQkuu+yyYF427Pzdq8TYK45h7jB3IUmVOKS0iAZLXv6MvZ0oz5rncfUXJVTCjsZy7pZE5hLZRESB\nIx9aLn+OZyVmCb8Z7LAJQ3wnJU/gbwqKKkGtqGzYsAFvvPEGrr32WgDAHXfcgWXLlo34ioq/xqee\nBGCgi9ZkMeGDw58C4PLCFHzy8jc+ddyw0vO1ERkRuWe1WlFRcdRnuLFjT/K5OhqNbPKhWuNTx2HV\nzJM89pDwuUzRLqgVFa1Wi/j4eOlYo9FArVYH85IhJd8gb7AtyM6T4mBX4O1D/wLQv0xxQ08jW6bJ\no+GWPcD3EIDB9pD4u4gEEYkqKo7ii1tuQk5Cgscwdd3dwPrHUFTEoTqj2YTkov7J8Z39k+MnJI+H\nCjEen7V8LlO0C2pFZcyYMdiyZQt6e3uxd+9ebN26Vdr8cSQYbkuF82T4HUcGliEuzSnB5r3iBHo+\nWMhZIFrJfA0rHOw1/F1Egohc5SQkQJ/EpVLJu383f4uX9wwsEx8zPaZ/SK4HfC5TtAtqReWPf/wj\n/vKXv6Crqwu/+93vcOqpp+K+++4L5iVDKpAtFc6t2z3WHuEcW0BILhStZIO9BidpEhEFV01Hneux\nh3mDAJ/LFP2CWlFJTk7G2rVrg3mJsApkS4Vz63b/8oEDcwfYAkJyoWglG+w1uPADEVFw5aeIG0Xn\nJfO5TCNbUCoq5eXlUCg8b0X8ySefBOOyIeE8bl+fko8V0y9HTWcd8rW5mJg8PiDXYAsI+SLf9Muf\nMiKfczIxeTwOdRz2OAeF5ZCIKLKUpk1H79Re1BkbkavNwoz0U8KdJaKgCkpF5ZVXXvEZ5sCBAygu\njr7NCZ3H7Z+unynsQ6GdqQ1IqwVbQMgXd5t9+iKfc7Ji+uV44bvXpGP5HBSWQyKiyPJN8//hb3ve\nkI7V09Ve56gQRbugVFTy8vJ8hvmv//ovvPjii8G4fFA5j9s39fW6nOOPOopULnNO5GOdWX6JiCLa\nYOeoEEW7oM5R8cZut3s8ZzKZ8Nvf/hbNzc0wm8247rrrcOaZZ4Yuc144j9PXxGg8niOKNC5zTpK5\nGgwRUTQZ7BwVomgXtoqKtzksH3/8MaZOnYqrr74atbW1+OUvfxkxFRXncfsFyXmYkTkVDaZGv+cJ\nEIWLfM7JxOTxSJ6ZzDkoRERR4tS0Utin21HTWYe8pBzMTJ8R7iwRBVXYKireXHjhhdL/a2trkZMT\nOS0G7sbtzyuahUZD+6A2xyMKNXdl1/nYDhsOdBwc1iaSREQUPEqoUJY+C7piLQwGo/Tc5m8PGqki\nsqLisHjxYjQ2NuLpp58Od1Z8CsQGfEThxDJMRBRd+NymkS4i56g4bNq0CQcOHMBtt92GN99802tY\nnW54O/oON36DqcHleF7RzJBdn/GjZ0fnQOc1UOl92ji8MuxNMP4+kfo5BjvNUAlU3mPVMUCf5/Mq\npRI6nRYqlSok+RluOq2tSX6FS0vrD3csQOEcYT3lP1I+n3CIhns3WHkM5HN7tH6OFNmCUlHZuXOn\n1/Nz5szBAw884PH83r17kZ6ejuzsbBQXF8NqtaKlpQVpaWke4/i7RKs7Op122PGzNFnCa1maLL/T\nDMT1GX948UNpOHmVG+57dzacMuxNIPMYrDSjJY+hFKi/vdnSB3iekgirzQaDwei1ohKozzIQ6bS0\ndIYlnCOsu/xH0ufjSCeUouHeDVYeA/XcjpZnYDDySJEtKBWVp556yuM5hUKBOXPmID8/32OY3bt3\no7a2FmvWrEFTUxN6enq8VlIiATfHo2g3lE0kiYgofPjbg0a6oFRUNm7c6PHc+++/7zP+kiVLsGbN\nGixduhS9vb24++67A5m9oODmeBTthrKJJBF5ZrXaUNfd7fF8XXc39FYbVCpOfqah4W8PGumCOkel\ntrYWL730ElpbWwEAZrMZu3btwnnnnec1XlxcHNatWxfMrBEREQWZHa9Mi0FCmtrt2e6WGJwG3/M1\niYhGq6BWVH7zm99g/vz52LZtG6688kp89NFHeOihh4J5SSIiooigUqmgK86BNjfV7XljbZvPRQSI\niEazoPY3q1QqXHvttcjIyMDSpUvx3//933j55ZeDeUkiIiIiIhoBgtqj0tvbi/r6eigUClRVVSE3\nNxc1NTXBvCQREUUhq9WKTZvcN2RptRoYjSYsXryUPRBERKNIUCsqK1euxM6dO3H11VfjkksugUql\nwsKFC4N5SSIiikIVFUfx36/tRFyi+2FSvV1tmD17DoqKuKoREdFoEdSKyrhx41BUVAQA+Oqrr9DV\n1YVjx/zZ1oqIiEab3ElzkTQmz+25zlb2xhMRjTZBmaPS0dGByspKrFmzBlVVVaiqqkJdXR2am5tx\nxx13BOOSREREREQ0ggSlR+Xbb7/FCy+8gP3792PFihXS60qlEmeccUYwLklERERERCNIUCoq5eXl\nKC8vx9/+9jcsWbIkGJcgIiIiIqIRLKjLE19wwQV48MEHcfvttwMAPv74Y7S0tATzkkRERERENAIE\ndTL973//e8yaNQvffvstgP6d6e+44w48++yzPuM+9NBD+Oabb2C1WnHttdfinHPOCWZWiYiIIp7V\nakVFxVHhtdbWJLS0dAqvjR17EpdyJqKoF9SKSktLC5YvX44PP/wQAHD++ef7teHjrl27cOTIEWza\ntAltbW249NJLWVEhIqJRr6LiKL645SbkJCRIr8nX0qzr7gbWP8alnIko6gW1ogIAFosFCoUCANDU\n1ITu7m6fccrKyjB9+nQAQHJyMnp6emC326V0iIiIRquchATok7ThzgYRUdAFtaKydOlSXHbZZWhq\nasKvfvUr7NmzB3fddZfPeAqFAhqNBgDw2muvoby8nJUUIiIiIqJRJKiT6cvLy3HOOecgPj4eP/74\nI5YvX46zzjrL7/j/+te/8I9//AO///3vg5hLIiIiIiKKNEHtUVm9ejVSU1OxcuVK2O12/Pvf/8bq\n1avx1FNP+Yy7Y8cOPPPMM3j++eeRlJTkM7xON7xucG/xrTY7vtpbj+N17Ribk4KykmwolWIPT1p6\nks8wQ70+4wc/figFOq/BeO+Rkkdv954/afpz7w43j95EU7mUC1TeY9UxQJ/n8yqlEjqdFh0dvp/z\naWlJYX9WtLb6zifQn9fBhJPPMwlE2KG812gss6G6d309T4b7vApEHiMpvWCkGY3lk4YnqBWV9vZ2\n/M///I90vGTJElxxxRU+43V2duLPf/4z/vrXv0Kr9a9QGgzGIedTp9N6jb/3eCvW/e1b6fjWJaUo\nKRwjxN/xTZXXMMO5PuMHP34oDSevcsN976FIczjpebr3/E3T170biDx6EozPMZQCkXedTguzpQ/w\n0mZjtdlgMBhdVq1yp6WlM6zPCkcewhFuKGEH+14DVWajsaw68/Q5+HqeDPd5FYg8Rkp6wUgzWHmk\nyBbUoV/5+fkwGAzScVNTEwoLC33G27p1K9ra2vDrX/8ay5Ytw/Lly1FfXx/MrHpV1dDp9djfMEQ0\nOMO9r3hfElGg+Hqe8HlDFHhB7VGpra3FOeecg/Hjx8Nms+HYsWMoKirC0qVLAcDjUsWLFi3CokWL\ngpm1QdFniV34BVmuXfr+hAEAm82GXQcNqKzvhD5bi9MmZ7iEsdvt2FfZhqqGTuizkjC5MBUKb02S\nRCOEvOzL7yNP95Un7u7LvcdbeW8R0aD5ep64e17tPd6K+m9rkJOWwOcN0RAEtaLy61//OpjJh8zk\nwlTcuqQUVQ2dKMhKwpTCVJcwSiUwvzQPPb19iI+LgcpDX9WugwY8+8Zep1dK8BNdihBmX2XbkIeR\nEUUzedn/1aUn+3VfeSK/L7tMFjy95QfpPO8tIvKXr+eJ/HnF5w3R8AW1olJWVhbM5ING3qo7qSAF\nHd1mtHeZkdJtgR12KKCA1WrD5/saUG04jIKsRCTEqtDT2wcFgOrGLhQXuD6QKus7vR4D7ruP+XCj\naOO4jxyticX6FOyvbJfuK/nx5MJUl7Jf3dgl/V8BoK65G1Yb/G6hrJLFd04P4L1FRP5rMppQmKVF\nbXMXcjMS0W40Ceflzyv586a2qf+YPbpE/gv6ho/RSN6q+4uLJuOv7+x3ClGCOZOz8Pm+Bun1+aV5\n2P5tjRTimktK3Katz9bKjoc+jIwoksnvo2suKRF6E+XHty4pdSn7aSkavP35wBpHyy+cPKjexqQE\ntXBf/uKiycJ53lvkYLVasX37Nq9h5s9fEKLcUCTqswAb3zsgHS+7oFg4n5EqPq/kz5ukBDVHSxAN\nEisqJzj3ovSYxbU0nVtFMlLi0G3qwysfHUZMzEBLSE+vGKe2qQuvbjsCfbYWZcUZOHCi5Tg/KwlX\nXTwFx+uNyNclYdZknUteivUpuOaSkhPzWJIwuTDFJQxRpJP3jjS19eBnC8ajud2E9BQNmtp6XMKf\nV5YvDLOsqG8X4jS2eO8RkXo5G7uQn5UEs+y+7O7p8zqMcyjzwzinbGSoqDiKhz56FAlpiW7Pd7d0\nQa/3vRgMjRzy50mrsUca2pUQF4PmdhN+tmA82jvNGJebjM5us3DebLbi1iWlqG/pRnZaAkdLEA0B\nKyonOLf+lpfmCefynVpdy2cU4OX3D7qES4gTP0pNbAxe//wwAMDSJ/bIOPe+xKqVLnNU9le2Cy3N\nyQlsdaHoI+8d0SbE4cV3B+6D5Re49m4ooEBJ4RipvBvaTGKcC8U4KdpY4di5l9PdNVK0sUL6ckOZ\nH8Y5ZSOHrjgH2lzXOYgAYKxtC3FuKNzcPU/e+qxCOl52QTE2vntAOO/cg7v8wskoKRyDM2fqYTAY\nXZov2KNL5Nuoq6i4a/0EgIbWbqnlNnNMPH55UTG6eqwoyErCxIIU2Gx21Bg6oVQqkKiJQZepD3uP\nNmHpeZNQ39KN3IxEnHeaHi3GXsTHxaDeqeVXPk7V0fuSqIlBe6cZmz44IIy3Z6sLRSP5vTUhPwXL\nL5yMGkMn8nVJMLR2C+ENbd1OPYdaTC5McUmjqV3sdTG0douTVbstwqo78nutrrnLJbw38nvPnzHl\nvF+JRib586S5Q+xRaTOahGP586quSYzvz8I8RCQadRUVd62fmbpkxKiUeH3bQen1X1w0GeeXFQAA\ndu5vwItbXXtEppyUIfWuOF7fva9B+r9Dnk4cShB/ovfl1MlZ2PzRj0JeSgrHcI4KRSX5vbX8wsnC\nfSPv3dClJrj0HAIQ03AT511ZD4tz+BWyHpecjEQhD7cuKfX6HuT3nj9jynm/Eo1M8u/u9OR4vPN5\nhXS87IJioYdF/rySx5f3GBORb6OuouLcc5KeopFaeeuaxNbe2qYuvPdVFfRZSS4rcyVoYvAfMwsQ\nq1YJryeeeF03Jh5NbT2YNSUL8XExsNtsUitKflYSOrp6ERujQoImRmiNqWvqQknhGLa6UFSS9yzI\nWxMbW7ukHpY8XRJ6TOL8EXebo9W3dAn3q6XPgivOnYSGlm5kpSegu8cshFcp7PjFRZP7x5RnJmLu\n1CzoUjTSvTRZn+J1HxX5vedPbwnvV6KRSaWwDzxv0hKEkRIAUN8s/m5obBOfcXOnZoUyu0Qj0qir\nqLjrOQFcV+PSJsRi88f9vR2/WDhFOJeoUeP1nYdxxXmThNcTNGq8u/Mwykvz8KnTONVZJ1phSwrH\nYO/xVjz35j4AwM8WjHe7UhhbXSgayXsWcjLE1sTMtEScOS1HOt7+Q71wPiE+BhnJGuG13IxElzHi\n3ua5jEmOd7lvnO+lvcdbvfaQyO89f8aU834lGpnSkuPxsKyX2FlOeoJwnJkq9uDqUjR8LhAN06ir\nqMh7ThzH/TvE94+Xz0yLx7tfDCwxaOuzCqtwpSfHYtFZE9Brtgjj3602G847rRDjcrWYWZzptoXV\nuYW2plFsrW03iq3DAFcUough71moaxLnh/RZrEL45rYe4XxzuwnzpmYLaRQXpkAdo5TuPXnvZkNL\n96B6M9z1kACe92VhbwnR6CW//3+sbhN6eLtMZuEZ1irbV4Xz1YiGL2IrKocOHcINN9yAX/ziF1i6\ndGnA0vW0j4kSSsyZnIU5k7Ow73grmtp7pTC6MQkoKRyDOZMHunEn5o3Bzv2NeH3bUem1ay4pEcK4\ne0A5tzrHxYpDx9y11nJFIYoW7nojXvnwkHRePj8kPTUeb30m7jngrnfCcV86ruJMn500qN4Mea9P\nijZ2UD0sRDR6uKxC2G5ymXf3+rfibwBnnK9GNHwRWVHp6enBfffdhzlz5gQ8beeeE312Ek5zs4+J\nv62oXd3mQa0oJE97bE4SZhZnSmusO67jbU8XttBQtHDsB1TV2ImCTNf9gMy9fcL9YzZbPaQ0wHH/\nOtJ0d/96467XxxnvLyJykI9oMJnMwpyVvj6L8DyZXJiC5AT2wBIFUkRWVOLi4vDcc8/hmWeeCXja\nzj0nzuQPpCmFqT5/sORmJHptMXbHXQutY411B297urCFhqKFr/2AhnL/OO7fn8wfD4PBCLvdjr2V\nnifHyw1lDgoRjU7yEQ1XLZyCDW/vk46vvaTE5fucPbBEgRWRFRWlUonY2FjfAQNoKEOsHK2z8h6R\n4XIeR//1/gYsPW8SLBYbW2goqvhaMSsQ8z+GOzQyWPcwUSSwWq2oqDjqNczYsSdBpVJ5DTNayZ9h\ndrtN6AVOTQrt7xSi0SgiKypDodNpfQfyor6l2+X4zJl6n/EydcnDuq6Dc/4n6Ad+aHWZ+lCYk4I5\nU3PcRXMbf7jXH43xQynQeQ3Gew9Ems7lGADG68e4pDuc+0en06LeadU8wP/7NlB58CWayqVcoPIe\nq44B+jyfVymV0Om06Ojw3ZuVlpYUtGdFa6t/1/eXv2Ed4Y75CDeUsB0djfjilpuQk5DgNkxddzfS\nXtiAiRMnSq9FY5kN1jNQ/gxrMVqElToLMpMwb4Z/z5tIfU4HM71gpBmN5ZOGZ8RUVJyHTg2WTqdF\nTpr4IM9OS/A7TZ1OO+zrO8c/KTtRaGkuyk70mn6grz8a44fScPIqN9z3Hsw0HeXY0VvhqxwPhiOP\nw7lv3aUXSIFOMxrLqU6nhdnS5zrGzonVZoPBYERLi+s+OnItLZ1Be1b4e31/+Rs2GGk6h81JSIA+\nyXPZcf5MA1Vmo7GsOnN8DvLvYnkx9vd5Ey3Pl9GaR4psI6aiMlyRtAwpVxqikcBRjuVzsAIpku5b\nIhpZ5N/Fdtj5vCEKsYisqOzduxd/+tOfUFtbi5iYGLz//vt44oknkJwcvCEarBwQRR/et0QUKnze\nEIVeRFZUSkpKsHHjxnBng4iIiIiIwkQZ7gwQERERERHJsaJCREREREQRJyKHfhEREQ2X1WrF9u3b\npOOUlAS0t3e7hJs/f0Eos0VERH5iRYWIiEakioqjeOijR5GQlugxTHdLF/T6whDmioiI/MWKChER\njVi64hxocz0vI2usbQthboiIaDA4R4WIiIiIiCIOKypERERERBRxWFEhIiIiIqKIw4oKERERERFF\nnIidTP/AAw/gu+++g0KhwJo1azB16tRwZ4mIiIiIiEIkIisqu3fvxvHjx7Fp0yYcOXIEd911FzZt\n2hTubBERERERUYhE5NCvnTt34uyzzwYAFBUVoaOjA11dXWHOFRERERERhUpEVlSampqQlpYmHY8Z\nMwZNTU1hzBEREREREYVSRA79krPb7eHOAhERDUFPeyN6zd2ez7dUSP/vbm/0GE5+7uWXX/R63aVL\nlwMAugxGr+Gcz3sL62+4oaZZ1+35M3KcHxfgsM7hiIgikcIegbWAJ554ApmZmVi0aBEA4Oyzz8ab\nb76JhISEMOeMiIiIiIhCISKHfp1++ul4//33AQB79+5FVlYWKylERERERKNIRA79Ki0tRUlJCRYv\nXgyVSoW1a9eGO0tERERERBRCETn0i4iIiIiIRreIHPpFRERERESjGysqREREREQUcVhRISIiIiKi\niMOKChERERERRRxWVIiIiIiIKOKwokJERERERBGHFRUiIiIiIooq411+AAAgAElEQVQ4rKgQERER\nEVHEYUWFiIiIiIgiDisqREREREQUcVhRISIiIiKiiMOKChERERERRRxWVIiIiIiIKOLEhPqCJpMJ\nv/3tb9Hc3Ayz2YzrrrsOZ555pnT+iy++wPr166FSqTB//nxcf/31oc4iERERERGFWcgrKh9//DGm\nTp2Kq6++GrW1tfjlL38pVFTuv/9+bNiwAZmZmbjyyitx3nnnoaioKNTZJCIiIiKiMAp5ReXCCy+U\n/l9bW4ucnBzpuKqqCqmpqcjKygIAlJeX48svv2RFhYiIiIholAl5RcVh8eLFaGxsxNNPPy291tTU\nhLS0NOk4LS0NVVVV4cgeERERERGFUdgm02/atAlPPfUUbrvtNo9h7HZ7CHNERERERESRIuQ9Knv3\n7kV6ejqys7NRXFwMq9WKlpYWpKWlITMzEwaDQQrb0NCAzMxMn2na7XYoFIpgZpsoIFhWKRqwnFK0\nCEVZ/a/1G7CzOt3j+QnaGjzyBy78QxQMIa+o7N69G7W1tVizZg2amprQ09MjDffKy8tDV1cXamtr\nkZmZiU8++QTr1q3zmaZCoYDBYBxynnQ6LeOP8vihMtyyKjfc9x6KNJnHwKUXKoEqp4H6DJhO9KUT\nKoF+pgKun0N3j8Vr+N5ei9c8RMvzZbTmkSJbyCsqS5YswZo1a7B06VL09vZi7dq12LJlC7RaLc4+\n+2zcfffdWL16NQBg4cKFKCwsDHUWiYiIiIgozEJeUYmLi/PaSzJz5kxs2rQphDkiIiIiIqJIw53p\niYiIiIgo4rCiQkREREREEYcVFSIiIiIiijisqBARERERUcRhRYWIiIiIiCIOKypERERERBRxWFEh\nIiIiIqKIw4oKERERERFFHFZUiIiIiIgo4rCiQkREREREEYcVFSIiIiIiijisqBARERERUcSJCdeF\nH3roIXzzzTewWq249tprcc4550jnzjrrLOTm5kKhUEChUODhhx9GZmZmuLJKREREREQhFpaKyq5d\nu3DkyBFs2rQJbW1tuPTSS4WKikKhwHPPPQeNRhOO7BERERERUZiFpaJSVlaG6dOnAwCSk5PR09MD\nu90OhUIBALDb7bDb7eHIGhERERERRYCwVFQUCoXUW/Laa6+hvLxcqqQ43H333aiursbMmTOxevXq\ncGRzZLDbYN7/A3qrqqApKIB68smAwsfUpKHECWR8Gr1sVpi++hymyirE6/WIK5sLKFWew7Os0Wjh\nrqwDMO//AZWGeijjNDC3G3kfENGIErY5KgDwr3/9C//4xz/w/PPPC6/ffPPNmDdvHlJTU3H99dfj\ngw8+wLnnnhumXEY38/4fUPHII9Lx2NWrETtlWsDjBDI+jV6mrz5H5XMbpGM97NDMnu8xPMsajRbu\nyjoAVDzyCDLmnYGmHZ8J53gfENFIELaKyo4dO/DMM8/g+eefR1JSknDukksukf4/f/58HDp0yGdF\nRafTDis/IzV+ZX2NcGytr4Gu/HSv8f2NE6z4QzHc+KEU6LwG472HK4+Hq6qF496qahRc7D6uTqcd\nUlkbbh7DnWaoBCrvTCcw6bgr69L/TSaXc5F0HwRbsO/dhHi117BxcWqfeYiG58tozSNFtrBUVDo7\nO/HnP/8Zf/3rX6HVal3O3XzzzXj66aehVquxe/dunH/++T7TNBiMQ86PTqcdsfFjsvOEY1V2nktY\neXx/4gQz/mAFIn4oDSevcsN976FIczDpaQoKhOO4gny3cR1pDrasBSKP4UozGstpoD4DpuP+ueoY\nMK2K17icC+d9EI1l1Zn8c+jusXgN39trCep3XCjSHM15pMgWlorK1q1b0dbWhl//+tfSJPrZs2dj\n4sSJOPvss3HmmWfi5z//OTQaDaZMmYLzzjsvHNmMDMMcg68uLoF+5VXSmP/Y4pJBxinwK44Qf/LJ\nGLt6NXqrqhBXUIDYE2OpaYQZbNn0I3xc2VzoYYepsgoafQE0s+bCvO97j3FY1iia2a1Wr+UbgDRv\nq7euHvoVy2DpNgllfezq1bA2NUA/cSIs7UbeB0Q0ooSlorJo0SIsWrTI4/lly5Zh2bJlIcxR5Br2\nfJEDe4Ux/2OTU3zPUXGJkzq48c4KJWKnTOMY6RFusGXTr/BKFTSz50Mz+0Scfd97j8OyRlGsZffX\nPu8Jl3lbK68SwsROmSa1NHNBfyIaabgsSITrraryehyM+MO9Jo0Ogy0nLItEoq7jx4Vjd+XbVFnl\n9ZjIH1arFUeO/Ojx36FDh2C1WsOdTSIXYV31i3xzHbNf4CFk4OIP95o0Ogy2nLAsEokSC8cKx+7K\nd7xeLxxr9LwHaPAqKo7ii1tuQk5CgtvzX3R3Y+76x1BUNCHEOSPyjhWVCDekMfiyuQD6/3cNTMcq\n+ueoTJriOiZafk3nOSqFekCpgPH9d/rDT5oC0+4vxH0uAm2we2lQYJwoN5X1NYjJzoO6uATmA3uH\nPj/kxN/xcFU1NAUFiJs5W5wvNaEYph0foae6BgkFeYibUw6oxEfSUOZYEUW8E/eata4G+l8sg7mt\nA7EpyTD9eBBWQyOUmjiY2zr675tZcwbmbeVkw9prgeXAHtitNvRW999btrllvue6eMmHI5593pzg\nv3cKm5yEBOiTOHmcogsrKpFuCGPw5XMBnNfY18Muzj9ZvRrIFJexlM9REeKvWIbKFzZK5/SwAxdf\nNLj35MNg99KgwJCXG/3Kq1zKymDmh7j8HS1msez0mlC58eWBYzugmfcfYp6GMMeKKNLJ77W8S38q\n3Rvu9kTRzJ4PZfLAfC15GHSuRMUzzwlx/LlP5PmIi/sNUDRlyO+LiCjQOEdlBJKPc3ZeY18+vtnd\nmGhv8XuqxbX8gzFemmOyw0P+d/enrHgjjy8vOz21tV7Pu7sm56jQSCAvx+aWFun/8j1RHGGd48jD\ndB+v9Jq+v/mQz5khIgo3VlRGIPm4fpVmYC2YeL3vMf9e4+eLa/kHY7w0x2SHh/zv7k9Z8Ub+d5SX\nnfi8PK/n3eWJc1RoJJCX69j0NOn/8j1RHGXeOY48TEKh3m2cweYjsbDQr3hERKHCoV8jkDB3ID8f\niFFBnZ3TP4+guARjk1O9znnxGn/SFOjV6oF9LsqGtvuxNy57aQThGuTK8Xe31tdAlZ3nV1nxxvF3\n7K2qRlxBPjSz5mJsum4gvYmToVco0FNdg/j8PGjmlnvME/dJoZFEutfqaqBITIS11/L/2bvzwKaq\nvH/875u0TVuatqRtuqQL0kpbaq1AWUqFKiII8hP5KoLsjg4zggiiCOKCM86IC4s+8jiK6AiiwyOO\nuI/iDIIL0II4gEWsbN1butK0adM2Pb8/Qi65N9vN0jRtP69/6M2995yT5JwTbnI+94PE+36H9upq\n+A8ciMTUVHQ0Ngn6vGAsDEpCyIiR0JeVQZGQgJjc0WAhoU6PE/H4Uo0aidq6lu586oQQ4hS6UOmL\nrMQOBKRe/uBiXVcOA9BedAol35Qag6dNAZj2zgcEeS66p/0cZKHhkIdpIQ8NBzjO8TnEfZff96i8\nXD77r934KEcJHC+/jwERLcb3UWbZrwLH3WQ/94O7eVLcTJhKSLewMtYACMYC/zfrEgTKKwYlQX+h\nGIEJCVBOmgJwMsj8/KSPE9GYCEi/hj+Pk9HYIIT4FrpQ6WfsBdo7m0yyu7ib5JJ4h6P3yRfeR19o\nAyHu8PScTWOCENKb0Ncn/Yy9QHlfCVSmAOrewdH75Avvoy+0gRB3eHrOpjFBCOlN6EKln7EXKO8r\ngcoUQN07OHqffOF99IU2EOIOT8/ZNCYIIb0JLf3qZ4QJ9BLARUQiKEFjDJ5OvxLHIkgCJpfxa6Kt\nJgG0co7TsQDm5w9KwqCHHuIDRSmA2jeJ+5I4GaP/kHQkzp+L1ooKBGs0CLg6zXFSOg/HlFAwPul1\nzMZAQFgoDIYuJC6cj7bKKgTGxqCzrRUJc+4GpwxBe2UlOEBaosbL5bZXViLpvt+h/ZKWxkQ/YjB0\noVKns7m/UqdDoqHL5n5CeorkC5Xm5mZotVowxvjH4uLiXK74hRdewLFjx2AwGLB48WLcfPPN/L6D\nBw9i8+bNkMvlGD9+PJYsWeJyPUTIIoHeypVInHWXIKDTbsJIK0kAoc51e92ztfOVkz2bSJJ4lmUy\nxnDBe9526FtRQkcm2LbWRzy+ft7dYHxCvMxqMsg9H/HbpvnYfF6WkqiRYlP6O4b3rvVDsMrf6l5d\nvR9Gg1ndR0hPknSh8vTTT2PPnj0YOHAgf6HCcRz279/vUqX5+fk4e/Ysdu3ahcbGRsyYMUNwofLX\nv/4Vb731FtRqNebNm4fJkycjOTnZpbqIkJT1ya4kjLRWrjMfgu6eT7zP0XvmKMGjtfeY+gHp7+wl\ngwSuzMfm83JLcTGCHFyo0Njq3+RyOaLSYqGMC7e6X1vRCLlc7uVWEeKYpAuVH3/8EQUFBVAoFB6p\ndNSoUcjKygIAhIaGorW1FYwxcByH0tJShIeHIzo6GgCQl5eHw4cP04WKh0hZn2w/YaT1xGLurnum\nddO9j6P3LDjBfoJHKX2P+gHpb+wlgwSuzMfm8/KApCQ4WrRDY4sQ0htJulBJTU1FR0eHxy5UOI5D\n4OVJdvfu3cjLywN3OVdGbW0tVKorE7NKpUJpf7grSZcBbQU/XF7vnwguIgL6c+c9nvtBsGY/MQFd\nTZdw5tXXjffnHzUWkMklJIwMs1jz724sgKTzKSdG97r8+pZUlQvz6tjgnzoUiQvn8wkbA1KF3+gq\nxoxHoqELrRUVCNJoEJgzHoMi1Vfe47QMi5gVj8eUUJ8hvkQUf1Kib4NMEYjONj38AgPQfkmLwIQE\nDFr1CPQXiuEfpuSTQXZc0sI/TInOllYMeughwbxsNVGjuO+nZWDQypVor6yE34Ag6EtLwQE0Jggh\nPk3ShcqECRMwceJEJCcnC34a3LFjh1uV//vf/8aHH36IN9980+Yx5jExfVlbwQ+C9f6aGbej8vK6\nZI+uJTZbs992+FtBnYlgCBwz3mHCR6tr/t2NBZBwPq2x7l7Ovr5tRw6iZPs7/Haiv7+x/5jKK/pF\nGJMSqRa8x+2nTlitz5MxJdRniC+xFf+nmXE7indeiUOxFqNnLTGqaV62lqjRXt+nMUEI6S0kXahs\n3LgRq1evRkxMjMcq/u6777B161a8+eabCAkJ4R9Xq9Woqanht6urq6FWqx2WFxWldKs9PX2+vrRM\nsG2+LtlQVY6ovFyP139GVKe+tAwJ/59rz8Mbr19JlTDmwfx1cbd+b/J0Wz1Vnr3X1xpH/cdRec7W\nJ+Zun3GlPGf1pn4p5qm2UzlXWPTHy3EmFnEoTo4Fa+2x1fcdjYne2Ge7e+wGB1kPQDdRKPwdtqGn\n55eGhhCHx6hUIT77+dRd5RHfJ+lCJSUlBTNmzPBYpc3NzXjxxRfx9ttvQ6kUdjqNRoOWlhZUVFRA\nrVZj//792Lhxo8Myze9a5ayoKGWPn2+xLtls+Zs8RmO3fFfrt1yzHO9SOd56/fxihDEOptfFE/V7\nkzttFXP3uZuz9fra4qj/OCrP2frMudtnXC3PGZ4uszf2U0+9Bn2lHIv+eHkJdEBEhPBxJ8aCrfbY\n6vv2xoQnXx9v6u6xq2vtsHu8Xt/RLZ/R9jhbZn19s6RjfPXzqTvKM5VJfJukC5XBgwdj9erVGD58\nuGDp15133ulSpV988QUaGxuxYsUKPoh+zJgxGDJkCCZOnIh169Zh5cqVAIBp06YhKSnJpXp6E8Wo\nsUgEQ1tJKQITEyCLiETszJl8HEnj++8iKDGRjyNxmShfSeLv74W+pBSKhHgEjnLuGzy36zfPwyIB\n5cToXqbX11BVLsyrY2LoRNvBA2gtK0dwggaKUdcjcWG7MUYlQYPAkWOdKs8b7yf1GeJLzPujf5gS\nrLMTiYOS0FZbh8QF89De0AB/pRKcXAawLsmxI8xgkBzvRWOCENKbSLpQaWxshEwmw3//+1/B465e\nqNx111246667bO7Pzs7Grl27XCq715LJEThmPALHXHko4OqhtuNIXGRt3XLKkqke/5bCmfqhlniB\nRDkxutfl1zcqL9dqf2g7eEAYk2LoEsagqKKE742D8rzyflKfIb5E1B87Cr7H+a3b+N2R465H1aef\nA3AudqT+yFHp8V40JgghvYikC5X169fjwoULGDRoEADg1KlTGDrU/j3biWeI85a0lZQKLmacJSWP\nSnfq6fqJ61zJi0IIsU1XXCLYNs+N4sx4aikuFmzTWCSE9BWSflfevHkzXn/9dX779ddflxQ3Qtwn\nzlsSmOjeve97+l76PV0/cZ1lXpQ4wTa9l4Q4J3iQcFmzeW4UZ8bTgKRBgm0ai4SQvkLSLyr5+fmC\npVgvv/wyZs+e3W2NIleIY1fcjSPp6fXJPV0/cZ0iJw+JDHzeFGNelGh6LwlxUcykiTAYDMb5PSkR\nsrDwKzmrnBhPqlHZNK8SQvokSRcqHR0daG9vR0BAAACgpaUFBoOhWxtGLhPFrjBmQP3xH9BWUoKg\npESEKEJRsq8MfjEaMLkM+gvFCExIEPxtaNGi7fwFYzD+yBy+aA5Ae9EplHxTKkzw526SPNH55m3x\nT7+G1kf7KkcJH2UyyCKi4KdrgzwiCjDL3cABQFcX2o58zyctVQwfhbZD36KoogLBGg0UOePR/ttp\nvl/4pQ1F/cl8tJWUIDApEarMMeA4N24UQYgPY12d0B3+Fh3llfAPC4OfSgU24UbIQsMhD9NCrgyD\nf2oGAtIyjSd0GdCW/61xPMXFoNMABESrwQxd0JeV8Ukc208XovTymFVOmgIAlOSUENJnSLpQmT17\nNqZOnYprrrkGXV1dOHnyJB544IHubhuxov7EYdS98gYAoAUALicMA64kD7P3d2JHuyAg2nyfKQDT\n3SR5tpKauVIW8R5H77t4f+J9vxPe6GHhfGGw/bxWlOx878p2V5dgO+53C1D3ljFpbAsALAMisrxw\n5zlCekDL4W9R8daVJMmR465HZVsbit++8pj5mLOWBFh74Tw/lwKWY3DQ5btlUkJHQkhfIelrlpkz\nZ2LHjh2YMmUKpk2bhvfeew933HEHAOD06dPd2kAi1FZiO/hSyt/igGhx8Kb5v+LHpRIfb60O4nsc\nve/ibfGNHiyC7Ssr7W6Lk5yK+zYhfYm4vxva2qArEydNvTKmxOOrvb5eMJdaO0ZfWko3LCGE9CmS\nflEBgLi4OMTFxVk8/uyzz2LHjh1WziDdITAp0fjt82XmwZdS/g5KsJ5wDLgSgOluwLv4fFcDRIl3\nOXrfxfvFN3oIihcH24u242JF5ccLyxeVR0hfokgU9nd5YCCC44WPmY858fgKUKnAWJfgMfExioQE\n4zJMG2USQkhvI/lCxRbGmCfa0Xu4kbDQE1SZY4Blxm+fgxITERIYiqAEDeTRcYCf3BiIGR8v+LtL\n1wx1UJAxGH/kWAxSRRmDLi8fF5SgESTk80vPQMSy3xtjBxIT4Z+e4VQbBQHz5m2hIE+f5p+WgcT7\nfgd9aRkCE+IRkCZ83y1uhJCWgUGhYVe2U4ci0d+fv/GDYvgoJDKG1ooKBMXFQTF2PAZFxfDH+6cN\nRcQABd/PVNe6cd9tQnyFjRi/AaPHQwMOHWUV8A8NBTcwDIWDgpC07PcIqGq0mB8FN1KJjYGhCwhJ\nSUHIiJHQl5UJxqA4qSoF1hNC+gq3L1Q4Tvz9Td/mVsJCD+A4uXEdv9la/qjrx/IJ9QJSr3womf8d\nOPLK8eJg9qjrcwQJ+X7VnsErdR8DAwDU/YRl2kikhaY60UjLhGLmbSG+qf10oXC9e2i41QSOgvdV\ntG1+44fTTb/iFcPXQDQAQyGWtSYiTXS8uC8T0tvZivXiZH4YkHMjgMtj4+ibwM/GY5bl3Gs5x1pJ\nAmwSkJF15W8rSVXphiWEkL6CbgXipP6w/rdcW2l3m/RNnu7b1I9IfyRlHNHYIIQQaehCxUn9IWGh\nRhlrd5v0TZ7u29SPSH8kZRzR2CCEEGkoRsVJvpSwsAsGHK07hvLiSsQr4zBCNQwyCPNQMHTh16bf\nUK6thEYZi9TQq8E5uD5NDb0ay7LvFZzjFHfzsJAeYerb4vXutjjqW6nKFPw5Yjr0ZcaxolKmOGyD\nuMwhoSkoajrjVP8lpCeJY/z80ofidNOvgj5smmNr9bUIkAWgXFsBAI77N82thJB+RvKFyqlTp9DU\n1CS4MMnJycH69etdqrioqAhLly7FokWLMHfuXMG+CRMmIC4uDhzHgeM4bNiwAWq12qV6PM7KOv2e\ncrTuGLYf381vsyyGUREjBcf82vSbcS30ZcuyrayFFuEgQ1poqnNxKWbczcNCesjlvi1e726Lo77V\n8Ushn/OnGYBypdJhPxCXuTBrpqCPS+m/hPQkcYzfwvoAq304LTQV5/V+2PDD6xb7bKG5lRDS30i6\nUHnggQfw22+/CS4WOI5DTk4O4kW3V5SitbUVf/nLX5CTk2N1P8dx2LZtGwLNbmtLLJU3VVpuR4iO\nsbIWurv/o2dtjTZ9mPY9jvqWK/3AokxxH/dC/yXEHc704ZJL5Tb3WUNzKyGkv5F0oVJRUYGvvvrK\nY5UqFAps27YNW7dutbqfMdbvlpS5Ij5MmNdGE2q5zrkn1kL3hzge4rhvudIPxGVY9HFay098nMW4\nCLU9ThLDNDb3WUNzKyGkv5F0oZKcnIz29nYEBAR4pFKZTOawrHXr1qGsrAzZ2dlYuXKlR+r1BdbW\n9dt63NpaZfPjBocn4YmBt6CjtAL+CRqoVddZHJMUqsFTA6dCX1oGRVICIpWD+fXS8aFxYKwLBy5e\nRHRgNF+nvbbYar85X4rj6a8YM6D+xGHjOvmkRKgyx4Dj5PbPufzeHrhYLegPtgxRJuPJgVPRXlqG\ngMR4RJn1LY0yFinpaVAuXYCO0goEJMSBS09FQd0RlDdVIj4sDsNV1+G3prNW1+6bx6gos5Wux0sR\n0o2szYeDQwfh7szpqNReRKxSjWAoMDdzBqqbaxAfFgddpw6fX/gC2Y3BiGlsx/OBt6Cp7iIUSY7j\nuGhuJYT0N3YvVFatWgWO49Dc3Ixp06YhMzMTcvmV/+y88MIL3dKo5cuXY9y4cQgPD8eSJUuwd+9e\nTJo0qVvq8jZr6/rVUdmSY0nMj3soJA/arca1z20AApYFICIr1+KYS2bHsKUMrzR8AQDITczGDyVH\nLeq01xZb7RfwoTie/qr+xGE+PqQFAJZdzllih7PxTA0n8tH0v28DMPYtLAXftwDg7szp+EfDl0AI\ngIYTuLtWgX+c/Jjf35HZgXdP7rGoTxwf5U68FCHdydqYqdXXCvr57Gtuw66fP+G3cxOzMbhMj0s7\n34f/uOtR+933ACTGcdHcSgjpZ+xeqIwdO9bmvu5M9Dh9+nT+7/Hjx6OoqMjhhUpUlNKtOr11/oGL\n1YLt6rZqwb/mj49LFl0AiM6XV9ahy2yfvqwUUROVdo9pLyszBnkCaOvUW63TWhtNbbHV/t7y+vsC\nT7fVWnkVZaK17Jf7hj323ndrxHWY9y0AqNReFOwXb1c0VzlVn5g3XkdfLNNbPNX2vlyOtTFzsblO\n8Fhls7Dft3XqEVLTAgAwtLUJ9hmqyhGV51oCVF97fbypu8ducJC/3WMVCn+Hbejp+aWhIcThMSpV\niM/Pq72xfxL32L1QmTFjBgBgw4YNeOSRRwT7Hn/8cdx+++0eb1BzczOWL1+O1157Df7+/jhy5Ahu\nueUWh+dJuUuRLVFRSq+dHx0YbXXb2uPWyjQ/zhAbKdiniE9ATY3W7jEB8fFAwwkAQKCf8GYFpjrt\ntcVW+3vL62/rfG9yp61itp67IiEBzebbl/uGPVL7oM06EuKB+hP8dlyosLxYpfDOfXEhMU7VZ87d\n97y7y+uOMntjP/XUa+Cr5VgbM34y4RLLuBDhMYF+CrREcQgAIA8SzsHyGI1L7fPF18ebunvs6lo7\n7B6v13fYbYMvzC/19c2SjvHlebW7Xkfi2+xeqHz99dfYu3cvDh06hIsXr3wr1NnZiSNHjrhcaWFh\nIZ577jlUVFTAz88PX331FSZMmID4+HhMnDgRN9xwA2bNmoXAwEAMHToUkydPdrmunmAvxsNWjhKp\nuUvMj+NCExCx7D7oy8oQmJiEzs52lHyyEwOTErBy5GJcaCrjj2krKUVgYiIGXjsay7SxKNdWIiFU\ng+HqTFS3XURMUAwY68J/yvcjIVSDRVl3oaypAvGhcZBxHP5Tvp+PGbjSzhjIOBk+KPxcUkwD8R5V\n5hhgGfhcDqprxzg8x9S3qtsux6goU9B+6gSfs8EvPQO/aq/kNLk6cxQ6lnagvbQMisR4RGSNxcL6\nAShvqoQmNBZZEZlgmYxfq58dNRyyTBkqtFWIC43ByMgR8Mvy448fEuo4zwohPUE8p0dEDgcgnI8T\nwjRo1DeipU2HuzOno7alHpEDVGjQXcLdmdNRp2tAZPBAXNI3Q58WhPAH7kHQpXYk3jcEHZe0tmNO\nKHcK8UEGgwEXLpyzuX/QoMFebA3py+xeqIwbNw4qlQo///yz4FbCHMfhgQcecLnSjIwMvPPOOzb3\nz58/H/Pnz3e5/J5mb62/rRwlUnOXWByXlYyoiUqc3PupIF4gdOki3DTsBv4YZF0pQ1zPuOSR+O7s\nUbxy9C3+MVP8iq04lrTQVJxu+hUvH3nD6vMkPYvj5MaYFAdxKYJzLvetccnZqKnRov3UCUHOhohl\nvzfmh7hsYdZMbG/4whiDUn8CC+sHCPJFzM3sFKzVl2XKBDEpfll+guNDs0Op/xCfJJ7TFQo/XKVI\nFszHBXVHRP1/hqC/35Y2Ce+ZjYdl2fci4/JYs3cjfsqdQnzRhQvncPChBxEbHGyxr1KnAzb/D2Ji\nhvdAy0hfY/dCJTAwECNGjMDHH3/ssTt+9Qc9kbukvbTMcnuY9PPFbTbFr4jjWMyfS088T+I94pwN\nbSUlghgUq3l8zFRoq+xuU44U0luI57qSS+W4Sp0sPMZB/8MxJqUAACAASURBVG9obbRbpi2UO4X4\nqtjgYCSG0NIp0r3sXqikpaXZDJqXy+X4+eefu6VRvV1P5C5RJCXAPCwzIMG5RJwWOTD8FJf/DbR5\nXE88T+I94pwNgYmJQN1P/LajPD6aUGEMSpwyRrSf+g/pHcR9U5z/BLAcD3Gi/j8wKNxumbZQ7hRC\nSH9m90KlsLAQjDG89tprSE1NxZgxY9DZ2YlDhw7h/Pnz3mpjryM13sSToq7NBVvKjDktEuKhvu56\np84XtjkGMk6O6CA1H8dSrq2yeC4WMQ2U46JPEeds8E/PwDJtpM0cJ0NCUxCaHXolhiU0GfIsP5Q3\nV0ITEosREcOgylbZPJ76D/FV4jk9W3Mt6mpbBMeMUA0Dy2J8zNWV/l6BoIBAaNu0mJs5A63tbdAo\n4yT3d8qdQgjpz+xeqJhypuTn5wtiUqZOnYr77ruve1vWi0mNN+mCAUfrjqG8uBKJofFoN+hR3lSF\nQeGJ6OhqR3lTFTShMRgVORJyB7k5OU6GxuRYlKuB+NA4NGrP8BcXMk6G0qZy/j+HRU1nHPznkMPV\nymQMUV7ZlxaaZvN5mmIaiG/j+9vlhIsjVMMgw5U7FFlL+Gies4EJbnQNdKEL9fp6NOgbEKxQoB3t\nqNXXol7fgECFPwZjEEL9Q9Ea1IpQ/1DIrIwLypFCfI2tm6Gkhabi6tBk5NccwVs//h8iggeirqUB\nkQNUaG5rRkjgANTqGhAeFAo/mR8OVxXA3994W1uVQoWcqNGu3WyEcqcQQvoxSZnpW1tbsWvXLowY\nMQIymQzHjh1DfX19d7etzztad4wPvjQPWr8tTYlPTu/lj2OZwNioHKtlmJgHe4oD4M23F2bNFAR8\nOptwkvRe5v0NAFgWw6iIkfy2oz4g3n935nRBsLwhs0uwzTIh2KY+RXoDe+Mgv+YI3j25B7mJ2dh7\n8lv+GHGgfG5iNiKCVfjk+F6r5RBCCJFG0tc7L774Io4cOYKVK1dixYoV+OGHH/D88893d9v6PPPg\nS/OgdXHQpTgo02pZWutlibetBTCLz7e2TXo/R8HvjvqAeNtRQkfxNvUp0hvY6/emuVg8x4rn7LZO\nvcvB84QQQq6Q9IvKVVddhY0bN3Z3W/od8+BL86B1lSjoUhyEbI15YKY4AN4UGA/YDmCmwPi+z2Hw\nu4M+IN52lNBRvE19ivQG9vq96QYR4jlWHCgf6KdwOXieEELIFXYvVFasWIGXXnoJeXl5Vu/+tX//\n/u5qV78wXHUdOjI7UNFchcTQeAwOT0RZUwWiAiMxJ3M6Ki4nylP6h2DP+U+sxhWYDAlNwcKsmShv\nqkRSeAKGqTNRoa0SBMbbC2DuiRsAEO8SB/tmRwjvcS++OcLVockoqDvCx7QMU2XxfUwTGovrIq4V\nJHQcETUMyAS/PTJqBAKyAlDeXIl4ZRwldCS9grW50BS3YujsMiZvbKnH3ZnTUa9rhCo4HE2tTcbt\n1kYE+QchLCAErJNhYdZMtLa3IiggCOXaCr58SoxLCCHS2L1QeeKJJwAA77zzDh9YTzznt6azgoRg\ny7LvxZjBo3G66Vds++97/OPmMSbiuAKToqYzFrEnN2lu4LeFgfGuJ5wkvZcMcmPfibC+X3xzBHEC\nu47MDkF/RZYwBiUyOxLXR+UCUcbt002/Cs5XZiupfxGfZ20uPN30K145+qZF/J845i83MRtfnTkA\n4EpMiulcE4pVIYQQ6ex+rRMZGQnAmCn++eefxw8//ACO46DRaKDRWN5HnjjH1lpoW8kXAcu4Akdl\nEeIqRwns3I1xIaS3MPVdiwS4TXbmaor/I4QQt0mKUdm3bx9OnjyJ/Px8PPnkk2hoaMCIESPw+OOP\nd3f7+jRba6FtJV8ELOMKHJVFiKssEtg5mbCR+iTpK0x9VxybIh4jgrma4v8IIcRtki5U5HI5MjIy\n0NnZia6uLhQUFODYsWPd3bZexda99+3h40ouJ8Tzk/nhP+X7kRCqwbLs36FcW4U4ZQx0nToEyQOt\nxhWYUIwJcZa4z14dmowf635CebExpmSYKstGAjtpCRspISjxFdZyBEmNE2HogozjcOfQqdC1t2Ju\n5gy0d3UgJihalPRUGA9I8X+EEOI+SRcqixYtgkwmQ0ZGBrKzszFv3jwolcrubluv4koeEnFcifn6\nZ3GMyQiV9QsUE4oxIc4S99m5mTMEMSh8PJRZTIszCRspISjxFe7kiRKfm5uYjZyEEbhKkQzAcgyY\nxwMCNDcTQog7JH2llJ6ejo6ODpw+fRq//vorzp49C4PB4FbFRUVFuPnmm/Huu+9a7Dt48CBmzpyJ\n2bNn49VXX3WrHm9xZR2y3VgUWsdMupm4jzmKQSGkt3InTsTaPF1yqdwj7SKEEGKfpF9UVq9eDQDQ\narUoKCjAa6+95tbyr9bWVvzlL39BTo71bOt//etf8dZbb0GtVmPevHmYPHkykpOTXarLW1xZh2w3\nFoXWMZNuZpkXxX4MCiG9lTtxItbm6cQwupkMIYR4g6QLlaKiIhw5cgRHjx5FUVERkpOT+YsXVygU\nCmzbtg1bt2612FdaWorw8HBERxuTyeXl5eHw4cM+c6Fivq4/IUyDRn0jyosrkRSagAeyf3c5d4m0\ndcjmMSrxyjgMDBiI6CA14kPjwFgX/lO+HxplLGScDKVN5RaxL+6suya9m6P33lrMFADBYymhgzE3\ncwYqtFWIC43ByMgR8Mvy42OmRkQMw+mmXwUxKUVNZ5yKwyLEF9iKl+qCAUfrjvG5gmSQoeRSGeLD\n4jBcdR1+azqLcm0FFl13F+p1DQj0D0SI/wBUa2tQ01wPbVszNMo4GguEENJNJF2oPPPMM8jNzcU9\n99yDzMxMQfLH06dPIy0tzalKZTIZAgICrO6rra2FSqXit1UqFUpLS50qvzuZr1e+LW0SPjm9l9+3\nMGumIK7EEVu5T4z33X+Lf1wcu2Ja6+zOumvSuzl6763tB2A3JsUvyw+jIkYiKk2JmhqtRf4Hcc4I\n6m+kt7AVL3W07pjNOEFx3iDzMSTOp0JjgRBCuoekC5V33nnH5r5nn30WO3bs8FiDxBhjko6LinIv\nuF/q+QcuVvN/N7Q2CvaVN1ciKk16O8zLAoDqtmqMS862eNw8dsV0jL3zXeGt189Xz/cmT7TV0Xtv\nbb9YRbMoJsWs/0ZFKS3KKG8WrtV3pr91x/vj6TJ7Qxu9yVNt9+VyyottxwmKx4f5GBLnU+nJuddX\ny/Gm7h67wUH+do9VKPwdtqGn55eGhhCHx6hUIZLLbGgIwXkHZQG9Y54mvk3ShYo9Ui8kpFKr1aip\nqeG3q6uroVarHZ7nzl2FoqKUks+PDozm/1YFhQv2aUJinWqHeVmm7ZoarcXj5rErpmPsne8sZ55/\nXz3fmzxxByxH7721/WIWeVEu91/T6yk+RxMiXKsvtb+5+/54o8ze0kZv8kTbPfUadFc58UrbeVDE\n48N8PIjzqfTU3OvL5XhTd49dXWuH3eP1+g67bfCF+aW+vlnSMVLLdFSeaX9vmKeJb3P7QsV8GZgn\naDQatLS0oKKiAmq1Gvv378fGjRs9Woc7zO+JnxSaIMiDYivHiaOyxOumhffdt35vfnvnk77P0Xtv\nK3eD+WNXhyYbY1Iu50kR919xGY7yphDS24xQDeNzBSWEacCB43NWifMGmY+hOn0tUrJmCmJUCCGE\neJ7bFyquKCwsxHPPPYeKigr4+fnhq6++woQJExAfH4+JEydi3bp1WLlyJQBg2rRpSEpK6olmWmVx\nT/yQZH5Nv6tliddNW7vvvvje/PbOJ32fo/feVu4G8WPiPCmOyqB8EKQvkUFuMQbMc1bZGkNRUTTn\nEkKIN/TIhUpGRobduJfs7Gzs2rXLiy0ihBBCCCGE+BK376fo6RgVQgghhBBCCLH7i8qhQ4fsnpyT\nk4P169d7tEGEEEIIIYQQYvdC5dVXX7W5j+M45OTkID4+3uONIoQQQgghfZ/BYMCFC+fsHjNo0GAv\ntYb4GrsXKvbiSL766iuPN4YQQgghhPQfFy6cw8GHHkRscLDV/ZU6HbD5fxAT49ydVUnfICmYvqKi\nAjt37kRDQwMAoL29Hfn5+Zg8eXK3No4QQgghhPRtscHBSAyhnCbEkqRg+kcffRTh4eH473//i2uu\nuQYNDQ144YUXurtthBBCCCGEkH5K0oWKXC7H4sWLERkZiblz5+Jvf/sb3n333e5uGyGEEEIIIaSf\nknShotfrUVVVBY7jUFpaCj8/P5SXl3d32wghhBBCCCH9lKQYlfvuuw+HDh3Cvffei+nTp0Mul2Pa\ntGnd3TZCCCGEEEJIPyXpQuWqq65CcnIyAKCgoAAtLS04f/58tzaMEEIIIYQQ0n/ZXfrV1NSEkpIS\nrF27FqWlpSgtLUVlZSXq6uqwevVqb7XRZzHGUFjcgF17T+NUcQMYWE83iZA+jcZc32N6T78sKKX3\nlBBCiIDdX1R++uknbN++Hb/88gsWLlzIPy6TyXD99dd3e+N83amSRmz8x0/89sN3D0NG0sAebBEh\nfRuNub6H3lNCCCG22L1QycvLQ15eHv7xj3/g7rvv9lil69evx/Hjx8FxHNauXYvMzEx+34QJExAX\nFweO48BxHDZs2AC1Wu2xuj2ptLrZYps+YAnpPjTm+h56TwkhhNgiKUZlypQpeP7551FbW4sXX3wR\n+/btw3XXXQeVSuV0hUeOHEFxcTF27dqFs2fP4vHHH8euXbv4/RzHYdu2bQgMDHS6bG9LjA4RbCeI\ntgkhnkVjru+h95QQQogtki5UnnzySYwcORI//WT8eb69vR2rV6/GG2+84XSFhw4dwsSJEwEAycnJ\naGpqQktLCwYMGADAuF6Zsd6xRjk9KRwP3z0MVfU6xKiCMTQpvKebREifRmOu7zG9p6XVzUiIDqH3\nlBBCCE9SHpX6+nosWLAA/v7+AIBbbrkFbW1tLlVYW1sr+CVm4MCBqK2tFRyzbt06zJkzB5s2b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YvHix1bLEff3UqVNujTN75bkyFzj6LHGljZ7+fPL0Z5OUMp1to6c/m4h39coLFUII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\n5wWDDdtofD+jAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f15b05198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean, hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sure enough, the strange distribution of the petal measurements exist because of the different species. This is actually great news for our classification task since it means that the petal measurements will make it easy to distinguish between `Iris-setosa` and the other `Iris` types.\n",
    "\n",
    "Distinguishing `Iris-versicolor` and `Iris-virginica` will prove more difficult given how much their measurements overlap.\n",
    "\n",
    "There are also correlations between petal length and petal width, as well as sepal length and sepal width. The field biologists assure us that this is to be expected: Longer flower petals also tend to be wider, and the same applies for sepals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enough flirting with the data. Let's get to modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Classification\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Wow, all this work and we *still* haven't modeled the data!\n",
    "\n",
    "As tiresome as it can be, tidying and exploring our data is a vital component to any data analysis. If we had jumped straight to the modeling step, we would have created a faulty classification model.\n",
    "\n",
    "Remember: **Bad data leads to bad models.** Always check your data first.\n",
    "\n",
    "<hr />\n",
    "\n",
    "Assured that our data is now as clean as we can make it — and armed with some cursory knowledge of the distributions and relationships in our data set — it's time to make the next big step in our analysis: Splitting the data into training and testing sets.\n",
    "\n",
    "A **training set** is a random subset of the data that we use to train our models.\n",
    "\n",
    "A **testing set** is a random subset of the data (mutually exclusive from the training set) that we use to validate our models on unforseen data.\n",
    "\n",
    "Especially in sparse data sets like ours, it's easy for models to **overfit** the data: The model will learn the training set so well that it won't be able to handle most of the cases it's never seen before. This is why it's important for us to build the model with the training set, but score it with the testing set.\n",
    "\n",
    "Note that once we split the data into a training and testing set, we should treat the testing set like it no longer exists: We cannot use any information from the testing set to build our model or else we're cheating.\n",
    "\n",
    "Let's set up our data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')\n",
    "\n",
    "# We're using all four measurements as inputs\n",
    "# Note that scikit-learn expects each entry to be a list of values, e.g.,\n",
    "# [ [val1, val2, val3],\n",
    "#   [val1, val2, val3],\n",
    "#   ... ]\n",
    "# such that our input data set is represented as a list of lists\n",
    "\n",
    "# We can extract the data in this format from pandas like this:\n",
    "X = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
    "                             'petal_length_cm', 'petal_width_cm']].values\n",
    "\n",
    "# Similarly, we can extract the classes\n",
    "y = iris_data_clean['class'].values\n",
    "\n",
    "# Make sure that you don't mix up the order of the entries\n",
    "# all_inputs[5] inputs should correspond to the class in all_classes[5]\n",
    "\n",
    "# Here's what a subset of our inputs looks like:\n",
    "X[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our data is ready to be split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our data split, we can start fitting models to our data. Our head of data is all about decision tree classifiers, so let's start with one of those.\n",
    "\n",
    "Decision tree classifiers are incredibly simple in theory. In their simplest form, decision tree classifiers ask a series of Yes/No questions about the data — each time getting closer to finding out the class of each entry — until they either classify the data set perfectly or simply can't differentiate a set of entries. Think of it like a game of [Twenty Questions](https://en.wikipedia.org/wiki/Twenty_Questions), except the computer is *much*, *much* better at it.\n",
    "\n",
    "Here's an example decision tree classifier:\n",
    "\n",
    "<img src=\"iris_dtc.png\" />\n",
    "\n",
    "Notice how the classifier asks Yes/No questions about the data — whether a certain feature is <= 1.75, for example — so it can differentiate the records. This is the essence of every decision tree.\n",
    "\n",
    "The nice part about decision tree classifiers is that they are **scale-invariant**, i.e., the scale of the features does not affect their performance, unlike many Machine Learning models. In other words, it doesn't matter if our features range from 0 to 1 or 0 to 1,000; decision tree classifiers will work with them just the same.\n",
    "\n",
    "There are several [parameters](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) that we can tune for decision tree classifiers, but for now let's use a basic decision tree classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Create the classifier\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "# Train the classifier on the training set\n",
    "decision_tree_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Validate the classifier on the testing set using classification accuracy\n",
    "decision_tree_classifier.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heck yeah! Our model achieves 97% classification accuracy without much effort.\n",
    "\n",
    "However, there's a catch: Depending on how our training and testing set was sampled, our model can achieve anywhere from 80% to 100% accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f0ffdf6d8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JRYsWYfPmzdi0aRMef/zxaNdFRDHQ1GlDW7cD5RPToVbKY10ORej6+YUAgL/u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\nsyQDU4vSUJqXgnxjMmSy+Dm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f15c5ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracies = []\n",
    "\n",
    "for repetition in range(1000):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75)\n",
    "    \n",
    "    decision_tree_classifier = DecisionTreeClassifier()\n",
    "    decision_tree_classifier.fit(X_train, y_train)\n",
    "    classifier_accuracy = decision_tree_classifier.score(X_test, y_test)\n",
    "    model_accuracies.append(classifier_accuracy)\n",
    "    \n",
    "sb.distplot(model_accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's obviously a problem that our model performs quite differently depending on the subset of the data it's trained on. This phenomenon is known as **overfitting**: The model is learning to classify the training set so well that it doesn't generalize and perform well on data it hasn't seen before.\n",
    "\n",
    "### Cross-validation\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "This problem is the main reason that most data scientists perform ***k*-fold cross-validation** on their models: Split the original data set into *k* subsets, use one of the subsets as the testing set, and the rest of the subsets are used as the training set. This process is then repeated *k* times such that each subset is used as the testing set exactly once.\n",
    "\n",
    "10-fold cross-validation is the most common choice, so let's use that here. Performing 5-fold cross-validation on our data set looks something like this:\n",
    "\n",
    "<img src='./images/k-fold.jpg' />\n",
    "\n",
    "(each square is an entry in our data set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f15293a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "\n",
    "def plot_cv(cv, n_samples):\n",
    "    masks = []\n",
    "    for train, test in cv:\n",
    "        mask = np.zeros(n_samples, dtype=bool)\n",
    "        mask[test] = 1\n",
    "        masks.append(mask)\n",
    "        \n",
    "    plt.figure(figsize=(15, 15))\n",
    "    plt.imshow(masks, interpolation='none')\n",
    "    plt.ylabel('Fold')\n",
    "    plt.xlabel('Row #')\n",
    "\n",
    "plot_cv(StratifiedKFold(y, n_folds=5), len(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that we used **Stratified *k*-fold cross-validation** in the code above. Stratified *k*-fold keeps the class proportions the same across all of the folds, which is vital for maintaining a representative subset of our data set. (e.g., so we don't have 100% `Iris setosa` entries in one of the folds.)\n",
    "\n",
    "We can perform 5-fold cross-validation on our model with the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3f0f5f9f28>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bkgIDNxFRBLIioNUEvyLZx03JhIGbiCiCiE3lzLgpCTBwExFFEN5UzsBNyYOB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\nEGnkv+KGyOcIFL31AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f1543e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "# cross_val_score returns a list of the scores, which we can visualize\n",
    "# to get a reasonable estimate of our classifier's performance\n",
    "cv_scores = cross_val_score(decision_tree_classifier, X, y, cv=5)\n",
    "sb.distplot(cv_scores)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a much more consistent rating of our classifier's general classification accuracy.\n",
    "\n",
    "### Parameter tuning\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Every Machine Learning model comes with a variety of parameters to tune, and these parameters can be vitally important to the performance of our classifier. For example, if we severely limit the depth of our decision tree classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3f0f5ecf60>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AIgQ3AAAWIbgBALAIwQ0AgEUIbgAALEJwAwBgEYIbAACLENwAAFiE4AYAwCK+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\n+X+1C/Z1MSkwNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f15b2ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_tree_classifier = DecisionTreeClassifier(max_depth=1)\n",
    "\n",
    "cv_scores = cross_val_score(decision_tree_classifier, X, y, cv=5)\n",
    "sb.distplot(cv_scores, kde=False)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the classification accuracy falls tremendously.\n",
    "\n",
    "Therefore, we need to find a systematic method to discover the best parameters for our model and data set.\n",
    "\n",
    "The most common method for model parameter tuning is **Grid Search**. The idea behind Grid Search is simple: explore a range of parameters and find the best-performing parameter combination. Focus your search on the best range of parameters, then repeat this process several times until the best parameters are discovered.\n",
    "\n",
    "Let's tune our decision tree classifier. We'll stick to only two parameters for now, but it's possible to simultaneously explore dozens of parameters if we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9664429530201343\n",
      "Best parameters: {'max_depth': 4, 'max_features': 4}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "parameter_grid = {'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(y, n_folds=5)\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(X, y)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the grid search to see how the parameters interact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3f0f5645f8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jxgxdeumlysnJ0cSJE9Xc3Kz6+npNmTJFaWlprRY3dNG/W/0emPPei2/bXYL/a2ywuwLg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f0f5487f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_visualization = []\n",
    "\n",
    "for grid_pair in grid_search.grid_scores_:\n",
    "    grid_visualization.append(grid_pair.mean_validation_score)\n",
    "    \n",
    "grid_visualization = np.array(grid_visualization)\n",
    "grid_visualization.shape = (5, 4)\n",
    "sb.heatmap(grid_visualization, cmap='Blues')\n",
    "plt.xticks(np.arange(4) + 0.5, grid_search.param_grid['max_features'])\n",
    "plt.yticks(np.arange(5) + 0.5, grid_search.param_grid['max_depth'][::-1])\n",
    "plt.xlabel('max_features')\n",
    "plt.ylabel('max_depth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a better sense of the parameter space: We know that we need a `max_depth` of at least 2 to allow the decision tree to make more than a one-off decision.\n",
    "\n",
    "`max_features` doesn't really seem to make a big difference here as long as we have 2 of them, which makes sense since our data set has only 4 features and is relatively easy to classify. (Remember, one of our data set's classes was easily separable from the rest based on a single feature.)\n",
    "\n",
    "Let's go ahead and use a broad grid search to find the best settings for a handful of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9731543624161074\n",
      "Best parameters: {'splitter': 'best', 'max_depth': 3, 'max_features': 4, 'criterion': 'gini'}\n"
     ]
    }
   ],
   "source": [
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "parameter_grid = {'criterion': ['gini', 'entropy'],\n",
    "                  'splitter': ['best', 'random'],\n",
    "                  'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(y, n_folds=5)\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(X, y)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take the best classifier from the Grid Search and use that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
       "            max_features=4, max_leaf_nodes=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_classifier = grid_search.best_estimator_\n",
    "decision_tree_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even visualize the decision tree with [GraphViz](http://www.graphviz.org/) to see how it's making the classifications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sklearn.tree as tree\n",
    "from sklearn.externals.six import StringIO\n",
    "\n",
    "with open('iris_dtc.dot', 'w') as out_file:\n",
    "    out_file = tree.export_graphviz(decision_tree_classifier, out_file=out_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<img src=\"iris_dtc.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(This classifier may look familiar from earlier in the notebook.)\n",
    "\n",
    "Alright! We finally have our demo classifier. Let's create some visuals of its performance so we have something to show our head of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f0f2786d8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+vY9ydpg5c2bcc889ERGxffv2uPzyy9PmP6qcPSIiNm/eHFu2bImpU6emzX5UuTtk3n9O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\ngAEggQADQAIBBoAEAgwACQQYABL8N5LQNQomDdoGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f0f278be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_scores = cross_val_score(decision_tree_classifier, X, y, cv=10)\n",
    "\n",
    "sb.boxplot(rf_scores)\n",
    "sb.stripplot(rf_scores, jitter=True, color='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm... that's a little boring by itself though. How about we compare another classifier to see how they perform?\n",
    "\n",
    "We already know from previous projects that Random Forest classifiers usually work better than individual decision trees. A common problem that decision trees face is that they're prone to overfitting: They complexify to the point that they classify the training set near-perfectly, but fail to generalize to data they have not seen before.\n",
    "\n",
    "**Random Forest classifiers** work around that limitation by creating a whole bunch of decision trees (hence \"forest\") — each trained on random subsets of training samples (drawn with replacement) and features (drawn without replacement) — and have the decision trees work together to make a more accurate classification.\n",
    "\n",
    "Let that be a lesson for us: **Even in Machine Learning, we get better results when we work together!**\n",
    "\n",
    "Let's see if a Random Forest classifier works better here.\n",
    "\n",
    "The great part about scikit-learn is that the training, testing, parameter tuning, etc. process is the same for all models, so we only need to plug in the new classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9731543624161074\n",
      "Best parameters: {'n_estimators': 10, 'max_features': 3, 'warm_start': True, 'criterion': 'gini'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features=3, max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0, warm_start=True)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "random_forest_classifier = RandomForestClassifier()\n",
    "\n",
    "parameter_grid = {'n_estimators': [5, 10, 25, 50],\n",
    "                  'criterion': ['gini', 'entropy'],\n",
    "                  'max_features': [1, 2, 3, 4],\n",
    "                  'warm_start': [True, False]}\n",
    "\n",
    "cross_validation = StratifiedKFold(y, n_folds=5)\n",
    "\n",
    "grid_search = GridSearchCV(random_forest_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(X, y)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare their performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f0d4c44a8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YY2ZV0f/vm6LDMLvbn5l9wsw+nLf+XOBUd38UuJSo/UYKaV+Dqt3dm6L3Pzz6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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f0d4c8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_forest_classifier = grid_search.best_estimator_\n",
    "\n",
    "rf_df = pd.DataFrame({'accuracy': cross_val_score(random_forest_classifier, X, y, cv=5),\n",
    "                       'classifier': ['Random Forest'] * 5})\n",
    "dt_df = pd.DataFrame({'accuracy': cross_val_score(decision_tree_classifier, X, y, cv=5),\n",
    "                      'classifier': ['Decision Tree'] * 5})\n",
    "both_df = rf_df.append(dt_df)\n",
    "\n",
    "sb.boxplot(x='classifier', y='accuracy', data=both_df)\n",
    "sb.stripplot(x='classifier', y='accuracy', data=both_df, jitter=True, color='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about that? They both seem to perform about the same on this data set. This is probably because of the limitations of our data set: We have only 4 features to make the classification, and Random Forest classifiers excel when there's hundreds of possible features to look at. In other words, there wasn't much room for improvement with this data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's extract the core of our work from Steps 1-5 and turn it into a single pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.6  3.4  1.4  0.3]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
      "[ 6.3  2.5  4.9  1.5]\t-->\tIris-virginica\t(Actual: Iris-versicolor)\n",
      "[ 5.6  2.8  6.7  2. ]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 5.6  2.8  4.9  2. ]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 6.6  2.9  4.6  1.3]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
      "[ 5.6  2.9  3.6  1.3]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
      "[ 5.5  2.4  3.8  1.1]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
      "[ 7.   3.2  4.7  1.4]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
      "[ 6.   2.9  4.5  1.5]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
      "[ 6.4  2.8  5.6  2.1]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f0d22fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "# We can jump directly to working with the clean data because we saved our cleaned data set\n",
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')\n",
    "\n",
    "# Testing our data: Our analysis will stop here if any of these assertions are wrong\n",
    "\n",
    "# We know that we should only have three classes\n",
    "assert len(iris_data_clean['class'].unique()) == 3\n",
    "\n",
    "# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\n",
    "assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5\n",
    "\n",
    "# We know that our data set should have no missing measurements\n",
    "assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['sepal_width_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_width_cm'].isnull())]) == 0\n",
    "\n",
    "X = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
    "                             'petal_length_cm', 'petal_width_cm']].values\n",
    "\n",
    "y = iris_data_clean['class'].values\n",
    "\n",
    "# This is the classifier that came out of Grid Search\n",
    "random_forest_classifier = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "                                max_depth=None, max_features=3, max_leaf_nodes=None,\n",
    "                                min_samples_leaf=1, min_samples_split=2,\n",
    "                                min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
    "                                oob_score=False, random_state=None, verbose=0, warm_start=True)\n",
    "\n",
    "# All that's left to do now is plot the cross-validation scores\n",
    "rf_classifier_scores = cross_val_score(random_forest_classifier, X, y, cv=5)\n",
    "sb.boxplot(rf_classifier_scores)\n",
    "sb.stripplot(rf_classifier_scores, jitter=True, color='white')\n",
    "\n",
    "# ...and show some of the predictions from the classifier\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75)\n",
    "\n",
    "random_forest_classifier.fit(X_train, y_train)\n",
    "\n",
    "for input_features, prediction, actual in zip(X_test[:10],\n",
    "                                              random_forest_classifier.predict(X_test[:10]),\n",
    "                                              y_test[:10]):\n",
    "    print('{}\\t-->\\t{}\\t(Actual: {})'.format(input_features, prediction, actual))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There we have it: We have a complete and reproducible Machine Learning pipeline to demo to our head of data. We've met the success criteria that we set from the beginning (>90% accuracy), and our pipeline is flexible enough to handle new inputs or flowers when that data set is ready. Not bad for our first week on the job!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "This notebook covers a broad variety of topics but skips over many of the specifics. If you're looking to dive deeper into a particular topic, here's some recommended reading.\n",
    "\n",
    "**Data Science**: William Chen compiled a [list of free books](http://www.wzchen.com/data-science-books/) for newcomers to Data Science, ranging from the basics of R & Python to Machine Learning to interviews and advice from prominent data scientists.\n",
    "\n",
    "**Machine Learning**: /r/MachineLearning has a useful [Wiki page](https://www.reddit.com/r/MachineLearning/wiki/index) containing links to online courses, books, data sets, etc. for Machine Learning. There's also a [curated list](https://github.com/josephmisiti/awesome-machine-learning) of Machine Learning frameworks, libraries, and software sorted by language.\n",
    "\n",
    "**Unit testing**: Dive Into Python 3 has a [great walkthrough](http://www.diveintopython3.net/unit-testing.html) of unit testing in Python, how it works, and how it should be used\n",
    "\n",
    "**pandas** has [several tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) covering its myriad features.\n",
    "\n",
    "**scikit-learn** has a [bunch of tutorials](http://scikit-learn.org/stable/tutorial/index.html) for those looking to learn Machine Learning in Python. Andreas Mueller's [scikit-learn workshop materials](https://github.com/amueller/scipy_2015_sklearn_tutorial) are top-notch and freely available.\n",
    "\n",
    "**matplotlib** has many [books, videos, and tutorials](http://matplotlib.org/resources/index.html) to teach plotting in Python.\n",
    "\n",
    "**Seaborn** has a [basic tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html) covering most of the statistical plotting features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgements\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Many thanks to [Andreas Mueller](http://amueller.github.io/) for some of his [examples](https://github.com/amueller/scipy_2015_sklearn_tutorial) in the Machine Learning section. I drew inspiration from several of his excellent examples.\n",
    "\n",
    "The photo of a flower with annotations of the petal and sepal was taken by [Eric Guinther](https://commons.wikimedia.org/wiki/File:Petal-sepal.jpg).\n",
    "\n",
    "The photos of the various *Iris* flower types were taken by [Ken Walker](http://www.signa.org/index.pl?Display+Iris-setosa+2) and [Barry Glick](http://www.signa.org/index.pl?Display+Iris-virginica+3)."
   ]
  }
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