IntroToPython/Intro_to_python_from_hello_world_to_scientific_computing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Intro to Python: From 'Hello World' to Basic Scientific Computing\n",
    "\n",
    "Z. W. Miller - 7.24.17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## A few general notes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Coding is a skill just like playing an instrument, knitting, or wood-carving. It takes a lot of practice to become proficient, especially to the point where you're making things that are pretty (continuing the comparison: the time needed to not just be flailing wildly at guitar strings, but instead making chords that fit together can literally be weeks or months just to reach basic proficiency). However, the key thing to remember is... all practice is good practice, and it's just software. You literally cannot harm your computer by writing bad python code that throws an error. You might accidentally shut it down by generating an infinite loop... but 'oh well, try again after it reboots.' **Be tenacious, practice a lot, and make cool things.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## What is Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Let's get the tech-mumbo jumbo out of the way first. What is Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "> Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and \n",
    "> first released in 1991. An interpreted language, Python has a design philosophy which emphasizes code readability \n",
    "> (notably using whitespace indentation to delimit code blocks rather than curly brackets or keywords), and a syntax which \n",
    "> allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java. The language\n",
    "> provides constructs intended to enable writing clear programs on both a small and large scale.\n",
    "> \n",
    "> Python features a dynamic type system and automatic memory management and supports multiple programming paradigms, \n",
    "> including object-oriented, imperative, functional programming, and procedural styles. It has a large and comprehensive \n",
    "> standard library.\n",
    "> \n",
    "> -Wikipedia [https://en.wikipedia.org/wiki/Python_(programming_language)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "What does this actually mean? Let's paraphrase it:\n",
    "\n",
    "> Python is used by a lot of people as a go-to coding language. It's interpreted, which means we don't have to muck around\n",
    "> with compilers like we do in other languages. Python's design emphasizes the ability to look at a code and understand it\n",
    "> without need for elaborate directions - and the idea is that we should always do things the simplest way. \n",
    "> \n",
    "> Python also has 'dynamic memory systems' which means that we don't have to keep track of all the memory usage like we do\n",
    "> in C-based languages. Python also allows for 'object-oriented programming' which is a more advanced topic we won't get to\n",
    "> today. Basically, Python is really damn convenient, because it's designed to be easy for the user to use.\n",
    "\n",
    "Let's get started with the most basic programming example: 'Hello world.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## What is an iPython Notebook/Jupyter Notebook?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "What we're using today is an iPython notebook. It allows us to work in an interactive bunch of cells where we can write **real** Python code and then execute it on the fly. Memory persists across cells - meaning I can write a function in one cell and use it in another. We'll talk more about that later. The main controls to remember is that you can hit `shift+enter` to run a cell once you type in your code. \n",
    "\n",
    "You can also switch cells to 'Markdown' mode which allows you to write notes/text like I'm using here. You can also include images and stuff. You can learn more about 'Markdown' here: https://blog.ghost.org/markdown/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Which version of Python are we using today?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "If your version isn't newer than the one below, you probably need to do an update.\n",
    "\n",
    "I recommend using https://www.continuum.io/downloads to get the newest version of Python 3, with a lot of really handy data handling tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.1 (default, Apr  4 2017, 09:40:21) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.38)]\n"
     ]
    }
   ],
   "source": [
    "import sys # Don't worry about imports yet, I just need it here to show you Python versions\n",
    "print(sys.version) # It's Python 3 (version 3.6.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Hello World - the starting point of champions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print('Hello World')\n",
    "# To run a cell in a Jupyter Notebook you can either click the \"Run button (Triangle with a line in the toolbar)\" or press \"Shift+Enter\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Woo! A thing happened. We told Python: \"Hey, I want you to print out the phrase 'Hello World'\" and it did. Now let's try it another way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "word_to_print = \"Hello World\" # Here we're creating a variable with a string in it!\n",
    "print(word_to_print)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Now we've introduced the idea of variables. In Python, we don't have to tell the code in advance what our variable 'type' will be. A type helps tell Python what our variable is storing for us. So for instance, let's look at the type for `word_to_print` and the type of a number. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'int'>\n",
      "<class 'str'>\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "print(type(word_to_print))\n",
    "print(type(1))\n",
    "print(type(\"bob\"))\n",
    "print(type(2.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Our print statement can also handle multiple things at once, let's use that to talk about types some more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a string: <class 'str'>\n",
      "This is an integer: <class 'int'>\n",
      "This is a string too: <class 'str'>\n",
      "This is a float: <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "print(\"This is a string:\",type(word_to_print))\n",
    "print(\"This is an integer:\",type(1))\n",
    "print(\"This is a string too:\",type(\"bob\"))\n",
    "print(\"This is a float:\",type(2.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "What if we have multiple things we want to store all at once? Python can handle that with an object called a `list.` Lists are really excellent because of a few things...\n",
    "\n",
    "1. They can hold correlated things.\n",
    "2. They're editable... meaning we can add and subtract things as necessary.\n",
    "3. They allow for indexing (don't worry, we'll explain this in a minute)\n",
    "4. They can work with `for` loops (this one too)\n",
    "\n",
    "Let's start by looking at how lists work. They have square-brackets as their identifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['steve', 'bob', 'emily']\n",
      "['steve', 'bob', 'emily', 'DOUG WAS HERE']\n",
      "['steve', 'bob', 'emily']\n",
      "['steve', 'bob']\n"
     ]
    }
   ],
   "source": [
    "our_list = [\"steve\",\"bob\",\"emily\"]\n",
    "print(our_list)\n",
    "\n",
    "our_list.append(\"DOUG WAS HERE\")\n",
    "print(our_list)\n",
    "\n",
    "our_list.pop()\n",
    "print(our_list)\n",
    "\n",
    "our_list.pop()\n",
    "print(our_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 1:** Let's do 'hello world' but with lists. (solutions to all exercises at the bottom)\n",
    "\n",
    "> Create a variable called `hello_world_list` that has two elements: \n",
    "> the strings 'hello' and 'world'. Then print out that variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Let's talk about Python's data structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Numerical Lists, For Loops, Indexing and MORE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Now let's talk about indexing. Lists in Python know two things about each element: \n",
    "\n",
    "1. The element is value X (for instance the first element in our hello_world_list below has a value of 'hello')\n",
    "2. The element is at position #x\n",
    "\n",
    "For instance, the 0th position of hello_world_list has the word 'hello'. Let's take a look at this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n",
      "world\n"
     ]
    }
   ],
   "source": [
    "hello_world_list = ['hello','world']\n",
    "print(hello_world_list[0])\n",
    "print(hello_world_list[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The square brackets directly after the list tell Python, \"Hey, I only want you to get the element at that position number in the list.\" Also, let's note something special: **the numbers start at 0!** All Python lists (and sets and tuples for that matter) start at position 0. This is sometimes called \"zero indexing.\"\n",
    "\n",
    "So, can lists just hold strings? Nope. Let's check out some other lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a list of integers:  [1, 2, 3]\n",
      "This is a list of floats:  [0.5, 0.2, 3]\n",
      "This is a mixed list:  ['bob', 1, 0.75]\n"
     ]
    }
   ],
   "source": [
    "list_of_ints = [1,2,3]\n",
    "print(\"This is a list of integers: \", list_of_ints)\n",
    "\n",
    "list_of_floats = [0.5, 0.2, 3]\n",
    "print(\"This is a list of floats: \", list_of_floats)\n",
    "\n",
    "mixed_list = ['bob', 1, 0.75]\n",
    "print(\"This is a mixed list: \", mixed_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'int'>\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "print(type(mixed_list[0]))\n",
    "print(type(mixed_list[1]))\n",
    "print(type(mixed_list[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 2: **\n",
    "> Okay, so let's create a list with the values 1, 2, 3, 4, and 5 in it. Then let's use indexing to calculate the sum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Alright! We can do some manipulations of our data with indexing. But, there's gotta be a better way of using all the elements in a list than manually typing each index in, right? There is, they're called `for` loops. Let's take a look at an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "values = [1,2,3,4,5]\n",
    "for element in values:\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "We told python, \"I want you to grab each element in the `values` list and then do something with it one at a time.\" There's nothing special about it being numbers, we could do that with a list of strings as well. All that Python knows is, \"This user said 'get everything in this list and give it back to me one at a time' and I'm going to do that now.\"\n",
    "\n",
    "We've also just seen our first Python indent! Did you notice that the `print()` statement is one tab in from the left side? That's how Python knows what belongs to the `for` loop and what doesn't. For instance, what if we added another print statement just below the `for` loop, but without the `tab`? Let's take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "alice\n",
      "2\n",
      "alice\n",
      "3\n",
      "alice\n",
      "4\n",
      "alice\n",
      "5\n",
      "alice\n",
      "bob\n"
     ]
    }
   ],
   "source": [
    "values = [1,2,3,4,5]\n",
    "for element in values:\n",
    "    print(element)\n",
    "    print('alice')\n",
    "print('bob')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "So that's a big key to how Python structures it's code. Things that are inside another structure (like a `for` loop) are indented. All the indented parts for a single structure have to go together! You can't do this (which is why we get an error):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-13-07435e37adbd>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-07435e37adbd>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    x = i\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "x = 0\n",
    "for i in [1,2,3,4]:\n",
    "    print(i)\n",
    "print(x)\n",
    "    x = i\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "But you can do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0\n",
      "2\n",
      "1\n",
      "3\n",
      "2\n",
      "4\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "x = 0\n",
    "for i in [1,2,3,4]:\n",
    "    print(i)\n",
    "    print(x)\n",
    "    x = i\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 3: ** \n",
    "> Use a `for` loop to compute the sum of `values`, where values is a list of all numbers 1 through 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "One last note about lists for now, you can 'nest' them. Which basically means, you're able to create lists of lists! Let's think about this from a data perspective. Let's say you do 5 measurements, and each measurement has 2 sensors. You could have a list of 5 lists, and each sublist could be the 2 sensor values. Like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 0], [2, 3], [5, 7], [1, 2], [5, 3]]\n"
     ]
    }
   ],
   "source": [
    "list_of_lists = [[1,0],[2,3],[5,7],[1,2],[5,3]]\n",
    "print(list_of_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sublist:  [1, 0]\n",
      "Sublist:  [2, 3]\n",
      "Sublist:  [5, 7]\n",
      "Sublist:  [1, 2]\n",
      "Sublist:  [5, 3]\n"
     ]
    }
   ],
   "source": [
    "for measurement in list_of_lists:\n",
    "    print(\"Sublist: \",measurement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Speaking of nesting, we can also nest for loops. Let's take this chance to introduce the \"range\" function and comments in python. A comment is something you add to your code that Python doesn't try to run as code. It's denoted like this: \n",
    "\n",
    "```python\n",
    "for blah in blahs:\n",
    "    # This is a comment. Python will skip anything after the #\n",
    "    x = blah # I can even put my comment after a line. It will run the line up to the start of the comment\n",
    "```\n",
    "\n",
    "Okay. So those are comments. What is the `range` function? It's a way to make a list of consecutive values. Let's look below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for x in range(5):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Now let's use this to create a nested `for` loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i =  0\n",
      "  j =  0\n",
      "  j =  1\n",
      "  j =  2\n",
      "i =  1\n",
      "  j =  0\n",
      "  j =  1\n",
      "  j =  2\n",
      "i =  2\n",
      "  j =  0\n",
      "  j =  1\n",
      "  j =  2\n",
      "i =  3\n",
      "  j =  0\n",
      "  j =  1\n",
      "  j =  2\n",
      "i =  4\n",
      "  j =  0\n",
      "  j =  1\n",
      "  j =  2\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(\"i = \",i)\n",
    "    for j in range(3):\n",
    "        print(\"  j = \",j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The idea here is that the outer `for` loop only repeats when it gets to the bottom of its indented stuff. Since there's a `for` loop inside, it runs all the way through EVERYTIME for the outer `for`. We can see that for every `i` all the `j`'s get a chance to show up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Slicing Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Ordered collections can be sliced in a variety of ways using bracket notation. This notation takes three optional limits, which are each separated by colons:\n",
    "\n",
    "> `a_list[start:stop:skip]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b']\n",
      "['d', 'e']\n",
      "['b', 'd']\n"
     ]
    }
   ],
   "source": [
    "a_list = ['a', 'b', 'c', 'd', 'e']\n",
    "\n",
    "# First two elements\n",
    "print(a_list[:2])\n",
    "\n",
    "# Last two elements\n",
    "print(a_list[-2:])\n",
    "\n",
    "# Every second element starting with the second one\n",
    "print(a_list[1::2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## List Comprehensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Python has a very unique and compact way of iterating over things called a comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n",
      "[10, 9, 8, 7, 6]\n"
     ]
    }
   ],
   "source": [
    "a_list = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
    "\n",
    "# Subtract 1 from each element in the list\n",
    "b_list = [x - 1 for x in a_list]\n",
    "print(b_list)\n",
    "\n",
    "# We can also use extra conditions like:\n",
    "c_list = [x for x in a_list if x > 5]\n",
    "print(c_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Some other data structures: Sets and Tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Let's talk about a few more built-in Python structures that are similar to lists. Let's start with sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5}\n",
      "\n",
      "\n",
      "1\n",
      "2\n",
      "2\n",
      "2\n",
      "4\n",
      "3\n",
      "5\n",
      "\n",
      "\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "list_of_numbers = [1,2,2,2,4,3,5]\n",
    "print(set(list_of_numbers))\n",
    "\n",
    "print('\\n') # This just puts a big break in the output. I'm telling python print a '\\n' character, which means 'new line'\n",
    "\n",
    "for i in list_of_numbers:\n",
    "    print(i)\n",
    "\n",
    "print('\\n') \n",
    "\n",
    "for i in set(list_of_numbers):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "So a set has a curly bracket around it, and it doesn't have duplicate values! It also sorted all the valued into ascending order! Otherwise, it behaves very similarly to a list in most ways. One **BIG** difference is that in sets you can't change values on the fly like you can in a list. As an example, let's try changing an element in both a list and a set (we should see an error from the set): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[7, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "li = [1,2,3]\n",
    "print(type(li))\n",
    "li[0] = 7\n",
    "print(li)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'set'>\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'set' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-bc5c65d548c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'set' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "s = set([1,2,3])\n",
    "print(type(s))\n",
    "s[0] = 7\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Sets can be modified using some extra functions like `update()` but that's best saved for another time. Sets also don't have indexing! Meaning something like `s[3]` will throw in an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3}\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'set' object does not support indexing",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-ef7fb4c7cc04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'set' object does not support indexing"
     ]
    }
   ],
   "source": [
    "s = {1,2,2,3}\n",
    "print(s)\n",
    "s[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Sets can be very handy when you're trying to find only unique values, removing duplicates and the like. However, in-practice they aren't used as much as lists.\n",
    "\n",
    "So what about 'tuples?' Tuples are denoted by parenthesis around a bunch of items. Tuples are just like lists, except they are _immutable_. Being immutable means once a tuple is constrcuted, you can't go in and make changes to the values. They DO allow for duplicates and indexing, unlike sets. Check it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4, 5, 5)\n",
      "3\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "li = [1,2,3,4,5,5]\n",
    "tup = tuple(li)\n",
    "print(tup)\n",
    "print(tup[2])\n",
    "print(type(tup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-bac9cd458040>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "tup[0] = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Tuples are great things when you have data that you don't want to ever be changed. For instance, if we were reading our sensor data like above and we never wanted to lose the correlation between sensor 1 and sensor 2, we might cast (meaning: convert the data) into a tuple before we do any manipulation. That way, we always have sensor 1 and 2 together for each measurement, unchanged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "** Exercise 4: **\n",
    "> Given the list [1,4,4,8,9,3,2,7,5,5,5,5,5], get me a tuple that contains only the unique values in the list, ordered from low to high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## A few last notes about lists, indexing, sets, and tuples before we move on to dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "There are a few really useful things in Python we haven't covered yet. One of them is the length (`len()`) function. Let's see an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(len([1,3,4,7]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "This prints the number of elements in a list! This means if we have a huge array where we're not sure how big it is, we can just look up the `len()` and we can find out.\n",
    "\n",
    "There are also interesting things about indexing that we should check out. For instance, what if we want to get the last element but we don't know how long the list is? We could find out the length and then do it that way... _OR_ we could use the power of Python's indexing. Check this out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "9\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "li = [1,2,3,4,5,6,7,8,9]\n",
    "print(li[len(li)-1]) # this is a silly way to do it, but it works\n",
    "\n",
    "print(li[-1]) # much better\n",
    "print(li[-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "What if we want to get a sub-section of the list? We can also do that with indexing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "print(li[2:5]) # remember, Python starts with element index = 0, so index 2 is actually the third element in the list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "There's also some special notation that tell's Python, 'hey, just start at the beginning and go to this index' or 'hey, start at this index and go to the end.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2]\n",
      "[6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "print(li[:2]) # The beginning of this section is 0 since there's no starting number\n",
    "print(li[5:]) # The end of this section is the last element, since there's no closing number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "# we can also do reverse sections!\n",
    "print(li[-5:]) # this gets the last 5 elements!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 5: **\n",
    "> Given the list [1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9], print the last 3 values, the first 9 values, and the values at indexes 7-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Dictionaries - A complement to lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Dictionaries are a handy way to store things when you want to be able to look them up by a name and the order of storage doesn't matter. Let's take a look at an example of a dictionary. Dictionaries are also denoted by curly brackets, but we'll see they have a different internal structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "dictionary = {'Alex': 1, \"Ron\": [2,3,4,5]}\n",
    "print(dictionary['Alex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "print(dictionary['Ron'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Right away, you can see that dictionaries aren't storing indexes... they're storing `keys`. These keys are how we look things up in a dictionary... and while the traditional example is to use a string as the key, we don't have to. We can also do something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5]\n",
      "[11, 12, 13]\n"
     ]
    }
   ],
   "source": [
    "dict2 = {1: [2,3,4,5], 2:[11,12,13]}\n",
    "print(dict2[1])\n",
    "print(dict2[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Dictionary look-ups are super fast compared to lists in many cases. Thus, they can be quite useful in many circumstances. However, dictionaries don't maintain order... so it's very important not to rely on ordering when using dictionaries. Dictionaries can also have sub-dictionaries. For instance, let's look at how we might store information about some baseball players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1914: ['Red Sox', 5, 10, 0], 1915: ['Red Sox', 43, 104, 4]}\n"
     ]
    }
   ],
   "source": [
    "# Let's assume a format like [Team, Games, Plate Apperance, Home Runs]\n",
    "career_stats = {'babe_ruth': {1914: [\"Red Sox\",5,10,0], 1915:['Red Sox', 43, 104, 4]},\n",
    "                'gavvy_cravath': {1914: ['Phillies',149,604,14]}} # Yes, that's a real baseball player's name\n",
    "print(career_stats['babe_ruth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Phillies', 149, 604, 14]\n"
     ]
    }
   ],
   "source": [
    "print(career_stats['gavvy_cravath'][1914])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1914\n",
      "1915\n",
      "1914 ['Red Sox', 5, 10, 0]\n",
      "1915 ['Red Sox', 43, 104, 4]\n"
     ]
    }
   ],
   "source": [
    "for key in career_stats['babe_ruth'].keys():\n",
    "    print(key)\n",
    "    \n",
    "for key, value in career_stats['babe_ruth'].items():\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## If statements - making decisions with variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "If statements are a ubiquitous part of pretty much all programming languages. We often want to treat things differently based on whether a value is big or small... true or false, etc. So let's see how this works in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fight the coffee-archy, comrade.\n"
     ]
    }
   ],
   "source": [
    "do_you_like_coffee = False\n",
    "\n",
    "if do_you_like_coffee: # This by default is asking if it's true. The same as: `if do_you_like_coffee == True`\n",
    "    print(\"I'm sorry you make bad choices.\")\n",
    "else:\n",
    "    print(\"Fight the coffee-archy, comrade.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "If statements can also work with numbers by checking for greater than, less than, equal to, etc. Let's see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Math works\n",
      "x > 7.0\n"
     ]
    }
   ],
   "source": [
    "if 4 > 2.33:\n",
    "    print(\"Math works\")\n",
    "else:\n",
    "    print(\"WHAT HAVE WE DONE\")\n",
    "    \n",
    "x = 7.5\n",
    "if x == 8.0: # we use two equals for comparison, since one equals tells python to set x to that value\n",
    "    print(\"x = 8.0\")\n",
    "elif x > 7.7: # this is an else-if statement. It allows us to do multiple checks in one if setup\n",
    "    print(\"x > 7.7\")\n",
    "elif x > 7.0:\n",
    "    print(\"x > 7.0\")\n",
    "else:\n",
    "    print(\"x <= 7.0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "We can also do type checking with if statements. For instance, we want to throw an error if we try to add 2 to a string. Let's try that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR: NOT AN INTEGER OR FLOAT\n"
     ]
    }
   ],
   "source": [
    "user_input = 'bob'\n",
    "\n",
    "if type(user_input) == type(1) or type(user_input) == type(1.5):\n",
    "    print(user_input+2)\n",
    "else:\n",
    "    print(\"ERROR: NOT AN INTEGER OR FLOAT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "If statements are super handy... but luckily for us there isn't much more to them. It's always asking true-false questions, then modifying the codes behavior based on that answer. We can chain together things with `and` and `or` as shown above; but the logic side of how and when `and's` and `or's` should be used is best saved for another time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Functions - How we SHOULD reuse code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "One of the beauties of Python is that it allows for function building. What is a function? Let's demonstrate with a simple example. Let's imagine we often want to compute some mathematical function on our variables. For instance, let's say we often want to get the square of some number. We could of course do this with Python in-line, but we could also make a block of code that does that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "def square(x):\n",
    "    return x*x\n",
    "\n",
    "x2 = square(2)\n",
    "print(x2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "This is a bit of a silly example, but it demonstrates some important concepts. First, we've defined a function with `def NAME_OF_FUNCTION()`. This means we can control how our functions are named, which means we can make our code easy to read. If we name our functions well, our code can become instantly interpretable. Next, we've demonstrated that we can send information between functions and our main code. For instance, here we told the function, \"you should expect an input, and I want you to call it `x`. If I'm still inside this function and say \"hey, what is x?\" I want you to tell me the number that the user put into the function call.\" That's called an argument of the function. By specifying the function with an `(x)`, if we try to call the function without a value, it will error (see below). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "square() missing 1 required positional argument: 'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-ad4e0b284611>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: square() missing 1 required positional argument: 'x'"
     ]
    }
   ],
   "source": [
    "square()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "We also see that Python continues to use indentation as a marker for being inside a function. We also have a `return` which means that we can send information back to the main code. Let's look at what happens when I call print inside the function and don't have a return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "def square_then_print(x):\n",
    "    y = x*x\n",
    "    print(y)\n",
    "    \n",
    "square_then_print(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "So it's also possible to not have a return in Python. Can we send variables to functions? Yep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "def add_one_to_var(x):\n",
    "    x += 1\n",
    "    return x\n",
    "\n",
    "z = 0\n",
    "z_plus_one = add_one_to_var(z)\n",
    "print(z_plus_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 6:**\n",
    "\n",
    "> Write a function that takes in a number and returns the number cubed plus 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "What about lists, can we send lists to functions? Absolutely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "def print_list(li):\n",
    "    for el in li:\n",
    "        print(el)\n",
    "        \n",
    "print_list([1,2,3,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 7:**\n",
    "\n",
    "> Write a function that takes a list, set, or tuple as input and computes the sum of all the elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "As a sidenote, Python actually has a really handy built-in function that computes the sum of a list really quickly. You might see it around. It goes like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "values = [1,2,3,4,5]\n",
    "print(sum(values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Args vs Kwargs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "One of the main complexities that comes up in Python functions is: what if I need an argument with a default value (e.g. it's USUALLY this, but I want to allow the programmer to change it as necessary)? That's where Kwargs come in. Let's look at an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "64\n"
     ]
    }
   ],
   "source": [
    "def raise_to_power(x, power=2):\n",
    "    return x**power\n",
    "\n",
    "print(raise_to_power(4)) # no Kwarg, so use default of power=2\n",
    "\n",
    "print(raise_to_power(4,power=3)) # Kwarg found, so use the value assigned!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Here we've created a function that raises a number (x) to some power. In this case, `x` is an argument of the function. However, `power` is also something a user can specify, but it DOES have a default value (2). So if we call the function and never say, `power = #`, the function assumes `power=2`. Thus, `power` is called a Kwarg or **K**ey**W**ord **ARG**ument. Kwargs must always be defined with a default value in the function, and must always be listed after all of the 'regular' arguments. Kwargs are a really handy way of setting default values in functions, while still givng the user some flexibility. It's a slightly more advanced topic, so we'll just cover the basic idea here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Key Take-Aways about Functions\n",
    "\n",
    "Functions are a HUGE part of programming well. There are some key things to remember about functions:\n",
    "\n",
    "* If you are ever re-using code, put it into a function. Functions are there so you shouldn't ever copy-paste code. It's good code design to use functions.\n",
    "* Name your functions well! If I have to read your code later a function called `function2()` is not easy to understand, a function called `sum_of_list()` is!\n",
    "* Use your arguments and Kwarguments well (arguments are the things inside the parenthesis, Kwargs are in the parenthesis but with default values). If you need to have arguments, name your arguments well. We didn't do a good job of that here since they are very simple examples. Good variable naming is VITAL for bigger projects.\n",
    "* Functions will save you time. It's a pain to think about functions at the beginning, invest the time as they are wonder-workers when done well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Imports - How we can use Python's huge library of pre-made modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Importing things is how we can gain access to the best that Python has to offer in terms of pre-made modules. For instance, we're going to want to use random numbers in a later example. So let's learn how to import the 'random' module. In this case, I know that there's a module called `random` that allows a user to generate and use random numbers in their code. If we tell python `import random`, it will give us access to all the functions that someone else has programmed into the `random module` to generate the random numbers. Once imported, if we want to use one of `random`'s functions, we have to tell python \"check random for this function\" which we do by saying `random.FUNCTION_NAME()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "print(random.randint(0,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Importing it also makes `random` accessible in other cells... as long this instance of the python notebook is open, once we import we can use `random` in any other cells. We don't have to re-import it every time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "10\n",
      "8\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    print(random.randint(5,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 8:**\n",
    "\n",
    "> Generate 500 random integers between 0-10, store them in a list, compute the average value of the random integers. Is it the value you expect? Run it a few times, does it change? What happens if you make it 5000 random numbers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "We also are going to want to use a library called `numpy` which is an excellent library for mathematical manipulations. It's VERY fast and has the ability to make slightly more efficient lists (called arrays) and to do more complex mathematical things with those arrays. We can also give `numpy` a special name so we don't always have to type `numpy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "vector1 = np.array([1,2,3,4])\n",
    "vector2 = np.array([2,3,4,5])\n",
    "print(np.dot(vector1,vector2)) \n",
    "# This is the dot product. If you don't understand it, no worries.\n",
    "# It's just an example of the cool math numpy has built in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Numpy is also **REALLY** handy for doing matrix math, since it knows all about matrices and even as a np.matrix() object. Numpy also has a random number generator as well. It's a really great library that really deserves its own lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Plotting - now we're getting somewhere cool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "One of the main Python libraries used for scientific computing is `matplotlib` which is a module designed around making plotting super easy. Let's look through some simple examples to whet your appetite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ0REqkXBISIi1aLgEBGRavl/syp/MWmJOqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103b86710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt # this is a standard convention when using matplotlib, we name it plt.\n",
    "%matplotlib inline \n",
    "# This is a special command only used in Jupyter Notebooks, it allows plt to draw in the notebook\n",
    "\n",
    "x = range(50) # this makes a list from 0-50\n",
    "y = []\n",
    "\n",
    "for value in x:\n",
    "    y.append(value**2) # This makes a list of x^2 in the SAME order as the x list.\n",
    "\n",
    "plt.plot(x,y); # this reads the two lists and grabs element 0 from x and y, then draws it. Then element 1, then ele...\n",
    "plt.xlabel(\"X\"); # LABEL OR DIE\n",
    "plt.ylabel(\"X-squared\");\n",
    "plt.title(\"Just a demo plot, yo\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Now let's make a scatter plot! And this time, let's do it with a random number generator and a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRGSMOgYRERnz/wE1KoS4xCQZ8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104ad42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_data_point():\n",
    "    value1 = random.randint(-10,10)\n",
    "    value2 = random.randint(-10,10)\n",
    "    return value1, value2 # Yep, Python can return more than one thing from a function. Check below to see how to handle it\n",
    "\n",
    "x_values = []\n",
    "y_values = []\n",
    "for i in range(250):\n",
    "    x, y = get_data_point() # we have to put two things left of equals, since it returns two things.\n",
    "    x_values.append(x)\n",
    "    y_values.append(y)\n",
    "\n",
    "plt.scatter(x_values,y_values,c='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "BOOM. We also changed the color of the points by telling it to have \"c='red'\" There are hundreds of plotting options in MatPlotLib... but we'll stop there for now and jump to the crowning achievement for today. Calculating Pi using random numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Let's SIMULATE some stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Okay, we need some math background for this. We're going to try to calculate pi using random numbers. How can that work? Well, the idea is that we're use the areas of the circle and square to get this. The area of a circle is A_circle = Pi x radius^2. For a square, the area is A_square = length x width.\n",
    "\n",
    "So if we have the radius be 1, and the length of the squares side be 2, we can have a circle set in a square, like shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARAAAAD8CAYAAAC/+/tYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104c98ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_circle_square():\n",
    "    x_square = [-1,1,1,-1,-1]\n",
    "    y_square = [1,1,-1,-1,1]\n",
    "    plt.plot(x_square,y_square)\n",
    "\n",
    "    x_circle = np.linspace(1,-1,100)\n",
    "    y_circle = []\n",
    "    y_circle_negative = []\n",
    "    for x in x_circle:\n",
    "        y_circle.append(np.sqrt(1-x**2))\n",
    "        y_circle_negative.append(-np.sqrt(1-x**2))\n",
    "\n",
    "    plt.plot(x_circle,y_circle,c='r')\n",
    "    plt.plot(x_circle,y_circle_negative,c='r')\n",
    "\n",
    "    plt.axes().set_aspect('equal') # This is just to square up the plot instead of being a rectangle\n",
    "\n",
    "draw_circle_square()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "So, if we generate data points and keep track of how many fall inside the circle AND the square, versus just the square, we can get an estimate of their areas. If we then do the area of the circle, divided by the area of the square, we expect it to be:\n",
    "\n",
    "$\\frac{\\pi*1*1}{2*2} = \\frac{\\pi}{4}$\n",
    "\n",
    "We can do this with just a few functions and some of the knowledge we've already gained from today. So, give it a shot. I'll slowly reveal parts of the answer along the way.\n",
    "\n",
    "A hint: You'll need to calculate some distances. The distance from a point to the center of the square/circle is:\n",
    "\n",
    "d = $\\sqrt{x^{2} + y^{2}}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARAAAAD8CAYAAAC/+/tYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niDEBMQwjYkxADMOImP8HjXPDf1+ptjgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb66d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_circle_square()\n",
    "example_points_x = [-0.1,0.5,0.75,-.9]\n",
    "example_points_y = [-0.5,0.6,0.8,-0.55]\n",
    "colors = ['r','r','b','b']\n",
    "plt.scatter(example_points_x,example_points_y,c=colors);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "### Solution without numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1604\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "def get_data_point():\n",
    "    '''\n",
    "    We're going to generate a value between 0 and 1. We can multiply by 2 and subtract 1 to \n",
    "    get a random number in the range of [-1,1]\n",
    "    '''\n",
    "    x = random.random()*2.-1.\n",
    "    y = random.random()*2.-1.\n",
    "    return [x,y]\n",
    "\n",
    "def get_distance(data_point):\n",
    "    '''\n",
    "    Here we just unpack our data point to x and y coordinates\n",
    "    then calculate the Euclidean distance\n",
    "    '''\n",
    "    x,y = data_point\n",
    "    return np.sqrt(x*x + y*y)\n",
    "\n",
    "def is_in_circle(data_point):\n",
    "    '''\n",
    "    This function takes our data point, finds the distance to the center of the\n",
    "    circle, an checks if it is less than the circle's radius (1). If it is, \n",
    "    it returns true (the point is in the circle), if not, it returns false.\n",
    "    '''\n",
    "    dist = get_distance(data_point)\n",
    "    return dist <= 1.\n",
    "\n",
    "def calculate_pi(num_data_points):\n",
    "    '''\n",
    "    For the number of data points requested (how many random draws to do), generate a data\n",
    "    point and check if it's in the circle, as well as the square. Keep track of how many are\n",
    "    in the circle, then return (circle/square)*4 to estimate pi.\n",
    "    '''\n",
    "    data = []\n",
    "    points_in_circle = 0\n",
    "    for i in range(int(num_data_points)):\n",
    "        data = get_data_point()\n",
    "        if is_in_circle(data):\n",
    "            points_in_circle += 1\n",
    "    return (points_in_circle/num_data_points)*4\n",
    "\n",
    "print(calculate_pi(10**4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Now let's plot the value we calculate for Pi for a bunch of different number of points to see how it goes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN9kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e886320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_points_to_try = np.linspace(100,10000,1000) # this just gives us 1000 points between 1 and 10000\n",
    "pi_vals = []\n",
    "\n",
    "for num_points in num_points_to_try:\n",
    "    pi_vals.append(calculate_pi(num_points))\n",
    "\n",
    "plt.plot(num_points_to_try,pi_vals);\n",
    "plt.xlabel(\"Number of Points Sampled\");\n",
    "plt.ylabel(\"Guessed Value of Pi\");\n",
    "plt.title(\"Estimating Pi with Random Numbers\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEWCAYAAACaBstRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGLExoWEYhmHE5v8Brz0OXpnNujQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ec59588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors = [estimate - 3.14159265 for estimate in pi_vals]\n",
    "\n",
    "plt.plot(num_points_to_try,errors)\n",
    "plt.xlabel(\"Number of Points Sampled\")\n",
    "plt.ylabel(\"Error on estimate\")\n",
    "plt.title(\"Estimating Pi with Random Numbers\")\n",
    "plt.plot([-10,10100],[0,0],'--r',lw=2); # just adding a line to show where 0 is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "### And now with numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1536"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_pi_numpy(num_data_points):\n",
    "    data = np.random.rand(int(num_data_points),2)*2. - 1. # This generates all the random numbers.\n",
    "                                                          # Numpy creates an array on the fly to store them.\n",
    "\n",
    "    in_circle = 0\n",
    "    for point in data:\n",
    "        vecx, vecy = point.T # Just grabbing the X and Y components here\n",
    "        if np.hypot(vecx,vecy) <= 1: # numpy has a really fast way to calculate the hypotenuse\n",
    "            in_circle += 1\n",
    "    return (in_circle/num_data_points)*4\n",
    "\n",
    "calculate_pi_numpy(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUnAcx3Hm4UrBcRzHmcf/A8hWLw5NYoUBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb91ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_points_to_try = np.linspace(1,10000,1000) # this just gives us 1000 points between 1 and 10000\n",
    "pi_vals = []\n",
    "\n",
    "for num_points in num_points_to_try:\n",
    "    pi_vals.append(calculate_pi_numpy(num_points))\n",
    "\n",
    "plt.plot(num_points_to_try,pi_vals,'r');\n",
    "plt.xlabel(\"Number of Points Sampled\");\n",
    "plt.ylabel(\"Guessed Value of Pi\");\n",
    "plt.title(\"Estimating Pi with Random Numbers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "### Sidenote: Why does this work? \n",
    "\n",
    "Because with enough points, we actually can fill in the circle and square. So we are literally learning about the areas by randomness. This is known as a Monte Carlo technique (not important to know, but you might want to read more later). Check it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR0AAAEKCAYAAAAvuYFYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsfXd4lGXW/v1OSyY9kx4S0kiA0HsRBBSUoti7rnVxP0Vd\nd+1rwXWta921r4ttPxURG4IiRRGQnoQSakJ6bzOTZCYzmZn398ed4xOKbRfl+77fnOvKlWTmfZ96\nzv2cc57znEfTdR1BClKQgvRrkeFENyBIQQrS/18UBJ0gBSlIvyoFQSdIQQrSr0pB0AlSkIL0q1IQ\ndIIUpCD9qhQEnSAFKUi/KgVBJ0hBCtKvSkHQCVKQgvSrUhB0ghSkIP2qZDrRDfg1KT4+Xs/MzDzR\nzQhSkP5P0vbt25t1XU/4sef+vwKdzMxMbNu27UQ3I0hB+j9JmqZV/JTnguZVkIIUpF+VgqATpCAF\n6VelIOgEKUhB+lUpCDpBClKQflUKgk6QghSkX5WCoBOkIAXpV6UTCjqapi3UNK1R07Td3/O9pmna\n3zRNK9E0baemaSN7fXelpmkHe36u/PVaHaQgBek/oROt6bwBYOYPfD8LQG7PzzwALwGApmk2AA8A\nGAdgLIAHNE2L/UVb+hOpuxsIZoA9TuTzfTeYgQBQVQVUVwfH9387ndDgQF3Xv9E0LfMHHjkLwFs6\nEzlv0jQtRtO0FABTAazUdb0VADRNWwmC17u/bIuBjg7gkUeA7duBYcOA++4DIiMBvx946y3gm2+A\niAjgxhuBAQN+6dYc3bYvvwQcDmDyZKBfvx9/p7ubfensBIYOBRISgNJS9qWzE5g7Fzj55J/XjkAA\nKCpiOwYMAFJSfuSF3buBPXuAPn2A8eMJNv/8J7B1K2CzwX/jzXh1RQY2b+bjkyYB11wDGI6xZPp8\nbP8HHwD19RyHs88GLJaf14fvI13nmJnNgKb95+WVlgKvvw60twNz5gAzZvx4uX4/sGUL0NYGDBoE\nZGTwfY8HsNmOPS5C9fUc7shIYNQowHQCEEA70YnZe0DnM13XBx/ju88APKbr+vqe/1cDuBMEnVBd\n1//S8/l9ANy6rj/5Q3WNHj1a/ykRyZl3LQMAjMuyHfXdvv1Ac5NijPh4ICcHaG4GmpqByAgyRSBA\noT+SgXSdzOLxEJwiI3+0OT+ZKioAdxdg0FhPZiYQEnL0c11d/D4kBKirI8MCgNEEpKdRo9A0QDMA\nbheQmAhERbFfmgaEhf1wOxoa2EeAApCRAYT4XTB1dcLW6UCssxXRHXZEOVoR4WxFWFsLrD4PQrxd\nMBsNMAZ80Lo8gNGIADR4DBa0mmLRHRKKjtBwtBkjYU6Lgjs2BvboOLRFx8MeFYfugAGVlUBjI8HH\nbKZQ9ekDJCf/cJt1/ceF3eejptXVBVitLPfHhNbn41ybTGoufD4FDKWlrNdgALxeoG/fHx/f+nrA\nblfttdmA1lb+HR7Odh2rL14vUF4BBPz8PyoKSE394brOGt4Hl47r+8MP9ZCmadt1XR/9Y8/9nz8G\noWnaPNA0Q9++P23wvo98PsDeRgYJBMioLS383NvNSTVonHifj88YjYeXUV9PDcBo5O+4ODKI2QxE\nR39/3d3drEvXgdhYIDT06LZ1dQGhPYzt8QAu19Gg09AAtNkBDYDZAng9qqyuLsDpZLtDQgCXm9pO\nfT2BKDSU7Y6I4N+6zjYfpkX4Awgvr8MYRymyWsqR1lSJHEc5Uuw1sHU6jtk3r9EEt8UKr8mMbqMZ\nfs2AAABoGgwaYPT5YfDrsPrcCPe4YNL9R8+N0YSG6CSUhfdBSVg6SmIyURKTiYMJ/eBwRB8TdJqb\ngaYmjpMlBIiKBNLSDp8zXecYBAIUdK+XY2O38/O0NArvsairCzhwgHNnMgNJiYDDyTG3WKhVBgL8\n2+vlc11dxwYdv5/8IouW1dpThweoqeE7ug60tLL9SUlHazzt7YAeUPPtbAeS/HzO4WBbIiLUfO6p\ncwLATwadn0r/00GnBkB6r//Tej6rAbWd3p9/fawCdF1/FcCrADWdn1KpaDiLrp9w2OcNDcCpLwEH\nD3Ll8nj4uT+RQKD5AK8ODB1N0+v233FCq6tpLdTVAXs2AOnpQFYWmb5iIzB4MMs67TTg9NOBffs4\n+cOGkYFcLuDeewGbs0cg6oC//IVMK9TVBfz+90B4jyZSXg3Mv4jt0nVqY62twB13ACf3ZbsOHCAj\nDh5MRjtUDVx7FvDJJ0DVQeDbbylgQ8cChYVA9gBgdJ4D696qR3yqBWFZSYg3OfCnad8ias8mYMsW\n6IWF0ER1AuAxhsFoAkx9+wDdsZSuyEg2ODkZiIiAZe9eWARxW1uB/rmUal0HfD505w7E7qoY7Nf6\n4+vcecgbaMRNl7bA3NZIRCwthWn1argL25Ha1oah1SsRpTu/a4MjKg3Rp4wGxowBJk4ERo3C/u0d\n+NsqI9CSgKZmDUYjkJMPTM0Cfvtb4R3ghReAzUUEhMaDwOSTgAPFQEclYI0AEgYAN93Eoo+k664D\n9qwF4uPYreJWAlR4OJCUSo1kQDiw/iugy82FI38O8MQfDl8sXC5gwQJqcIEA0LwTGHcy53bfIZpL\nPgO/7+gAnNGAKxV49FGaa0JffQUsXAhkZ3P42zqAZ68CXnwR2NGzldPeDlw5j+9dvnDjTxGXn03/\n00HnUwDzNU17D3QaO3Rdr9M0bQWAR3o5j08DcPcv0QBdJ8PIynTRRcDjj3PlMRo58R4PV8yQEP4/\nfDgwezbw178SWA4doomyZw8BqKMDqK1l+cnJXC39fuCdd4Dnn2ed6enAGWcAV19NsHI4CCalpfx7\n+3ZgZi8XfGgocPPNwEsvcSUcNw546imgvJzlT5gAXHIJn5VVtaMDGDuWzwQCtPFPOonMf889bBPA\nlTQQAKwGD4asfBqWRuBU50ZkFxYjzlMLvA12fvhwaFdcgYrY4fhyQxiGVCyFN60fTmr/HPB5OQh2\nOxATw0IrKqjqSefi4ymV7e0Eh9JSoK0N5uhwDBnUB+nF+3BS1jtIuvsGmC2RADLZwL/9DYiLg2/6\nSXh8/URUmHPR2AgM0XbjnH47cLZtPVHz44/5vKYhKyoOd2op+DL8HLyf8ntkt2xBfGEnviwdg9jY\ndJx1FsdxyxbOYV0df775hiAQGgrk5RFAdu48HHRWrQI+/JD+NYuFz/p8HPPISHa/vp7DcdppwPr1\nHIbBg6lplpUd7g+sqGAbJEGCw6FMrMhIpY15PKwnOpp/v/wyy0nvWbYnTiTfFBdz0bzuOvaluJjP\nfPghp2XLFvry9OHHx291JJ1Q0NE07V1QY4nXNK0a3JEyA4Cu6y8DWA5gNoASAC4AV/d816pp2kMA\ntvYU9WdxKh8v6uwE/vAHoKCAE+RyUTu58ELgiiuo7TQ1EZC8Xk62200NYtcuYPFiYMgQaixFRQSB\nlhY6VT0eMkxODoFM1znZxcV8PiwMqKwEVq9mfTEx1LL27KFa3d0NvP8+Hby9VfH8fMqf00kAKiqi\nDJeX03ybNYv1LV7MdoeHs77Zswlg4oSsqCAAjRoFbN6kI6piN642foxT136IDHsRNADu7nCUhw3C\nivhLMWGcjqxZA4ApU4CYGGQkJOCyFeugLQxFSH8TDM3jgDVrWJnDwUHYt4/SIU6XpCRg2jRg/352\nXhwc2dlAeDhMFgPi+8cDeiVwpFN4506gb18MMHrwgL4aBw1VCEuKxPDODQiv2s+Jyc7mYCQmAgUF\nMPp8SGsvxnX2Xbiy5lE0GpJQa85AZ1I2lrz7GEymVEwZ70FrswlVVUYEAtQOamrY1Oxs+qoqKwnq\nQqWlwNtv01eSns45NRrZhD59OA/NzZzD4cOBzZvJFwJqQ4YcbRKHhxP0ZTMvJAT4r//ikB44wHZZ\nLBxeQL1vMnG4BXRCQsjTra3ko/Bw8nlbG8G0ulph/+rVQG6/4+tzFDrRu1eX/Mj3OoAbv+e7hQAW\n/hLt8vnIXFH1BJbmZmowTicB47e/pSq7cycnx2rlpHs8PapzEk0T8b/oOlVbt5vMk5zMz2SlPHCA\n2pDBQHDTda5kbW0EDr+ff7e0kGnHjuUK1NioVj8hTVN+irAwlu9wkLkcDpYxcCDrDAnhM6tXc2UT\nH0Bykg5b1U6cbl+EBYcWI8lZAl3T4Mkfie7oftjXnYMXXFfD6zdhTuJ+ZPZpoIq1cCHQvz9wzjkI\nM5sBVwtQ1bNUnnIKt5KsVg6QppG7Q0M5wG1tRLvsbODPf+YkJCQAmzYRYT0eduCcc46esH79gJIS\nID4emZ4yZLoKgVoPO+lwcBLEURETA6SlwWixwDF2Otyrv4WlqQbRzhb08WyAXvktcjuL0FQ5DrE5\n7TirbjIeqroMTd4YREWxSTk5BJrmZuDUU9k1oZYWjmNICBea8nLOe1oaF62WFnb7tNOIgV98wff8\nfn5XW3u0jyg9HTjvPGoiJSUEkwce4LNjxgAjRpBPbDbW1dJCQExKIm73Jl2nOXboEJCby+ny+zm8\ngQDB0O/nzy+1x/Q/3bw6ISQrijhOZSKamjipH39MO1jTqBo/9RR5ed06TrzVyp/WVjJfIECA6ejg\nQg6QAVet4srW1ET12m4nkDQ1EVz69AGefJIMNGAA3zeZ+JzZzPq6uo7emrfZyLg2G8uTZxwOynZH\nB/82m49wNjc0AM8+i9mvvIo5ba0IwICSvtNgvvt22K6ai1CrFbjkEgytqcGL+mMItHfCnDsCcEWS\naw0GajGPPMJGAxT8uXNpM3zxBQdjzBjg4os5kNKA556jNBYW8ic/nxJ+yim0CXbvpo04d+7RE3b9\n9cAbb1DtyM3ls+3tBJvGRnZabMXERA5AdDSiO2oRffkMoKICe3d1I2rvZsR6GxDrKEefbbuA7Rqm\nxZSi1mPHq8Yb0emJQWgoi73uOmoqvam2lnPT3c1Fo6SEfDF7NrB2LbBjB99JTOSC1dgIbNtGPsrO\nVtpQQwP5QUjTgDPPJM889hh5YNMm8klBAed2xAhqq8uX832jkeb5kQD28cfAu+9yOux2YORI8rn4\nGN1uamspKcpZfbwpCDrHIIuF28e9YtNQX08Gslo5KXv3Ui5OOolMsHEjrYXmZqqpZ5xB5ior48o4\ncSIn2eHgO/v2sZ6kJKrg0dH8fP16rlTi6qiuZv19+pBhN24kswLARx/RQX3ddcCllypNxWYDbrmF\nctjSwrr37wcWLaLp57DrMHa74Xd60LSpBfdeUoKoq14Fli4FfD5ooaFAair01DTkLvgDtCGDgX/8\ngwVUVQGRkTDGxcFoNVElTE0lt/btS/BpbqaDYu9eCvzixXxm6FBKQ2EhnWN5ebQpqqqoCjY3EzCs\nVuCzz4Bzz2WHV6+muVVZSQmbM+fwCYuNZWDUxo30/BYXq31qgCgQCPDvoiLaDC4XJ+bCC4H770f/\nvV/AZQhHReoExLprEeKugMFihq2lDA8E7sUtxqfxvuEqfOH5HRITc5Gbe3gTdu4kbuo6q87MJP+k\npFBDbmgg+Dsc1H4EXKxWgsf+/Wq7vPcGQW8yGrlQ1NQQTJxODu+331L7LijgQtevH3lg1SpqVAC7\nX1bGYa2v51B3dXHhMpvZDrOZbY6KItA1N/+EGKt/g4KgcwwyGIC+6cDkXKq2lZW0eVNTuSKYzZxw\ngJMzfjx/dJ2rXUMDsGIFF3ankxO5Zg3fTUnp2TloI3PFxRFw2tqo9QwbRrkTB15iIk0kTeMCbTZT\nhioraY1ERZHZIyOBs85SfRg6FHj6af69ciXrB4AEYwuS3BUYadmNk63rMbliGZIfqWVjzjuPHJyV\nBQAw1tcDG78F3nyDHdJ1gktXF0Ggq4vLZFUVJSYmhigZH08AOHSI3N7WRo2nqoqgYbXyvUGDgGXL\nlLNqzRpg+nS25ZtvgIce4nsSdBMWxs6efDIdGGVlRNWDB9nJTZs4iJMnAxs2sK0xMcoh0tVFrUr2\nvZcsIXJ3dsJg0BARYcBA40HAXt1jI1lQbB6OGM2O2Eg/rmn+G+btfhq+5Jkwbbj1sEi+Tz7hvNTU\nEHSSk+mMf+QRzqf48yRkIjKSTUpLo1LW2cluCK9ceOHRTtzsbIJZSQkBAWA50dHsuixGLS1Kofvt\nb4GpU7ngbdnCthw8SACzWFiH18sFtL6e/CRalrjfjjcFQecY5PWST2fPBh5+mPwXFaV4NSzs2NG+\nmsYV7L//m0xQUsLPBg6kvM6axZWnupoMsG4dmbR/fwKay0XgGDuW8uD1knHKyuhjTUpi/V99RYbw\n+ymrKSlk1N6gI1RXxxVv+7pO9DE1ILHxAE71LMe1roUIRydqTX2BxGTg1luJnIcOKftNtlzEmSUa\ng6ZRgNPSgNGj+ffll3OQQkIIFFddxecNBhV80t3NKONHH6WkFBayM6GhHKz2dgLQ8OEEqLg4FTWn\naZSmLVuA+fMJJm1tHLQtW4iyJhPfP+00AtOIEVzav/pKReA5nZRICZTq7CTIBgKcGOlfWBjgdmNM\n+B4UZpyFrq5OVJ8zH2NSamB65SXGNgweDNx5J3DRRejqMmP7di4QbjfnPyaGGuezzypfnd/P7hoM\nbL6m8TOzmdpJczPNn0mTyEu9KSSEIQ8jRgD/+hdNs7Q08suKFezGwYMEn4MHacWGhNAVAJCHJLhR\n18k7qanscloa/66t5VTHxgLGYwSWHg8Kgs4RtHQpcKiMwXP3388Jys2lYG/bRhNozhwu5keSrpPH\nFy7kxHs85N29e/meuCkSEsg406eTyRISqGJrGmWnuprb562tlIvkZIJKZyfNI5OJMuP3k8GTksiw\nR1JLC/DIn32wH2zC0OatuN7/MqYFVsEEH4qMo1CuZ+DU8K3k+IIC4LLLuHpv3EgpufZaaibLl1Pw\nxdbsiZ+BwUDurKsjd48fz+9tNnbcbidHi1QZDPzRNALF3/9OgGppIddbrURhm42glJpKxO3sZJ26\nrpxdixfTbqyqYv0tLXw/IoIqYEoK6+ns5POtrXxXQnkbGtinzk4u/xaL8vxHRhIRfD6EZiVgwsxY\nwGkEBriB+fcDd99JU/OJJ7iVef/9uOjMe/CJ70p0d5sREkKX1pdfUgGrq2NV48YRZ00mAoTPpzYI\nQkPZvK4u5dj1+TjPovH4fDwy8a9/qV1O8QU5HOSz7m7lN6+ro/YiSml4OPmtTx+Wr+vk47lzCZAW\nC9vs93OxS/+eoMf/lIKg04scDu4QhPRsrtTVcaJzcvh9RgbV3rAw2vB+v9qBAsjrS5YQROrqOKld\nXZSz0FAc5QfIySEDPPooZQugBjV7Ns2sujqCkfgJ1q4lU/l8ZJLsbH4eGUl8OIw8HrQ/9hou+2I9\nkluLkdJ9AGZ0o8A8Dh+EXIL5ltcw0vkJEJEERERR+EtLgXnzgN/8hg3u25dC+cAD/F+cxRYLtYXE\nRApvUtLhXtWEBIWMAIFHlnqbjciekkJNZuhQqnzNzXQUx8QQ7VetooZitXLAZfepTx+WazDQxtyz\nh3+bTJSu2FhqH14vcPvtan86PJzAY7Ox7rY2glpBASdJSHYMQkKooubmqsCYpiZO8NixLPvyy2ke\nPvQQhv7tt1gT8Qg+Hv4givIvhddvxOefqzgsr5dVhoZSCbNaOSxDhxJIysr4fXQ0QxXWrQO+/ppd\nv+EGWqKffMI4LouFoBAXx8XNbufa0NpK31C/fuzuwYMcGgGu9evZrSFDOLX79/Nc2o03sj2ffso2\n2Gx8fmPr4Q7t40VB0OlFssEBAJ0uwFNLG/rQIcreGWdwVbnhBjKEz0dm+Mc/uHJ1dChzqqqK74g8\nmHoc00dSSYnaKQbIfFJOVBTLMZm4QhoMZK6SEiUnl13GSOSj4imWLUPS8oVIrSyGOeBBmSkXD4Y9\nAS0mCpdo7yEtMQTACApuWRm58fnnyfW3305P93vvsWHnnsu/W1qU7j19OnDlleTW/v1VkAhASenf\nn1wtKpmmkdszMtT2SlcXnWbx8eT0pCSWk5ZG7/i0aQSN998nApeXUwqqqvhcaSnbYzTyc7ebdkle\nHvuQkKC8rR0dlNbISPZZAmYASqjJpDQ4Ob/ypz/xubIyqhHbt3MSCgrYljfe4LbSGWcAn3+OyD/e\ni3nrf4OW4gfRfd7FeNFxIQJpQ6BpGjIz6VvZvJlNDglh94cM4dB+8AHBY+ZMfvfyy+Q9t5sK4bPP\nUjkMDVWA1dlJN1xBAYfV7ye+hoYSH8PDifUeD7sRGkq87e5m984/n+af2cxhcDo5PImJLD/Q8cts\nmxsXLFhw/Ev9H0qvvvrqgnnz5n3v96GhXC1WlVbD7QYimtJhs6nt86oqbjmuW8eJ6+4mAHz+Ofl5\nwgSaYE4nVzbZhRo/Xjnshg07vE67nT7T6GjWY7fTNzNgAHncbueC3NHB9+XMj9vNerZvZ7tOOaVH\nGdB1Okevvx7milK4k7OxKfls2ALNOGVsOy4dvg95j19Lbl+xgtwperzfTxDYupUc7vdznzc7m42Q\nSMj0dNoKF19MgLBYKJiFheRyOVQk4CDezvBwlil7zh9/TE0lIoKDlJdHTSs6mgMWF8e6kpKobaSm\ncml3OAgSM2ZQcuW0Y2oqnXCBANXC3Fz2q66OkhXbcwzD6eTktbXxfZtNSWy/fpQ6m4127ujRtIVf\nflkd4fD5OLk5OXwuIgLIzYX5ht8CLhfCtqxFxKbVGN/4KQ66+2CvcTBCQqhAjhlDHmloYCzlmWdy\nuCZPZneysohvxcUs2mxmM6dOJVbKzpPdzu/vvJPNrKzkkJjNrKO1ldvh4tgOCeEwDhlC7eqmm+gm\nEMAB2I116/huSwvg7VONiAjggtHp+Cn04IMP1i1YsODVH3suqOn0Ik3j4v3MDk7S9Onk1507afdq\nGuVUwuBdLgUm33xDQPnTn7hx0tTE9BBpaZTZpia149Wb+vUj861bx/9PPZUazptvUh22WChDI0bQ\n3pYALk0j03V28rldu4DxtgPUlVetorAOG4YIkwmTvVuB68+E4cYb6OF8+GFyYmMjzRmJDRAhFD9I\nbCyBoqSEHSspITgkJFATKiigqbFrF7fK5BTsZZdRwmw2AlhBATm6vp7AcPfd/NxqpVnmctEpe5SN\nCNoIzz5L6fjmG3Y8OZltyc6mahATQ6A4+2y2OzSUnxUUKJulooLSJL6huDi2qa6OtobJRM2so4Pt\ncrn4/zvvUHNrb+ePeGEDAfqVPv+c/YmNpYQ3NhI4d+9G6Ndf48ZvL0NdZiHMd96PhDTa4U89dezD\nwEIDB/I7cerm5nIa+vfnGmAyEe9mzSLOjxnDoa6sJHCdcQanf9UqYmRoKPlZ9gXOP591HEnZ2bSk\n9+5ld54s+DcF6UcoCDq9KBCgfd3lAbq9QNF+TlZUlFIzk5NV5KjHo3YAjEZ1Qruriw5lXScgVFfj\nO/X6SDIYeL5q9mzKUyDAHD2LF5NBLBbysTgAo6O5Usr5KYMBMPo8SP/no8Bbj7JB06eTC9evB0wm\nGNLSgJpqxrv8858U/pAQPut0slO1tRSu2Fguu998Q05vaODy+9BDKpR1yBB1EhWgtzQ8nGZSVxcd\nz6efTrPM4aAwZmbSk15RQe3m44/ZmaQkdqaoiGW53fSbHDqk4noMBrbN62V5oaHUTHbv5iBHRHDg\nli0j4t93Hwd740YV+Sj2hdutlv6YGJYfCFBab7+dICPR0dOmsY3ffsvB93r5vt/P/uXlUdJ//3v+\n7u5mXfHxwJQp0Gw2oKoKqf96AvjmPW4jzZkDTft+wAGIgffcw65ERXEh+sc/aJqZTOS3M87gwiTO\n55EjiZ/V1ez2vHn0DW3bxubb7RzOiy4ivn8fpadzWHbtIsb2tpqPFwVBpxcdOECNwxrL9AObNtHp\nN3o0zRibjavLww/zlPemTZTbtDTyc1ISV4rCQmo2Q4ZQk6mpoRxkZ6sd2SNjMOTU+saNVDTMZmVG\nASq1BUDehx6Ap7kDUyzf4gX/bejzWjHNnQsuoEAbDCzQ7SbH7d7NDlZUsBGpqRRk2T5JTWVg3ahR\n7JSmsSODBpELv/qKjeroYMdnzOB3ACVDjtzLVozfz44vWUKtqLyc3H7gAPDqqzS9mpqoqVRVEdic\nTjo4NmygJrJkCdsj6OtysVxJH+j308H2/PN0ahgMysyrqKAkxsQQfGtrqR4EAvQF+f0cF5+PUn7x\nxXxu0iSqFm+/zfGRnBY+nwon3raNv3VdhRjIHrTRyBVi3z6qEy+9xLG//noixWWXMdboRzy0OTlq\nA6O1lUpbTg6npayMQxMRQaWzoYGHi6Oi+NmIEdxYGzmSPPnMMxyGYyX3kmMRBw+SBfr3J/ZWVwPV\necqaPJ7JvoKg04vETyOZGSwW8m97O+UtMZHhJxYLF0SXi6tIRweBaetWAkVKCn+XlhK08vK40C9Z\nQkWju5vWxyWX0Ha/7z6qtOnp/HG5qDg0NancNrIDoeuA0aijn1aKV3zXYax7HWCJ5tbG3LnUUAA2\n2uvl30YjBdtqZWPFIWS1cjUfOpQNeOwxCkx0NPDHP9KD6fOxsRLl6/dTYG67TYXOnn02weTbb1nP\noEE8v9HeTk5ub+cyLFFzOTkckK++ou9ITizedx87n5qqzo+43dwyXLGCoLV7NyUrJEQd8TYaVQCh\nnIjs25fRmTU1Sjtqa1POEDkTYrPRVLv+egKfyUSta84c1imneLu72YeyMkrimjUqT0V6OsEnPJx9\nHD2auUiys/nupElEjUcf5YolGufs2T+JL2X+RUELBDj0FRXsblGROvBpNKpMkOnpbG51NVNj9KZA\ngF1YupRaTWYmh76ujjyvaUB0ModV1oTjRUHQ6UX9+pE3vV61KjQ1qY2N4mJaLrNm8TuxUNrbyZvC\nEP36cWGVsyy//z0XvqefJs+3tND1IiZUaSlXotpayt+hQ2yDycTJl00YoOecTOduvO66CAMCe1Ed\nNwxpMwaq/fhRo2jufPghOcZiYSdk61nTWJimfef3gdHIBrpcNA2qq6nfe7304Xi9BBuJ5o2NZefk\nwI7PxxW8vJzagNVKcPB6+cyUKRT+xESadnKaUA4pJSWxTdXVBCWfj8+2ttLUmzuXP04nNSMBL8nM\nNXcu+2vgdKenAAAgAElEQVQ00gM/ZgzLeOop5bhOS6Pw79nDsXrjDUppejr70N7OugBKc1YWQbeg\ngIFX0dFs68aNSoU4dIgAumgRJ7aqitrNSSexzJoaAmVcHMduwQIeWL3iCoLaWWeRESZM+EE7Jjyc\nJvjrr7M7I0dy2hYsoMnl93PaKyu5prhcxP3YWP5UVKiFTOjLLxnvU1bGpoaFsYm7dpFndR0I61mA\na2qCoPOLkdXKM0xr3gXQ4yqw29UkuN3MpTN4MEHpo49oaYSEUNGYP58LbE0NtZs77lAbLE8/TRVW\ndqksFoJYUxPrrq0lyMiGj6apMHWzmbJhtwMXuN7EnztuRAAGrOt7KbKm9wP8FepsUXg4TY5du8hJ\njY3kyrFj6YWMj1f7osKpmqZiWbq7WVFHB00D8UImJvJvsf3eeYeOhoYGajl9+yr/j0T/Dh/OTlos\nBIHzzmN7PvhA1StHJ8rLlQ0wY4bKtdPUpLbux4yhBvHee3yuvp7t6Ohg2/r0IUJLPFFmJge1qYkD\nvGkT1VC/n2PjdFIaBeTknEkgQC1GoqS9XpYZH8+xSEhQ/q+vvuKERkWxzpEjOQ4rV7JNUVG0s8vK\nCHYXX0wbfuZMMs2aNQQ7AZ/YWOi6CihMS2MXTjqJQNPVxSY/8ACLljNd+/ezeW43n5XQqH37OJ3v\nvUeFUUIrCgv5fFsbF73NmzlNwuuASuFaUkK8Pl4UBJ1eJNoMejY4wsNVkrvQUBWV/9e/UjvfsIGW\nSU4OJ3/NGvKSwUDQiYsjrz7+OOWqtztCNCmJ15AIUTnVLgGF0dFktrdedqH10vlIW/k66gdMgWvi\nqRjVXoIwfwVX97w81RERALebBTidBIMhQ+jviY2lWTF4MPdsTSb+/cYbqoExMQSSTZsoEHfeSSG3\nWCiIpaUcLIOBHZXlcuNG1hUXR5OlokLtVNlsND0mTqQXdO9evldVRcHv6CD4fPGFyify6ad8Ly+P\nXtAzz+SAb9rE7cHaWkpYSwsBzWjkZNxzD4FKAqoCAX5eU8PxcLvZppAQgu+111Izamqi53bpUmpr\nXV3UjuT8whlncBzEg9vZSa0nLIzv7tjBbaOwMIJeeTlXm2HDCDY+n5LgrCyOz/Ll7Me0adAfWIAl\nK6OwjGm6vzuT6vUqSzI+njxSXa0SdnV3sykTJyotuaqKP6NH013V3MxpBOgC27ePf3u9ymUmQyVB\nhRYLWeF4UhB0elFpKVeA2HgmJA8NpWn0xhsc/OhoTsShQ1zQ9u3jT0YGefnAAcoRwDgIWaibmykz\noaFccXw+dUZHUls4nVwIRfORfLj5+UA/UzmsM85BWlERcO+9SF6wgF/u368S8/QOuAgPp423dy+5\nLy+PAvfYY7xG4cEHyaWrVrHi6dPZuBkzuPq6XFxiDQYV+vzxx1ytX36Z5be0cDDEXLJa2ZnsbO7s\n5OdzwPbuJcf7fHRUDxzIXa2TT6bzISqKdXd3U4MoK2O5ISH8OzmZ39lsRPW5c+ntLC5WiYzkiIbs\nZJWW8rNLL6VNImZhWZmynyMjCXJGI/t64ADVyTFjCBLbtlFaCws5UaecoiLz5s+n76y2Vu2ItbVx\njJubWXZkJOvzesksoolu28b5kHwmTicnu6gIcLthn34Bli2bgLQ0dnvRImJVWRmbJ6dF5KiC3c5i\nZQteEq3L9nlMDDFR1zkVPh+7Wliodvjz8ghklZVqs8JuJ+tERR2e8vR40InOHDgTwHMAjABe03X9\nsSO+fwbAtJ5/wwAk6roe0/OdH8Cunu8qdV0/RqKVn0ceT49ZY6ZvNn8wLYIxY4BXXqGaKfw9ZAgt\nllWruOK43ZSn+HhO7sMPcxegqIgrVEsL5cdkUhsfchtDdDT/zs7md2FhwP79AfRJ9OIk1ze4de2l\n0DQ/t4R7Ox9l9+hYNH48GyYHbUJCCCISXRgZScGtreVKDlCwjUY2YMsWcmdbmwKvm25iUOCmTVzl\nKyv5XkMDhV3XGejU2wHw2WcEk6IiakgHDlCSrr2WzuqCAnUQ1OlkO8VBJtvyYWEqdaNQRgalz2BQ\nx7SNRjppR41SsQ7Z2fQNlZervKGiXsqRDrebms2oUXwmIYFS6XQSHORUfWws6/H7ab5KrhPZJezb\nV51ZkSs/Bg0iqIizJDmZ3/XpQ4AKBDjpCQnAjh2IuGs+ovKXw2BIQmUlu93VxUdbWvh/djZ9+3V1\nnM7ERA6JpM6QM6zZ2bT+oqM5BOnp7NKzz3JIhw/ndIjy2tFBXhfzLiKH/NzbF3Q86ISBjqZpRgAv\nAJgBoBrAVk3TPtV1fY88o+v6rb2evwnAiF5FuHVdPyKN0n9GkhGuoIP/p6fzs/x8as1xcZzgZcu4\n0OXkUMbmzSMoVVdTPnfsIL/fcgvfyc+nTB06RN6KiuIES+6TlhZq4pdfTi08JcaNjideRPvHq5G8\nYwW0zAzupBx5eOuHaOZMouTevazwhhv4ud3O1dZsVmnqJJmK06m2yfLyyInbtrFRXi9BQsyshQsp\ndPHx5PK+fY8GHIBgVFOjttIjIljnwYMcuGHDiNYPP0zNzedTxybMZkpEbCwB0uejP+jcc6k9zJtH\nUCspIaiEh6s8IC4X2zdkCCemqkr5Zlyuww8lyV5zfT37096udgUiIpSWFAjQYb5zp8oBMW0aTcrY\nWBUm4PVS4nNzmQXxpps4xv36kcG2bmX7165lG0UbnTABpjffxEM14/CY6zPUtQ/+7pCmhCu1t3Pa\nRo2i2f7II3RTiY9QslOmprKqs86iG6BfPzqjW1vV2T2AXZgzh02X7JYWC11n/xeDA8cCKNF1/RAA\n9CRfPwvAnu95/hIwh/IvRqGhwF13Adt6UgHcdaOKLWts5ARZLDTJAwFG8mdmcuGaM4eWh+SgGjKE\nO8jh4eS/mBiuHlZrTwxEtbK7u7ook089BXy2VAdefgURLz+JiPp6ruhTphydlxQgl735JgXpnHMO\njz4MC6Oq1VtrAGjS5OWxARKlpmksQ5y6YWHqoJjDQRR1OtmZhQtpPo0bR2Fra1N6+LE0r8sv5/a5\nycS2ZGZScnr7oMaNIzJLdHBDAwd8/HgOnuj6MTE0/+QYxowZ/PnyS+YTkRQVVVXKMX7bbVQLUlMp\nWeXlynnm9fLZtDT6nyS147hx/F4c21VVnNAzz6TUysl2STDvcPRcOubm2JnNdA7fcgvHZcQIglJC\ngkrHIYdPW1rIVPX1wIQJ0D78EJHXXIOH1kxE4Z+W4JVDM/D110rb8Pvpt0lMJD+ecw6xS9PIP198\nQUt4xQo2/f77+bnDQVBxOlU6XDmTO2kSFbDzz2dzwsJ6NtP+D4JOHwBVvf6vBm98OIo0TcsAkAVg\nTa+PQzVN2wbAB17I9/H3vDsPP+Peq/BwleKxd9LzCRMob7KrdPPN5Jl77qF10tFBU2zaNGrmBQVc\noXSdPNbWxq32r7+mXBuNlD25LVKyS+x9fyeGP/oohc5m42rZ+1pJIYeDWdjDwoiWr79O4emd6OdY\nN+MZjRTakSOpbaxdqxLkZmZSuLZuVTtZJhNR0WhUCaCLiylkLS0qCjc3l2Vs3kyJyMpiuyMjeUyg\nrY2mT10dtZukJHWlRnc3tR1JqffFF2zLoEFso9NJ/wpAAJWrNIRGjqT6WVbGOk8+WV0ilp5O8JLj\n+B9+yLGLjiZImM18f9aswyM2IyN50NRgYBk33EDH9c6d/KylheZleDj7KicrxbG8dy/riYqiND/x\nBMFKrhDZtIn9krxAfj8T6YwZA23FChgvvxyj75uFQdfOx3+feyOW7stFdDSn+vrr1VRedRXjvUwm\n8uCKFUqp83qpQPbpQ8BpamLT5SJGs5l+cYk8kHVp795jp245XvS/xZF8MYAPdP2wW9YydF2v0TQt\nG8AaTdN26bpeeuSL/869V8ciST4nG0LJyeRJkQmDgZr/nDnqPGL//srHOGsWg4UlqDcqisGCEm8X\nFgaEwYWMB68h4Ejqh0OHiGaSsLa2lv6Rjg6+nJBA5i8vJyNL2Or30dy5BI3qajZ82DAKnYQ8P/cc\n0XDtWjpuZcensVHl2NQ0go5ELhuNFObGRoKCrjNlXVkZgcZuV4dJvV4Cz+bNBINLL1V35chdK7qu\n7nSR2yJEwLu7ObAuF8vweglICxbQqy9qZu8xOPdcArQc0tyxgxMWH88lfedOjodcmwBwwvLzOUaZ\nmWSA4mICSlMTQenCCwn0zzzD+aqr42S3trLPjzxCqc/K4u8VK/jMZ59x5TKZqHJIlrjYWOXEufFG\n4P77YX31OVw75lucct/raEwYhOxsssWzz3LTKzWVx+Jyc5VPu6uLfCaZY598kkMomQwqKrh3IDgu\ndOgQ9xrk0r/G/F8GfE4k6NTg2BfpHYsuxhG3Qui6XtPz+5CmaV+D/p6jQOd4kK5Tgx87lnzS0ECf\njsfDCZQIZquVmkzfvmQCOXEeFkb/r9lMJomIoAwlJqrDm6FeJz41nIGYskKq95LFT6Jv//QnMu/W\nrco3UVPDSqqq2MgnnuDKecYZqvF1dWxwSoq6HuDRR/l5UREFQe7fDQRYbmIiEXL6dHK3HGvw+WhS\nSYCHZPSTUOn6empaTidjfOSqyR07iMSHDrHeAQNYpghfZSUlIiWFbf3Nb3iQUhJM33MP415aWmi2\n5OayDyUl7OPnnxN0Jkzg/w4HP3M6+XxpKaXI56MfaP9+TmDvtB4PP8xdvaQkCv62bSp3iQBpVxcj\n6rxejlluLsd81Cg6iiXz4ODBNKnEl2SzkYGWLycqVFQo/1xnp8pV0a8fF5PISC4gl10GfPABtK1b\nkf3yHUj94DN0dGp4/nliaFQUi/rDH6gkeb3ESvH/m818RpzLHR38EVfVkfThh9Tav0ssZuUtoJIy\n6XjRiQSdrQByNU3LAsHmYgCXHvmQpmkDAMQC2Njrs1gALl3XPZqmxQM4CcATx7Nxfj9XCbkLKjqa\nExcdTd6LjlY+x+5ulYQuK4u8JqlK+/Ylb7/yCoMFJ02iDO7aRV6bOBFoK3fgrYbTMdi9HdqbbxJY\n1q5VOSQfflgdEc7J4fZtIKBW1shIlXz5X/+ib2fxYgJEaSk5xm6nVnHeeQQm+fnmG+UkHTv28LuN\nY2MZhVZTw+CkwkKVWsJoVFFk4pSV5L2SaSw9nQMpYa3iOyoqok/H6aTUiF4v0ZBTp1JDCgmh1rB4\nMQV78mRqHQIW7e2s0+9nRNwFF7Dep5/mM6GhFHSDgQLd1sZgwqeeUibppEkss6KCYxUby76WllKr\nrK8ngJx/PqW5uVn1d+dO9kGccxJkefAgtSRJXKbrajUKBPi93c6FxWBQ26A1NdRE83oOPX3zDX9H\nRADLl2PtqFuxaOACbN4XhdBQA2Ji2I3aWip9ixax+mHDCB7h4VSEW1o4RPv2kRVSUjgFEpg+ZAjL\n2bKFAFNf36NEdwMd7QyVkltPjwedMNDRdd2nadp8ACvALfOFuq4Xa5r2ZwDbdF3/tOfRiwG813MH\nltBAAK9omhYAYAB9Ot/ngP7ZJInibr+dwX833sh4seee4+oxYgRXlF27yLNtbdS8Q0M54d3dXKgG\nDiQv79tHGcjMpEZ+662UoRdeAGI0B+7cdTqyvduhffABBa+tjc6h1lYKiUR7ORz8SUigcJWXqzMb\nISFEvdBQHlbctIkremsrmRmgnl1eTgfnvn187847KUyRkdS3vV46lyT5jziXHQ62LSSEHGyxEOTk\n6gDJlSM+Grebmk1ZmboPVzQij4ccn5JCYG1pUdeMzp5NiTAaOfjl5dRiZCfr9tsZJHjoEIVecn8s\nWkRHb3s7AUCuwCkvp4AXFqobIvbu5cS99hoBvryc4B4draKj4+OV/8ZsZvk1NXzGauVY19ay/jfe\nYJ8FsKuquIsQEUGmkZsM5fL6gQNVIGReHsE5KoplrF/Pfs+cSe2oJ3ixcXMpTt/7HEJbarHd9Dzq\nu+PhdBrQ3k5Tf/lyatQxMerCPIuFQBIezr/DwoiFffrwwLtsNObkcANFUhO5XBx+8bVLrrPjRSfU\np6Pr+nLwFs/en91/xP8LjvHetwCG/BJt6ugA6hs4SX37Uv7++7/piJOzi5IfR3ZNbTauEHI4026n\nZVJUpK4ZGTKEGv9pp1E2ly8HrIFO/OHrOciyb4e25APubz79NFdb2cqW+BNA5b0pKGBD5swhgxcU\nqHNAt97K4wl9+hDxAMXsHg+3NhYt4mc2G8EtMZGr74YN5DjJcxkXp1KVSoi2pKwzmVTIdlISNQmv\nl1wr57gmTGAftm0j50rKUdHvU1OJ4OeeyzojI2nOrFihgumkTZGR7POjj9IH1TtePzRUHd2QBDIO\nB9sXGqry5Iif54Yb2H45bS4ZsORKBPH+A3xHovEiIliv5BWZOJH1Se5nSYYWFkaJlt20zz8Hfvc7\njntlJRcNSYjtdnNbfcgQtbP3/vt8fvRoICUFel0d2ja1wB0RhSmNi/GALQq3hb0Ih8uCpCR1eF8s\nNoOBwzplCoe9f3+yx759bE55OZsvB91LS7lGtbaqLvp8arimSaTccaL/LY7kX408HgC6clUEAlxw\nTCaCkMNBv+HixZys9HS1uyq7B1deyR2GN98knw0YQD6rqVGJ/ZxNHtxfeA6yWjdi0YRncYlcVB4S\nQsYPDaWGMHAgTQiXi0KbkcFt4s8+o6aQkUHGnzqVgRgxMeQyyYVQWkqtorWVZXZ1sVOxsezM4sUs\nw+mkSWM0Uh0LDVVpOgcNYl1btnBQ0tLYTrkRQUKnt22js0EiinftUhHHR+ZIkNSn4hkVvxFALWTj\nRnWCUd6RXDgLF3LgOztZpoBSbCzrvvlm2rP19Wzj1q3qbImAquRdtlioFsghppQUmqBvv8264uIo\n0fX1bKvskI0bp+74uegi9tvjYXkXXMCxlTNocmr3ySfZhy1buBMm9fv9Kv+srFxZWdSCliyBVlOD\nWEsU9hgHwxziwrmt/4QrLQ87L7njuyNkXV3EXbudVZ53Htng5pvplisv51C2tancO6tXs0qr9fBr\nrnfsUMfZEhPZ1eNJQdA5giT7ZHs7sL+cq4PknJWJ9fmoWcfEEBc6O2luxcdzRcnOZsoYu50KQHMz\nJzI/n2W023347aqLkFO1Eu9HXo1NJQm45PHH+eDQoeQiu50ckpHB30YjK5LTfP37sxFGI5nz6quV\nen/11WTc0lKqXBs2qIOfEqNiMrE80aklmbrkvpQtcIDOqH37+KykOuzuJqBkZNAHYjZTuAYNIiiJ\no7YnodV3xxZ6rv/FlCls0/btbNfixWzbtGmM7fnTnyjoQ4ZQE7DbFbqXldGvtWuXGhvZogeI8s88\nw/+dTgKQ280fTVPpAWSbsbNT7SF7PNSMLBbORUYGwfTUU1lXdTU/P+00Sm5UFO3vhASaY6NG0ZH/\n8MPss99PtaNvX7YzIoJjKJfGt7erZGUSO3XNNQT7gwdZf1UVEtCE/tYqBEJtcIeG4vKdd+LFyDTs\nGX4p7Hb6nIcNYxEtLepcVW4umyhrlwSp93ZWSIrpkBB1rbzDAbTnqljL40lB0DmCNE2ZUcOTyTPZ\n2XTUbd/OSQkPJ7/FxiqNSK4c7uigG8Zm4yQ6nfzM6eQO9OxZOhZrFyO36hO8Z7oMyz2n49LY1Szc\nbCaTpaerk3aZmSo/8cknUw+OjOTukNerruV94gkK8eTJ5EDJBf3ccwSIoiI+LwckJQOeXCMguzsx\nMexsVRUjz4qLeURAtIyWFgqQ3K971VUq4ZDkwUlMVFfAzJ6t/D+/+Q3bI9cVCCgsW6bSL65eTbMi\nP5/13Hwz2/nkk9ROBg6khIgmYLPx/7a2oxO/aBqB+MEHGSTY2EgJzMlRx7jlhor581n/xo1cIXJy\n2N99+wiiTifjh7xe9nf7doLJFVfQl3TFFYcz0tChBCHRdgTAAfbr7LPVHcEAtcvMTDJbfDzH3GT6\n7gY9zWJBSmhPwuxX30bghhvxuw1X4dMRSYi65lRMncoq3nyTu1CtrRzi2bOJc8XFakdeMp1MmsRu\nbd5MbWjSJE6TycSpv3vlLyNjQdA5Bmlaz67lCi7YhYXkBblGqbaWcuxwUGbNZnVfkMXCEJP8fKUR\nVVTwPYsFuLTxOeS6l8DeJx8JRjPuaHsOg7zl6uqItWvJlHJ+SBpjt9NuOzKP8N69DL4zmymEK1fy\nfTHEp0yheWE2U/MwGtkZi4Wmx+23U6v49lsCS0sLgaehgeHVEvAWHs72dHTw72uvpUnR3c32BQLU\n1CRKGFCHIgMB6ujTplGIFi5kO0aO5AD+85+UikBAXXcqZDRSk2huZlniY0lNJdhkZrJNu3YReM84\ng2pn7zR5c+ey/q1b6dOSmKG+famaGo00VyWwT6K05WZFTaNTT0BKfEcxMSxvzpzD62tuJkOMGKFy\nFRUVKTvF5eJu1fjxnBOJE0pLowY7cKACvY4OgpDdru4Wy8+H4eOPgMmTcdZb52JZ2rd4bNMg9OnD\nNUlSYtjttLLj4tQuq5hMLpdSNKOjORTLl3P44uPJEvuSqcBJiovjRUHQ+R6qqgLqCimbDQ3kiTlz\nVOhMbq46jC35qaKiOLnFxVxwd+4kTzU2cvWYY/gcD7n/iG9CpmPyyYk4dfd2wNtE9JIdKAkYGzGC\nK25FBdFrxoyjc0Zu304zYvdudYhRdmCEhg+naSAJVwAK3f33UxgBpvtcv56cJsm+SkspGJJHWLbB\nExIIVpMm8dxHRAQHQmJXzj6bjmGAbd6zh5w+fDiX3w0bWKbXy3c7OvhMZ6fyu7z5JgXPZuNzS5YQ\nFMvL+f3AgVQnk5P5WWEhJSs8nAErcgVHb0pKokTNnEkfzX33EaBrawnWU6YQWG65heWUlnIyR49m\nfw8eZDmSvjQQUCfWe0uky8Vj4XV1rNNkUk5mgOP8xhvUqBITVciDx8P2bNjAOTjlFPqKKis5NpMn\nc1GQsY2JAT7/HK5BYzD2L2di46Vb8Mm2+O+uFgZUBtX/+i9GB6xYoTYf//53dTHG4MHE47IyWqI1\nNT0bpD1W9LZtKlbzeJDhxx/5/5NkcZczemL52O0EkR07KEMTJ/J5q5XPW62UBYuFWHLKKeSt3O49\neLH1YuwzDsKnw+6DZuoJnBs4kE7g1FQimc1GxKqpoWCPH0/V/7LLjm7k2rWsNDaWwrNiBYMqVq8+\n/BIv0Znj47nCa5pKawrw/9xctZ1RX0+hslpVjmSrle+PG8ddKU2jgCYmsq5AgJx5+umqXJuNfRg9\nWl3563RSDZRgkqeeUqCmaTTLWloogM3NLO/662kDtLcrM0puEL3zTmpLycnsU3Exn/0+MpkI1jt2\nUNC9XuWoBzgGd92lrk84cIDtHD+efU9NVYzR2Un/i+w4fP45gfzpp9mHpUsJVnL3S3s74yQAanly\nlMTpVJHY8fGU+IICvrNsGTW9jAz66iQAEgDS0vC3Uz5GrLsW8744F2lJ3d9p5NXVnLKTTmIYkNOp\nrhZzOOim27yZVmBpqRpSuV20vR3QAypvz/GkoKbzPRQRAdR2cgJl40fyQQEq2v2117hZsXCh8lNe\ndx13v5uaCFjTxzpx/ydz4dNM+Fv0fbjf8Dfg5JkEm759udpffTUZVKK9Fi0i4w8eTIE61vUBYWEU\nNMn3Kyr+hx/S7HjgAQUokgC6u1uBSG9KSmLw4Esvsf6QEHJsWBgD46ZOJUcOG8ZOAeTm665jW+VA\n5Qcf0Aw8FknkrZwlk8xlMTHqKg0R4DffVMcj5MS3xPcLQHm97GdsLLUym00lrv7d71hmTQ3HJS0N\n311iJtuINhufcTqpUUyZwue2b2fZo0ax3q++4gn7NWtUVsGEBILd7t3quMpTT6lcyeI4nj2bQBQZ\nSRDaupV/WywE3/Z2go/cF2+1su6QEJV58FhX8/SQYfw4vG5fiHlfX4Y5X90GzxPPYehQKrZWK5tx\n113EcLNZKZhWK/H5tNOIZxUVZLW1a9VmmsGrEg4cTwqCzjFI7l2z2ahyRkSQH3fuVOlGJctafT0X\nyoULyasDB1ITfuUVym7Bdh0L269BZqAMB1Kn4LGIpxDb0gq81UjHqsXCit57j0b1M89QiDo6lHAu\nXUpn5ZHp/JubyeSi1RgMZOjOTqr4jY0Ek2nTGJvy0kssr6mJPhDJzyPe8PPPZ+O3b2esSEUFhXHQ\nIA7AnDlHD1ZNDZfLyEii7u7d/LyxkVqCpOvUNJpYEydyy9hgoPa2eLHadpcT3wcO8LmWFqWlyV1T\n4m9pbGS/rFaacTt3UlDz8/nMRx/R1JLUHsOHMyPbihWUrN7+q7w8moXDhyvQkzGRLe/4eHVDhhwH\nWb2aquyGDSpnj92urtAcOpTAFRFBJ/Tdd6vASblxVBzbXi/7XVNDELvhBnUS8wdo3jzgDcul+Nq+\nBacXPYfuF0tRkzERBZHzsL08Hj6fSk0qx/UMBoKR3Dgybx6V63feYZmSn8dgAqKifzht079DQdA5\ngnw+FbJx7pnEAVEQJDdT791Xt5vKxj/+wc0lgLJcVET5HbzqOUzYvwSBiRMxYG8R4LeoROd//CM1\nifPOo9ZwzjlckioqVEZ4QDmUe1NXFzUaq5U/DQ0qZsVqJXAJGY00A049VZ3LKinhXVZiMgHqIrs5\nc/j+I4/QKSsnrgcPPvxQJECB/eIL1iG7Z9XVNAn37OHAzZjB3Sezmbs0F1zAPq1cyQEVQR48mMK/\nfj3b4XCoTIISW3TxxfR3SZY/ATM5OuB2U5CXLSOoxMaqU7dPPslyRo9WuW8mTmQ977zD2JyrrybA\np6dTFejuVk75Dz9kOXKcQ27UaGsj4FitlFjJviVJ2ffsoR9Hbkhsa2O7Bg2iFiWZAGw2JrI5/fSj\nT1q2tVGjbGjgGE+bBmgaYmMZD4qJlwBnvgPjqi9RmhKHrztKUZtqQ94AAzwedUWS5CLTNJUArLmZ\nkRVjx9K9t3072as0DLDF4rhTEHR6kddLRaOxkX8X90S9l5RwAe/u5pZ5TQ1XCDlg3dZG/AgPp8y0\ntQPIECMAACAASURBVJH/+jQU4NzNd6AwfS6GnDkJhj171B3FssdusVADSUkho59+Ov0UTU3KxzF9\nulK5hWQrQu5mEoM8Lo7m1Ny5askCKAQffaROTdfVURjuvpsAkZPDuuSdyEiaEJIAXU4LAqznwAHW\nKxe8f/aZOkv12mssOyyM9bz3HgVREhINGkSH6Kef0iyrrGRZJhM1n4QEOo/FlLnwQvpUZs8+9gVM\n/frx1OPq1Sox8I4d6u5y0V5kJ8hoVDtHJ51EcJUsfnv20Nk8erS6TrOggGZRZaWK7pZzVnImLjmZ\nQLRzpwo9aG+nyWWx0BuraXzf5VI3itpsKrVIdDRBUACnpoaAHBmpUlTK+bHx4+mYyc9nm1etgj5+\nPLqXr8Ko1pUI0X6D5jov2hyhh11/lp9PXq2tJbYvXUp8vuUWlTJ76VI2PZCuLODg7tUvRDt3cnGL\nSFJpawcNIk8XFCgZl8DT0FDFyzt3kjdKS6muap0dOOuzS9BpjkFgyFCYKg7RByC7NLL/LocCGxoI\nOnv20JQJCWED6uspnEVFBCNZFSMiCCwLFxKQwsPVVRUWi7qVASBq/vnP/O1wqGMKvZMDDRhARL3r\nLtqIWVks97PPWEZoKDWH9nZuccvlSKJhlJaSsysrlV/C7VaBiPv2fZcdDzt3UkhFY5D8NHKOrLZW\nBTpFRPA92WkDOF6FhQSXnBxO0rBhfOf++/l8ZydNSNkRiIqihrV4MYW5q0sFKNbXq2tcJU/RokVs\nd3Y2t78lqlrCBmJjVRiC5IIoK1NZD1tb6VvavJlgYrGoq5tDQwksSUkEnlNO4XwEAgpwqqs5Z1K2\nHHMpLCR6LF9OM7H3RfeNjaiPy0dG43bcYH4Gl5imwdyjVMt11RYLh/K555TP2m5ndwcPJovk55MV\nP++JlujsPL43fQZBpxcJv0iSusQ8rgAREYz9kqNHFgv5zedTmfh371a5jwHgscDtiHYeROuoGRie\n5QTs7epi6kCAILJ9u3KIrlihfBhyQq+1lZyRkUGmKypS22WaxoC3MWOo2o8cqZIuZ2Twe5+PAvTc\nc+SshAS10yVnqb76SuXHcTjIfQsWsNMpKRRoSQP60EMqt/Kpp3IQxJ/h9RJI5FxWV5fKvZOUpM4l\nyQBbrUqI5OKw+HgCR22tSmHX0MBnrrpKTZRc2mQyUYgvvZT+qPZ2BaRjxvD3zJn0reTlsf/9+lGD\nSUzkztqGDcpONhopmV9+qeKjpH1RUfw+OpoSOXAgtdIvv1SBLzU1CmS7ug4/BBoZyTkEVDbCsDC2\nQUzia69Vd8QUFfH9jAylgRUXU5uUrG8Oh0ryFBkJLTYWqZ31OBA3HjNbluGypNXYlTwDVVWcArH8\nursP115cLi6q8+dTiZPr2v0BFclxPCkIOr1o8GAu9kVdAHRaDlOnku8lTbDZrPKXl5dT3jo7+Z1c\nmxS16UvEvPsycM45iIuJAeJjAYuRKrbDofwvsnPjdhOA9u1T+WgkGfiAAaqBhx20BzlF7hzft49b\ntuKUOvNMlrV0qWLSzk5yUHy8Emgx0+RoscOhyo+NVdHEpaV8XwI2Vq+mcNXUqNOBHg/LjYoicK1a\nRYdrerq6OUF2o8aNo++osJDC+MorFND8fGpXbW0qME/u7xGn7qpVBJBdu1j/o48SVC+5RN0uBxDU\nLr9cCTLA8ew9phJd3dKiMq+JOltTozKuye2hERH0DeXlsd9yElwOeoqzW9LEDh2qEtTI3VmapqL1\nHnjg2JmyoqIUj3R1sb4dO1S2Q7kSx2xWd09HRcGcEo7U2xag/pqb8GDVNbgkZjf8/mg0N7MrJ59M\nfDvrLHajooJsOWAAh+ngQYJTRQUQiKc89E5YeTwoCDq9KDycfrwdL5Evrr+efJ6SQv+iHA+Sq6Ke\nfVbltpLriKePcWDY36/lSnjnnfTMud1UteXuV8lfk5BA3414qDMzlVpdW8tVuKiIgpqWRuQTKilh\nzIfNRhPh3XfV9bWtrdQEUlNVBG93t0pxmJlJIFi3TqUtlOtYepsx2dk037q62L7wcJYvQJCQQMYX\n/0VrK/++8kpqAbNmcWltbuYgSg4cQXeA5taECdTUXniBQDhhAvsUEcHyOjvpJ9qyRV2pUV6uMhWm\npHB8Tz+dCb/WreNETZ58OOAci+69l/0yGJSZUl/PvoiADx/OepuaKLVyb5XPxzE6cIBjN24c27Jp\nE+fL4+HvqCjuYslVPCYTx+3GG78/Nd/48Zz7ggK2SxKEJSYqAEtNZbs2bVIXYqWlIWJIFpwL30TM\nORPw+8pb8dLohTAYGN0waZKyih9+mFPy1FMEHVHuxoyhb/Lqt4//uSsgCDpHkSSllhQtNTWchJkz\nyW8+H/2iG3tSijmdKlGS1Qrc574HpsZa4NWPqateeCEDfMxmClN5uboRQbZto6MpmJ9+SvMpLo7M\nOmYMwUUOWvaOrZE8wVFRfD8Q4PLU0aHSX5rNKlG4ycQyxUktka9yPa7fTyDoHVS4d6+6blTSzxkM\nbI+kRywtVbcZREbSBDOZGK+zZQsH5/zzKYwpKd8/8AMHqmMVJSU8niH5QlJSqMXl5al4JEn7ISZg\nUxOBJi6OEvNTSNc5H36/ujZZTkTK4cxAgOAloQ0XXKDeN5nowJb0G+PGqW1un4/O5+JijtXUqVyA\n5DjFuHFkqu8ji4W3SNTUEEijozm/djvbJXeNNTURsOvr+X9PAGO9IwcFQ+7EGbseRZPtcqw1nvLd\nbaFCCQnEqWHDVGYRl4vK5i9x0PO7Yftliv1p9BPuvboKwF+h0pg+r+v6az3fXQng3p7P/6Lr+pvH\nq11dXcCBg8Cer7gC7NypdliLiui/GT+efFFdTR5ISwPGYTMGffMStH79VOb2WbOAv/yFKLVwIWe9\nvZ2xK42N6ra0sjLlo3jySTp0JSXmsaisjKt7QQEFz+djw4T5dJ3MvWABGx4I0A8j2evOOos+obVr\nWd5ppxGoHnqIQj11qgpIkm1eg4FCJelM165VpoTFQlC46iqClculLvT6+muqhf37//DAy3GBN95g\nW+Qmzs2bqXnU1nK3yW6nAP/1r+qcyZgx7NvPIU3jRL7+unLQSZ6dmBi121ZXxzHbuZPgN3MmdxT7\n9iWoH+s2OpOJi0xWFk3cV17he5MmsZ7Bg3/86kyXi9qfaDJtbWyHw8FxFWdNfr7KJ1RUBDz3HCLO\nvgMfD7kPE6rfx5ylv8P6OTsREREKv19dh52WxlCx+fN5AmbbNipkP3cYfy6dMND5Kfde9dAiXdfn\nH/GuDbyOZjQAHcD2nnfb8B+S28259HQBSVHKm//KK+QvXSdPpqSoFAGtrUBXhw+/rb8egbBIGGUn\nprGRmsz06fjOqNZ1znJqKkEoKkpl/UtMVLEbDQ1EvIsvPloFLyigJjF0KEFm924yeEwM9z9NJpWr\nZeBA1n/4ABLYCgsVsKxapUJVJS/HGWdQYLq6lMYTGckBufxybmFLfEFkJP0T9fVqO1luAW1rw3eJ\nfX9s+SwvV+G0GRn0WVks6ra57m46XKOieH5KEo7l5R0dPPlT6N576cQvKVFAHQhwUjs6WG5JCTXN\n6mpqG/X1lNoHHlBm4rHI5eKOojjXd+wgqP+QxtebVqzggpGUpNJehIQoJ4vcKSwnisPDv0tVmdsn\nDbNHTMDrFU/itg3n4G7tUSQnP4iVK3lG1Wzm9O/dSxz//HNafGvW8Lunnvr5Q/lT6URqOj/33qve\ndDqAlbqut/a8uxLATADv/qeNkrN8Zoviv5YWpeUDVEv371fa/cSJwLTil9GvdAf25s3FwLLP1RZp\nfT0FurKS2o3Hw52Iq65iUF1cHB2iJhML/uQTMlhrK1f6ujqlfQAqqG/3bqJgXJy6qkKuN0lJIQhJ\nuP+R5PFQAKxWlSRLbiFISaHgr1pFoLj5ZjqzfD4K+oABNClCQwmcco+ty0UNTmIJxNkl6UTXr2fb\nf+jCwKYm7rRt3MixMBo5+JGRFO7GRoLONdfweUnvWFurcgIdSXJRngQYBgLUPtato/ljsdC/1dBw\n+FEMGe9AQMXltLZyfMPCWO7evQp0fD6+HxmpQEHKlIBKifAWRioro1bndBLAp09X0dZNTVz9wsOJ\nCoWFnKusLI5jWBjNvs2bOfaSb6ioCACgPfwXnBsTA09SX/gHDUX2+48DD16FQ4ey4HaT5QwGTvOW\nLWQFOY0uJ9V/KTqRoNMHP+3eq/M0TTsZwAEAt+q6XvU97/7AkvPTyWbr8fP1HH3p7KT2vGoVlQgJ\n17jiCmLIwle7Eb/nW8xafw92x5wEfchIoGLF4Qmz5HpG2eFoaaFKJRfWTZxI27+piQyUmKiuAJYk\nU+IQffdd+mA6OghoVVXUhHbsoHEu29HV1eqA4pFkNlO3FrPBZFI7JT4ftQpJQ7dvH0GouZnfLVlC\nM+nIA6iSoOWtt9j23kczYmKUX+j7yOVifpmNG9XWoGS/l5PcycnqHhWAoPrYY+qw5o030r8h1NVF\nje7gQeXszckh0JSUcBydTgWQVqvKVi6xPXJflkSEyr1hzc3qBrymJqoGjY3s6x/+wPGVTIayzS0H\nOn0+gs8DD6iAzrff5lwFAjRFASKAz8f+JyZSQ/Z4aAO5XCzzySfZvjvuIC+YzeqMW1MTQlwuoKOn\njzfdhAG3f4ZXXlH3X/XtS1Yxm9X9bGlp7M7+FiAiXN3sfLzof7ojeSmAd3tufbgewJsATvk5Bfzc\ny/ZCQshTTU3AjNOUD3PrVpWjOyKCC1NmJhDyyYdo/2wxQn2dqM6ajNOnxgOFfRSj2Wzc7nr1VbW9\nnJZGJrr2Wq5cDQ10Gv797wST3bvJEU1NyqsntHcvmTArixqT2axyDA8axOA4k4k/sjW8fz81hNxc\ndqKsjHV7PFzm5PoZiS/RdUbkfvABDX1R9eQ6jEWLjgYdiRvKziZCr1zZc1RZV7H3vQHjSKqtVat1\ndze53GikiSU5muV6nOpq9mXZMn5ms3GF//3vmVNHblCor+e4yEFQOUMl19l0d6uk1pI0Xm6kiI1V\nu2MrVvD/K69kwuwtW9i+99/n2YHFi1Vys8ZGAu899xCAbr6Zh1fdbmq3SUn08W3eTAd0cjLbm5FB\nPli9Wm0OyP1aEyaw32PHHn7wV4Jt6uq4ECQmElw7O9Vd6nFx6jqTZcsw+bZvMG3ayVizhlUmJJCV\nDhwgawUCnDanEzCdDrR30Ad0rCQH/y6dSNCpwY/ce6Xrekuvf1+DumamBsDUI979+liV/NzL9tra\nKPduN1BUQsVCFg6LhfPq8ajnx9Z9Ct35EQIjR2LmJBfgdjHPzCuvkCHmz6cAJyRQpQ8LoxotKnjv\nHYyzz1Z5YurqqAHdeuvhvoqcHOrGXi+F0WZjWZGRfH/UKPVsIMCt5g0bCHbR0XyuoYErtdPJLbm6\nOmUeyMHKU08liFRVcUB6n1A3m9UdJrm5ylwICWGsjMHAQSopYdkGA8+X2GyHD3YgQG5fvpxCuGOH\niimS1T05mebFCy8QRMrK2P/Bg9WRjW+/paT4/dwXHjCAoCMHSeXWOZ+PbTIalSYqF0FZLPTbpKfT\nDIqJoWaWnc32HDpELUzuJAIIOv36EbRkYYiIODwJ2eDBdHgLffaZCh0IC6PJFhLCOjMz1XNdXerG\njLPOOjazBgIqd8/UqZznujqVn1nX2e5x474LADXc9gfct3IroqI0FBVx3Zo0iU2RRIVNTVwfbe3U\ndKqqjl39v0snEnR+9N4rTdNSdF2v6/l3LoC9PX+vAPBIz/1XAHAagLv/0wa1tzN2obrnQKdeoc4x\nhoWRDzo7KQffnXs8cACawQDj2NFc7U47jfaYOG/FcZqRoa5F+T6aNYtaUEMDmT0n5+hnLriAjCTb\n1+3tdDzPm3f0lY11dVzV/X5115Kus221tSp9gzB+WprKy/z44+Q8t1ul25TUiImJHCiAGsC999K3\n0TsJuaZxHCSH8eDBh7dN16kBLFlC01LXWX5TEwX45JNZ9sCBBKSsLBX4KDk38/NZVkODuh6nsZGr\nRFoad5vS0xnAJ5Gdu3bRDA0LI+D5fARim40AlJlJgNy/n9qnyUSQ6OqiSSSOXYmVefFFnl87cIAg\n0d19dOrS3uR2q8ReKSkqd+i4cZzzCROorVVUqNP1VVVHH7QFuCKuWKGc9fPnM6Ds+efZXgH9/fs5\nNv37A1u2IOarjzBv3rm46y5Om8PB2NSwMDZFbjr2eoFuy+Hr2PGgEwY6P/Heq5s1TZsL3lfeCuCq\nnndbNU17CAQuAPizOJX/Eyot7QlMjeJ8SyI7uUzPaOQkSTpdFBdztiQr+4wZNHFe/X/snXd43NWx\n92dXvaykVS+WZdly7xXbGBe6qaEFCAkECD2Q3BtCCYEAL6TQE0gAB0IIzRTTsTG44IaNe5GLLNnq\nVu+97P7ePz4aj2RsSuKE+97X53n0SNr9lVNm5syZ8p15MOncuUcWND4fWcuffQaT/uQnMMqYMV/d\nycGDcQ9//DGdvOACKGTfvi97hnbt4vmKwxERAUVt22aRsyLYEkaOtPreX3yBiu/zmcahwsBxOP6p\n7SM0lIRJzf+64QYMnPv3s/N6vTDvoZ6lyko8U9HRfFddTX9CQ/m+upot+Ec/Inyguxuh0N6OkNAa\n7p2dfKY2loYGjl+apX7LLZwbJk3i+bW1cFFSEp657GzsQP36wdwTJzI/CrC+YAHzl5KCxvXRRwge\nxbCOj8f+dMcdCKe0tK9ew+nTOUJ5vQgxEYMiXbmSI62iqyckYFN75RU2FUUU0LZiBcJS66Lfcw/x\nTbm5ZvMRYf1mz+bvnByRu++WypfOFbc7QA4cYKnq6w0VRTNUGkMZ9kknfTVJftv2ndp0vq7uleM4\nd8oRNBjHcf4mIn87mv3RKPagYNaso4NJb2piEYKC+P9g1d7f/IaVeuklK6Z2112W0LltGxrBoccK\nEQjlvfewA1RVsbs+/HBf5tRI5o4OBE18PNrBli10tK4OBhg4kJ3R5+sb/fXOO/yvdhKFM21psVyi\nujoY6NJLYbwFC9AGkpKAkFi6FAZITYXJAwOh0v79zSDb1YWR68ABktVuvRX7x5VXmvZzaFP1X9EK\nVROJiYEhMzIA4lL7ziefIDC0+PukSQiJ6B7Al82bmZP0dIRAeTmL9eSTHHl62zc0j021NgWzbm01\ntKvFi1nf008HCXDQIPp13HFmN0lJYe5DQtDIhg//eiKLjyfCWYGM09KMBv7yFwRNaysCtLsb4nvz\nTQRnYyPwJxdcwPfR0dCR2qbi4hBEHo/tkllZdnzs6uLdK1bIgPVvSHPzpQfjP71elLxx41AcBw8W\nWdVz0v1nIhG+qv1PNyT/R9vgwSgn29aLhIWLnHmeFXrUk05xcQ+MSt4OGPQ3vzGhUloKwajBuqiI\nXetwQkfxJLXuUlERQkHdBN3deCa2boXJHnwQRszNRQMIDkbQ5ObSofHjTeDU1qJJdHRAmFFRFnnb\n0ABA1KmnIkyam7lX0QCHDEEYqrfm7LOZABEraN3UZK724GBLVd64EUH5/PPs+tdf33fM3d2W0BoY\nCJVv3szEt7TAHB4PgiAyknE89BDamkZZq7u6tJRjkuIMK0bspEloarGxaBUuF0Jm0iS46ZRTEKSZ\nmcxPdzeMPGQIXiNNls3PR8s4/XTe+fLLCIZLL2WMr75K/9atYzylpV8ds6Pt9dexDcXFIRiTkri/\ntdXCFjwextTdzbyEh0N4FRUYoffswahcVMTfPh9HzcGDuWfMGEv8LShA2Ch8yC9+IU5trYQ9fJ9M\nvf5iyclxH6xx2NzM8BQpcN2zXz+cf6YdEzq9mstFLN5bPQe1P9yINqqniZ5kXgJJf/kwxPKzn3Fx\nd7cctMwpZo2I7eSHtuHDDWiquRmppqpzXR0RXG+/bRpNSwu7lsvFsWX0aJhv/37zSO3cCTM/+qiV\nHa6vpz8hIfRp+HD6nZSEJvTFFzD4lVciiEaNIv/qoYfMk5OezjNE2OXDwhA8AwaggbW38+62Nhhv\n2DCY8aqrrKrnvn0wdXMzzFBZafleM2diKH7sMfqsMBRr1ljpYhEYSYF+ExNJr5g/346K4eG8NzAQ\nbXDvXsaTlMTxJDTUigquWMG7tBa5loNpbzd4VA1T0HyrujpipOrqGH9TE2pvSwsa1e9+9/XBjzt2\nICDDwizwUAHor7sOo7pGnaanW3yUIgo2NqL1bN3KM2JjmZfiYkMR0Mz4nBy8lJddxjWRkSLJybJ1\naY2M/+OV4v7oA2ltPfcgTM/MmV9vdjwa7ZjQOUzr7ZUcOhR6ff99NsYTTxTxFRRjxPvpTyHkV1/F\nK3HgAISSnQ0D3nWX7X5tbQSCbd1qR4jkZO6fMgUPhcsFcV14IZqQVvQLC7Nd7/jjOXKtXYuw0Wjl\nyEg0LwUJ0yzpfv3YUTUTevt23q1Bi9nZEPzy5aRM3HQTAmHiRN67dKm56MvLeb/bjdekpIQJuuIK\n3MSVlVzX1ATDFhcTdV1fz7UDBqAFqjdt1iyYOzeXcTz2GMIuNxfNIj6e70eN4h7152ZkcKyZMIEx\nl5YitM88k/4WFiLsWlpg0MmTLeiqq8tK9Wik9fr12E2uuYY5KC9n6+9df8XtRquoq2OMqk1q4q5q\nEko8paXASVZWss4KuxoRwQaRnIzAmDQJzWTkSMO6ranheYqZrIZtFYSbNpmXMTPTtM2gIOxCXq/F\nGQ0fznWtrSIHDkjX7x6RP3/6PXkoJFnO3v2Q/Cn8XElMZKrLyjjhpaX1MiH8G9oxofMN2uTJ0HJZ\nGTQet+MPMt3viOuCCyDWlSthFK0JNXUqRKXYNyJIrbVrIYw33mCnHj8eRvjFL2w3f+UV1Gi1xWi8\nhUb27tqFYFMsjZYWBFrvsrRuN0eBoCCem5WFVqXG18pKDK/79hksRUsLGtKPfsS7uru5TlHwJk9m\nDNnZPLu+Hg0iKAjB0tTE0e3dd2HqX/yC42FgIIy9Ywdj9nrpg1Y/0PcHBnJNbi6M5POhxcXEMLYx\nYzgWJiXBrNOmYd8KDLSi3RpY2NqKUKqvx0iRlMR3zz9vyZ0JCaa6dnfD1JMnWz7U2rUI///+b9Rf\nNZorsHxCAmtRX898TJrE8zRX5u67rcbvBx8wp1pu+fTT6cO4cby/q4s+amRxXFzfVInf/Q472fr1\nrFlUFD+q4aSmIimqq60sSVAQtFNRgYG5pYVgs4wskcgoWRN7tpxd9leZlLZW3OnTDmLbR0djiiwq\nEpGvSdD/Z9sxofMNWkEBm9PA+EaJXPupTNnyrHSGRUrIVVex4FOnsjsVFrLQkZFoCj6f7XwFBVyT\nlwdhdXTgFqioQKNRsCcRiFYJWBMuMzNhWHV7Z2ejAWi5YJ+PI0lXF6kEWpFT8XnS0uhnTAz9iItD\nu9H0Cc3B2raNbS47G6Jub0ejaG6GMfLyeFdQkKUWaPncpCS0hD170P727UP7CAlhnjS/SSuXqvfm\nsstgFHUTqmYWHIzwUthStSlpa2iwI6kGmgwdynsqK/k7KAgt4m9/YzsPCYH5ysuZo8pKhKGWxNGE\nz8BA5nrRIoRRVhbXTJmCHUqEYMSgIPq+axdeuoQEbCsrVjD/Woc6NhZhXFNDv++/H2Gg8ULLllnC\na2wsEcaqab3xBmkk1dVcr6iFquUMHMjf48djj6utZS2zsvjd1oaGuWWLBJUWyCWjdshbLRfL7IA3\n5JrGR+Rt7wLZvRv5p4VZs7NFnKnHoC3+462mhg1CRMRp75CgpYvkgl13SZB0S4vfJSGVlRZJO3Kk\neY/i4rj5jjsgfK1m/+yzfXEf8/JY6d52n0svxVuxdy//Dx2KZnPjjRwJpkwxm0NVlVXUnDLFjloP\nPEC/NmxAe8jKMlCo2lr6cO21HJE2boTZAwMhzhtvhCEeeAD1TlMTYmNhqoULYYRt26yy4IABVnWh\nrQ1BkJDA+LZvNySoyZMRYBMmwCRlZQgL7XdcHBqHRjKfcgrXHcl9csIJBE9qBvyPf4xd5PzzLWAw\nMRFNdNMmPE9JSQaKFRRkgX2XXso1r75Kv1QrKSkxkKzAQI7UeXnMbWgofViwgLXIyODeefMQrPv3\ns06dnX01u+xs+rRuHf2OjUUIZmby3rIy7HnXXYewf+MNy+Tv6GCj0oquTz7JhhUXh4Bxu7HTZWQg\nFNeutXyyjAyRXbvk5KAVMnxstHSMukhOfu8F2R1TJqHjU6S0lG7qibDtfyO0xf/k1tBAyIMIPDNz\nWIV4Pl4qqd3F4hO3hHc2iARFWMHyvXthoJgYCHnvXph54ULsE089BZH4/ZY1LIINpTeAeno697z4\nIl6b+HgE2sknYw/S4LTYWAy+V1755c4rdk3vqK7qajQbv5+YjX/8A4OvCpDAQMOCmTeP5y9ciA0n\nKIjdXWsnKwxERIRV79y/H0Erwk6s9oT0dI52zc1oNoWFeIuCgth9q6vRElJSOD6MHm04Ips20ecT\nTzz8lpufz1y0tbFgCxawS/zyl7jw/X7ihjSv6pNPmMuAAMY6apRFK0dFoXnedRfXK9hWQ0PfIM22\nNuanutqKFkZE8ExF9mttZW7VIxkdDY3U1aGVxMWxAbW3WwyUYgdFRiIsP/8coRMYaOEFmsOndHLn\nnbZpBQYyhv/6r75zNGcOwq2oiDW99VaR1FRJGzKEMbzznPys8L+l+9E/yq+eSJR337UgwY6Oow9V\nKnJM6By2OY5IeYXI+CQmPT9f5LgTguR031sSKJ3iuFzi0vB6RZ1ThL6AAEP3LyuDqMrLETpnnQUB\n+Hyo4A8+2LdUjLaEBIjjppsg4pgY3nHVVbjoP/wQQr7uOghXCVurHnR1medJBAYqKzOIiEcfZSf1\neKyAXXe3JaM2NeGl6e5mJ6+sRKho0mVdHUeNMWMMCOuZZ9iR8/IMaH3kSHbXt9+G2Xw+xr90KcyQ\nl8dR0HGshOq2bZYQGx+PHSYoyOr7aGtvx5ifk4OQz8gwAPgbb7SFU2+YuvcrK9lFFPTM5WJeHX+U\nuAAAIABJREFUQ0IQdCEh9LWlBa0wJaUvXmdREf1TN09hIdqoeho1JmbLFoO0SE21Ppx+Or+XLUPA\n5uczXxrF3d3d931hYQSO3norz9P8MMcBKiQmBgF0662HR0n0ekXuu4+xRUVBjwUFRJy//TbvfvNN\nCWhukY64f8iFF8YczPrQYpFHux0TOodpjiMijoW9BASIdHW7D/7vUhyT8HAW1eNBiGzdimu1vNzA\nsdQFu2yZJeI5PQ9/8kmMlP36HT6N99AI1LAwiGTiRAjvt7/l885OGOnkk9GO2tpgiqwshFFuLt6f\nHTu4XjPK8/P7Zpfv3AnxawG6HTv4TKOFhw3ju4oKM7hqYbn2divxMmAAiIkhIUQEa96TBrvdfDNC\nq74ebUhhW9VD1dZmNcM3b0YwTZ7cdy6WLTOPkM8H88+YgdaUnGxlKxVLWEMTTjvN4pauuYb3q+Bv\na0PTUChUETSt889ng9Cignpk0lTtIUOY6+ZmM0qnpxsq44ABrL3iIldVYQgvL2ct29oQTLm5rFdd\nHWO77TbGkZdnR1CXC8GxZw9amNJdaysbhUZ0H0rQGo3uOFa7Xo98fr/I52tk2HGrJD/xbNm/vweV\nNtKW+GjiJB8TOodpbncPvncu/4eGikwb1wbRKEynCARRVQUzNjRAwBqqHxUFc3Z2whhJSQiGdetQ\n62NjLWM5M5MjgQYVOg7MrkSlx6/SUnbf9HSE1HvvQQ3t7Qi5Dz5gZz7uOI4ACpLS2QljaDKiwkwo\ng8fGMobiYqsJlZjIseLcc/n8s8+YiPJyAtHi4mASl0vk4ouxhaihdt8++rl4MWNOTKTvLhd9CA/n\nOQrdobt8VxcazaZNZnQODjaD7vnn2yJVV9O/tDSYrqqKhWto4HdlJfOSnk6/taylahMNDbhrysvR\nPhoaiGnZvZtnK8Srjucvf0H4paUBYKbxQRdeyLqXlPAsdaNr7fhBgxBIinqek9OTY9BotV1uugmt\n8JlnsLHl50NHn39udpr+/S3dQ6uGaKZ8ZydR8Rs3crwaOJAxDxoEff3hD8zZoEFoRDU1rKnff3A6\nXY2NctKJfrl3M9MfGyvii7LySkcz/+qY0DlCS0wU+d5IzDFdXSKb522UM7q7YbjkZI4Hp58OIZaV\nQWD33guR3HMPdovaWktZmDrV3NlqTBSBeX0+iPi222C0Bx8kcE8DyO6/H8Z84w2OH9nZMFNTE7/r\n6uhkdbX9rq2FcpqaYMioKAPZUg9YcjLCQAl3yhQ0uN51ls48Ewbq3x/B1r8/Y9uxw+JhioutGKBW\nIGxpMTvFxImGmhgXx65bXs53W7Yw/oEDMQQvXIgQ1kDHzk7Trnq3KVPQdrTiwpgxLFpDA+PVahpq\nZPX5YPiCAnMrJycjUPLzmcOSEkswVQxix+EdHR2sbUgIGuWsWczxpk3Ykioq+FHQNK0u4fUy74sX\nI2zHjEHYnXYaGtD+/bwnKor1f/dd4nsKCnifptQ0NRlukNtN/6urGafPx7Oam7FJ6QYzeTLH044O\nM5rPn4/A08KJ2rq7JenUsTIthFdPmSKy1i3SJXThaLZjQucIzXGwOw4cyPrGvviWdMYkSPD8+QZL\n4PWi0mp8jpZw6e5mx1RmGjHC7AcJCRCjJiiqjeSzzyCgtDQwezXreds2VOGBA6EGrdtdXo4tobLS\nYB0UY7mpiUFomH9bGwyldaHa2npSiLsQhtnZMHZCAjulxpdMmGARxd/7Hj+trQjXdesQNk1NELJG\n1WpZDI2EXrvWnhcVRf8Uv0ZzmdrbOXKNGoVW8Je/sFPrzr5pE1pO72JNw4aBWbNlC/0+4QSMusnJ\njFmL0KenIygKCjjCKBCX2kWamxFeWtzb5eLvggJLS0lLYw17hzV0dhKHVFVlGfyaFa4AZlqpoqvL\njoaKE9HebmNRF6kI71C7YHu71dFSV/mwYfxWgKemJgv+i49nfuPimOunnkIwKpZzWRn2tLg4c3i4\nXAfL2Lx/90ZZHTpAqqqQozHnswzHapn/h5rfz7onJYkEdbfJuNIP5cCJl8mA1ast8nfWLJCt3W6E\nQ2ioIfKp21UE5ggJMdgI3SGXLmWnW7fOtA4tnq6VHEpL+yLxJSTgzSkqwm7y858bM3o8EGFoqJWd\nqapCYznlFIO66N/fKlG0tTFI3fkGDeoLGnZo05IyGtfT2AhzV1TY0S0+nuNWeTmCa8sWxj91qkGL\napWLgAB2ac0vy85Gi1LhrPE0L74Ic159tQn39naemZHBcxctwqZTXIygmDKFOdi3j/freH0++rB6\nNf31+fDShYTwnO5ujoxnnAFjDhzIWvd23efnMw8DBkAo6upXG5nfD7eqR1BTLNraECz19Qir6mq0\nkbAw5mr6dHa7DRtMsxo8mPFoZRG1kUVFcU1Li7nca2pYvzVr6E9AADQWH884IyMttkjd8JMni2/d\nehm89U0pSL7wIIyL3w+pfF0Vn2/bjgmdI7SAAGKtNmwQOe7AxxLa3SJhZ58M9IBix3zyCRerK9px\n2BEzMyFU9WL5fH3xZPr3x808aRKRqwoXumIFhKLHm6gomO/UU7kmLc1sHSecgFt+yhTzlKkRMTUV\nZlBAcYWC2LvX8p3S063MrcvFz9q12ADmzDnyxKghVWtdud2mxRQUYMxNT4f4d+7EYFpYaNG2Bw5Y\nlQWfD2HldnOkyM9nF9dihBp5rfWvVqzgqBYSYtn8sbGkYaxcyTtOPx3Nq39/5iE/33LBysrsuBIY\nyHuysliLujoAz1at4r3nndc3Y7+mBs2hpARBN3GiGeBTUuhHWBjvUAEbE8PvhQvZjPbutfiZrVsJ\nWJw8mfl4+mlSQBYsgOjCwlh3DQe4916M7+ecg7Bav57vFBI1NhbaCw83u9TkyfR7927mPjXVChSq\nph4aKjJggLQnZsrY+a+IL6JVgiPCDyrEFRWQ4+Hs0/9sOyZ0vqJdfz2yYvC9b4nPGydJp44V2bTQ\nhMSuXRB1RQWLqIFZgwdD6ArFdjjAcBGIPzYWom1tZbeKi4N4CgqQen/4g3kdxoyx6N8NGyC8qCh2\nx40b2fFOPhmmaWmxondPPglT1tVh92hsxPshYgGDmtRZVfXVk5KWRhnfV14xO5HHw9gHDkRIqAE4\nNZVnZ2TAcKtX831xMeNUj11XF/1ra0MoxsTANKp1BAWhSWiRvWeeMdtMZydCRoR5DAxEiGiKwJAh\nuNBvuQXuqaxkl29pgZOSk2HIQYNgUq1gemj785/R8uLjCVnwejluvvcen6unyuPhnWpHmz2bY/BT\nT/E7OBibjoYGbNnCvHm9rOHKlZYIVVqKhtXUZOV7wsNZx5QUBFdnp3kMS0sNxmPAAMakCAbTp0Ov\nGhHe2opATEsTqa2VCI9HxNciwwoXyTtyAQqrH3n32muHDwf7Z9t3KnS+Qd2r/xaRnwggXlUicpXj\nOIU93/lEpMcHLEWO45wjR7kFB4ucOKNTZOsHqLaZmfzoGb6ri1gLZRDNGWpsRKVPSCAGJDPz8C9Q\nt/OoURwN2tpMW0pNxUui2er5+YYJrKq3lpTdsgXGfuQRoCiUGEePhgnuuIOddtky3rlpE3ahadNg\nZjWifhMQMRF24NmzESi//jUMNHIkx6MFC+hPaCiMFhUF5V5xBfNQXc271MOXkWFpGvn5Fpd04olc\no0GUjoOgKC1lDrRgYWUlDDhnDhpnZCRjvvFGjMJaEfTcc1mz4mJztVdV0dfoaNPuOjt5Z3i4HY00\nvEC9X9HRCL3rrkNI3XEHm0djIxtQbS33jB6NMKqthS5GjuTZL73E8zXGa9Mm1qKhQQ6GBWuYwoED\nliEvwnr9/e8cBysqLCCzuRntKyICAabCvaaGTVCxhFwu1njHDvqk4/f7pdMdKt/velXecp8nLpdb\npCfdTJFNjlb7zoTON6x7tUVEJjmO0+pyuW4QMJIv7vmuzXGccf/2jm7YwE4zdy5Mfuut7Eh5edhH\nNKtcbQ/qVk5NteoDh7bOTha8f38Eyz33WDBcR4clhE6Zwk6nGdMFBRCtejUUvElBnAMCYPStW822\ncs89MPGMGdgDFFqiqwsiTUtjd01Kovrn0KH0bfFinqNu80P16+BgmPK55/p+fvPNVjNdgzu0SJzW\n5Q4Pt+htxQmurYUpgoNhEgUey8y0fDEt85uUZKWY29vRvE4+mbEcOABTTZjQN4r5nHMQStu3M/9q\n25kyhTF/8QXG6qeeQjM4cAAh8YMfcAwePZp7FTtZoVLr6lh/tdl4PFZxwuMhMC8mhnd+/jkCurWV\n6wcNoo+qHb78Mmusc9PQwHqtXm3pMJWVlqCrWEkjRiC4iooQtHV1CBwV1GFhzJ8W5FPUgB/9iDAL\nl0tk61bZ5R4lM7qWy+jkUslrTxe/T6Szy/B1jlb7LjWdKfI1da8cx1ne6/p1IvLD/2gPRSzIb9Ys\nfoeHE0ty/PEQ7oYN2AVCQiD6bdv4PWAA3z3+OGd4De1ctQqjqM+HxvD97/PMxESIKCenR8U6ETtE\nWhpqvGpRra0wZHe3xVlER/P5Y49xr8JP5OejUk+fjgr/618jML1eduX33+e6U08lpF77+MknFnej\npXF+8hOI+rXXeO8ZZ+B5EjGbUlCQAXD1bjEx5J+98ILFnSi2TWOjlQV2u9EciothFgWjUrTw+HiO\nlCNHMt/t7TDO3Lm857TTjryOISEInoYGbBx1dfzs3Wtg6xojo1AcRUVsIDffjLbx9tt8ftZZCHG/\nH1uMlplpa0O7HTKE4+uTT8LQqaloILm5rKHCUdbU8P3xxyOg1KaVkWHH5dxcBFRjIzYf1bbq683a\nu3Mn/y9Zgv0sNZU5VHudz4dQVpf+3r1cv3QpZoD33hMJDpac4FEyrnujzGxbLKXhP5GAQJEoz1dD\nPv8z7bsUOmnyzepeabtaRBb1+j/U5XJtFI5ev3cc592j0SmttOLzoeHGr1jB8efQKpsBAeT1nHMO\ni6vHiuhodp6VK83W88ADVjDvhRfYqQMDOVINHGhMWlwMU6al8RyfD0IU4ZobbgDmICiInV7R+9QY\ne+AAnU9PtyJ0zzzDseryy3mXloFRG1Fjo9kUNmzAVrNhgyF2f/EFx7q//Q3GDw9nd927l108MxN7\nx7ZtPPecczDC9m4ulyVmfvIJjKxg8F1dMMBpp1lyouLIhIQwl9u3YyCOi2OsGRmAp4WEfH1Bpu5u\nDLnbtlkdq4oK1lON4UVFaLAKZp6by/oqhMQXXyC4L7+877NzcmBcTcBNSUE4hYezuTQ1sT45Odjn\nhg61OmVaRz4uDkGWl2ehD5WVzI8GGCqAV2srm9l//Rfa8J49htccHGzwKImJEK/a2urqEJitrQg6\n9XYuXcrxf/Bgkc2bJXlQuMgOkeGh+TJpkkh7FmRx6B7yr7b/JwzJLpfrh0IJ4Vm9Ps5wHKfU5XIN\nFJFlLpdrh+M4+w5z77XyLepevf56TyiFS+SeO7vkqdVrxH3e9yCitLS+tZvcbj57+mkYcvBgCOfd\nd1no6GhU6NJSBI/XiyAIDrbdVfOwFFZBjxv5+Xx3Ti9T1Q9+wI75yivsqtOnc83TT/cU4Qoxpqmv\nR2iotvDoo+yUmsHncvWttllRwXPi4vjRelilpZbT5PczhrY2nlNUxHg3bzZN7P332bk1irq6mus+\n+ID+nHEGniqPh3sOHGDuKiuh7iFDGL8IC6HGUDXUd3aa5+WbtMWLydoPC0N4dnaaUDvlFN57++0c\nsz791Gpv9Y45OlzBQhG0YDVK19fzrDVrGL9uIJoq0dRE2sVrr/Gd14vgdLuB7wgPR2jpBuLxmOdK\nsZ8jIgyTurMTb1Zzs1XD0By8DRv4vrHR8gE12LGlxXCWFJb1mWdEnnxSZubslc7cKDk5fa+MvVvk\nkc3fbIq/bfsuhU6pfE3dKxERl8t1sojcJSKzHMc5WHHKcZzSnt/7XS7XZyIyXkS+JHS+Td2r9nY2\n4uBE1rLq403ibm2Run214n3+eatj1TsmXL1OKtAqKy3XpbMTQldbxdatnP9zcmCwqVPZ/X7wA4Sa\nupHz8mD6H/2IY1ZBAfaY8HCOQhdcwBFhwwaYOinJ6sCGheGpcbvxsng8huLX0GDCQF30xcVochq8\nFhFhgiovjzFHRVkEscZ+qN1Kq0JoJLKIhbAWFqKZdXRwVBwzhnEnJVkaQHAwkcg//SkG1g0bsKEs\nX26wD2432kpCAlqOBgCKIKxLStBaBg/+sqcwOxshqhG+mh6gial33423S4T/x4/nt8LODhuGcf5w\nze02VENF72tqQnhp/pjmprW2Ynw/5RSLLNYIcq1O4Tis2cyZHJkKChBm06cjrF9/HSHa2YkNZ8IE\nNsGyMgO237YNwT1sGDSi9qGpU/m+uNjql2mck8cjcued4mpvl+DODhmwcqUMmOSI/C8UOt+k7tV4\nEXlWRE53HKey1+deEWntqfwZLyLHixXi+6ebBn+2tmJAO77rMxEReb77crl1wB4IYOHCvkJHvRwH\nDpinRj0r6j0YPZoF37MHwgoLY0eaO9diVsaPh2C3bIEAp09HY8nORkvRUPatWxFSjzyCttHQYOVi\n2tsh6vPPh2A//BCNS/OremsH8+dDwBERHL9mzKB/e/caIFR3Nwyt2c2at+T3w7iaa5aUxI7pONyX\nksL/Dz+MEB45Em1l8WLsRu3taGZaC/0nP2HOLr3U7Czf/z4Bes88Qx9nz4aZQ0KwlWzcyHx8/DHX\nR0SwLnfd1dd4n5VlYQY+n4F9eb0k26rAEaGfegQbOZKj3e23HxnP58wzOfrp8/x+06Y0kjskhL5l\nZdH/G25gTI8/btUaxoyBflTrdRwriaMZ6Tt28My5c6HDigqEnQb6KVJAWxtzr3ClgwbhydOSRaNH\nI6QCA9HIBg2yulphYczz/PkI5X9T+86Ezjese/WwiESKyJsuPBHqGh8uIs+6XC6/iLgFm86uw77o\nW7SgIDbdz/4h4vhFpnWukPKwAbKvNUWk6BMW69CCdm43Z+xXXmHnOuMMK3AXH8/OXFODka+uzjw6\n7e2GhOfxAIv5+56IAc1Y3roVV3NJiRkj/X7sRVrVs6iIHfmHP+S7tDSOOx0dGDx37oSAL7+8b6rw\nypXs+nV1CJulSzliVFcbWO6nn7K7KmJdRARjamlhN9Yo6CuuwB6TkoIm9sEH5IsVF8MImhvm8fTF\n2lFXvmpZMTFEbyviYl0dYwsPt+oW+/czjk2b0DJqa9HeWloQwB9+aGD5Imgpra14jpKSDOZj2LAv\no5AnJ+Pt08pzapA/UtNo4ZISGD0317LPOzr6lnfW4ME9e7CPeTx2RE1OxtkQEkL/brkFLbGxkWeF\nhZl9sLgYoaPP0wz/iAjzYvaund7ejmDNzERA1tYabrZiLQcHQ3+jRpnDZMUKETnK+Q897Tu16XyD\nulcnH+G+z0Vk9L+jT8cfL5K5SuRAqSNjm1fL6n6XyPH+1SxCSAgaSn4+BNDYyAImJEA0IhDNww+z\nOymzP/ooi6zx5SUlEF1vsJLBg/E+XXKJBY5p5nVHh2GuKPj7mjVoQRoV/OKLHNUcB7V63DhLOkxO\nRhBceaVBjB44YEDjXV0G6TBtGkece+9Ffb/6anbZTZvoZ3Ex47jwQv7ftMm8NCIIlb//nWcOGoQW\nsncv8zZtmpU03rgRr8mhoFMi9LGpCaG7fXtfSAiPx4qSNTQwF3qU6Q2Mri04mHF8/jnets5Onuf1\nAg3yi1/0tW2p9nWktn+/aXCKZaTeNbebedQqjVouU2tmnXkmgknEEBY7O1nvJ56wfLSBA6GNd9+1\niOzAQMZeWMj9Hg/PU2FbXm55W2of9HqZq8hINDePx1AFCwq4VosdLlyI0Bk6FOG8erXItP+FQud/\navN6RbwdjRLa1SzppwyT2U3bRBLPZvHKy/FAaWZ1XBxeHD26BAbifr6zp0ZgYSHagSYk+v3ck56O\ndvSrX9mLlegmTIApq6utcLrWx1b4zoEDsZPEx0OgK1bAYGFhPEezv7Wk78qVaGnjxpkA9HjY+UJD\nD9a6lqIi/p8zBy3phBNgsnnzONIFBdH3RYusRpbXa/lEH31kAig4GO0nKopjxVtvWdTsuHEIlaKi\nLwdPNjYC97BqlVW6zMqCubROd3g4wkMjq8PDef+55yLUd+/m3v79EYIKYLVwIUyVlcXYX3oJreyb\ntNZWNpGqKgvci4lh/tWb2N1tuMeJiXiZoqN5Z0gIXiStj1ZaytxlZdHPri42qzFj+D82lndpUmdT\nE1pTSAj35eez8SQlQQdquwoP51la4zw3lyNTaKjFd+lROTiYvqi26XKxroWFItO+BdN8i3ZM6Byh\npfuqRURk6Jw0kU922kKVlrIzxcaya6sx9OGHDw+pmZ6OvWTVKhY2MLCvAbB30/iVAQMQDCUlpnIr\ncFRlJZGwWr4mKcmgUTs6rApEdbUBnH/2GbubGjADAlDHNYpXAaYCAqx+VEsLYywuNhyc2bNNC9i6\nlfeFhpq3SY9QF1+M1qaeugsuAHw9OZnPNYq6tRVNSYVObS3Cc/16dtqoKPMMZWaiVWi9JzUkBwXx\njuhohFNDA2uxZQtCoH9/hNgZZ/RFUtR7Ow76Jr6+ZWezJhERpoVWV7MuLhf2rM5OA90aNoz1GDuW\n+197DbQ/RSRISrJ0Gc0H05SYkhLuy821QL/QUPqenIy9KSfHCult344dcMoU1qCmxspz/vWvPLe+\n3jbO1FSL14qMZI20paYyf/+mdkzoHKHF1vfkIO3cyQKqETgigv/37bN8mI0b2Rk0ZaF3U5jRk05C\nM/noI4iivBxG6N108d96CyLxeCCcsDAIJiqKd7e2sqOddhpHH8eB4FTl79ePY4DG1Gih6mHDYJxn\nnmGX3LcPAla7yfTpMMnmzXzf1IRgam01QHPHgbFjYqwc8SOPwBihoRwPMzI4UsybB+NHRcGMc+ag\nIb37Lp8NG4bNaOhQ5u53v+P927cbOmFEBMKouBiD88svm3ajCZkKs/rFF1yjQiA1lTGEhDBP8fFs\nAgEBrJfj9K1CumED7ku1kSQlYRPyeBAYr79O/+rqzK2usBJBQWgac+dyjIyOZuzLlxOY193Ns9VV\nrVg/w4ezbgUFPOvss7m+qIiNSuucax5aaSlj8PsNqlU1rKVL0SBHjeLn008R0j1F9qR/f+5/+mn6\n0N7OWDQkQVtKCnT6b2rHhM4hbdMm+G3UfjQd2bmTHWfQIIC1ZsxgJ1u9GsbQImdXXcVCXnop1uiV\nKwkYDAnh/zFjYCwFqMrIsKL2vduZZ0I4LS1E5372GZ8rTL9iHwcHW+qEx8Pzhg2DQe+6C3uFx2Nx\nHKecwsCuucbwdhQudMIEQ92LjDRExPh47nniCWwf+fnYRrxeonTdbsbz+9/Tv4QEiyRbvZrd2u+H\nWbXYX3GxJakWFrKj/va3zG91NePWSNvcXLStsWMxrjY2ouVphcvNm9np1bsWEQHjb9jA/7GxzFVW\nFsI8JIRnBQXRt6Qk1qSpCRvWa69ZxQWPx5IkQ0I4nnR1mZakRuKAAPMc1dWhpdXXo3GoF6qsDEfC\ngQPM7YYNZp9S7OfQUPp40kmGFLhmDWPQGmOqZXV1QSebNhnmttttNcaam827N348Y9i61RKBn36a\n94wadXjcitRUkeZmCW1vkfbQr4A5+SfbMaHTq5WUEFzrjxeJbUDo1AfFS4zbzU6g0KDjxiF44uKs\nkJsalnW3VLS9ri4MrQ89BFH6fCz44bQibVoV9Gc/Y9dUTeSXv4QAFWxqwwaD41y+nOd7vUTXipjR\neeBAiPfaa9F0euPtaFSw14u2UF2N0bGxEYHiOIy1uRmBddVVljioTWOBtCRPWhrHpMJCs73Mm8fz\nkpPpw+rVMMCgQRhyd++mH+oBiouzWlG3386xRN3Kxx+PreT++9mR4+O5b+BABGxHhwGrh4TwM3Gi\nudK1soIan199lQ2lpcU8dW1taAXd3bxXj0SKYKg11ZXRW1sR2v36GTDYqacSSX3llYYfpIZsxYz+\n/e+xtcXGMpbp0/lctdycHPql2lR6OuEEQ4awHp99xrqHhlplz/vug5A7O82YrBCl4eGszYYNRMkf\nak9Tr5mIeOurpSz5mND5t7bycn4HBIgktVZLS7BXqr1DJGZ/T5rtjBkQRHk5Lurjj4eBtIxwRAQE\nt2sXjDBkCMRQX49QeP99Y9ZbbkF4ffop3yUk8Mze5Wg0ovWppyyKdcAA3qO1nkaNwk2ck8Pn9fUw\n23nnsZtecgnMeM89fK6CU0FSNCR/9Worpbt8OQTr8fCdAorp5IjwrNdeYxefMQPt5bnnYEqNhtYa\nJi4XfdGUj1mzYEq/3yBBIyLYlVetgukVYvTss3nHpk3013EwXl9zDYF9o0cjsNLTEQ6NjfRFI4H7\n9YPJn34aW48IBvwlS/h76lQ0T3WNqyDq6DC0QS3D3NFhQZAqFNraDDitf38EW3o6mq7WSF+7luvV\n/uZ227G2qYnPvF52vZtvpt8eD3M2fjzfV1SgRTsOEdbTpkF/N92EwCwspH8DB3I8uvxyoHRbWnjX\nL35B7l19PeNUIP7eQmfHDrPFiUhsab6UJR8SVnAU2jGh06ulpppRP7axRhoiUiXp6rNEPmiEea68\nEqbRY41mkc+bZzE4mv1cW2uVFaKjUYPj4iCm+noEhc9H/ankZOwUjz8OPITbjZr+zDPsSnV1vKe2\nlmc88QSBdh9/jHZRWGgQnFrRUSFUExOttrmWnKmqgngVr7mqinHFxxuujYKUHXccmlPvSgxa/qSk\nhLG9/LIZnktKeKZGaqugCwvjaObxcL+6arVo34knwkizZll8j8Iw5OQgBBMT0YYqKzlaRUez64vw\n/T/+gdFUCxH6fNitXC7GMnMmx6slSyxxcu1ahK3Oh2pBwcEIhVtuYe3vuIN3RkXRz4wMBOull6Jl\npaWxHr1tUsXFjFkz7EtLrZqqz8dYNBO8q4u1mTaNNVZv5PTphjqpZY60rHNUFNpvYiIY6v88AAAg\nAElEQVR2Mk1f+fnPEbC33kq8VUWFBYp6vTy3rIz+926ffMLm0pPq4z1QJHIUAdm1fUXk0/9/LTWV\nsJHgYJHE1mrxDokXz9/+iOTPy0PjCAlhx9IdPzMToK3ERNN2kpIgcA0mvOMOq36Znc0uv3GjRZlq\n5YHt27FxdHUBYRkZaYygcAhNTQiQuXNhnMJCg5FQYm5tRXg0NiII5s+HASoqEEYapKfBhTfdZK7V\nmhqeN2MGAufKK837oq2zEy2gXz9DJayvt5ghZViF7WxqMo9LezvErlUv9aihRQejohBAKiD69cM+\nkpJi+MpdXbi6b7qJ3VqEd159NVqGVuHo7rYjUFwcjKk40b1/Tj4ZDSMqijlXlIApU+hTayvM+8QT\njHXLFo5CEydizP/pT9Es77iDdcjPt4odHg800drKz7Rp/KgWq9AkgYHcqyEBsbEGB6taZ2UlWt3e\nvdgJZ88mp09BxLKzmZ8VKzjO6wYyciTCX9+jIF/Dh/dd16Ag6HL1ahER8TbV/Vv47Jimc0gbNQpe\nTm6rlrC4DMvsdblg3PLyL0eynn02nqg//5k4kJEjId6wMNzHMTG4jG++GY1Gi7498ghCoKPDAgAf\neABC7uiA4HrX9w4NxUaQkGB5Qzt3ohLv38+xLiwMlVx3tDff5Jo5cyCovXvtuBQcDNFv2UKo/Pz5\nCLGMDJhNkfIUy1gjmoODuaa01GA1zj2XnVLtCz4fjJOVhTDVsikqhDo6zPtTXIy2dPPNHDFffx3m\nuPZa+pGYyFjnz0dQR0YyXg0y/Pvf0Qi0tE7//ryvsdGKCZaW8nlGBsfe3bsNhF2N6ePGsYE0NzO/\no0dzLNWWnc3YtRJnRwces8WLEVgK6J6QwPsaG7l2/Hju93gQXjExzHNxMSktaq8JDeXzoCCuuegi\n6GXAAI5Hu3cjOOPjGb/joEmFh1vtenW7r1vHM1R9V1B6BY6bMOHLgG3qeAgOFnG7xdt8TOj8x5rL\n75eYhp7API38HTvWsIAP1wICrDRLfb1lnefmIgAGDYLx29vZiffv52+Nn3C72R2Li2G62lquUewc\n/X7oULM/BAbSr3vvxRaSn89RZMgQq7y5davlEwUH0w81Xrnd9LGuDoK75hp24Wef5Zpzz+X48/jj\nMMb06Wgho0Zx7HjlFa675BLDn1GbVWws72togCk02TIqyqpput1oIMnJ9NNx0DpOOqlnIXqeVVqK\noLrwQu7/5BOYo7YW4XLFFQieGTMQKpp7pMBgmhqQlmYpJzt2GHj62rW4m9vbmSfFPl66lPkZOJBn\nFhYaykBJCUeZbdsQAmvXIvSDgixmqLqauenutvrxCpEyaJDlXqkLvqMDD9bAgQi8Q50N48ebw0C1\n48BA3jd+PHQTF4dmXlPDO0ND8YJqOsxJJ7GGyclfTvHo7GQOIyNFCgvF21z/zzHQ17RjQucwzdNc\nL4H+HtjRcePYbdauxRj5VZAK6ensyJs2QUDLl7Oz+f1oPBMmGKyDovl3d0Ogzc0cx9RYqUKmtham\nzcyEYFauRBtQraOwEOKPjbXo4YICnlNTYwZREUPeU/tNaqoV4svJgdhHjcJe4zgw5qOPck92NhrR\n2rWAiiUmIkRHj+a4+PTTfQ3OU6eaF+nyy2HIri6E7PLlFrmryaMDB5qQ6e0ZW74cIdHURL8zM63+\nuAZTqj1nxgw8MtdcY56z9HRLVdEM9JAQPi8rYzwvvsh4Nm5ESOo879tnx0bNO2tqQtBqlQkNMdBA\nTLfbUP1OOIF5rKjg+zfe4EiozB4WxrhDQpiLmhrGExnZ16HQu02bhuFYtTGtaz94MEfujRt5X79+\nPKe0lM2rXz9CKj7+GEF3uFSPKVPYZEVEgoLE29785WuOQjsmdA7TvD3ucomIgAlHjEADSU421+1x\nxxEroe5rbf378/PyyxCSQoO+/TaG4XHjLJBQMXdqaxEICi2p8R/NzRCOBve1tnLPAw+wo82a1ePj\n93N/VpZhFi9fzlEqNRUG274dgdm/P1pYSgpG0E2bMDJ+9BFE+d//bbFAGs+zZYsdsYqKCKgLD7c6\n216vaRQKKxoSggCcNAnhumULfa2ogLFHjkR70Hrwgwdj+IyNRUhpasEzzzCP/fox9uZmmOullyyy\nWit4ijCud97h+LpgAUyopXo1o3zXLo6kfj/jiYpiftPSYNLCQq7XLP7164lzuuACtKywMKv4sXkz\n89TWxjM6Oqz6xdChaEQDByJ8Nm2y4D4RBMaFF9JPdaWvXg3jr1xJKo1mq4tAH5pfNnQoYx0+3BJ2\nNVCxu9viofSopsgIYWGW6vL3v9Pv005DG1qwwAoyjhgh3p25/xb+OiZ0DtO89T1CJzISAdPVBaG8\n8QYE5fVij5k3D0ZXEPDeLTDQ4EQ1a7q9HYHxxBMWBDd1KgT2+ecWFFZWxuIrRk5mpuXVdHejgZWV\nGbRBayu72YYNHLfGjeNI1NwM0WsmcUyMualFYPy33jI1ftcufkaMQDAp5nBRkXlaSksZT3AwY3z5\nZbS7hARLjQgPR1gvXkxfNm3i3RpxfMst7PxDhvC7qYmE1K4uPEsffID2kZb25XSDjg6M2wkJvNvt\nRiu44gqb+6Ag7r3uOoOIGDvWAvEWLLB8s+BghHNKCr9bWy3htqdSgjQ0IIRuuIH5f+UV+rRzp6Wj\nTJzIGpaXmyE4JweB5zhW+fPQhNSTTmI9VXhpLFFBAXOtLu2ODuKVFi0yo/PEiRyVduyAbioqLJZJ\nK5ImJTHPxcUmFLu6iLdSo/r27cxhWhrrVlAg4vXCB9rvo9iOCZ3DNG9jDX/cdpup0CefjCtSC7tV\nVVkqwJ/+hCBRVD4RdsaNG2FYxcK5+WaY4M47IZzYWBjM74eJ2tt5dlub2RI6O9E+xo5lF/zgAwg5\nOtpiYsrK7FjzzjsWi6EG3ZYW1PJduwwQ/rjj+p7p1ZPT0IDxtLKSPmt1SsVw1aRSdc97vfTt5psx\nwjY2YvsJDoZ4zzuP56SkYEyfO5dd9d13uVYBuVpa8BCqZvfQQ8zrmDHEMjU22hHkqadYmzPOgDGH\nDmWu3nuPOZk0iaNsSEjf+uciaATvvmvMqTExu3ezJpdeyppv3co6tLayNuHhJPqmpxtEhlbY3LTJ\nAhprarhW13HsWGhA7VUpKdYXx0H727GDtd+8GbryeqGt3mEKRUWsn4Kx19dz/UUXQT8NDQiW3huC\nz2dQHk1N3PejH7HJKYi8Yj7t28daBQSwBp2dEtLVIeFtR/+IdUzoHKYd1HSmTOFcrm3YMHavhgYW\nVMP+a2rY1ZuasHGMHg0B3n8/xPf66zCgqu9vvw2TikAQSogq4IYMwR2q7lcRy7Tu6kKDaW5mVwwP\nx6sTHIwgaWhAe8nLs/QArxcto64OBunXD4YNDeWedessu3jfPn60NpLmdnk8ML4eZTRlYto0dmP1\nAP3hD1ZxsqjIyr2EhqJhnHYaQkFBpWJjOQIqnKlmbft8MMT27aYpulyMOzsbW9mTT7JGIgii9evp\n08qVHNEmT2bs7e0cH3fswNs0ejQM3N0NE2uu1qxZMLpqDS0tCIHGRvqi0CZjx9LPjRvpn8vFOmqN\nrtZWBEFioq1zYCACq/dRvLWVsQwYYCiCRUXQ04QJfVEQVdBrDFVzM30ZOpQ1fPxxS9NwHOjCcQxC\nIzYWZ0NFhZX1UVufhoF88QUBhwqK/8YbxgtHsX2nQucb1L0KEZF/CCFKNSJyseM4BT3f3SmAtftE\n5BbHcRYfrX55G6qlMTJGonprLiLEgfzpTxBxZqbt5q2tHL0iIhA+t9wCY4WHIyhaW60OttZq0nbc\ncSx2YiLPmzkTRlm82I4OChw2ahRHi08/ZWf/4Q8hKK1frjXKNWZIUe2mTiVa+LHHEKKFhRwl+vfn\nCHLiiQY69vOfc0xToRMUBAO53XwWH48WFhOD4Dr9dCtPk5ZmR6g33qAPlZUIrJISixpWWIzycvrb\n3g6Rb9xoO3BUFJrFjh3mWg8IoB+aqKj127u7uVdxmjduROAPGcKxa+dOjj51dfwdHW2g7pqPpDFL\nXi9z+ZOfoIVERPBTXo5Q+v73WfO6OtN2Y2Lo244dzIPPx5yore1IxcDVm9fQYOWABw2iL4pxrI4L\nhSRVbSY2FoEfGMhnc+cyrs2bWb+ICMaotPfpp4xP8XgUccDvh74yM/ls1Cj+76mvrulAR7MdUei4\nXK6FInKjMvnRbt+w7tXVIlLnOE6Wy+W6RET+ICIXu1yuEQK86UgRSRWRJS6Xa4jjOL6j0TdvfZXU\nRcdLH+e4BuwVFbGYo0dDTHFxqPX9+kEA1dVoDr0RBk84AfuDuo572x8mToTRt2+HaWfP5jlZWRyV\nNGpXkfpOPJGf3u3eexFcGv+zdCnEmpLCbpeczA7X1YW35u9/N8/Pr37FbllbC6OuWWOZ7GqTUlW/\nu5uj0+OPH7neuTJpVhZalRagCw8noK20lOeOGMHPvn0w3cSJzOHGjQiCyy7DiDxlih0Purvph89n\nzx8xwrw9GptTXQ1DJiXh1VLgNHWxv/uupTMotnBHB0cqEXLezjmHeRwzxiK8TzqJY01cHO+88EIM\n2s3NCKHe+ViOgwa3c+fhhY5GPv/sZwg3LS1UWmp2lH37TADMm8d4MjL4fupUggDHjDHjr9vN96pR\nNTcjGLdtgy78fvs8JIQ52r+f8QUEsP5qQ+oBG/P+J4WOiLwgIp+4XK4XReQhx3G6jvK7p8jX1L3q\n+f/enr/fEpGnXOCWnisi83uA2vNdLldez/PWHo2OxTZUS21MvPQJAdQYmIwMiF5rjWuFTE0MrKrq\nWxmyqQmV1evlHi1fq83lQpWeMME+8/s5bmgm8nPP8S4NMju0JSQATZqXB1N1d8PIunO/9x5MHBzM\nUUyNnwcO4I256ioYOCcH4uzstIJ5SUkQc3Q0gu+GG44scHq3uXMhXI1FuvhimE+JX0P/w8IQILt2\n0d+2NgS6At0nJZE4uWYNwm/ECH7v388RKykJQ/Ytt8C8e/fymbrg/X6ESlkZ72pv57nqIYyJMYjX\n5GSY/+WX0Wq07pXLhfBLTcWBoILP52PeN27k3qAgi6Fpb0eA9C5dpJrWokUGmXrddYQlaJ6Z2oQy\nMmzzKitjvL1xgDRK/i9/scBSj4dx3XYbc5qbizdTY5ICA60CiceD4Jw8GdqbNYt3x8TYhiVswEe7\nHVHoOI7zpsvlWiQid4vIRpfL9ZKI+Ht9/9i/+O5vUvfq4DU9mMoNIhLX8/m6Q+5N+xf7Q3Mc6Xcg\nX/IG9PJG+XwQwO7dlvVbVgZBqk1hzRqYNDERBtq7F+1j2TKI9qKLMFJ+k6YgThr53NwMAR1J6IhA\nzA8/bOVG3G7sJ+pmVcgJzf3RceXmYmNqb0fbCg62ag56TBg+nMC2Q/F/tBUUIOQGDDBvzYIFCLJt\n23j3736HhjRuHOPYs4cjwg03sDNfc40VCtywgXf++MdoKmFhMPv3v8+c33mnCRVFz5s2jXdUVBCr\nU1TEuGfNQoPats0wa7R1dNBfTcLUGJvGRo6io0cj5OrrTSAp8qMI7/jFLyzq9+yz2WRUYKemEjsk\nggD67W/Rpioq0Bj37CHDfdYs7hk6FC0tNJR3aTT4X/9KH+rqmGfdwAYP5qeyEsF51lkGZJaTg2Ze\nXW2JwFp4LzPT4qRSUhjPH//I+CMiEOJJSdIVEChjdn3xzWj2W7Svs+l0ikiLiISIiEd6CZ3/V9q3\nrXslIhLo65bgrk774O230Qg6OyHQxERLwgsM5P/YWHZ3BYj64gvcv6oZvfEGDNfbe9G7OY4VQouN\nZbcuK0PD0eoJIhDTokW8/7zzTKtavdoKuHV3Exejdg/1+rhceC/mzeOeffsgWD16aVPB5Dh8rzg1\nh2sff4z2pOiCd91Ffz/91OpvVVezOycn08/Vq9nBzzoLRvP7ETizZ/OcykqEybXXcozw+RAYn39u\nIFhqXNZ5e/ttNAQN+RfhODRpEmObNKlvUW49Bqlxftcu0xJjYhBkI0ZgF9LIXi0To0e9wECYvLKS\nUIj9+9kgFMO5tRWNZMAA5kNB22NiLDVl0SLgKUJDEWJ6/IyL48hZWorGe/rpBid70UWsr9fLeB2H\nZw4fjta0d69V8lQPoRq45841Z0daGs+47DLLGWxuxuB84YXichyJaP0Peq96jLyPicj7IjLBcZzW\no/zuUvn6uld6TYnL5QoUkWjBoPxN7hWRb1f3SkREXC4pTh0oDVGx9tm6dQai3tSEkImJsaNVczPC\nRHcpEXZQjSxW4+eCBSz0nDmWA0MngYn49FP+PvFE7DwvvcQzL70UNfjAAbQZxcDZu5es9PBwBFVb\nG/drhrni8HR3W6Df1KkIwnnzCApsa6Mvo0dbJK6OKTTUMsTTDqNI+nx4ytLSrKbSZ5+hySgEqkbb\n+v0IoMREM5C//DIaitvN0WvnTvpSWkq/8vIQTE1NeADdbt4zZQpCvbsbbeDNN82gWlWFttTYCBNp\naIAe5drb6Y8I71LBmpTE+/r3Zx7UBlRUxLVXXIFGdtNNrGN4uB0177kHrSUmBk0sLMzwpx991IDr\nNcO/sNCOOZWVCK64ON41YQKCsKICwTJ7Nu/3+xFeWgZ6+nSOk4oseM45VkJowADGtX+/VRkNCmKN\nr76671F+/nzuCw8380GPjSvQ75P142fLIYg7/3L7Kk3nLhG5yHGcnUf5ndo2yNfUvRIE3hWCreZC\nEVnmOI7jcrneF5FXXS7XY4IhebCIrD9aHauLjsdV6PdDOLGxlsinQiQ6mp0oIYGYnAkT2G2bm9Em\nBg+GATXqNi+PZ5WU8LznnmPne+01K/uix6elSyGqX/6yb8eys60etubdFBejLZx+OkSjMKXHH8+7\ntTRt78jWykrcyyqIKisxMD/6KLvehx/y/fDhMGNlpdkTejeN7dHqEps2sZtOnAjhb95sAXiaVV1Y\niIDR7Hlt118PM2siox4JFDR90iSEYHk5Y7rxRtzku3aZ96a4GMYOD2d9Nm6EyUSsOkNnp2W/+/0I\nTb8foe5ymVE1PBwjb0wMAjI5mWe3trJpjB/P2muZnK4u01Q17igw0AD1TzuN445iFwcGQlOxsQio\n6mqe73IhRDSgMzGRY+ZLL5lb/tFHsZH97nfYa8rLoS+FDdF5HTgQu9Hq1Sb0li+HDufMYZxLlyL4\nFROqrY3jXg9+d11ML5vUUWpfZdM54UjfHY32DetePS8iL/UYimsFwSQ9170hGJ27ReSmo+W5EmGi\nM0r24h7fts3AqOrq8FgMGwYxzJzZN4Dr0HIqv/41hL9vH0RYVAQzVFdzbtbyuJ2d7DZDhsBsRUWA\nP82cSeh9RAR9ePttiKu1FSKKieHYN3QoBHfLLYZ5e9ttBvvg8VhNLRErW6IZ05rJvmQJdpMHH8T+\ncOAAPyNH9i2Xo83txm3/0EPsyuqm/eMfRX7zG/r3+ecQd0EB/VKIjbw8+qtxK5GRVuPr5z9HuObl\nGaxHSwuM0tWF8NHM7pAQnp2dbR42PaJpdK/LZX/7fPwfF2fu+YgI3jtuHEzschGaoLlT771ncCQd\nHazZkCFWKlohYZubDcA/MBChqe9NTkZQlZUxD93d0IXW7GpvR8sqKUHTGzuW8YogxFetwluVlkZ/\nX3qJ4+ezzyLIXS40oBEjDM/7uOMQ4qNH89zsbOZrxQr68aMfIfTa2wlc3L8fjSkmBmEubMBHu32n\ncTrfoO5Vu4hcdIR7HxSRB/8d/aqLjpOYxjormRIQwM795psWN6Fwk1r58nAtNhZC2LsX97cCRYWE\nQKBadygiAqbLyYEpi4ogkrVrYb777zeMmLQ0M5KOHm2CZdkywvNVDdccpcZGg9HUNmwYGsyOHebe\n1TrqmiJw1132/dixBoJ+aBs4EEYLDGRcWou7rY1n6C78t7+hure3wxQ//rGlJfRu9fUY7OvqDsaK\nSGwsTKBu6zPOYLcPCeG79HSOLRMmYO+oqcFYes013K9F+ubMQftSbJvOTp6pkA81NcTnXH+9rema\nNQj7jg7mWgVbczP9nDHDsrxbWgyf2eNBWPc+Rs+cCXqkxna5XMzX+PEWCdzVZZU/NA1Gc8gUoH/P\nHo7zn3yCYdrrtfy+jz8293tMDJtPWhobSns7mk10NHQVF4f37PbboT2v1+AuvgtN5//nVhcdL27H\nz4JqFnNtLYbIcePQAj78EKL/3vdIVOyNpn9oGzwYo++jj5o3SSt8aoG08ePZbR59FAERF8c7lYgU\n9S8lBYYWQVPp3x8GffllMxhu2QJjZGRYzlbv6p6xsdgD7roLYq6sZFfs1w/G272b49r06XaPevAq\nKmAmrYShCH6xscxHfj59iouDqbSqhccDIQcEWFrBXXfxjPBwmFXDAsaONQHc3s71Xi+C5PzzefbZ\nZ7PL19YidB54gCNY73GKsHZvvcXRLz0dgbx2rbnO9bgzciTHqVmzECiffmrwGWFhXBccjECJikKI\nvf46m0NiogkExUxOSEAD0UJ+veFB9dipnq3MTOjohRew54hYIF9CgkWzl5VBPyUl3BMZabl0ClDf\n0WFro0UUX32VvumxS21/Dz7IMT80FK3O7QYt4Ic/PAjkVfu/TdP5n9r6qJTNzRZUtXgxDLlokRlo\nP/oIlVYrXh6uuVwYHceO5QgWHg5z5edD8BkZEF18PNpKYSFM3tRkxFJeDsPGx3M804J1115rQkgz\n3qOiYHY1gk6b1reKpQgC6rnneNazz8JEmsWuRNu7vf66ec3ef9/wnF2uvpgt0dEI4d6xPJGRoOq9\n9x5CYOJENJ+QEEDPSkqsdtXnn/P9qaeyK+fkMM/jxsF8n32GgJs6lfGqgbk3yFjv9s47rFtmJpqb\nFv/TY5YG6aWnm9H2scdYG4X1rK42m5TbbblUjsOcZGXRx+xsA0BThAA9IpWX0/8TTuC7tjY2nfBw\nvG0jRvC8nTsRHAEBjFOE+Zg/n2jh3bv5/atfocW++aZ5JseMMW+m0kNMjOXiadP0iMJCtCb1wjkO\nc/744yJ5edLtDpDm4K/YTP/JdkzoHKb1USk1sS8gAAZQQlU1Wz0Sh31QnZ2jJ0wwmAzVToqLraj9\nzp0Q1/XX46HSJM4hQ2CSqCi0kvHj8d5cdJERpc+HoMjONpfsbbfZjpaWdvia3C4XxKaAXIWFAHed\nemrf6xyH41tGBv3dsQOPzbBhVu64vJxx3HDDlwWcCIx1+eV4zR5+GHtGezvj1ONETY0dO8vKEGTn\nnovGuWIFzFNYSF9uu83qQoWHY1T+1a/oU++2axf9y8w0jOURI9B+tPxvZGRfwHmFHnEc00pCQiwv\nrqODNYyPNyfDvHkIhEceMUjQ6dNN+KomrFn4BQUcOzMy0K5iY+nn2rW8T0MtRNA6g4Kgke99D6O0\nCtjf/x7B4/FgNNd4LBFMA6qR9w6J6A1Cn5XF+2pr2eS0TI7fL/UR0eJousRRbMeEzmFabXQvsCcN\ntIuJsTSAzz6DyZShJ0+GEZuaIMr4eBbx+uthGC078/TTMMXWrQiQmhqIb8UKCG/hQgywb71l+UWP\nPGIZxPn5ME9ZGdrBvfdaVc6f/xyPU0cHWsGR4moO17Ts7+Harl3YDgoLIdL0dHbHQYMY+7JlzMvg\nwbi3D8VT1ub3o0GsWcNuXFJCX3sH62nUbG4uBs25cxHEzz+PwI+NRdjX1tocpafzWVMT2tqhQmfI\nEDQdhRaZNYuj5MqVzGlQEHN62WV2NAoKQvgFBfGuCROs/Mu55zK38+cjKKuquF/LN999N8fb2FjW\noauLe2NjMZT/n//DexVT58QTORbq5nXxxczDli3Qy623IrBPPfXLm4EImpRqU9r27+c4uWcPwigx\n0bB2NHI5Lo5+jxwJisEHHxi86vbtIkVFUheRIOI+gr3yX2jHhM5hWkOUV/wul7ijogwutKkJopo7\nFyJfuBD1++KLYcxFi1jQ8HAIb/16Ft7rRTgtWcJnN9+M52HVKphP0f1aW9kVd+xgJxOBMK+7DpW3\nqamvMCkqskxjEQSkZsQ3NsIUdXUw2YgR/9xEbNpk2DcaF3TccQjgzEw+r6xE4MTFYVd5/XUY//rr\nEb5VVQjN3gbZwkKYsKGBcetRRTF/6urQhJ56irlMTEQYaIVRBakqLUUTGT6cudNYoh072BjCwpiT\nri60kIkTec6Pf4zAGDGCXf700+nX7t0866c/Je+rpoZ3a62whgbGf+mlfLdhA2O/6iqbMxXGTz/N\nOoeF8bwRI1iLBQugo+ho1kkzvoOD6VtuLs8tKWH+ly/nOaNGfbM1y8/HVqPCQ+uuh4czt2PHWjS9\ny8WajhqF8Ln+ejbV3btFOjulLjFGxHMEeN5/oR0TOodp/oBAafDEilfhK7R6otaG7p102d4Olk5G\nhiHrrV1rpUWaeyI6Ozpg0PBwjMqPPWZ1nDTCVqEWtKmxccYMNKMnnrA6S14vzzxwwKpT6G759NOW\na7R+PYyr2L5fGqwfAbpokRk+L7yQOJ8lS9jpFWqipgbP0ahRvEOxlhsb0Ty0EmVBAdrJzJmE8DuO\nxf1s326AXFOmICyzsxEaMTEQvGIme73M58yZ9GX5csarXrKxYxE8e/cSnjBmDMLq0UeZox07CMy7\n+mo2i3nz6IcW4auoYE2ffBLhMmoUGt8JJyDw/H5ipT75xIINFWfo8sv5OVxbuRJNJTOTd910E/M5\naRJzmZfHOjc0oInl5Zld5c03DTM5NBQa+vOfme9v0rQGeXU1tFJVhUBLTYVGZ8xgbaKjEWw33MDa\na7JwWRl93LlTauMOg6N8FNoxoXOEVh8dJ95QB9Xa47GiZYe29nZ2p337YCqfD4LWYDPFPNYqkBpF\ne/75eKYUHzkkxAjzcG3iRHbYJUsgoPh4q/k0eza795/+BCNt3syOrEXvNmywap5VVRCzBuitWoU9\nZ88eO9LV1sJg8fGmWXR2Qpjx8fTxt79lV73vPjQ9DYL7/HMM15WVeNQiI+m7RmfDCPAAACAASURB\nVCR3d9OX8HCE3IknwtiLFqENOQ7fx8QY5GZMDAbz6dM5BqxezftqahAmPh/jFYGBfT76lpDAuq1Y\nYfOmeU3aj85OrmttpT8vvghjahZ6SAjXaB15FbRHao6DYAwM5Bl5eYyjqQkt58wz+buwEObes4fo\nasUNiohgzbQyg8ZxHa75/czb+vU86+KLbTMqKmLu1VCdkWFZ745jKR0lJRzHXnmFY6hiC3V3S13y\n0UlnPLQdEzpHaLUx8ZLZVcvC1Ndz1Lnggr4X+f3YUvLzIaTt2zHwzpoFwc6ZAwGpZ+Wzz1jYxERi\nSAYOhCHS09EgIiMhirg4CERxh7WM75ln8lNaShS0Zg2//jrCpLub3XXDBrxqKSkIkEcewWC4ZQua\njNeL9nD11bbLappAbS2ayn33IVwU5CswkGOI2g+SknjOGWfwbq2K2d3N9Wo4dblwPyclwbS1tTBU\ncjJMddlljKGiwgoK1tcbfMWQIQiMl17iHR99hJDw+xFsH33EWO6/n3fOnIlW0txsx7a8PNZHkR41\nNkkEIVRba+7xxkY0VQ0X0FpYvWuja1N7n37W0kJg5KZNaGxNTWxGWVl2zD5wgL52ddH399+3YMG2\nNquVVVNj3rJDE20dBzr629/Q8qZMQcjU1+P237mTjcfvZ721uGJ7OxvAunWMt7MTIbdqFZqUgpAF\nBoo0N0udN+lostTBdkzoHKHVRXpFtm6B6TRhcNcuKzSnNbA+/NAAvUpLIS71IFxyCXYMl8sKxQ0b\nBkE9/bTt+Hl5CCGvF7V4xAh2W0WsGz0a244+d98+7k1I4NklJTBtWhoEk5RkyahqI9m8md21oIA+\ndHaibQ0eDL6M2m1ELPVjzRq0lt/+ls+Tk61UylNPMTYtHxwczE9nJ8wTGgqR799vuU8nncT1iojn\nchnhi9B/1VSysti5PR6ObprEqSh9Ho+FFWzciOE5MJCxNDfzHgUQU2PqkiWMPTbW3NoK+dpbGC1b\nZkKnf3+ObUlJCBWt8PrXvzKnqanY6VJS0DpycjimeTzM+ymnWBJoXR32Ok0N0ehprcrqcvGe/HyE\n3cSJ9DMnpy9xbtuGFqmQKjt2oDHu3o3Q0OKJzzzDPKSn058JE6izlZ/PnEVEYI9av577AgKYgx7N\nqi76WzgjvkU7JnSO0OoCwyCE9HRsDvffDyNkZ6Oh5OUZvm9xMd9phPH+/TDinDkQjxoy9+wx0O7s\nbK7NyOBZ3d0wu9cLocyfz3tGjODad94hbF0EwgwJ4b2hoWbQ/ugj06wmTmQHXLIE4tSjhNtNPxTq\nYMYM3lVQABPX18Mk+n9X15fTO5YsQR2vrOR7DV4cNIhdVUu1qCY2YAACp6LCDNCRkYxt8GDGk5nJ\nbj1oENrieedZZr26fCMiWI9DU0Ha2mCck0+GoYYNw96zYweaQFYW41GN7vnnwaEpLmYtdu6Eub1e\nNoTeqS1nncXxWY8r116LcNm0if5VVsLc991n5YJcLvrpdiOwX30VgX/22eYkEMF2tGCBYRZ3dVlk\ncFAQfRk+nI3oySfpx5gxVlPM67VM8pwcNkbVukaNQusqKgJCtrOTOWpoIG2lpIR57dePa4YP5/v2\n9oN2nLqYXnCpR7EdEzpHaHWenhiYhgY0i4gI2w2rq83OM306GkFZGcQdEwMB+v0sbL9+VjKkqckA\nrI4/HmJX+4IGqWmqRGurwThERx8sai9dXRBqYiJEGBCA1+iiiyC6oiIY7LXX2LFTUiBKLYSngkCP\nF243sT/d3ezab7/Nu8PCGPuhgWUiCNWCAsPqdbnQUkJCmCdNJG1vh0FmzoTB9+zhOvUIXnutIdX9\n6ldWIlnrjO/bh0BsbzdAslmz8OKtW2eCyHEQdrm59GnnTssm9/msKoPXyxgLC/l/zBgEhcJHVFay\nxr3jjKKjiUlqbORdQUEY3j0ey+HStVFaKCnh/aefzv1HCkdITobZHYf1rKhAAKn9q6qKd06cyNE4\nKYnvR42iP6rxdnVxreI1FRbyf79+/N3Wxjw7jiE1auSz42AX0wBNl+sgAkDtMaHzn211qT3YO1r/\netgwg7dMTma3Vq/AD34AA8XEoBFpxQitCJmaCuFfdRXMmpKCOrx1K7aK/v3Ni6PgXUOHGsZwUxPx\nISIwb24uu+SMGTxvyBC+y8xECDz/PIxTW8v7b7wRwi8tNcQ8RQ6MjmY3z87m2RERMEJAAP08nKs2\nMZHnaCqBy2Xu8WHDYJgDB7h/5kwikdvauD4ykvcdONA3RickxMYhwvcPPojwCwtDg7j6agRkYCD2\njFdfRTNSuFARS6kICrJsbbUjTZliRQ4VQkM1BoX6HDYMY/XcuRaAp3Fa2iZNQivw+Xj3nDlotiNH\nktqRnY2AmDr16wktPp41iIvjyKNwJEFB9E2P0ImJVuv8xReZg5IS1mr6dOa+ro55mzfPYFXOOMOC\nL4uKuKajg3fX16MNbd7M+E46iaPiffeJhIRIS7jn6/v/T7RjQucIrc6byB9ZWRCuxk4EB7N7JyfD\nVFlZeJGU6VSD6A3boBAQI0f2DeSaMsWQB2+/nYVXw+YFF/COwkKOIePHG9CUwjKoOv6nP7Grnnsu\nAik4GCJWjJYpU9BgrrzSsqibmmBi/fntbxFg9fUcnVTNvuoqSwNR1f2443hmfr6p6dHRFr09aRK7\ndkYGR55lywzcvLwcb43H8+UaUNreeccCAocPR7OprbWI8DFjsHGNHIkhPjsbwbF+vRUXbGiwo6xq\nEd3drNmppzKmBx6AAT0ebFdDhlgVCp/v8GkVOv6AABwHkZHYWK67DqF/yy1fLnvzVe2CC9A+1MOV\nlYXg0NpdaWl89tJLZojX+Y6MtEqfCoT2xBOGf11Tw5zNno0NsaEBYXjJJWiMBQXQdXg4gvjzz0XW\nrpWmA00SFJsiHZ2uPlWVjlY7JnSO0Ooie7LJlRg0knPiRHawH/wA5szPh6nOPx/BM3EiO7vaG1Tj\n8Xgw/n3/+4eHiaivR7vR2tR1dWbDaW0l+ljLnaSnI4CWLeO7vDwYrrycZ+zfbxUO1E5RUQEhJiez\nmyqS3MqVGDvr6yHMzk6I/KyzYIbf/IY5GDgQpkxIwJYxa5bV+dJs6V//Gk/VqlUw+u23wyx798Lc\nilWjqQe9S+eWltKvjRuJ2tX+5eQgsHJy+F/hHX79a7Q9PWaqEbq21ipuBAczB5MmsQZ3320pIUuW\nsLP7fMb0breFNLz4Isx6uDw0l4uQiMmTyVOqqmJOtA799dd/MyLbuxeQ/M5ObD3R0fRLQdajo4li\nfucdgzzRLHQVPK2tzN0550CPGzdy/Nf6ZPX1xPl89hn0uGoVa7FqFSYBBdLfvVskKUnWto2T0G2L\nJCgkSfbvN8Tco9mOCZ0jtIb4HkDyqioYICIChtHCZs89h40iLg513ONBG9m5E6Kv7gEB0zpDxx8P\nczz8MOBLh2aljxnDM7Vu9ve+Z9+98go7v8/HfcXF7P4aadrZae7jNWt4d04OAuanP0Vd1kqd6hJt\nbUULcLutfG9UlCHIJSbyzn37IOD8fIReSgrjmTjRMs5HjECwLVzIc9PT0fReeYX+dHRYYbuAAIsR\nys7Go7JkCcclnw/bRVAQ86r3aFlhPert3s2O3r+/cUVQEHOyejVCU7Wd/v1hymnTLECys5P5KC3l\nmoYGhIvm1DU3c/T94guE7pE4z+dD0OvRKyrqICTE17a6Ogz0bW3MqZYW9nhMqDz0EHP75z8jYGtq\noI/e8LO33MLReeNG6HDiRDyqiv9cXEwQY2Mjm4ZGfnu99DUm5qBDwBcdK3fu/IG81PWC7AqedLDs\n19Fux4TOEZoTEAgR5+SwK7e0GMbLSSfhMk1NNY9VXp7Bko4ZAwMWFlr4/vr1ZGZXVxvi/8GXORBA\nfb2lRLzwAoTm87F7KoJ/ezu7eXU1n6n20NCA9uNy0ceoKPr77LP0NygIoafBZ1oqefx4iF6hNlwu\nrv3iC8tS7+ykT4GBUKGq4+pxE+Gz/Hyr861wmdq/6GjG7fNZYmp4OBrVAw/wHMeBOeLi6GNnpwmb\ndev43+dD+HV38/zQUJ7z/vv077zzYMSUFI49Gzfy9ymn2HyrIVtrS2nTBNypU62mmSZl9m5+Pwbu\nJUtgWJ+P+Whtxc62bJnVcO+9xgsX8hMZyXsUnL662qq2aj8V0jQsjP4fOMBYQ0MtDKBfPzaWN980\nj1tCAtcrxlFpKWvb0cEcKlRsaCjv7tePfvbrJweiRkjQ7jpJd4rkvfibxe8/PGDkv9q+E6Hjcrli\nReR1ERkgIgUi8n3HceoOuWaciDwtIlFCQb0HHcd5vee7v4vILBFp6Ln8x47jbD3qHZ05k503NdVA\nrn76U870n39udZ3q6iyj2OdjoRWkW2tHd3XBLAMHWna4CELtz3/GYO04GIC1NnlREQwYHQ0T6LMS\nEhCIq1YZ/GZQkNXVCgmB2ePjEWYVFTwnPR2GmzwZG9Dw4ezwAQGMSWujBwZCuCEhVjVAIVKVEiMi\nIN6mJquyMHs2hK2GS7cb+8G+fRbLo8Jbo7vfe4/5SuoJRCsvN0RFn4/5/cc/rELGgAGMZcQI+pGb\ny7NHjeJ3ezvjDgj4cmkfff6LLzIv/fvTTy3NrAK/rIx5iYzsmzjb0GAIkAsXGqbNJ5/Q38pKQxZY\nvBhhr3lzCQkYbEeOpI8ffICW4fNxj85BXByfu91WZULxsquqEC7jxyOoKit5t2rgCnsSG8tzNWWk\nXz+rW66xUbm50PLJJ/PMkBAJf2+1nLltnUiDyOqgE8Xl6vhWecPftH1Xms4dIrLUcZzfu1yuO3r+\nv/2Qa1pF5HLHcXJdLleqiGxyuVyLHcdRH+4vHcd569/ay7FjYWhVexWkXIRI2hdegBHCwghge/xx\nGPq55xAULS0WAKZBfFo1UgQB8qc/wYzp6X0T9NLS2I1iYjialZezE4eHEyy4bRv3q4tdq0kmJ8Pk\nalNqbv6/7J13eNbl9f/PMzKe7D1IQkIgQNgYhiJLcWMd1F13Xa3VVqt+1c5vxdZalbZqratuq6i1\nKjio7CEQVhiBhEwyyN77SfL8/njleAKFzvjzd/0u7uvKlfV8Pp/7c9/nnPvM98EPogiAU6bAAHPn\nHulTuekmNLTmZu5/+DDamRZsxsfDlD09EHVTE6r8RRdZr6/QUITxJ58gHO66CwZpaaFuSuuqVFhp\nt4q0NNPjY2JghpgYtEkFsGps5LlXXYVT/LPPYN72dgSOwoJqW97jAck/8QTzGTmS9VbBGhhoGDeB\ngWiN3/ueVc1v2oQJqE58LZNoa+P6zEwEVlsb73jwIO/r74/zvrPTDq7OTuadmso6dnSwTklJXHfO\nOSSLqmCOi6PVzZ13cjjl5DCvnBzrD1ZSQh5QQoL52h5+GG2soIDfNQro58f67tjBvg+M6OvOl8ve\nuUXay8LFN2myJCdvPSJoN1Tj6xI6F4rI/IGfXxGRNXKU0PH5fPmDfq50OBw1IhIrIsdIHPmKhkaa\nOjtNDd64EQJZt84IXlHhcnMJOVZWwqibN3PqKTi65mPo0M6RXq9FS+rrYfqFC0kIa21F5V68mNMw\nPR1mVPVZr1MmGDnStKHDh9HA1CwqL+dELihgvhqSFuEep55qc0tOBvJgzRr+l5/Ps51OtLDbbsOE\n0AhUWxv9qEJC0C56emAqjweH8rXX8v/2duuzrk0C9++HaXp6YN4rruAdXn/d/DoiMExGBgJdu1Uk\nJ3Pit7UZKLtqkg0NPCssDMZXKFSNaJ10EsL+4EE+s3s373rGGazXrFmmGbz0EnuoqQ1tbTxLEx09\nHuuL1d5u2cuHD6O9NDVxH3XohoaigSr649ixHEZOJxrjG2+wtlddZSgBfn7kY116Kfe75x6eqVjX\n8fGWduBysUdbt/KcgABoyeWyQtrgYN4zJIR7uFwyLH+N9J01V1582SXffmPoWUrk6xM68T6fTz1u\nVSLyD4s8HA7HDBHxF5HCQX9+2OFw/FREVorI/QPdPo917S3yb/a9+nLExEAwHR34K7xeog3NzTC3\nMr3Xy9/y8yHYG2+k0lnxX4YNs1B7eTmmi9rmfX0IMEUoHDECIaOnZGQkJ/ull1qWbU6OEajLZdGO\nrCyEU0gIpt+tt6J1KHRobCyE2d+Pafad72BG3HijmTe2cIbhkp8PWJSGkP39IeLBIe+amiOdyGvX\nkh+UkQHxjxyJj2rtWhy9ISHcMyGBSFR2Nus5cyb3VtPskUcQ4jExhOk7OjB5VbhpJbbbzVoFB+M4\nDwvD19Hayt5MnWpA8drLPCaGpMS9e9EI3G7WqrbWulyoE7yqCkHtdHLdRRext/PnozGsW4cG5fOh\n1V1zjZUvNDby7JAQhKCatYWFXLN7NwJWsZZfeslMpN/+ljWIikKILl/OGixYwLq++CJzvOgi5rhk\nCXO44gor0lV6q6qC5jQaW1dHfllBAcJqwQKRgwfFdeutR2CBDfX4yoSOw+H4XESOUZYtPxr8y0BL\nGd8xPqf3SRSR10TkOp/Pp9lkDwjCyl/oafU/IvKLY13/b/e9EoMwrmgMkqSsLOzmsDA2TATGnzED\n4snJYfPi4gxl//bbST3fudMgLFwuTqff/95Op4ULLbwZFgbBBwVxXWAg99SCxaeeMvt/xAieq+aT\nx4MW8utfw9CHD+N8VpOhutpC1FoPpRGwkhLmtHixMVpuLteNGcO8hg9HOBQXW+6LmjM6oqKsA0Jd\nHSfpeeehBS5ZwtcNN7BuXV3cW2E8IiOPBKhqbibKV1mJBvCd7/B+sbEw4PDhCOXVq3mHO+9ES/vj\nH9Eu4+JwoGvGclsb6zVzJnPU5oLqg7nmGhzQnZ1oIkuXsj4PPYTJef31vJdmZ9fXs14aTZs1C21p\n+nTWubSU+Zx5JoJv2zZz8MbGYkZpxvWIEeazCwtDgLS1mfO6tdUykx991KKp2dkI16efNjjbH/3I\natRef5110l5jCnI/ahQ0pr2uFJytuJhkSxGDbf2KxlcmdHw+3xnH+5/D4ah2OByJPp/v8IBQqTnO\n58JEZLmI/Mjn820edG/VkrodDsdLInLPUM27vp717+0V+dGPHfLDqdfIxBUrDCSqtdVO9N//HhNo\n7142eMsWBFJFBf6M+fNhsDfegOgDA/l/aipMdN99EGFnpzlotddRcDCE39MDE8+YwXM0ynTuuczl\n7rsNdlKFYkuLZaQqVk9LC/fZs4fnTJ0KsYeFIXgU4e7llw1FLi6O94uJQWt57z3uqYWYg0dEBO/8\n2mtWy6N9vA8dgugbGtC8+vutfcyxsI0/+wyBM3y4nd5TppiDeuJE7l1UhMDQ4szNm1lLTQxU01Wj\nYmVlzEE1xPZ2GHn0aPazuxsH8cqVXFNWhgAfOZL1HTcOra+8nHV58EH2MiLC+ldp6YiiFE6fblni\nBw6wpi0tzCEx0eBZHQ6u83igE0258PdH4GuZhmrrxcXkRGVkIMCbm7mn9jdzOqGPjAyep21zxo/n\nENXSm/LyL6EsfMHB4ggKYq2/wvF1mVfaRO+Rge8fHP0Bh8PhLyLvi8irRzuMBwksh4hcJCJ7h2pi\na9eyH9qK+tlD58uTTqc4tIZHhNMsIgICOPtsGCM/35y3UVEw6EknIRzcbiIw6svYvx/BpMJLE9x6\neyHAyy6DOD76COJSoomO5mRSBL2ZM48NDxobyzNbWgyCMjgYIRMbC0jX229bo7hx4yDWhgZyPLQy\nvLgY7emHP0TN14xrnw8na0cHiWtVVTBXdDTa24UXUlBZWcm7er1oIfn55kS/7TaYLiQEDWhw54n2\ndhNEeXkIkuHDmVdpqWHMjByJQ9zhwETJy4MxAwKO7IGljQm1j5aI1aBpi5+LLoLZKyq4JjTUcJtF\nmE9dHf/XpnZLlgAbEh6O7+XhhxFUkZGsy8SJEJEWiKaksFaZmaZlFBYi2JxODpPOTkL2KhgXLeL+\nGzag3ezbh1AoKGD/FNXgzDOtFMLjwffY0MCBk5rKHqekoAH5fAY2LyL9gYHS6oqU4PJKKU2dI0E1\nruN2vx6K8XUJnUdEZKnD4fi2iJSKyGUiIg6HY5qI3Obz+W4a+NtcEYl2OBzXD1ynofE3HA5HrIg4\nRGSXiPyLKaD/fBxdvdAZECG+088Qx6YNaBsjRkCwy5fjNJ4zBw1EmeuUUywBS0dAgCUVasW0whJs\n3mwlB6eeCoF88AE/X3st1z37LJ8LDITgFOJS0QK1lbEIhPTkk9ZxwOOxk1RfKiOD6MgXX+AHuvhi\n/qemh7a8raujjmfPHhjwpJO4386dpt5XVsJEH35oEBIeD3P/85+57ymn8PnSUqsSP3yYEzw6Gi1g\n3DhLsps3D99LcTFCZPRoq+8KDUUza2hA6JSUYAKtWsXedHXxDh6PwXpoHdOECQgn1TQ078ffn7yb\nrCz2RNsP9/Yyv9mzEW4PP2ydKDTxTkHCli1jrnPmWNO89evRCktKrHI8Ph6Np68PQT1mDD67YcOs\nb3pvr7XnEUFYL13K53fu5HmJiRw4Lhf7uG8fmnhZGTSgmdnNzQjr4GDu4/Xa4RYQIBIQIA0SLZ3d\nLgn3eeU912Xy7oWQh0I5DfX4WoSOz+erF5EFx/j7NhG5aeDn10Xk9eNcf/pXNbe5c0VcW9C0KxpQ\nOpxVl4t8voJIyfDhBpglwsbdeis79M47EFhyslX8imBKJSURSfB4LLKkKr5GnrKzTeXu77eSgVtv\nxUHd14fPYfNmiHfjRpjw5JMREKmpnNz79nGf5GSrOVIzIzHRCkqPbnYXFoZJ+OKLJhRdLghd+y2F\nhsIgzzwDMYeHG2BVZqaVA9TWIhQGa1grV+Iv0rkoHo32X1ehk55O2UdhIcL3o4/IEA4K4hnV1dbt\n849/RAvLzoYZOzpg8PZ2fo6LM7iO6dP5fd066xU1Zw7CrKWFcLQ24dP+6z09FEXeey/7oPNSaNbs\nbPZd/TS7dvGzZhWLcFDk5FjFe3Ky4SM3NFizRY2QnnPOkftSWwsdDB/OV3Ex89L9qahgXTIyWOdl\nyyzfSlMq/P1N6OgBNGBeu8paJKqvU3wiIu3t0hnok/XrHVI76u/jC0MxTmQkHzViY0VGpHHQ/fzO\nAUiX+gvx8u/aBbH09EAYqvLm5lrFdkcHYWCvl/+XlWF7K6qegmUp5rJGavR0+uwzVPXBkSElmtWr\n0R5Uw4mLwz7/7DPDcdbI1eDwg6bWBwdz78GakY7ubhyJjY2m2otAoHV1/F5WxjuUliJQ1EHZ18ea\naJZ1Xx9Mra11HA6DrVCEwu5u7rt3L6bV0dSdlMQ7//znOGI1z+jwYZisrAwNqrMThtaExa4u9kh9\nZQrTmZtrtUbnnw8zbtuGH665GQEQH89ntbo/NJR32rsX34+fn+E9h4ZCHE8/zbvU1iI0NDE0Lc1S\nLhYMnK/PP89nnU7oJTvbcmfS0ljPBQtM6Ozdi8DVjqSKLR0dDUrBa6+ZH0Czn9vbcb5v3sw79PQw\nb63qdzr5vbMTIeX1is8dK3JwrxS5R0tzbY809XbJgQMe6Ys/IXT+rw2XC9pMSxv4Q3Q0BL5zpyWp\n/e53bOL48TC914u50NGBPyMuDkbYtg0tRU82PYGcTh6i6n9AAFrI+PF/X31dXY1q39yMat7TY43a\n4uMxLzweTuBduwzlrr3dWsWmpHDaHzx4bLiKDz5AqCUlWZ9vxaQRMW1MT+XycsxN7e7gdiNka2vR\nTubP57PalWDePD4nYkQfEoKmcdNNx67oXryY9VP/Sna2dXRobsZ8+f73+azHgxm0ZQtzjo2FSWtr\nufasswzitaMDLfGss75styJJSaxfdrZpn0FBlhNUXIxGlJ7OfoiwznV1Vm/X04O2Nno0771rF9qk\n04kwefNN1kV9ScOHQ1fqS7r/ftNCKirwGQUHW3+u5GTuP24cwQm3m2t//WsDCktMJH/n299Gs9EE\nyro667g6bJjBw44fL5Fr1ogjt1necd0uq32nSX1bgNTtERk2mcuGepwQOscZXi+H2+HDIrNP9cnF\nfgHi1CxSbU7W0wNR+fkZyFNPD6dqVhYXNzdzMqqqrczV18dnNONZSxHq67nm/fetodt3vsM9tOap\npwdmCArCj9Hba+hxTU04c5uamIfi4oSHM5/BviYdbW34RLQ1bVYW9/N6rfNmTY1BV4SEIAjUFAwN\nRRC3tfHc6dPt5P3GN/hSaNHXXzfMmBEj0Aruvx/mS0pCcGi/+MJCmFejcX19MNlJJyEEEhKoZ9NM\n7MpKAwlbtMggV7WVj2piH35o7XgnT+b/YWEICZ+PZ6jfTSvVx4+HidUfpV1Ce3utlY06qwsK2Lfn\nnjOYUO28Wl5u+NdasNvQYN1SdVRUMEdNdOzpwdZPT8fUCw624tsdO0glaGvj66mnDHhOAdWUxpS+\nJk3iefX14ujslH5xSG5QljR6RomvxUkq1gA6hvqrh2qcEDrHGeXlIlsOwkPr1/TL1IBYSVdTIyXF\nNABNMdcNVYCs3Fz+53AYOJYKHPXXREWRpRsURBSkstKqh5cv55QqKMBP1NHBZJxONJFJk3jODTdw\nIm7bZoT8/vsmHFJT0QjUjFu6lO9aCZ+WhhM1P9+wdkJC8BNddBEakEIhdHQglFpaOLF/9Suc6TU1\nMLDO/6mnYMCFC21BnU6iWmpOOBwGE/rpp3xpVPD552HU4cPNt6JCIjycdYiMREN67z20ldRUIoUh\nIayNrvXRYPoizH3rVkNTvPFGy7jWnKbmZu4xfTrr4HRiznR1maZWU0Mk8N13WZuSEtYmKgqto7oa\nYfDssxBUTQ1zdToRikFBmIZBQeR2DR7araG11dIZNAVBtUQRhGJHh/n/Hn2UvRqc7d7fb109+vqs\nGWRmJuZ+UZFUZsyT7c4LpLzC/aUs9fcXEd+RWGtDMU4InWOMzk6R2jqRorWKbe6SP3ecK/8Tt17c\ndVVsblISJ43a952d1sXA5WLj+/sNTqG21nwQDgdEtmiR+VceecSiRfn5B6xPtwAAIABJREFUnHRN\nTRClnx/Eo1EohwO/zZgxRC6+9S2YITHR2rP4fOTiqNMyMBBB0teHsNBaqZoa605ZXMx9Ro/mmZMm\ncYorPozbjfBMTSVxLjQUJ+nOnQjH+nrrivnWW/gdtMxi40aYLyiItVA8GO3+oIWrhw5RNvHxx2gh\n06dbEeiIEYCN1day/grnEB+PWeVy4Xs71mhrI21h925+nzYNDSwjAyGRkYHGsGEDgnXKFA6E6dN5\n3ne/ayZtWxvaSl8fAqeignmr/yQnB6Go7WCee457ezwIwdJS1nj6dAOif+YZ3luT+1JTeeZf/8o6\nf+tb1gYnOpqAgh4y2oqoogIhFR5u2Md6OA4OyypyYHY2B2hJifgteVjkebfExFjUvXvAbTi4Pnko\nxgmhc4xRUyPichqUS2uryJYp35KKkbWS+uQ9/FGR7Ab6Pkt7OzsUFGQp5g0NVuOiJQinn87pqH2d\nNPtU4Qqam2EMBd12uxFUg5vxaSV0ZCTMrB0VysoQTjExCI9Vq5iPtkrp7UV7qKxEk4iI4OW0za32\nTB83znI89u/n3fr7eXZnJ0Lggw94zvz55KFoGNjlgpHU4a5CJzub98vPh4kVJS842O6v3T41Ajdl\nitW6tbRwMo8aZfi+r73GHLQVcG7u8Tf1jTcQ0AcPsjcHDnC/hx7i/3FxBlju9RrkgwjzCwy01Ife\nXgRtWJg50hUyQsHt+/pwpGs5hgKnKT5zUxNCUCvqDx9Ga7vjDpvzYGRJEWjl4YcNQbClBcH1xhv4\nZ9QGSknhHRVZcLDAUbhaBSvr7haJipKAKxZJ1g4r0N+/X6Q1jrNpsNU3FOOE0DnGUBhdb4gFCyJj\nXOK7/AqR535kLVZ6eiBeTVarqrIGb/39xtQilAQUFlJXU1bGSb1xIzusNUBaCKgQFQ6HwVb09RlW\njgj3z8vj9/HjOQkffhgiysoiz0V7V2t0bM8etAOPh+NMa5AU6Nvfn2fX10PEY8YQQVFo0dZWax74\nhz9YON7Pz4pOlULVz6WlDomJRPG0Q2ZyMut2/fUIwpdegtnDwmDe+noEdFcXkZhp0yyfSEdmJqak\n2819NcHw8GGEmwj3SkhA+1Cnamws67pvH89PTMQ3NHeugbLrwfLRRxYiV0RHt5u10pSE8nJzUCsy\nwMiRvMv+/byn9jg7dIj9jo1lrdXH5u9vuDcqGI4eJSU8PzHRCjfj4qDHPXugsVtuIas8LY2/a8RN\nNR4/P4TRtGnkUp11lvR953apagqUiRM577SPQEnMf8VGxx0nhM4xhkKjZGVxaCskb1pmEOpvcbFl\nvXZ0oJ2EhUE0mn+jXRwyMrjJgQM4TTMzIQrFED7/fB62cSNaQ34+jKEEo9nHir3S3893zSdpaYHY\nNm1ibg0NCDU/P068iAiYJjISpg4PNwiK+npO0rvuQqBopqrPR9g1IIB7BAZaYWtAgPVwUp+DIg42\nN1sh68iRR4btv/ENTJe8POY5fTpzzcpCuDQ0mPamGDBlZcz35pvNp5Kfz3oFB8M455yD8Jg3D5/O\ngw+iMShwfHIyp0ZZGe/W1IS9oBXiUVEG/XrvvZRVPPsswnnnTj7X0sL7x8cT8Tr/fMzj5mbeMSDA\nGvHFx6PhLFrEMxVCIyyMd3E4oJs1a5hvTY1l4PX1Ycr6+fHO06cfSZhhYeafcTjM0T9YuI8bx/tq\npDUkxHCYgoMRrAradu+9Il6vvOy4QdY/wm3T09mqMWNEbnrzq+GvE0LnGCMqCjq65WyDBmZE0o7k\nhhssFyQw0GAW3APLOXeuFX9qH6jCQr6Xllp/8+pqTv+pU9lxddZqDktQEAy5dSvEptpEfb2dXgoL\nqtnQp5+OkIuNNWd2VxcvFBZmPpTeXswUrxfmy8tDEPX3E/KeNAlz5IUXDGNGAbS2bbPGfKrlqDbm\n74/mdd99R4Y8goPRxFwu1qWqikiVdjt45BHCvi0tMGN1NdqPw8Hctevlvn18P+UU1uvKK60Z4BNP\nsJ4KEaLdE5qbYfRp07hezbv+fuYSGMh6PP44jJqby8GiJRH+/qxBSgqHQ2sr2pOu46JFOPO1f9m4\ncRYpa2vDbNu/3yKC2nq6rIzwthYDv/EGz9Oq9SVLWIuGBszC8HCqx999lwNLAchOPRVzuaQE4bl1\nK3SmB0V8PPO64Qbo4803v4Q4aY9MlnW5sZIyYMUpUm1Rkfmvh3qcEDrHGcHBf58YKiKo4x99RNZn\nVhZCQgHT/f0h9p07+TkwkJMmLQ0i2bDBIgoOB9pMeTlJZLNm4RspLra+T5r1On26hW61b7ifn3XO\nbGtDO9ICwIQEiPDAASRmYiJEW10NoeoJ2duL+XTvvRBpayumVkUFAmrtWiRweDgErXVB2sI2Kor7\nqVCbNAkNKSXlyN5ROrQ8RLW3OXPMHEtKYh461HGrOSwbNpDvtH27mTkpKTCuCEz9+usI67Y26zaq\nvcS0G0dQEJ/V/CqFK1X4j/fe4+e2NohA+5J1dtohohX7kydDJFOn8rcRI/i/CmQR5jN5soGfa7Z2\nfDzvERfHWq1ezbocPGhtjP/0JwT/iy9aztXNN+N0FjGsH58PwVZaCn2UlXHAVVebhnbVVaRedHSg\nadfXizQ1Sc30s+RQpUtylll2QmcnS94wxfDWjq7v/W/GCaHzn4yf/IR6mSlTSMK66SY2s6WF79XV\nR3Z5bG+HwLKyYNjt2w3aVIXH8uXWMVTVdhE0gvPO4zkeD4T40EMQqDa6U2bu6UEANjYiBKZMQU/u\n64ORduyAmrxetKnduy213ufj+dqypqDAnMIJCZgJhw5B0NdfL1925FyxgutbW3EWR0dzryeeIEo2\n2DfxyScw4UknsVYvvshpPnj4fGDbbN7MGqmJoXMLCTG/iEJN3H47GkhXF9co7KjCjXZ2sj6lpcxH\noTEU2VExpfv7WVPVhDweuK2jA42so4P5KOzIpk2YR2+9RXrA8uWmyX7722hyjwzYLfn5zEfhaLu6\nMDO1rEafr8706GjWSJsfBgfzHi+/zPorNk9QEHtaWIha3tuL4ImKMrM4PBwt/LbbDHz+ww9FwsKk\nI3mUNOeFfQk8qRkRfX0ijoFC9aVLeZ2hGieEzn8ypkwhu/WFF0hsGzeOzdyyhVMlORk9VXFbhg1j\nN3/8Y3b21lsNeKq1FUb86COiSkFBMNOoUYbA98c/oln94hdoRX/5C0yjDK2gWu3t/G3yZBivoAAm\nSEgwtLnaWmuvq6FTLcmoqYGYFdN30yb07NxcmOO117iXOj9XryYCc/AgvyuWTnIyjPCHPzCnuXMx\nh1pbLUnS47G2xAoX6nCgeb32Gmuanc26zJmDufbTnyKE29oQPOXlCHBtmeznB7Mp1vCdd1L8GhHB\n/mRmoil88gn/E+Ga3l5O/oAANDmvFzNFc4MWLeL5Gzcyn4oKi9YNH86eLl5s3T59PmBP9u61w6ev\nD21t4UK0xqAg/E8ffYRZ196OINc0+MRE1kdTKrTHeH09mMmKM+102vfOTvZLu9B2dJip+dhj+KKC\ngzGPGxpEbr1Vus+/XSa+EyghIdaRKD9/AFXDK+IXYvXFQzVOCJ1/cxQUsCkZF90rGX87Czv8Bz9A\n5VVUucmTEQoxMWgIDgfq7fjxMOYppyAMsrMNd7ijA4Fw8cVcq4h1OTkw0qFDCJ0XXiDSog3xTj+d\no2n/fvwVEREQ7OefI0A0+3X+fJ6zZ4+hlCUnc/Lm5iIQ2tpg9m99y8yvRYsGEpdqrbWyCM97+WXm\nqX3R29sRAgUFMHBzM2bT3r0I4dpa3kMT19LSEAIOB2ty440wtMuF4AgN5Zr2djSqYcNgoLAwmFux\naAZXhbe0UHLws59ZF87UVDOrgoIMhqOqymrWNNt6zx4YNSGBXlKafZ6UxN6sXw9nKlZ0by/vdPiw\nQZBoqx3tIKqaS3o66zV1KlGm3l40Ia+Xee3ezXooZk56OgKpuJh1UHD7ggLWPCICc7aggJ/XrbMy\nlAMHWDftGaZ1f5mZ0ElUlMiSJZLe7ZHwFRZZV2jvri4RPx9bNf24yFj/2TghdP7JKCiwdJfUVGTL\n/v0iZYfOkM89J0vG/Q9JYNm14nr0UU6f557jRPPzM8Jrb+d01RYlN98MHk1KCoS6b581R/P3h5C0\nViswkPsePszvZ53FKab1QUVFaD933IFJsnu3Ye7Mn2/Zsfn5ZAR/+ikMrGZHURGne1AQzFNXZwBe\ngwsE3W7TUkRgBJcL5tRr1evY2opwCAmxDgYvv4xACwzk97o6zBK3G1+N9uhOSzNh29rKGiUlGV5y\nXZ35WgaH+N1utDPthRUaSn5Mfj5z1QxuxZGeNo1wu76j2801Gn4/6STTOjXqVFDAQaF1aIWF/E0r\n2ysr0W5jYnjXWbPsXTweTMnBdW/V1VZqUV3NnuXmogHpiIoiQ/udd6zwdu9e5tbYyPwDA9GiFy9m\nzdQUViAzDXbs2MH/Kyv5rMcjER4u3bCBWyk4QF+fSG6QiCfoOL7N/2KcEDr/YFRUcBCpZnvoEHRR\nVSXicjvk567F8teGM+TAfc/L2Ke+xwmsOC4Kp5CQABGvXs2FGzYQgcjIQNPYt4+an+ZmhMBrr1GD\ndfvtZOXu2AFxKRzEgQNWde7nB7EuXYopNH8+EYreXiIU6ucpK0PdDwvjmnnzuF9EBEJHhOfv2wcx\nL18Oc0ydaqUR1113ZAg8Odl6Z23fbnnzI0ZYNbrmrlRVcfqq9rJmDc9TLOK334ZBtZXP975HEWNs\nLBqQZvvql6b9a2vdlhbeZdYs67O+fDkaX3s7QmnHDnxIGmH89a+tYr2xkbU//XRLCIyL47kej5Uo\n7N5t5qNqOGFhvLOWTajTOSGBNQsLgw4CAkxL1H3UzOZdu6xMpaaG91Efz5YtCDatTtfiT23lU1EB\nZk9UFAfSu+8iVLRFkNatzZ7NPYqL+ftdd325lXFxVHpos1JF23CniMREDz2mztcidP6VvlcDn+sT\nkT0Dvx7y+XwXDPx9hIi8JSLRIrJdRK7x+Xw9Qz3PoiJoSLsAFxayKdoCKjv0dNnmmC/jXntY5NEb\nIZAXXoBxIyIMwiIigtNana+ad6O5I5qroXCobjd+jMmTOfk2brQcGQX/1twLLVDUk1i7Uqak4HSu\nquJ/2t9q0yYYNSkJzcjthhA1Tyc2lpN061a0k6uv5v6DG8eJkIty7bVm9pSWmunm9VrXhqoqtIyA\nAO5fW4ugPHzYco40w1d9QzNmgJHzy18iMCoqDIMoJcWypfv6WNNLLmEtHQ4Y8fbb8Vv4+eFPUtMk\nMhJGDwlhUx96CDNV8Ys0k/r889EctQ5NNTwFEdNQub8/JpBCser6BQdbhbqIhfvVhnn4YQS85hCl\npFhXCH9/6EETC1euJAK5dy/30ABEXBzh9NtvRxDfd59pv5mZ/K+8nENMkwijo7+E2K1sCpKSXbxS\nRgbT37sXyzQ3F7dTSPDQFnrq+Lo0nX+l75WISKfP5zsWYOuvRWSJz+d7y+Fw/FFEvi005hvSERVl\n7aS8XjT/iAhkAF1pHPJK/EPy5K45nJya3KBHg8/HrjU2wmiff24RoZtu4lTKy7OcjqYmiLa0FCYJ\nC+PUb2riGq3o7u01YC6PBw3C4zGmE0Fg/e531smhvd0S+U45BWd0ZCRmREUFWs3OnfysXRFefBGA\n9KMFjgjPOvNM60hZXc1ntduBovXNmYP/4tVXeVe322A7tV+X08lib97M8z//nPe44gqY7De/QevT\nbGgVmhkZvMODD/L5Q4cM7e+ccxCClZWmUT3yCOv3wANoIrNmWRbzq69y7YgRMP3rr+Or09HXh09u\n1iye098PQXg8CEuvl7mkpJippGPDBg4ARSBoaWEexcW8h7bq2buX74q/vHMn2lVSEuus5ujw4Qi7\nk09Gm1u8mPkMH86znnwSmpgzh0THrVv53P/+r0hKihScdrM88lODSb7mGm6l2QVjxw4kn7sskXko\nx9cldC6Uf9L36nhjABf5dBG5atD1P5evQOiMHIk80P71v/yl5VZ9+ikH4HlXzhbfcxeL47HHSOUc\nNcpO3dNOw9y5/34Is6sLRnzxRYhz9mx2WEsaFCh72DCiK9/9Lg/s6KAr5YEDlsJ+yy34Ct5809L0\nj26fK2Lh8OXL+d3fH4G3f7/lA/n7G9ZKcTGCLyuLk1Wdo/9oeL1oFqmpPP+MM6wFc2Ii93/gASh4\n5UoYXLGifT4DaN+3D0GpvqX334fx9+yByZqbYWgFop84kfVqb0eILFuGJqdJccHBVqKSkMDvFRVo\nN83N/DxjBhrbZ58xn6oqhIti/4jAtC++yH1OPhmG3rqV90pLIyK1bJkVXHo8VmHv9bJ38fGsZXMz\n76x1UX19cP1LL3GiJSfz7n/4A2ZzVRWCPDoaX6D2qpo50zpo6DNbW9kHhcx45x3p25Eja5ynSW9h\njpxdvFN6X3lDPlsT8GUVR14ekfSZMyG19euxloOCRLraLANgKFvSfF1C51/texXocDi2iUiviDzi\n8/n+KphUTT6fbwAvVMpFJOmrmOSaNfD7FVdAL++/z6GflYW5/qWtO/o3IpnLDAUvKgpG+PnPv0zC\nkqQkGLqtzarIJ03i+nHjrLQgPNxatzz9NF8XXsiXiEVZfD6rAt+xg79Nm/b3SP6BgQixCRPM7Nu4\nES3h4EE0kq1b+buGWbU9bXy8dZlsaUGTCw1FAAyuAly6FCkcFoZwW7GCyM+cOUc6BNQPs3s36xMd\nbTkyTU3MZelScxK73eT7aK1TWprBXGi29Zw5CJaSEgROair/27EDLmpsRChrx0+Xi8jA8OG887Jl\nCK6qKgudr15tztzmZoID0dHM4b33LGNcW7dER2OXvPwy3HnJJQD2634VFvLOwcGsW1wc8+rqYh7n\nnos5qf3VenoQLJ2dCNeuLj5/441okUePhQsRitp1IyQE4dzSIvWNbnG4QmVWyyrJC5goHxy+Unxi\nSfErVvB9xw7I99e/Rk6OGiXyaQ+vWlRkpDoU4ysTOkPU9yrV5/NVOByOdBFZ5XA49oj1L/9X53GL\n/IfN9urqOEC0ZnLdOtOazz6byLKIiIwcKf13fF+cSx6HAGJiUO/T01HVNdqheTEKj9DSggawcSNM\nWlBA9fauXda18qWXyMzSnJyqKtTn6mpCvwsXQrghIZzQKgy6utCCcnKsJ3pKimXU3nEHCWKa0FdS\nYn6S8nLMuvPOQwupq8MP0dQEo118MZ7HPXvImP3gA3O8FhXhIKiu5vMqLEUQlH/+M9pCfT2nsoa5\n1d+hmL5hYTCdwpB2d8N4LhdHtM53zx7WT/t+a5V6XR1zmzGDuQ0Oj2tHivff59nbtrGWyckcCIcP\n87cpUxBomq8jAlP39FhpRlMT2ps6vbV8RSN5H32EOqFlF7GxCI7WVoNWVRNWs4s1a1yzq4OC2LPY\n2GMT6ty5HBC//S3CTUtpfD6pc4XJKN9uCe1rkp8GvCYfL3ZIcLA1m/V60egVEOGFF1ii7m4R38CW\n6XIN1fjKhM5Q9L3y+XwVA9+LHA7HGhGZKiLviUiEw+FwD2g7ySJS8Q/m8Zz8m832uM40Yk0o1WLh\nvj7cDosWsTlPPy1SfvjH8mjIG+L57HNxb9sCgR04ACNMmsSOBgbaSR0Vhf8gI4OvlSvx8ZSXW61Q\nVhaSbupUy8x96inrg712LUSdkMA1mZkk0AUGIgjWrDGs4cJCrvd4yBlSjJZJkzAt1JHc04PAmDPH\ngNI3bkQI1tbCCAcPwuRPP8399u0zPJv+fkzGYcPQPBYuJDq1eTOmlha6aki8u9scoCqc+/qsDU10\nNFzh54cQXLcOxtIkRaeTdISHHsLcWruWZwQGco3WZ2lk6Yor+Myjj8JpGs3avp2TRK89cAAfyO9/\n/yXmjLhcMH5DA+ugWNDd3fxdO4wqZo8IcwsLYz26u/l64gnybxoaoAPV/MaMMf9QcbEV1La2Wn/3\n440xY6CXTz5BEA7AWYzs2it+0isHHaNkbcAZ0tfFFqlPPToapbu5mb+vWmX5g/7nsGUKCTVU4+sy\nrz6Uf973KlJEOnw+X7fD4YgRkVNF5NEBzWi1iFwiRLCOef1/M3p7ccv8eSs8qNnq2vZKg1JOJ7hL\nmzaJJCSEy58m/06+v+Ey/BLd3Ugqnw8NZOxYBEBnJ0QYGWmdFPPzKRlobORUU2S40aMhIHUO9/fD\n/MnJVrulFe5FRZz6fX0InsJCKCogAKqJicEnIAKh5+XxcunpzKWw0KrYi4o4Pe+917Ss/Hxrz1JT\ng1/Gz89O4a4uq+yOimLO48cjTD/91LQT9eXU1VlSnHrqVdNRFLzgYASOOtuzsxHKxcXMVfGCNUo0\nbRpaivaR6ulBGBw4wFp4vQhtLdbs7DT8I8XGqapiDcrKeK9336Uoc/VqPn/KKQjpF17gfjNnIuw3\nbeJeNTVH2iKJiYYrpGkK2ls+MtIQDPv7WR9/f56jFeLx8Vx/LL9aYyMO9oYGzLsJE6xx48DwF6/0\ni1Oe8L9f3B5/CRReV7sLX3cdvkqXCzpvbLSytq5upvD/C57Ov9L3KlNEnnU4HP0i4hR8OorS9D8i\n8pbD4VgsIjtF5MWhnFxjIzJDM9wPH0YDXrKEg6itDSUhOxslwONh3990XSLnjDxXxvz0p5hXEyfC\nRMXF9DFqb4ewYmIg1OhoHpidDYNpdm1tLebHxx9DdFrm7nRC5Bs2QA2q6u/ZAzF3d2MyHDhAVKq2\nlusaGqwjqQiMGxEBk+hRtmOHFaIqCNnrrzOnyy8/ElJjwgQro1CTbPhwhOjf/sa106bhIH3vPShc\ngedLS49kwI6OI8Hf1al86qkIk337+F9WFtc2NrIGOTk8PzmZzXj+eRi/qso6aWrdkddrGMSffcY1\nSUl8TkM4MTGWuatz6ezknrfcgvkrwvVPPGHAXWeeiUNX20FPnYpjWsfddzPXggLWQTuyzpmDqVlc\njGY1ahQaT3S0+QG9XoSf08leXH21lUnk57O+xcUIsaeeInyu6zcAgeEQEV9UtCSPipGxISJbtnJW\nNDWxRffdxxJ/8QW31TKv2FiRsoEzbqjH1yJ0/sW+V5tEZOJxri8SkRnH+t9/M/r7iUpqTWR1lx2Q\nQUEEfX7yEzZl2TLoODmZqGxAgEhdvUOK7/2DjLl7HLuo2ad9fRCFRi6mTOHE1NHSAmHW1xtzjx0L\nBWjVuhLbDTcgQLZswcarqrL+TqGhUFRoKEQ+d67BQKjfob8fk2P8eLSEDRt4XmjokXAVPT0Ip+ef\n5+/nnYevKTaW3y+8kHuvWoW5FBwMhX7zm0Sc1Gk6fjzP8/Oztr7z5rFga9dyL03ic7s5buPjMT/8\n/PB5BQZaOYHDAbdERiI0TjsNAbxlC8JT+8prJfrhwzyzvx+tqb2dZ7S0WL7P+PFWXhAQgDDr7+e+\nqglBeHhaNTpUUoJAeuUVBM1gYaMjJgbI0YoKPv/MMwiQ8eOtS8i4cTxr926LxqnG43TagfLgg/jp\nvF7C/4cPG315vTjhFcaiqor/uVzinjpJ7v3NKLnQzynr1llrrttvZ8nvvBNyVcQUBbBU/K+hHicy\nkgeNXbswiUMGtO9VqzhgL72Uzdm4kc8lJEDbO3ZYku3hw/D4mTenifQ+iqN2xQoY8sABPjBiBAyn\nzl2FQti82ZyTqhrX1HBtXR2+hfx8TtWTTkIAvPYaKpfHwzV1dRDoiBGWBzN5MlqBz4fQ0aiXw2HP\naWnh87NmIcS07YwizHm9mIva4WHsWE7zBQu439VXmzbn9TLvxYthSu0z7vWizYWHM1+FgdBM6/R0\n1iQvz1oPxMbyNWoUOUUKijZhAtpHYSGCeNkynt/ZyTM0cU8d4NrhVDnt4osHHBYDRbKjRrGJ2jkh\nL8+YODzcOj309CAw8vIMkdHl4uv113mHkSP/nqiKihBKVVX4o7SjRXMzc5k7Fw2uvZ292rXLcpwO\nHbKTrasLwSViAG7qj/L5kBg9PaztlCki2dnia2yUvXf9SbamXy5J1QFfWmBHj+xs3FkxMbi2du/m\nsdWBQ4+PLHJC6BwxtM+c281iJ4wmVebUU42u6+uhC6+X3887z1oKhYXBA9/4zu3i/OtfYY7gYC7q\n6kKP1ZNVT08VFnFxMEFhIZ/VVH/tNnngAKHgH/0Ic6u7m8+FhRmO8rBhForQELMIk7v8cojf6USY\nBQRA7GPGWKq1YiQnJnL/UaMsTD5yJKfwBRcgeHSoA3jkSHw1P/mJHZk7dpATo501NfxcUoIvQ1H0\namsRVpGRONXj4tDApk1DO4qPt8jVt7/NPdXH0daGGTo43BwYaLCo6gVta0MTee45Q1y88kr8L8nJ\nzGHPHoRhSwtrO2MGm/3666zZtm0cGsuXW6Lm/PnMS/1ug0d9PZqQarh68ISF8bzsbKsC7++3xM9z\nzuHnDz+0Ill/f4NrDQ1lnWprWUeF9Jg8mbVau1akrk52X/aw3LX9Wqn7jO284ALcBMca6krLzETW\nX3ONyO/3WtBuKMcJoTNojBljlkW/D5o/5RR+/8MfoJHsbPY/IoL/l5ZC/2Fh8PE774hERDhkwmMv\nSfi8yeLY+IW4RqRyAnd3I3gmTyaiExtrHRMSEkiU6OmBCVpbIWSHw8Kve/aQ+/PAAxDz2rXc1+VC\nQNx+O0KluxvVfXB7xtNP596rVxOynjmTa1NS8InU1yO4urt5tiYKdnQgdVVLch+HZDZtonJw2zYk\ndmIiQnLrViSxFifOnMnvUVH8rNjSCrh18CAC44ILjkyajIrCjt21C0Hx9NOsi+IST5pEONHrhRmT\nk4ns6TtlZrJewcG8T0sL97/8coTCj39s+S0KMDZsmDURjIjgedoq+ZNP0CgiIkyQHz1efhkCiY01\nc0fBarSja1aWIQMuXMg6bNoEMSYno37U1iLo77mHezidmLDp6VZR3tmJD7G6WnylpVI27mz5dsED\nUlBowAN//SuoKopr39uLvNaqF+3AnJ6OnBtc3zuU44TQGTSSk1E6hFI9AAAgAElEQVQkrntFpLML\nftQusTt2cLjU1PD7vHmmFZWWWg++kBCspTcKU2TStBfle6sWSUdhhQRFBBn0xcqVEJn6Be69FwaJ\niUGbmDkT06y7G4Hk54fpo+USv/0teTNvvcXPvb2o6eeff/yw6sGDfD44GKL+y18Mh6W11RzCPh+q\nvoZdw8LQhHw+XvpY8dOmJut/rrBzyvg5OTBVaqplLk+ZYtg9u3axYE1NVsSqzmqtSeroMIyfvj40\nkKAg/hYdDaNu2YLWmJ6OELrxRoSJ9hz7xS/gOq+XZ7W0WLsXjaqpn23ZMoRzRATXzp6NcFi+HG70\nelGBFSdp4cJj59CUlZk/RsHA0tIglAsuQJjl57NmqtWOH89hcfbZPE8zyg8dMnoQwfS75hrb21/9\nikPiww+l3xMid0W+Ik1VDvF6bfm0IF+FzvLlwOwoNFBlJds0fDjZFnUDSudQjxNC56iRnDwAcTOQ\nf3P//dBWSorlbjU2Yp6PGYOSsGoVNBEZCQ1q9+C6ORfLpsobZdaBP4n0ONltxc3RsOgXXyB07ryT\n/z32GEzQ3Y2TNCkJIVVdzamWkQExV1Xh45k92/B5H3gASMqTTvr7F8vLQ8DExlqW7JQpMHVOzpGh\nVrfbNIEzzmC+c+di2hwrfqoFk3o8aoO3wEAYSK/p67PCxdxc5lFZaXg2fn4wk4Jxud2YQKtWGYTn\n1Kn8T/GYtSp7/HhLTFy8mDW66aaBVh6R5hH99FPzq1x7rYHIaKFqfT33USznqCg4MSWFVIRduxDE\n0dHslfYY/8EPLHTf0oIgiI5GyDQ2ct/vf5819HrRWAsKrK9ZVBRCsq2NjGYt31A0Q4+Hr9JSq+6v\nrkZorVrFWgzUda34waey84P4LzvbaA5ObCzLMmMGW7J6NWSglTAKqaR9/fI7h5i5BsYJoXPUqKmB\n33v7RMpLrEFCRQX7ra6C6mprn9TbC29FRFjTRI28/vWMpyS1brsktezHnt+1y1LqtV+4nl5BQQiO\n4mI0nS++gAoyM1HrY2PN8ZSYyDXr1jGJESMQHH/8I5Glzz9nQjfcwNEVH2+mk4KWa3LdmDGmFehw\nOCD8+HijyOMlbMTEIJk1e1iRALOyEIwKFK5IeFq1npeHkFIkQw1fn3yyaWw332zckpWF2ZOTw3VV\nVbxzQADCSP/32mtWvXjDDfhdHn+ctdKQTGwsmkZhIYfBJZfgMM/L4zMZGVZgqiXYaWnU1zU0oFUq\nLGxjI+90+eW81+LFfEZB2IOCcKh///sG1pWXZw791la0LBVIK1ZYkWpxMQLpjjtIP1ixgvk3N7Mv\nu3ZxUjqdaHt33il7h50tSUkWI6ioYItHjybtaOxYFEktWB+cHlVdjQwLCxMJnv0VMJicEDp/N0JD\nRcSBpqPtqePiDIFCU2MUqkbLY7S7cEoK9KC5b06nR1pffV/kW1mcRN/6FmGwlhYI5bzzMHX6+tBs\nurtxIClRzZ0LQ3k8OIz6+/FdqL9GjyaHA8Kvrsbxqdi7jz+O93DCBF5m2TKu064OMTEcfb/+NSbJ\nF19Y5bfTyVHZ2Wl5KkeP9etJFOzuxsmlfbKCg2GU6GiSQSorYbRHHjHYVNU41G/l8eB7+vGPzaEQ\nFEQGtfZZF+HaykrDL6qpgVNUWzvjDEzH9nbed/Zs/GEJCTi729qsmv4vf2F+ra2saWsrGltFhfUf\n277dnHfjxmGmFRQg6Lu7EV67dqGRan6UOt43b7a8oldfpbpSe8NromhXF4Jo2TIER1oaRLVrF4fL\n9OnsheJRNzZi/4SGSlt9l6w/HCVntH8oh8ImS/O0m2SExzoy5+Xx2hkZPEJ9+Y89xuupIqWWrZa3\ndXWJBH8FTmSRE0Ln70ZYmEjSMDRsNcWrqqB5rSV0OKBFhb3QXKyGBg5bLf/57nfZxJiYEeRXLFxo\nhYjFxWgwxcWoyA4HvhZth5Kaam1hJw6kKz3wgE20pATC0/qkoiKoR80nVccPHYKRdu1ikmedNWCw\n1/FyI0bACAEBnN7d3da7y+VCw7j7buZ89NA2MfHxFo278UbeZeZM3uH55y0fZ/x467KgSIkKcpaU\nhIBcuPBIjcrnwyR64QWuycxE2mt5gNoG3d34c2prWc+EBDgnOtoa0yksq25sSopVumdnozGEh1vH\nDn2vyZMRUJs2sX9RUdyzqMiii4mJEMrGjVYkW1rK/nz+uSEIpqcbYL5Ct2rpR20tAkVB5fPykBI9\nPcyvv5/37evj7wEBsqdjpCzo+lBanOFyce9S6frZCElO46z64AODzv7oI0unKinh/MrLY7kPHuQM\nTEtDmRQZwODvHvq6K5ETQueYIySEYMjFd8AzgYEDSGpua8TndJp2XFzM30NDoUmFE87MHLRp55zD\n8XL33dz8+ecxix591DBxDx3i5iNG8HtwMOr/qlUwx/79mEzf+AaOWxHzhWguUGAgTKWQFJoo19rK\n5/SEd7kg/rVrLUt4gJAlKooXDAxkbkfDZfh8UO7+/ZYTI8I9x441ys3ORmCUlsKQWocUEGC4NNrT\n++qrj93n5N138X/097MeXi8ahJaCqFN45EiEQ1kZ3FRaylF+7bXW+0qZWDOgtdmeCByXk4PwGDfO\nkBMffZTrRoxAGEVEoIWUlzN/VYW19EN7vZeWcgp1dhrwfVcXJlxMjGmnaub29LA2mnqQl8e7jhnD\nulRUoEXt3fulQ7nf7SeZPbvFJf3ye8+9ctgvXUJ8bqmoQOnKzGR5a2vJGwwMRO4rvrvTyWsnJSGP\nAwOtHli7FH0V44TQOc7Qgyk6Gn5MS2O/k5OhvfBwO0R9PtAf33uPz2hn2dWrUR5EOG3eDP6BTJ6U\nKzN++UvpPVwr7qR4BIWCZ9fWErXQvlIVFVBATg6h9qwsCPXRR2GokSN5+NKl1ntKcZXz8jju7rkH\notfoS1GRdfxctQqtQItFNaxbX29+Cu10MVj7+MtfyCHp77fWAQEBvMfg6Jb2SdfImAq0ri4Y8vbb\n8YMcjYfZ38+7a0xXs6y1PUxNDe+vwjUmBkYtKICjtOThZz/j5+xsM8ciIhDQLS2myVRWIpyef557\nZGWxsU88wVqpH27iRGsnPXo0grO5mYzxqireccECggIFBdgxpaWWcxUQwD4uWABHq7BKTeUe5eUQ\nVmIiAiY42GrN+vu57q670JwOHxbn3r0S3t8g+2Lny2r3IuntdEtkpFVotLfzalVVLFFSkinBZ57J\nFPPyMLsUBFE7Zl91lcjLh74a3johdI4zmppEfAN8HBTE5px5JgGRjz9G09Ycuuuuww/47rsoD2lp\n0OX+/Qidri58hNnZDkmI/oM8HZwrk156UfLGnC9RAV6Jzv9EnH5utITKSvP+ORwwmDJ0aSlHVXn5\ngLe7F4JubzcQcZcLCjrlFAhYHbIpKWgTBw9i4Le2crwlJiLYtm+3fBfNynW58Encfz+Ca/58nLwa\nynW5LKN43Dj+P7gwcdw466Pe08M7hIWhkdx0E76kd9+FK2bMwCTr74dZtwxU6peWInB27rSs7cmT\nLdITGgozNjXBjJGRmI4ffshn3nzT6tQUHkTNugULEPpdXdbsb98+0hA2bOA+w4djsjqd+IomTsQx\nnpVFsl5XF/VuJSUI+fnzefYLLxg+kkIVaPh/40Z+1o4OWuG/ezfvPG0a73rqqeT6REUhhDZsQFjv\n2QNdNDRI/4yTpeYbD8uwPSOlaCNkoQnfcXFc3tNjyCann86thg0jj3PJEs6YadN49K5dyOIDB0S8\nfic6fP5fHT4fdOjnhxxwOjlkMjIQLOvXW1BmyxbDw1ZckvR0aFoEqyInBzr3djnlN757ZLH7Phl5\n8BPJjZsrHvFJyJgEiKu7GwYbOxaGKCyEuXp7EQRlZWbCfPIJTDhvHmaMfi40FIKPjYUptHdSQADU\nOHw4k1yzxqpVq6o4cRUgfvJkFmHPHmPct99GO9Fwh95zzpwjTbDduxFWMTHkxzz2GNc3N/OOY8ci\nYP7wB+4fEmLZucHBXJuWZlm8ewZgskNCmFtLC87hv/2Ne37xhUXzamvhHvXBvPACETAtsAwIsIS+\njRvhxMG5/m++yRyio2Hs+noEWXi4tXlW/1ZhIc4Sp5NkzdRUuPyVVyyTOz7eAMtmz0a71KpzhZlt\nbgbIa+tW9n/YMANumjXLMstLS5EKZWVIhawscV36TTn9nllymg8/9Ntv83q33kq5X2ioNYX44ANi\nCip7taxscLbAwYMs5WefibROP3Zlx387Tgid44ywMDZM8/kuuMA65apvUSOz+/fDM+eeCx2WlHCa\nnHsun9fmjk1NIqGhTinsTZWPEm6Wm+t+JROqV0tbcDyMX1lpgFHFxfgVNANx4kQoICMDAK2oKKJY\n2gpXQxOaDXbwIAz2y1+iqQQFIXD8/c0zHhFhfa/CwzHttGtoU5O1wdA+VA0NCLerrsJ21ND9zJm2\ncPv24RAODobZJk6EESsrua+qjW+/DdNPnMjfe3sRLtOmWWmFCO8ZF8fz/fy4f2GhOYTHj7f+41oE\n19/Pe/r78+yDB82ETEvj65NPEGAHD8Khzz5rgF1OJ0K9qop1iIzEnFIIUhG0oEcfNfv6wAHW+Y9/\nRCg0NbFeWteVkGCQHFlZEJXXC6E1NloWnjqKFdCps9Ouy8hAMG3YgEaamfllJrRapG43j96wAbml\nvvauLv5+551sj8vFWaAglhUVLF9kJIdoSIglDBYUWELhUIwTQucYo7MTepk7Cj9eWhr7q+kRLhcb\nt3YtNJORwaY3NrLJnZ0GX9zVRXqIll/19jokZVK6xPf1y/7Qc2VC3l8kqKdJxJPCxeHhaA1ffIEg\nuewy7LeUFMtIVofT6tUwofZPGuwvOe88NJyiIj57yikQ8QMPYIZ4PPyckwOjK7auw4Ga5nLxIpqn\nozk0gYFI03nzLMNW5yXCnAMCYCKfj/lFR6M29vcTMs/Ls6pudby2t1vzv/R0Ax2bONHMPF38mBgL\ntc+bh+azejWCYft21kO7P7S0YA9ffDHvUlXFnIqKLMu5vp6M3iuuQAv7/HOr4Zo61YDTH3sMQT97\ntkERqIZVVoZAaG7mvj09rFl/P8RTW2sxaTUZ/fwwM5cuZT4xMQjN9nYOhIULee6vfiX9u/eKrFwl\nzvXrpCxiolS5Z0rGpGkS8Y1viAgy74svIJ9NmxAcGRlMUesE/fxISu/owDdfUoIFrPW22ji1uZnp\nBWUhf6urTwidr3RUVoocKhNxOkRyD0Mft9yCNvPxx9BrfT0bp8GWvXvhqf37OU3GjcNCys1lw0NC\n0MjVf3vBBRGydc/9srmnV74TEyQZ2//MKT9xorUwmTcP382DDx6Jiu3z4fP4+GPrWd3cbFmNChJ1\nvES+tDQIXcfo0Zgp5eVQYWYmZoii+Xm9mFSlpRaSi4n5e+evwnCEh0PhGtaNiLAwiHa1UHNh7FgL\nFZ95Jv4Qt5u8mRUrWNAJE5jvD36A/eB2w4gTJ1otloZedu7EjNqyxcxFt5v3am01plb7wu1GWCcm\nEu7Zt88cIE4n/qvoaBznmZmcQC+8wGbHxrIXmo0tglmkCYEKu+pysbYhIajLoaFoV7fdZt0s7rsP\nrlcMUa2/CwsT38pVUra5QhoLfDK5Yp2UutLkooQt0t3pkTkFIk+7YeKeHrZr924eGxTE17hxBj4X\nFMS0N2602EB3t+VLRkRYCURXl0jkQFAyJeXfZKJ/Mr4WofOv9L1yOByniciSQX8aKyJX+Hy+vzoc\njpdFZJ4YXvL1Pp9v11DM7dAhEfGJ+PlDQ59/Ds1o4OTccw2jfO5cvtfVcfhpfzntaaYuioYGNKKY\nGD6vqu+iRW4Zdf9DIk9EiDz7R46r0aNh8P5+iPRoGP6aGk5UjdHX1tqpHBkJJQUHQ1mpqRxj2ttc\nQ+WDxxtvwHChoQiHM84wO1LHtdfiPG5q4qjMyUH7mjEDCs/Pxzzx82MOEyfyLjExVoSam4uAaGhg\nMVXTuO8+vOyDR2kpKQG9vWg4OTloMH19LFxJic198WJMl7Q0juTiYkondu5EiDQ28rydO3mvCRPY\nsNRUayQ4bJidEE4np0N8PIJQtStd17o61nz6dBKxli41UJpJkzD3qqtZC6fTmqR1dxugWXg4pvJg\n/OXB+EqDRuWeOuneVyCT6zZLoXu0bJUZIv0+aWhm20pKEBZpabxGdjaP1Nri8HC2f/t2i9Rr4Oz6\n68lX7OvDQi4sZFoxMchtCeLnfxNa/J+Or0vT+ad9r3w+32oRmSLypZAqEJEVgz5yr8/ne3eoJxYT\ng9Tv70fY1NYiUA4d4oDatQt+qa3FbNLDbuVK+EEPr44Ow2basAFZUV7O3xQe5fnnRT7+OFE8nsfk\ne2/dKmO+uwDH5JNPErnQfJ3BQ6u8tc6no8MoSYtnzj3XTsv/+R8ywVavRjDdeismg47Nm6EqbYO8\nbRsvPHjs2IE5oZWBNTVQtEa+FE40OJgX7OlBaKi2tWMH4euiIuuCoDAc69eTd6Q+jd5enM8bNlhX\niLVrrYBIxGzZ99/HpggO5v9xcdzrnnsQAmvW8M4JCWhhTz7Jsb14sbUvdjqZR0kJzK95Ti6XnRw+\n30Ax3UAHiOXL8d2MHInZprjW/f2Eg664Ak1L69c0Waa0FM0pLo7IQ3Iy7+DnR7nGjBk8KzeXe6am\nimvLJsmo2yyN4WmS65smuzpPkpJqj4jDkD5GjkSW3XYbr/fMM9abUVtkeTxm1T74INs1dy7CRlO9\nfvc7Ht3RQeL8asf/X9Grf7fv1SUi8onP5+v4aqeF7aogXW63td9WWN3Vqy36O2YMPDNmjLXtdrko\n48nMNKTJxx5j49etg6dFoPGKCnyKXV0iT3yUIU98tkGCL1gA9SxdeuyK7uhoNKAPPoBxNGs4NBQt\nIjYWYdXVhZRcswbzSZMwnnmGkLD6YZKSmIii0Gvl9eDR1GT5OBq50nCw18uCaHV4RYX109Lx+ecI\nHMX+KS5GqCYmIs0bGkzolJailTQ1mf6vhUE6fD7+rw3JFI+mq8swZwIDj0xOqa1lXaKjDRRNhaTL\nhea2eTPcGR5uMBNvv41wamhAk0lK4tqUFNSK999HiHd1ISiuuooIw7vvssllZUiFujpUjtNOY512\n7sT5oi2Kn32Wz+3eTZb3QC1VwoEDkpN2oWxNuVjKOmLk9eIF4vA5JDycZS4tZUrt7ZDMNddgfWty\n+rBhbPv772ONqomlIyDAcjvvvpvg2ssvM73iyKE3rUT+3+97peMKEXniqL897HA4fioiK0Xkfp/P\n1z1Uk4uI4Ou2GRzQn34KfwzGDO/ttWh0QwO81tkJIVx5JfdRBM6AQbaxHnh6ALrdCKyGBpHm8OES\nvGEDBH/hhRCimh7bt2PatLQg1X71K5hi40Zio4rFU1ZGbZBmw27axENiYmCmujrL6xHBYfX00zB/\nVhaawtEjNpaX0J5bmo4fEmK9uzsHSpI7OmDqwaO93WqttMhHI3UKCbplC0IgONiaMmkUSzUNzSTW\nbN6gIJ7vcPCM+Hj7zNlnky/T2Mi6TJxoTrioKPw1g8cvf0lHicZGuHjGDN7D6UR1VZyd3l5+1+w7\ndRiLEGe+6ipOoR/9iL9t28YeBATwzo2NCMwJE9Ac/QaSYVS1/vhj5vf55/z/lFMkdulfJHidUxq/\nEBkZZFu1b5/h3GuLd2233tPD5c3NvJqf37EhfwYPl4uDMTYW+s9pN5jtoRxfmdAZor5XMtCiZqKI\nfDbozw8IwspfaC/zPyLyi+Ncf4v8B32vurqgwcBABI4iiWonk+5u+Pvcc6Ftlwt+cTqty+v8+fg1\ndYSHk5DV3g7t/eIX+DV7enhGbKyI+MWjTl16KQ7UggJ8B0uWGFTm6tXc6JprDPRbazVqa5mYVpBr\nEtrHH1vngsF5KfHxTERDxSIIrKYmK9zJzIRJtPOm243vQnuYq09JHbwa0RGBkQoLuUbBpE8+GabW\nMPjVVxs+8tlno27W1BhomHYfLS9nnklJtmg1NVb7NGIE6/Too3ju//d/2Zy4OCq8j5fXn52NZhIV\nhf9q+nSO/K4u88j29KChXX45APdu98BJ0cyBoB0bOjqOjOZNm8ZXby8aZ34+p82UKcxPT6uSEgO3\nKSlhnWfOFLniChmW7JQJEzj85s7lIwcPIhgGV4l3dVmbrJ4eAzA4uorlHw3FahOh79VQd4IQ+X+8\n79XAuExE3vf5fN5B91YtqdvhcLwkIvf8g3k8J/9B36uaGpHa3RyObjdf/f3QlFafn3sueWe9vdZV\n5pNPOLTDwnBlhIVZ083KSoRMYiIa0YMPIj9CQuC1L+3n0FB8O3fcQYh561b+ri1VGhvRZhYtMkyc\ntjb+r0VhQUFQncvFA8PCyO8599y/dyaLmMDZsYOkvcZGKHvCBCh39mwzK+67D0bevRv/SWoqDKtz\n0GREEetWcM01cIu23lVAsf5+AyIPCcE59vjjaHjV1azF6aezYO+9h5BzOjHP0tJY1LFj+VteHoLx\noYeoVL/8cr6ONbS8uqEBTS8oiHV5+WWEbFKSYeoEBvLz7Nlomr291pFUTxptCVxQcOx2mEowmrwl\ngja0apU5yUNCWKP2dsywrKwv59/ebilbEybwUU1biomxUp0dO3AnBQby+a4ua/qpo6fHSnmUFEpL\nsSS3brXSNt+4f9xq6z8dX5d59U/7Xg0aVwqazZdjkMByiMhFIrJ3KCfn84m0DsJNKikxy0IbRPr5\nwQvp6fh7WlrYrI4OA9rzesnVmTKFQMlvf2unyPe/j4l/3EZmfn74X8aMgbGDgowCFGPjjTc4mc87\nD4rx97dOBV1d1tXA4+EzmkxYVUX4ubMTdWzCBDNj/vQnpOfBgzBCZye/R0fD9Hr09fQYellvL8wr\ngqYz2BEQFcUcXC58Frm5CLT4eBhUG9iFhbGAsbEIkU8/tRa+55yDkGputjKL3l58X9ddB4rili3W\nv3zLFrhv/vwj11Tjw42NaESVlayj5hYprnRlJU6QqCjeKyAAh8cll7CGTifailb3z5vHfbWh4r86\nUlKYf1sbAvO993jWwoXsg0KkitV9lpSYnFaQx5oagl933IF5pP360tP50uUWYbpLlvBIl4vrNm/G\nb+/1WqBOQb/+f8JI/lf6XonD4UgTkRQRWXvU9W84HI5YEXGIyC4RuW2oJ+jnFhk/2dALxo+3aGlv\nL4ImJoYT5MEH4b/2dlwsetIEBGBvd3WhmISEwDPqA5082Z6n/tigoEEqrcMhctdd0pkxUVyXXypy\nqErqg/0lMcbDabp+PUzym9/gN6ipgbqGDWOCo0ejOQQHWwZjVxfYOZok9+STMNODDzLpggI+o6X0\nWoOlPbF0bN4Ms06ciAbU3Y3Z9IMfHPm5ceNIcPzoI5g6MtJKmNV/o9jEYWGETTwe/CObNvHs7Gyc\nxcHBHM9NTWhLNTW813nnWe8w7aKwcqUVmA4fjnd1505z2FVWwlV/+5uBwmvTwbY2vk+fjml06BCm\njsKV+vujAcXF8c7V1bzrggWYhiUl1v3z8GGeczzTXqFWV6zgfrNni1x+ufhiYoGPHvD/DR+ORb11\nK9v06qsomBMmGBpgcjLIJWvXmuvNz+/I2MBzz/G9uhqlavlyqwqpq+MeLtdANsaQeUmPHF+L0PlX\n+l4N/F4iIknH+NwxwF2GbjgcA+2DBtLKNcrZ3o4mExbGJq1aBY3ExqIFv/kmn9FmlR6PfdbtNh+n\npm7o2LkTN0FdHVrRYAyr/n6RJ3LOkIZzdsqda78pqfU7pDnlZAnPzOQULi+3Zndam1VZSb7N+vUw\nSUgIk4uKQhC1tPA51cFzcqA+BQyvrTVMmXHj+JtGhXRoAWVKCi/b0sLEj/ZWOhxUvWoJwT33IIAU\n+3XSJOY+ezY+rNGj+bsWlarTubiYe7W0GBh7SAh+keZm/ubzsXBq56oPSDOchw/nVNi3D6EcGWm1\naopemJkJF/p8BkWq7Ye3bmWeO3YY4Pu4cQjEGTPQrF55hYhhayumZGYm65qQwDVjxpAgExqKTXPl\nlYT9r7kG8zcpSerTp8vvfmYHWWYm8u/ii5G5UVG85oYNyFmPx4J/MTHUrT79tCF3PPcccYmMDOR1\nYKBFspqazHTT/LKAAOj/31Ha/p1xIiP5OCM4mE1JSOCkKCridPD3R5DMnGkdT1RALF1qpUqKM3Xp\npVxz2WUoJGVl8NEVV3BNVxfq7r590P/evfD7XXdBSH/9Kzw6ZkyavHLrJjn1+etlQe5bIrWFUjpx\noRRNmiVjt6yVxLQ0KEiPNwV+GjcOoi8tRTNRgNzmZos+ORyYLwoqpclGPT1Q8LBhUPPzz4NK6HYT\npamvR7CJWLnE8YZqPz/5CffKyYEBL7sMs2swhWs/n44OuKy3F8F14ABa1dSphBW1CfeIEQiv/ftR\nHw8c4LrUVDhp9WpMtvJyhEhYGO+Wm8smezwGs3rJJbz/xInkGnm9nCqbNyNg1Wnc1YVg2rABQfjZ\nZ3Dy2rU8d+tW4/DSUq676CIElr+/NPgiJfy+W8Qp/eJ4660jfE+vLuE1NYjX3IzZtH69LUtVFWZT\nRQXLeeqptnzZ2fx9zx7IYcoU7vfww2hCb7/NPZuaEDYNDaaZa5CyqEgk/Ss62k8IneOM7gHVctw4\nvldWWsvutjboeNEi+Ezt69pa27yuLmj1oou4fuRIbO78fEw1rWUZ3Dyht3cAd+dNs268Xp63datI\nb2+AHFzwmqQ4x0r6Xx6ThDVvy7q2LNkYPkHuqHhZwqsPcpPRow2/RuFMKyut7/WVV0JViprX3MzD\nMjL43KZNTPLiiw3kfcUKsse0wPF3v0NzmDbNWiL/7W//vCw5IoKar7Vrcdo+/jgLGx0NF82aRZfQ\nm2/mGZo79N3vWl8olfKan6P94uvqzDwMDzfHmvaQr6w0oer1cm8tem1oIF3gwgsReqNGIXiSkxFM\nK1fif3nlFdPwysv5X1kZG7dkiXV70HXSnKYB06/XEyqtv+9l27QAACAASURBVPi9RFXmSmHsTHnn\nojfl1rPSZRAgiFRVWXQ/MJBbNzQwFS3dqq5GXvf28nNrqwEWrlqFhhMYyPnQ2srjKyqgx9RUNKGV\nK4/Ekp88mXupEqqH7FCPE0LnOEOjVd3dlhunplNbG3T/zW/Cx+8O5EVrUq6/v3VKaWjg5CkshJf9\n/bEc7r0XPv/kEza3vp4Dt6vLTLoVK+DBOXM4UPfvF5k+3S2/7P6ZNE64WR46/G25Zusdcih+utS5\n/SUkfZR4y2vEr75BXAkJHHEtLVBpRASmQX09iYWpqUyqu9sSiIqKDMVOc0l05Ofz8oGBLIxm26ak\nIDQqKv71Rkm9vWhMCQlc8/77aEonnWSteE4/HRCt1lYrZC0rY2H0OTNmsKgHDpjjQ9vqrFqFNhUb\ny+nQ0sJGaD+WykoEi0K23nMPdswzzyA8S0vN9lCNyelEo0pNhSvffdfKJ5RTExLw6QQFsZbr1vG8\nlBTxbtkhvs9XSlhvtzwd97+y7cwHxNfjJ+vWIet0nHoqiqf2WIyORpZrJF6rWTROoKlEOmJirAFk\ne7uZXnFxVkB/2238T4N0ar0qHU6cKLLXwWsN9TghdI4zAgIws996i2hAWholEFoioU7iNWs4iLV9\nVHo638PCyGxfvJiN3L4d+j7jDGj59dc50QZ3XQkIMFfBsmVW49XUxFdyMkJs7FiRoqJh8q2Ij+XB\njOdl0fq7xN3fI/nO0+Vg+DxJ7CmR9O/eI9FjYkhO05bCLhcT27gRKu3s5IVUKNXUEF4fMwbGGpwZ\nNmkSGpE6TT0eTIINGyw7+bzzjr+gGrZTYLLcXF44PR3qDw83P01xsT1DcZ4ffdSQDR94AM4JCEBY\nKMj54sWWbpuVxWL39FCwqfn9I0dy/5NOsn7xYWE4TX78Yz7b3s4mut2s39ataHRacqERu7g47qXg\nS4r7fN99IrW10vr0q/J+9SzZ1x8t1+39lYxr+0hqg1Pl7hFvS1HsTOnYx7JqdYcIgmbKFJa5tJRD\nRxFeo6KQu243KBrbtvHz7bcfGdq+9VbktZpiHg9CZrBDeepUFOIvvmDJ7r6b137qKWtBI76vxq9z\nQuj8g7FgAbT46qvWMlt5x+3mNFHgrrg4C85oSH3tWv43YgS0WVdnDR5bW6HbpCQsg6oqvmsxaEMD\nxLhtG/IiNNS69SYnQwzFJQ65rewWeTJinvy5ZaFklq2QZHe2bB5+qSw9OFW+c46bhyclEaXauRNq\n9niYeGIiGkp7Ow8fOxYmTU9nAoPxDIYPx9YsK0NwJCWhCV1wAUInIeHIpLjBIzeX59fUkJBUWMhn\nFWc4LAyB2NSEGqk2rY533sFsUpS/Sy9FNTjtNBzUiYkmBMrLDZh6yhRMHu2ZFRRkXSf6+9mIujoE\niuYlaWadv7+FEqOi0KpyczmJ2ttxwnm9CC9N/R07FlNt/Hhp3Z4v9+7+lsSX7ZAfdf1QAn2d8vqw\ne+WdSYvFGegvrYXIydBQK3Xr7kZebd2KsHC7TQlta0PuZmSwXCEhZEsca6SlkW+5ZAmCpaYGf05G\nBss2Zw4KZXExS9fTQymZpoPExQ3I4sQT5tX/9bF9O5vR3g6/6okUHGzukspKiGXPHiwETboaORKF\noq2N/2dkIFi09ZN2gNFE4vHj4ZHycgSTolxWViI3FAGiuxuCUf4LCBCpih4jU1p2yUMRj8hVbc/J\nmUXPSezznSLffER6YoZJfdopEp65SYLKytDde3tJsw8L4/iLiqIcYsYMa39z3XUwowhUWVHBkTh7\nNoxYUmJI9YN1+6NHZyclG3v2wPjbtvFyKSloVL29OCd8Pp5x4YV/X6LQ0WFA69pnPD8fgaEpt243\nWs877yC8rr4ac2lw2FBLEBTVqr2dHJtFi3h+aOiRVeEeD59VEHgRNiwqCrU3MJA16+7m3qNHs24i\nUra+WO4t/r6M9ObJdv+T5ad+v5L9jpPFVQAXjxiB/L35ZrZBhKl/9BHTOHTIsNjCw1FE33zTQBhF\nzP+3d6+h0WrnnXXr2JaICKzKxkaWevt2amE//phrAgJwUHd24mRua+NaDXKqD/NosIP/ZpwQOscZ\nClsTGQnteb3WWLKvj0P+8GEIp7oaxUAB3vr64OmuLk6SNWvg18WLET6K/93WhgZ11lnUx4wfz0H8\n0ks8T+uyKisNrTQ0FAJRZDefD0HWIyHySPBieSP5frmm/FdyS+5j0p36nryReI+snX6vhNScJXel\ntcmokcLNMzIQOAEB6NJZWUxQO4EuXw6j6YTLy63jYHQ0WWXjx//zhWxttY6Zqq6pSdLfj6SeMuXI\nLN7ycmtLEx+P2fb735unXeHxUlLQMjTPPyaGDqeDx/XXg4HjdMJFiYnmk8nKIloVGGidFzZvhtu1\nS4U2Nywu5v6an9TaiuCbPp01GjlS5Ic/5GRYtEjGvf++1ASmyA9D/yQbQs+R4uZoCff4y/Tp7PvY\nsZg0g1Oa9u83GamuNS2/Oe00rqupMSfzm28ipPbsYVl/9zum3dTE/4OCWNqKCu6xeTP3fvxxaDcg\ngCXUfoUiLG9zM69zsEekqxuS0AYfQzFOCJ3jjNpakY3LEAIRERCA1wu/+flhR69fjwnV3w8/REXx\nv6Iic8hVVPD31lZoWw/roiJo2eOBxlNTuc/VV0NcOTkQV2srUVZttBAaCnGGhUEgnZ3mrgkIEIlO\nDZH34h6Wff7flst2PSA3lv1CLjr8jKyacKe8unOi/CJ4BRfefDOUX1LCy73yCs5XhfzMyTF0vMBA\ng2fYuxfGVbTCY/XwHjyiolhA7V/b3c310dG83LBh1tdLBE3o6actAnTPPUh+bRusqqN69gd7YAdv\nXmcn9z71VMy1/HySIhU7R/P+8/KIjD32GE7pU09lI5KTLfXX6UQ4jR2LnTJihKXtpqTgeMnIwDZ6\n6SURj0f6HnpY3gn4gRxYEySdZSKhLkOt+D/tXXl4lNW5/52ZTJYJWUnIJGEISdgCkUXCEhUrWnA3\n2gUFW7XlarnorVVvK9r2saULyG2fa3uvtKLSe9vbqtU+t4pdFIpgLfsWAgEEQ8hKyEIyWchkmXP/\n+H2Hk8AkmWiYCdfze548yXzz5fves73nfd/zLl4v+duFcU15eTQptbRww0pM1KdL3d09bC0Wjh3T\nXaJS/dTVaa24vZ1zRRUZVXOloUH7kZWUkHSXi12i7OZKOIQceq9kw3T8oLFRV9htbOQaDAujWUOZ\nHObN4/pQaVTCwzlJ0tIoGicl6QMN5VK+aRP9e1patAtKdzeZ0P79/DlgpSJbtIiT8p13tN+POrG4\n7jqqbip0KSuLgkJxMSfMhAlAUVEWVsnX8F8Jj2NZ+dP4woHv4rNRLmDmAvrYnD7NhVZUxFlXU8PG\njhyp9UiXi7YLr1dHsMfFUW2pqmIH9Iwl8oewMC72xx7TjMDtpi2kvPzi3D2vv86OjInRVR0WLaKh\noq5O+yJ1dZGm7dupDyxcyI7ZuJGZDwEtfSQkcEVfcw13ilOnKEFNm0ap6rvf5eo8d476xzXXcLCm\nTGHnVlVRz7n6ap3POSyMnD8vjzSuX893Pvww8PTTsKek4CttwJRZHKd160iq4qX+Ailvu418/k9/\n4isnTCDjiInhfLr3Xr6yvl7H3P7+99ozQIXPKV+b1FQ2/1e/IoM6fVq7ZsXHU0qvrSUvfeABnRCs\nqIgRGe1WtPmkSYNaPgPCMB0/aGvTQZxOJyfAxIk4X5A+OVmXUrr1Vk6AI0e4bnfuJBNR/1tdzTWk\nHF1TUmiLfOopzbCU45YqjOnzUYJqb+ezlNF6504+Z/lyakLFxZzraWn8H6eTDro1NTpT3D9K5mBf\n1t9wz6jN+FrN95nrRXn7ut3aqJyTw23P4+Hii4vjaklK4melUlxxBTmsOsILBLm5lCrKy7niNmxg\nozMzee2rX+U9Dz2ko7dVcl+Hg4xw7FjtNa1sL6dOkSvbbNRT581jp8yapRPA79rF//3tb9m2/Hwy\n0dxcnfisooIrVyUSVoYUZWyJjeUAJCRQ4nn9dUp5FRX8LATb8NRT5z2yW1oYq6se3dKikxuqQ64L\n4XAAjzxCvtXZyWGJjtbCpM9Hf8V//IOvvOMOXZUa0LX7VFYTt5smp7AwCrJC8L0OB7vn7Fl2RX4+\nm6Yq1RYXW6Ytu667OJQwTMcPlF3m9Gm9tkaO1NVr29poJlD2Fp9PV5ttbua9SvqeOZODHxfHwOmP\nPiJDGTtWn7yqUsTbtumy1QCvqURgKmLBbudmPX8+n7l4MZ/ncNBAmJSks4Ru2cK1lZsLZGZeD5F2\nPRq2bEXCsysgdu6gqhQby1ntdPLGmhptNdy+nUymro6rJjOThJSVUYa/0ODbHzo6yAA6Oii5ZGXx\nRKuwkB20Y4du7JYt7MC4OIZGrFxJWlXemoYGDkZkJJlXSoouzufx8PurruKzWloo0am4s40bKcF9\n+KH2dvZ4dBE9Vd9LMUrFhO+777zOIUtLIbZsgS/KCbH8YYgnHr8oturvf9fhX6mpXOCqkKrPx7ml\nBKYLoSoljx3b+/rhwxTqxo7lM958k7bAe+6hRlpaqsu8O53cJx59lFrf2rVsvprPu3ezu3NzKRA+\n+qhOyq5SYrQl8llDDcN0/ECF4zhH8e/cXJ0Qbvp0DohKfj13LjfuceN0IQDlHZqdzchym02HET32\nGD/n5+tMFBkZurKiclGZMoXS/IMP8nkqgLqiggHYb71F2kaM4O64bBkna0MDN3ifj35CyjFs0yYa\nEKX8DKbethGPuB+EY98uvlDlLS4ooNik5PpTp3SYQXg4Xz57NlUqlyvwXJatrdz2VaXLwkKmn1DO\nfk1NupRuaysZ0qRJXEHbt3OFtrbqcr5KFFX1VerrdWiHOllraSEHz8jg/cnJFBVbW3Xun0ce4Wpd\nsIC6bVcX2/aklcQyNZW7hKoXvWwZcPIk2uJS8ZcZP8J7E76GidkjkVkIOIo5XsrI+8c/kvzkZPLL\n8HBuQC4Xx7G6WteYio/XXbV1K30OHQ4ehPUMCm5v53xR2UsAXYVHpUf5wx/YvJgYdsemTWzyM8/0\nttV/tkfimSee0CXUTp3iEOXkAN52oDngVRM4DNPxg/BwDkDBYk6OsWM5oAcPUrJ2OjmPb72VE2PC\nBM7daivLj8NBJlFayrUmBKWc2bM5EdxuXnO7uZOUlHD9ZWZqFe7oUe6Cq1cz7UptLT9LqYv/RURw\n7axdy4OejAyqaYWFfP60aTRXuN086VB8Yt+xEdj31ecx5/5tOt3cb37DB0VEAFOnoiNnGt6tnYVx\n7YeRECaRMgZ8uTruBno7/F2IAwfoaXbkCCWR+nod3BMZyc6ZM4fbdU0NV9SMGZRAzpzhd8rrWQ1K\nS4uuGa46WnkY22y6flRGBg1vkyeT848Ywfcpzj56NJ9ts/H+5mYenZeXMxRCHamfPEm1zFLr5Lx5\nqP6XVfj2nrvgzg5HdDsdmKdMISP54AP2t8PBhe5y6RRD48ez6aqc/OHDDOxPSKA95dpr+br16/l/\nXV0UBNes0TnXJk4kjy4p0Z/T0/VplZQUxnw+zjOVpNHppCDpL81Pdzfpdjh0mSSAXQL4T730SWGY\njh+MGgWUV+j4x4MHKaHU1GiPfKVCAVwfKopgzBidkkUIfm5vZ5jR7NncDbdu1XmhfvADbsBnz3KD\nf+cdrtEpU7hDTphA8fjtt7kjKs2jrU3/RERQ129r035Cyu3ljTdoA1IZP4Wwkjs5E4HP3MYGLFpE\nY9Dbb9MusXcvwnfvxqzIDJwcPQ+tZYfgmJmExOwEXbZ09256TXZ10TM5L49SiNvNVfazn7Hjursp\nJdTVcTWdPctVkZ1ND+DYWDIegNKNqhRRXs6BWLRIJ3OvqOD9ymoPcFCUn1B9PVdgbCylN4+H33/r\nW1zNqmqcEhVSU5mK48UXyegWLOC77ruPNHk8kKmp2D//cfx+xFI0JE9E0w7ayyZY/pCdnTrNyaFD\nlEx9PvK4hgZKHOnpHAOPhxloi4u1LVwVBc3LI/l2u7b3qHp9CQlkDIcOUbJWbhwzZ+oTrRtuoOao\nspB0d3MYpk5lU/tKxqWKYShG1jN7qvKmGGoYpuMHDgeQlQk8888sSpaaSinj4EEyhpwcXdsO4By+\n4w6K0g8/zF0sMlI71irJx+ejZJSdTSY1bZp2DcnM1OlllFDgcnHSLVnCZ+zdyzXd2KiNfQCvXXEF\nVS6VpVTZlABOnBkzyCdUkruep9QAOFsLCoAbb0Tn7gP4zTf24trGNzH7o1dgk91oa00DUm6lXWby\nZHpNqvpQa9ZwlSlnppUrya19Pl2rW9XicTi4Ek+eZNTs7bfTqPXzn1O8GzNGlzUYP573Pv20VRsI\n3MK//nX+BjgwV12l83c6HFTHVGbylSvJWAoKcHZcHrBtO2IifQi7/36utspKvquwEHL5cojWVnRE\nx6Prts/DuXQx3um4Hr97zY7MTGD/n3XZ82PHyEdjYrT7wvbt2jTk8XBsz51jl2RmUmpVeW6U3aSs\njM/o6GDTw8LIJ1UZmXXryGsbG/mMpiZqjXffre34QvBkKytL28qrqtid0dEc65tu8j/XbTb6C6ko\nkeRk0uP1Aj6H9hpQTt5DgZAwHSHEFwF8D0AOgNlWHh1/990E4GcA7ABeklKutq5nAngVwEgAewF8\nWUo55KFpSjJQv0eP5kCqOMLvfY8DVl/PQd23j1JPdLQ+CDp1ihPo9tu1S/n8+b3fM24cTQrKy1j5\nop05w006IoKT6t57qX3cc4/esFUEAqB9fU6coGQzbRojBmw2+szNncvn5+b2TpMMULB4910AiMQN\nN8xF8fy52O99GOkRdUjctgG3df4Rkf/zW9hefFFvr6r8qXIkGj2aK2bdOnZYWZnOcaOCzOLiSJCq\nzdPSwu34m9/kinI6Lz4Vi4joXYtr1Soew9fV8ZxZVSMdM4YD8MorFCmzs4HycsgVK3C8KQWlJ30o\nS5qBqHFuLHh2PexbN2Nk9WEAgExPx77JX8Lm2LtwIGE+KqrD8dkdOmoC0MfS+flaaJoyhe5Nzc2q\ngisXb2cnJZJZsyjJPvkk543qAq+X5EvJe+Li2GUrVtBm19FBj4SODp2RY/x48si9e/mTn8/mK4dp\nl4t81+1mV6SkMA4rP7+3Buzz8R3vv69PU19+mXw/KopDIiVwykFm98ILnOdDhVBJOocAfA7AC33d\nIISwA3gewAIAFQB2CyHeklIWA3gWwL9LKV8VQvwSwFIAvxhqIlNSdKlvu51rYvJkHeKzdKk+UXrv\nPe54t9zCdZOSQtUoP5/Xc3L6fs/NN3OivvwyNQyloUh58f9NmMD0BOoo/rXXaDZpaGDqjBtvpFFw\n3z6uOZWm0uGgeucPDQ1cx7W1bOtPf8qdVErgWH0Ssm7+Cr5f8RVgcjtyG97HfUl/Qtzbv6Oup9Aj\nJ3PL0TJsi7sZEyMq4G46DJuUtMBLyZepspNnzpBrbthAJhEVRU45fXr/xoS5c4G5c1Fe2IDCGjeS\n3FGY+ZMlcGz4Xy25JCaSER0+jLbSWsiGcszprsTC6l8DRUC7zYmTaVdj86z7UTZpIe56Ziqe/5GA\nywWUvMdFvnmzjo9SFZWVvSQqiqfleXnMNnD8OE+rAG4u586RER0/zt9Tp3LDqq/XfFVK8u2GBgZo\nfu5zfNeJE2RQVnA6kpOtyrNlWoMtL+dp59atnA93362Fw6NHSUdqKrvzQpPbzp3kyy4Xx/u556jm\nqwD5oiKrxnmYlvBVAsmhQKgyBx4BANF/qvnZAE5IKUuse18FUCCEOALgegBLrPv+G5Sahpzp2O20\n5ezaxfW0YQMnW3c3B0blPVF5sISgtDBnjk4/3LOuXV9QSb1cLjKepCSuRX8TBuAp8nPPkbGoksUd\nHVzH111HAULl+F6zhj49/QXuqTif0lIySOUj98QT9JNbvdoSblIjcXTEQvxyzEI8ueopWtWPHeOW\nXFhITiglRqAcN2AfvLYoNEbGI8HRAeHx0N06Pl4XrFNhFHv3UvcDOMsXL6Ztqa/TsRMnUF7UiB+W\nLIazoxExxyrQ2FaFG6/Mgjh+nGLESy+xUQCiAaTbYlEfkYoKkY2fxP8QFePm4+r57JTycmChlXC/\nsZHt7+riAldJ+F0uSqs+H5nzjBk61ZCqJTVxIv9HpX0eN07bXTweSj8NDRwrdbq5fz+fl5dHxl9d\nrQPwVQXkqVP5PKWlpqezu6Oj+Zy//pVzYNQoSiQHDpD+adP8q0UlJToNS3Q0pfEPPqBtqWd6JRkH\ndHbpFKZDBSHVCUQIIITYAuBf/alXQogvALipR77kLwOYAzKYHVLKcdZ1N1iIL/fCZ1yIvLw8uWeP\nX02uF+5+YTuKqz2YnBrb63ptLXcqVWNIxT6pvOMq+6UqMfVx9OCGBh0ykZTU/2CrIpKKoXi9nJwq\nqFRdUyfefcHr5URUGUiVo2FyMmlQKX/tdqCrm/mjL6qhVFsL0dqCmMZWxJdWY8rpIoxv+AjpTTVI\na6lFeHdnr9u7bHZ4omLQGhmNVns4zjki0BEeiU6bHd1hDvhiYyHCwmDz+RDW3QlHZwcivO2Iam9F\ndKsH0c1NiGlvgQ29529zdCxOj3KjMiUDFSljcMo9HkXNTjQgEj6fxFlbIjwRoxAZCURGAdIKNs/O\nZvurqgFPDy/znqW5BqpgpGwvSirKzCTDqqzU/i4qF45yd/A0M9F3QgI3trY261jcrl0qUkZxHMLD\ndaiCqnSj7C8DjXGvPmoGKiqBcAfHMyJcJ2VXaUttNsBj9yDJHos3H80/LzH3ByHEXill3kD3XTJJ\np7+6V1LK/qo/DDUdD2GQda8Kpl+UlhmAZgK1dawJJH0cfFVwQeXE8VckM1AkJl5sb+kLI0Zwkqtg\n0Kio3rmVfT5OXn8OaD2hHNhOnuTO5ozSZheA7Va+dwCQ6m9Uk5Mhk5NR2QzsiszF+znXwtlUjYiu\nFsTFAgl2H5IdQGJjLRIbaxHXWIvYmiqMaGuGs60ZMd42RLQ1Iay7C3bpg63GBggbfDYbuuxh6AiP\nRIcjHK3OGNSOTEVddwSqEI/G6DjUxrpQMTIDzukutI2IvYg0b7uEp7Qd3k4BnyMSORlc4HV1bKfa\nyWNjuYhbW6niKMag7HoDIT2dm43Pxw1H9XtaGhe6YkZt59jPwsY5BJtOxhUWxu8g+F3yqN7FNTIy\naHM5fVrXulIHeoEiJgZwpWg1cdQobjpKqhaC94x3xaJgenpADGcwuGRMp7+6VwGiEqwEoTDaulYP\nIF4IESal7OpxvS86Bl33asmcMVgyp28G1dhIO2ZVFSdTZiZF8iQnj0wvRSlWf5CSJybbttF2c+ed\nXCTvvsvUBTExdBAcKIOowrlztGM0NtL+09N2W1ZGcV+FTvUFVSF3zx7AbpN46M4qzJ7ZrVPU9YTP\nx+182zbtjKSCNL/znX5FRe85H9auasKxo+EIj3fiqeXifH0xv/d7dXbSQBhIZSXtLKrg5+OP9p/B\nYyDs30+3JWlJplfO5DWvD4hwAMlR1CqrqqjO1tbSdejH3/bfDW1tujb5pEmfXP3ZuJGJ5VTg8NPf\nuHTzeDirV2EAPgSrRlQC2A1giZTysBDidQB/6GFIPiilXDvQ+wJVrwKB18tFGBPDk2MDDWUXUHmw\nAoJKjGW3cxUFoJuqIhQqL/VQo7tbF6obivijmhpKV2lp/TOwri6t3gUTJSVkshkZH88/J1D1KiRM\nRwhxF4D/AJAMoBHAASnljUKINPBo/BbrvlsAPAcema+XUv7Iup4FHpknAtgP4EuB1DIfSqZjYGDQ\nG8Oa6YQKhukYGFw6BMp0hjho3cDAwKB/GKZjYGAQVBimY2BgEFQYpmNgYBBUGKZjYGAQVBimY2Bg\nEFQYpmNgYBBUfKr8dIQQtQBOBXh7EoC6S0jOpcblTj9w+bfhcqcfGFwbMqSUAxRC+5QxncFACLEn\nEEen4YrLnX7g8m/D5U4/cGnaYNQrAwODoMIwHQMDg6DCMJ2+sS7UBHxCXO70A5d/Gy53+oFL0AZj\n0zEwMAgqjKRjYGAQVBimY0EI8UUhxGEhhE8I0ae1XghxkxDimBDihBBiRTBp7A9CiEQhxEYhxHHr\nt980UUKIbiHEAevnrWDT6Q8D9akQIkII8Zr1/U4hxNjgU9k3AqD/ASFEbY9+/6dQ0NkXhBDrhRBn\nhBCH+vheCCF+brXvoBDiyk/0Qiml+aGKmQNgIoAtAPL6uMcO4CMAWQDCARQCmBxq2i3a1gBYYf29\nAsCzfdzXEmpaB9unAJYD+KX19z0AXgs13YOk/wEA/xlqWvtpw7UArgRwqI/vbwHwFzCH/FwAOz/J\n+4ykY0FKeURKeWyA286XxZEs7vcqgIJLT11AKADL8cD6fWcIaRkMAunTnm17A8ANYoD6RUHEcJ4T\nAUFK+T6Ahn5uKQDwa0nsAHOUp37c9xmmMzikAyjv8bnCujYckCKlrLb+Pg2grxz+kUKIPUKIHUKI\n4cCYAunT8/dIJuNvAqu7DgcEOic+b6kmb1hlky4nDOm8/1TVMh8uZXE+Lvqjv+cHKaUUQvR1LJkh\npay08kxvFkIUSSk/GmpaDXphA4BXpJReIcTXQKnt+hDTFDJ8qpiOvHRlcYKC/ugXQtQIIVKllNWW\n6Humj2dUWr9LrGocM0CbRKgQSJ+qeyqsKiFxYCmi4YAB6ZdS9qT1JdD+djlhSOe9Ua8Gh90Axgsh\nMoUQ4aBRc1icAIF03G/9fT+AiyQ3IUSCECLC+jsJwNUAioNGoX8E0qc92/YFAJulZeEcBhiQ/gvs\nH3cAOBJE+oYCbwG4zzrFmgugqYcqP3iE2nI+XH4A3AXqql4ANQDesa6nAfjzBZb8D0Hp4NuhprsH\nXSMB/A3AcQCbACRa1/PAsj4AcBWAIvCEpQjA0lDT3VefAlgJ4A7r70gArwM4AWAXgKxQ0zxI+lcB\nOGz1+3sAJoWa5gvofwVANYBOaw0sBbAMwDLrewHgC4dHNAAAATZJREFUeat9RejjdDfQH+ORbGBg\nEFQY9crAwCCoMEzHwMAgqDBMx8DAIKgwTMfAwCCoMEzHwMAgqDBMx2BYQQjhFkKcFEIkWp8TrM9j\nQ0uZwVDBMB2DYQUpZTmAXwBYbV1aDWCdlLI0ZEQZDCmMn47BsIMQwgFgL4D1AB4EMF1K2RlaqgyG\nCp+q2CuDywNSyk4hxDcB/BXAQsNw/n/BqFcGwxU3g675uaEmxGBoYZiOwbCDEGI6gAVglrrHPknC\nKIPhB8N0DIYVrIyAvwDwDSllGYB/A/CT0FJlMJQwTMdguOFBAGVSyo3W57UAcoQQnwkhTQZDCHN6\nZWBgEFQYScfAwCCoMEzHwMAgqDBMx8DAIKgwTMfAwCCoMEzHwMAgqDBMx8DAIKgwTMfAwCCoMEzH\nwMAgqPg/mO0+4QCwkMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eeb6b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_circle_square()\n",
    "\n",
    "x_vals = []\n",
    "y_vals = []\n",
    "colors = []\n",
    "for i in range(3000):\n",
    "    data = get_data_point()\n",
    "    \n",
    "    x_vals.append(data[0])\n",
    "    y_vals.append(data[1])\n",
    "    if is_in_circle(data):\n",
    "        colors.append('r')\n",
    "    else:\n",
    "        colors.append('b')\n",
    "plt.scatter(x_vals,y_vals,c=colors,alpha=0.5,s=14);\n",
    "plt.xlabel(\"X\");\n",
    "plt.ylabel(\"Y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Bonus Stuff: Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Classes are amazing, somewhat complicated, and an unavoidable part of learning Python. They let us do a ton of really cool things. We're not going to get into too much detail today, but let me demonstrate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spot says woof-woof\n",
      "Alejandro says woof-woof\n"
     ]
    }
   ],
   "source": [
    "class Dog:\n",
    "    \n",
    "    # Method that is run upon initiation\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        \n",
    "    # Method that is run on demand\n",
    "    def bark(self):\n",
    "        \n",
    "        print(\"{} says woof-woof\".format(self.name))\n",
    "\n",
    "\n",
    "d = Dog(\"Spot\") \n",
    "d.bark()\n",
    "\n",
    "d2 = Dog(\"Alejandro\")\n",
    "d2.bark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "class Cat:\n",
    "    \n",
    "    def __init__(self,name):\n",
    "        self.name = name\n",
    "        self.lives = 9\n",
    "        self.alive = True\n",
    "    \n",
    "    def lose_life(self):\n",
    "        self.lives -=1\n",
    "        if self.lives <= 0:\n",
    "            self.alive = False\n",
    "    \n",
    "    def how_many_lives(self):\n",
    "        if self.alive:\n",
    "            print(\"%s has %s lives left.\"%(self.name,self.lives))\n",
    "        else:\n",
    "            print(\"%s has used all lives. :sad:\"%self.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steve has 9 lives left.\n",
      "9\n",
      "Mika has 9 lives left.\n",
      "Steve has 8 lives left.\n"
     ]
    }
   ],
   "source": [
    "mika = Cat('Mika')\n",
    "steve = Cat('Steve')\n",
    "\n",
    "steve.how_many_lives()\n",
    "print(steve.lives)\n",
    "\n",
    "steve.lose_life()\n",
    "\n",
    "mika.how_many_lives()\n",
    "steve.how_many_lives()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steve has 7 lives left.\n",
      "Steve has 6 lives left.\n",
      "Steve has 5 lives left.\n",
      "Steve has 4 lives left.\n",
      "Steve has 3 lives left.\n",
      "Steve has 2 lives left.\n",
      "Steve has 1 lives left.\n",
      "Steve has used all lives. :sad:\n",
      "Mika has 9 lives left.\n"
     ]
    }
   ],
   "source": [
    "while steve.alive:\n",
    "    steve.lose_life()\n",
    "    steve.how_many_lives()\n",
    "    \n",
    "mika.how_many_lives()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 9:**\n",
    "\n",
    "> Write a class for \"person\" that stores a person's age, hair color, and eye color. In that class, add a function that allows you to change the hair color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Where to go from here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "This has been a whirlwind of information that I hope has been useful. It's just the tip of the iceberg. Here are some other great resources:\n",
    "\n",
    "* https://learnpythonthehardway.org/ (this costs money)\n",
    "* http://codingbat.com/python (Lot's of simple test problems to play with)\n",
    "* http://learnpython.org/ (Excellent interactive 'course')\n",
    "* https://py.checkio.org/ (A series of Python tasks in a game framework; I've never played it but know people who have and learned a ton. Requires Log-in.)\n",
    "\n",
    "At the end of the day, finding cool ideas and trying to code them up is the best place to go. Fight with your code, love your code, passionately defend your code. It's all part of the fun. Take a look around and see what other people are doing and see if you can do it (better). GitHub is a great resource for finding cool projects and great coding examples. At the risk of self-promotion, let's say you want to learn how to work with microphones... you can look at my github for an example here: https://github.com/ZWMiller/PythonProjects/blob/master/streamAudio/streamAudio.py That code will never win an award, but if you were trying to figure out how to begin working with microphones in Python, it's a great place to start. There are tons of people doing _WAY_ cooler things than I am, so find them on GitHub, look at their code, figure out what they're doing... it's a great resource.\n",
    "\n",
    "** Best of luck. Feel free to ping me with questions! **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Solutions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 1:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hello', 'world']\n"
     ]
    }
   ],
   "source": [
    "hello_world_list = ['hello','world']\n",
    "print(hello_world_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 2: **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Sum is:  15\n"
     ]
    }
   ],
   "source": [
    "values = [1,2,3,4,5]\n",
    "sm = values[0]+values[1]+values[2]+values[3]+values[4]\n",
    "print(\"The Sum is: \",sm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 3: **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "values = [1,2,3,4,5]\n",
    "sm = 0\n",
    "sm2 = 0\n",
    "for val in values:\n",
    "    sm = sm + val\n",
    "    sm2 += val # Note, these are completely equivalent! += just means, add it to the current value on the left side.\n",
    "print(sm)\n",
    "print(sm2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 4: **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4, 5, 7, 8, 9)\n"
     ]
    }
   ],
   "source": [
    "li = [1,4,4,8,9,3,2,7,5,5,5,5,5]\n",
    "s = set(li)\n",
    "tup = tuple(s)\n",
    "print(tup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 5: **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 8, 9]\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "[8, 9, 0]\n"
     ]
    }
   ],
   "source": [
    "li = [1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9]\n",
    "last_three = li[-3:]\n",
    "print(last_three)\n",
    "\n",
    "first_nine = li[:9]\n",
    "print(first_nine)\n",
    "\n",
    "middle = li[7:10]\n",
    "print(middle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 6:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "def cube_plus_seven(x):\n",
    "    return x**3+7\n",
    "\n",
    "print(cube_plus_seven(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 7:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "def sum_of_list(li):\n",
    "    sm = 0\n",
    "    for el in li:\n",
    "        sm+=el\n",
    "    return sm\n",
    "\n",
    "print(sum_of_list([1,2,3,4,5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 8:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.9858\n"
     ]
    }
   ],
   "source": [
    "def get_random_number():\n",
    "    return random.randint(0,10)\n",
    "\n",
    "def sum_of_list(li):\n",
    "    sm = 0\n",
    "    for el in li:\n",
    "        sm+=el\n",
    "    return sm\n",
    "\n",
    "numbers = []\n",
    "for i in range(5000):\n",
    "    numbers.append(get_random_number())\n",
    "\n",
    "avg = sum_of_list(numbers)/len(numbers)\n",
    "print(avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "**Exercise 9:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brown\n"
     ]
    }
   ],
   "source": [
    "class person:\n",
    "    def __init__(self,age,hair,eyes):\n",
    "        self.age = age\n",
    "        self.hair = hair\n",
    "        self.eyes = eyes\n",
    "    \n",
    "    def change_hair(self,new_hair):\n",
    "        self.hair = new_hair\n",
    "\n",
    "zach = person(29,'red','blue')\n",
    "zach.change_hair('brown')\n",
    "print(zach.hair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "354px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}