{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Python Lists and Arrays\n",
    "===\n",
    "\n",
    "## Unit 4, Lecture 1\n",
    "\n",
    "\n",
    "*Numerical Methods and Statistics*\n",
    "\n",
    "----\n",
    "#### Prof. Andrew White, Feb 8 2016\n",
    "---\n",
    "\n",
    "*Suggested Reading:*\n",
    "\n",
    "1. https://docs.scipy.org/doc/numpy-1.12.0/user/whatisnumpy.html\n",
    "2. https://matplotlib.org/users/pyplot_tutorial.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "List Methods\n",
    "----\n",
    "\n",
    "A method is a function, but it is associated with some data. We can put a `.` after a list to call methods associated with the list. It's best to see some examples. Notice that the methods all modify the list, hence they are methods and not functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "x = list(range(10))\n",
    "print(x)\n",
    "x.reverse()\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4, 3, 5, 8, 42, 42, 246]\n",
      "['A', 'B', 'C']\n"
     ]
    }
   ],
   "source": [
    "x = [3,42,8,5,42,0.4,246]\n",
    "x.sort()\n",
    "print(x)\n",
    "x = ['A', 'C', 'B']\n",
    "x.sort()\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3]\n",
      "[0, 1, 2, 3, 5]\n"
     ]
    }
   ],
   "source": [
    "x = list(range(4))\n",
    "print(x)\n",
    "x.append(5)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Creating Python Lists\n",
    "====\n",
    "We saw you can explicitly declare all elements of a list, like `[5,3,2]`. You can also create lists in the following other ways:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Using the `range` function\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = list(range(10))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = list(range(5,10))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = list(range(0,10,2))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = list(range(10,0,-1))\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Notice that the second argument to `range` is not inclusive, even if counting downwards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Creating Python Lists as needed\n",
    "----\n",
    "You can use the `append` function to add elements as you need them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 3, \"Let's put a string here instead\", 'And another for demonstration porpoises']\n"
     ]
    }
   ],
   "source": [
    "x = [4,3,41]\n",
    "x[2] = \"Let's put a string here instead\"\n",
    "x.append(\"And another for demonstration porpoises\")\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, \"Look! I'm putting a string in here :)\"]\n"
     ]
    }
   ],
   "source": [
    "x = [0, 1]\n",
    "x.append(\"Look! I'm putting a string in here :)\")\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[43, 'X']\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "x.append(43)\n",
    "x.append('X')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5, 3, 4], 'ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ']\n",
      "[[5, 3, 4], 'ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ', [[54, 3]]]\n",
      "[[5, 3, 4], 'ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ', [[54, 3]], [...]]\n",
      "[[5, 3, 4], 'ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ', [[54, 3]], [...]]\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "x.append([5,3,4])\n",
    "x.append('ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ')\n",
    "print(x)\n",
    "x.append([[54,3]])\n",
    "print(x)\n",
    "x.append(x)\n",
    "print(x)\n",
    "print(x[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# List Assignment\n",
    "\n",
    "Lists can be assigned when `[]` are used on the left-hand side of the assignment operator(`=`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can assign a single element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5, 3, 4], 'ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ', [[54, 3]], 'end of list']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[-1] = 'end of list'\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can assign a slice, but the right-hand side should be a list with the same number of elements as the slice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['new 0', 'new 1', 'ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ', [[54, 3]], 'end of list']"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0:1] = ['new 0', 'new 1']\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can also delete items instead of assigning them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['new 0', 'new 1', 'ᕙ(˵ ಠ ਊ ಠ ˵)ᕗ', 'end of list']"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del x[3]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "NumPy Arrays\n",
    "====\n",
    "\n",
    "Python lists are great. They can store strings, integers, or mixtures. You can even put lists into lists! NumPy arrays though are multidimensional and most scientific/engineering python libraries use them instead. **They store the same type of data in each element and cannot change size**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.zeros(5)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Notice as opposed to lists, we have to state the dimension. The dimensions are passed either as a single number, like `5`, or as a tuple, like `(5,2)` creates a $5\\times 2$ array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros( (5,2) )\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0.]]\n",
      "\n",
      " [[0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0.]]]\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros( (2,3,5))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Creating NumPy Arrays\n",
    "====\n",
    "\n",
    "There are many convienent methods in numpy to create arrays. You saw `zeros`. Here are others:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Ones\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "x = np.ones(5)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.04166667 0.08333333 0.125      0.16666667 0.20833333\n",
      " 0.25       0.29166667 0.33333333 0.375      0.41666667 0.45833333\n",
      " 0.5        0.54166667 0.58333333 0.625      0.66666667 0.70833333\n",
      " 0.75       0.79166667 0.83333333 0.875      0.91666667 0.95833333\n",
      " 1.        ]\n"
     ]
    }
   ],
   "source": [
    "print(np.linspace(0,1,25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Notice **linspace includes the end point** and **arange does not include the endpoint**!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Remember:** we cannot append to NumPy arrays because their size is set once and never changed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "NumPy Functions\n",
    "====\n",
    "\n",
    "Functions from the `numpy` module all take arrays as arguments, so whereas we cannot call `math.cos` on a list, we can call `np.cos` on an array. This is very useful for working with probability distributions. Numpy also treats `*`, `**` and all other arithmetic operations as per-element calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.   0.5  0.5 -1. ]\n"
     ]
    }
   ],
   "source": [
    "from math import pi\n",
    "x = np.linspace(-pi,pi,4)\n",
    "print(np.cos(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  4  9 16]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "print(x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  4  8 16]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "print(2 ** x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This only scratches the surface of functions that take numpy arrays. There are many more that do things from computing integrals to evaluating boolean expressions to writing out files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "NumPy Methods\n",
    "====\n",
    "You can always play with these by using `TAB` in your jupyter notebook. Let's see a few."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n",
      "4.95\n",
      "8.332500000000001\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(0,10,0.1)\n",
    "print(x.argmax())\n",
    "print(x.mean())\n",
    "print(x.var())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Plotting\n",
    "====\n",
    "\n",
    "Just like we use the NumPy library for working with arrays, there is a library for plotting called Matplotlib. It's import syntax is a little funny. This is how you activiate it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#the line above is for jupyter notebooks only\n",
    "import matplotlib.pyplot as plt #we import a sub-module called pyplot and call it plt for short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f12cb4b9f28>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2*pi, 2*pi, 500)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To get rid of that extra line at the top, we use the `show` command. This is sort of like the difference between using `print` vs just making a variable the last line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 5\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fCnFbd46Ojo6qNHV2dpJMJkmlUuX9RtGUSCTK50yn0yQSCUtpcrvduFwuhoaGVNf0vQPFxQJ3D/g5f/58QzWVvk8Ao6Oj79F0/7o2vn14gu8dHKbfNW0KO1X+RuRyOYaGhkzf92b+7lX+LmqhaSEclXXbFoMkSR8FHhRCfEp5/3HgFiHEp2c59veAlUKI36zYtkIIcUmSpHUUHdduIcTp0v49e/aUG7h9+/a62jofkUiEzs7aKjknMnke/cfDxUriH72O1R3GWrQsX5D51986xEQyz198cAPb+tv0bpKqLMZmZkALXWcnkvzqP79Li9vJt/7VFnxul6rnr5a5tI0lMvzSk0dwAN98dAvdDapsoRZ2X1wcg4OD5de7d+9+T2xXjRDfJWBVxfuVyrbZeIQZ4T0hxCXl3zPAi1w9PtUwpqamav7MnlMTpHLF1HKjOScoLhR3e1/xi/4jC4b5FmMzM6CFrtIcvd0bunRzTjC3th6/h9sHOsjL5uyrdl/UBjUc1D5goyRJayVJ8lB0Qk/NPEiSpGuBTmBvxbZOSZKaldc9wB3A0ZmfbQTBYG3lXmRZLg84f+g6486BeOiGfhzAq2cnmUrl9G6OqtRqM7Ogtq5kNs/zJycA+Nlr9e2r82n7OeV79PS7Y+QL9UV2Go3dF7WhbgclhMgBnwaeBY4VN4kjkiR9SZKkypTxR4AnhRCVPe86YL8kSQeAn1Acg9LFQU1MTNR0/NGRBOciKTp9Tdw+ENCoVfXTlI5x88o2sgWZ50/VptHo1Gozs6C2rpfOTDKdLbB5mb8hS2rMx3zatvW3sjLQzFgiy5sXzPVEYvdFbVAl91gI8TTw9IxtX5zx/o9m+dxrwFY12lAvtY7FPXuiaLj3X9ON22Xc+c6yLPPBTT3svxjjR++O8/MmTeOdjXrHT42K2rqeVsJ7H7xW/1Vf59PmcDh48JpuHt93medOTnCbgW/8ZmL3RW0w7i9rg6nlUTaVK/Dy2QgA79tonNTy2QgGg9w6EKDT18TQZIqjowm9m6QaeocftEJNXecnU7wbnqbF7eSudfoP4i+kbfeGLpwOeON81FQhabsvaoPtoBRGRqqvqLx3qBgy2RRsMWRyRCUjIyM0OR1lR/rjE9YJRdRiMzOhpq49ytjTnWs78Dbp/3VfSFu33832FcWQ9ItnIg1qVf3YfVEb9O+xBqE0b6AanlO+9EZ/eoIruu5X2vry2UkyuYKeTVKNWmxmJtTSVZBl9pw2Vl+tRlupraXvmRmw+6I22A6qRsYTWQYvxWhyOrjHACGTalnT6WN9t49EJm+6AWibxXFoOM5oPEtvq4ctfeb5Ab19oIMWt5Pj4WnOR1J6N8dGR2wHpRCPV1diZc/pCQoy3Lq6nXav8evbVeravb7oUPdYJJuvWpuZDbV0lbI279vQaZgakdVoa25ycrdy8/fcSXPMibL7ojbYDkqht7d3wWNkWS6HHe43SMhkISp13bu+OAD95oUpUw1Az0U1NjMjauhK5Qq8cnYSgPsNVCOyWm2lMN/zpyKmmBNl90VtsB2UQji88IJpZyaSDEVStDe72LlS/2U1qqFSV7ffzbb+4gD0K+cmdWyVOlRjMzOihq7KRJ5VBkrkqVbb9b1++to8jE9nOTJi/KcTuy9qg+2gFKqZG/TSmeKP+p1rOww996mSmbp2b7BOmM8q87lmooauF04VM+CM9PQE1WtzOBzlMN+Lp41/M2X3RW0wx69sA+jqmv+LLMtyee7T3SZKjpip646BDppdDg6HEoRiaZ1apQ4L2cys1Ksrns4xeCmG0wF3re1QqVXqUIu2e9YV2/7KuUnDh/nsvqgNtoNSWOhR9uR4kstTGTp9TWw1UUbUTF0tHhe3ri7O0C+NUZgVvcMPWlGvrr3no2QLMlv7Wuk0WFXwWrSt6/KxMtBMNJXj7csxDVtVP3Zf1AbbQSm0t88/pvSyMmnwzrUduJzmeZyfTVeposDLJndQC9nMrNSr62UlFG3EJ/1atDkcV6ZyvGTwSbt2X9QG20Ep5PP5OffJslwef7prrfG+9PMxm66dq9ppbirOMzFzmG8+m5mZenTF0zneUsJ7d6wxXi27WrWVHNRPz0XJ5o07wdzui9pgOyiFRGLuGnXHw9OMxDN0tTSxpU+fpbIXy2y6vE1Obl1VvDMyc5hvPpuZmXp0vTYUJVeQuWF5K50+Y4X3oHZtqzu9rOvyEs/keeuSccN8dl/UBttBKfT19c25rxQKu2utcSY8Vstcuu5UBqDNHOabz2Zmph5dr5w19pP+YrRdyeYzbpjP7ovaYDsohVAoNOv2giyX4993rzNWRlQ1zKVr16qA6cN8c2kzO4vVFTN4eA8Wp63kbF8/b9wwn90XtcF2UApu9+zhkHdHpwknsvT43Vy3zFzhPZhblxXCfHNpMzuL1bVXCe/daNDwHixO24pAM2s7vUxnC7xz2ZiTdu2+qA2qFJOTJOlB4GuAC3hcCPGVGfs/AfxH4JKy6b8KIR5X9j0G/IGy/U+EEE+o0aZaCQRmv+MszX26a22H6cJ7MLcuKIb5Xjo7yctnJ/nFG8xXqmU+bWZmsbpK4do7DRreg8Vru2NNB2cjIX46NMnOVcbLmLP7ojbU/QQlSZIL+DrwAWAz8KgkSZtnOfTbQohtyl/JOXUBfwjcAuwC/lCSJF2+XWNjY+/ZJssyrw1FAbhzjfnCezC7rhK7VgVodjk4Hp4mnMg0sFXqMJ82M7MYXZWTc40a3oPF26ykae9Q1JCTdu2+qA1qhPh2AaeEEGeEEBngSeDhKj/7APCcEGJCCBEBngMeVKFNNTPbncLZiRShWIYObxPXmjC8B/PfAXmbnNys1BTcqzhiM6H33Z1WLEbXvotT5EqTcw0a3oPF22xdl4++Ng+RZI5jBlwV2u6L2qBGiG8FcKHi/UWKT0Qz+YgkSXcBJ4DPCCEuzPHZFXNdKJFIEI1GyWaz9PX1EQqF8Pv9uFwupqamCAaDTExMIMsywWCQkZGR8oJb8Xic3t5ewuEwDoeDrq4uwuEw7e3t5PN5RkdH8fl8hEIh3G43gUCApw8Um7ZtmYeLF86Xr+nxeGhra2N8fJzOzk6SySSpVKq83+v14vP5iEQidHd3E4vFyGQy5f0+nw+Px0M0GqWnp0czTYlEArfbTTQaLWsaGxsjEAiQyWRIJpPsXOHntaEoL54Y5b7VPlNoKp0zlUrhdrvfo8mMdiqd0+12k8/niUajNWl65UyxOv2WLidTU1OG01Tqe7IsL9pOWzudhGLw4yOXaEt7DaOp9G80GjV935v5GzE2Nlb+XdRC00I4ZLm+x2VJkj4KPCiE+JTy/uPALUKIT1cc0w3EhRBpSZJ+FfiYEOI+SZJ+G/AKIf5EOe7/AZJCiL8sfXbPnj3lBm7fvr2uts7H0NAQAwMDV237je+9y6nxJH/8/nXcstqcd0iz6aokmsrxsX84hAP4p1/aSmuz8de4KrGQNrNSq65MvoD0zUNMZws88bHNLG9r1rB19VGPzY6E4nzmhyfpbfXwjY9t1r2QaSV2X1wcg4OD5de7d+9+j0HVCPFdAlZVvF/JlWQIAIQQ40KIUi7z48DN1X62UczM9x+NZzg1nsTb5OSm/jY9mqQKC81jCHib2NLbSl4uhonMhN5zNLSiVl0HLseZzhZY1+U1tHOC+mx2Xa+fTl8TI/EMZyaSKraqfuy+qA1qOKh9wEZJktZKkuQBHgGeqjxAkqTlFW8fAo4pr58F3i9JUqeSHPF+ZVvDmZnvX0qO2LGyHU+TebPxq5nHcLsyAP2aycah9J6joRW16iqNH94+YPxEnnps5nQ4uG2g2Fd/es5YfdXui9pQ9y+vECIHfJqiYzlW3CSOSJL0JUmSHlIO+y1Jko5IknQA+C3gE8pnJ4A/pujk9gFfUrY1HI/Hc9X714aKKbu3D5gztFdipq7ZKH3p912YImPQiZCzUY02M1KLroIs89r5Yl+9zQR9tV6b/YySTfuqwRbctPuiNqgy4CCEeBp4esa2L1a8/jzw+Tk++7fA36rRjnpoa7sSxptK5Tg4HMfpgFtWG2/ORS1U6pqL5W3NrOvycmYixYHLcUPOM5mNarSZkVp0nQhPMzGdI+h3s6Hbp2Gr1KFem924vBW/x8W5SIpL0TQrAsYIadp9URvMG7tSmfHx8fLrNy9MUZCLX4Y2EyUNzEalrvkohYfMlG5erTazUYuuK+G9gKGSBuaiXpu5XU52riz+aL5xwTh91e6L2mA7KIXOzivzg6+E94wf01+ISl3zUQplvnZ+kkKdmZ2NolptZqMWXaVxQzOE90Adm5Uyat84bxwHZfdFbbAdlEIyWcwKyuQK7L9YLOtvli/9fJR0LcT6bh/LWt1MTOc4Hp7WuFXqUK02s1GtrkvRFEOTKfweFzcsN0eISQ2b7VzZjtMBB4fjJDLGWIdpqfdFrbAdlEIqlQLgYChOKldgXZePZa3mH/gs6VoIh8PBbcqd6esGujOdj2q1mY1qdZXCe7tWtdNkklWe1bBZu7eJzcv85GV465IxpkYs9b6oFbaDUijl+79xvtjhbzFJosBC1DKPYdeqK9l8ZkDvORpaUa2ufcqT/q0mSuRRy2ZXwnzG6KtLvS9qhe2gFEKhELIs86Yy8LrLRF/6+ahlHsONy1tpbnJyajzJeCKrYavUQe85GlpRja5kNs/hUBwHsH2FefqqWjYrZdcWE5r0HzNdyn1RS2wHpeD1erkQTTMcy9De7OLaoDmLw87E6/VWfaynyclN/cUaXm8aKENqLmrRZiaq0XVwOE62IHNNsIWA1zyZpmrZbKDDS1+bh2jKGGOmS7kvaontoBR8Ph9vnr9SPcJlkpj+Qvh8tc2NKYX53jBBmK9WbWahGl37lbJUO1ea5+kJ1LOZw+HgllXGyeZbyn1RS2wHpRCJRMo/ymYtDDsbkUikpuN3KWNvg5dihq8qUas2s1CNrtL40w6TOSg1bVYK8xnhZmop90UtsR2Ugretg8OhYvWIHSvNkbJbDd3d3TUdv6zVw7ouL6lcgUPDxlxeu0St2szCQrouRdNcnkrT1uxiU7ClQa1SBzVtdsPyVrxNTk6PJ3VfcHOp9kWtsR2UwuvnJsjLsLnXb/rqEZXEYrGaP1MK871pgDvT+ViMNjOwkK5SavX2/jbThaLVtJnH5eTmFUpVCZ2z+ZZqX9Qa20EpDA4XB1pLcW2rkMnUfmdZSrE3QuhkPhajzQwspKs0/rTDhFMh1LaZUapKLNW+qDW2g6JYEfpYpDgjfZcJv/TzsZh5DNcu89PW7OLyVJpLUeNOQNR7joZWzKcrky/wzuVi6HWHidLLS6hts9L39e3LMVI5/cZMl2JfbAS2g6JYEXoylWdZq5s1ndZKF13MPAaX01EefDfyU5TeczS0Yj5dR0YSSqUTL91+dwNbpQ5q26yrxc2mYAuZvMyBy/qFo5ZiX2wEtoPiyljLrlXmqAhdC4tNEy3dmeod258PvVNgtWI+XfuVvmq27L0SWtislGq/X8cVoZdiX2wEqmQDSJL0IPA1wAU8LoT4yoz9nwU+BeSAMPDLQoghZV8eOKQcel4I8RANplS230wlY6plsQuOlQpyHgrFmc7kafG4VG5Z/ei9mJpWzKerPP5kUgelhc12rmrnm2+Hyqn3erAU+2IjqPsJSpIkF/B14APAZuBRSZI2zzjsbWCHEOIG4DvAX1TsSwohtil/DXdOkeksJ8eSuJ1wo0kqQtdCNLq4weN2bxPXBv3kCjKDOoZO5mOx2ozOXLrGEhnORlJ4m5xc32vOSida2OyanpaKMdO06uevhqXWFxuFGiG+XcApIcQZIUQGeBJ4uPIAIcRPhBCleiSvAytVuK4qvHWp+OO7pbeF5ibrRTx7enoW/dnSREg9QyfzUY82IzOXrtIyMDf1t+F2mbOvamEzl9PBdiXdXK++utT6YqNQo5evAC5UvL+obJuLTwI/qnjvlSRpvyRJr0uS9GEV2lMT+5QOfV2H8UJYalDPHdDNFbF92QAFOWei992dVsyl60p4z7xP+lrZTO9xqKXWFxtFQ2ekSpL0S8AO4O6KzQNCiEuSJK0DXpAk6ZAQ4vRsn08kEkSjUbLZLH19fYRCIfx+Py43pkIYAAAgAElEQVSXi6mpKYLBIBMTE8iyTDAYZGRkhNbWYvHTeDxOb28v4XAYh8NBV1cXI6Oj7FfGn1Y2JUin04RCIdxuN4FAgLGxMQKBAJlMhmQyWb6mx+Ohra2N8fFxOjs7SSaTpFKp8n6v14vP5yMSidDd3U0sFiOTyZT3+3w+PB4P0WiUnp4eVTWFw2Ha29vJ5/MkEgny+TxDQ0OL0uROJgk0uxiNZ3n79CU2LGs3hKbSOWOxmGXsVDqn2+0mkUgwNDR0labgst5yX72uq4mhoSFTaSrZKZVKMTo6qrqdNrYV77XfuRzj3IVLOAq5hmkKBAJMTk5e9f9o1r438/s0NjZGd3e3ZpoWwlHvnbEkSbcBfySEeEB5/3kAIcSfzTjufuC/AHcLIUbnONffAz8UQnyntG3Pnj3lBm7fvr2uts7kxNg0n/7+cZa1unn8wxt0r9yrBel0mubm5kV//s9fPMeeUxF+7dYV/MKWZSq2rH7q1WZUZtN1ZCTOZ/7lJCvam/m79wzxmgctbfbr33uX0+NJ/uzB9eWn/0axlPqimgwODpZf7969+z0p1GqE+PYBGyVJWitJkgd4BHiq8gBJkm4C/gfwUKVzkiSpU5KkZuV1D3AHcFSFNlVFZcruyMhIoy7bUOqdx3DzCuOOQ+k9R0MrZtO136TFYWeipc126BjmW0p9sZHU7aCEEDng08CzwLHiJnFEkqQvSZJUysr7j0Ar8E+SJL0jSVLJgV0H7Jck6QDwE+ArQojGOSilptmOFe34/ebMilqIenXdrIx3HByOk9Zxpv5sLCWblZfXWGXe8SfQ1mY7V5YSJRqfdbqU+mIjUWUMSgjxNPD0jG1frHh9/xyfew3YqkYbaiWRyXNsJIHTATetaCOTMN4Tghq4XPUlf3T63Gzo9nFqPMmhUNxQd/D1ajMqM3VNJrOcCE/jdjnY2teqU6vUQUubbe5tpcXtZGgyxWg8w7LWxs3hWSp9sdGYM1dVBd6+HCtWL1/mx+8pDjZaETV0lTKk9hkszLdUbDZ4KYYMbO1rxec29w+hljZrcjq4qb/4FNXovrpU+mKjWbIO6i2lA5cGU4PBoJ7N0Qw1dJWqZr+l40z92VgqNjN79YhKtLZZqa/ub3ANyaXSFxvNknRQsixXDDoX77gmJib0bJJmqKHrumV+WtxOziuhE6OwFGxWqOirO008/6mE1jYrPe2/fTlGrtC4uXtLoS/qwZJ0UBejaUbiGQLeJjb2FFckNeJEVDVQQ1dl6MRI2XxLwWanx5NMpnIE/W5Wd5h/GoTWNlvW6mGgw8t0tsDRkcatCL0U+qIeLEkHVfqR3b6iDadSvVzvR1mtUEvXzQaoGD2TpWCzyvCeFSrtN8JmpahII4vHLoW+qAdL0kGV6u9Vloyx6jwotXSV/q8GLzU2dDIfS8FmpcH+nRYYf4LG2EyP+VBLoS/qwZJzUJlcobyw2c0VK5KWSoNYDbV09bU1szLQzHS2wLujCVXOWS9Wt1kik+doxVQIK9AIm23ta6XZ5eD0eJLx6azm1wPr90W9WHIO6vBInHReZl2Xj64W861IqidGTTe3Km9filGQYXNvcSqETXV4mpzcqIyZvmX3VVOz5BzUXBlR8XjjBlQbiZq6SqETo6SbW91mVgvvQeNsdrPyxFkK52uN1fuiXixBB3X1/KcSvb29ejRHc9TUtXV5K26Xg5Nj00wmGxM6mQ8r26w4FcI6859KNMpmO8tz96bIN2DM1Mp9UU+WlIMaS2Q4N8eKpOFwWKdWaYuaurxNTm7oa0WmmCyhN1a22fnJFOFElg5vE+u7fXo3STUaZbMV7c30tnqYSuc5NT698AfqxMp9UU+WlIMqPe5v6299z4qkVkjhnQ21dRkp3dzKNqucSO60kM5G2czhcFSMmWp/M2XlvqgnS8pBVS6vMZOurq5GN6chqK2rsmJ0QedJfFa2mRXDe9BYm5Uq8TciUcLKfVFPloyDyhdkBmdJLy+h96OsVqita3WHlx6/m8lUjjPjSVXPXStWtdnF0CgHQ3EcFCeTW4lG2mxbfxsuBxwbTRBL5zS9llX7ot66loyDOjE2TSydp7/dw4rAe1eIbG+31p1qCbV1ORwOdpQWMbykb5jPqja7mHaTzctcE2yhw2etqRCNtJnf42JzbysFuVibT0us2hf11rVkHFQ5e2+WpyeAfD7fyOY0DC107VhVCp3omyhhVZu9PVwc1LdaeA8ab7MdKxvTV63aF/XWpcqChZIkPQh8DXABjwshvjJjfzPwDeBmYBz4mBDinLLv88AngTzwW0KIZ9Vo00zeWmDJ7EQiQU9PjxaX1hUtdN3U34bTAYdDcaYzeVp0mkRqVZu9Eyo5KGuF96DxNtuxsp2/2z/MvotTyLKs2aC/Vfui3rrqfoKSJMkFfB34ALAZeFSSpM0zDvskEBFCbAC+Cvy58tnNwCPA9cCDwH9TzqcqsXSOd8MJmpwOblw+e+mOvr4+tS9rCLTQ1dbcxLVBP3kZDgzrN5HPijYbnkozMl2g1ePi2qD1lhFvtM3Wd/vo8DYxlshyfjKl2XWs2BdBf11qhPh2AaeEEGeEEBngSeDhGcc8DDyhvP4OsFuSJIey/UkhRFoIcRY4pZxPVUolY67v9c95tx8KhdS+rCHQStfNK/VffsOKNqustO9yWi91udE2czoc5b6qZbq5Ffsi6K9LDQe1ArhQ8f6ism3WY4QQOSAKdFf52brpa2vmA5u6uXd955zHuN3WGowuoZWuctkjHRMlrGiz/QuEos2OHja7UqJLu75qxb4Ic+vKF2T++vWLvDY0qel0E1XGoBpFIpEgGo2SzWbp6+sjFArh9/txuVxMTU0RDAaZmJhAlmWCwSAjIyO0trbS7YRfWC3T29vKhQsXcDgcdHV1EQ6HaW9vJ5/PMz09TTqdJhQK4Xa7CQQCjI2NEQgEyGQyJJPJ8jU9Hg9tbW2Mj4/T2dlJMpkklUqV93u9Xnw+H5FIhO7ubmKxGJlMprzf5/Ph8XiIRqP09PQsShMU62T19vYSDodn1ZRIJAgEAgwNDamuqT03RUuTg8tTGfYdO8NAd2vDNJXOKcsyiUTCEnbq6+vj4uVhBhWH38cUk5Mu02ua+X1qbm5mdHS0oZrW+IoLPR4YjhGZipOYmlRVUyAQIJ/PMzQ0ZBk7lb5Plb+LlZrCBR/fOxzmldPjbP/IJkZGRhalaSEc9a6YKEnSbcAfCSEeUN5/HkAI8WcVxzyrHLNXkqQmIAQEgc9VHlt5XOmze/bsKTdw+/btdbV1PoaGhhgYGNDs/Hqhpa4v7znLS2cn+fTtK3loc+MXNrOazd65HON3nz7FilYXf/fIDXo3RxP0stm/+/67nBxL8uUH1pfr9KmJ1fpiibl0fXNwmG8MhvjQtT381s+sWvT5BwcHy6937979npi2GiG+fcBGSZLWSpLkoZj08NSMY54CHlNefxR4QQghK9sfkSSpWZKktcBG4E0V2lQzgUBAj8tqjpa69C57ZDWbladC9FtzbSHQz2bluXsa9VWr9cUSc+kqhaJv1jjTtG4HpYwpfRp4FjhW3CSOSJL0JUmSHlIO+19AtyRJp4DPcuXJ6QgggKPAM8C/E0LoknifyWT0uKzmaKmr1DkPDMfJ5guaXWcurGaz0o/n5m5TRd5rQi+b7VilrYOyWl8sMZuuuJIV7XIUq3VoiSrfBCHE08DTM7Z9seJ1CvjFOT77ZeDLarSjHpJJfcv2aIWWuoJ+DwOdXoYiKY6OJMqLxDUKK9lsPJHlzESK5iYnq1sa7+wbhV42u26Znxa3kwvRNCOxDL1tHlXPb6W+WMlsut6+HKcgw5Y+7RfSXDKVJBZC73x/rdBa104dw3xWslmpbNS25a2s6l+uc2u0Qy+bNTkd3KTcQGlRostKfbGS2XTtb+BCmraDUtA7318rtNZVWrl0vw7rQ1nJZqVK+ztXtVtK10z01FYO811Q30FZ1WYzdcmyXJ5aMnPRVy2wHZSCx6PuI79R0FrX1r5Wml0OTo8niUw3dpVdq9isstL+jpXtltE1G3pqKyVKvH05Rk7lVXatarOZui5E04zGswS8TWxowEKatoNSaGuzXt0z0F6Xp8nJVqV81FsNfoqyis2Oh0uV9pvpb2+2jK7Z0FNbb5uHVYFmprMFjo0mVD23VW02U9dbFZVOGrGQpu2gFMbHx/VugiY0QtcOncahrGKzKzH94o+BVXTNht7atOqreuvSipm6Kld6bgS2g1Lo7Jy7DJKZaYSuK2WPGrvKrlVstm/G6rlW0TUbemvTykHprUsrKnVlcgUODs+96KsW2A5KYSmliarNqkAzy1rdRFM5TjVwlV0r2CyaynEiPI3b6eAGJVRqBV1zobe2rctbcbscnBxLEkmqN2aqty6tqNR1eCROOi+zrstHV0tjag/aDkohldKuFL+eNEKXw+Eo31FpWZBzJlaw2eClKWRgS18rPndxTokVdM2F3tq8TU5u6CveCAyqOGaqty6tqNTV6PAe2A6qzFKax6AFV0InjUuUsILNSktA7Kz40ltB11wYQdvNGlQ3N4IuLajUVfr/akR6eQnbQSkslXkMWnFTfytOBxwdiZPINKZaldltVpDl8pd+R0UBU7Prmg8jaNtZXstMvTFTI+jSgpKusUSGs5FipZPrexu3kKbtoBS8Xq/eTdCERulqbW7iumXFVXbfudyYpyiz2+zsRJJIMkeP381AxxUtZtc1H0bQtrrDS4/fzWQqxxmVxkyNoEsLSrpKU0i2LW/F42qc27AdlILPp/2kMz1opK4roZPGOCiz22xfRckYR8WcErPrmg8jaHM4HOUyPftUCvMZQZcWlHTt1yG8B7aDKhOJRPRugiY0UteOFVdqndW7zlg1mN1m+y/Mvnqu2XXNh1G0lSrxq3UzZRRdahOJRIqVTi41PkECbAdVpru7W+8maEIjdW3saaGt2UUoluHyVFrz65nZZolMnsMjcZyO4vhdJWbWtRBG0ba9vw2nA46oNGZqFF1q093dzcmxYqWT3lYPK9qbG3p920EpxGKNL3baCBqpy+V0lIvH7mtAmM/MNhu8FKMgw+ZeP63NV696Y2ZdC2EUba3NTVwbLI6ZHhiuv01G0aU2sVisXAh6x8q2q0LRjcB2UApLacExLdmhQQrvXJjZZvsuzL1kgZl1LYSRtF2pbl6/czGSLjXJZDK6pJeXqGvBQkmSuoBvA2uAc4AkhIjMOGYb8NdAO5AHviyE+Lay7++Bu4GocvgnhBDv1NOmxbIU5jE0gtKE3XeG42TyBU0zfsxqM1mWy4Pzu1a990tvVl3VYCRtO1a08Y23htl3sThmWs/TgZF0qUlbV5Bjo8eVUHTjC+LW++vxOWCPEGIjsEd5P5Np4N8IIa4HHgT+SpKkjor9vyOE2Kb86eKcwPrzGBpFt9/N2k4v6VyBIyPqVoyeiVltdmYiyfh0lu4WN+u63pv9ZVZd1WAkbRt7WmhvdjESz3CpzjFTI+lSkxePXSqHorVePXc26nVQDwNPKK+fAD488wAhxAkhxEnl9WVgFAjWeV3VsXqaaCPRYqb+bJjVZleKw84e0zerrmowkjaX08H20phpnYsYGkmXmhyNFAC4ZVVAl+vXFeIDeoUQw8rrENA738GSJO0CPMDpis1fliTpiyhPYEKIOW9lEokE0WiUbDZLX18foVAIv9+Py+ViamqKYDDIxMQEsiwTDAYZGRmhtbWYIRWPx+nt7SUcDuNwOOjq6iIcDtPe3k4+nycSiRAIBAiFQrjdbgKBAGNjYwQCATKZDMlksnxNj8dDW1sb4+PjdHZ2kkwmSaVS5f1erxefz0ckEqG7u5tYLEYmkynv9/l8eDweotEoPT09mmlKJBK0tLQwNDTUUE1be1r4DvDamXE+fmOP6ppK18zlcvj9ftPZ6fVzcQA2d7oYGxu7SpPb7cbpdDI0NGQqTfPZqbLvuVwuRkdHDaNprS/Li8Des+Nsb0suSlMgEGB6epqhoSFDaFLDToFAgFQ6zTuhaQBWuuIMDxdU17QQjoXmq0iS9DwwW4D1C8ATQoiOimMjQohZ685LkrQceBF4TAjxesW2EEWn9T+B00KIL1V+bs+ePeUGbt++fUFBi2VoaIiBgQHNzq8XeujK5At85H8fIp0r8K1/tYVujSofm9Fm8XSOj37zEADf/fgNs4ZNzKirWoymbXw6y6P/eJhml4PvfvwGPE2LCyoZTZcanByb5t99/zg9LW7+4dHrNcngGxwcLL/evXv3ey6w4BOUEOL+ufZJkjQiSdJyIcSw4mxG5ziuHfg/wBdKzkk5d+npKy1J0t8Bv71Qe7Sip6dHr0trih66PC4nNy5v5c0LU7x1cYr3X6PNHBEz2mzwcjG9fGtf65wxfTPqqhajaSuNA56ZSHJ4JM72Ra5zZDRdalDONF3V3vD08hL1jkE9BTymvH4M+MHMAyRJ8gDfA74hhPjOjH3LlX8dFMevDtfZnkVTzeOmGdFL1w6VS8nMhhltduVLP3dGlBl1VYsRte2oKB67WIyoq17evDB3pmmjqNdBfQV4nyRJJ4H7lfdIkrRDkqTHlWMk4C7gE5IkvaP8bVP2/YMkSYeAQ0AP8Cd1tmfRZLPqLV5mJPTSVerU+y/GyBW0KXtkNptVppfPNv+phNl01YIRtamxyq4RddXDVCrHu+EELp3Sy0vUlSQhhBgHds+yfT/wKeX1N4FvzvH5++q5vppYdR6DXrr625tZFWjmQjTN0ZE4NyxXv5ObzWZnJpJMTOfmTC8vYTZdtWBEbdf3+vE2OTkXSRFOZAj6PTWfw4i66uGtS1MUZLixz0+LDunlJexKEgpWncegp65bVhdTU984r02Yz2w2e/PC7NXLZ2I2XbVgRG1ul5NtSj3ExRaPNaKueij11WvaC7q2w3ZQCn5/4xbhaiR66iqF+d6oc47JXJjNZpWDzvNhNl21YFRt9Yb5jKprMeQLcnk8bpdO859K2A5KweXS7zFWS/TUtaWvlRa3k/OTKYZj6lc3N5PNYukcR0eLMf3S5NC5MJOuWjGqtpKDGrwUI7+IMVOj6loMJ8amiaZy9LV5WBVobPXymdgOSmFqSvvipnqgp64mp6P8xX9TgzCfmWxWql5+fe/c6eUlzKSrVoyqrb+9mf72ZuKZPEdHay/RZVRdi6GykLHeVdptB6UQDBqu+pIq6K3rSphP/TRcvbXVQrXhPTCXrloxsrZbVxdt8/pQ7X3VyLpqpbKQsd66bAelMDExoXcTNEFvXTtXteMADgzHSWbrXxiuEr21VUu+IJfH4aqZU2IWXYvByNpuVZJ6Xj9fu4Mysq5aiCSzHA9P43Y5uLG/TXddtoNSaMQS5Xqgt65On5trgi1k8zLvXI6rem69tVXLu+EE0VSO3lYPazq9Cx5vFl2LwcjatijVPS5E01yKpmr6rJF11UIpe+/G5a14m5y667IdlILej7JaYQRd5XRzlcN8RtBWDa8r42+3DQSqKhljFl2LwcjampyO8hPu3hrHTI2sqxb2KuHN2weKJVb11mU7KIWRkRG9m6AJRtB1y6oriRJq3pEZQVs1lMY0SmMcC2EWXYvB6NpKNnqjxjCf0XVVQzpX4C1lefdblP8HvXXZDkqhVJ7eahhB14ZuH10tTYxNZzkzkVTtvEbQthDDU2mGJlO0uJ1s7auuvWbQtViMrm3HynacDjgUihNL56r+nNF1VcM7l2OkcwU29vjK1TT01mU7KBvNcTgc7FqpbVUJo1IacN+5sh23y/66GZ225ia29rVSkOurzWdG9ip99bbV+k7OrcT+xijE4+oO4BsFo+gqhQz2LiJDai6Mom0+SnpvHaj+S28GXYvFDNpK2Xx7a0g3N4Ou+SjIcvlm6tYKB6W3LttBKfT2zrsYsGkxiq6bV7bT7HJwPDzNWCKjyjmNom0u4ukch4bjOB3zVy+fidF11YMZtJV+oPfVUInfDLrm49RYsZBxj9/N+u4rhYz11mU7KIVwOKx3EzTBKLq8TU62Kz/StdyZzodRtM3Fvosx8jJs6W2l3Vv9wgFG11UPZtC2IlCsxJ/I5Dkcqu4Jwgy65qMyvFeZaaq3LttBKei1YqTWGEnXHUqY6zWVHJSRtM3GlZBJbQu+GV1XPZhFW62Tds2iay5KN423zQhF662rrvWgJEnqAr4NrAHOAZIQIjLLcXmKixICnBdCPKRsXws8CXQDbwEfF0KoE/+pka6uLj0uqzlG0nXr6gBORzFbKJ7O0dpcV/czlLaZ5AtyubzRzC/9QhhZV72YRdttAwH+6dAor5+P8qu3rFjwh9osumZjJJbhzEQSn9vJDcuvztrTW1e9T1CfA/YIITYCe5T3s5EUQmxT/h6q2P7nwFeFEBuACPDJOtuzaPR+lNUKI+lq9xYzpPLylRnr9WAkbTM5OBwnnsmzMtDMisDC1SMqMbKuejGLtuuW+WlvdnF5KsP5yYWrSphF12yUnhJ3rGzHMyPTVG9d9Tqoh4EnlNdPAB+u9oOSJDmA+4DvLObzatPeXlsYxiwYTdftKob5jKatklfOTQJw55qOmj9rZF31YhZtLqejHOZ79dzCfdUsumbjp0PFvjpberneuup1UL1CiGHldQiYK+XDK0nSfkmSXpckqeSEuoFJIURpNtxFYEWd7Vk0+by6hUyNgtF0lcJd+y5OkcnVt1qn0bSVKMgyrykO6mfW1u6gjKpLDcykrWS7V85OLnismXRVMpnMcnA4TpPTUZ4KUoneuhYcBJAk6Xmgb5ZdX6h8I4SQJUmaKydzQAhxSZKkdcALkiQdAmq+hU4kEkSjUbLZLH19fYRCIfx+Py6Xi6mpKYLBIBMTE8iyTDAYZGRkpDwTOh6P09vbSzgcxuFw0NXVRTgcpr29nXw+z+XLl2lrayMUCuF2uwkEAoyNjREIBMhkMiSTyfI1PR4PbW1tjI+P09nZSTKZJJVKlfd7vV58Ph+RSITu7m5isRiZTKa83+fz4fF4iEaj9PT0aKYpkUiU/zWKpvTUFGs7mzkbSfPcwbPs3ryiZk2lc8ZiMXw+n+6aZtopLPuZSOYItrjoZJqhoXDVmtxuN4lEgkQiYShNi+l7lZpKfS+VSlEoFEyh6brODrwuODOR5MhQiFbSs2oKBAKMjo5epdmommba6Znj4xRk2NzVRGx8FOeM34hQKFT+XdRC00I46qmNJknSceAeIcSwJEnLgReFEJsW+MzfAz8EvguEgT4hRE6SpNuAPxJCPFB5/J49e8oN3L59+6LbuhDpdJrmZn1Xj9QCI+r65uAw3xgM8YFN3XzmztWLPo8RtQH89esX+d7hMB/ZEuRXb11Z8+eNqksNzKbtz35yjp+cjvCpnf1IN849J8hsukr8/jOn2H8xxmfuXM0HNnW/Z7/WugYHB8uvd+/e/Z5MlHpDfE8BjymvHwN+MPMASZI6JUlqVl73AHcAR4UQMvAT4KPzfb5RhEIhvS6tKUbUVaqUvHcouqjltUsYUZssy/y0jvAeGFOXWphNW2kMsTSmOBdm0wUQS+d4+1IMp+PK2PBM9NZVr4P6CvA+SZJOAvcr75EkaYckSY8rx1wH7Jck6QBFh/QVIcRRZd/vAZ+VJOkUxTGp/1VnexaN2+3W69KaYkRda7u8LG/zMJnKcWRk8aVUjKjt5FiS0XiW7hY31y3zL+ocRtSlFmbTtmNVO81NTo6HpxmNzz0Dxmy6QLlBlItrPwXmmEiut666JqIIIcaB3bNs3w98Snn9GrB1js+fAXbV0wa1CASMUyBRTYyoy+FwcNe6Tr59YISXzkxyw/K2RZ3HiNpKd9p3rAngXOQkRyPqUguzafM2Odm5sp1Xz03y6rlJfmHLslmPM5sugFdLT/rzZJrqrcuuJKEwNjamdxM0wai67q7IkFpsmM9o2mRZ5tWzC3/pF8JoutTEjNruVPrqq/Nk85lNVyKT562LMRzAHfP0Vb112Q5KQe87Ba0wqq713T7625uZTOU4VGW9s5kYTdu5SIpLU2kCyoTkxWI0XWpiRm23rGrH7XJwZCTBxHR21mPMpuuN81GyBZnr+/x0tcwdxtNbl+2gFDIZXSosaY5RdTkcjvJT1MtnFp5nMhtG01aaL3P7QACXc/E1zIymS03MqK3F4+LmFW3IXAmLzcRsul6tciK53rpsB6WQTKq30quRMLKuu9ZdyZBaTJjPSNpkWebFM8UylHcuMnuvhJF0qY1Ztd25wM2UmXQls/lynciFMk311mU7KIW+vtnmIpsfI+ta1+VjZaCZaCrHweHaw3xG0nZyPMnFaJoObxM39S8u6aOEkXSpjVm13T7Qgdvl4FAoPms2n5l0/fRclHReZvMyf3lp97nQW5ftoBT0zvfXCiPrcjgc3KXcwZWePmrBSNp+cmoCgLvXddYV3gNj6VIbs2rze1zcujqAzOx91Uy6fnK62P77NnQueKzeumwHpeDxzH8nYVaMruvudcUvyavnJsnka6vNZxRt+YLMi0rop5ov/UIYRZcWmFnbveuLti39wFdiFl2RZJa3Lk3hclz57s2H3rpsB6XQ1lZfWMaoGF3X2i4f67q8xNL5mpfgMIq2w6E449NZ+to8XBtsqft8RtGlBWbWtmtlO36Pi9PjSYYiV4/NmEXXK2cnKchw88r2OSfnVqK3LttBKYyPj+vdBE0wg67dG4qLou05OVHT54yi7QXljvre9Z2qrEBqFF1aYGZtniZnOett5lOUWXS9cEoJ762v7klfb122g1Lo7Kw/NGNEzKDrvvVdOB3wxoUpplK5hT+gYARt2XyhnLJ7b5Vf+oUwgi6tMLu2ezdcCfNVFto2g67hqTRHRxM0NzmrXuVZb122g1LQO51SK8ygq9vvZlt/G7mCzMtVrL1Twgja3rwwRSydZ22nlzWdPlXOaQRdWmF2bTf0tdLV0sRwLMO74enydjPoKj313T4QwF/aIXMAABTwSURBVOd2VfUZvXXZDkohlVp4WWczYhZd9ythvudrCPMZQduPTxTb+75r3rtUwWIxgi6tMLs2l9PBvUpywY9PXAl/GV2XLMs8r2SaVhveA/112Q5KQe98f60wi6471gRobnJydDTB8FS6qs/orW1iOssbF6K4HLBbhey9Enrr0hIraHtAWTfpJ6cjpJRVoY2u68hIgovRNF2+JnasrH4Zd7112Q5KQe98f60wiy6f28XPrCnGxZ+r8ilKb23Pn5qgIMMtqwN0+tRblkBvXVpiBW1rOn1cG2xhOlsoF5A1uq5njhef9t63saumeXp667IdlILX69W7CZpgJl3vV8Jkz5wYr6r0kZ7aZFnmWeVL/4CK4T0wl81qxSraSk9RpR9+I+tKZPK8pDjSB2dZNXc+9NZlOygFn0+dAW6jYSZdNy5vpb+9mbFEln0XF54Tpae2Y6PTXFBCJrtWVR8yqQYz2axWrKLtnnWdNLscHAzFuRRNG1rXS2cipHMFtva1siJQm8PRW1ddCxZKktQFfBtYA5wDJCFEZMYx9wJfrdh0LfCIEOL7kiT9PXA3EFX2fUII8U49bVoskUiE9nZ1f2iMgJl0OR0OPripm8f3Xebpd8e4dfX8qbB6antWGSC/v8aQSTWYyWa1YhVtfo+LO9d18vzJCX58Ypz7lmUNq+tHylPeg5u6av6s3vaq9wnqc8AeIcRGYI/y/iqEED8RQmwTQmwD7gOmgR9XHPI7pf16OSeA7m51wzRGwWy63ndNF01OB29emCKcmL/Uv17apjP5cj2296sc3gPz2awWrKTtQcX2Pz45QUdn7T/+jeDsRJLj4Wla3E7uXFt7Io/e9qrXQT0MPKG8fgL48ALHfxT4kRBieoHjGk4sFtO7CZpgNl2dPjd3DAQoyFfi+3Ohl7bnT02QzBbY0utndYf6MXqz2awWrKRta5+fFe3NjE9nefmUMVfUffrd4nfo3vWdeJtq/7nX2171OqheIcSw8joE9C5w/CPAt2Zs+7IkSQclSfqqJEnNdbZn0ei9MJdWmFHXB6/tAYqhifmSJfTQJssyTx0t/hg9fH1Qk2uY0WbVYiVtDoeDhzYX++qzZxe3KrSWTGfyPHey6KA+dF3Pos6ht70WHIOSJOl5YLZk+C9UvhFCyJIkzflrIknScmAr8GzF5s9TdGwe4H8Cvwd8aa5zJBIJotEo2WyWvr4+QqEQfr8fl8vF1NQUwWCQiYkJZFkmGAwyMjJCa2tx6e14PE5vby/hcBiHw0FXVxfhcJj29nby+Tz5fJ50Ok0oFMLtdhMIBBgbGyMQCJDJZEgmk+Vrejwe2traGB8fp7Ozk2QySSqVKu/3er34fD4ikQjd3d3EYjEymUx5v8/nw+PxEI1G6enp0UxTIpGgq6uLoaEhU2natGwZy1qcjCayvHxqlHXNqas0lc7pcDhIJBIN1XRoZJrzkykCHgebWnNEIhFV7FS6ptvtpr29naGhIcPbqRZNpb7X0tLC6OioZTRd55vG2+TgRCTHq4dPs3PjSsNoevZ0jOlsgc3LfHimx7mYnKjaTqW+V/m7qIWmhXBU1pOqFUmSjgP3CCGGFQf0ohBi0xzH/t/A9UKIX5lj/z3AbwshPlS5fc+ePeUGbt++fdFtXYihoSEGBgY0O79emFXXdw+N8j/euMQNfa385Yc2znqMHtq+9PwZXj0X5ePb+/j49uWaXMOsNqsGK2r7+msX+cHRMA9e081n71qtd3MAKMgyn/rOMS5G0/zBfWu4q4qlNWZDa3sNDg6WX+/evfs92Ub1hvieAh5TXj8G/GCeYx9lRnhPcWpIkuSgOH51uM72LBq90ym1wqy6HtzUTYvbycFQnJNjsw9ZNlpbKJbmtaFi5YhSGFILzGqzarCitoevL/aFF05P1FTsWEsGL8W4GE3T43dz+5r5l3WfD73tVa+D+grwPkmSTgL3K++RJGmHJEmPlw6SJGkNsAp4acbn/0GSpEPAIaAH+JM627No9F6YSyvMqsvvcZUnFf7z4dFZj2m0tn8+HKYgFxd6625Rr3LETMxqs2qworaVAS839fnI5OVySrfelL4zP3ddD011TIPQ2151zYMSQowDu2fZvh/4VMX7c8CKWY67r57rq0k0GqWjY/F3GkbFzLo+fH2Q7x8J8+LpCJ/c2U+P/+ovSyO1TaVy5R8f6YaFcoHqw8w2WwirartruYu3Q/D9I2F+fksQj0u/Gggnx6bZfzGGt8nJz9b5pK+3vexKEgo9PdqFbPTEzLr62pq5Y00Hebk4JjWTRmp76tgY6VyBnSvbWdetbdjDzDZbCKtqu2vTctZ2ehmfzlZdS1IrxIERAH722m7aq1g1dz70tpftoBSqySgxI2bX9eiNxaeVHx4bI5LMXrWvUdpSuQI/OBIGQLphmebXM7vN5sOq2mJTUzyyrZjsLA6MVFVLUgsuRVO8cm6SJqeDX9haf1/V2162g1LIZrMLH2RCzK5rQ08Lt65uJ52X3/MU1ShtTx0NE03l2BRs4YblrZpfz+w2mw+rastms9y1toMV7c0MxzLvWRK+UTx5YISCXFz+Jeivf/xIb3vZDkpB73VPtMIKuv71TUUNTx0dI1qRJdUIbYlMvhwyeezm5Tgc6tbdmw0r2GwurKqtr68Pl9PBI9uKT/z/+E6o4U9RFyZTPHdyAqcDHrlRnXFSve1lOygFvdc90Qor6NoU9LNjZRupXIEn37mipxHavnckzFQ6z5ZePzevaNP8emANm82FVbWVdO3e0EV/u4eL0XS5oHCj+MbgMAW5uPxLrVXL50Jve9kOSsHv9+vdBE2wiq5/u6MfKD5FlVbc1VrbVCpXDis26ukJrGOz2bCqtpKuJqeDT9xc7KvfGBwmmc035PqnxqZ56cwkbpeDX9qu3lOP3vayHZSCy+XSuwmaYBVdG3tauH9DJ9mCzN/uvwxor+3v3xomkclzU38bN/Y35ukJrGOz2bCqtkpdd63r4JqeFiamc3zvcFjza8uyzN+8eQmAh67rUWXsqYTe9rIdlMLU1MIL5JkRK+n6xI5+PC4HL52Z5OhIQlNtp8enefrdMZwO+PXb3jOFT1OsZLOZWFVbpS6nw8GndhWfop48MMJoXNuCq6+cneTty3Haml3lTEK10NtetoNSCAa1qUytN1bStazVw0e2FFNnv/bqeTq7tZmjIcsyX997kYIMD28OsqazseVerGSzmVhV20xd2/rbuGMgQCpX4L/tvajZdZPZPP/99eLT0y/v7CdQ57ynmehtL9tBKUxM6Du5TiuspuvRm/pY3ubhbCTFP76lzRf/mePjHA4lCHib+LiK8fxqsZrNKrGqttl0/cbtK/G5nbw2FOW1oUlNrvu/B0OMTWfZFGwpL6CoJnrby3ZQCvVUdTcyVtPlbXLy73+mWDH6qdPFpS/UZHgqzX9/o3hH+uu3rqC1Wd070mqwms0qsaq22XQF/R4+cXOx4v1//elF1QvJHrgc47uHRnE64NO3r8RVR829udDbXraDUtD7UVYrrKjrphVtPHBNF7kC/OkLZ0nnCqqcN1+Q+Y8vD5HMFrhrbQf3rl/cEgX1YkWblbCqtrl0PbQ5yHXLWhibzvKfXj6v2g9+LJ3jL14aQgYe3dbHpqA22XZ628t2UAojIyN6N0ETrKrr125dyTKfkzMTKb7+mjqhvm8MDnM4lKDL18Rv3rGqYWnlM7GqzcC62ubS5XI6+Py9a/B7XOw9H+X7R+rP6pNlmb969QLhRDG0V5rIrgV628t2UAqlVTWthlV1+T0uPnvbMjwuB8+cGOf/vDtW1/lePhPhW++M4HTA794zoPpgcy1Y1WZgXW3z6epra+Y/3FkMS//Nm5d5+1Ksrmt98+0Qr5ydpMXt5HP3DNS1nMZC6G0v20HZmJaBgIffvGMVAP/lpxd49dziBqIHL03x5y8OAfCpnf1sX9GuWhttbAB+Zm0HH926jFxB5v99/gwn5liEcyF+eGyM/z0YwumA379vjWoVI4yK7aAU4vG43k3QBKvqgqK2B67p5uPb+yjI8OU9Z3nhVG1ZR3uHovzhj8+QLcg8vDnIR1SoAF0vVreZFalG16d29XP32g6mswV+7+lTHBmp7f/i+0fC/H8/vQDAb9y2kl2rAotqay3oba+64hiSJP0i8EfAdcAuZaHC2Y57EPga4AIeF0KUVt5dCzwJdANvAR8XQmg7q20Oenu1XYROL6yqC65o+6Wb+sjkCnz74ChfeXGIsxNJHtvRP2/oI1+QEQdHeOKtYv2yD2zq5tdvW6HbuFMlS8FmVqMaXU6Hg9+9Z4D8/9/e/cdGXd9xHH9e2yt3lPZK25NulFWICgEckTnwx8ZUlsUpQtzIO85o/LXMEWUaYTglS7YlJltc5lymS8yG2TKS8c5GpkndmBA34yaokA2dwFYdJxBKj5a2cFzvSnv74/ttbQP0oPfje/ft+/FX7/rl+n71U+7z/Xy/n/t8MvDGwR42tLWz5toWbp3XOO7fXXJgkBd2HaFtv7O23zeWzmTl/OJMXvC6vXIdQb0HfAV4/XwHiEgl8BzwZWA+8DURme9++0fAM6p6GXACeCDHeiYsHi/8kiRe8Gsu+DhbIBDggSUzeXDpTCoCsGVvJ2u27ueNgz1nrSg9OJThrUO9fOvlA7z4jtM53XVVM49+bhYVJdA5weRoM7+50FzBygo23nQpK+c3MTCU4Wd/P8S329rZe/TkWTP8BgaH2P7fbh7cup+2/V0EKwI8fkMrq4s4yve6vXLd8n0fgIiMd9gSoF1VP3SP/R2wSkT2ATcBd7rH/RpnNPaLXGqaqFI4cy4Ev+aCs7N99cpLmBudytN/ixHr6ecH2/9HJFTFvOhU6sNV9KUGOdCZoDvpfB6lcWqQdcs+xdUtpXXPaTK1mV9cTK7KigAPXzeLBTNqeP7NI+ztOMX6tnaiNUHmRmuoqa6g6/QA7x9LcHrA+QjFnIYQ65e1clnT1EJFOCev26sYU5VmAodGPT4MLMW5rNejqmdGPT/uomeJRILe3l4GBgZobm6mo6ODmpoaKisr6evrIxqN0t3dTSaTIRqNcuzYsZFZKKdOnWLGjBnE43ECgQANDQ3E43Hq6uoYHBykv7+fVCpFR0cHwWCQSCTC8ePHiUQipNNpksnkyM+srq6mtraWrq4upk+fTjKZpL+/f+T7oVCIcDjMiRMnaGxs5OTJk6TT6ZHvh8Nhqqur6e3tpampqWCZEokEkUiEWCzmq0zDr5nJZEgkEmMy1aaSPHfbHLbs/ojXD6c4mjjDrkNj1xNrnhbkuuYqbrk8Qn0txGKxkskUDAYJh8PEYjHftNPov71QKERnZ6evMkUizr2gWCx2UZk+Ew3z/Wum8Y94Bdvae4gnBognxk70aZlWye0Lmlg0PUMmESdVW7xM6XR6zPtiIdopm0C2D46JyHbgXBPtN6rqS+4xfwXWn+selIisBm5W1a+7j+/G6aC+B+x0L+8hIrOAP6nqwtH/fseOHSMFLl68OGugiYrFYrS2thbs9b3i11yQPVsmk+FIX4oPupKcHhgiVFXB7IYQrfUhz88MxzOZ26xc5ZprKJPhYHc/sZ4kyYEh6sNVzG4I84naKXms8uIVur327Nkz8vXy5cvP+k+ZdQSlql/MsYYjwKxRj1vc57qAehGpckdRw897oq6utC7z5Itfc0H2bIFAgJZIiJYym4o7mdusXOWaqyIQYE5jmDmNxV2YOBuv26sY08zfBi4XkdkiUg3cAbysqhngNWC1e9w9wEtFqOecBgeLs7FYsfk1F/g3m19zgX+zWa7CyKmDEpHbReQwcC3QJiLb3Oc/KSKvALijo4eBbcA+5yn9t/sSjwOPiUg7zj2pX+VSTy4SiYRXP7qg/JoL/JvNr7nAv9ksV2FkvQfltWLdg0qlUkyZ4u313kLway7wbza/5gL/ZrNcE5PtHpStJOHq6OjwuoSC8Gsu8G82v+YC/2azXIVhHZQrGAx6XUJB+DUX+DebX3OBf7NZrsKwDso1/FkGv/FrLvBvNr/mAv9ms1yF4d2eAhMw+nqlMcYYf7MRlDHGmJJkHZQxxpiSVPLTzI0xxkxONoIyxhhTkspqkkQxiMha4CFgEGhT1Q0el5Q3IrIO+DEQVdXjXteTDyLyNHAbkAY+AO5T1Ynt/V4Czre5ZzlzF4L+DTADyAAvqOqz3laVP+6ed+8AR1R1hdf15IuI1AO/BBbitNv9qvpmMWuwEdQoInIjsApYpKoLcN7MfcF9k/gS8JHXteTZq8BCVf008B/gCY/rmbAsm3uWszPAOlWdD1wDPOSTXMMewVnGzW+eBf6sqvOARXiQ0UZQY60BfqiqKQBV7fS4nnx6BtiAhwvyFoKq/mXUw518vPhwOTrn5p7A+55WlSNVPQocdb8+6W5WOpMyzwUgIi3ArcBTwGMel5M3IhIBlgH3AqhqGucqRVFZBzXWFcDnReQpoB9nj6u3Pa4pZyKyCufyw7+y7H5c7u4HtnhdRA7Ot7mnb4jIpcBVwC6PS8mXn+Kc+NV6XUiezQbiwIsisgjYDTyiqkVdPXbSdVDjbcCI8/towLkM8VlARWSOuzVIScuS60mcy3tl6QI3zdyIcylpczFrMxdORKYBfwAeVdW+bMeXOhFZAXSq6m4RucHrevKsClgMrFXVXSLyLPAd4LvFLmJSGW8DRhFZA2x1O6S3RGQIaMI5kyhp58slIlfinA0Nj55agD0iskRVy2KFy2ybZorIvcAKYHk5nEyM43ybe5Y9EQnidE6bVXWr1/XkyfXAShG5BQgBdSLyW1W9y+O68uEwcFhVh0e6v8fpoIpq0nVQWfwRuBF4TUSuAKqBsp7tpqrvApcMPxaRg8DVPprFdzPOJZYvqOppr+vJ0cjmnjgd0x3And6WlDsRCeDs9bZPVX/idT35oqpP4E7KcUdQ633SOaGqHSJySETmquoBYDke3DO0DmqsTcAmEXkP54bgPWV+Rj4Z/ByYArzqjhB3quo3vS1pYlT1jIgMb+5ZCWwatblnObseuBt4V0T+6T73pKq+4mFNJru1wGZ3J/QPgfuKXYCtJGGMMaYk2eegjDHGlCTroIwxxpQk66CMMcaUJOugjDHGlCTroIwxxpQk66CMMcaUJOugjDHGlCTroIwxxpSk/wMx/alUezBC/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2*pi, 2*pi, 500)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Changing the Look and Feel\n",
    "----\n",
    "You may switch the look and feel of plots by using the `plt.style.use` command. You may change the size by using the `plt.figure(figsize=(4,4))` command, where you give the figure size in inches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "x = np.linspace(-2*pi, 2*pi, 500)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "plt.figure(figsize=(4,3))\n",
    "\n",
    "x = np.linspace(-2*pi, 2*pi, 500)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-colorblind',\n",
       " 'seaborn-muted',\n",
       " 'seaborn',\n",
       " 'seaborn-dark-palette',\n",
       " 'classic',\n",
       " 'grayscale',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-white',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-dark',\n",
       " 'bmh',\n",
       " 'dark_background',\n",
       " 'seaborn-paper',\n",
       " 'ggplot',\n",
       " '_classic_test',\n",
       " 'fast',\n",
       " 'Solarize_Light2',\n",
       " 'tableau-colorblind10',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-darkgrid',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-ticks']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Customizing plots\n",
    "----\n",
    "You should read the matplotlib [tutorial online](http://matplotlib.org/users/pyplot_tutorial.html), but here's some basic info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.figure(figsize=(4,3))\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.xlabel(\"The x-axis\")\n",
    "plt.ylabel(\"The y-axis\")\n",
    "plt.title(\"The title\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y, label=\"A sine wave\")\n",
    "plt.plot(x, np.cos(x), label=\"A cosine wave\")\n",
    "plt.title(\"A Title!\")\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "p = 0.2\n",
    "geo_p = (1 - p)**(x-1) * p\n",
    "plt.plot(x,geo_p,'o') #use circles\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "p = 0.2\n",
    "geo_p = (1 - p)**(x-1) * p\n",
    "plt.plot(x,geo_p,'yo-') #use yellow circles with dashes\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Specifying Color\n",
    "\n",
    "\n",
    "One of the most common ways of specifying color is the default \"color cycler\". Matplotlib has a default set of *categorical* colors that represent different line types. You can specify them by using `C0` or `C1` or `C2` etc. Let's see an example of way you might want to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1, 10)\n",
    "p = 0.2\n",
    "geo_p = (1 - p)**(x-1) * p\n",
    "plt.plot(x, geo_p,color='C0', label='$E_1[x]$')\n",
    "\n",
    "#plot the mean as vertical line\n",
    "plt.axvline(x=1 / p, color='C0', linestyle='--', label='$E_1[x]$')\n",
    "\n",
    "#create a different geometric\n",
    "p = 0.4\n",
    "geo_p = (1 - p)**(x-1) * p\n",
    "plt.plot(x, geo_p,color='C1', label='$P_2(x)$')\n",
    "\n",
    "#plot the mean as vertical line\n",
    "plt.axvline(x=1 / p, color='C1', linestyle='--', label='$E_2[x]$')\n",
    "\n",
    "#add legend, which uses the labels = .. from above\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Notice how I can use consistent colors for related lines in the figure. Categorical colors are for data which has no ordering. *Gradient* colors are for when a color has order. We will see those later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "For Loops\n",
    "====\n",
    "\n",
    "Now that we have arrays and lists, we need need new flow statements to do something with them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "24\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "x = [4,3,24,7]\n",
    "for element in x:\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n"
     ]
    }
   ],
   "source": [
    "x = [4,3,24,7]\n",
    "xsum = 0\n",
    "for element in x:\n",
    "    xsum += element # <--- this means xsum = xsum + element\n",
    "print(xsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "7\n",
      "31\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "x = [4,3,24,7]\n",
    "xsum = 0\n",
    "for element in x:\n",
    "    xsum += element\n",
    "    print(xsum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Python Tutor\n",
    "----\n",
    "\n",
    "[Python Tutor](http://www.pythontutor.com/visualize.html#mode=edit) Allows you to see your code as it executes. Let's look at the last example with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.display import HTML, display\n",
    "from IPython.core.magic import register_line_cell_magic\n",
    "import urllib\n",
    "\n",
    "@register_line_cell_magic\n",
    "def tutor(line, cell):\n",
    "    code = urllib.parse.urlencode({\"code\": cell})\n",
    "    display(HTML(\"\"\"\n",
    "    <iframe width=\"800\" height=\"500\" frameborder=\"0\"\n",
    "            src=\"http://pythontutor.com/iframe-embed.html#{}&py=2\">\n",
    "    </iframe>\n",
    "    \"\"\".format(code)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <iframe width=\"800\" height=\"500\" frameborder=\"0\"\n",
       "            src=\"http://pythontutor.com/iframe-embed.html#code=%0Aprod+%3D+1%0Afor+i+in+range%285%29%3A%0A++++prod+%2A%3D+i%0A++++if+prod+%3D%3D+0%3A%0A++++++++prod+%3D+1%0A++++print%28%27%7B%7D%21+%3D+%7B%7D%27.format%28i%2C+int%28prod%29%29%29%0A&py=2\">\n",
       "    </iframe>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%tutor\n",
    "\n",
    "prod = 1\n",
    "for i in range(5):\n",
    "    prod *= i\n",
    "    if prod == 0:\n",
    "        prod = 1\n",
    "    print('{}! = {}'.format(i, int(prod)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Python Data Types\n",
    "----\n",
    "\n",
    "Let's put all we know about python data types in one place. A `data type` is something which can be assigned to a variable. For example, a floating point number or a string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Here's a list of python data types we'll cover in the class:\n",
    "\n",
    "* floating point numbers\n",
    "* integers\n",
    "* strings\n",
    "* lists\n",
    "* dictionaries\n",
    "* tuples\n",
    "* NumPy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Floating Points and Integers\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.4"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Floating Point\n",
    "a = 4.4\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting a floating point to integer\n",
    "a = int(4.4)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Converting floating points is often needed when you want to slice a list. For example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "slice indices must be integers or None or have an __index__ method",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-86-7c5a81e5dd65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlength\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mhalf_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlength\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_length\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\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: slice indices must be integers or None or have an __index__ method"
     ]
    }
   ],
   "source": [
    "a = 'A string is a list'\n",
    "length = len(a)\n",
    "half_length = length / 2\n",
    "print(a[half_length:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'is a list'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_length = int(length / 2)\n",
    "a[half_length:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This is such a common occurence though that there is a shortcut to ensure that division results in an integer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 // 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'is a list'"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[len(a) // 2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "What are the mathematical consequences of `int`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(int(4.9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Often that's not exactly what we want, so we can use a few functions from math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from math import floor, ceil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(floor(5.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(ceil(4.0001))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Dictionaries\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Dictionaries are very similar to lists. Let's see how they work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    }
   ],
   "source": [
    "d = dict()\n",
    "d['thingie'] = 45\n",
    "d['other thingie'] = 434343\n",
    "\n",
    "print(d['thingie'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'thingie': 45, 'other thingie': 434343}\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Python prints it in the format above, you can also define it using that format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a string\n"
     ]
    }
   ],
   "source": [
    "d = {'thing1': 3, 'b': 'a string'}\n",
    "print(d['b'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We're learning dictionaries because they are often required for plotting and optimization. We'll see that later in this lecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Tuples\n",
    "----\n",
    "\n",
    "Tuples are just like lists, except they can't have individual elements modified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 'fdsa']\n"
     ]
    }
   ],
   "source": [
    "a = [0,1,2,3] # a list\n",
    "a[3] = 'fdsa'\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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-126-1a62624cd994>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\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[0;32m----> 2\u001b[0;31m \u001b[0ma\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'fdsa'\u001b[0m\u001b[0;34m\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": [
    "a = (0,1,2,3)\n",
    "a[3] = 'fdsa'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The most common place you'll see tuples is in creating numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "my_tuple = (5,4)\n",
    "a = np.ones(my_tuple)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Notice that's the same as this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.ones( (5,4) )\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Function Arguments\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Functions are a little more complicated than just the paranthesis part. There are three things you should know about functions:\n",
    "\n",
    "1. Arguments are separated by `,`\n",
    "2. Arguments may be named -> `foo(1,2,4, example=5)`\n",
    "3. You may have to pass in lists or dictionaries -> `np.ones( (5,4) )` or `foo(special_arg={'a':4, 'b':4})`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Plotting provides some examples of this. Let's see them in action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0,10, 100)\n",
    "y = x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uxeF0MSOzO2eeoq3ifZHKhoiItEv7DjYy4/kcGptd3HNNGuf0jjI7krSRyoaIiLQ7BaV2ZjyfQ22jk7uuSmX0gBizI8lJUNkQEZF25WBFE9MX5VBZ5+C2y7ty4eA4syPJSVLZEBGRdqO0upnpi3Ior2nh/36RwqUZnc2OJG6gsiEiIu1CRW0L9y/KobiymWljk5gwMsHsSOImKhsiImK6qjoH9y/KobCsiYmjEphyXqLZkcSNVDZERMRUDU0w4/k97C+xM354PDdelIzFYjE7lriRtskTERHT1DU6WPqJlaIqO5ee05mbL0lR0fBDOrMhIiKmqLc7mblkL0VVFsYNieU3l3VR0fBTKhsiIuJ1jU1OHly6l+/yGxiY5uL28alYrSoa/kqXUURExKvszU4efGEv3+TVM6p/NOefWq6i4ed0ZkNERLzG3uxi1gu5bM+tZ2S/aP5wTRrqGf5PZUNERLyiqcXFnBf38tXeOob3jeLeSWkEBKhpdAQqGyIi4nHNLS4eWpbLlzl1ZJwRxfTJ3QlU0egwVDZERMSjfigaW3bXcvbpkdw/JU1Fo4NR2RAREY/5oWhk767l7F6R/PHa7gQF6ldPR9Pmu1FWr17NokWLCAwM5I477qBXr17ce++9OJ1O4uPjefzxxwkODnZnVhER8SHNLS7mvvQ/RWNqd4JVNDqkNv2rV1ZW8vTTT7N8+XKeffZZPvroI5566ikyMzNZvnw5aWlprFy50t1ZRUTER/xQNL7YVcsQFY0Or03/8hs2bCAjI4Pw8HASEhKYO3cumzZt4vzzzwdgzJgxbNiwwa1BRUTEN/y4aMy8VkWjo2vTZZSCggLsdju33HILNTU13H777TQ2NrZeNomLi6O0tNStQUVEpP07dHtrLlv31DL4tIhDRSNIRaOja/OcjaqqKv76179SVFTEddddh2EYrV/738+PJTs7u60RTH1uOZzG2ns01t6jsT4xzQ546TMre0ss9Eo2uLRPFV9/tfW4Hqux9h4zxrpNZSMuLo4zzzyTwMBAunXrRlhYGAEBAdjtdmw2G8XFxSQkJBzXcw0aNKgtEY4pOzvbY88th9NYe4/G2ns01ifG3uxk9gu57C2p45zekdyfefyXTjTW3uPJsT5aiWnTua0RI0awceNGXC4XlZWVNDQ0MGzYMNasWQPA2rVrGTlyZNvSioiIT7E3O5n1Qi7b9tYxrE8UM06gaEjH0KYzG4mJiVx00UVcc801AMycOZN+/fpx3333kZWVRUpKCuPHj3drUBERaX8avt+9dce+eob31cqgcmRtnrMxefJkJk+efNixJUuWnHQgERHxDfV2JzMX5/BtfgPn9j+0qZqKhhyJtpgXEZETVtvo4I/P57C7sJHzBsbw+6u7aVM1+VkqGyIickKq6x3MeD6HvQcauWBQLHdelUqA9omXo1DZEBGR41ZZ28KMxTnsO2jn4rPj+O0VXbGqaMgxqGyIiMhxKatu5v7ncygobeKyjM785rIuWCwqGnJsKhsiInJMxZVNTF+Uw8GKZq4+N4GbxiWraMhxU9kQEZGjKixr4v5FeyitbuHa8xO59vwkFQ05ISobIiLys/KK7dz//B4qax3cOC6Za0Ylmh1JfJDKhoiIHNGewgb+uDiHmgYnv760C+OHx5sdSXyUyoaIiPzEN3n1PLAkh8ZmF3delcq4IXFmRxIfprIhIiKH2bqnljkv5tLidHHvNWmMHhhjdiTxcSobIiLSauPOaua9vA+Amdemk3FGlLmBxC+obIiICADrv6xk4Yo8AgOsPDgtnbNOjTA7kvgJlQ0REeGdTWU8/VYBnYKtzLmhB327h5sdSfyIyoaISAf32sfFLHn/AFFhgTx8Uw9OSQk1O5L4GZUNEZEOyjAMlq45wGsflxAfFcT8X/aka7zN7Fjih1Q2REQ6IKfL4JnVBby7qZwucSHM/7+eJEQHmx1L/JTKhohIB9PicLFwxX7+/VUVPZJtPHxjT2IigsyOJX5MZUNEpAOxNzt5+KV9ZO+upU/3MGZfl054J/0qEM/SO0xEpIOobXAw64W97NzfwNm9Irk/szu2YKvZsaQDUNkQEekAymtamLk4h33FdsYMjOH3V3cjMEA7t4p3qGyIiPi5glI7f1ycQ0lVC5dndObXl3bBalXREO9R2RAR8WO7Cxt4YMlequsdTBubxJTzErFYVDTEu1Q2RET81NY9tcxdlktTi4vbx3flF0M7mx1JOiiVDRERP/Tvryp5/LX9AMzI7M7wvtEmJ5KOTGVDRMTPvPVZKX//ZyG2YCuzpqUzoKc2VBNzqWyIiPgJwzB4Ye0BstaXEBMRyNwbetBT+5xIO6CyISLiBxxOgyff2M+HWyrpEhfCwzf1ICk2xOxYIoDKhoiIz7M3O5n38j6+2FVLr66hzL6+B9Hh+vEu7YfejSIiPqyqroUHl+5ld2Ejg0+L4I/XdscWHGB2LJHDqGyIiPiowrImZi7J4WBFMxcMiuWOK1O1Kqi0SyobIiI+aOf+ema/sJeaBieZ5yUydWySFuuSdktlQ+QY7HY7zz33HOXl5ezevZu4uDj+8Ic/kJqaanY06aA2flPNo6/uo8VpcMeVqVx8dpzZkUSOSmVD5BieeeYZJk+eTEpKCgD33nsvU6ZMYfXq1cTGxpqcTjqatzeU8uzbhQQFWpk1rTtnnx5ldiSRY9LewiJH0dTUxLJly3j99ddbj/3mN7+htLSUN99808Rk0tG4XAaL3i3kmdWFRIYFsuBXPVU0xGfozIbIUTidTqKjo2lsbGw91qVLFwDy8vLMiiUdTHOLi4Ur9vPJ11V0jQ/hoRt6kKw1NMSHqGyIHEVoaCjr1q077Fh+fj6A5myIV9TUO5izLJdv8urp2z2MB6elExGqH93iW/SOFTlBb7/9NrGxsVx99dVmRxE/V1jWxKyleyksb2JU/2h+f3U3goN09Vt8j8qGyAkoKirilVde4dFHHyUmJsbsOOLHtu+r46EXc6ltdHLNqASuvzAZq1W3topvUtkQOU7Nzc1Mnz6d2bNnM2bMGLPjiB9b/2Ulf1q5H8MwuOuqVC4aoltbxbepbIgcp0ceeYRf/vKXjBo1Cjg0QTQtLc3kVOJPDMPg1XXFvPjBQUJDrMyc2oMzT9H28OL7dPFP5DgsXbqUUaNGtRaNlpYW3nnnHZNTiT9pdrj404r9vPjBQRKig3jiN6eqaIjf0JkNkWP44IMPWLduHcOHD2fXrl0AFBcX079/f5OTib+ornfw8Eu5bN9XT6+uoTx4XTqxEUFmxxJxG5UNcas//elPfPrppzgcDubOncuAAQMO+/qtt95KbGwsDz/8sNezrVq1itdff52mpiZiY2OZN28eTU1NzJkzB7vdTkREBA888ACJiYmtj6msrOQPf/gDjY2NbNy48bDnW7Fihbf/CuKH8kvszHphLwcqmjm3XzS/n9iNEN1xIn5GZUPcZsWKFURERPDGG29wxx138Mgjj/Dqq6+2fv3gwYN89NFHjBgxwuvZlixZQlVVFUuWLCEwMJDf/va3/O53v8NmszF37lwKCgq44447eO6555g5c2br42JiYvjyyy+9nlc6hq17apn/8j7q7E6mjDm0mZruOBF/pLIhbuFwOHj//fdZtGgRALm5uUREHH69+fPPPwdg8ODBP3n8ihUrWL16dZte22KxMG/evJ9dZCs/P58dO3awcOHC1mOnnnoqzzzzDPPnzycxMZFFixZRVlbG6aef3qYMIifqnU1lPLO6AKvFwj0Tu3H+WdpnR/yXyoa4xeeff87o0aOxWCzs3LmTXbt2cd999x32PV988QUAQ4YM+cnjJ06cyMSJEz2S7a233uJXv/rVYcf27NmD1WrlggsuAODuu+9mwoQJKhvicU6nwd/fKeTtDWVEhgXwwNR0+nYPNzuWiEepbIhbnHPOOZxzzjkAvPbaawQGBnLFFVcc9j1ffPEFISEhXp9Yeeutt2K1/vcauGEYfP755/Tu3ZvIyEgAbDabioZ4XG2jg0eW57F1Ty3dE23Mvj6dxBjtcSL+T7OQxC0sFgsWiwWHw8F7773HsGHDiIv770JEFRUV5OTkMGDAAIKDg72a7X+LBsDOnTuprKxk6NChXs0hHVthWRO//9tutu6p5exekSy85VQVDekwdGZD3Co7O5vKykpGjx79k+OGYRzxEoq3ffbZZwCtZ2J+YBgGFosm54n7bdldyyPLD00EnTAynhvHpRCgiaDSgahsiFvt2bMHgH79+h12/GjzNeDkJ4g+/PDDdOvW7YhfLy0tpaWlhZSUFAA2bNiA1Wpl0KBBh33fbbfdxjPPPNOmDCJHYhgGb31WxnPvFGK1Wvj91alcMEhLj0vHc1Jlw263c+mll3LrrbeSkZHBvffei9PpJD4+nscff9zrp8vFfI2NjQCEh/93wpvdbmfdunUEBQUxcODAIz7OUxNEq6qquOSSS1rnaVRVVbF582aSk5MPy/jBBx/8ZE0QkZPR7HDxzFsFrPmigpjwQB6Ymk7vtDCzY4mY4qTmbPztb38jKioKgKeeeorMzEyWL19OWloaK1eudEtA8S0ZGRlYLJbWBbDq6uq4++67ycvLo0+fPnTq1MmreQoLC6mrq2Py5Mk4nU4efvhhJk6cSHl5ORUVFQBs2rSJlStXctNNN3k1m/ivytoW7l+Uw5ovKji1Syee/O1pKhrSobX5zEZOTg579uxpvTa/adMm5syZA8CYMWNYvHgxmZmZbgkpvqNPnz4sWLCAf/zjH6xevRqbzcZpp50G/PwlFE/nufnmm9m6dSvXXnst06ZN45JLLiEpKYkbbrgBm83GKaecwsKFCwkK0vLQcvJ2FTTw0LJcymtaGNU/mrsmdMMWrLn40rG1uWwsWLCABx54gFWrVgGHTp//cNkkLi6O0tJS9yQUn3PFFVccdtvr7373OwDOO+88U/LcddddPzl28803c/PNN5uQRvzZR1sqePLNfBxOgxvHJTPx3ARNOhahjWVj1apVDBw48GdXbDQM47ifKzs7uy0RTH9uOVx2djbr1q3jlVde4aabbmq906Ouro5//etf9OjRA8Mw9G/iBhpD7znesXa6YM3XFj7bbcUWZDBlmIue4QVs2VLg4YT+Q+9r7zFjrNtUNtavX09+fj7r16/n4MGDBAcHExoait1ux2azUVxcTEJCwnE914/vCHCX7Oxsjz23HO6HsZ49ezbNzc0MHTq0dewfeughLBYLTzzxBL179zY5qe/T+9p7jnesq+sdPPrKPr7MqSM1IYQHp6bTNd7m+YB+RO9r7/HkWB+txLSpbPz5z39u/fwvf/kLXbp0YevWraxZs4YrrriCtWvXMnLkyLY8tfiwLl26cMcddzB48GAMw2DRokWsXr2ap556SkVD/NKewgbmvpRLSVUL5/SO5J5r0gizBZgdS6Tdcds6G7fffjv33XcfWVlZpKSkMH78eHc9tfiIWbNmMWvWLJYuXYrdbueMM85g1apVdO3a1exoIm730ZYKnnoznxanwbSxSUwek6gdW0V+xkmXjdtvv7318yVLlpzs04kPS05O5h//+IfZMUQ8yuE0WPRuIW99VkaYzcofr+3O2adHmR1LpF3TCqIiIsepoqaF+a/sY8e+erol2HhwWjpdOmt/E5FjUdkQETkO2/fVMX/5PiprHYzsF81dE1IJDdH8DJHjobIhInIUhmGw+rMynnu3EAP41S9SuHJEvNbPEDkBKhsiIj+jscnJU2/ms35bFdHhgdw/pTv9e4Qf+4EichiVDRGRIyipgb8/s4v8kiZ6dwtlRmZ3Okdpc0mRtlDZEBH5kY+/quTZj6w0O5u4YlhnfnlxCkGB2t9EpK1UNkREvtficLHo3SJWbygjOBDun5LGuf1jzI4l4vNUNkREgOLKZh5Zvo/vChpIS7QxfkC9ioaIm6hsiEiHt2lnNQtX7Keu0cl5A2O4/cqu7Pj6S7NjifgNlQ0R6bAcToMX1h5g5b9LCAq0cOdVqVw0OFa3tYq4mcqGiHRIpdXNLHg1jx376kmJC2ZGZnd6poSaHUvEL6lsiEiHs/nbav60Yj81DU5G9ovmzqtStVuriAepbIhIh9HicPHC2gO8/kkpQYEWbruiK5eb5/eUAAAV1ElEQVQMjdNlExEPU9kQkQ6huLKJR17J47v8BrrEhXB/Zpoum4h4icqGiPi9T76u4sk39lNvdzF6wKG7TbSJmoj3qGyIiN+yN7v4xzuFvLe5nJAgK3dNSOXCQbrbRMTbVDZExC/lHmzk0Vfy2F9iJz3Jxv1TupOaYDM7lkiHpLIhIn7FMAze2VjOc+8W0uwwuDzj0N4mwUHa20TELCobIuI3qusd/L/X97NpZw0RnQKYPqUbGWdEmR1LpMNT2RARv7B1Ty0LX8ujotbBgJ7h3DOxm7aEF2knVDZExKc1O1y8+P3aGQFWuGlcMhNGJmC1ahKoSHuhsiEiPiuvuJHHsvLYe8BOSlww903uzmldtXaGSHujsiEiPscwDFZvKGPxe0U0OwzGDYnj5ktS6KS1M0TaJZUNEfEpFTUtPLFyP9m7a4kM0yRQEV+gsiEiPuOTr6v4y5v51DY6GXxaBL+7uhuxEUFmxxKRY1DZEJF2r67RwTOrC1n3ZSUhQRZuvbwLl57TWSuBivgIlQ0Rade+zKnlTyv2U1bdQq+uodxzTTe6xmslUBFforIhIu2SvdnJkvcPsHpDGQFWmDY2iUmjEwkI0NkMEV+jsiEi7c43efX8aUUeReXNpCaEcM/ENN3SKuLDVDZEpN1obnGx7MODvPFJCQYwYWQ8112QrH1NRHycyoaItAvf5TfwxMr97C+xkxwbzO8ndqNv93CzY4mIG6hsiIipmltcvPzRQVb+uwSXAZee05mbxiVrgS4RP6KyISKm+S6/nidW5rO/xE5iTDC/m5DKgJ4RZscSETdT2RARr2tqcfHS93MzXAZcdk5nbtTZDBG/pbIhIl61PbeOP7+eT2F5E0mxwdx1lc5miPg7lQ0R8YqGJidL3z/A2xvLsFjgyuHxXHdhErZgnc0Q8XcqGyLicZ9/V8NfV+VTUtVCakIIv7uqG73TwsyOJSJeorIhIh5TVefgH+8c2tMkwAqTxyQy5bxEggO1boZIR6KyISJuZxgG/9payT/eKaSmwUmvrqHcOSGV9KROZkcTEROobIiIWxWVN/HXVQVs3VOLLdjKry/twmUZnQmwak8TkY5KZUNE3KLF4eKN/5Sy/KODNDsMBp8WwW/HdyUxJsTsaCJiMpUNETlp3+TV85c389lXbCcmPJC7L+vCyH7RWCw6myEiKhsichJqGxwsfv8A739eDsAvzo7jxnHJhHfSjxYR+S/9RBCRE2YYBh9uqWDRe0XU1Dvpnmjjt+O70kcbp4nIEahsiMgJyStu5K+rCti+r56QICu/vDiF8cPjCQzQJRMROTKVDRE5Lg1NTl7+6CBvfVqK0wUZZ0Rxy2VdSIgONjuaiLRzKhsiclSGYfDxV1UsereI8poWkmKCueWyLgztHWV2NBHxESobIvKz8oob+dvqQrbtrSMo0MLU85O4elQCIUFaAVREjp/Khoj8RF2jg5c+PMjbG8twueDs0yO55bIuJMdqzQwROXFtLhuPPfYY2dnZOBwOfv3rX9OvXz/uvfdenE4n8fHxPP744wQH61quiC9xuQw+yK5gyZoDVNc7SIkL5uZLdMlERE5Om8rGxo0b2b17N1lZWVRWVnLllVeSkZFBZmYmF198MU888QQrV64kMzPT3XlFxEO+yavn7/8sZFdBA7ZgKzdelMz4EfHaNE1ETlqbysaQIUPo378/AJGRkTQ2NrJp0ybmzJkDwJgxY1i8eLHKhogPKK1uZvF7B1i/rRKA0QOiueniFOKjdGZSRNyjTWUjICCA0NBQAFauXMm5557Lf/7zn9bLJnFxcZSWlrovpYi4nb3ZxeuflLDi4xKaWlyc2qUTv760ixbmEhG3O6kJoh9++CErV65k8eLFXHjhha3HDcM47ufIzs4+mQimPbccTmPtPSc71i4DvtpvYe12CzWNFsJtBpcMMBiYVoe9/Duyy90U1A/ofe09GmvvMWOs21w2PvnkE5599lkWLVpEREQEoaGh2O12bDYbxcXFJCQkHNfzDBo0qK0Rjio7O9tjzy2H01h7z8mO9fbcOv7xTiG7CxsJCrQwaXQ814xOJDQkwI0p/YPe196jsfYeT4710UpMm8pGbW0tjz32GEuXLiU6OhqAYcOGsWbNGq644grWrl3LyJEj25ZWRNyuqKyJxWuK+HR7NQCjB8Rww0XJJMZoXoaIeF6bysa7775LZWUld911V+uxRx99lJkzZ5KVlUVKSgrjx493W0gRaZuqOgev/Osg72wqw+mC3t1C+dUlXejdLczsaCLSgbSpbEyaNIlJkyb95PiSJUtOOpCInLymFherPi3ltfXFNDS5SI4N5sZxKYzoG4XFog3TRMS7tIKoiB9xOg0+2FLBSx8epLymhcjQAG65tAu/GBpHkNbLEBGTqGyI+AHDMPjsm2peWHOA/NImQoIsXDMqgWtGJxJm0+RPETGXyoaIj/tqby1L3j/At/kNWK0wbkgc156fSGctyiUi7YTKhoiP+i6/gRfWHmDrnloAhveN4oYLk+kabzM5mYjI4VQ2RHxMXnEjL35wkM92HLqN9cxTIrj+wmR6pYaanExE5MhUNkR8REGpndc2Wfj69e8wjEO3sV5/YTIDekaYHU1E5KhUNkTauQMVTSz/6CD/2lqJy7DSI7kT0y5IYujpkbqNVUR8gsqGSDt1sKKJrPXFfJBdgdMFaYk2hqXXM/Wy07BaVTJExHeobIi0MwcqmshaV8yHWw6VjK7xIVx7XhIj+0fz5dYtKhoi4nNUNkTaiaKyJrI+PlQyXC5IjQ9hynlJnNs/mgAVDBHxYSobIibLK7aTtb6Yj7dV4jIgNSGEzPOSGNlPJUNE/IPKhohJ9hQ28Or64tadWNOTbEw5L4lhfaJUMkTEr6hsiHiRYRh8nVtH1voStuw+tBjXaV1DmXJeou4uERG/pbIh4gUul8Hmb2vIWl/Mt/kNAPTvEc41oxI469QIlQwR8WsqGyIe1Oxwse7LSl7/pIT8kiYAMs6IZOKoRHp3CzM5nYiId6hsiHhAvd3Ju5vKWPVpKRW1DgKscP6ZMUwclUBaYiez44mIeJXKhogbFVc2serTMtZ8Xk5js4tOIVauGhnP+GHxxEdrF1YR6ZhUNkTcYOf+et78Tymfbq/CZUBcZBCTz0vkF2fHEd5J/zMTkY5NPwVF2sjhNPjP11W89Vlp66TPHsmdmDAynpH9ogkKtJqcUESkfVDZEDlBVXUO3vu8jHc2llNe04LFAmefHslVI+Lp3yNcd5aIiPyIyobIcdpV0MDbG0r5+KsqWhwGnUKsXDGsM5dnxJPSOcTseCIi7ZbKhshRNDtcfPJVFW9vKOO7gkOXSrrEhXBZRmfGDoolzBZgckIRkfZPZUPkCIrKmnjv83LWZpdTU+/EYoGhvSO57JzOnHlKhHZeFRE5ASobIt9zOA02fVvNu5vKW5cSjwwN4OpzE7hkaBxJsbpUIiLSFiob0uEVlTex9oty1mZXUFnrAKBv9zB+MbQzw/tGEay7SkRETorKhnRIzS0uNuys5v3N5XyZUwdAuC2AyzM6c/HZcXRP0iqfIiLuorIhHYZhGOwpamTtFxWs/7KSOrsTgL7pYVw8JI7hfaMJCdJZDBERd1PZEL9XWdvC+m2VfJBdQe5BOwAxEYFMHJrAhYNi6RpvMzmhiIh/U9kQv9Tc4mLjzmo+3FJJ9u4aXC4IsMLwPlFcODiWQadGEhCgO0pERLxBZUP8htNl8HVuHeu+rOTT7VXU210AnNqlE+efFcuo/jFEh+stLyLibfrJKz7NMAxyihpZt62Sj7dVUV7TAhzaCO0XQztz/pkx2tJdRMRkKhvik/KK7Xz8VSX/3lZFYXkTcOhuknFDYhkzMIa+3cO18JaISDuhsiE+Y3+Jnf98XcUn26vY9/1Ez5AgC+f2j+bc/tEM6RWpNTFERNohlQ1ptwzDIK/Yzn+2V/Gf7dXkFR8qGIEBFjLOiOTc/jEMPT2STiHan0REpD1T2ZB2xeUy+C6/gc++qeazHVUUlTcDEBRoIeOMKEb0jWJo7yhtgCYi4kNUNsR0zS0uvsypY9POajburKbi+yXDbcFWRvSNYnifaM7uHUmozmCIiPgklQ0xRUVtC59/V8OmnTVs2V1LU8uh21QjQwO4YFAsw/pEceYpEVrRU0TED6hsiFc4XQa7CxrY/F0NX3xXw+7CxtavdY0P4ZzeUQztHUnvbmEE6C4SERG/orIhHlNR00L27hqyd9WydU8tNQ2H9iIJDLAwoGc4Q3pFMvT0SC0XLiLi51Q2xG3szU6259azNaeWrbtrW/chgUOLbF00OIohp0cysGeEJniKiHQgKhvSZs0OF7vyG1j3jYWs7N3s3N+Aw2kAh+4eOfOUCAafFsGg0yLolmDDYtHlERGRjkhlQ45bc4uLXQUNfL2vjq9y6ti5v56mFgOwYrHUc0pKJwaeEsGZp0RwRlqYJneKiAigsiFHUdfo4Nv9DWzfV8+OfXV8V9BAi8No/Xr3JBv908MJo5jx5w8gMkxvJxER+Sn9dhDg0GJaReVN7Nxfzzd5DezcX8/+EjvG993CaoH05E707R5Gn+7h9EsPb91BNTu7WEVDRER+ln5DdFAVNS3sKmxgV34D3+Y3sLuggTq7s/XrIUFW+qWH07tbKH26h3NGWpgmdYqISJuobPg5wzAor2khp6iRPUWN7C5sYE9hY+tW7D/oEhfCkNMj6ZUaSp+0MNKTOhEQoAmdIiJy8lQ2/Ehzi4v9pXb2HbSTe7CRvUWN7D3Q2Lq+xQ/iIoM4p3ckp3QJpVfXUHqlhhIRqreCiIh4hn7D+KCmFhcFpXbyS5rYX2pnf4mdvGI7RWVNuIzDvzc5Nph+PcLpkdSJnimdOLVrKLERQeYEFxGRDkllo51yOg1KqpspKmuiqLyJgtImCssOfRRXNbdO3PxBmM1K77Qwuifa6J7Yie5JNtKTO2mehYiImM7tZWP+/Pls27YNi8XCjBkz6N+/v7tfwi84XQZVdQ6KK5spqWympKqZ4spmDlQ0c6CiiZKqZlyunz4uJiKQPt3D6JZgo1u8jW6JNlLjQ4iLDNKiWSIi0i65tWxs3ryZvLw8srKyyMnJYcaMGWRlZbnzJdo9l8ug3u6kotZBRW0LlbUtVNQ6qKxtoaymhbLqFsqqmymvacF5hDIBEBMeyOmpYSTHBpMcG0KXziF0iQ8hJS5EZypERMTnuLVsbNiwgbFjxwLQs2dPqqurqaurIzw83J0v43FOl4G92UVTs4uGJif19h/+e+ijrtFJbYOT2kYHdQ1OahocVNU7qP7+40hnJH5gtUJcRBCndQ2lc1QwiTHBJEQHff/fQ3/uFKJCISIi/sOtZaOsrIw+ffq0/jk2NpbS0lKvl42V/y5h09cW3v82F8MAg0O3gDqc4HAaOF0GDuehj+YWF80OF82OQ5/bmw99fqLCbFaiw4JIjg0hKiyA6PAgYiMCiYkIIjbi0OdxkUHERARpC3UREelQPDpB1PjxLMYjyM7OdvNrwhv/tlJZb4WC6p/9PgsGAVYICoDA7z9CAiAiAoIDf/gwCA4EWyDYgg59hARBp2CDTsEQ+v2HLRgCrU6g5cgv1gh1jVBXAnlu/du2H+7+d5Sfp7H2Ho2192isvceMsXZr2UhISKCsrKz1zyUlJcTHxx/1MYMGDXJnBACWDnSxYdNWBgwYgMVyaKltLBBotRAQYCHQasGqswtuk52d7ZF/R/kpjbX3aKy9R2PtPZ4c66OVGLduyzl8+HDWrFkDwI4dO0hISDBlvkZwoJVwG0SHBxIVFkhEaCARnQLpFBJAcKBVRUNERMSL3Hpm46yzzqJPnz5MnjwZi8XCrFmz3Pn0IiIi4oPcPmfjnnvucfdTioiIiA9z62UUERERkR9T2RARERGPUtkQERERj1LZEBEREY9S2RARERGPUtkQERERj1LZEBEREY9S2RARERGPUtkQERERj1LZEBEREY+yGMezD7yHaEthERER//FzO8qaWjZERETE/+kyioiIiHiUyoaIiIh4lMqGiIiIeJTKhoiIiHiUyoaIiIh4lN+Vjfnz5zNp0iQmT57MV199ZXYcv/fYY48xadIkJkyYwNq1a82O4/fsdjtjx47ljTfeMDuKX1u9ejWXX345V111FevXrzc7jt+qr6/nt7/9LdOmTWPy5Ml88sknZkfyS7t27WLs2LG89NJLABw4cIBp06aRmZnJnXfeSXNzs8cz+FXZ2Lx5M3l5eWRlZTFv3jzmzZtndiS/tnHjRnbv3k1WVhaLFi1i/vz5Zkfye3/729+IiooyO4Zfq6ys5Omnn2b58uU8++yzfPTRR2ZH8ltvvvkm6enpLFu2jCeffFI/sz2goaGBuXPnkpGR0XrsqaeeIjMzk+XLl5OWlsbKlSs9nsOvysaGDRsYO3YsAD179qS6upq6ujqTU/mvIUOG8OSTTwIQGRlJY2MjTqfT5FT+Kycnhz179jB69Gizo/i1DRs2kJGRQXh4OAkJCcydO9fsSH4rJiaGqqoqAGpqaoiJiTE5kf8JDg7mueeeIyEhofXYpk2bOP/88wEYM2YMGzZs8HgOvyobZWVlh71ZY2NjKS0tNTGRfwsICCA0NBSAlStXcu655xIQEGByKv+1YMECpk+fbnYMv1dQUIDdbueWW24hMzPTKz+IO6pLLrmEoqIiLrjgAqZOncp9991ndiS/ExgYiM1mO+xYY2MjwcHBAMTFxXnl92Sgx1/BRFoc1Ts+/PBDVq5cyeLFi82O4rdWrVrFwIEDSU1NNTtKh1BVVcVf//pXioqKuO6661i3bh0Wi8XsWH7nrbfeIiUlheeff55vv/2WGTNmaD6Sl3nr96RflY2EhATKyspa/1xSUkJ8fLyJifzfJ598wrPPPsuiRYuIiIgwO47fWr9+Pfn5+axfv56DBw8SHBxMUlISw4YNMzua34mLi+PMM88kMDCQbt26ERYWRkVFBXFxcWZH8ztbtmxhxIgRAJx++umUlJTgdDp1htTDQkNDsdvt2Gw2iouLD7vE4il+dRll+PDhrFmzBoAdO3aQkJBAeHi4yan8V21tLY899hh///vfiY6ONjuOX/vzn//M66+/zmuvvcbEiRO59dZbVTQ8ZMSIEWzcuBGXy0VlZSUNDQ2aS+AhaWlpbNu2DYDCwkLCwsJUNLxg2LBhrb8r165dy8iRIz3+mn51ZuOss86iT58+TJ48GYvFwqxZs8yO5NfeffddKisrueuuu1qPLViwgJSUFBNTiZycxMRELrroIq655hoAZs6cidXqV/+/rN2YNGkSM2bMYOrUqTgcDmbPnm12JL+zfft2FixYQGFhIYGBgaxZs4aFCxcyffp0srKySElJYfz48R7PoV1fRURExKNU10VERMSjVDZERETEo1Q2RERExKNUNkRERMSjVDZERETEo1Q2RERExKNUNkRERMSjVDZERETEo/4/VLe4u9mQd5AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.text(2, 40, '$y = x^2$', fontdict={'fontsize': 24})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Most arguments are optional. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.302585092994046"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import log\n",
    "log(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log(10, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "More about Notebooks\n",
    "===="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "How to get help about functions\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To get help about a particular function, type `help( fxn_name ) `. This will post it in the output. You can instead, type `fxn_name?` to get a popup with help. The most useful thing though is to type `SHIFT-TAB` to get a tooltip about a function. To get suggestions, press TAB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What exactly is saved for a notebook?\n",
    "---\n",
    "\n",
    "A notebook is just a JSON file. You can open one and see this:\n",
    "\n",
    "```\n",
    " {\n",
    "   \"cell_type\": \"markdown\",\n",
    "   \"metadata\": {},\n",
    "   \"source\": [\n",
    "    \"To get help about a particular function, type `help( fxn_name ) `. This will post it in the output. You can instead, type `fxn_name?` to get a popup with help. The most useful thing though is to type `shift-tab` to get a tooltip about a function.\"\n",
    "   ]\n",
    "  },\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Jupyter will save checkpoints whenever you save. These can be accessed at file->revert-to-checkpoint. Pay close attention to the top messages to make sure you are saving your notebook. In an emergency, like your notebook failed to save, go to `file->download as->jupyter notebook` and it will appear in your downloads folder. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Rescuing Python Kernel\n",
    "----\n",
    "\n",
    "It's easy to write code that will rek your kernel. You'll see this if the circle indicator in the top right corner is solid. If pressing interrupt (stop button) a few times doesn't work, save your notebook and then close the command line/terminal window and reopen one."
   ]
  }
 ],
 "metadata": {
  "_draft": {
   "nbviewer_url": "https://gist.github.com/9a23ea69051e53f12428"
  },
  "celltoolbar": "Slideshow",
  "gist": {
   "data": {
    "description": "Unit_4/Lecture_1/Unit_4_Lecture_1.ipynb",
    "public": false
   },
   "id": "9a23ea69051e53f12428"
  },
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}