Lecture_6.ipynb

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   "cell_type": "markdown",
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   },
   "source": [
    "To generate a presentation run the following command:\n",
    "\n",
    "``jupyter nbconvert Lecture_6.ipynb --to slides --post serve``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# How to use this notebook\n",
    "\n",
    "This notebook serves as both a presentation and interactive environment for students to experiment with Python. If you run it in the interactive mode using [Binder](https://mybinder.org/v2/gh/krzysztofarendt/deap/master), you can modify all code cells. Press `Shift+Enter` to run the modified code.\n",
    "\n",
    "Link to the repository: https://github.com/krzysztofarendt/deap "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# NumPy - vectors and matrices\n",
    "\n",
    "[NumPy](http://www.numpy.org/) is the fundamental package for scientific computing with Python. It contains among other things:\n",
    "\n",
    "* a powerful N-dimensional array object\n",
    "* useful linear algebra, Fourier transform, and random number capabilities\n",
    "\n",
    "To use NumPy, first import it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The following presentation is based on the official [NumPy tutorial](https://docs.scipy.org/doc/numpy/user/quickstart.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Array creation\n",
    "\n",
    "NumPy's main object is the homogeneous multidimensional array, called `ndarray`. It is a table of elements, all of the same type (usually floats), indexed by a tuple of positive integers.\n",
    "\n",
    "In NumPy array dimensions are called *axes*.\n",
    "\n",
    "An array can be created from nested lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0.]\n",
      " [0. 1. 2.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1., 0., 0.,],\n",
    "              [0., 1., 2.]])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "...or by using special functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "b = np.zeros((3, 3))\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1.]\n",
      " [1. 1.]\n",
      " [1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "c = np.ones((3, 2))\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5. 5.]\n",
      " [5. 5.]]\n"
     ]
    }
   ],
   "source": [
    "d = np.full((2, 2), 5.)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "There are also two very useful functions for creating one-dimensional arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 4 6 8]\n"
     ]
    }
   ],
   "source": [
    "e = np.arange(0, 10, 2)  # Start from 0 with step 2, while < 10\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.  0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5. ]\n"
     ]
    }
   ],
   "source": [
    "f = np.linspace(0, 5, 11)  # 11 elements from 0 to 5\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Some of the important attributes of `ndarray` are as follows:\n",
    "\n",
    "* `ndarray.ndim` - the number of axes (dimensions) of the array,\n",
    "* `ndarray.shape` - the dimensions of the array,\n",
    "* `ndarray.size` - the total number of elements of the array,\n",
    "* `ndarray.dtype` - an object describing the type of the elements in the array.\n",
    "\n",
    "Standard Python types cn be used as `dtype`. Additionally NumPy provides types of its own: `numpy.int32`, `numpy.int16`, and `numpy.float64` are some examples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0.]\n",
      " [0. 1. 2.]]\n",
      "ndim = 2\n",
      "shape = (2, 3)\n",
      "size = 6\n",
      "dtype = float64\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print('ndim =', a.ndim)\n",
    "print('shape =', a.shape)\n",
    "print('size =', a.size)\n",
    "print('dtype =', a.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Basic operations\n",
    "\n",
    "Arithmetic operators on arrays apply *elementwise*. A new array is created and filled with the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3. 4.]\n",
      " [6. 9.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1., 2.], [3., 6.]])\n",
    "b = np.array([[2., 2.], [3., 3.]])\n",
    "c = a + b\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.  4.]\n",
      " [ 9. 18.]]\n"
     ]
    }
   ],
   "source": [
    "print(a * b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5 1. ]\n",
      " [1.  2. ]]\n"
     ]
    }
   ],
   "source": [
    "print(a / b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Scalars are also applied *elementwise*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5. 10.]\n",
      " [15. 30.]]\n"
     ]
    }
   ],
   "source": [
    "print(a * 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1.]\n",
      " [2. 5.]]\n"
     ]
    }
   ],
   "source": [
    "print(a - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Matrix product can be performed using the `@` operator (in Python >= 3.5) or the `dot` function or method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "c = a @ b\n",
    "d = np.dot(a, b)\n",
    "e = a.dot(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Please check that all `c`, `d`, and `e` are equal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Note, that if you compare two arrays using `==`, you get an array containing `bool` elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True],\n",
       "       [ True,  True]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c == d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True],\n",
       "       [ True,  True]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d == e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Since the return type is an array, you can use array methods with it. One of the methods is `ndarray.all()`, which checks if all elements in the array are true:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(c == d).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Two arrays can be joined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 3 4]\n",
      " [5 6 7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 5, 1)\n",
    "b = np.arange(5, 10, 1)\n",
    "c = np.vstack((a, b))  # Note double parentheses!\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "d = np.hstack((a, b))  # Note double parentheses!\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Reshaping\n",
    "\n",
    "An array can be reshaped:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 9, 1)\n",
    "b = a.reshape((3, 3))\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Note, that the number of elements in the reshaped array **must match** the original array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Indexing, slicing, and iterating\n",
    "\n",
    "Similar rules as in Python lists apply, however indexes for all dimensions can be given in a single pair of brackets.\n",
    "\n",
    "Analyze the following cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 9, 1).reshape((3, 3))\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[0 1 2]\n",
      "[6 7 8]\n",
      "[[0 1 2]\n",
      " [3 4 5]]\n",
      "[0 3]\n"
     ]
    }
   ],
   "source": [
    "print(a[0, 0])\n",
    "print(a[0])\n",
    "print(a[-1])\n",
    "print(a[0:2])\n",
    "print(a[0:2, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`:` can be used to select all elements in a given axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "print(a[0, :])    # Get first column\n",
    "print(a[:, :])    # Get a complete copy of a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Iterating over arrays is similar to iterating over lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 "
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 9, 1).reshape((3, 3))\n",
    "\n",
    "# Approach 1\n",
    "for k in a:\n",
    "    for j in k:\n",
    "        print(j, end=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 "
     ]
    }
   ],
   "source": [
    "# Approach 2\n",
    "for k in range(len(a)):\n",
    "    for j in range(len(a[k])):\n",
    "        print(a[k, j], end=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### NumPy and Pandas\n",
    "\n",
    "The `DataFrame` object in Pandas is using Numpy's arrays internally. You can access it with `DataFrame.values`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame:\n",
      "   0  1\n",
      "0  1  2\n",
      "1  3  4\n",
      "\n",
      "ndarray:\n",
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([[1, 2], [3, 4]])\n",
    "a = df.values\n",
    "\n",
    "print('DataFrame:')\n",
    "print(df)\n",
    "print()\n",
    "print('ndarray:')\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Numpy functions\n",
    "\n",
    "NumPy provide many common mathematical functions that can be used with arrays, from simple trigonometrical functions to linear algebra. In many cases their names are self-explanatory, e.g.:\n",
    "* np.sin(x) - sine,\n",
    "* np.cos(x) - cosine,\n",
    "* np.max(x) - return maximum number.\n",
    "\n",
    "The full list is available [here](https://docs.scipy.org/doc/numpy-1.13.0/reference/index.html).\n",
    "\n",
    "Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n",
      " 3.33333333 3.88888889 4.44444444 5.        ]\n"
     ]
    }
   ],
   "source": [
    "a = np.linspace(0, 5, 10)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.52741539  0.8961922   0.99540796  0.79522006  0.35584199\n",
      " -0.19056796 -0.67965796 -0.96431712 -0.95892427]\n"
     ]
    }
   ],
   "source": [
    "print(np.sin(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n"
     ]
    }
   ],
   "source": [
    "print(np.max(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Matplotlib - visualization\n",
    "\n",
    "Matplotlib is a Python 2D plotting library which produces publication quality figures.\n",
    "\n",
    "Check out what kind of plots can be generated with Matplotlib: [https://matplotlib.org/gallery/index.html](https://matplotlib.org/gallery/index.html).\n",
    "\n",
    "Today we'll learn how to create basic plots: **linear**, **scatter**, and **bar plots**.\n",
    "\n",
    "This tutorial is based on: [https://matplotlib.org/tutorials/introductory/pyplot.html](https://matplotlib.org/tutorials/introductory/pyplot.html)\n",
    "\n",
    "First, let's import Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  # We import the \"pyplot\" interface, which is advised for beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`matplotlib.pyplot` is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure, e.g.: \n",
    "* creates a figure,\n",
    "* creates a plotting area in a figure,\n",
    "* plots some lines in a plotting area,\n",
    "* decorates the plot with labels.\n",
    "\n",
    "In `matplotlib.pyplot` various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Generating visualizations with pyplot is very quick:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "You may be wondering why the x-axis ranges from 0-3 and the y-axis from 1-4. If you provide a single list or array to the `plot()` command, matplotlib assumes it is a sequence of y values, and automatically generates the x values for you. Since python ranges start with 0, the default x vector has the same length as y but starts with 0. Hence the x data are `[0,1,2,3]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`plot()` is a versatile command, and will take an arbitrary number of arguments. For example, to plot x versus y, you can issue the command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Formatting the style of your plot\n",
    "\n",
    "For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is 'b-', which is a solid blue line. For example, to plot the above with red circles, you would issue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PPHNi7ZJ0Ehn8p8JaPyzjD85ImgKD/1S49VbYuvWVbVu3Dtol6RQz+E+FuTlYWIDt2yEZTBcW/GJX0lR4V8+pMjdn0EvaELzil6TGGPyS1BiDX5Ia0yv4k1yb5NtJnkzy4SHr/1aSz3Trv5FkR5/jSZL6Gzv4k5wB/B5wHfAG4OYkb1jV7X3Ad6vqHwC/C/y7cY8nSZqMPlf8O4Enq+qpqvo+8GngxlV9bgTu7uY/C7wzSXocU5LUU5/bOS8Enl2xfBB4y1p9qurlJC8APw58Z/XOkswD3ZvL+H9JHu1R20Z2HkPOfxPx/E5vnt/p67JRO/YJ/mFX7jVGn0Fj1QKwAJBkqapme9S2YW3mcwPP73Tn+Z2+kiyN2rfPUM9B4OIVyxcBh9bqk+RM4O8CR3scU5LUU5/gfwi4NMlPJnkVcBNw36o+9wG3dPPvAf60qoZe8UuSTo2xh3q6MfsPAF8CzgDuqqrHknwUWKqq+4A7gf+S5EkGV/o3jbj7hXHrOg1s5nMDz+905/mdvkY+t3gBLklt8cldSWqMwS9JjdlQwb/eKyBOZ0nuSnJ4sz6fkOTiJF9Osj/JY0k+OO2aJinJ307yzSQPd+f3b6dd06QlOSPJ/0ryP6Zdy6QleTrJI0n2nchtj6eLJOck+WySx7v/B9963P4bZYy/ewXEnwNXM7gN9CHg5qr6s6kWNiFJ3ga8BHyqqt447XomLckFwAVVtTfJ2cAe4N2b6L9fgG1V9VKSs4CvAh+sqq9PubSJSfIvgVngx6rqhmnXM0lJngZmq2pTPryV5G7gK1V1R3eX5daq+qu1+m+kK/5RXgFx2qqqB9nEzzBU1XNVtbeb/x6wn8GT25tCDbzULZ7VfTbGVdMEJLkI+CfAHdOuRScmyY8Bb2NwFyVV9f3jhT5srOAf9gqITRMcLenewvpm4BvTrWSyuqGQfcBhYHdVbabz+w/Avwb+ZtqFnCQFPJBkT/d6mM3k7wFHgP/cDdXdkWTb8TbYSME/8usdtHEleTXwOeBDVfXitOuZpKr6QVVdzuAp9Z1JNsWQXZIbgMNVtWfatZxEV1bVFQzeJvz+buh1szgTuAL4RFW9Gfi/wHG/I91IwT/KKyC0gXVj358DFqvq89Ou52Tp/hn9P4Frp1zKpFwJvKsbB/808I4k/3W6JU1WVR3qpoeBexkMLW8WB4GDK/4F+lkGfwjWtJGCf5RXQGiD6r78vBPYX1Ufn3Y9k5ZkJsk53fzfAX4GeHy6VU1GVf1GVV1UVTsY/H/3p1X1T6dc1sQk2dbdcEA3BHINsGnurquq/wM8m+TY2znfCRz3poo+b+ecqLVeATHlsiYmyT3AVcB5SQ4CH6mqO6db1URdCbwXeKQbBwf4zaq6f4o1TdIFwN3d3WdbgP9WVZvutsdN6nzg3u6nQM4E/qCqvjjdkibuXwCL3UXzU8AvH6/zhrmdU5J0amykoR5J0ilg8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TG/H9sTSG9ra8nVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.axis([0, 6, 0, 20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "See the `plot()` [documentation](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot) for a complete list of line styles and format strings. The `axis()` command in the example above takes a list of `[xmin, xmax, ymin, ymax]` and specifies the viewport of the axes.\n",
    "\n",
    "If Matplotlib were limited to working with lists, it would be fairly useless for numeric processing. Generally, you will use NumPy arrays. In fact, **all sequences are converted to NumPy arrays internally**. The example below illustrates a plotting several lines with different format styles in one command using arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "# red dashes, blue squares and green triangles\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Controlling line properties\n",
    "\n",
    "Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see matplotlib.lines.Line2D. There are several ways to set line properties.\n",
    "\n",
    "Use keyword args:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mtqe4qpi0i2l28sUGnYSLm2D7Hag220l+Gvr/vElzKSVff/01p06dMo9df/31hIeH208WAgb92Wqoqg425RyBs58BEN4lnCCfxkO+o0aNMq+SS0tLze1AtbJ41mL2P7SfIb217U+4G4ZjaAmqCpSlqy2eAZCwEK5Lgz4zmzyFbRhJL5FdIwkLCOO6ftcpA7F3godNuKqqALK/0XS+bt26mfVmpJSNx3gbYW7yXJbMWcKy25bpsjPoABSmKWmpdSp9C+Luh8F/sR+3QErJ999/z6FDh8xjEydOZPhwlVVBA+HTIUypgq6TcM9FmHwOPtz4hKZUcm9vb8aPH29+vXXrVqqqtD1EgbJq8HAUNXBjXNXBbYYQIl0IcUIIYVfZIYR4RQhxoP7ruBCiyOKYyeKYSppCOyTzS5C11mP+fZQN5pFvNLlSACgsLCQrKwtQMh1SUlJ038LL018m58kcIoPrW2r7hELUHPuJapuAjWC5Cb1//35dudqBPoHMTZnb7itCDXRSdkYpYKtRWWH2uQFG/bvRNNIGtm/fzs6dO82vhw8fzjXXXNP0dYUwOxwBJPlAoAekyjw4pa0f2PDhwwkOVuqEPDw8yMvL02RnSXFlMX/Y+AeO5R/TbduWNDtdVQjhCSwCpgLZwB4hxHIppbk6REr5uMX8nwOWaQcVUsr2ud5qjDOf2I9F365sjGnAcrWQkJBgJWKnB0/b68U/aH9vF9ZC2WkIinV4vqSkJPz8/KisrKSoqIjTp087JcddW6c4zY4iUWzQCJX5itRFRY79sR5jYNwX5rqcxjh48CD/+9//zK+TkpK47rrrtCVi9LwGwqcjctbwbHdYGAzhXiib37F3g6dfk+aenp5MmTKFwsJCRo8e3Wj2X1M8u/FZXtv1GkfyjrBk7hLd9m2FK1YMo4ATUspTUspq4HNgdhPzbwc+c8F13ZOyM0retC0xd9qPqSClbFYYKfdyLmtPrsVUp5Ji13OCUlFqi8YnKC8vLwYNGkRAQABjxoyhWzf9Kaj/3vtvol+N5r9H/6vb1qAdUXsZNl0Ppcftj3VNggnfgldAk6fIyMhg+fIrQYTo6GhuueUWzQVnKzNWUtz/SgAjvMEHlWdDxluazjFw4EDGjx/vlFMA+OXVv2Rq3FQeG/2YU/ZthSscQ18gy+J1dv2YHUKIaCAWWG8x7CeE2CuE2CmEuMkF99O2nP3Ufiw4ufHNaBsuXLhgbkzu7e1t1WFKCx+nfcy0j6fx4Lcqm81CQLy9qion/6P019XAxIkTeeKJJ5g2bRohIU1r1atRU1fD+dLzrDph9J3usNTVwJa5imy8Lf59YdJq8G26er6qqoply5aZEzB69uzJ/Pnz8fLStspcfWI1sz6bxYTl/4/y8BvtJxz+q6LK2sL07dqX7+/6nnFR4xxPdiNc4RjU1nSNBZ/nA0ullJafQlH1MrB3AK8KIVQD8EKIhfUOZK8zsb5WQUr1MFLMnQ7jqA34+voycuRIAgICSEpK0v2kEuwbTEK3BG5MVPljAIi7B4RNiKninFIZqgF/f388PbWFxNRYMGgBm+7dxH9m/cfpcxi4MVLCrp9Ajorj9w5RnEJglMPT+Pr6MnfuXHx9fQkODmbBggX4+TUd+rFkQI8BxHeL5/p+1xMw9HnsPqaq8uD466q2jtCbkWdJe6njaXY/BiHEGOAPUsrp9a+fBpBSPq8ydz/wMynl9kbO9T6wQkrZZE6j2/ZjKDwAq1SqNmedhqAYXacymUxUVVUREND0clsNKSUS2XhWxOab7LORIm6Ga4zwjkEzOfBrOPI3+3EPX5i8FnqOtz/WBBcuXMDLy4sePXrovpXiymK6+nZV9iO2L7B/aPMOgdmnlWY/GsjNzWXDhg1kZGTw85//3LwxrYVLFZf448Y/knEpg5V3rtTza7gUrf0YXLFi2AP0E0LECiF8UFYFdtlFQoj+QCiww2IsVAjhW/9zD2AsoE/S0J04/bH9WNhY3U4BlI0vZ5wCKJlMTabKqdU0nPsWKlSKhRxQWlpKcXGxbjuAsuoy9b0Qg/bJsVfVnYLwgLGf63YKAL1799bsFM6VnLPauwr2C76ySZ36B/uVck0RHH1R872sWLGCY8eOYTKZ2LRpk2Y7AA/hwYdpH7L6xGoO5R5ybNDGNNsxSClrgUeBNcBRYImU8rAQ4jkhxCyLqbcDn0vrJUoSsFcIcRDYALxgmc3UrqgzmYtnrIhZ0CqXl1Ly5eEvKavWEDcNn6Gkz1qdoBZOa9dPysnJ4bPPPuOVV15h8+amZY3V+OPGPxL+Ujjfn/xet62BG3LmM/ihkUr+kW9CZNPbh7W1tSxfvpyiIufa11bUVDDjkxncuuRWvjj0hf2ELglKzYQt6a9Cpbbq5EmTJpl/PnDgAAUFBU3MtibEL4TFsxZz8OGD5kZY7oxL6hiklCullIlSyngp5V/qx34vpVxuMecPUspf29htl1KmSikH13/XptHgjuRuggobXRThBVFzNZnX1tZSUVHh9OW3Z21n3tJ5jHh7hOP6Ag8viFNpWn7yXbPQmCNqa2s5fvw4UkoOHTpEdbVK8VIT+Hj6UFZdxrYsFdllA/en9CTs/iks6QqfetRXNauQ+gelqLMJ6urqWLZsGfv372fx4sXqMhcO8Pf2557B95AclsyUuEbUfwf+ThHqs6T2Mhx+QdM1YmNjiY1V0rqllLpXDbck3UJqL/3Fqm1B+y3NczfUNp37zHSYfdFAeno6L774Ip9//jknT57Uffnaulqujrya2f1na8vxjld5eirNgDxtipARERHmJX51dTWHD2sT5GvgJ8N/wtGfHeXPk//seLKBe3F+FawcpDxI1JbSaK5JwkMw8PdNnkpKyerVq81NccrKyjh9+rRTt/XU1U+xb+E+ugc08jcXGAn9HrEfz/iXegc5FSZPviLy9+OPP3LxonMtedPz0916I9pwDK7AVAlZKvvlGmsXQHmT1dXVkZ6ebq561sOEmAlsu38bz0+x2/NXJyhO6QZnywltldBCCKtKaL0KlD0CejCgxwBdNgZugGXb2qaaUfWeCiMWOczG27Jli5UO0ejRo61kr5tCSskrO17hUsUl85ifl4PMpeSnFWkaS+qq4JC2B5SIiAgSExPNrzds2KDJzpL/W/t/JC1K4svDX+q2bS0Mx+AKzq2wL/n36gJ9G0kZtaGiosJKylqvkqoluvRZ1Dahs76Eam1x3kGDBpmLjbKzs8nNdU5J8nzp+XbfCrHToLVtbVCcw0r/H374weqDdeDAgUyfPl2zvPxru17jie+fYOpHU7UnMfj3gv6/sB8/uRjKTtmPq2C515Cens65c+e0XbuehG4JeHl4cbrIuZVRa2A4BlegFkaKvMVhZWcDlkqqffv21V1RvOL4CnJKVWQHHBF5E/jYXMtUCWdUivRUCAwMZMCAK0/9zshxL/x2IZGvRLIls/02NelUaG1bq5aIYUFJSQkrV15J24yLi2P2bI1h0HrmJM9hUK9B/Hb8b+3lX5oi+ZfgbZNqKmsh7Q+azHv37m2lX7Z+/fomZttz35D7OP7z4/x6nJ2snNtgOIbmUnUJzqvkJesMIzWgVwKjtKqUeV/OI/KVSP3a755+EHuX/biTwnoHDx6ktra2idn2hAWE4Sk8SbuYpsvOoI3Q2rbWQVXxkSNHzJ3Revbsybx58zRXNTcQ0TWCfQv3cXPSzbrs8AmFpKfsx898DMXakiInTpxodmKnTp3izJkzmi/v7elNTEiM5vltgeEYmkvWUns5Yb/e0KvpTlQNFBUVkZmZCShx+4ED9aWyXaq4xPSE6UyKnUTPQG3N061Qk8go3A+XtO0ZxMXFmQt9KioqSE9P13X5x8c8TvYT2Tw66lFddgZthNa2td5Nzzt27Ira6KhRo/D1dU5112khxv6/AF/b+ggJaU1vljfQo0cPBg++0vzKsqmWHnZm72R5uvuJShuOobm4UEk1Pj5et5JqdEg0y25bxpoF2iQt7AhJhe6j7cc1rhqEEFZNfJzZhHbKoRm0DTELAAfvbeENMSor0XrKyso4e/as+bVePbBv079lwX8XsOmMvnRRK7y7KBvRtmR9pd5LRYUJEyYQFxfHfffdx5QpjaTINsG2zG2MWTyGh1Y8RHlNuW77lsRwDM3hcibkqhR3xbaOkqolzWoKorZqOPMJ1Gp7s1q2/Tx16pTTRUrtTbO+U5L0pLo6miUO2tZaPl1HRUWZO6Vp5dNDn/LJj5+w93wzZXH6PWJf6Alw8LeazENCQrjrrruIinKs/aTG1ZFXMzFmIg8MfcDtUlcNQfzmoLZJ27U/hA6zH1fh4sWL5uYf3t7eVhu5WtiauZUQv5DmV1JGz1eqVmsvXxmrKYHMpRB3t0Pz4OBg+vfvj5eXF0OHDtWlIdPAzE9msvrEavYt3MewcG3/fgZtQEBfpXBTqmQBCW/FKThoWzt48GDCwsI4evQoPXvqXy3+edKfGdZ7GHOSVRpP6cHLXyl622NT25CzGnK3Qs+WVUQVQrD+7vW6NtxbC2PF0BxUlVQXaFZSTUu7suE6YMAA3Uqqj695nNQ3U1mV0UwJa+8uEHWb/biOTejbbruNOXPmEB8f79QbPbFbIiF+IZy45Fys1qCVyFmr5P3b4t1Vc9taIQR9+/ZlypQpTqVmx3eL55djf0l0SLRuWzvi7odAlSZVac9oVgGwpLa21pxhqAV3dApgOAbnKUyDYhUxrJhGpAFUiIuLIykpCU9PT91hpBpTDcN6DyMmJIZJsZMcGzhCraYhbwuUaNtMbu4b/PcTfs/5J84zL2Ves85j0MJkqyjwxt4Dc4s1ta11Ozx9FNkOW3I3K90NNWIymdi3bx+vv/66VW9qrazKWMWkDyZRUK5df6klMRyDs6itFnqMUQp7NJKQkMC8efN46qmniI/X9wfl7enNv2/8NycfO+m42lMLPa5SGgrZcrJ15Ku6B3TH39u/Va5l4CR1NZCtkkETeUurXL62rpa5X87l7X1vu1aVN+ZOpaucLQe1rxp27drFihUrKC0tZePGjeZUXK28uutVNp7ZyOu7nOsR4WoMx+AMsk69U5uO2gVL/Pz8NLcrtKVZm86WCKG+ajj9AZj0CeTV1NSQlpZGWZn+Dll1so5d2bt02xm0ArmbofqS9ZhXoCJ/oYGamhr27dvH5cuXHU9WYcvZLSw9spSXdrzkuvc9KBmEg/5kP35pr33fkkYYPnw4/v7Kg01hYaHuYs+/Tv4rL0590W2K3gzH4Ay5m+1Ft4QXRLVOGCQ9P531p9e7vpdBzF3K5qEllblwfoXmU+zevZuXX36ZZcuWcfDgQV2Xr5N1DHpzEFctvorjBSq9gg3aliyVMFKf65RNXA2cOHGCFStW8NJLL/HNN9o+cC0Z0nsIi2ct5pnxz7g+Nh95i3rSSNrvNLW99fX1ZezYsebXmzdv1lXsObzPcJ68+km3WTUbjsEZ1MJI4dPBL0yTeVlZme6lpiX/3P1Prv3wWv6w8Q9On0MVvx5KJzdbNArrgfIHUllZCSg1DXo0kDyEB6P7jiYmJIasYv1CggYtiKyD7GX24xHaw0gNRW1SSrp06aL7FkL9Q7l/6P3cPdhxppxuhIDBKkJ6xYcgU6W/gwqjRo0yp96WlpZaiQPqocZUQ1GlcynfrsJwDHoxVUKmiiqijjDSd999x0svvcSKFSsoLCzUfQuxIbHEhsTqlwLQglo4KWc1XNb2QZ2cnGyuYr106ZK5qlsrr8x4hZOPneTaOBXlV4O2I38XVNjocXn4QN/rNJmbTCarqvikJJWYflsTPkPpuGhL2rOahAO9vb0ZP/5Kl7qtW7fq7lOyLXMbSYuSeHLNk7rsXI1LHIMQYoYQIl0IcUIIYRckE0LcK4TIE0IcqP960OLYPUKIjPqve1xxPy3K+ZVQY9PK0isQImapz7ehQUm1oqKCffv26Upta+DJq5/k5GMnGdpbpb90c+l9LQTapgFKOPWeJnNvb2+rDCu9ldBdfbu6Nn5s4BrUspF6T1XSVDVw+vRpqqqUNNfg4GB69+6t6/Jv7nmT57c8z7kSfUqmuhACBv3FfrzsBJz6QNMphg0bZq7jKS8vZ+fOnbpuoVdQL84UnWFH9g6qalVrae6tAAAgAElEQVTSgluJZv8FCiE8gUXATCAZuF0IoZLewhdSyiH1X+/W23YDngVGA6OAZ4UQoc29pxZFLYwUcYviHDRgKR7Wp08funfX1sjHFiFEy+RACw+IU6mEPvUfJZygAUthvSNHjphDS3oorynnf6f+p9vOoAWQUn1/QUc20tGjR80/JyUl6XrvSil5eefL/Gb9b1q+zqXXBPXN9EPPgcnxB7WXlxcTJkwwv96+fbuuzowJ3RLYeO9G0h5Jw9fLOf0oV+CKR7NRwAkp5SkpZTXwOTBbo+10YK2U8pKUshBYC8xwwT21DNVFSu8FW1pJSbWsuozPfvys5XVV4u5VHIQll8/ChXWazMPDw81PhLW1tVa/sxYqayuJfS2W6R9PN/Ya3IGiNPteBcID+mpbJTc0oGpAbxhJInlp2ks8PPxhxkW1bDUyAINU9hrKs+DEvzWZDx482PzAV1VVxfbt23VdflzUOOfFAV2EKxxDX8Dyrze7fsyWW4UQaUKIpUKISJ227kHWVypKqj2V8IsGiouLzeJhziipfn3sa+747x3M/lyr33WSwEgl3mqLk3LcesNJfl5+TIyZyIg+I/RLiRu4HrXVQs8JSrKCFvOsLHOKamBgIBEREbou7yE8mNV/Fm/e8Ka+vgvO0mMURKj8jR3+i7VsTCN4eHgwceJE8+u0tDSnkk3Ka8pZekSlM2Qr4ArHoLYmtE1F+RaIkVIOAv4HNATstNgqE4VYKITYK4TY26Av1Oqc/th+LGo+aPTulk/OcXFxusXDgn2DGd13NLcm3arLzinUNqGzl0Flvibz1NRUs77+hQsXyMnR10jog5s+YNeDuxjeZ7guO4MWQM0x6MhGsgwjDRgwwOmanVZl0J+w+3iqzIX0f2oyT0lJITIykvHjx/PII4/g6anPodXW1TL4rcHM/XIuO7P17VO4Alf8D2UDkRavI4DzlhOklAVSyoYA3TvAcK22Fud4W0o5Qko5IixMW1qoSynPhlwVmd/YBZpP0Vwl1Rv738jOB3fy0PCHdNvqpu8NymrIkroaOPORJnM/Pz+Sk69sNTmzajBwA0qOq0u/RN6kyVxKadV7QW8Y6XThaZ5c82TrFz2GpCrikrYc/bum1rdCCO677z4mT56Mn5/+97KXhxdzk+cyPHw4wqGcretxhWPYA/QTQsQKIXyA+YBV3bwQItzi5Syg4RFiDTBNCBFav+k8rX7M/TjzGXaLmS79oNsITeYXL14090T28vLSraRqSasIb3l4Q+y99uMn39UsE2DZpyE9Pd2pDKzcy7lu2cik06BWu9B9NARoCwdduHCB4mIli8/Pz4+YmBhdl196ZCkv73yZ13e3gVRE6h9B2DzpVxfCsZc1mTf37/TZCc+y5yd7GB2h0i+lhWn2DoeUslYI8SjKB7on8B8p5WEhxHPAXinlcuAxIcQsoBa4BNxbb3tJCPEnFOcC8JyU8pLdRdyBMyphpJg7nVZS1dux6pO0T5gSN4VeQb102TWL+AeUJyRLio9A/k4IG+PQPDo6mpSUFOLi4khJSdEdQiisKCT61WhMdSbOP3meHgHaYtoGLqSZ2UhhYWHMnz+fY8eO4ePjozukMjV+Kk+UPcG0+Gm67FxC135KIoatXtixVyDx55oLWi2RUmp2GG2ZlST0VKa6CyNGjJB79zazSYceig7BSpXQz40Z0CXBobmUkldffZWSkhIAbr/9dhITEzVfPqMgg8Q3FFnq3Kdy8fb0dmzkKv43wb4ZUdz9cFXriOvN/ny2kq44/WUSujn+tzZwIZez4BuVJjQ3HFc+NDsDlzPh2372SScAXl0Umf2kJx2qyl66dImNGzfi4eHBTTdpC8OZbSsu8Y9t/2BCzARmJDQvaVMIsU9K6TDM0Q52gdwAtdqF7qM1OQVQ0vXGjh1LREQEAQEBupVUK2sruTHxRuYkzWldpwDqm9BnP1ca+bQC/533X5bfvtxwCm1B9tf2YyGpnccpAARGQUIje3q1pUpodeUgON94T5TCwkIWLVrEjz/+yMGDB80hZa18ePBDXtj2Ak+ve1qXxExzMByDI2Sdeqc2HbULnp6ejBo1igceeIBHH31U93I6tVcqy29fzts3vq3LziVE3greNh3ZTOVwVpt+THNplfREA3WamY3UXP629W8sT1/ephXAAEQ30WNF1ih/D1vmQOlJ1SmhoaFWD4MbNmzQdfmHhj/E7QNv563r32q1xj6GY3BE3lYot9H7EZ4QrdLxTAMN0rzO0CbdnrwC1J2gjpoGUMJpZ8+eZcWKFU7ldB/LP8aSw0t02xk4SWUe5Kn0M9exv3DmzBldCqOWFJQX8Mz6Z7h1ya0tX9DpiNMf4vCjsq5G2XtohEmTrjTTOnbsGOfPqyZfquLv7c+nt37aqpvQRs9nR6iFkXpPs0/lbCFWHF9BXGgcyWFqKiOtRPyDkPEv67GC3UoXu1BtrRk/+ugjTp8+rZwuPl5X2mJmcSZJi5II9A5kZsJMuvjqV+Y00Mm55fYSKEHxSihJA0VFRXzwwQf4+PgwYMAAbrrpJt0PNs9Neo6LZRcJ9W9jlZwzHwMOMupkfSr3yDdUD4eHh5OcnMyRI0cYOHBgsx4QWwPDMTSFqbrZSqqFhYWEhIQ49bRfW1fLA8sfIPdyLmkPp5HaS3/tg0voNlTRqi+0qUU4uRhGvKbpFBEREWbH8MMPP+hyDFHBUVzf73rCg8K5XHPZcAytQWPZSBrfxw1FbdXV1VRUVOh+/3cP6M5vxv9Gl02LUaux4VRN0/OmTJnCNddcQ69erZhZ6CSGY2iKnFVK3rIlngHq5fIqVFZWsmjRIkJCQkhNTWX8+PG6UjZLq0q5MfFGDuUeYmBPffIZLifhQdjzU+uxMx/B0L+Bp+MCnqFDh7JlyxZAadhSXFxsVqHUwre3f+u2jdM7HNXFcEFFwFBHGMmyqK05NTtugVeQstHsCO+mlQxCQ91bH9QSY4+hKdQkMCJucvgGaKBBSbWgoID09HTdefyh/qG8O+tddjywo+0/FKNvB0+b5W91IWSpFECpEBoaSlzclX7YBw4c0HX5Nv/9OxPnV9qnZ/r3ge6jNJmXlZWZ+3AIIejfv7+uy285u4X39r9HQXmBLrsWI2YBCAfZgMJb6YDYQTAcQ2NUF8O5b+3HW1ECowG3+FD0CYGoufbjOjahLSuh9+/fr7sSWkrJpjOb+CRNZd/HwHWoZiPdbK+42wiWq4Xo6GgCA7VJ0jfwxp43uH/5/Xx48ENddi1G0pP2LW9t8fCGAY+3zv20AoZjaIys/0KdTZqcb5jmxuclJSWcOXMGcE5JdVf2Ljae2Uidxh4IrYJaTcPF9Y2m6dkyYMAA86ZbcXGxec9BK2kX05j4wUQeW/0YlbX6ezwYaKC2Qlkx2NKKYaTr+13PtPhpLdOh0Bm6xMP4pUoY2XblILyV8fFLHRa5tScMx9AYatlI0bc5paQaGxuru8ftc5ufY9IHk1j8Q+tUGGsibBx0UanYPvUfTeZeXl4MGnQli0mvsN7g3oO5IfEGfjrip1Sb9LVMNNDIhe+VvHxLfLpBz2s0mVdUVFg5fGccw92D72bNgjXEhMTotm0x+syE69IgYWF91zoP5XvCQmW8z8y2vkOXYmw+q1F+TnkStqWVGvJIKRkRPoLjBce5aYC+8vkWRQhl1XDg/6zHT72nCI5pcJrDhg1j1y5FKfPYsWNcvnxZV6jh29tVwnsGrkM1jDRb8wPR8ePHzSHCvn376kowcHu6xCvpqI2kpHYkjBWDGmc/x05JNShekcHQQG5uLhcvXgSUp2S9UsNCCP446Y8cf/Q4YYFtIDHeFLF3g7D5kKjIaVISwJKePXuaG7XU1dVZiQsatDF1NZCtomTbSmGkGlMNr+x4hVOFpxxPNmhRDMeghloYSYeSquVqoX///rqVVBtwi01nW/x7QYRKS0cnN6EvXdIvpltVW8WSw0v44IC2Bu0GGrm4EWpseg14BUHvKZrMq6urOXHiSk9mvQ9Em89u5onvn+D6T6/XZWfgeoxQki3FR6Bwv/24xjCSlLJZYaTM4kx2Zu/kxsQb8fd20+rIuAfsQw7nv4Py8xDQx6H5wIEDKSkpYciQIYSEhOi+/O5zu7lt6W2EB4Vz56A727w/bodBLYzU53pNdSqgPMjMmDHDHCJs6HuslVD/UO5IvYOBYW1cs2NgOAY71FYL3UZAV20y2ZmZmebGJP7+/iQk6FMFff/A+zy78VnuH3I/i2e70cazJeHTwb8vVJy7MiZNcPoDSHnaobmPj49VT1y9jIsax80DbmZy7GRMdSbDMbiCOpN6Ux4dYSRvb2+GDx/O8OHDnWrKNCx8GJ/cYqQiuwNGKMkSKRtRUtVeuxASEsL48eMJCQkhOTlZt5JqfGg8I/qMYG6KSs2Au+DhCfH324+fXGyvr9MCCCH4723/5dFRj7ZpM5MORcFOqLxoPebh63S2Tbvo62zQKEajHkvytsHacdZjwgNuOgf+vXWdSkpJTU0NPj4+Tt2Knk5PbULZGVgeh90m/bXrodckNQsDd2bfE5Buow7a90aY0DptVVdmrKRHQA9G9hnp3u/7dk6rNuoRQswQQqQLIU4IIX6tcvwJIcQRIUSaEGKdECLa4phJCHGg/qttm/uqSWD0mqLbKYDyVOusU2iwd2uCYtQ3JU/ok+MuLCxk/fr1fPGF/v4OhRWFvL7rdWMTurlICdnNa+FZXFzsdBMZKSU/X/VzRr87mu1Z2506h4FraXZwVgjhCSwCpgLZwB4hxHIp5RGLafuBEVLKciHEI8DfgYaGBhVSyiHNvY9mY6qGTBW9fx21C826fJ2Jd394l1uSbnG/FNXGiH8QLqy1Hsv6Cqr+Cb7dHJpXVVXxr3/9y6zZf/HiRV3Kkzuyd/CL1b8gPjSeuwff7f7O1F0p3A+Xz1qPCU9lxaCBuro63n77bby8vBgwYADXXnutroeiytpKru93PduztnNVxFV67tyghXDFimEUcEJKeUpKWQ18DljJj0opN0gpG8opdwIRLriua8lZA9U2qZOe/hCprSy/urravOnsDOtPr+fh7x5mwvsTnD5HqxMxG3xtMk/qqtQ38FXw9fW1ynXXWwk9PX46d6TewQtTXnAv6ZD2hlo2Us+J9v+3jZCZmUl5eTklJSUcOXIEb2997Wf9vf15febr7F241+jY5ya4wjH0BbIsXmfXjzXGA4BlNZSfEGKvEGKnEKLRMl8hxML6eXvz8vKad8dqqH2YRcwGb21SFocPH+bVV1/l/ffftyry0UqQTxDX9buOO1NbZ4XiEjx9IeZu+/GT7yjhCQ1Y1jSkpaXp6vjl6eHJJ7d8wpzkOcYHSnNorPeCRhp6L4BS1Gas3No/rnAMau8C1U8FIcQCYATwD4vhqPrNkDuAV4UQqkpUUsq3pZQjpJQjwsJcHGqpKYFz39iPOyGBcfbsWaeKtsZEjuG7O77jmWue0W3bpsQ/YD9W9CNc0pYcEBsba65lqKystPqQMWgFio9Cicq/eYQ2KRYppdWDkN6ituySbFZlrGr7vs4GVrjCMWQDkRavIwC7hqZCiCnAM8AsKaX5XSClPF///RSwERhqa9viZC0Dk41ap293JV9fA6WlpVbCYXqVVNs1ISnQY4z9+EltNRhCCDs5br2cLTrLM+ue4aODH+m27fSo1S70GKOpUBHg/PnzlJSUAErdTnR0tAMLaz5J+4TrPr2On373U8eTDVoNVziGPUA/IUSsEMIHmA9YZRcJIYYC/0ZxCrkW46FCCN/6n3sAYwHLTevWQS2MFHWbYw32eiwrnWNiYujatauuy3+c9jHp+em6bNwKNTnuM59C7WVN5kOGDDGHH06fPq17xbXr3C7+uvWvvLjjRaczYzotLgwj9e/fX3fdTs/AngzuNZgb+2vb6DZoHZrtGKSUtcCjwBrgKLBESnlYCPGcEKJBVOcfQBDwpU1aahKwVwhxENgAvGCTzdTyVOTAxXX2404qqVrKSmshvzyf+765j5R/pXCpQn8Iyi2Imqdo6lhSW6reL1uFrl270q9fP/NrvauG2f1n89Dwh3hjZsdXvXQpl8/CpX324xHaEi6klHb7C3q5b+h9HHj4ALP7a2uXa9A6uERLQEq5ElhpM/Z7i59VVbiklNuBNupwX8/Zz+2rdQNj1cMjKuTl5XHhwgUAPD09dcdYK2oqWDBoAeU15XTzd5zi6ZZ4BymtP0++Yz1+8l2Iu1fTKYYOHcrx48cBpe3npEmTNFfP+nr58tYNb+m5YwNQb8saMlhzw5m8vDzz6s7b25v4eOcb1Rgb1u6FUbfuQiXVxMRE/Py0CY41EBkcyXuz3+OLOfoLvNwKtXBS3jZYEgS7f+qwy1u/fv0IClJWHWVlZWRkZLTEXRpY4sIwUmJiIl5e+p4z15xYQ1FlkeOJBq1O53YMxcfUl9JOKqnqDSN1KLqPhMAY+/Hay8rKYeWgJns2eHp6MnjwYOVU3bs7tVdw8MJBHlz+IJ8f+ly3baej4iLkbbUfb6XeC3mX87ju0+uIeDmCipoKXbYGLU/nlqVUWy2EDoNgbW/yrKwsioqUJx4/Pz/dSqqrMlYR5BPE2KixeGhstO62lJ2CCrtkNAVZA6Ya2DJHaYPYSKhi5MiRJCYmEhkZ6VRoYV/OPhbvX8yx/GPMHzhft32n4tw32GWVd+kHwSmazKWUDBgwACkl+fn5VntEWsgvz2dizEQCvQPdV16+E9N5HYOUcFZNSdW5Tefk5GRdS2kpJU98/wTH8o+x+s7VTE/Qlhrrthx9yXFRW10NHHul0daIwcHBzWoFOS9lHhkFGdw75F6nz9FpaCyMpNEhCyGYMGECEyZMoKysTHczqqSwJNbdvQ5TnUmXnUHr0HkdQ/5O5SnXEuEB0dqfNFNTU6mtreXo0aO6w0jVpmpmJc4iwDuAybGTddm6JWc+VlYGTSFr4MxHLdYzN8gniOenPN8i5+5QVBfBBZVMvAjtYSRLGvaGnMGoWHdPOq9jUAsj9ZqsubAHICoqiqioKK6//nrd+du+Xr78berfdNm4NbVl2ubVaJxXj9vLj7dHzq0AaSM9EhAB3R2qMbuE9Px0/L39iQqOapXrGeinnQe2naSuBjJVsoCcVFL18vIyPrxs6xgaw9vxvMuXL7N7927ee+891q5d63C+Ld8d/46bv7iZbZnbdNt2CtTCSBE3KytmDejRs1Ljtxt+S/Sr0XycpiJzb+AWdE7HkPM9VOVbj3n66crIaA4HLhzgqyNfUVlb6XhyeyFmAQgHleLCG2LucniqnJwcVq1aRWZmJocPH9adobT57Ga+Pva1kZ2kRu1lyFltP67jvf/ZZ5/xzjvvsHXrVsrLyx0b2BDkE0SQTxDXRF+j29agdeicjkEtjNR3Fnhrk7IoLCykqsp50a/Xdr3GnC/n8LetHSiUlPSkYwkRDy8Y8LjDU8XGxprrQUpKSjh37pwDC2vuGXIPr894nafHO+4/3enIWQMmm/RQ3x4QNk59vg0VFRWcPn2a8+fPs27dOqdWD+/Nfo/8X+YboSQ3pvPtMdSUQvbX9uM6wkgrVqwgMzOTAQMGMHHiRLp316Zb38DovqM5eOFgx0qp7BIP45cqKal1Neob0QkLNVXVenp6MmDAAA4cOAAokuYREdpbeCSHJZMclqx5fqdCNYw0W3HaGkhPTzev4CIiInTrgjVg9Op2bzrfiiH7a/snJp9uED5Dk3mDkmptbS2HDh3SXe0J8PCIh/nhoR/o36O/blu3ps9MpU4hYSF4qHTwKtYuqZ2SciWf/siRI4Y4niswVcO5b+3HdWQjNaeorcZUw/6c/cb/ZTug8zkGVSXVueCprRXhoUOHzG/s6OjoZuXdd0i6xCvpqFM22R+7uA6qCjSdprnhpBpTDa/vep3Zn882cuUbuLhe6T1iiVcX6H2tJvPq6mpOnDhhfq1XF2zjmY0Me3sYMz+ZqcvOoPXpXI6h4qJ9j2JQNk41YlnUlpqqT//vUsUl3tzzJgXl2j4c2zXdR0OATQxZmtSF21RoCCc1cPjwYV2X9/Lw4tWdr7I8fTlbM1WkHzojamGkvjconfg0kJGRgcmkONlevXrRrZs+0cf88nx6BfZiZJ+RuuwMWp/O5RhUlVSjIexqTeb5+fnk5OQAygdXcrK+OPaXh7/kpyt/yoJl2h1Ru0UIiJpjP65RihuaF04SQvDcpOf47NbPGN5nuGa7DkudSX1vrZW0kQBuT72dc0+c49fjfq3b1qB16VyOQS2MFH2H5vxty9VCv3798PfXp/ESGxrL9PjpLEjtBI4BlD4NtlxcB5X59uMqxMbGmv+NS0pKyM7O1nX5BYMWMH/gfIJ8nK/M7TDkb4Mqm17pnn6a99Zqa2vNsuigP4xkvqSHJ4E+gU7ZGrQencMxlJ6EbXfCpT32x5xUUtUbRgKYFj+N1QtWc+cg5wrp2h3dR6mHk9TaSarQ3HCSgQVqYaTw6ZoKDgFOnTpFdXU1AN26daNnz566Ln/i0glqTA4kUwzcBpc4BiHEDCFEuhDihBDCbp0ohPAVQnxRf3yXECLG4tjT9ePpQgjXK8mdX6VIPp9VK3YSUJ6p6TTZ2dkUFhYC4OvrS2JiogtvsoMihLKxb4uOcFJDuM7Pzw9vb22tVi3JLsnmd+t/x/NbOrGGkpSNpKk613thwIABuir9pZRM/WgqvV7sxYlLJxwbGLQ5zXYMQghPYBEwE0gGbhdC2AbfHwAKpZQJwCvA3+ptk1F6RKcAM4B/1Z/PNZSeVPLqTeVAncoEqRx30EQGrMNISUlJupVU/7nrn53zj0LNMVxcryucdOedd/LUU09x7bXasmcsyb2cy5+3/JnXdr3WebOTLu2D8izrMeGlbDxrJCgoyCyWpzeMlFeeh7+XP75evsSFxumyNWgbXFHgNgo4IaU8BSCE+ByYDVj2bp4N/KH+56XAG0J55JgNfC6lrAJOCyFO1J9vhwvuS5GCrnOwfHUgBQ1gMpmswhh6lVTTLqbx2OrH6L6pOxeeuoCXxmKiDkFDOMlyZdYQTkr4iUNzT09P3X0uLBnaeyi/GvsrpsRN6bx6VmqrhV6TwFd7VtG1117L5MmTyc7Opm/fvrou3zOwJ0d+doS8y3ntv+9IJ8EV/0t9AcvHkez6MdU5UspaoBjortHWefRIQTtgxowZJCQkEBISQkxMjK7b8PH04a5Bd3H/0Ps7l1MAJZwUrbIJnbmklS4veGHKC0yJm9I5P5SkhKyv7Med0AUTQjjdRAkgLDDMKTuD1scVn1Jq7xLbvMLG5mixVU4gxEJgIShy15pwkRS0p6cnqamp5v4Lev8wksKS+PDmD3XZdCgi58LRF63HLm6AyjzwMz4sWpSSo1B63GZQKDIYrUBRZREewoOuvs5JZxi0Da54hMoGIi1eRwC2PR7Nc4QQXkAwcEmjLQBSyrellCOklCPCwjR+mLhQCtp8SickMDo93Ucq9SKW6MhOAqXq9vDhw3z55ZesW6fSZMYBO7N38siKR9ietV23bbtGLYwUdjX4h7fK5d/c8yZh/wjj5R0vt8r1DFyDKxzDHqCfECJWCOGDspm83GbOcuCe+p/nAOulUq20HJhfn7UUC/QDdrvgnhRcKAVt0AxckJ2UlZXF0qVLOXLkCAcPHtStt7M8fTlv7Xur8/UAaGY20oYNG/jss884cOAAFRUVjg1sOFt8lhpTDfGhjsUTDdyHZjuG+j2DR4E1wFFgiZTysBDiOSHErPppi4Hu9ZvLTwC/rrc9DCxB2aheDfxMSum61BFNUtDemqSgDZqJarHbeiWcpAHLYrfS0lKysrIcWFhzZ+qd/Gbcb3hkxCO67No1ZaehcL/9eOTNmswbaneOHz/ON998o/vfHOCtG94i58kcZiRoK6QzcA9cshsnpVwppUyUUsZLKf9SP/Z7KeXy+p8rpZRzpZQJUspRDRlM9cf+Um/XX0q5yhX3Y6ZBCtozwH7lILyV8fFLNUlBGzSTbiMgMMZ6TNZpDid5eHhYpUkeOXKkidn2pPRM4S/X/oXUXvoLE9starpUoUMhKFaTeW5urrl2x8fHh7g451JNewX1MmS22xkdP03DUgrauyvgoXxPWKiM9zGUHluFxsJJZ7VnJxlS3DrJVgkj6chGsixqS0xM1L2/1inEIjsoHd8xwBUp6LnFcIdJ+T7yDWOl0NqoOYbcDZrDSTExMQQEBADOhZPqZB1LDi/hvm/uo7aueX2L3Z6KHMhT2Wh30jHoFc27WHaRXi/2YuL7E6mzFa40cHs6h2MwcA8aCyepbZCq4OHh0SztJIHgmfXP8P6B99l8drMu23ZH9jfYZX537Q9dtVUtFxQUkJubCyjp2v369dN1+QMXDuDt6U2gT2DnrB9p5xj/YwathwuykyzDSUePHtUtxf2rsb/ilemvkBKW4tigPdNYNpLGGhxLie2EhAR8fLQ1smpgesJ08n+Zz6LrFumyM3APjKR8g9Ylah4c/Yf1WO4GqMwFP8eKnQ3hpPLycnM4SXPBI/DgsAf13nH7o+qSUkBoSyuFkRoI9Ak0JLbbKcaKwaB16Ta8kXCS9uwkQ4rbAedWgLTZQwmIVP7tNWDZSlUIQf/++nqTl1WXGYkB7RzDMRi0LkKo1zTo0E5qCCd5eHiYewTo4VLFJRbtXsSrO1/VbdsuaCwbyYkwkmX9iFbuXnY3/d/o3/mqzDsQRijJoPWJmgtH/249lrtRVzjppptuIjExUfeHFkBWcRaPrnqUsIAwHh31aMcSNqwpg5w19uM6wkjl5eV4eXlRW1urO4xkqjOxL2cfmcWZRHaNdGxg4JZ0oL8Ig3ZDt+EQGAuXT18Za8hO6vewQ3MPDw8GDx7s9OUH9RrEQ8MfYlzUuI6XSpmzGkyV1mO+YdBjrOZTTJw4kbFjx3LixAld+zegtO48+dhJ9ufsJzLYcAztFdzn+/wAACAASURBVCOUZND6NJqd1HpS3G/d8BYLBi3Ax1Nfto3bo5qNdBN46Ot/5e3tTVJSEoGB+jePvTy8GNl3pG47A/fBcAwGbYNaj4bcTVBxsfXvpaNgqlI2nm1xoveCU5evM3X8wsFOguEYDNqG0GFKOMkSWae+cdoIUkrOnj3LqlWr2LRpk+5bOF5wnD9u/CM7s3fqtnVLLqyD2lLrMe+u0Gtyq1x+7am1hL8Uzp83/7lVrmfQchiOwaBtaLSzm/Zit7Nnz/L++++ze/du9u7dqztF8qODH/GHTX/gvf3v6bJzW9Scat8bQWO4LD09nW+//ZaMjAxqa/U/+W86s4n88nxj1dABMByDQduhlraqI5wUFRVljoGXlZWRmZnpwMKa+QPnc/+Q+7k99XZddm5JXW29DIYNOsJIaWlp/PDDD3z66ads364/1fSv1/6VHx/5kZ8Mc9zL28C9MRyDQdsROhSCbKScdYSTmlvsltIzhcWzFzMxZqIuO7ckbytU5VuPefpD+HRN5jU1NWRkZJhfO1PtLIRgYM+B9O3qurbtBm2D4RgM2o7Git2clOI+evQodXUdLP1UK2rZSOEzwEtbVtGpU6eoqakBoHv37mhun1tPh0v77eQ0yzEIIboJIdYKITLqv4eqzBkihNghhDgshEgTQtxmcex9IcRpIcSB+q8hzbkfg3aIWtpq3maouKDJPDo62iqcpFeKW0rJpjObeHLNk+03Nt6YQm0ztJGExippUP4Nkxclc8sXt3Cp4pJmOwP3pbkrhl8D66SU/YB19a9tKQfullKmADOAV4UQIRbHfymlHFL/daCZ92PQ3ggdCkE2fTF0SnFbdnZzRjvp4e8e5uWdL7PhtIrwXHugYC9UnLMeE17Q9wZN5iaTifT0dPNry39PLRzNP0p6QTq7zu0ixC/EsYGB29NcxzAb+KD+5w+Am2wnSCmPSykz6n8+D+QC+tapBh0XF0hxJycnm3/WG04SQvDoyEf5v6v/j7hQ51pXtimlJ2GPSrV4jzHgo+1D+uzZs1RWKtXSXbt2pU+fPrpuITksmezHs/n0lk+N3gsdhOb+L/aSUuYA1H9vUuhGCDEK8AFOWgz/pT7E9IoQwmgM2xlpNDupdcJJPxv1M/429W/Ed2tnHf3Or4KVg6Bwv/2xgl3KcQ00J4zUQN+ufZkQM0G3nYF74tAxCCH+J4Q4pPI1W8+FhBDhwEfAfVKad6qeBgYAI4FuwK+asF8ohNgrhNibl6etFaRBOyF0iH04Cdmq4aR2R+lJ2DIHTOXqx+uqleOlJ9WP1yOltFJT1RtGMuS1OyYOHYOUcoqUcqDK1zfAxfoP/IYP/ly1cwghugLfAb+VUu60OHeOVKgC3gNGNXEfb0spR0gpR+jNmDBwc1woxQ1QVFSk+xYqair46shXvLPvHd22bcLRl6Cupuk5dTVw7JUmp2RnZ1NWVgZAQECAbtG857c+zzXvXcPak2t12Rm4N80NJS0H7qn/+R7ArsJGCOEDLAM+lFJ+aXOswakIlP2JQ828H4P2ito+Q6727KSoqCimTZvGY489xh133KH78icLTzLnyzk8ve5pakwOPnDdgTMfg3Rwn7IGznzU5JSioiI8PRWBvf79++Phoe8j4fuT37MlcwtVpipddgbuTXNlt18AlgghHgAygbkAQogRwMNSygeBecA1QHchxL31dvfWZyB9IoQIAwRwAHCsuWzQMQkdAkEJUHbCYlBC1leQ+DOH5h4eHowZM8bpy6eEpXBbym2M6DOCmroavD29nT5Xq1Bbpm1eTdPzUlNTiY2NZe/evcTGxjY5V42Vd67k47SPmRI3Rbetgfsi2mOMcMSIEXLv3r1tfRsGrubAb+DI89ZjPSfAlI1tcjtuzZKu9oJ5anh3hbnFLX8/Bu0CIcQ+KeUIR/OM3DID90FVinszVOS0/r24O+EzHM8R3hBzV4tcvqy6DFOdqUXObdD2GI7BwH0IGayEk6zQnp3UQEFBAVu2bGHbtm26byH3ci5v7X2LPef26LZtVRxtPAN4eMOAx1UPNTdS8PsNvyfxjUS+P/l9s85j4J4YjsHAfWhUilt7dlJ2djZvvPEG69evZ8eOHbq1k/6151888t0j/Hvfv3XZtSoVOZDzXePHhTd4BsD4pdBFvTbj008/5bvvvqOgoED35aWUbDyzkVOFpwgLMDIEOyKGYzBwL1Szk7ZoDif16dPHXOx2+fJlzp49q+vy81LmcV2/65ger02VtE04vkhlxSCUL++ukLAQrkuDPjNVzc+fP8+JEyfYu3cvb775JhUVFbouL4Rgz0/2sOGeDQwNH+rc72Dg1hiOwcC9CBkMXfrZDErI/EqTuYeHh5VExpEjR3RdPjksme/u+I65KSoOyh2orYATb9mPp/wG7qhTNppHvtHoSgFg584rHetSUlLw9/fXfRueHp4dQ67cQBXDMRi4Fy4odmuOdpLbc+YjqLIJ/3h4a0rpBSgpKbGqDL/qqqt0Xf5U4SnKaxqptjboMBiOwcD9UJXi3grl57WZR0URFBQEOBdOAkjPT+eFrS+4V7GblHDsVfvxqPngH67pFLt37zY7yujoaMLDtdk1cM/X9xD5SiQ7snbosjNoXxiOwcD9CBkEXRJtBuuL3TTgCu2kW5fcytPrnmb96fW6bVuMnDVQctR+vJHMI1uqq6vZt2+f+bXe1UJpVSnVpmpMdSZSe6XqsjVoXxiOwcD9cIEUt6V20rFjx3SHk+4fej/3DL6HXkG9dNm1KGq6Rz0nQjdtG8AHDx40y2uHhoaSmGjrfJumi28Xdj6wkx8f+ZEgnyBdtgbti+ZKYhgYtAxR8+DwX6zHGsJJAY77BURGRhIUFERZWZk5nKRH8uGJMU/oveOWpegwXFCpGdC4WpBSsmvXLvPr0aNH69ZFAiUjKTI4UredQfvCWDEYuCchqW0eTnIr0lX2FoISNHdpy8jIMNcs+Pr6MmSIvi66606tI/eyqniyQQfEcAwG7omLpbjPn9e2cW1JnaxjW+Y2PvvxM922LqUyD06rqKT2/wVo7JhmmaI6bNgwfH2198S6XH2ZuV/OJeqVKM4UndFsZ9B+MUJJBu5L1Fw4/GfrsbxtUH4OAvo6No+KYty4cSQmJhIREaH78un56Yx7bxyhfqHMSZ7TdoqrGW9BnY2stXcIxN2ryVxKybBhw6iqqiInJ4dRoxpte6JKYWUh46PHk1+eT0xIjC5bg/aJ4RgM3JeQVOjaH0rSLQbrw0n9H3NoLoTg2muvdfrySWFJzEyYSUpYCuU15QR7Bjt9LqcxVUHGIvvxhJ+At7YNYCEEAwcOJCUlhYKCAkJCtPWCbiCiawTfzP+Gqlqj50JnwXAMBu6LEBCpsmrI/FKTY3AFK+9c2SrXaZSzn0PlResx4QmJP9d9KiEEPXr0cPpWfL2MluydBWOPwcC9URPVy9uqhJM6OlKqp6hGzoHA1skMenXnqxwvON4q1zJwHwzHYODeBA9Uwkm2aMxOaqCiooL9+/eze/du3bdQWVvJN8e+YX/Oft22zSJ3IxQdtB/XmKJaW1tLRkaG0xLbBy8c5PE1jzPi7RGGDEYno1mOQQjRTQixVgiRUf89tJF5JiHEgfqv5RbjsUKIXfX2X9T3hzYwuIILspNyc3N58cUXWb58OZs2bdJd7Pbi9he56f+3d+fRVdbnose/DxkIIQMglAAhCYQwCCKjOLdaqIJU9J7gqRbKcaj3aLUO7ak996x17uk5di3b1XOgpULLFRxBUGjLpKig1iqVMMoYIAGERIYAIQSSkIHn/vHuhCR7J3nf7Axm7+ezFivZv/3+9vvby7if/RufZffwu6zfeaoXtEC9hZ43QM8Jrqrv2rWLJUuW8OKLL7J7t/d06gmdE/jhmB/y2LjHiI2K9VzfdFzB9hh+DmxQ1Qxgg+9xIKWqOsr37+5a5b8CZvvqFwIPB9keE4oCnp30mevhpF69etUcxV1SUsKRI0c83T7z6kzG9BnD+L7jPdULyvmDkL/Gv9zDhrbqJapnzpyhqMh7es8B3Qew4LsL+NWkX3muazq2YAPDNOBV3++vAve4rSgiAtwOLG9OfRNGEkdAwlD/8qPL/csCEJGgNrsN7TmUrY9u5fHxj3uqF5T9vwXqDQF1TYXke11VP3z4MKdOORvSoqKiGDt2bAs30ISyYANDb1U9DuD7+Y0GrosRkS0i8rmIVH/4XwWcU9VK3+M8oMHF6SLyqO81thQUFATZbNOhNHR20rG2OzupTZUXwqGX/csHPwmd3C0k/Pvfr5x+OmrUKGJiYlzfvqyyjAdXPsinRz8NOgWo6ZiaDAwisl5Edgf4N83DfVJUdRzwADBHRNJxUk7V1+BfoaouUNVxqjquVy9LJxh2As0zFHwGJXmuqvfv35/4+HigecNJAGdKzrBw20LKq8o91/Uk5/9BVb3J3sg4SH/EVfWCggJycnJqHns9RXXxzsW8suMVfvxu2ywJNl8/TX79UNWJDT0nIidFpI+qHheRPkDAw1RU9Svfz0Mi8jEwGlgBdBORSF+vIRnwfm6BCQ+Jw53hpPPZdcuProChTzVZvXo4qXpV0p49exg4cKCnJkx6fRLbT2wnKS6Juwbf5amua5cr4MBc//KBD0G0uw12tQ/LGzJkCD169PDUhCkZU/i3W/6N0UmjcUZ8TbgJdihpFTDL9/ssYGX9C0Sku4h09v3eE7gJ2KtOH/UjILOx+sYALX52UnMyu/3j8H/kjvQ7iO8c76meJ0dXBOgFiesNfSUlJXzxxZUlrl57CwB94vvw/O3P8w9X/4PnuiY0BBsYXgAmichBYJLvMSIyTkRe8l0zDNgiIl/gBIIXVLU6Ee9zwLMikoMz57AwyPaYUBYoMJzeCBePuapeeziptLSUw4cPe7r9czc/x7oZ67g19VZP9VxThf0BlqgmT2s0h3NtW7dupbLSmbZLSkoiNTW1JVtowkRQgUFVz6jqt1U1w/fzrK98i6o+4vt9o6peo6rX+n4urFX/kKpep6qDVHW6qtphLKZh3YZDwjD/cpeb3USkTj7ovXv3NnJ1Ozj9dzgTYAOeyyWqVVVVdTbwXX/99Z6GgrJPZ3Pry7eyev9q13VMaLKdz6ZjacHhpEOHDjVr1U3O2ZzW+fAMtKGt+xjodYur6vn5+ZSUOJPWcXFxjBgxwtPt522ex9+O/o01BwLsnzBhxQ7RMx1LynTY/Yu6Zaf/7gwnuTg/KDk5mbFjx5Kens6gQYM8T64eLjxMxtwMEjsncvKnJ1vuYLkLRyDvT/7lQ59x5ldcSElJ4emnnyYrK4u4uDgiIiI8NeGXt/+SjB4ZTEqf5KmeCT0WGEzH0m04JF4NRfWGgY4tdzXkIiJMneou61kgA7oP4OaUm0lNTKXoUhHfiGxo645HB+aC1psM79IncA+pEfHx8c0+ajy+czxPTvB+aqsJPRYYTMfTfzoU1es1HH3b9Vh8sD75p09adhlnRTHkvuRfPvgJiGj948OqLlchInRymQ3OhD77SzAdT6Bd0NXDSW2gxdf25y6CivN1yyK6wKD/3bL3acCbu99k2IvDWL7X3REjJvRZYDAdT/VwUn3HvH2wVVVVcfDgQXbs2OG5CarK53mfs/uU91NL67hc5TsXqZ4BP4DOV7l6iQ8//JB169ZRWFjYrCa8vfdtDpw5wLmyc82qb0KPBQbTMQUae//S/eqkwsJCfvOb37BkyRLef/99qqqqPN1+zudzuGHhDfz6s197qucnfxVcDLCfYsjTrqqXlZWxadMmNm3axNy5czl+/LjnJqy4bwVvZb7F96/5vue6JjRZYDAdU6DhpDOfw8Wjrqp369aN6Ghn/L60tNTz2Ul3Db6LfvH9SOuW5qmen0BLVPtMhsQAp8kGsG3bNsrLnbObevbsSVJSkucmRHaKZPrw6XSJ6uK5rglNFhhMx5R4tXN+Un0ejuKuvdnN61Hcg68azLFnjvGft/2np3p1nNkCBX/zL3c5iX758uU6G9omTJjgaf6jsLSQssoy19eb8GGBwXRcgXoNR90fxV07MGRnZ3seTgp6EjpQbyFxBCQ1eG5lHfv27atJwBMbG8vIkSM93f4/Pv4PUmansDLbjigzdVlgMB1XkMNJycnJJCQkAM07OwngUuUl1hxY4/2bd0l+4B3bQ592vaGtOkMbwNixY4mKinJ9e1Vlx8kdFJQUBD8cZkKOBQbTcbXwcFJzzk6a9Pokvvvmd3k/931vFQ/8HmpyVPl07gVp7iaA8/LyyMtzTmHt1KkT48d7SzsqInw862O2PbqNa5Ou9VTXhD4LDKZja8Gzk5oznHTnoDu5trfHD9bKi5DzR//yjMcgwl2mtdq9hWuuuabm1FgvRITRfUZ7rmdCnwUG07EFHE7aBBe/dFW9X79+QQ0nPXfTc+z45x3cPeRu95UOv+ak76ytUzRkuMspXVRUVKd34zXnwt6CvZwpOeOpjgkvFhhMx5Y4zJmwra+NVidFdPJ2UB16GbLn+JenPQBdert6iaysrJpTYdPS0jwvUX1k1SP0n92fvx75q6d6JnxYYDAdX6Bew/afwlsJkPU4FOc2Wr32cNKBAwc8Z3YDZ+mnqw/ar96F4gP+5S43tAH07duX3r2dIOK1t3Cx/CKJMYnERMYwru84T3VN+JDmnEdfU1mkB7AMSAOOAPepamG9a24Daq/LGwp8T1X/IiKvAN8EinzP/ZOqNnk+wbhx43TLli3NbrcJMUX7YG2AIzIAJAo6RcEty6Hv5ICXqCorVqwgNTWVYcOGERcX5+n2Jy6cIGV2CjGRMZz6l1PERDYyT7BhIpzcULes9+3w7Q2Br2+AqnL06FFSUlKatWz2bOlZenTxlgvadHwislVVm/xGEGyP4efABlXNADb4Htehqh+p6ihVHQXcDpQAtZdw/Ev1826CgjF+OkUDDXw4agVUlcDfMhvsOYgImZmZjB8/3nNQAEiKS2JU0iiu63cdpy6eavjCwp3+QQGadSqsiJCamtrsvRQWFExjgg0M04BXfb+/CtzTxPWZwLuqWhLkfY25Yt9/02BgqHa5IvCGshby2UOfsf4H60lJTGn4ov0B5hbiB0PfKa3WrtpW71/NocJDbXIv07EFGxh6q+pxAN/PprKWfA94s17ZL0Vkp4jMFpEWSodlwsqRN4Am5gW0Ao687urlVJXs7GxPaT+jIq5sLqu8XOl/QelJOLLYv3zIU+AyD0J+fn6zUpECXCi/wMw/z2TQ7waRczanWa9hwkeTf5Eisl5Edgf4N83LjUSkD3AN8F6t4n/FmXMYD/QAnmuk/qMiskVEthQUFHi5tQl1lRfcXVfh7rrNmzezbNkyFi9ezPnz55uuUEtWfhaD5w7mg9wP6j5xcD5cLq9bFt0dBs5y9bonTpzgpZdeYv78+c06JvxC+QXuGXoP30r7FoN6DPJc34SXJgODqk5U1REB/q0ETvo+8Ks/+BsZYOU+4M+qWlHrtY+r4xLwMnBdI+1YoKrjVHVcr1693L4/Ew4iXc4LiMCFxodSCgsLWb9+PQC5ubnMmzePHTt2uP6mPjdrLofPHa67E7qqDA7O87940KMQ2dXV61ZvaCsoKCAnx/s3/qS4JF655xXW/2C957om/AQ7lLQKqP7KMwto7DSu+6k3jFQrqAjO/ESQWU9MWEqb4aw+aopWwTsjnW/v9fMr+8TFxTF27Niax5cuXWLlypUsXbqU4uLiJm/x8rSXeXHKi/zX7f9VU1aW+ypcqtfLlUgndacLxcXF7Nq1q+ax1yWqtVn6TuNGsH8lLwCTROQgMMn3GBEZJyI1SWxFJA3oD9Rf6L1YRHYBu4CewPNBtseEo2E/cZakulF5ETY/Dh9+J+Du6KioKO644w4efPBBunfvXlN+4MAB5s2bx65duxrtPUR2iuTx8Y/XLFktr7zETSuf5senoKR2LEqZDrHJrpq8efPmmr0VycnJJCe7q1ft+U+eJys/q+kLjfEJah9De7F9DMbPV+86S1IvVzgTzW5ExsOY/4H0hwOeaFpeXs6GDRvq5DwAGDp0KFOnTqVr16aHgT7e9t9MWv1TUqLgixSIq/4qdkcWXNX0wXcVFRXMmTOHkhJnIV9mZmadDXlN2X58O2MWjCEuOo7jPzlOXLT35bgmdLTVPgZjvh76ToYpO51x+6gEoJPzs9806NI3cJ3KYsj6IXw8BUry/J6Ojo5m8uTJzJo1i27dutWUZ2dnM2/ePPLz85ts1rfOf0hWf1jc+0pQqOp5E6UJAY7xCGDnzp01QSExMZFhw4a5qlctKS6Jn934M569/lkLCsY16zGY0FdR7ByRkbOg4WuiEmHs72DAzIC9h0uXLvHBBx+wdetWwPmQfuyxx+jcuZEV1kXZsNb/g/zXiTNZdDiL1+59jev6NbjeAlVl/vz5VK/CmzRpEjfeeGPD9zOmCdZjMKZaVDxc90e47b2Gx/UriuDzWfDJNCg94fd0586dmTp1KjNmzCAxMZG777678aAAsP+3fkVVsWkszdvN/jP7mzzhNDc3tyYoREdHM2bMmMbvZ0wLscBgwkef78CUXTDwwYavyV8Na4fDkTchQG86PT2dJ554goEDB/o9t3XrVkpLS50Hl87A4Vf9rokY+hQbH97IW5lvMTnjytlN58rO+V1bO+fC6NGjiYlxl6sBoLSilHuX3cvq/aubvSnOhC8LDCa8RHeD6xfBN1dDlz6Bryk/CxsfgE+nQ5n/1pzIyEi/suzsbNasWcP8+fM5ePCgM2xVVVqvYjykP0RMZAzTh185ETb3bC5pc9L494/+ncu+ZbTFxcV1ckNMmDDB09t8Y+cb/CX7L/zir7/wVM8YAP+/cGPCQb+pMGU3bH3Kd6RGAMdWwKm/wvj5kJLZ4EuVl5ezZs0awPlAX7JkCaO6Z3NH987ERFy6cmH6I76J8brez32foktFHDx7sGafQXx8PE8++SRZWVmUlpbWWTrrxn3D76O4vJiMHhnNPmjPhC+bfDbm2J9h8z8H7B3USP0ejPs9dL4q4NPVPYaLFy/WlCVEFnH3N1aS3vWQcx7Sd3MhLi1g/U++/IThvYZzVazz+mdLzxIfHV/nDCZjguV28tkCgzEAZQWw5Udw9O2Gr4npDdctgOTAaTxLSkp4Z+1a9tRKuwkwNmELk0Z1ofNt7nJRqyqTF0/mdMlplmYu9Xy2kapaL8EEZKuSjPEiphfc/BbctKzBXgFlJ51VSxt/4J+zGYiNjSXzm0lkJr1Nl05XTpbfen4cf9g+1nU+6a+Kv2Lf6X0cPnfY896DPaf2MPIPI3n9C3cnyRoTiAUGY2pLvQ+m7IHkRlKLHHkd1o5wdlvXlz2b4fF7eDz1RYZ23VdTfK64jNdee61mH0RD9uzZw95Ne/n0gU9Z+8BakuKcfM6qSt55/0149S3cvpDdp3az8djGJq81piEWGIypr0tvuOVPcMMbENUt8DWlXzk7pjc9AuW+zLQXDkHeXwCIi7zIfX2WcW/vFcREO/+bxcTEkJGR0eBtVZVPP/2UjRs38vIfXiaxOLHmuaW7l5IxN4P5m+c32vQXJr7A6/e+zrM3POvhDRtTl61KMiYQERjwfeh9m3NsxlfvBL4udyHkvwNXjYPj6wCt8xIjkwoZcP+PWL12HcOHDychwX9VUrUvv/ySEyeczXURERGkpqbWPJeVn0VZZRnREdGNNjs6IpoZI2e4f5/GBGA9BmMaE9sXvrkGJiwKuNQUgLLjzsa4ywEO70uaRHxiD+6//35Gjhzp9/SmTZvIy3OGiGpvaLv22muJjY2teTz7ztl89tBnPDT6oZqyQ4WHavY9VFRVUF5VLxGQMc1kgcGYpohA+oPOvoek73ir++UyKM5FRPxWCuXl5fHee++xaNEi1qxZw/79+2ueC5Rz4cb+N9a8xumS09yw8AYmvjaRwtJCluxaQtqcNF7Z8Yrnt2dMfRYYjHGra3+4bZ1z7pLbrHGXKyB7tl+xqrJu3TpUFVWtMyk9aNAgevbs2ejL5pzNQVWp0ioSYxJ5N+ddjl84bsdfmBZh+xiMaY4LR2D1ICcrXFOiEmB6kV/xuXPnWLVqld8y1hkzZpCent7kyxZcLKCssoz+if1RVTYc3sDNKTfXJAkypr422ccgItNFZI+IXBaRBm8mIneKyH4RyRGRn9cqHyAim0TkoIgsE5HGZ9aM+bqIS2swPaifigsBi7t168bMmTOZMmUKUVHODufk5OSAB/QF0qtrL/on9gdARJg4cKIFBdMigl2VtBv4X8AfG7pARCKAF3FSf+YBm0VklaruBX4FzFbVpSLyB+BhoPH1eMZ8XUTGOcl+mhLV8LCTiDB+/HiGDBnC0aNHSU9Pt13Lpt0F1WNQ1X2qur+Jy64DclT1kKqWA0uBaeL89d8OLPdd9yrQyK4iY75m0maANHGWkURB2swmXyohIYERI0bQpUuXFmqcMc3XFpPP/YBjtR7n+cquAs6pamW9cmM6hmE/gU5NBIZOUTD0mbZpjzEtpMnAICLrRWR3gH/TXN4jUL9YGylvqB2PisgWEdlSndXKmHYVnw63LIeIWP+eg0Q55bcsd64zpgNpco5BVScGeY88oH+tx8nAV8BpoJuIRPp6DdXlDbVjAbAAnFVJQbbJmJbRdzJM2eksST3yujPRHBXnDB8NfcaCgumQ2uJIjM1AhogMAPKB7wEPqKqKyEdAJs68wyxgZRu0x5iWFZ8O43/v/DMmBAS7XPVeEckDbgDWish7vvK+IvIOgK838ATwHrAPeEtV9/he4jngWRHJwZlzWBhMe4wxxgTPNrgZY0yYsEQ9xhhjmqVD9hhEpAD4spnVe+JMfIcTe8/hwd5z6Av2/aaqaq+mLuqQgSEYIrLFTVcqlNh7Dg/2nkNfW71fG0oyxhhThwUGY4wxdYRjYFjQ3g1oB/aew4O959DXJu837OYYjDHGNC4cv1v8AgAAAtlJREFUewzGGGMaEVaBoaGEQaFIRPqLyEciss+XTOmp9m5TWxGRCBHZLiJr2rstbUFEuonIchHJ9v33vqG929TaROQZ39/1bhF5U0RCLkORiCwSkVMisrtWWQ8R+cCX3OwDEeneGvcOm8BQK2HQZOBq4H4Rubp9W9WqKoGfqOow4HrgRyH+fmt7Cuf4lXDxW2Cdqg4FriXE37uI9AN+DIxT1RFABM4ZbKHmFeDOemU/Bzaoagawwfe4xYVNYKCBhEHt3KZWo6rHVXWb7/dinA+LkM93ISLJwF3AS+3dlrYgIgnArfjOGVPVclU9176tahORQBcRiQRiaeRk5o5KVT8BztYrnoaT1AxaMblZOAWGhhIGhTwRSQNGA5vatyVtYg7wM8BlQuYObyBQALzsGz57SUS6tnejWpOq5gO/AY4Cx4EiVX2/fVvVZnqr6nFwvvwB32iNm4RTYPCUGChUiEgcsAJ4WlXPt3d7WpOITAVOqerW9m5LG4oExgDzVXU0cJFWGl74uvCNq08DBgB9ga4iMqN9WxVawikwNJQwKGSJSBROUFisqn9q7/a0gZuAu0XkCM5Q4e0i8kb7NqnV5QF5qlrdG1yOEyhC2UTgsKoWqGoF8CfgxnZuU1s5KSJ9AHw/T7XGTcIpMNQkDBKRaJzJqlXt3KZWIyKCM+68T1X/p73b0xZU9V9VNVlV03D++36oqiH9TVJVTwDHRGSIr+jbwN52bFJbOApcLyKxvr/zbxPiE+61rMJJagatmNysLTK4fS2oaqWIVCcMigAW1UoYFIpuAmYCu0Rkh6/s/6jqO+3YJtM6ngQW+77wHAIebOf2tCpV3SQiy4FtOKvvthOCO6BF5E3gW0BPX0K0/wu8ALwlIg/jBMjprXJv2/lsjDGmtnAaSjLGGOOCBQZjjDF1WGAwxhhThwUGY4wxdVhgMMYYU4cFBmOMMXVYYDDGGFOHBQZjjDF1/H9gfz4KmqCdoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 10, 1)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x,       y, linewidth=5.0, color='orange', linestyle='-', marker='o', markersize=10)\n",
    "plt.plot(x + 0.5, y, linewidth=3.0, color='gray',   linestyle='--')\n",
    "plt.plot(x + 1.0, y, linewidth=2.0, color='green',  linestyle=':')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Use the MATLAB-like syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'r-o', x + 0.5, y, 'g--', x + 1.0, y, 'b:^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Plotting with keyword strings\n",
    "\n",
    "There are some instances where you have data in a format that lets you access particular variables with strings. For example, with `pandas.DataFrame`.\n",
    "\n",
    "Matplotlib allows you provide such an object with the data keyword argument. If provided, then you may generate plots with the strings corresponding to these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some map with random numbers\n",
    "data = {'a': np.arange(50),\n",
    "        'c': np.random.randint(0, 50, 50),\n",
    "        'd': np.random.randn(50)}\n",
    "data['b'] = data['a'] + 10 * np.random.randn(50)\n",
    "data['d'] = np.abs(data['d']) * 100\n",
    "\n",
    "#           (x)  (y) (color) (size)\n",
    "plt.scatter('a', 'b', c='c', s='d', data=data)\n",
    "plt.xlabel('entry a')\n",
    "plt.ylabel('entry b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Maybe a more familiar example with a `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.linspace(0, 10, 21)\n",
    "b = np.sin(a)\n",
    "df = pd.DataFrame(data={'a': a, 'b': b})\n",
    "\n",
    "#           (x)  (y)  (color)   (size)\n",
    "plt.scatter('a', 'b', c='blue', s=100, data=df)\n",
    "plt.xlabel('a')\n",
    "plt.ylabel('b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This is the `DataFrame` from the previous plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       a         b\n",
      "0    0.0  0.000000\n",
      "1    0.5  0.479426\n",
      "2    1.0  0.841471\n",
      "3    1.5  0.997495\n",
      "4    2.0  0.909297\n",
      "5    2.5  0.598472\n",
      "6    3.0  0.141120\n",
      "7    3.5 -0.350783\n",
      "8    4.0 -0.756802\n",
      "9    4.5 -0.977530\n",
      "10   5.0 -0.958924\n",
      "11   5.5 -0.705540\n",
      "12   6.0 -0.279415\n",
      "13   6.5  0.215120\n",
      "14   7.0  0.656987\n",
      "15   7.5  0.938000\n",
      "16   8.0  0.989358\n",
      "17   8.5  0.798487\n",
      "18   9.0  0.412118\n",
      "19   9.5 -0.075151\n",
      "20  10.0 -0.544021\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Plotting with categorical variables\n",
    "\n",
    "It is also possible to create a plot using categorical variables. Matplotlib allows you to pass categorical variables directly to many plotting functions. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(names, values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(names, values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(names, values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Subplots\n",
    "\n",
    "It is sometimes useful to present multiple plots next to one another. It's easy in Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['group_a', 'group_b', 'group_c']\n",
    "values = [1, 10, 100]\n",
    "\n",
    "plt.figure(1, figsize=(9, 3))\n",
    "\n",
    "plt.subplot(131)  # rows, columns, plot number\n",
    "plt.bar(names, values)\n",
    "plt.subplot(132)\n",
    "plt.scatter(names, values)\n",
    "plt.subplot(133)\n",
    "plt.plot(names, values)\n",
    "plt.suptitle('Categorical Plotting')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}