intro-to-numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Numpy!\n",
    "\n",
    "Numpy is a numerical computing library in Python.  It provides $n$ dimensional arrays, several useful array generation methods, linear algebra techniques, and much more.  It is a cornerstone in the Python scientific computing ecosystem.\n",
    "\n",
    "Today we'll cover a range of methods that Numpy provides, and even do some exploratory data analysis.  Some of the topics we'll cover include:  \n",
    "* array creation methods\n",
    "* array selection and manipulation\n",
    "* methods from `numpy.linalg`\n",
    "* bit of matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # common abbreviation\n",
    "import matplotlib.pyplot as plt # common abbreviation\n",
    "\n",
    "#   Tells matplotlib to use the inline backend, which allows plots to be\n",
    "#   rendered in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "#   Sets up some matplotlib parameters telling it to set a larger figure size\n",
    "plt.rcParams['figure.figsize'] = (15, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `ndarray`\n",
    "\n",
    "The main object in NumPy is the `ndarray`.  It is a table of elements, usually numbers, but can be arbitrary objects of the same type.  The array is indexed by a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12).reshape((2,6))\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the array as an $n$-dimensional tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 6)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of dimensions of the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The datatype of the elements of the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float32')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.astype('float32')\n",
    "b.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often times one would like to create different types of matrices rather than generating everything by hand.  NumPy provides several ways of creating arrays, including creating them from python lists, linearly spaced ranges, and creating random matrices of arbitrary sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an array from Python lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [1, 2, 3, 4],\n",
       "       [1, 2, 3, 4],\n",
       "       [1, 2, 3, 4]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1, 2, 3, 4]]*4)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when creating arrays with `np.array`, it takes sequences as objects rather then sequences unpacked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1,  0.2,  0.3])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([0.1, 0.2, 0.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "only 2 non-keyword arguments accepted",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-160-f83d877829fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: only 2 non-keyword arguments accepted"
     ]
    }
   ],
   "source": [
    "np.array(0.1, 0.2, 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linearly Spaced Sequences\n",
    "\n",
    "Note that the bounds are inclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,\n",
       "         4.5,   5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,\n",
       "         9. ,   9.5,  10. ])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Linear Spacing from 0, 10.  21 elements\n",
    "np.linspace(0, 10, 21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd063ed2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#   An example of a linearly spaced range that provides a whole period of a sinusoid\n",
    "#   Method signature:\n",
    "#       np.linspace(start, end, # of steps to take)\n",
    "x = np.linspace(0, 2 * np.pi, 1000)\n",
    "\n",
    "#   Applies sin elementwise across x, returning a new array\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the start and end bounds are inclusive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bounds: [0.0, 2pi]\n"
     ]
    }
   ],
   "source": [
    "print(\"Bounds: [{}, {:1.0f}pi]\".format(np.min(x), np.max(x)/np.pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_matrix(matrix, name):\n",
    "    \"\"\"\n",
    "    Prints out some nice information regarding the matrix.\n",
    "    \"\"\"\n",
    "    \n",
    "    print('{} shape: {}\\n'.format(name, matrix.shape))\n",
    "    print('{}:\\n{}'.format(name, matrix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other useful array creations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_matrix shape: (2, 3)\n",
      "\n",
      "random_matrix:\n",
      "[[ 0.65358959  0.11500694  0.95028286]\n",
      " [ 0.4821914   0.87247454  0.21233268]]\n"
     ]
    }
   ],
   "source": [
    "#   Random matrix of arbitrary shape:\n",
    "random_matrix = np.random.rand(2, 3)\n",
    "describe_matrix(random_matrix, 'random_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ones shape: (4, 6)\n",
      "\n",
      "ones:\n",
      "[[ 1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "#   Matrix of all ones\n",
    "ones = np.ones((4, 6))\n",
    "describe_matrix(ones, 'ones')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zeros shape: (5, 7)\n",
      "\n",
      "zeros:\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "#   Matrix of all zeros\n",
    "zeros = np.zeros((5,7))\n",
    "describe_matrix(zeros, 'zeros')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagonal_matrix shape: (5, 5)\n",
      "\n",
      "diagonal_matrix:\n",
      "[[ 0  0  0  0  0]\n",
      " [ 0  4  0  0  0]\n",
      " [ 0  0  8  0  0]\n",
      " [ 0  0  0 12  0]\n",
      " [ 0  0  0  0 16]]\n"
     ]
    }
   ],
   "source": [
    "#   Ranges and Diagonals\n",
    "\n",
    "#   Steps from 0, 20 by 4.\n",
    "#   Bounds here are [inclusive, exclusive)\n",
    "a_range = np.arange(0, 20, 4)\n",
    "diagonal_matrix = np.diag(a_range)\n",
    "describe_matrix(diagonal_matrix, 'diagonal_matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vector Stacking\n",
    "\n",
    "Create higher rank arrays from a sequence of lower rank arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  4  8 12 16]\n",
      "[0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 20, 4)\n",
    "b = np.arange(5)\n",
    "\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 0,  4,  8, 12, 16]])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack([b, a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  0,  4,  8, 12, 16])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack([b, a])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Operations with Arrays\n",
    "\n",
    "The `ndarray` allows you to perform arithmetic operations with it, in a natural way just like you'd write it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "b = np.ones(10)\n",
    "\n",
    "#   Elementwise subtraction\n",
    "a - b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.,  10.])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Scaling a vector\n",
    "10 * b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying a boolean operator to an ndarray also performs it elementwise, resulting in a boolean array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False,  True,  True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a > 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Elementwise squaring of the vector\n",
    "a ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `*` provides element-wise multiplication rather than a Matrix-Vector product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#   b is all ones\n",
    "print(b * a)\n",
    "print(np.allclose(b * a, a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45.0\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#   dot product\n",
    "print(a.dot(b))\n",
    "print(a.dot(b) == a.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection and Manipulation\n",
    "\n",
    "You can index, slice, and iterate over each dimension in an ndarray.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(9)**2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] # 3^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Get the first through the fifth element\n",
    "a[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gets every other element from the array.  Note that this is equivalent to `a[0:len(a):2]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4, 16,  0, 64])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also set values after slicing an array.  Set every $3^{rd}$ element to 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  0, 16, 25,  0, 49, 64])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::3] = 0\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-Dimensional selection\n",
    "\n",
    "For $n$-dimensional arrays, you can take an index or slice from each dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(24).reshape((2, 3, 4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0, :2, 1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean Indexing\n",
    "\n",
    "You can also use boolean arrays to index whole `ndarrays` use them along a certain axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  3],\n",
       "        [ 4,  7],\n",
       "        [ 8, 11]],\n",
       "\n",
       "       [[12, 15],\n",
       "        [16, 19],\n",
       "        [20, 23]]])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.array([True, False, False, True])\n",
    "a[:, :, idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = a % 2 == 0   # True for even elements, False for odd\n",
    "\n",
    "#   Same shape as a\n",
    "idx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22])"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   All elements where idx == True\n",
    "a[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Algebra\n",
    "\n",
    "There's also a slew of useful linear algebra techniques, from matrix and vector multiplication to eigensolving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.],\n",
       "       [ 3.,  4.]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1.0, 2.0], [3.0, 4.0]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  3.],\n",
       "       [ 2.,  4.]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Transpose of a matrix\n",
    "a.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  3.],\n",
       "       [ 2.,  4.]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Or also\n",
    "a.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 4)\n",
      "(4, 3, 2)\n"
     ]
    }
   ],
   "source": [
    "#   When applied to n-dimensional data:\n",
    "b = np.arange(24).reshape((2, 3, 4))\n",
    "print(b.shape)\n",
    "print(b.T.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2. ,  1. ],\n",
       "       [ 1.5, -0.5]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Inverse\n",
    "np.linalg.inv(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.],\n",
       "       [ 0.,  1.]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Identity of rank n\n",
    "u = np.eye(2) # unit 2x2 matrix; \"eye\" represents \"I\"\n",
    "u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.,  0.],\n",
       "       [ 0., -1.]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = np.array([[0.0, -1.0], [1.0, 0.0]])\n",
    "np.dot (j, j) # matrix product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.trace(u)  # trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.],\n",
       "       [ 4.]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.array([[5.], [7.]])\n",
    "np.linalg.solve(a, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Eigenvectors and eigenvalues\n",
    "eigval, eigvec = np.linalg.eig(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the $i^{th}$ eigenvalue in `eigval[i]` and the $i^{th}$ eigenvector in `eigvec[:, i]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Check that we actually have eigenpairs\n",
    "np.allclose(j.dot(eigvec.T[0]), eigvec.T[0] * eigval[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  basic line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random_vector(vector):\n",
    "    \"\"\"\n",
    "    Takes the values and vector and adds a random value to each element\n",
    "    taken from the distribution N(vector_i, vector_i), so that we have\n",
    "    a higher variance for larger \n",
    "    \"\"\"\n",
    "    \n",
    "    return np.random.normal(vector, vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_array = np.vstack([generate_random_vector(np.arange(100)) for i in range(1000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MYxlEREREJOgWL7ZqmC/JHkDevNZgpHBhazISrGQPrJHLkCHw7ruWXKZkxAhr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd063f72b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(100), np.max(random_array, axis=0), label=\"max\")\n",
    "plt.plot(np.arange(100), np.mean(random_array, axis=0), label=\"mean\")\n",
    "plt.plot(np.arange(100), np.min(random_array, axis=0), label=\"min\")\n",
    "\n",
    "plt.legend(loc = 'upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### putting it all together + matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['target', 'DESCR', 'data', 'target_names', 'feature_names'])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Labels for the data\n",
    "print(iris['target'].shape)\n",
    "iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   Using the labels as an index for the target names, printed below\n",
    "labels = np.array(iris['target_names'])[iris['target']]\n",
    "labels[::50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['feature_names']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 4 Dimensions, let's plot 4 3D Plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4, (3, 150)\n"
     ]
    }
   ],
   "source": [
    "#   4 boolean index arrays to get the 4 combinations of our 3 features\n",
    "index = np.array([1, 1, 1, 0]).astype(bool)\n",
    "index_array = np.array([np.roll(index, shift) for shift in range(4)])\n",
    "\n",
    "#   Get the 4 plots as a list\n",
    "plot_arrays = [iris['data'][:, index_row].T for index_row in index_array]\n",
    "print('{}, {}'.format(len(plot_arrays), plot_arrays[0].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)'],\n",
       "       dtype='<U17'),\n",
       " array(['sepal width (cm)', 'petal length (cm)', 'petal width (cm)'],\n",
       "       dtype='<U17'),\n",
       " array(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)'],\n",
       "       dtype='<U17'),\n",
       " array(['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)'],\n",
       "       dtype='<U17')]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_features = [np.array(iris['feature_names'])[index_row] for index_row in index_array]\n",
    "plot_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['r', 'g', 'b']\n",
    "colormap = dict(zip(np.unique(labels), colors))\n",
    "color_labels = list(map(lambda key: colormap[key], labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+GiLrAjmYF0q35FtQ8zmvxuhYr63VeTWvMYXA5Rr+Stdg1cKZamtrY/78+RUd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\noDdR7ErQ0dHBU0+9wYgRj+DzhWhoWMiuXa/y+uu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RRFr2tToIgkD//v0RBH+1orvuuivs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//KFVc87lefgTZgevue16VkSWe6jIwM3n33XQ4cOEBiYiJt27alXbt2vPLKKx6zOc6FfDwz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vKfA0mUx8RZUCSaKSe6rGDNhT+tQMPNlgjc63J0+eROPGjfHKK68gLCwMKSkpWLVqFZo0\naXLfBlP3vAAFGW13Jc6BMvlF4cBfYRBFw3lZegIZb3qodDqdQ8U0Tx4sNeY6CEEPFR2ihbxipegK\n3lDoA25Xb4jLTr+dkk2nQuOtRrMx3U/Ue0f3ndKS7YB94KGk1LkYRUQYKMpVFJSyprD/qrS0FJ9/\n/jl+//13fPrpp2jbtu19x/uGT4BCLu56H+lofIhckN0OCgoqF0SRvaBDNIFsCiUA3aEzi4nMCJVh\n6TBLYjLeZh4o8dnC6g5Qnkbtrt8VQki1U0sYif3OaOCuUqqyLKSqGnoCKd+ZsIpF/99VnxILb6nl\nssEaDV0+fvw44uPj0bZtW0ycONHtYbt3Me5fAQqWEmYymdz+DMqQkPGnQxhJn7M9UUIKnKMMiKPy\nsFSZc6kZME8grHiJzRFRiq7gjayhM4cnle7ozNARWOOtZsWL1tHpdAgICLALDNl9sKIdrjK4Yuuo\nOTWdpY7SwY4ybVKqWHJ7sVgpVxrS/eeff2LKlCl47rnn8Pvvv6vye/ngw50I1r9REOLu51DFSGwg\nPVuJoufZ0wSgM+qV2Wzm51Gx6xLVUKngwxu0QbLrJHVNfcNiQZYnVEjWp3iTaufoO5PCyHDmA1zR\nE5WCVGq9O31K7HmQ7jUSbVFjL6y/p++sqKgIM2bMwNGjRzF37lw0a9ZM8XXvdtzzlSkKUtyVOAdu\nT1AmOW6agwEoP0xQaDzEMhdE51J7FoKw4iWnOVlIyaB9AOXpXORYdTodjEajV4y31IqXWObTEe2C\nDg3U1Oot401ZI7n7cCbZTvez2pU1ujapKn3CXixHVV6W+kifJSbleu3aNUyYMAFarRYzZ85ERESE\n4vtzhc8++wxff/01NBoNWrRogSVLllRkE6+vMiUPd72PpGSLO9LmBDqAEXNBbJYim4z0BouCHThM\ntlmulLmrdbwxK0pOxUt4MHfkr8SCD7EeL6WhhE8R+jL2nCEMPCgBqBbDRc1gTcjWoGSEO8ldqesJ\nfxuO47BhwwbMnDkTY8eOxfOCGaNqICoqCqGhoXyC5MCBA+VeM27cOGzatAmBgYFYunQpWrdureo1\nMfBVptztmbJay+ZdFBQUwM/Pjw/G2CCK4zjFqirO+pcomGNpEiwPV26G3hFYOpe7jZNi+wDgNMsE\ngM+8qUFXcIcnLaXplFVFpL2Qw1OS8w24b7yl7EOseZbjyma/qFn1lKPSJ7UXi63G0WstljL53kqV\nKoHjOHz55Zf48ccfMW3aNDz66KMVQum7cuUKvvjiC5w4cQIGgwGDBw/Gjz/+iOHDh3v9Wny4P+GJ\nj6RnmASeNBoNL2jBsjXYIErNZnyWRSEmviTmW+X2YXnqU6TCnYoXu0ehUintUSgGRIEHJZnUTs4C\nnrE1XPky+m1oD/SbKhl8yO2Ddge0R/LJwgSFo546dxSAxXqH09LSkJCQgBo1amDLli0ICwtTdH+O\noNFosGPHDr5XWYhNmzbh7NmzOH36NPbv348xY8Zg3759Xrk2Z7jngymCXEdBh1Z6KNlZUWJBlJpG\nle29Yqsqjgykuw2OVCFSg/NNIMMtRlcQ0uuk0h3FoDZdgcr1Wq0WpaWlvMoNSd8KaWnu0OuE+1HD\neAtpB2JS52S0PTXYLMSMt6f7EDsoUaaN9rlkyRLMmjULoaGhCA8Px+DBg+3oRxUBCvI0Gg2KiooU\nU8L0wQc5kOsjie1BPZQajQYFBQUoKSmxk4ZWu1LP2noAHosvASjnj4Tz9DQa9WZFCXtvlDhXiPX+\nkg02m828Paczj7s0QTGwEuRq9/hQwE7tCFIEPeT4Mm/SINl7QOjvhcGykHXCnqGcKQCLsUJMJhNm\nzZqFLVu2YObMmXjwwQe9mmhkq9liWLt2LZ9sbN++PXJzc3H9+nWEh4d76xJFcc8HU2zPlLMfiMAG\nUQaDASEhISgqKuL7rehm9MYwQVd9RGIGUvhQsbLaQuNB7/WWVKyzHi+NRiPKIxbrlxHbhyO6glx6\nohywB3VH94CQkiAcEOsq6BWW3tUM2FnjHRISInot7hhs4WfIHbzrDtgKHmX0cnNzkZWV6upl6AAA\nIABJREFUhR49emDUqFHIycnBkSNHsHjxYvTs2VPxa5CCyMhIvPXWW6hTpw4CAgLQo0cPdO/evUKu\nxYf7E3IrU8Igih1Ir9freZ9Dn8VS7ZSGWuJLZNOEs6I4juP/To1ZUSw90ZsHdfIpzip1cvtUhb3j\nas3TdMbWcFSpc3VOEhO7YBOAap4r2MSA1HvAWS+W8LdkmT9ms5kXZjEYDNi9ezc++OADDB48GDt2\n7KiQ3mGO4/D444+D4zi8/PLLGD16tN2/X758GbVr1+b/XLNmTVy+fNkXTHkDrJNwRLuiB5+dyE4B\nmE6n429wlv+t1jBBT2gErh4qYaMqVbhIwEApeh0LNliTOl/LGd2R3Qc5UpYnTcGaN+S0XTlwKfQ6\nR4EJAD6IV0vEgtaQkmmTcm9JkfJVOyhkpVyJcrRy5UosWLAA7777Lp5++mnVed9SkZOTg7Vr1yIt\nLQ2hoaEYOHAgli9fjqFDh1b0pflwn4GeUUeggIK1ryydj+jNpCxKPUREH2ZttZxDuRicJeaUhJRZ\nUVJEi1wlmJzRE5WClGDNVaVOjCYo3CcAr9AgPWFryPFl7L/r9XpJPcruQOn+K0e/pcVSNneVErp7\n9+7Fc889h8qVK0Or1WLIkCGoV68eiouL+ZmL3sTevXtRo0YN3LhxA4899hiaNGmChx56iP93sYRP\nRVD0hbgvginAXjFMaAyFQZRWq7VrTqUHjh0mCNgbdCWyU2wQpWRmSvhQkREiOobBYIDVakVRUREA\nz5TShPtRUqHPUWBCNAKTycQHI6S4KNyHJ+sLg0JPgjVnxpxUqNjAXahApURvnFCQwZ2srhTnazKZ\n7JpnAfC/lRzqpitYLOXnbpw8eRIJCQlo1qwZtm/f7pYAjZrYtm0b6tWrx/PRBwwYgL179/qCKR+8\nBleVKeFAepo1w/ZEEZ2P6MFilW3AXuJbSE2XQrkSUtHVqnZIDdac9WEJqWVAed9KPkVNQQ7A834l\nZywYMcl2ADAYDPw6SvYOs5VCtc9JpaWldr6X7nOxQNLd84UrSp9SYNehdhGr1YorV66gVatWGD16\nNPz9/XH06FEsXLgQTZo0qZBgqkaNGgCAatWqoX///jhw4IBdMFWrVi1cunSJ/3N6evodQY2/b4Ip\noHyFisr0Op3OLohieyiIluUsYyQnOyX2sHmzvM+uQwMUWbD7cMfZ0Treykyx6ziiK7ibMRRbR03Z\ndgC8yIhYb5zwN3E3gBdm9NQ4kFCwJzyQCCuLSvSUsXQSWufWrVuYOXMmDhw4gM8++wytWrVSdH9K\noU6dOti3bx9u3boFo9GI7du3o23bthV9WT7chxAGU5ScMJlMToMolsrl6iBIB05hEskVNZ3jON6G\nq0lFFyrnuWMbHVHLHFU9KLFG34OS7BA1K3hsopnW4TjOzm95QhN0tB+aD6kmW0OYmHN3HIwrsQup\nkuqegtYh9olGo8HRo0fx3nvvoWvXrtixYwf8/PwAoEITeVQxCwoKQmFhIX777TdMnjzZ7jV9+/bF\n/PnzMXjwYOzbtw+VKlWqcIofcJ8EU2xFiqWHabVah0EUaxyorOuMyuUoOyVmPNkHjQ62Go3GK/OI\nXNEIWK44+35Xzo6lcnmjOdNVE6gUmqAjShob9KqRARPbjzDIFeuNE/tN5MwY8Wb/lbN1nP0mcoJF\nsXU4jsPWrVsxffp0jB49Gh9++KFqDsoRTp06hcGDB/Pf+blz5zB16lSMGzeu3GvbtWuHgQMHIjY2\nFnq9HrGxsXj55Ze9er0++ADc9pNsEOXn54eAgACnQZQSCraOqvRCBVs6pLMiCUrYMG/0DbM+hZRy\niRVCto9suqdVD3eo9e5A6jpyaIKO3q8UK8TVfqRQ7dhAkiDlfEH75TiOr7CqSVMV6x3Oz8/HtGnT\ncP78eXz11Vdo2LCh4uu6i+vXr6N///58P9ewYcPQo0cPLFq0iO+h6tWrFzZu3IgGDRogMDAQS5Ys\nqejLBnAfzJkCwB808/Ly+CqLcOgdgWgEapTd6WGjmQHsEGGxw7wS66rVoCsMFslBAGV7IaU7KZUf\nOVBDXIKlZNB/7D1B0uBKZgwJbKbN09keQnoJ/UfqU/Ssq+mMlNoP65TY/VCwSIkRi6VMMtZoNOLy\n5ctISEhAaGgoZsyYgWrVqim8O/mwWq2oVasW9u/fb9c0ewej4snndxfueh9JlXeTyYT8/HxwHAc/\nPz8YjUY7ZS2yF96YrSSkiItV6YU2QQ7bwNk63tqPo3WEvlXOPr25H5at4e5MTTEbz/YNU88dUdNo\n9pHa+1FyHWEympSMAdidk5Q8K4ntx2azYfXq1Zg7dy7efvttDBo0yCu9w1arFXFxcahVqxbWrVtn\n92/Lli3DO++8g1q1agEAxo4dixdffFH1a/IA9++cKaCsGbKgoAA2W9mMC39/fz5DAnhvmCCtQzLT\nQiUkqjAIqXXuBFi0H8qAKZ35IMMOlAWr9L2xAaqUmR1SwXLllc7ksNQTyuTQ90YOnKgSnjhuFko3\nmwLi9BJax2Qy8fc08cCV4nwD9lQ7JYI1Z9lqWkej0eDcuXPo2bMnoqOjcePGDQwbNgwDBgxAQECA\n22sriW3btqF+/fp3SyDlw30Iq9WKwsJClJaWAgDfV0iKfMIgypvUbbGqthS2AeCcVuZNKjpbPZfC\nbnC3D4vWUptFoRRbw5mNZ89CBDrTqJFwVpNqx1axSktLodFo+GBN7n0rBWK9w2fOnEF8fDxiYmKw\nbds2hIaGKrpHZ/j888/RtGlT5OXlif77kCFDMHfuXK9dj1q4L4IpoOymoiDFZDLxB2A2iFJrmCDg\nnLvMca6HwkotiwsbdL1RDndW3nelwOfKaHhLtl3YR+RIGtxT2diKaDYVyt8K9+IJ59tb1EHgttOj\n/jgKDOPi4vjMV2pqKl5//XW8//77eOqpp1S5DjlYsWIFnn322Yq+DB98cAh6xoODg5GXlwez2cz7\nJHrGvDEGxB3xJfLjYqJEjhKUAPjepDuBWi8FzvqwSktL+UCYzjQU7EjpcZYKlhWi1vdG10j9tAEB\nAdDr9U57uYW+V+o+SYCJ2A1qiX84S5y6S3l3JAgi7B0uKSnBxx9/jD179mD27NmIjY1VZY+OkJ6e\njo0bN2LChAmYPXu26GtcsOPuGtwXwRR741KVis1QGQwGGAwGVZyEMLiRyl12lLVhy8VCOW36d28F\nHVKCAUfOzpXRoP1QcFNR/T3CvQgzhoBz0Q7hfqg/zhvNps4ybe72lLFGnZyR2s3ArOogVXOzsrIw\nefJk5OXlYdmyZahbt64qa3sCk8mEdevW4eOPP67oS/HBB4egwAIA9Ho9/6xRbxRl0tUaUEvBjpJ9\nw8I+U9bOA+ArOTQsW0rfjlSoRa0XwlGCVk6PsxSfqgaLwtE6jvqiXPVyU5JcSBMUY494Q4CJ1pGT\nOHV1VnLUH02/IVs15jgOv//+O6ZNm4aRI0di+/btqp03nOHNN9/EzJkzkZub6/A1q1atwq5duxAT\nE4PZs2fzlL+7DfdFMDVhwgT89ddfqFmzJiIiInD69Gm0aNECCQkJfCaHAiyxaok7UKOiIhZgCWlP\nFDBSuV+JvQDK0yKcBYu0DoH6sZTaC/u5FHR44sRdGXrqkQNuG0zKvCm1F8Bzpycl8GWdM+2dqKp0\nAFOSa85KuVIj/LfffotvvvkG77//Pvr06ePVTJscbNq0CQ888MAd0bvlgw+OcOHCBYwYMQI6nQ6N\nGjUCx3HYuXMnli9fjrp16/KHT6p2KFHxEFZuPO0XdQZns6KcJSjlBljO2CdKwpXogyv6nBzBInfn\nOMmBMOiQerZwtU8x9gjHlQkbqFllBZSlDrI0QQIlPikRQXTccePG4dSpU3ybwqRJk9ClS5cKCaQ2\nbNiA8PBwtG7dGjt27BCtQPXt2xdDhw6FXq/HokWLMGLECGzfvt3r16oE7gsBCqBsavLYsWPxxx9/\n4NFHH0VpaSlu3LiB6tWro3Xr1mjdujViY2NRuXJlO6MDlBeHcHZgZA2dtxpAdTod/Pz8HMqC0/+X\nuxf6LDZzqNYke0Dc6Yk147JCF1LpaCwqyukJ++PE9sJWsqRek9DpUa+X0hCqDrJDO4V78aQ/DrCX\ncqV77tixY4iPj8eDDz6IiRMn3jF9UY7w7LPPomfPnhgxYkRFX4oc3JmR6Z2Le8JH2mw2/Pjjj3j3\n3XcRHByMTp064cSJE7DZbGjcuDFatWqFNm3aoFGjRtBqtXbPvNzAo6IrN64gRwCCFUhQW2lOaXEJ\nR4JFwG3VRK1WC6PRqOr8Kwo61BqGC8CuvYBYIp7SBMXgrSoeey/QPWexWDB//nzs3r0bDRs2RF5e\nHo4ePQqTyYQjR44ofg2uMH78eHz33XfQ6XQoLi5Gfn4+BgwYgG+//Vb09VarFWFhYcjJyfHylcqC\nwx/zvgmmbt68if/+97947bXX+EFkNpsNV65cweHDh5GYmIjDhw/j+vXrCAsLQ6tWrfgAq1q1anaH\nRqD84Ze45d5Q0WEPtFKDGyGFi/5z1rvEDvojp6cG5Do9d/ZC71NSJMHZ9UlVOpK6F/Y+Y0H3gtqB\nLvHwnan0ie2FnLVUpyUm5VpYWIiPPvoIx48fx2effYamTZuqskdXyM3NxUsvvYTU1FRoNBp88803\naN++vehri4uLUadOHZw7d65CBh96AF8wJQ/3jI/84YcfUKNGDXTr1g3AbTt2/PhxJCUl4dChQ0hN\nTYXZbEbDhg15/9i0adNyfS1iAZa3+pOF/bxK2HlHCUo6P2k0Gl71VcnqPK3NUtGNRqOqCc2ioiI+\ncATA71epqiR9Jgk9qRlQO/P5UtQE5SSd1VIDFMJkMvG9w7ROYmIiJkyYgN69e+Ott96CwWCwu7aK\nZm/s3LkTs2bNKqfmd+3aNX5I7+rVqzFz5kzs3bu3Ii5RKnzBlFTYbDZkZmYiKSkJSUlJOHz4MC5f\nvozg4GC7ACsyMpIvI7NKaVR2djcj7+y6WFqEUsGNmINgZcH1ej3fT6b0A6l0Rs+RxDn9DhaLhTdA\namfAPA1uxAIsNgtMwS6rCqkGlAhAxQIsodMSk8AFgF9//RWzZs3CuHHjMGzYMK9IuTrCCy+8gC5d\numDkyJH8oYOUz5RCQUEBLl68WGEBI3zBlFzcdz7SbDbjxIkTvI9MSUlBSUkJoqOj0bp1a7Rq1Qot\nW7aE0WiE1WpFQUEBf7jTarW8j/REPVQMQmq9mhV6UkTV6XTlAkmp4kpSQP5ESZ8vBlcJTVf+SGof\nljcTmvTdyUluu2L1iDEuvFVdY3uH6TfKzs7G5MmTkZWVhdmzZyM6OlqVtT0FG0xNnjwZbdu2RZ8+\nfTB+/HisW7cOer0eYWFhWLhwIWJiYpx+VgUHh75gyhPYbDZkZ2fj8OHDfIbu/PnzyM3NRU5ODp54\n4gm8/fbbiIyMLMfZVcKoeosWwfZfkZFj96JUo66cyo0nYA0q9QKRU2AP8Ur0YXkj00bXzsq0k2GR\nS9+UshabDWVppErthe4tduaaxWLBiBEjEBUVheTkZNSvXx9ffPFFhU84z8/PR+vWrXH27FlV11m5\nciW2bduGWrVqYdKkSaqu5QC+YEoefD4SZc/tmTNnkJiYiEOHDuHIkSPIzMyExWLB1atXsXDhQnTq\n1IkfS+KKOicHStPfnK3DDot3lCxjfSZ7KJfCNCDcCZUbKe+VSocEbp9j1PAnLFhGjRLBjZBxwf6+\n5IP1ej30er1qfp/tHab5bz/88AO+/PJLjB8/Hv369fNKgOFsZlRpaSmGDx+OpKQkVK1aFStWrECd\nOnUUWzsjIwMbN25EYGAgBg0apNjnyoQvmFISFy9eRPfu3REZGYlnn30WRUVFSEpKwrlz52A0GtGs\nWTOeY16vXj0AcGhUnQVY3urtkdJz46wkLjUz5S5F0R2wtDShM5LjBKSU972daWOdkbuUR2eg7w5Q\nl97Jfnd0fxOl78SJE7BYLLh06RIuXLiADz74APHx8apchxQcOXIEL7/8Mpo2bYojR44gLi4On3/+\nOfz9/T3+bJa/D5TRCYcNG4a+ffuiZ8+eijokCfAFU/Lg85EimDVrFj766CMMGzYMcXFxOHbsGA4f\nPozc3FzUrFmTZ3m0bt0aISEhbvX2CvuG1aa/eTIsXmplh+M4r4w2USuh6agPi86ZFHAozdwBvNuv\nRJVJvV7PU1hZxoUYHdKdaxHrHT5+/Dji4+MRFxeH999/H0FBQYrv0RE+++wzJCUlIS8vr1wwtXDh\nQqSkpGDBggVYsWIFVq9ejR9//FGxtfPz83H8+HGMHTsWCQkJeOyxxxRnhkiAL5hSElarFXv37sVD\nDz1k9/c2mw1FRUVITk7GoUOHcOjQIZw6dQparRZNmzblKYINGjSARqNxaFQ1Gg2vQuMtg+pOtkhO\nUGKxWHiFPjUP6O7y5eUGJQA8+u7kwJ3gxhnl0VF1sSIpGBzHYdeuXfjggw8wdOhQvPrqq/w+i4qK\nUFxcjCpVqih+LVKRlJSEBx98EH/99Rfi4uLwxhtvIDQ0FFOmTPHoc1nKAv0+ALBr1y5s3LgRwcHB\nGD9+vMfXLwO+YEoefD5SBMeOHUN4eDiqVq1q9/dWqxXp6el2NPqbN2+iWrVqfIDVpk0bp0JQRAku\nLS31SlJOrYQm+Rur1corvtJ5jKVCesqaEK4ppbqmBIS+mM48YswdT/qwxCo3alVpxIIb4bXIpQmK\nQax3uKioCDNmzEBycjLmzJmD5s2bq7JHR0hPT8fIkSP5mVHCYKpnz56YMmUK2rdvD4vFgho1auDG\njRsercn6RPKV27dvx6pVq9CoUSOMGTPGrj/MC/AFUxUFOqCmpKTwAdbff/9tp5IUGxuLxo0bo7Cw\nEFeuXEFUVBSA8oNglepbUrNCJKQ5ms1m3phoNBq7njKlZbRZqodSDaCOKBtAmeKRwWCw24+SUDrT\nxjpvYfBLlE42w6uWwpZQyOL69euYMGECbDYbZs2ahcjISMXX9RTXr19Hhw4dcO7cOQDA7t27MWPG\nDPz6669ufR7rJIAy5aObN29i4MCB6N69O4AyadlNmzbh6aef5kUBvABfMCUPPh/pIWw2G65evYpD\nhw7xAdb169dRqVIl3j/GxsaiatWqOHPmDCpXrsxn45XqTRJCDRELMYiJSwAol9RTgsrtjK2h9J6k\nqMsq0YflLQEmseBGDh3SEU1QGExqNBpePZnOMUCZL5g5cyb+/e9/Y/jw4RXSOzxo0CBMmDABubm5\nomISLVq0wJYtW3j/3bBhQ+zfvx9hYWEer3306FHExMTw38f69euxdetWdOjQAUOGDPH482XA4Y9+\nX8yZqkhQU2K7du3Qrl07ALczKX///TcSExOxdOlSbN26FVlZWejatSt69uyJZs2aoXnz5tBqteUG\n0nkSYLE9RGoMWiWjT3QFmnfANumKDYN11xkKA0OlZ0ewhpxtACUHQQ7Kk6GIYnuSM+xPKkgghQVl\n2oj3TdVV2rtSBxUxKVer1YrFixfj+++/x9SpU/HYY49VuOqQI4SHh6N27do4deoUYmJisH37drdF\nIthAKisrCzt27EBGRgZatmyJr7/+GllZWRg8eDDi4uJw4MABXLlyRcmt+ODDHQWO4xAZGYnIyEj0\n6dMHwG0hKAqwFi1ahOTkZAQHB+OZZ57hg6yaNWvy/pFoeJ74FGFSTs3ZSqyglNAXsz5MeBhnZydJ\nqXZUVGDoal4UK9jFfoaUeVgA+PlK7tAu5eyJrXq5cz9Q0CtnfuPp06fx22+/oW7duvj5559Rp04d\nbN68ucLYGVJmRgn/zl2hCIvl9hBxk8mEt99+G7/88gv69++PBx98EMOGDUOXLl1w8eJFHDt2DFlZ\nWRXKWiH4gqkKAMdxMBqNaN26NVq0aIFPP/0UXbp0wcSJE1FaWoqkpCSsWbMGU6dORXFxMaKjoxEb\nG8urJAkDLClNvN5saGWdEWtQtVqtnTFhjabZbOaDL6nOkG00VSMwFNuTo4npUp2Aq4yiksP+XO1J\n2K/EXpOz30buXA5WypWmsycnJ+O9995D9+7dsXPnTj7j5E1ERUUhNDSUd3IHDhxw+vq5c+di2LBh\nMJlMqFevHpYsWeLWuhqNBjk5ORgzZgwfyC5YsAC1atVCtWrVsHz5cnTv3h3h4eGoXLkydu/ejWHD\nhlW0ipEPPngNHMehWrVqePzxx1FUVIQFCxZgxowZeOqpp5CamorExESsX78eaWlp8Pf3R8uWLfk+\nZeoxlONThJR3T4fSO4NcXyx2GBcGWGLBJNHqSFBKrcAQUM4Xs/6SwPZh0TrsTEOiRyrdh8VS+tTw\nxRRMarVau348rbZM8fLEiRNYuXIlMjIykJycjIsXL+Lzzz9H48aNFb0OKdizZw/WrVuHjRs38jOj\nhg8fbjczqnbt2rh06RIiIyNhsViQl5eHypUry1qHfkcA2LJlC/Ly8tCwYUOcPXsWa9aswfLly9G+\nfXs0aNAAjRs3xtGjR1U798mFj+Z3ByA7O9vhTUcqSaQimJycjIKCAtSpU4dv4G3VqhUCAwNFaQFs\nb5bRaPTaQFd3S+5SSv8cx/HBitqNpp4o2jlqxgXKV30A8Nx8uTQCd/ckt/HYmcS5WIaUreTRnnJz\nc/F///d/uHTpEubMmYMGDRoovkepqFevHpKSkmQbfLkg1Sf6Pf/8809Mnz4dTz75JB544AEMGzYM\nixcvRrdu3XDt2jXMmDEDBoMBM2bMQE5ODgYOHIilS5eiVq1aql7nP/BFa/Lg85Eqgx3kLoTNZkN+\nfj4OHz7M0+jPnj0rSQiK7BVQdnDWaDSqS1urXSFi2R9ms5n/e3eSYFLX85bog9AXAyjni5QYxOsJ\npc/dPbG9w3v27MHkyZPxzDPP4LXXXoNWq8WVK1eQnJyMjh07qu6vXMHRzKgFCxYgNTUVCxYswI8/\n/og1a9a4JUBx4sQJTJo0CRkZGbh48SIGDhyITz75hJd/LywsxJw5cwAAXbt2xXvvvYfHH39ckb1J\ngI/mdyfD2cOh1WrRqFEjNGrUCEOHDgVQ9rBfuHABSUlJ+PPPPzF37lzk5OQgMjKSpz9ERUVh586d\n6N69OypXrgyO43h6gCe8azGw1RRPK0SOSv9ipXCNRlMuK6VUNlGJTBubZXNUkaOqDb1ep9Px+1W6\nb4kVsnBnT1JoGZQJpiQNx3HYtGkTWrVqheTkZCxcuBDvvvsunn766QqdGcVeu5pgKX0XLlxAlSpV\ncO7cOezZswdffvkl6tSpgxEjRuDbb79Fy5YtUaNGDfTt2xezZs3CpUuXULlyZbz44osIDQ1V9Tp9\n8OFOhbOqNcdxCAkJQZcuXdClSxcAt4Wgjhw5gkOHDmHevHl2QlDkIwsKCnD16lWeXmyxWFBUVKR4\nn7KQ2aBmhYhmbdlsNgQGBtr5E6EP9aRlwJt7Ynu9hH5LKnVOCgVfCUqfJ3u6ceMGJk6ciJKSEvz8\n88+oWbMm//qaNWva/flOATszatSoUXj++efRsGFDVKlSRVIgxVL6AOD48eMYOXIkevTogZUrV2LB\nggX4888/+eG+AwcOREJCAlasWIHBgwfjxRdfxKVLl9TcomT4KlP3CEglae/evfjqq6+wZ88edOjQ\nAWFhYXZKgmFhYeVUktztjfEmdVCYlSLBBKUbdb2ZaaMglBqPxSo/SoiQsMMs1dwTAH6YrUajgcFg\nQHZ2Nl555RWkpKTgxo0biI2NxQMPPIBnn30WDz/8sCrXIBX16tVDWFgYOI7Dyy+/jNGjRyv22bm5\nuXwAdOvWLcTHx2Pnzp1YtWoVQkJCkJCQgKpVq+Ljjz9GaWkpBgwYgCeffBL/+te/+ENKcHAwAODk\nyZNo1KiRYtfmAr7KlDz4fORdADr8p6SkYN26dVi2bBkKCwvRqVMnVK9eHS1btkRsbCyaNGnC0+jJ\nDrtrgz1Vy5UDuX7LkRIvO0hdrGWA9VtarRb+/v6q7kkJvyVFdRgoU+dVuzrpqHd42bJl+PbbbzFl\nyhT07NlTVTp3SUkJOnfuzFcvBw4ciMmTJ9u9ZtmyZXjnnXd4NsTYsWPx4osvKnodbKJx2bJlqFKl\nCtq3b4/x48fj8uXL2LhxIwBgwIAB6NKlC1577TWYTCb88ssvqF+/Ptq3b489e/YgIiKCrzx7Ab7K\n1L0OjUaDOnXqYOXKlQgNDcWRI0cQExODq1ev4vDhw0hMTMT//vc/XLt2DZUrV+ZlaGNjYxEeHi6r\niddbDa2AvWCGkLfsiFtNgYiwUVeYlRJy5u+ETJs7zbiOAkYhjUDNPgAxasStW7fw5ZdfoqSkBBs2\nbEBUVBSOHj2K5ORkfkhvRWLv3r28fOtjjz2GJk2alBt3IBclJSX47rvv0KRJE3Ts2BEA8MEHH6Co\nqAgHDx7ks6gvvvgiPv74Y+zcuRNdunTBsGHDsH79egwbNgxBQUEwGo181s6LgZQPPtyT4DgOfn5+\naN26NUaPHo23334b//rXv8BxHI4fP46kpCT8+OOPSE1NhclkQoMGDXgavTMhKLGh70JxCbV7X93x\nW856k4QsAwC8jyHhKH9/f6+IPighAuJor+QfqXrFCkq5k1x2BWHvsEajwdGjR5GQkIDOnTtj586d\niswtdAWj0Yg//vgDAQEBsFgs6NSpE5544gleII0wZMgQzJ07V9G1S0tLsWfPHnTu3BlarRY3b97E\nyy+/jBs3bmDmzJkICwvD2LFjMXXqVKxatQoDBgzAm2++iddffx2dOnVCXFwcz9ACgLi4OF4Bs6Jx\nV1emKnIa850KV03qNpsNWVlZdnM+0tPTERQUZBdg1axZs5ycp1ar5Q2uTqdTNSulVNVLyvwoMqpq\n70mJOU5S51hwHMc7c7UzbazjI0rOtm3b8OGHH+Kll17CqFGjVFtfKUyZMgXBwcH4z3/+4/FnDR06\nFP3798egQYOQl5eHF154AdOnT0fjxo1RVFSEgIAA5OXlYdmyZdi8eTM2bNigwA7dCElJAAAgAElE\nQVQUg68yJQ8+H3mXwZWPNJvNOHnyJO8jjx49ilu3biE6OprvUW7ZsiX8/f3LVTqoVxTAHd3PK2cd\n8sUURNKZUUm1VwLbMuAtv8XKtzsaheJJH5ZY73BeXh4+/PBDnDlzBnPmzKmwhFlRURE6d+6MhQsX\nom3btvzfL1u2DImJifjiiy8UXW/jxo3YvXs3pk+fDgBITEzE4sWL8d///pd/TUlJCX744Qds2LAB\n33zzDYKDg7Fw4UL069cPERERil6PG7g3K1Off/45mjZtiry8vHL/9vXXXyMsLAynT5/GihUr8O67\n7yo6jflOhasHnOM4VK1aFY8//jjftGez2ZCTk4PDhw8jKSkJGzZswIULF3iVpBYtWiAzMxNXrlzB\n+PHjodPpYLVakZ+fb2dIleCYs6V9JapejmRJKctI/HKO43iKmnBPSnDmlaoQOcuykfIeVReBsgCL\nhluqoXZEfWWUfb18+TISEhIQHByM9evXo3r16oqspTSKiopgtVoRFBSEwsJC/Pbbb+WoDnJBtIVn\nnnkG27ZtQ79+/RASEoKSkhKsWLECkydPRkBAAICyZEGPHj2Ql5eH7OxsXlVQyCH3wQdP4POR5eHK\n/ul0OjRr1gzNmjXD8OHDAZQ9r2fPnkViYiK2bt2KTz/9FPn5+bwQVO3atbF79248+eSTfIb/1q1b\nvCCTkn3K3lR8ZRNlgYGBduwUT5R4hfCW6APgXKXPUS8VGzSLjakRG64spgJss9nwyy+/YO7cuXjr\nrbfw+eefV0jvsNVqxQMPPICzZ8/i3//+t10gRVi1ahV27dqFmJgYzJ49WxEBpObNm2PSpEmIj49H\naGgofv/9d1y/fh0AUFhYiMDAQBiNRnTt2hW//fYbFixYgPj4eLzyyiser6027trKVEVMY76fYLPZ\nUFBQgEWLFmH27NkwGAxo2rQpioqK0Lx5c7Rs2RIPPPCAU5UkOVkcoeGWozInF4745VKHCMoJsNiA\nQ+1MGxuwCdWOlOzDEpNVN5vNWLRoEdasWYOPPvoInTt3rhAZb2eZeBbnz59H//79+SB62LBhSEhI\nUOQaduzYgQ0bNuDZZ59FmzZtkJKSgl69euG7775Dq1atMHHiRAQFBWHq1KmqUWU8gK8yJQ8+H3mf\nwmq1IiUlBR988AG2bNmChx9+GIWFhahevTrP8mjdujVCQ0PL9Sm7op2LwZs9yuS35AzDdcd/Sh3w\nqwSoQkSKuZ7YXld9WORXWCGQM2fOID4+Hg0bNsTUqVPvCFGhvLw89OvXD/PmzbObm5idnY2goCDo\n9XosWrQIK1euxPbt2z1ai84NZHP69++PzMxMtG3bFqtWrUJsbCwAYOvWrWjTpg0yMzNRt27dChmb\n4gT3XmXqzTffxMyZM5Gbmyv675cvX0bt2rUBlBmuSpUq4ebNm4pMY74fQFmvxMREzJ8/H/369QMA\nO5Wk+fPn49SpU9BoNGjSpAlPEYyJieFL5q6GDQsNt7f45WJcbGdKgmQ4He1HmNHylpAFIF4hIsjZ\nj7PGY3qvsMLGcRz279+PiRMn4qmnnsKOHTtgMBhU2acUOMvEs4iOjkZycrLb65hMpnIqUkDZPdS0\naVP88MMPSE5ORr169dCiRQu8+eabWLJkCY4fP462bdti4sSJdjNjfPOjfFAaPh+pLjQaDW7evInK\nlSvj9OnT/ADhy5cvIykpCYmJifjqq6+QlZWFqlWr8gFWmzZteCEoKQPslWZrOIMwYJMTcLhS4hWe\nB6hHyZt+39GcSLlwxBCh74+k9v/44w+MGTMGUVFRyMrKwujRo9G/f3+eoVDRCAkJQdeuXbF582a7\nYIpVmB49ejTi4+Nlfa6YTyOGkL+/P9LS0pCZmYmqVavijTfewIgRI/Cf//wHGzZsQHp6OlavXs1T\nH1mhijsZd2UwpcY0ZmcZbW8om9yJ0Gg05WgfgYGB6NixI99cz6okHTp0iD8w2mw2NGrUiHcgTZs2\nLdfEy/4eBoMBBoNBNd43q0Akx3CTgxB+njNhCKAswDEYDF4TfZAasEnZj7DxmALf0tJSO9GMrKws\nTJ48GTk5OVi+fDmioqJU2adUpKenY+PGjXwmXg2QzHtkZCQAIDU1Fc2bN7ejv1SvXp0fRhwQEIAh\nQ4bgP//5D2w2Gy5evIi6devyr1WSdumDDwSfj/QOunXrhm7duvF/1mg0qF27NmrXrs0nIG02G65d\nu4ZDhw4hKSkJ33//Pa5du4ZKlSqJCkGxtDlKOJJynlrVKLUCNrEAix28S9LtBQUFHivxioGSfxqN\nRlVfLEaJBIDg4GDExsaiffv2qFSpEo4ePYpnnnkGc+bMQc+ePVW5FlfIzMyEXq9HaGgoiouLsW3b\ntnKsDJIiB4C1a9faBVrOQAGr2G9mtVphNBrx4IMP4qeffsK+ffvQp08fvP7664iMjMSpU6fQuXNn\nvPbaa3bvuxsCKeAuDabUmMbsKqOthrLJvQBSSWrbti3Pu6UKxt9//43ExESsWLHCTiUpKioKKSkp\naNiwId59912+JF5SUiJqUD15mJwp53myZ7GsFBlToCwIMZlMMJlMblE6nMGbakdms9lOFXHTpk34\n6quvEB4ejiNHjuCNN97AK6+8UqHVKIKrTLwS+OWXX/Dtt9/ik08+wdChQ5Gbm4vhw4ejf//+qF+/\nPn9AHTRoEMxmMzZv3oxbt27hhRdeAMdxqFu3Lv/d3i1Owoe7Dz4feeeA4zhERESgd+/e6N27N4Db\nQlAUYP3yyy+8EFTLli2h1+uxadMmLF26FHXq1OEpUp70JYlB6EvUDjgcCTB5osQrBlb0QW6FTS7E\nmCHXrl1DQkICjEYjVqxYgfDwcNXWl4urV69ixIgRsFqtsFqtGDx4MHr16mU3M2ru3LlYt24d9Ho9\nwsLCsHTpUpefu3v3bkybNg2bN29GQUEBPv74Yzz88MPo2LEjP+YDAB5//HH8/fff2LlzJ4qLizFo\n0CAMGjTI7rPuxt7hu7ZniqDENGZX3HK1lE3uN+Tn5+O9997D0qVL0aFDB3Ach/z8fERFRSE2Nrac\nSpJQqU5OxupOoEZIURKU6xC91YMF2Eu5klrUwYMH8dlnnwEoqyampqbiwoUL2Llzp2gTq7ewYcMG\nbNq0CfPmzcOOHTswa9Ys/Prrr4p89pkzZ9CgQQMAZY7oxRdfhM1mw8SJE1G5cmUsWrQItWvXxhtv\nvAG9Xm/nCA4dOoT33nsPCQkJiIuLs3Mqdyh8ZTJ58PlI+HykErDZbNi1axfGjRuHjIwMPPLIIzh7\n9iz8/f3RokULtGrVCm3atEFUVFQ5+pw7fbBs1UZqX5S7+3JHedAd/+nNHiyx3mGLxYKvvvoKP//8\nM6ZPn46uXbveF8wDm82GvLw89OrVC126dEFaWhof0D788MN4+eWXERwcDLPZDJ1Oh5ycHOzfvx9T\npkzB7Nmz0bZtW2i12ruB8n7v9UyJwd1pzFIy2moom9xvoApUamoqTwkjlaSkpCRs27bNTiWJbeL1\n8/NzmrFinYeSVRtnEBvAJ+zBcqQkSE7QkRKSUEnQmz1YbFaPqnmUaUpJScGcOXPQrFkz/vWFhYUV\nXpmSkol3BxaLBRMnTsS4ceOQmZmJkpISjB49Gs8//zzatGmDgIAAdOvWDTt27MDWrVvRq1cv/lBi\ns9nQpk0bzJ8/n/89ffChIuHzkXcuOI5Deno6XnnlFYwaNcqOAnf48GEcOnQIs2fPxpkzZ2AwGHgh\nqNjYWNSvX5/vQaK5SY4CLG8JWQD2yT+5zBBn/pN8KOs/SZae+nLUFOhge4cpQZaUlITx48ejV69e\n2Llzp1d8opQBvGqOQNi1axfPTCotLcWsWbMwcOBATJ8+HcOHD8f69evxxx9/YMOGDRgyZAj/+1eq\nVAmPP/448vLykJycDK1Wi7Zt297pgZRT3PWVKU8hJaMtV9kkKiqKlzrW6/U4cOBAudeMGzcOmzZt\nQmBgIJYuXYrWrVsrvre7FVarFWlpaUhMTMShQ4eQnJyM7OxsREREoFWrVoiNjRVVSWJFAAwGA/R6\nvSo9KWrM+HCmhATAK7O9xLJ6ALB+/Xp8+umnGDt2LJ5//vk7np7mKBMvB2x1afr06Zg4cSL69u2L\nWbNmoX79+ujduzfi4uIwZcoUZGdnY/HixUhLS0NCQsLdfoi8e71ZxcDnI+Hzkd6EzWZDcXExLwSV\nlJSEkydPQqvVokmTJnwSsnHjxnazkygJCZQJExkMBlV7sLyZ/KNRF3RYV0q5Vgi2bYACtuzsbHzw\nwQe4ceMGZs+ezSscews0u5AG8M6dO9duAO/ChQuRkpKCBQsWYMWKFVi9erXHIxD27t2Ljh074tix\nY+jSpQteeuklHD9+HN988w1Gjx4NnU6Hn376CYWFhfjmm2/4ynbjxo3LVZ9IrONOP1f8g/ujMuUO\npGS05SqbaDQa7NixwyH/fNOmTTh79ixOnz6N/fv3Y8yYMdi3b58yG7oHoNFoEB0djejoaJ5La7PZ\nkJ6ezg9SXLx4MW7cuIGqVauiVq1aSE1NRVxcHN5//327DJ0nlDoxqNGDBYg36hLNDgCfpczPz3c5\n38IdsLM3iDd/4cIFJCQkIDIyElu2bLkvVL5YB2y1WnHlyhWEhISgWbNmfAbYZrNh2rRpeOGFFzBk\nyBA0adIE7dq1Q0hICEJCQip6Cz74oCh8PvLOAsdxCAgIQIcOHdChQwcAtxNhqampSEpKwrJly3D8\n+HFYrVY0bNgQGo0GmzZtwqZNm/gerJKSEhQVFSnap6x0P6+rtZxR+hwJRbE+U+p+xVgoNpsNy5f/\nf3tnHhZV2f7xzxlAFFFJBTfchbRUQHxdSMwtFV7c3khBs4QUrTQ1+6WWqWhahpm7abmbZeZWampa\n7rnAgIa7koggbgiKEOv9+wPmNGwKyqaez3XNxcych+c858zM+Z7nfu5lLYsXL2b8+PFqqY3ixpAZ\nMCkpidTU1Bxj2LJlC/7+/gB4enoyfPjwx9pfeHg4fn5+BAQE4OzsTKVKldi4cSPnz58HYNasWfzv\nf/9j3759vPzyy7i6urJ48WL27t2rTvCNKax7qJLmmV+ZMiYvi7ZxZpNNmzYREBDA4cOH8+ynfv36\nBAYGUqVKlVy3Dxs2jI4dO9KvXz8AmjRpwt69e0tVkOKTwN27d3n//fdZv349Xbt2JS0tjaioqBxZ\nkqpXr57Dx7ygEyzjGKzisLTlZdV7WH2LgmZCyq1YYnJyMvPmzWPXrl0EBATQpk2bEhGJ/LgwFBU7\nd+5k9uzZVKxYkXXr1nHjxg0aNWqEXq9X46f8/f3ZuXPnA68FTyDaylTB0DQSTSNLIyLCvn37GDp0\nKIqi0KlTJ06dOqUmgjJoZLNmzShTpsxj60lxxWBl31dBvDUKqp/wr6HR2AvlzJkzjB07FicnJyZO\nnFiisbDZC/B+9tlnWbY3a9aMnTt3qhlo7ezsOHr0aL6No4aVJGNPmenTp3Pz5k0GDx5MfHw83bt3\n58CBAzRt2hTIiMfcsGGDujodFBSEo6Pj0+Dqrq1MFZTHyWyiKArdunVDURT8/PwYMmRIlu3G9T0A\natWqRWRkpCYUBcTMzAxra2suXryItbU1kDNL0saNG7l69Srly5fPMsGytbVVs9UZ+1xnv6BC8cZg\nPWxfeWXeM/Yjzy2uzNgaZwjUNeyrTJkyqhgcOHCAyZMn4+3tzd69e0vUamRubs4ff/yRxYXBzc0t\niwtDYWKwPr733ntERETQq1cvxo8fz7Jly/D19WXo0KH4+fnx+++/89tvv/HWW29RtWpVNY28Tqd7\nEgJoNTQKBU0jSzeKohAXF8e0adN49dVX1etSWloa586dIygoiF9++YVp06aRmJhIvXr1cHR0xMHB\nAQcHB8zMzPIVp2xwOyyOGKzHzdL3oMy1homVce0vw/FdvXoVExMTatWqxcyZM9Hr9cyePZtmzZoV\n9iEWGJ1OR3BwsFqA9/Tp01lSmRe0BEJ2bt26hbW1tTqRmjFjBoGBgRw8eJDnnnuOiRMnMmHCBLy9\nvfnrr78AeP3111m/fj0rV67kzTffxNnZ+ZH2/SShrUwVAQYr3c2bN3nllVeYP38+7dq1U7d7eHjw\n0UcfqbWaunTpQkBAgFoB2piH+Zbv27ePXr16qX66//vf/5gwYUIRHt2Th4gQFxdHcHAwQUFB6PV6\nLl++TNmyZdUsSY6OjjRo0CBH7JLBGmdmZoaZmdlj+1znhcH6pShKoWTpe1AmJEOgrqIoREdHU79+\nfe7cucOECRNITU1l1qxZqhWrtJCQkED79u1ZtGhRoWUNzO3CnpCQgJeXF/PmzaNu3bp89913rFy5\nkuXLl1OrVi2cnJywtLTE3NyctWvXYmNjUyhjKUU8nUpXdGga+QhoGlm6SE9P59KlS2qc8okTJ7h7\n9y61a9dWk0A5ODhgaWmZJfmDARMTE8qUKfPYbvR5UdxZ+pKSkkhKSsLU1BQTExO++eYbFixYwPXr\n16lTpw7dunXD2dkZLy8vypUrVyTjeBSmTJmCpaUl77//vvqem5sbkydPpnXr1qSlpVGjRg1u3LiR\nr/42b97MqVOn+PjjjwGYO3cuW7ZsYevWraxZs4ZFixaxevVqXnzxRezt7fHz8yMlJYVKlSrRr1+/\nPFeen2C0lanixODuYG1tTZ8+fTh27FgWobC1tSUiIkJ9ffXq1TxvXh/mWw7Qvn37xwq2f9pRFAUr\nK6ssBRYNWZJCQkIICgpi9uzZapakOnXqEBUVBcD333+Pqamp6g5n7HNdGEGtRRWom1smJIMFMSUl\nRV1x8vHx4dy5c5ibm+Pi4kKPHj24detWqZlMZXdhKKyJlLHLwu7du0lJScHBwQFzc3MsLCy4c+cO\nNWvWZMCAAcybN4/JkyfzzTff8MsvvxAeHs5LL72k9vU0W9s0NIoCTSNLFzqdDjs7O+zs7PD29gb+\nTQQVFBTEoUOHmD9/Pnfu3KF69epUqFCB33//nblz59KpU6csSZkKO065uArvQtbY4QoVKqDT6YiI\niODPP/+ka9eujBs3joiICIKDg9mzZ496rkqK/BTg7dGjBytXrqR169asX7+eTp06PbRfg6b16tWL\nHj168Mcff9CxY0eSk5Pp0KED5cqVY8iQIYSEhPDFF1+wcuVKNm7cyPTp0ylXrhwzZ85Uf4/GWvs0\no02mChlDVhlLS0vu37/Prl27csR59OzZkwULFtCvXz+OHDmClZVVnu4LBheuB/GQ1UWNXFAUhQoV\nKuDq6oqrqyuQ4f4wdepUZs+eTceOHalcubIaVNqkSRPVQvf888+rqzuPGtRa3IG6xqlcDUkSTpw4\nQYUKFfjggw94+eWXOXXqFEePHuXChQt8+eWXRTKWgvIwF4bH6TchIYHFixfz9ddf4+rqSkBAAHv2\n7MHKyoo//viDGjVqUK1aNTp27Mju3bu5cOECdnZ2arY+Q8Y/bSKloZF/NI18MjBOBOXp6QnA2bNn\n8fX15cSJE3h6erJixQpmzpxJlSpVsrjRW1tbP7D0R37jlIur8G722GEzMzOSk5NZsGAB27dv54sv\nvsDFxQVFUWjUqJFqlC1p8lOAtyAlEAyTKMPnEhkZydWrV+nXrx/nz5+nfPny3Llzh9jYWKysrHj7\n7bdp3rw57u7u9OvXj2+//VZNiGHo61mYSIE2mSp0rl+/rt6Ap6amMmDAALp27crixYtV/3B3d3e2\nb99Oo0aNKF++PMuXL8+zv4f5lgMcOXIEJycnatasSUBAQKHcbD6LmJiYYGFhQUhIiFoHyzhLkl6v\nZ9WqVWqWJHt7e1VAXnzxRUxNTUlLSyMpKSnPoFadTpfFpc9QMb2oMGQfhH/rfMTFxTF16lTCw8NZ\nunQpdnZ2AHTu3LnIxvG4VKxYkQ4dOrBjx45H+n4b3BoNIhEVFcXrr79O/fr1+euvvyhTpgz9+vXD\n39+fKVOm8H//93/o9XpiYmKoWbMmtWrVYunSpXz++edqn09BMK2GRrGjaeSTS3p6OgMHDsTPzy9L\nPb3r16+rccrff/89165do1KlSllqRdaoUSNHnHL2mF5Dn8YufRYWFsUSp2wcO3zo0CEmTpzIa6+9\nxt69e4t0IgcZK69vvPEG0dHRmJiYMGTIEN57770sbfJyV9Xr9Tn6M2Tvg4zY4x9//PGhY8juYbF6\n9WoiIiIYO3YsXl5e+Pv74+/vT//+/VmxYgXe3t4cP36cdu3aqefNMJF6VlajjNFipko5D/Mtj4+P\nR6fTYWFhwa+//srIkSPVFJW5ERcXx+DBgwkNDUWn07Fs2TJat26dpY1W3+PhpKSkcObMGdXHPDQ0\nlOTkZBo2bKjWwmratCnm5ua5+pibmppiZmamBusWhY+5oTq7cSrXDRs2MH/+fD744ANee+21Un3B\ny+7C0K1bN8aNG4e7u3uB+jG+sJ89e5ZatWpRoUIFpk6dyv79+1m1ahU1atTgypUrdOnShc2bN/PC\nCy/w448/YmJiwquvvsqYMWPo0KEDPXr0KIpDLU1oy2wFQ9PIEqYwNVLTx8JBRIiJiVEnWEFBQVkS\nQRkmWXXr1s2RXc+gh4Y6WMUZp3zz5k0++eQTEhMT+eqrr4qtZmB0dDTR0dE4OjoSHx+Ps7MzW7Zs\noXHjxmqbwqifmBvG+piSksLatWvp1KkTf//9NzNmzGDbtm1ERkby3//+l6VLl2Jqasrq1avZv38/\nderUYf78+arb7jNAnl9EbTL1BOHv70+FChWyBBdmp379+gQFBeWZ9nLQoEG8/PLL+Pj4kJqaSkJC\nQpbaOIbijNu2bePo0aOMHDlSq++RT4yzJOn1ek6ePElCQgK1a9fGxMSE3377jd9++4169eplSQ4B\neWfdKygGy1/2VK4XLlxg7NixPP/880yZMoVKlSoV9uE/kPxY3rLz119/5XBhMATC5ocbN26oCSKS\nkpIYM2YMx44do1u3bpiZmTFu3Djc3NwYNWoUbm5umJqa8umnn/LDDz8QGhoKZAjY6NGjcXR0ZNGi\nRWoh46cYbTJVMDSNLEU8rkZq+lh0GBJBhYSEqEbIv//+m3LlymFra0tYWBi1atVi4cKFqhu9QSML\nM07Z2NBocB9MT09n1apVrFy5ksmTJ+Pm5lai7tu9e/dmxIgRWbxF9u3bx8yZM3MUzC5M9u/fT9eu\nXfHw8OCnn36iS5cuDB48GC8vL5YsWcKqVas4ePAgABcvXlTLhGT3/HiK0SZTTyLZfcu7du3KpEmT\n6Nq1q9rm+vXrqi/5sWPH6Nu3L5cvX861v3v37uHo6MilS5fy3KdW36NwOXToEG+99RZlypShc+fO\nhIaGcu/ePWrXrp3FBcI4S5Ih697j+JgbakYlJiby5ZdfcvjwYb766qtcs2EVB/mxvBUWIsLkyZMx\nNzdn6NChVKlShe+++47IyEjGjBnD2LFj2bFjB8HBwaxZs4bt27cTEBCgunYaig1Cxm8qKSlJjat7\nBnjq1bCQ0TSyBClMjdT0sfhJTk7mk08+YfHixXTr1o2yZcty4cIFzMzMaNq0Kc2bN8fJyQk7OzsU\nRcmygpV9gqXT6R4ap2wcO1y2bFkUReHkyZOMHz+edu3a8dFHH5V4dr7Lly/ToUMHQkNDsbS0VN/f\nt28fnp6e2NraFoq7qmECZPgM3N3d6dChA9OmTWPDhg307duX8uXLExERwWeffYaI0LNnT0aNGpXl\n92WIHX5G0LL5lSSPOmvPj2/5Tz/9xKJFizAzM6NcuXKsW7cuz/7CwsKoWrUqPj4+nDhxgpYtWzJn\nzpwsFw+tvkfhcv/+faZOnYqnp6f6+aenp3PlyhUCAwM5fPgwCxYsIDY2lmrVqqkugo6Ojjz33HOq\ne+CDgnghp485ZGSp+/TTT/H19WXq1KklesGrXr266gpgaWlJkyZNiIyMLPTJlMHvu23btqxbt47g\n4GC6dOnC0aNHqVKlCl5eXqSlpbF161bMzMzw8fFh48aNrFmzhnHjxmFqaqpOpIAiq2mloaHxL6VB\nIzV9LH7MzMywsLAgNDRUdakzZJ09efIker2exYsXc+7cOTURlMEI2bhx4xyJoPKKU84tdvjevXtM\nmzaNCxcusGjRoiIx7BWU+Ph4PD09mTNnTpaJFICzszPh4eGqu2rv3r0fGNKRG3/++Se//vorU6ZM\nUe8dzM3NqVOnDjt37iQoKAhnZ2fs7e05e/YsO3fuJDExkX/++Yfy5cuzceNGypYtm6XPZ2gi9UC0\nlakSpLhTKgcFBdGmTRv+/PNPWrZsyahRo6hUqVKWYMWC1PeAh/uYazU+8oeIEBkZqboIBgcHc+PG\nDapUqaKuXjk6OmJjY5NlBcuQxUpRFE6cOEHZsmWpWrUqkyZNwsLCgpkzZ5a6Wkh5Wd4eh/j4eCwt\nLbP8piZPnkxCQgLjx4/nt99+w8fHh++++47evXsDGS47L774Iunp6YgI9evXL5SxPOFoK1MFQ9PI\nIqQ4NVLTx9KLIVHEqVOnVBfB06dPk5aWhr29vaqPxomgjN3oISMe6ODBgzg6OqLX65kzZw6jR4/G\ny8urVMQOp6am4uHhgZubGyNHjnxo+4eFdOTG/v37mTZtGhMmTMDV1ZWUlBTVzVGv1/Ppp58SHx/P\nwIEDefPNN1m2bBmjR4/m3LlzqjHUMGd4Blz6ckNbmSopwsLC1KxEbdq0wcrKSt2W25exKLOg2Nra\nUrt2bVq2bAmAp6cnM2bMyNEmv/U9AEaOHIm7uzvr169Xfcyzo9X4eDiKomBra4utrS29evUC/s2S\nFBwcTGBgoJolqWLFijRs2JBLly5RoUIFFi9ejE6n48CBA6xfv56wsDDq1auHq6sr69evZ9iwYaXG\nevQgy9ujkJyczPTp06lbty7e3t6ULVtWdTvw9fVlwoQJbN26FRcXF1599VUCAwNp27YtX3zxBb//\n/jtLly6lRYsWgFYvSkOjJCgtGqnpY+lFURTMzc1p0aKFer2GfxNBBQUF8aMJbGoAABi1SURBVNNP\nPzFp0iSSk5Np0KABlpaWbN26lZUrV9KiRQtiYmJYsmQJJ0+e5J9//sHFxYXQ0FCOHz+eI8lISeDr\n68sLL7yQ50Qqu7uqiORrImXshteiRQt69OjBokWLcHV1VSdSOp2Oli1bMnnyZPr378/IkSNp164d\nvr6+DBgwIEussKaRuaNNpoqQ2NhYli1bRnR0NLt372bt2rX4+/tTv359jh8/TnR0NK1ataJatWrq\nF9pYJApbNKpVq0bt2rU5f/489vb27NmzJ4fPbUHqe9y7d48DBw6wYsUKICNDnXGwrgGtxsejoSgK\n1atXx83NDTc3NyDjwhgQEMDnn3+Oi4sLJiYmeHh4YGpqyo0bNxg0aBDvvPMO586dQ6/Xc/bs2VJh\ndYMMy5unpycDBw5UJ4yPS5kyZbC2tiYwMBAHBwdatGiBiYkJIkKdOnXo0qULhw4domnTpkyfPp1x\n48bh5+dHrVq12L9/v5rSFTSR0NAobkqTRmr6+ORhZmZG8+bNad68OT4+PgBcunQJX19fLl68SI8e\nPZg+fToJCQnqpGLHjh3UrFmT4OBg9Ho9165dK+GjyIit/u6772jWrBlOTk4oisL06dMJDw9/pJAO\n+Pd7ZZzC3tLSkp49e3L06FEWLlzIO++8o+qeiODo6Mi0adPYuXMnOp0OEcHc3PxZi4t6NETkQQ+N\nx+DevXvy8ssvy4oVK0REJCEhQURElixZIlOnThU/Pz9p1aqVrFq1SkREjhw5IsePH5fbt2/n2Wda\nWpqkpaU98phCQkKkZcuW4uDgIH369JHY2Fj5+uuvZfHixWqbd999Vxo2bCjNmzeXoKCgB/bVqlUr\nGTRokDg5OcmQIUPUYzSwd+9eqVq1qjg6Ooq7u7ucOnXqkceuIZKeni7jxo2T06dPZ3kvJiZGjh07\nVoIjezgDBw6U0aNHP3Y/aWlpkp6err5OSkqSYcOGyZdffik3btxQ24hknJuRI0eKv7+/3LlzR0RE\n7t69q/5vamrqY4/nKeNhmqA9NI0sNEqbRmr6+OQTGBgon332mSQlJanvpaWlyYEDB3Kc/2eBffv2\niaurq3zyySfy7bffiojI5s2bxd3dXcLCwkQkp6Zq5EmeWqAJRRGzevVqefvtt2XWrFmSmJgod+/e\nFXt7e3n//fclKipK9u3bJ35+fvLPP//Iu+++Ky+99JL07t1bbG1tZcOGDSIikpycrN4kGhMbGysf\nfvihLF++XESk2H8MgYGBYmpqKsePHxcRkZEjR8rEiROztLl3757cv39fRES2b98udnZ2efZ37tw5\ncXR0FCcnJ3F0dJSKFSvKnDlzcrQbMWKENGrUSBwcHCQ4OLgQj0ijqDh48KDodDpxcHBQP+Nff/21\nwP0Y3ySdOnVKrly5IiIihw8flgEDBsiOHTvU34FhorRv3z4ZOHCgXLp0Kc++NFRKenLypD00HpOn\nVSM1fXz2iIiIkI4dO0qTJk2kadOmuX4+IkX3GWU3Dv7www/SunVr2b9/v2zatEnq16+vfh/HjRsn\no0aNyrMvTR9zRZtMlQT37t0TEZG///5bevXqJVOnThW9Xi+Ojo4yduxY6dq1q7Ro0UIcHBwkNTVV\n+vXrJ0OHDpX09HQJDAwUNzc3uX37tly7dk18fHykefPm0qVLF9V6FRkZKQMGDJBt27aJyL8/pFu3\nbsmkSZNk7969RXp80dHRUr9+ffX1gQMHxMPD44H/U69evQdaFQ2kpaVJjRo11JtlA9u3bxd3d3cR\nybBStm7d+hFGrvG4+Pr6io2NjTRr1qxY9md8YY+Pj5dBgwaJi4uL+Pr6yo8//igiIjNmzJAxY8bI\n+fPnc/x/fHx8sYzzKaCkJydP2kPjMXiaNVLTx2ePa9euqZOje/fuib29vZw5cyZLm6L4jNLT03M1\nFFy8eFHu378vq1evFicnJ/Hy8pJWrVqJiMiJEyekXbt2smfPnsfe/zNEnlpQOoIpnkLS0tJYtmwZ\ngwcP5vDhw7Ru3Zq7d+9iYWFBamoqn3/+uZqK8vDhwyQmJhIdHc2IESNQFAUHBwdCQkIoW7Ys1apV\nY9GiRZw4cYJhw4bx888/k56ezp07d4iLi6Np06YAqu/43bt30ev1mJmZZRmTIWtZYWHsYw7k6mN+\n/fp19XlBgiZ3795Nw4YNs6ShBdiyZQtvvPEGAK1btyYuLi7LPjSKBx8fH3bu3Fnk+4mJieGvv/5S\nv9tpaWlMnz4dBwcHDh06xO3bt1m4cCF79uxh6NChxMTEcODAAeLj47P0U758eTXzoYaGRsnztGuk\npo/PHtWrV8fR0RHIWgLEmKL4jAxlBUJDQ3nllVf4+OOP2bRpEw0bNuTixYusWbOGjRs3smLFCq5f\nv05AQADNmzdn1qxZdOrU6bH2rZGBloCiiDAkBhARdu3ahb29PZ988gkigouLC59//jndu3fH2tqa\nKlWqcP78ebWeEMDp06epUaMGZmZmbNy4kblz55KYmIilpSUpKSmMGzeOyMhIdDodderUAf4NoL91\n6xYiol64o6KiqF69eq6BuiKi1vh4lEDeuXPnMmDAAFJSUmjQoAHLly9/5DpYxqxbtw5vb+8c72t1\nPkoH7dq1Izw8vMj3Ex8fz8yZM+nevTtLlixh/vz5jBo1itTUVLp164alpSWurq5s2rSJtm3b4ubm\nxi+//MIrr7ySI1tgaUnEoaGh8WxopKaPzy6XL18mJCQkR6bAwvqMRCRL8ogff/yRrVu38uqrr1K5\ncmXeffddXnjhBa5cuULt2rWpV68ee/bs4aWXXiIkJIT09HT+85//5OhL4xF50LJV8ayaPXsEBgbK\n0KFDpXXr1uLl5SU3btyQ9evXi6Ojo3z99deSnp4u77//vrz99tsSHBwsbm5ucvToUUlOTpaPPvpI\n+vTpIyIiixYtksGDB4tI1oD7n376Sfr16yciIocOHRJfX1/5z3/+Ix06dJCLFy+KiMidO3ceGHyf\nmpoqqampJRKUmJycLFWrVs3VB/6///2vHDp0SH3duXNn0ev1ufaTHx/zvXv3SqVKlcTJyUmcnJxk\n6tSphXswTzGXL18uEje/7L7a7u7uUrFiRQkICFDfW7NmjYwdO1ZEMtwmGjRoILNmzRIRkZs3bxb6\nmJ4hStpt7kl7aBQBmkbmjaaPpZ979+6Js7OzbN68Oce2gnxGuZGenp5DI8+cOSMNGzYUb29v9b0J\nEyZI//795datW9K7d29xc3OTRo0ayZEjRx7hiDQyyVMLtJWpIsT4RBunlXR2dsbZ2RnISBdtamrK\n0aNH6dy5M3q9HltbWwYMGMDHH39MVFQUNjY21KpVi9jYWLWwIGTU57Czs1P3BRl1d8LCwnj++eeJ\njo7G39+foUOHsnTpUrZv386CBQuYNWsW8+fP59ixY5iYmFCxYkU+/PBDXnzxRXWMuaXBDAkJoWbN\nmlSpUqVI02T++uuvODs7Y21tnWNbQep82NvbExwcDGS4b9ja2tKnT58c7bQ6H6UH41TH586do2HD\nhgwfPpzExESaN2+ubk9ISOCHH35gypQp7N69m27dutG+fXsAqlatqlnaNDSeADSNLDiaPpZuHlYC\npKC1yrJjcOkLDw9n/vz5eHh40KZNGyZOnMiGDRuIjo6mevXqTJkyBTs7OxITE1m+fDl6vR4XFxfK\nli0LoKU7L2Q0v5cixOAWkP0Lm56eTlpaGunp6ZiaZsxnDx06xMsvv8zixYuJjIxk6tSp1KpVCycn\nJywsLGjbti0zZszgwIEDtGjRgqSkJCIiIrJc3AESEhL4+++/cXZ2Zv/+/Vy9epW5c+fi4uLCO++8\nQ2hoKImJiYSFhWFiYsKKFSuwsrJi7ty5QEbdj6+//prevXuzdOlSwsPDERESEhJYt24dt2/fxsTE\nRK3Wfe7cOfVYCovvv/8+VxcGyKjzsWrVKoCH1vkwJi8fc9DqfJQmdDod4eHheHl58d577zF58mRs\nbGyYOHEis2fP5uLFiwAMGTKEV155hXbt2hEXF8fs2bPVmy/QakZpaDwJaBpZcDR9LN08rPjuo3xG\n2T+DVatW0atXL2xtbdm+fTtvvfUWr7/+OsnJyaxZs4bbt29z9uxZXnjhBaysrLCysqJTp05qUXvI\n3Rig8ehoK1MlQHa/axFh7NixWSzvhorTpqamLFy4kIULFxIbG0vHjh1p3749t2/fJiUlhcaNGwP/\n3jzGxcVx8+ZNGjduzM8//8zrr7/O+PHjSUhI4OTJk1hZWXH+/HksLS3x8PCgUqVKNGnShIMHDwIw\ne/ZsgoOD8fPzY9OmTaxbtw4PDw8sLS1ZsmQJMTExfPjhhyQkJPDPP/9gY2NTqD/KxMREdu/ezZIl\nS9T3jH3M3d3d2b59O40aNaJ8+fIsX748X/3m5WMOGRc0JycnatasSUBAQI4gYY3cMViUH4fcim7O\nnDkTLy8v2rZti7e3NxEREaxcuZJvv/2WzZs307JlS06ePMk333zDjRs3sLGxybMvDQ2NJw9NI3NH\n08fSTX6K7xb0MzJeQUpKSsLc3Jxr166xf/9+goODWblyJR4eHuh0OkaMGMH48eM5ffo0p0+fxsvL\nK0fssDaJKiIe5ANYDP6HGvmgIPn+g4ODpUOHDnLr1i05ffq09OnTR06cOJGlnz/++EOGDBkioaGh\nIiIyduxYmTNnjhw5ckTefvtt2bp1q4iIrF+/XlxcXOTUqVOyceNG6dy5swwcOFCWL18u69atEw8P\nD5k5c6ZMmDDhgbUSDh48qMa3lAQP8jEvSJ0PEZFZs2bJiy++KM2aNZP+/ftnKQwoklFAtl+/ftKo\nUSNp06aNhIeHF96BlCK8vb2lRo0aUqZMGaldu7YsW7aswH0Yf68NhXRjYmKkZ8+esm7dOnF1dZXh\nw4ern8/Fixdl5MiR4uDgIDNmzFD/Nzcfco1HpqRjkJ60h0YpQNPIR6cw9VFE08ii4ObNmzJixAg1\nBqtz587SuHFj6dmzp+zbt09tIyIyevRoGTJkiERHR5fYeJ9i8tQCzYz7BGBspXuYq4CjoyObNm2i\nSpUqNGnSBFdXV3x8fHBycqJv376ICFevXiU9PZ26desCEBERQa1atQC4f/++mka2cuXKPP/889jY\n2BAZGUn37t1ZtWoVgwYN4uzZs8TFxWFjY8O1a9eYO3cuMTExOca5a9cutm3bRseOHXOMtbhSVT/I\nx9zS0hILCwsA3NzcSElJyXIcxkRFRTFv3jz0ej0nT54kNTWVH374IUubpUuXUrlyZS5cuMCoUaP4\n8MMPC/+AioAdO3bQuHFj7O3tmTFjxkPbr127lqioKJKSkrhy5Qo+Pj4F3qdOpyMuLo4333yTAQMG\nsHr1asqUKUOVKlUYNWoUK1asYN68eVhYWLBhwwaqV6/O559/zuHDh7Oc10fNRKmhofF0oGnko1NY\n+ghPj0a+9dZbVKtWjebNm+e6fd++fVhZWdGiRQtatGjBp59+WmRj2bZtG127dsXGxkb9jgwZMgQT\nExM2b95M+/btiYqKYtKkSYSFhTFy5EhOnjzJiRMnVJc+jaJHuwN5wsjPTaOVlZXqfjV69GiCgoLY\ntWsXEydORFEULC0tqVu3LpaWlsTHxxMWFoaNjQ0NGjRg//79qoDs2rULExMTqlatyoULF6hQoQKx\nsbEAnDx5Em9vbwYOHMi3337LpUuX1HgWMQr+X7ZsGba2tnTr1g3IqO9x48aNPI8lLS2t0H20H+Rj\nXtA6H2lpady/f5/U1FQSEhJyBI5u2bKFN998EwBPT0/27NlTCEdQtKSnpzN8+HB27tzJqVOn+P77\n7zl79myR73fDhg34+fnh4OCAj48Phw8f5oMPPmDYsGHUr1+fmJgY/v77b1577TXWrVtHQkICZcuW\nxcLCotBrpmloaDwdaBpZMApTHw3je9I1Mj91FNu3b49er0ev1zNhwoQiG0tkZCQ9evRgwoQJ3Lp1\ni5CQEJycnOjSpQvdu3dn7NixuLm5UaNGDRo0aEDdunV54403MDc311z6ihEtZuopxXChNviXW1tb\nq5an3r1707t3b7XdBx98gL29PZUrV+a1116jbdu2tGnThqCgIF555RXS0tKIi4vDzs5OFaHbt2/T\nqlUr0tPT+eeff4iKisLe3j7Lvm/evMm1a9fo0KEDkGG1+uqrrzh8+DA3b97E29ubyZMnExwcTO3a\ntbG2tn7gj9+QKckQI5MfHuZjXpA6HzVr1mTMmDHUqVMHCwsLunbtSpcuXbK0Ma4hYWJigpWVFTEx\nMfkqxFhSHDt2DDs7O/UGwcvLiy1btqixBkXF0aNH2blzp3rOHRwc+OSTT0hMTGTYsGHMnj2bCxcu\n0Lt3b8aPH5/lf7WVKA0NjcdB08jC1Ud4ejQyP3UUi8uY17BhQ5YuXcrRo0fVVc4GDRqwePFifv/9\ndwIDA9myZQv16tVT/+edd94plrFp/Is2mXrKyasIoeFiXr58eV599VUg48Lq7+/P7du32bBhA7Gx\nsdjb22NiYoK9vT3+/v5YWFhgaWmJmZkZNjY26HQ6zp07h7m5uSoihr7PnDmDpaWlGrD65ZdfEh8f\nz6FDh4iPj+fUqVMoikLfvn156aWXuHbtGiLC999/T1hYGBUrVqRx48Zqn3PnzqV79+707ds338df\nrlw5bt68meW9oUOHqs/fffdd3n333Xz1FRsby5YtWwgPD6dSpUp4enqydu1a+vfvn+Xc5nWu8+Kr\nr75i6dKl6HQ6mjVrxvLlyylTpoy6feXKlfzf//0ftra2AAwfPhxfX998jTk/ZC8iaGtry7Fjxwqt\n/7z46KOP+Pnnn9VzWL16dSpVqkRcXBwDBw5k4MCBxMbGYmVlBWipXDU0NAqfZ1kjC1MfoWg0sqT1\nMS+KKzFH586dWbt2LVWrVqVixYocOXKEjRs3kpaWRqdOnejUqROAWlhay2RbQjwooEp7PBsPwCTz\n73+B7cCqzIc/UCVzW23gI6A/4AGsBZ7L3DYc+M24r8znfYANRv+/FWiYbd/mwMXMfssD64BFwFfA\naWCU0fjeAwYZ76OYz5Mn8I3R64HA/GxtfgVaG84FcOMhfdYEwoAyma/XAW9ka/MmMLeIj2uJ0evX\ngTnFdE5fA84DtTNfHwC6Z2ujA5SS+My1h/bQHtpD08h8n6dC1ciS1EegLnAyj22WgEXmczfgfBGf\nVx0Zix+DgWBgTLbtmj6W8ENbmdJARNIy/25TFOUY0AaoAGwVkbuZ2yKA6Yb/URRlu4gYomPvA1uM\ntukyt9UBEjLffg6IBawy25iJSArQAogTkbWKopgDZ4HmItJHUZTawH4RmZ3ZRxDgJyIrCv0k5I8r\nQBtFUcoCSUBn4Hi2Nr+QcXE/SsZE4fd89GsClFcUJR2wAKJyaVOU5qarZHxWBmzzGEOhIyLrFUUZ\nAuxUFOUEGedzZ7Y2xZOpRENDQyMXNI3MN0WhkSWtjzkQkXij578qirJQUZTKIpJ3do7H3CXQFXAH\nfEQkJNt4tADiEkabTGlkQURuknGxy4KSsXasE5E0RVEU4xtcEVlu9DxN+Xed2Qww/OjPABFkWOyC\nAEOamf8A5zKfVyPDArMj87UTEGk0jHJA/Uc/usdDRI4pivITGZahFEAPLFEUxR84LiJbgaXAakVR\nLgC3Aa+H9BmlKMqXZIhQArBLRHbn0vR/iqK4krGK876IXC20A8sQu0aKotQFrmWOOfeI5KJhBLAC\n+C7zHJL5HdMEQkNDo1ShaWTeFLZGlrA+KuQxSVMUpZqIXM983oqMlaGimkghIqIoyi4R2Z65T13m\n25pGlhK0KG6NfCEZGKxzWX7AiqKYZG+b+XQrYJ154UkB1gOOiqJcBOZktulEhqsCQGWgEhkXToBW\nwEmjrluScbNfYoiIv4g0EZHmIjJIRFJEZJJhEiAiSSLSV0TsRKSNiFx+UH+KolgBvchwKagJWCqK\n0j9bs5+BeiLiCOwBVhbyMaWR4YayCzgF/CAiZwpzHw/Z/zkyrLYTQZtIaWhoPHloGplBYWpkSemj\noihrgcOAvaIoVxRF8VEUZaiiKH6ZTTwVRQlVFCUYmA30e9x9PgwRSc0cm05E0jWNLF1oK1Maj41B\nQHJ5/6yiKJeAW5mv9WT4iKMoiqGoxSH+XeavCdQgwzoHGUKx1qjLFkBuVqknmS5AmMGqpSjKRsAF\no+MWkTtG7b8BHl4IqoCIyA7g+cLutwDMA6Izx6KJhIaGxlODppGPTInoo4hkn7Bl374AWPC4+3kU\nNLf30ok2mdIoar413BxnLk2TaVW5mfk8wKjtXjKsaoYiR1WAvzL/ty0ZbgOlryjF4/FQH3NFUaqL\nSHTmy178a6V8ahCR+2S4+mloaGg8S2gamTeaPmo8ESiaEVijJFEUxSQvq51RGx3wHRnuZ1se1PZJ\nRFGUSWT4jRt8zIcAE8j0MVcUZTrQM3N7DPC2iJwvqfFqaGhoaBQPz7pGavqo8STw/+cw4mgksOro\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd063938160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "fig = plt.figure()\n",
    "gs = GridSpec(2,2)\n",
    "\n",
    "for ind, data, feature_labels in zip(range(4), plot_arrays, plot_features):\n",
    "    ax = fig.add_subplot(gs[ind // 2, ind % 2], projection = '3d')\n",
    "    ax.scatter(*tuple(data), c=color_labels)\n",
    "    \n",
    "    label_funcs = [ax.set_xlabel, ax.set_ylabel, ax.set_zlabel]\n",
    "    for label_func, label in zip(label_funcs, feature_labels):\n",
    "        label_func(label)\n",
    "        \n",
    "fig.suptitle('4 3D traces of the Iris Dataset')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}