Advanced NumPy Patterns Class.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# About me\n",
    "\n",
    "Juan Nunez-Iglesias  \n",
    "CZI Imaging Software Fellow  \n",
    "Monash University  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "http://elegant-scipy.org\n",
    "\n",
    "<a href=\"http://elegant-scipy.org\">\n",
    "<img src=\"https://github.com/elegant-scipy/elegant-scipy/blob/master/_images/cover.jpg?raw=true\"\n",
    " alt=\"Elegant SciPy Cover\" height=128 width=192 align=\"left\">\n",
    "</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Quick example: gene expression, without numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Gene   | Cell type A | Cell type B | Cell type C | Cell type D |\n",
    "|--------|-------------|-------------|-------------|-------------|\n",
    "| Gene 0 | 100         | 200         | 50          | 400         |\n",
    "| Gene 1 | 50          | 0           | 0           | 100         |\n",
    "| Gene 2 | 350         | 100         | 50          | 200         |\n",
    "\n",
    "hello"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene0 = [100, 200, 50, 400]\n",
    "gene1 = [50, 0, 0, 100]\n",
    "gene2 = [350, 100, 50, 200]\n",
    "expression_data = [gene0, gene1, gene2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[100, 200, 50, 400], [50, 0, 0, 100], [350, 100, 50, 200]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is this a bad idea?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now with NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100 200  50 400]\n",
      " [ 50   0   0 100]\n",
      " [350 100  50 200]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array(expression_data)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to:\n",
    "\n",
    "* Obtain an *RPKM* expression matrix\n",
    "* Quantile normalize the data\n",
    "\n",
    "using the awesome power of NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inside a numpy ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 12\n",
      "number of dimensions: 2\n",
      "shape: (3, 4)\n",
      "data type: int64\n",
      "strides: (32, 8)\n",
      "flags:\n",
      "  C_CONTIGUOUS : True\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : True\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "def print_info(a):\n",
    "    print('number of elements:', a.size)\n",
    "    print('number of dimensions:', a.ndim)\n",
    "    print('shape:', a.shape)\n",
    "    print('data type:', a.dtype)\n",
    "    print('strides:', a.strides)\n",
    "    print('flags:')\n",
    "    print(a.flags)\n",
    "    \n",
    "print_info(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<memory at 0x7f586e26a7e0>\n"
     ]
    }
   ],
   "source": [
    "print(a.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ravel().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "abytes = a.ravel().view(dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 96\n",
      "number of dimensions: 1\n",
      "shape: (96,)\n",
      "data type: uint8\n",
      "strides: (1,)\n",
      "flags:\n",
      "  C_CONTIGUOUS : True\n",
      "  F_CONTIGUOUS : True\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(abytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100   0   0   0   0   0   0   0 200   0   0   0   0   0   0   0  50   0\n",
      "   0   0   0   0   0   0]\n"
     ]
    }
   ],
   "source": [
    "print(abytes[:24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "abytes[1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ravel()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: take the transpose of `a`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 12\n",
      "number of dimensions: 2\n",
      "shape: (3, 4)\n",
      "data type: int64\n",
      "strides: (32, 8)\n",
      "flags:\n",
      "  C_CONTIGUOUS : True\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : True\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 12\n",
      "number of dimensions: 2\n",
      "shape: (4, 3)\n",
      "data type: int64\n",
      "strides: (8, 32)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : True\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(a.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: skipping rows and columns with *slicing*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 12\n",
      "number of dimensions: 2\n",
      "shape: (4, 3)\n",
      "data type: int64\n",
      "strides: (8, 32)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : True\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(a.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 6\n",
      "number of dimensions: 2\n",
      "shape: (2, 3)\n",
      "data type: int64\n",
      "strides: (16, 32)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(a.T[::2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 4\n",
      "number of dimensions: 2\n",
      "shape: (2, 2)\n",
      "data type: int64\n",
      "strides: (16, 64)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(a.T[::2, ::2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting a copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[356 200  50 400]\n",
      " [ 50   0   0 100]\n",
      " [350 100  50 200]]\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  5 200  50 400]\n",
      " [ 50   0   0 100]\n",
      " [350 100  50 200]]\n"
     ]
    }
   ],
   "source": [
    "a[0, 0] = 5\n",
    "print(b)\n",
    "a[0, 0] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = a.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100 200  50 400]\n",
      " [ 50   0   0 100]\n",
      " [350 100  50 200]]\n"
     ]
    }
   ],
   "source": [
    "a[0, 0] = 5\n",
    "print(c)\n",
    "a[0, 0] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced operations: axis-wise evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "expr = np.load('expr.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 7687500\n",
      "number of dimensions: 2\n",
      "shape: (20500, 375)\n",
      "data type: uint32\n",
      "strides: (4, 82000)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : True\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(expr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This has the raw read count data. However, each sample gets a different number of reads, so we want to normalize by the *library size*, which is the total number of reads across a column.\n",
    "\n",
    "The `np.sum` function returns the sum of all the elements of an array. With the `axis` argument, you can take the sum *along the given axis*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = np.sum(expr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib_size = np.sum(expr, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(375,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib_size.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Generate a 10 x 3 array of random numbers. From each row, pick the column containing the number closest to 0.75.\n",
    "\n",
    "*Hint:* use of `np.abs` and `np.argmin` to find the column j that contains the closest element in each row i. The final result should be a vector of integers of shape (10,)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.34659102, 0.88736773, 0.9552061 ],\n",
       "       [0.45635929, 0.08955531, 0.16221448],\n",
       "       [0.91208995, 0.5806005 , 0.94956507],\n",
       "       [0.95798454, 0.2356055 , 0.63938993],\n",
       "       [0.69597717, 0.58500954, 0.27611599],\n",
       "       [0.81587436, 0.66722308, 0.1153436 ],\n",
       "       [0.7748871 , 0.24224122, 0.80395726],\n",
       "       [0.97636427, 0.88578983, 0.06151782],\n",
       "       [0.91746781, 0.791986  , 0.12767417],\n",
       "       [0.42508357, 0.17744768, 0.64231658]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.random((10, 3))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.40340898, 0.13736773, 0.2052061 ],\n",
       "       [0.29364071, 0.66044469, 0.58778552],\n",
       "       [0.16208995, 0.1693995 , 0.19956507],\n",
       "       [0.20798454, 0.5143945 , 0.11061007],\n",
       "       [0.05402283, 0.16499046, 0.47388401],\n",
       "       [0.06587436, 0.08277692, 0.6346564 ],\n",
       "       [0.0248871 , 0.50775878, 0.05395726],\n",
       "       [0.22636427, 0.13578983, 0.68848218],\n",
       "       [0.16746781, 0.041986  , 0.62232583],\n",
       "       [0.32491643, 0.57255232, 0.10768342]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist75 = np.abs(arr - 0.75)\n",
    "dist75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 2, 0, 0, 0, 1, 1, 2])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_indices = np.argmin(dist75, axis=1)\n",
    "col_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.88736773, 0.34659102, 0.34659102, 0.9552061 , 0.34659102,\n",
       "        0.34659102, 0.34659102, 0.88736773, 0.88736773, 0.9552061 ],\n",
       "       [0.08955531, 0.45635929, 0.45635929, 0.16221448, 0.45635929,\n",
       "        0.45635929, 0.45635929, 0.08955531, 0.08955531, 0.16221448],\n",
       "       [0.5806005 , 0.91208995, 0.91208995, 0.94956507, 0.91208995,\n",
       "        0.91208995, 0.91208995, 0.5806005 , 0.5806005 , 0.94956507],\n",
       "       [0.2356055 , 0.95798454, 0.95798454, 0.63938993, 0.95798454,\n",
       "        0.95798454, 0.95798454, 0.2356055 , 0.2356055 , 0.63938993],\n",
       "       [0.58500954, 0.69597717, 0.69597717, 0.27611599, 0.69597717,\n",
       "        0.69597717, 0.69597717, 0.58500954, 0.58500954, 0.27611599],\n",
       "       [0.66722308, 0.81587436, 0.81587436, 0.1153436 , 0.81587436,\n",
       "        0.81587436, 0.81587436, 0.66722308, 0.66722308, 0.1153436 ],\n",
       "       [0.24224122, 0.7748871 , 0.7748871 , 0.80395726, 0.7748871 ,\n",
       "        0.7748871 , 0.7748871 , 0.24224122, 0.24224122, 0.80395726],\n",
       "       [0.88578983, 0.97636427, 0.97636427, 0.06151782, 0.97636427,\n",
       "        0.97636427, 0.97636427, 0.88578983, 0.88578983, 0.06151782],\n",
       "       [0.791986  , 0.91746781, 0.91746781, 0.12767417, 0.91746781,\n",
       "        0.91746781, 0.91746781, 0.791986  , 0.791986  , 0.12767417],\n",
       "       [0.17744768, 0.42508357, 0.42508357, 0.64231658, 0.42508357,\n",
       "        0.42508357, 0.42508357, 0.17744768, 0.17744768, 0.64231658]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest = arr[:, col_indices]\n",
    "closest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "closest = arr[np.arange(arr.shape[0]), col_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 2, 0, 0, 0, 1, 1, 2])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88736773, 0.45635929, 0.91208995, 0.63938993, 0.69597717,\n",
       "       0.81587436, 0.7748871 , 0.88578983, 0.791986  , 0.64231658])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.34659102, 0.88736773, 0.9552061 ],\n",
       "       [0.45635929, 0.08955531, 0.16221448],\n",
       "       [0.91208995, 0.5806005 , 0.94956507],\n",
       "       [0.95798454, 0.2356055 , 0.63938993],\n",
       "       [0.69597717, 0.58500954, 0.27611599],\n",
       "       [0.81587436, 0.66722308, 0.1153436 ],\n",
       "       [0.7748871 , 0.24224122, 0.80395726],\n",
       "       [0.97636427, 0.88578983, 0.06151782],\n",
       "       [0.91746781, 0.791986  , 0.12767417],\n",
       "       [0.42508357, 0.17744768, 0.64231658]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Some applications, such as clustering, are computationally expensive, and wouldn't work without first doing some form of *feature selection*, where we discard most of the data and keep only what we think will be most useful. One simple version is to keep only the genes with the most variance (as these will be more informative than genes that don't vary between patients).\n",
    "\n",
    "- Find the variance across patients of all the genes (rows) in the expression dataset.\n",
    "- Use `np.argsort` to find the location of the 1,500 most variable genes.\n",
    "- Use these indices to produce a shape (1500, 375) matrix containing only the most variable genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_variance = np.var(expr, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20500,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_variance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20500, 375)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_rows_variance = np.sort(row_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_variance_order = np.argsort(row_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_variable_rows = row_variance_order[-1500:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_variable_data = expr[most_variable_rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 375)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_variable_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced operations: broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to normalize every column by its corresponding library size, we have to *align* the two arrays' axes: each dimension must be either the same size, or one of the arrays must have size 1. Use `np.newaxis` to match the dimensions. But let's first do some simple examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 200,  50, 400],\n",
       "       [ 50,   0,   0, 100],\n",
       "       [350, 100,  50, 200]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[105, 205,  55, 405],\n",
       "       [ 55,   5,   5, 105],\n",
       "       [355, 105,  55, 205]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + 5  # simplest \"broadcasting\": scalar - array operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[101, 202,  53, 404],\n",
       "       [ 51,   2,   3, 104],\n",
       "       [351, 102,  53, 204]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([1, 2, 3, 4])\n",
    "a + b  # broadcasting: coerce arrays to same shape by repeating as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (3,4) (3,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-43c2c47166d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m  \u001b[0;31m# broadcasting: not just magic!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,4) (3,) "
     ]
    }
   ],
   "source": [
    "b = np.array([1, 2, 3])\n",
    "a + b  # broadcasting: not just magic!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[101, 201,  51, 401],\n",
       "       [ 52,   2,   2, 102],\n",
       "       [353, 103,  53, 203]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([[1], [2], [3]])\n",
    "a + b  # broadcasting: shape compatibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, back to our expression data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib_size = np.sum(expr, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20500, 375)\n",
      "(375,)\n",
      "(1, 375)\n"
     ]
    }
   ],
   "source": [
    "print(expr.shape)\n",
    "print(lib_size.shape)\n",
    "print(lib_size[np.newaxis, :].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, NumPy will automatically prepend singleton dimensions until the array shapes match or there is an error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all([True, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all([True, True, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(expr / lib_size ==\n",
    "       expr / lib_size[np.newaxis, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "expr_lib = expr / lib_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also multiply by $10^6$ in order to keep the numbers on a readable scale (reads per million reads)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "expr_lib *= 1e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, longer genes are more likely to produce reads. So we must normalize by the gene length (in kb) to produce a measure of expression called Reads Per Kilobase per Million reads (RPKM). We start by loading the gene lengths in *bases*. (1 kilobase = 1,000 bases.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20500,)\n"
     ]
    }
   ],
   "source": [
    "gene_len = np.load('gene-lens.npy')\n",
    "print(gene_len.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: broadcast `expr_lib` and `gene_len` together to produce RPKM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reminder:\n",
    "\n",
    "$RPKM = \\frac{C}{N \\times 10^{-6} \\times L \\times 10^{-3}} = \\frac{10^9C}{NL}$\n",
    "\n",
    "where $C$ is the raw counts, $N$ is the library size (in reads) and $L$ is the gene length (in bases). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20500,)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_len.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20500, 1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_len[:, np.newaxis].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpkm = expr_lib / gene_len[:, np.newaxis] * 1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20500, 375)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpkm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to admire our handywork, we will use a custom plotting function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "def plot_col_density(data, xlim=None, *args, **kwargs):\n",
    "    # Use gaussian smoothing to estimate the density\n",
    "    density_per_col = [stats.kde.gaussian_kde(col) for col in data.T]\n",
    "    if xlim is not None:\n",
    "        m, M = xlim\n",
    "    else:\n",
    "        m, M = np.min(data), np.max(data)\n",
    "    x = np.linspace(m, M, 100)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    for density in density_per_col:\n",
    "        ax.plot(x, density(x), *args, **kwargs)\n",
    "    ax.set_xlabel('log-counts')\n",
    "    ax.set_ylabel('frequency')\n",
    "    if xlim is not None:\n",
    "        ax.set_xlim(xlim)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 397
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_col_density(np.log(expr+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 388
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_col_density(np.log(rpkm + 1), xlim=(0, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that the most \"disparate\" data column is now a much better fit with the rest of the data. This should improve downstream scientific analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: 3D broadcasting\n",
    "\n",
    "Below, using broadcasting, produce the array containing the sum of every element in `x` with every element in `y`. That is, produce an array `z` such that `z[i, j, k]` contains either the sum of `y[i]` and `x[j, k]` OR the sum of `x[i, j]` and `y[k]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.random(size=(3, 5))\n",
    "y = np.random.randint(10, size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = x + y[:, np.newaxis, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 3, 5)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = x[..., np.newaxis] + y[np.newaxis, np.newaxis, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5, 8)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: explicit broadcasting and stride tricks\n",
    "\n",
    "Now, use `np.broadcast_arrays` to `xbroad` and `ybroad` that are the same shape as `z` (so that a simple element-wise addition will give `z`). Then use `print_info` on `xbroad` and `ybroad`. Notice anything weird?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((5, 5))\n",
    "b = np.ones((5, 5))\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "xbroad, ybroad = np.broadcast_arrays(x[..., np.newaxis], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 120\n",
      "number of dimensions: 3\n",
      "shape: (3, 5, 8)\n",
      "data type: float64\n",
      "strides: (40, 8, 0)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(xbroad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 120\n",
      "number of dimensions: 3\n",
      "shape: (3, 5, 8)\n",
      "data type: int64\n",
      "strides: (0, 0, 8)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(ybroad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stride tricks\n",
    "\n",
    "By manipulating the shape and strides of an array, we can produce a \"virtual\" array much bigger than its memory usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def repeat(arr, n):\n",
    "    return np.lib.stride_tricks.as_strided(arr,\n",
    "                                           shape=(n,) + arr.shape,\n",
    "                                           strides=(0,) + arr.strides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.28361179 0.50731328 0.0200835  0.02085825 0.42432864]\n"
     ]
    }
   ],
   "source": [
    "row = np.random.random(size=5)\n",
    "repeated_row = repeat(row, 4)\n",
    "print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be careful, though: some operations, such as `np.copy`, actually materialize the much bigger array!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 20\n",
      "number of dimensions: 2\n",
      "shape: (4, 5)\n",
      "data type: float64\n",
      "strides: (0, 8)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(repeated_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 20\n",
      "number of dimensions: 2\n",
      "shape: (4, 5)\n",
      "data type: float64\n",
      "strides: (8, 32)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : True\n",
      "  OWNDATA : True\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(np.copy(repeated_row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.random((3, 2)).astype(np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to answer these without looking at `x`. Then, try them out with the `print_info` function.\n",
    "\n",
    "- What is the shape of `x`?\n",
    "- What are the strides of `x`?\n",
    "- Is `x` C-contiguous, F-contiguous, or neither?\n",
    "\n",
    "Now let `y = repeat(x, 4)`. What is its shape? What are its strides? Is it contiguous?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = repeat(x, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 6\n",
      "number of dimensions: 2\n",
      "shape: (3, 2)\n",
      "data type: float32\n",
      "strides: (8, 4)\n",
      "flags:\n",
      "  C_CONTIGUOUS : True\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : True\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of elements: 24\n",
      "number of dimensions: 3\n",
      "shape: (4, 3, 2)\n",
      "data type: float32\n",
      "strides: (0, 8, 4)\n",
      "flags:\n",
      "  C_CONTIGUOUS : False\n",
      "  F_CONTIGUOUS : False\n",
      "  OWNDATA : False\n",
      "  WRITEABLE : True\n",
      "  ALIGNED : True\n",
      "  WRITEBACKIFCOPY : False\n",
      "  UPDATEIFCOPY : False\n"
     ]
    }
   ],
   "source": [
    "print_info(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: `np.lib.stride_tricks.as_strided`\n",
    "\n",
    "Use `as_strided` to produce a sliding-window view of a 1D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sliding_window(arr, size=2):\n",
    "    \"\"\"Produce an array of sliding window views of `arr`\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    arr : 1D array, shape (N,)\n",
    "        The input array.\n",
    "    size : int, optional\n",
    "        The size of the sliding window.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    arr_slide : 2D array, shape (N - size + 1, size)\n",
    "        The sliding windows of size `size` of `arr`.\n",
    "        \n",
    "    Examples\n",
    "    --------\n",
    "    >>> a = np.array([0, 1, 2, 3])\n",
    "    >>> sliding_window(a, 2)\n",
    "    array([[0, 1],\n",
    "           [1, 2],\n",
    "           [2, 3]])\n",
    "    \"\"\"\n",
    "    stride = arr.strides[0]\n",
    "    n_windows = arr.size - size + 1\n",
    "    return np.lib.stride_tricks.as_strided(arr,\n",
    "                                           shape=(n_windows, size),\n",
    "                                           strides=(stride, stride))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [1, 2, 3],\n",
       "       [2, 3, 4],\n",
       "       [3, 4, 5],\n",
       "       [4, 5, 6],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test your code here\n",
    "sliding_window(np.arange(8), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: mean filtering\n",
    "\n",
    "Use `sliding_window` to implement mean filtering, in which every value in an array is replaced by the mean of it and its neighbours. This is a basic operation in signal processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_filter(signal, window_size=3):\n",
    "    \"\"\"Apply a mean filter to the input with the desired window size.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    signal : 1D array, shape (M,)\n",
    "        The input signal.\n",
    "    window_size : int, optional\n",
    "        The size of the window along which to compute the mean.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    filtered : 1D array, shape (M - window_size + 1,)\n",
    "        The filtered signal.\n",
    "    \"\"\"\n",
    "    signal_sliding = sliding_window(signal, window_size)\n",
    "    filtered = np.mean(signal_sliding, axis=1)\n",
    "    return filtered  # FIX THIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test your function, we will use the example of a *difference filter*, which finds the location of changes in a signal using *convolution*. When the signal is perfectly noiseless, it works great:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal = np.zeros(100, np.float)\n",
    "signal[30:60] = 1\n",
    "\n",
    "diff = np.array([1, 0, -1])\n",
    "from scipy import ndimage as ndi\n",
    "dsignal = ndi.convolve(signal, diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 424
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2)\n",
    "ax[0].plot(signal)\n",
    "ax[0].set_title('signal')\n",
    "ax[1].plot(dsignal)\n",
    "ax[1].set_title('change')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if the signal is corrupted by noise, a standard difference filter convolution doesn't work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 424
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "signal_noisy = signal + np.random.normal(0, 0.3, size=signal.shape)\n",
    "dsignal_noisy = ndi.convolve(signal_noisy, diff)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "ax[0].plot(signal_noisy)\n",
    "ax[0].set_title('signal')\n",
    "ax[1].plot(dsignal_noisy)\n",
    "ax[1].set_title('change')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try mean filtering with different window sizes to see whether the change signal becomes more apparent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5861534630>]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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elYiI5jMPTLh8bbvxMQN+soMBP5HHnRyZ1T4udkmPmuFf3Ows4NeadkvQdExEVI7UpVstdVW4TAn42bhLdjDgJ/K4+U27RQ741Qy/w4C/pa4KIl56iom5CCLRmBsPjYiooo2bAv5L1rQbx9IDp8e1HSlE2TDgJ/Kw8ZnwvJFsxc6QD7pQw+/3CTQrzb7pxswREZFOD/gDaKmrwjlLmwEAMQns7h4t1UOjMsOAn8jDzBN6gBLU8E/kn+EHOKmHiMgurYY/kTTZsbbD+NzOExzPSdYw4CfyMHPDLlCCKT0uNO0C3pnFPxeO4nO/OYJvPXoCMXWjGRGRx5hLegBgxzqljv846/jJmkCpHwARZWau3weAyWAE4WgMVf7inK+7leFv9UiG//O/OYJvPHIcALC6vR4vOWdxyR4LEVE25qZdALhsTSrg339qHNPBCBpqGM5RdszwE3lYuoAfiC/DKoa5cNT4WX6fQEej/S27SW0eyPCHIjH86OmTxsfPnRwryeMgIrIiXYa/raEaW5Y0AQAiMYlnelnHT7l5KuAXQqwQQtwlhDgthAgKIbqFEF8QQrRZ/P4GIcRtQogfCCGeF0JMCyEmhRBPCyE+IISozn0vRN5xcnQ27eeLNalHbdjtaqoxtjw6oS/fKk2G/+FDg9rVhZ4MJ1RERF6QLuAHgB1rWdZD9ngm4BdCrAewG8DtAJ4E8HkAxwG8D8ATQoiOLN+e9CIA3wPwMgD7AXwZwA8ALAfw7wB+J4RwXpNAVGRqDX9TbeqSbbHq+N2q3wdMs/hLlOFXs/sA0DM8XZLHQURkhXnxVtJlSuPuLjbukgWeCfgBfA1AF4A7pJSvllJ+WEp5A+KB/2YAd1q4jwEAbwGwVEr5usR9/BWATQCeAXAlgPcW5uETuSsak+hTpvRsX9FivF+sgNmt+n2g9FN6Bsbn8IcjZ7XP9Qwzw09E3pWuhh+AtoBrz8lxzIWjRX1cVH48EfAnsvsvBdAN4KumL38cwDSAtwohGrLdj5TyOSnl96WUIdPnJwF8NvHhdW48ZqJCG5yYQzganyLT0VCN5a11xteKFTCrGX6nW3aTSj2l56fP9ME8lGd8NlzSiUFERNloJT1K0qSzqQbrO+MhUSgaYx0/5eSJgB/A9Ym3D0optRWciWD9MQD1AC7P42cknzWRPO6DqGjUht2V7fVaScxIsUp6XFi6lVTKkh4pJX6yuy/t15jlJyIvCkdjmA7FM/c+ATRW65N4dqxLlfU8eYJ1/JSdVwL+zYm3RzJ8/Wji7aY8fsY7Em/vz+M+iIpGDfhXtdejraH4GfJBF0t6Stm0+3TPKE4Mxev1m2oCuGZTp/G1btbxE5EHTZjq932moQls3CU7vDK4NVmcPJ7h68nPtzq5cyHEXwO4EcBzAO6y+D27M3xpi5PHQGRXn5bhr9Nq4Eemy7Gkp3QB/4+eSjXrvvKCZWisCeCRRD1/LzP8RORBmSb0JF2uZPifPTkKKSWEcD5JjSqbVzL8BSOEuBXAFxBv6H2tlLJ0G3+IbDBn+EtRAz84ETTeX1qmJT1TwQh+ta/f+Pj1F6/AqvZ64+NuBvxE5EG5Av7FzbWorYqHcXPhVPkPUTpeyfAnM/gtGb6e/LytLTlCiFcD+G8AZwBcL6U8bvV7pZQXZ7jP3QAusvM4iJww1/Crm3WLETDHYlIv6ckz4K+v9qPa70MoGkMwEsNsKIq6an++DzOnX+/tx0zihXBjVyMuWNmK6WDqhbF3hCU9ROQ9uQJ+AGivr8bpxJXY0ekQGrlxlzLwSob/cOJtphr9jYm3mWr85xFCvB7AjwEMArhWSnk4x7cQeUrvSGrp1sq2+qKPtRyaDiKSGGvTUleF2qr8gnMhhFbWU6wsvzp7/w2XrIQQAqs7mOEnIm/TZvDXpg/4Wz2w34TKg1cC/t8l3r5UCKE9JiFEE4CrAMwA2GnlzoQQtwH4/wCcRjzYP5rjW4g8ZTYUxdBUvJwm4BNY2lJb9JKYwfFUOU++DbtJxf5/OH52Ck/3xMfVBXwCr75wOYB4eVKVP17renYyiJkQh3cRkbeYm3bTaW9Qj6msWKbMPBHwSymPAXgQwBrMX4z1SQANAL4rpTSuvQshtggh5jXQCiH+DMB3APQCuMZOGQ+RV5xUFm4tb6tDwO/TLumOz4YRNQ+Vd5k6knNxnuU8ScVu3P2xMorzhi1d6GyqAQAE/D6saEtl+Tmak4i8xkpJjzq9rVgb2Kk8eanY6z0AHgfwJSHEiwEcArAD8Rn9RwD8ven2hxJvjZZ0IcT1iE/h8SF+1eD2NB3rY1LKL7j+6IlcpE6OWZkITJNB//hsGFLGXwzU7I7b1IB/qUsZ/mKW9ESiMfxUCfjfcMlK7eurO+qNUZ09wzPYurS5oI+HiMiOibnUlceMAb82vY0BP2XmmYBfSnlMCHEJgE8hPkLzJgD9AL4I4JNSSitr5FYjddXiHRlu04P41B4iz1Iz/CuViTJt9VVG1mdkOlTQgH9w3P0Mv17SU9gM/yNHz+LMZLwsqbOpBtdt7tS+vrpdzfCzcZeIvGV8xkKGv8QbzKl8eCbgBwAp5UkAt1u87bzUvZTyHgD3uPuoiIrPPJIzqa2h2mgyLfTBfcDFpVtJaoPZeIEf/4+eSmX3b71wOQJ+vYJxdUeD8X7PCEt6iMhbLJX0qBl+BvyUhSdq+IlId9K0dCtJzeYU+vKtunRrSUuNK/dZrElDkWgMvz18xvj49ZesmHcbdVIPM/xEzvx6Xz8+8KM9ODwwWeqHUnFs1/CzaZey8FSGn4jiTiojOdUMfzGbXrWm3TKb0tM/PodQJAYA6GqqwYaupnm30TL8bNolsm1kOoT3//dzCEVj+OPRs3joA9dmHB+5EOW7+dZahp9Nu2QNM/xEHiOlzFjS017EsZZqDf/Slrost7SuWCcsagCv/v5UK9vrkHwtPj02a5wgEJE1z/dPIBSNP2/OTAbx2Qe47ibpuZNjuOLTv8WtX3sMc2FnG3AtLd5ihp8sYsBP5DFDUyHMJl4gmmoC2oFevXxbyHrN6WAEk8H4hIjqgE8rxcmHfvm5cI+/R9meu6ojfcBfE/BjWeJEJiaBvlFm+YnsODakl8J9Z2cP9pwcK9Gj8Zav//4YBibm8EzvGH5zcNDRfUxwLCe5iAE/kceYJ/Sol4S1iQzThcvm6OU8NXldlla11hUnw6+ONV3d3pDxdqvaOYufyKkTZ/WAX0rg7/5nHyJRXi3bf3rceL9/fDbLLdOLxqSRdBECaKpNX4FtbtqVsrD7Wah8MeAn8piTGcp5gOJNZNAadl2q3weKtwZeLYlanSHDDwBrFlVu4240JrGvb5ylSlQwx4em5n3uYP8E7nm8u/gPxkPGZ8PoG00F+Wcng1lunZ6a3W+qCcDnS590qavyoyYQD+VCkZhxdZjIjAE/kcdoS7fa9dp59fJtIcdyqgG/Ww27gF7DPz4bRqxA24J7htPvMTBbpWT/uyssw//Bn+zFK7/yKF7/jSeY9aOCOKGU9Nxy/jLj/c/95ghOjdnPaleK5/sntI+dBPxq/X5zhnIeABBCFHV6G5UvBvxEHpOpYRco3ljOQszgB4Aqvw9NNfFL01ICE3Pul/WYm56zZviVr/VW0Cz+aEziF3tPAwD2nBzT/j2J3BCMRI2rkUIAd77mPGxa3AgAmAlF8fGfHyjlwyupQ6aA/0yeAX+m+v0kPRHExl1KjwE/kcdk2rILAG0NxamBH1QDfpe27Ca1NhR2Fv/IdAhTidrXhmo/OrJsI1YbersrqKTn1Kg+dWi0gP0etDCdHJlB8gLdspY6NNVW4Z9fs834+kOHBvHAgYESPbrSOuhyhj9nwK+WejLDTxkw4CfyGHUGvzngb63Ta+ALVRKjL91yN+Av9Cx+7QpJR0PWhmN1Fn/fyCyiBfp9Fpu5trrQI1xp4TmmNOyu64w/jy5Z0443X7bS+PzHf37AOPleSOYF/FPFy/DzuU6ZMOAn8pBQJIbTiYkOQgDLW/Ua/uqAD42JkpiYBCbnCvNiOligkh5Af/EqRB+CXhKVfX9AY00AixrjL5ahaMzRNA0vOm6ansIggNym1u+vW5Q6cf7QjVuM59TAxBw+++DCms0fjsZwZFA/4R6bCSMYsddMq5Y75gr428ts+dapsVn8el8/phfgyWApMeAn8pDTY7NI9lcuaa5FbZV/3m3atJKYwhzc+wvUtAvoi2IGJ+xnvnJRG3bVDH4m6m16K6Rxd36GnyU95K7jZ1N/Y2uVgL+1vhofu/kc4+NvP96tTR6rdMfPTqedjDU8Ze9Y7bikx+PP9ZlQBLd8+VG85/vP4GP37i/1w1lQGPATeYianV7Zlr7ZVGvcLUDAH4nGMKRcgnY74N+8pMl4/6nuEVfvG8je9JzO6na1jr8yApN5Gf4yyPpRedEy/J2N2tduOX8ZLl7dBiB+JfKZ3tGiPrZSMjfsJtlt3LU6pQco3vQ2N+zrG8dw4nj0q339jrcQk30M+Ik8RAv4MwSr2vKtAhzcz04FjWa8RY3VqA64e5i4fF2H8f7OY8Ouj4xUs/RWAn61cVfd0FvOTgyxpIcKSz2pVDP8QHxU5EWrWo2PK+XKmRXm+v0ku427VrbsJpXTWE71CmwwEltQJ4OlxoCfyEPUCT2ZglV9IoP7l28LNYM/advyFjRUx0uVTo/PuT4OUw3as43kTFqjlPT0DJV/YDITimglWQAz/OSu8ZmwkaWtDvjm9RoB+vGrkkbe5qJm+NVA3W7AX6ljOc3T0B5/YbhEj2ThYcBP5CEnRzIv3Uoq9OXbQjbsAvFZ/JeubTc+fuKYewf8uXDU6Avw+wSWpQlEzPQMf/kHJuZyHoA1/OQutUdkbUdD2i2wq9QT6Qp4XlkhpcTB06mA/+qNi4z3Cxrwl9FYzh7T1Z7Hjg2V6JEsPAz4iTzESv15oS/fahl+l0dyJl2hlPU8cdy9gF89YVreWocqf+5DnJbhH54u+620x4fSBfzeDgKovOj1++kb49XemIVS0nNmMmhc+Wio9uPiVW3G185O2Vt+Zy/gL58afnOGf2/fOCYLsICR5mPAT+Qh6gz+jAG/NnPZ/QNlf4Ez/ABwxXol4Hexjr/HZv0+EM+OJbf/zoSiGLI5TcNNh/oncNejJ7SmabtOpM3wezsIoPKSrX4/aXlbHZKJ/4GJuQXRnKnW729Z2qyVRJ6xOZHMTsCvTj4rxCAHt0gp0W1KSERjEk+ecH94A83HgJ/II8ZnwsZBvibgQ2dTTdrbqZdvC1GbPVjApVtJ5y5rQVNtPMg+MxlMm5V2okdbumUt4BdCYPUipaynRBt3p4MRvPmbO/GpXx7Eh3+61/H9mEdyAty0SylSSnzt9y/go/fuc3yFMNuEnqQqv08rqesbrfwsv1rOc87SZu0Ybnf51viM9YC/vtqP6sTVzLlwDLMhb55cDU2FMJ3msT3GOv6iYMBP5BFqw+7K9vqMG2ILval2oAgZfr9PYEcB6vjVkp7VFjP88duqZT2lCUxeODNlNNw9+sKQ4y3K6Wr4p4KRtLPBaeF59IUhfOb+w/jezl586eGjju7jWIYZ/GZq03ypnlfFpDbsbl3ajC414LdRwx+LSUwqS6mSyZFMhBBF2c+SLzWZ4lf6Ph5nHX9RMOAn8gh9Bn/mZlO9XtP9zK26DKtQGX5AH8/pVh2/+oJitaQHMAcmpcnwqydac+EYTo3Z3/orpdSyr9VKD8PYrDeDACquvX3jxvs7HTzvYjGp1WGvz1DDDwCrPHAiXUxqSc85y0wZ/smg5dLFybmIsYCxsSaAgIVepHIYzanuObluUyeq/PGg//mBybzKGMkaBvxEHqG+iGbbEKtmctyu15RSFnwsZ5Jax7/ruDt1/E5KegBTwF+iiSLqdCQAOHpm0vZ9nJ0MYiqRGWyqDWiTnljWQ4DeQHv0zJTt8o/+iTnMheNXi9rqq9CqBJpm6vOq0kdzzoQixsm2TwCbFzehoSaA+sQI4mAkpmXts7FTv59U6ESQG9RkytalzbhwZaqp+XEXp7VRegz4iTxCfSHONj/ePJHBzakyE7MRzCaa6+qq/GjOcSk5H1uXNKM10Y8wNBXC0TPza8/tiMYk+pSm52wnTWbqbUu1bXfMdflQAAAgAElEQVTANDv/6KD938cxpZxn3aIGrZnPq5f5qbjUwDsakxkXRWWiNoVnqt9PWkiz+A8PTBpZ+bWLGlCXCPTVLL/Vxl07W3aTCpkIcot69XF1R71peAPLegqNAT+RR/RYDPhrq/yoq4q/mISj0sjoukGr32+pzdhH4Aafy3X8gxNzCEXjmceOhmo01lg/WdEykR4o6QHg6ARIbdhd19moZV+5fIuA+YH3/lPjGW6Z3okha/X7gB7wl6pUrlj0cp4W4/3ORvt1/HqG39pxrBxGc6qvcWsWNeCqDak9BWzcLTwG/EQeob4Q58pOtxdos2IxGnZV2jz+PAN+9cVkpY36fQBY3FSLmkD8cDiqTEsqpvklPfYD/hPmDH99YUe4UnkJRqI4Pa73hqg1/VZoV5Gy1O8D+on0ydFZx43o5UBv2G0y3u9qtj+pZ2LOfkmPNprTgyf3UkpT2Wo9LljZaiSvekdmtKEL5D4G/EQeoL4QCwGsyNK0C8AohQHcPbgXYySn6or1qQzPzhPDeQUEvSP6i4kdPp/Qyw9KUNYzaLrc/8LgpO1yreOmcYmtZTC5g4rn1OgszH9S+06N2boPbSRnjgx/U22VEYiGIrF5V7EqiXkkZ1L+GX5rAb/Xr+aNzoQxORe/Gt1Q7UdnYw2qAz5cplzl5bSewmLAT+QBJ0dSL8TLWupQE/BnvX2harPVF+RCNuwmbVrciI7E/8vYTBiHB+03qiZpJVE2M/yAuY6/+OUHg6Ya/ulQFP3j9gKk46Zxie0eDwKouNI1pL9wZgrTNsoCzWVjuaxcAHX8sZjE8wOpY5cW8Ks1/JPWns9OAv527eTee1fz9Pr9BqNc9Eqljp9lPYXFgJ/IA+xmp1sLNItfL+lJv/jLTUIIfTxnHmU92lhTRwF/6QKT6WAk7QQPO2U9oUgMJ0dT5RprFzXoo/qY4V/w0l25iklYbtwNRqLoG01dibQy+nZ1ia+cFUPPyAxmEtOOFjVWa0G+eTSnFXln+D34XFd7ONYoiw7VOv7HXdy6TvMx4Ke8BSPRiq7NLIbuIWsNu0n6tl0Xa/iLXNIDAJevUxp385jHb6cHIp01yu/dvP690DKVOhy1ccWjd2QG0cTzcHlrHeqq/WgrUK8HladMJ7L7LNbx9wzPGFciV7TVobYq+5VIwDzytjIbd80Lt9RhB11NqeNoIQP+do8H/N3D6Y/P5yxtNv4fh6aCeU9ro8wY8FNenu0dxWV3Powr/+W3eOjgYKkfTtlSX4jVZTWZFGoigx7wZ+8jcIt5Hn/U4cmj1SlHmazqKN2SIHM5T9ILNl78zOU8gOnE0INBABWX+nd96ZrUDPR9Fif1qFuc1y7KXc4DmCf1VGaGP1P9PpB/ht/yWE6tfM97J/dahl85Pvt8Qhve8NgLrOMvFAb8lJfv7+rF+GwYAxNzeOd3nsanfnEQwYi9RS6U+WCYiRrIuVmqMVjkKT0AsL6z0XhRnJiLaNkyq8aVyTo1AZ+20t4qdbuxeZJJoakZfvXf1k62S2/YjQf8Xm/ko+JSSwdfsW2p8f7ePmuNu1r9fo6G3SQ1m1upNfwHTRl+lRrwW90mO+Fk8VZDYQY5uEXN8K8xXYG9agPr+IuBAT/lRc0qAsBdj53A6/7jiaKXRJQ7NfNlZUNsW4P74xaDkSiGEy8UPhGvRS0GK3X8+0+N4x9+vh+/3tef9j70KyT1jvYHqE3KZyaCRa0lVQP+K5Wa1qM2JvWYR3IC5uZu72X9rIrFJB48MGA5MKX5pJTa8+Tl25bC74s/T44PTVva53HCxkjOpIWwfOuQNoNfD/jbG6qRPBwNT4cQSewKycZJhr+xJoAqf/wHzYajmAt7K/GmxgRrTCeL6jFv1/FhS78jso8BP+Ul3VbSfafGcfOXH8XPnztVgkdUfqIxiZOj9urP2wqQuVW3QHY21SDgL97hQZvHr9Txz4ai+OdfH8ItX3kU33miB+/9wTN4fmD+FYCePEZyJjXUBIxlXaForKg17+rv/oIVrWhKPI6JuYjlMoB001Na6qqMYGN8Nly2L6T/8Ydj+Mvv7satX3s8730NC9XZySDmwvF//5a6KixursXGrvjfiZTAAQtlPdpVJIslPV1NNcaOi7ES7bgopNHpkDFNqzrgm3flo8rvM+rrpYSRVMnGSQ2/EMKzjbtjMyHj/6m2av4V2HWLGowrypPBCPaftn+Vl3JjwE+Ojc+GjUuH1QEfPnbzOUaGYSoYwfv++zl86Cd7MRvyVqbBa/rHZxGOxrO4ixqtbYh1O3MbjETxbw8cNj4uVjlPklrH/+SJEUSiMTx6dAgv+8Ij+M9HjiNZ1i8l8O3Hu+d9v3aFxEIPRCbqkpxBiyP03KD2TixuqcWGxalgympZj15fHf8d+H1CCxjKMdiSUuIHu3oBAJGYxKd+edBxn8dC1mO6CgYA25anNsJaqeNXRyuutZjh98KOi0JSs/ubFzelTZTYreN3EvADpsZdD9Xxm8t5zFdghRCm8Zys4y8EBvzkmHqJbnV7Pf786rX4n3dfpdWg//Dpk3jLt3Zhcs47Bx+v6R2e/0KcS6s2pSe/TM7EXBhvv+sp/O+e08bnXq7U9xbDmo564yRjKhjB7fc8hbd8a1faEoCfPXtqXqPyyZH8GnaTFisTNcyLsArJvOE4mXkFrE3qGZ8JG5nDmoAPy1tT/QhtHs36WXXg9AROjaV6Kg71T+Cnu/vyus9YTCJcplc7nEpXNrh9RSrgz7Vxd2wmZCR4aqt8WGojKVDJk3rU+n1zw26SnYA/FpOOavgB0+uCh57rPcO5r8BeqY3nZMBfCAz4ybHu4fk1edtWtOAXf3M1XnXBMuNru3tG8dZvPamtC6eUTOPKsjEv3nJab94/Pos3fP0JrYzmrZevxl+8aJ2j+3NKCKFl+f94NHXAb64N4DOv3W68mM6FY/jhUye17+9xcNKUzmIlw3+miFtBzc3SG7uajI+tZPjVcp61ixrg86UyaPqknvJ7Dt6/f2De5/79wcO2lkWp+sdn8aLP/A6X3vkQ9lucTlMJtLG1yQz/ilbjc7ky/Go5z5oO/W8sl0pevqU37DalvY2dgH8qFDGuaNZX+1Flo7SyUAsZ83XC9LeTjtq4+3T3qOd6ECoBA35yTLu8q9QtNtVW4QtvvAAfu/kc43PPnRzDW/9rF8bLMOAoNCf153VVflQn6mKDkRhmHRwcDw9M4tavPa5tiPzgjZvxqVedazTzFZNax5/0im1L8dAHrsUbLl2Jt1+1xvj8d57o0co6tKbdfDL8auOuxdr5fEVjUvtZXc01tkt60pXzJGnLtzw4vSOXBw6kAv5A4u/yzGQQ33jkuKP7u/uxbpwam8XYTBh3PXrClcdYDnrTZFm3LGkyfqcnhqazJmXUv7H1Fjbsqip5+ZY2knNZS9rb2Nm2q75G2snuA96dyqUmZMwNu0lLW+qM/odgJIanukeK8tgWEgb85Fh3lrN2IQT+/Oq1+MdXnWt8bk/fOG771k5X58ZXgl4H8+OFEHllbnceH8brvv640WwW8Al87g3n4z3XbXA04cYN123pREN1fJHP4uYa/OdbL8ZXb7vIWFxzy/nLjP/nU2OzeOhQfO9DMBI1xmgKEV8I5FRXs1rSU5wM//BU0Dh5aa2vQm2VXyvpsTKLX2/Y1Z+LrQXa2VAMx85OGSc8tVU+/MMrU0mE/3zkGPptjk+VUmqTnnadGFkwmz170myirq3yY9PiVFY62xWPE0Pz9zxYtbqEOy4KKRSJ4ZgyqW5Lpgx/o/UMv3rSZTfgb9dGc3onudZtoaQHAK7Z1Gm8//ChMwV9TAsRA35y7IR21p7+SfzWK9bgn1+zzfh4/6kJ/Ok3d3kq+1Bq3Q4bTp1O6nm2dxRv+9aTmJyLl0Q01gRw9+2X4taLVli+j0LoaqrFve+9Cl944wX4zf+5Fi89d4n29doqP9502Srj42Tz7qnRWWP759LmWtQEcm//zEQt6SlWwG+u3weAZS11qE+c/IxMhzCcY3738bOZp6eoQUAhSnqkLFw9vJrdv3ZTJ27bsVor7fr3B47Yur/9pybQN5o6STg1Nqt9XMlOZthErdbxZ9u4e9zBSM4k9apbJZX09I3OGAMXlrbUork2fYCuJhLO5nguOxnJmeTVfp2eLDP4VS/Zuth4/6FDgwvmZLxYGPCTY90ZSnrM/nTHKvzra7cZ4wEP9k/gzd/cmTOIWQiklNqlditLt5KcHtzvfqwboUSA1tlUgx/+1eV40cbOHN9VHBsXN+HVFy7P+ML5lstXI1lt9PixYRwZnNSnj+RRzgPoJT3FatpVf07y5/t8Ahu6rJf1nEizdCupkJf558JRvOqrj2H7Jx7EbwqwafsBpX7/Zecugd8n8NFXbDU+99Nn+rIGqWa/3j9/j8PO45U/5nMqGMHQVGKimt+nTeHapjbuZs3wWzvep7Oirc44/p8en0UoUhkN0+b9H5nYyvA7bNgFvBnwm6f5ZZsAd9nadmMkcd/oLI4MWl88SLkx4CdHRqf1ubrqdJN03njpKnzmtduNg/7zA5N4y7eerJgDv1PD0yFMJ8aWNtYEtKarXJyO5uxTZv5/9vXn49wMdadetLy1Di9TMv/ffrxbL4nKYyQnoE/pKVbTbroMPwDLAX8sJvWAf16Gv3BBwEOHBrG3bxyz4Sj+XRnr6ob+8VnsSQTzAZ/Ai7fEs39XbliEl2ztMm73T786aCkTKKXEfWkWt+06Ufm1wupzZEVbndajs315qnE3U0lPrr+xXGoCfmOqj5T6MaicnbQa8Nto2nU6khPw5qI9bUJPe33WZu/qgE8r60mWbZI7GPCTIyeG7U9seP0lK/HZ159vZGgP9U/gt88v7Do99WBod0Os09GcaoNoPvXupfJnV64x3v+fZ05p00XyzfCrc/jPTAYRK8K890HTDP4kdVLPC1lGc54am0UwceLc0VCNlno9SFB7Pdyu632uN7X59vDgJI5YGCFq1YMHUi/2V6zv0P6//u6mrUaz6a4TI5auLjw/MJl2UeCuE5Wf4c/W1L5pSSOqE5NgeoZn0g5WOD2e/W/MilXaaM7KCPh7LY4D1pt2Cxfwuzmu2S3dFhp2VS85J3Uyz4DfXQz4yZFsDbvZ3HrRCvzFNamRj/enucS+kPRY6IPIxEnmVkrzRJjiLthyw4617diyJB4Mz4aj+NmzqY3O+YzkBOJ9AskX2UhMYqQIl8UzZfg3WszwZyvnAfTL/G437e7pG9M+/qWyyyFf6jhOcz/H+s5G3LYj1c/x6fuez3m1UM3uv/y8JairivdInByZ1eb8V6LeET3LqqoJ+LF5SerkMt14znzq91M/N/V9lTKpRz1+r8xy7GmuDRhT1WZC0awjZd3L8Hsj4O8Zsleyet2mLiMp+NzJMcubxik3BvzkiBbw26znfOX21Iz+hw+dWdBlPflsiHVSmz0+GzZ+3w3Vfktbfb1GCKFl+dXxnPks3UoqduOu+jPUn73R4mjO42ezT09pU4IAN09gItHYvODwl3v7XWm0G5kO4cnEWD4hgJeds3jebd73kk1oqo3//Z4Ymsb3dvZkvc/7lBOIV56/DBevbjM+3lXhdfz60q35fyN6Hf/YvK8fVkb32q3fT/3cymvctVrDL4RAl8Wynvwy/N4by2l3z0xbQzUuWdMOIF7+9bsFXgXgJgb85Ig6oWetzcz0ucuajVKSyWAEjy3grXpWLwmn42T6Srln95NefcHytC+G+dbwAzDGgALAmSI07g6oJT3Kv8mKtnrUJLKCZyeDGbPzx7UM//zaavUy/5iLdb1HBqcwF9ZP1o8PTWuLiJx66NCgcSJ34crWtH+r7Q3V+JsbNhgff+m3RzPu+Tg6OKmN97xucyd2rG03vr7reGXX8ecKTLcvTwX85jr+fX3j+MJDqWlI6hhPO9SfWwmjOaWUlmv4AVMdf5aBFeOzqex/c529hExzbcDoz5gORRGMlH55Vfew/WoAtUeHZT3uYcBPjjgt6QHi2Y6Xn5e6RP9Amk2aC4XV+cTptDqYyKAGsOoLULmpq/bjTZeu1D7XXBtwVFts1lXCDP8SpYbf7xPagqNM8/j1kZy5S3rc6kswl/Mk/WJP/mV6Dx7Qp/Nk8mdXrsHK9njyYGwmjK/+/oW0t1Oz+9dv7kJ9dQCXK5udK72OP1diQcvwK1OPTgxN4+13P2kMFljaUut4fO9qLcM/neWW5WHE5sAFq5N68snwx/ezqM/30jfu9jh4jXuxMp7zj0eHuHXXJQz4yTYppeWRnJncqAT8Dx4cRKRAc7y9rtfm5U6VkxFs6pbHrjIO+AF9RCdg//eXSTFHc86GophI7EOo8gu01+tBg5WynhM5MvxVfp8x6i4mkXWbqh17TqYC/kvXpMpjfrn3dF5lPVPBCB45mrrqly3grwn48aEbtxgf3/NYt5Z1TVID/uSxZ/uKFuMKSvfwTNH2LhRbJBrDKWXXwMq2+UHXpsVNRo153+gsRqZDODMxh7fdtQvDidKQlroqfOcdl9maJKbSavhHZsp+xrp5kVmugQta426Wv7V8An7A3KRf2rKeybmwNg52Wau1IRHrOxuN5MVsOIonjlX2CXmxMOAn24anQ5hMNB01VPsdZYovXNlmBJwj0yE81T3q6mMsB5NzYePF1Dwb24p2rV7TQUlPjlGqXreyvV7LBOU7oSdpsfL3PDhZ2CBQbdjtaqqdN+1Ka9xNM5N6NhQ1Gk79PpGxrKCtAOP6nlMC/r++YaNRT983mhqn6cQfDp81+ky2LGnK2SP0im1LccHK+GjJUDSGz5jGg54YmsahRJlRdcCHG7bEywVqAn5ctCp1olKp8/hPj80hkriq09VUg7rq+Yvpqvw+bE0sNAOAx48N4c/ufgonR+J/W7VVPtz19kux0WE5DwC01FehOfE3MheO5ZxW43V6OU/uQNZqSU8+c/gB83O9tAG/3tSsj4PN5cUs63EdA36yTc3ur+5osDVKMsnnE1rmbiFO61EPhitsHgwBoFWr4bdf0qOWrpSr916/AVX++O9NrfvMh5rhL3QNv1q/r5bzJG1QRnMePTN/5KWa3V/ZVmdkac3czvrNhCLGCE4hgItXt+FG5fn8izym9dxvsZwnSQh9Gdcv9pzGs72pBMJ9yrHlmo2daFKWuu1Yp9TxV+g8fqt9Qmod///98R7jJMnvE/jabRdpTc5OqVfhyr1xt3fYev0+oCdYrJb02N20C+jPdauJoEKxumE3HXXr7sOHzpT9FSEvYMBPtuWzcVGl1fEfGCzKzHMvUV/w7B4MAaCpJmDMIp8JRS3VOaoZ68UVEPBfsLIV97//Gtz73qvwmgud1RabqQ2iZwqc4c80oSdJLekx1/BLKfE1pWZ9fZpynqRWl0dz7j81geTTdWNXIxprArj5/NT0rV/t7Xf0fA5GotpUDisBPwBcsqZdO578868PGQHCfftSJxA3bdPvb8faVB1/pWb4e0bUXR+ZjzNqHb/ajP2vr92OG7bMn5LkhDaLv8wbd/XdBrmP31aWb0kp8y7p8dJoTr1Hzd5r3MWr24z//4GJORw4nf8wgKQf7OrFn9/zFHb3VOZJfiYM+Mk2reve5oQe1WVr241sxMDEHJ7L0ARYqXpsZojMhBCmQC53NufsROWU9CSt72w0SjrcUMyxnAMT6Sf0JK1urzeuYPSPz2FSqb//3s4e/HJvKnv9BlMTs0oNAtzI8Kv1++eviP/ur1zfoT2fd/faL9N7/NgwphLlgqva67F1qfUSkg/duMU4AX6qexQPHBjEyZEZY3RolV9oJWAAcOGqVmPp1PGz0wU/wSsFq5no7Svmb9z+u5dvwesududEGtB3APQOl3fjbo+NCT2AtZKe6VDUmE5VW+VDTWB++VUuXhrNqY/vtvcaF/D7cP1m97fu/vCpXnzkZ/vw8PNn8JH/2e/KfZYLBvxkW/dQfpnppIDfhz9R5mvfv8Cm9TiZXmDWbrOsp5KadgvFnImLFvDKk1bSkybgD/h9WLdofpZ/X984/vGXh4zP/+mOVVmz4W6P5lRPzs9PnGxV+X248bylxuedLOFSJ3a97NzFtsoF1yxqwFuvWG18/C/3HcL/Ko/hqg2L5mVMa6v8uGBV6mTxyQos67Fa0rOhsxENSn3/O69ei79UliS6QRvNWeYlPXZGcgLmpt30AX++9fuA3ttVjMWB2eRT0gMAL1HiAzcC/n194/jYzw8YHx8enNSSKJWOAT/Z5lZJDwC8XAkQ7t8/sKDq9PI9GAL2szmV1LRbKDUBv5ERj0lgOEuDXb7UE7B0NfwAsME0qWd8Noz3/GA3QonJVucsbcY/3HxO1p/jdhCgZvjVqyuvPD/1fP7VvgFbJ0uxmMRvDqZe1K2W86juuGGj0RjaPTyDLz581PjaTcqxRnV5hc/j15duZQ5MA34fPnrzOVjeWod3X7ceH7lpq6P+rGy8vnxrNhTFQwcHc17pmQtHjatzPgEstzB9ZlFj6jk4PB1K+9zIt5wH0Jt2Sz2W08kMftU1mzqNq3b7T01oCRK7RqdDeNf3ds9b9Kkulat0DPjJFimlqaQnv4D/yg0dxsjA3pEZV5b2lAu9BtRhhr/e+vSVqWAEM4m50dUBn+2lLguJevWjkKM5My3dUumTeibxwZ/sMaanNNUE8LXbLkJtVfZL/60N7tXwD00F0ZcY81gd8GHzklTZzY61HUYmc2gqaGuD7fGhaWNqVXtDtTZBx6q2hmr8zQ0bjY+TL+5+n9CuJqp2rKvcefxSSj3DnyMT/ebLVuGxD9+AD924Zd7EKDdoTbserOH/4E/34p3feRqv+erjWXuiTo3NIpmbWtqSuVleVRPwG1faojGZ9oqsKwG/R8ZyzoQiRoIp4BNY1mo/wdRcW6U11j/8vLMsfzQm8b4fPmdMNFMtpJiDAT/ZcnYyaASNTTUBdDicyZxUE/DjBmW6ykJZwhWMRHF6PH7wEQLG5mG72pSSnlyZ2zMTejmP29m7SqLP4i9cXbd6MpFpLOtGZVLPD3b14oEDqRe9z7xuu6WTbicjXDPZq5TznLesGVX+1MuI3ydwk9I8+4u91qdvHTidGuW5fUWL44DzbVeunvd8umJdh5b5VF20qs3okzgyOFXQKzrFNjoTNnoiGqr9jmfou2VJc63RMzE8HTIemxeEIjE8kJgQdWps1uj9SMfuhJ6kXMu33M/wly7gV0t/V7bXI+B3Fm6q03oeOugs4P/CQ0fwyJGzxsdXb1hkvH+IAT9ReieG9Oy+G0GjOs7vvgUS8J8cSWWIlrXUOWrOAuyV9KjBZaZsMsVpjbsFauSMxaRpSk+GgF8p6Ulu9gSAt1+5Bi/flr5MxUzL+uUZBDx3MhUInZ+mWVqd1nPf/n6ELS7VO6hM4Th3WXOWW2ZnXsYFAC/flrk8qK7aj+0rKrOOX+0TWuVwhLKb/D6hnYz1eKhx98jgpFbusTfLLolem/X7SVodf46A38lITkBfyFjKGn43etQAPeB/7NgwZkL2ThIfOjiIL/82Nc3svdevx7uuXW98fLCfJT1EaWl153mW8yRdu7kTtVXxP8WjZ6bmjR+sROpq+XwOhmrGaCBHJpoNu9YVY9vu8HTIWIjUXBtIuxAJiNe+mnc0nL+yFR+5aWva26fj5ljOTPX7SRevasPSRD/C2EwYj70wNO826RzQAv75E2PsuHn7Uly2Jl4K0FpfpSUV0tmxtjLn8dsp5ykWtXwx3VbkUtljmhK3L8vUOKflmLlGc6pNu821+TftjpVwDn+3Cz1qQPzqwObEwrdQJIZHj1o7ngDxKUF/+6PnjI9ftHER/s+fbNamfx0emCjoYAYvYcBPtpxQztrXurTZtL46gGs3pcZvPXCg8rP86olTPgG/ml3K9eJ5VmvYZcCfjTaLv0AlPWp2P1PDLhCvk1+j/I001wbwlTdfaKluOEkfy+k8CJBSaoHR+SvmB/w+n8ArlCsPv7RQ1iOlxH6lpOe8PAN+IQTuecel+PSt2/Dff3k5Ohqz/71fvq4y5/FbbdgtJvXEw0uz+Pee1DP6WUt6HGb4u3IE/G6U9DTVBpDMD0wGI/OaVIvF6e8oHXXr7k9291n6ntlQFO/63m5MzsWvCCxvrcMX33Qh/D6BjsYa499iLhzTKhcqGQN+sqV7yL2GXZV5Wk+l0wN+579HO9kybUIPS3qy0pt2Cx/w5yqxukY5If7sGy7ASpsvoPpYzpDjaVi9IzPG5I+WuqqMJ6tqWc8DBwYQjGRfCnd6fM6436baAFa2O+tpUdVXB/Dmy1Zhy5Lc5UEXr24zrqIcHpwsae2zm9wMutyiLqlyc5lSvvaaAvzjQ9MZRzY6ruEvQsDv8wmtrKdUf8vq8c1Jw67q5u2p48mDBwctLcz69wcP4/nEBJ7qgA//8ZaLtMTHOUrZ4EKp42fAT7aYa/jdcv2WLqNxbt+pcU/VdhaCVt+YxwuxWg/bNzqb9dKkmqnuZIY/q2KU9KglWJkadpM+8NLNuPM15+Gn774y47SZbGqr/KhPlAxFYhKTDpslnzupz9/PVBN+/ooWY1Th5FwE+7LUQwPAASXYOmdpc9FrzRtqAti2PH5VQcrKqePvdelKopu2KlOd/nfPafzoqZMlfDRxs6EojgzqtdxSpj8hMU8+chzwp2kOdyPgB/TG3VzT2wrFygQyq85Z1oybt6eSgnf+6lDWpMWh/gnc83i38fEnbzlX69MBgK1LUwH/QpnUw7l8ZJmUUstMr80jM23WUleFqzYswu8Pxzvpr/2336OzqQYr2uqwoq0eK9rqsKylFhAC4UgM4Wj8v1BUIhyN4bxlLbhp25KSN6Wpjg5O4o9Hh3DjeUuwzDSnuceFkZxAPIu5qLEGQ1NBRGIS/eOzWNGW/v7OsKTHMrVpN11znRsGx62V9ABAY00At+1YnfU2ubTVV2MmFJ8MNTYddlQjvEx8o3MAACAASURBVEcpe7ggzWbWJCEErljfYVx+f6p7FJesac94ezfr953asa7dOKHZeXwEL3WwB8BrepReIa9k+C9f14HrNncax/qP/GwflrbW4kUbO3N8Z+Ec7B9PmyzZf2pcK/cCgKGpEGbDqUl16tWzXDobs5cKuhbwe2A0p9WSRas+dOMWPHhgEKFoDM/0juG+/QO4Kc3QglhM4mP37jf+Pa9Y14E3pdlCrgb8CyXD75mAXwixAsCnANwIoANAP4B7AXxSSml5R7sQoh3APwB4NYClAIYB3A/gH6SU1oq/KK3BiaBxoGupq8o45s6pm85barwIAPFLnmcng3i2N3PzlOq/3naJtpmvlIKRKN78zZ0Ymgrh8w8dwb+97nzcmBhXGI1Jrfwmn5IeAFjVXoehRLbo5EjmgN9OCclCt6ixBkLEs3zD00GEozFt/KQbBor879HWUGXMoR6ZCTk60dyTZsNuJpeuaTMC/qe7RwCsz3hbdSTnecudT+jJx+VrO/CNPxwHUBnz+OfCUePqlN8n5iUdSsXnE/jKn16E13/9CRzqn0AkJvGe7z2DH7/7CkvlV4WgnshWB3xG3Xu6ST3mhl07SSZbGX4bJxJmpS7pCUaixk4Nn9CHSzi1sr0eb7tiNf7r0RMAgH+9/3m8ZOvieb1MP32mD0/3xEPGKr/AP7763LT/RueoGX4PlZYVkidKeoQQ6wHsBnA7gCcBfB7AcQDvA/CEEKIjy7er99MB4InE9x1L3M+TifvdLYRwd0/4AlOocp6k11y0HG++bBVWtNXNm0pixf0eavbtG53F0FT8gDc5F8G7vrcb//jLgwhFYugfn0U4Gs8+LGqsRmNNfufdKy027jLDb12V34eOhvjvSEoYJ1RuGijymNQ2bUmb/SAgHI1h/yl1Vn6ugD+V0X+6ZxSxLOVmXsjwX7wmtejr8MBk2W/9Vo8Fy1vrXD9hzUdjTQB3v/1So5RtMhjBO+5+qqA7L7JRd0vcrGSN0zXu9uZx1SRb0+6TJ0a0CVj5BMmlHs15Rjm2LWqscTyD3+yvb9hgbNLuGZ7B93f1aF8fnwnjX+573vj4z69ehw3KHhPV2kUNxnTAM5PBitq/kYlXMvxfA9AF4A4p5ZeTnxRCfA7A3wK4E8C7LNzPPwPYBOBzUsoPKPdzB4AvJn7OjS4+7gWluwATelRVfh8+fes2AEAkGsPAxBz6RmfRNzqLU6OzGJiYg0/Eb1cd8KHKLzAxG8F3d8af9F7KyqXLqnzr0RN4pndUK81w4zK7eh+Z1tXPhaPGtIKAqamL0lvcXGME+oMTQSxtcTdDqpX0FCHgz3c05+GBSQQTmc/lrXU5+0DWLmpAR0M1hqdDGJ8N44WzU9i0eP6L78h0CP2J30VNwIf1ne4nE6xorq0ysruRmMRsOIr6aq+8RNrn1iSwQlnSUou7b78Ur//6E5gKRnB6fA633/0UfvSuK/JOgtilZvLfcOlK/O+e04jEJE4MTWNiTi9/6x1ObWu1e/xuqatClV8gHJWYnItgLhxFbZUfYzMhvP+/n0XynPjyde15/Zvpy7eKX8PvdjlPUmt9fJP2nb8+BAD40sNHcetFK4zyp3978HnjysKyllrc8eINGe/L7xPYvLgJexL/9of6J3H1xspOhJX8lD+R3X8pgG4AXzV9+eMApgG8VQiR9VVACNEI4K2J23/C9OWvAOgB8DJm+Z0r1ISedAJ+H1a01ePydR143cUr8L6XbMSnb92GO1+zDZ+45Vx85Kat+H8v24KP3XyOcZZ+cmQWp9Oszi4F9SCrXq14tncM//fHe4yP85lPnLTSQsCvZlw6m2ocbzFdSAq9bVcr6Wkp/AtNu1bXaz8IUMt50s3fNxNC4BIla/5Ud/pGWLWcZ8vSZteygU4ks4cAMDXnnS2wTqjHArtTnYpl69JmfO22i4xj5MH+CfzND55BxOKyNjeMz4ZxPPHaFvAJXLCyVTsx3W/K8ufze/X5BBaZtu1KKfHBn+zF6cRJb2t9FT7/xgvy6kfLp4bfjZn0hSxXVDdpj86E8R+/PwYgfpXm+7t6jdv9wyvPzXnCrk7qOdifebCAlBIf+dk+/OzZvoyTm8pByQN+ANcn3j4opdSe5VLKSQCPAagHcHmO+7kcQB2AxxLfp95PDMADpp9HNqklPWsLHPBbVR3w4eLVqaDCK9M11ID/FduW4u9v2pq2TMmN2djaLP7RDAE/l27ZpjXuuhzwz4WjRr1uwCewqKHw/yb5Zvj3aBN6rJXdaGU93elbsfafcmfDrhualEzuRAUF/F5ZupXONZs6ceerzzM+/t3hs/inXx0q2s9XA/rNS5pQW+XHdqUh3TxhSu+/sv97NW/b/d7OHjx4cND43Gdeuz3vq4nalB6LAX84GsO/3Pc8tn/iAfz5PU/lVdI2UMCrlzUBPz6obNK+67ETODkyg4/eu9/YXn/d5k687Nzc/Xx6427mjbsH+yfwg129+Nsf7sG1//b7op6QuskLAf/mxNsjGb5+NPF2U5HuBwAghNid7j8AW3J+c4VSS3rcyEy7ZcfaVIuHV8p61Brp9oZq/MU16/DDv7x83sHPjUvtVmr41fr9ziY27FrR1VS40ZzqFYOuIl1x0ZdvOQn4U4FPuoVb6aiTeTKdjKsZ/lIH/GopyZTD0aVeoR6vvVjSo3rTZavw3utTTd3ffqJbCxoLSS3nSfalbFMCfvN8/nwnH6m1+Y8cOYt/VE5u/uyK1a5Mh2q32a/TNzqDN3zjCXz9D8cwHYri4efPZF08lkuhSnqSXrl9Kc5P/BuFIjG86T93Gv+O1QEfPnlL+kZdM6uTen6lLA+8ZuOikl6FzIcXHnXymZXpryv5+VyvMG7dD6URi+kjOQtd0mPHjrWpoGLXcW9k+NONV7tkTTt+dcfVxhKlhmo/rt6Q/yi6Jc21xg6DoakQptMEKvqEHmb4rShkSY82o7oAL4jp6Mu37F2WngpGcORMPAPmE8B5y61l+M9d1oy6qvj8/1Nj6UvuDnqgYTepSSnpKedL9+FoDLuVKyqZGhe95AN/shkXrYq/PEsJPPbCUFF+7l5tc3T872+b8vetXgFQJx/5BBxNPupSjr9f+u1RYyLQ1qXN+Lubttq+v3TaGpSSnhzP9YcODuIVX3p03jS8fMpjCz2QQAiBjyi/q1PKY333testT77bouyEeOHMVNoFgVJK/HpfKuBPNwq0XHgh4PckKeXF6f4D8HzOb65A/RNzRsNee0N1XjOC3Xb+ylZjNNfxoWnXyy+cUAMqNdDqaKzBPW+/FD9/71X4/f+73pUFWH6f0EZx9o3OP1DrE3qY4bdCPTEadHkWv52lW27JJ8O//9S4cbl80+ImNFhsqqzy+3DhqlSOJTkuL2kqGMGJRCba7xPaC3ApaBn+Mi7pee7kmLFcbXlrXckaoe3w+QRevDVVhvHYsWIF/PMz/JuXNBlJlJ7hGYwnjud9SsnkMoeTj9QMf/I5VVflx5fffCFqEyfH+bIyljMUieGffnkQ7/zO01qCKimfq5rFGEiwY13HvCWEq9rr8e7rMo//NWuqrTKu0kRiEkcHp+bd5mD/BLoTyc7GmoC29bzceCHgTz7bMqV2kp/PNYzdrfuhNLSGXY9dHq6t8mtNhE9maA4sJvUyqnkijs8ncP7KVle33aobd9M17qpNu13M8FuiZqbcPoksxU6EfMZyavX7Fst5ki7R6vj15+ah/gkj6NnQ2ehawOOUWsM/WcYB/+8PnzHev2ZTp6cWEmZzxfpUeeYTx4YLPhp1aCpoZIdrAj5sXNyYeN+v7QRIlrf05lm/D6Tfcv7JV52LDV2Nju4vHW0sZ5qT+2QJT3KmPRCfavNSJYDO56qmltAo4ECCD798i9Yb98lbzrV9DDknx8ZdtZznJVu7Sn6MyocXAv7DibeZaus3Jt5mqs13+34ojULP4M/X5R4r63FrgYpVuUZzsmnXPvXEyP2SnuLO4Af0K012A361njfXwi2zS7VJPXqG/8Ap79TvA6aSnjKu4f/DkdQCw2vLKCO5fXkLmhJXWfrH57TXnUJQy3nOXdasZezVOv7k379a1up0pLI54L/l/GV4/cUrHN1XJs11VUjGwZNzEYQTTaZSSvzwqV68/At/NLZKA8CLt3ThV3e8CDds6TI+5zTDL6Us2lLB9Z2N+NSrzsWKtjrc8eKNuF55/FZlq+OvpHIewBtz+H+XePtSIYRPndQjhGgCcBWAGQA7c9zPTgCzAK4SQjSpk3qEED7ER3+qP49sUDP8az3UsJt02doOAC8A8EbjrlbSU4Typ1U5GnfPsqTHto6GGvgEEJPx8W/BSBQ1AXeyO4OTxcmAqdSSntGZMKSUljO//colersTui5c1Wb8Hp8fmNDmmqsLt87xWsBfpjX8ZyeDxuSjgE/gqg2W9lZ6QsDvw4517XjoUPwKxWPHhrGu073Mt5naiG5eJKfW8e87FQ+O3Rh1qmbyV7XX487XnOf6FRi/T6ClrgqjidehsZkwQtEYPvzTvfjj0VSpVMAn8KEbt+CdL1oLIYR+VXPSWZJjbCZs9CU0VPu1q2aFcNuO1dpuG7u2Lk2VEZoD/koq5wE8kOGXUh4D8CCANQDea/ryJwE0APiulNKIOIUQW4QQ2rQcKeUUgO8mbv8J0/38deL+H5BSHnfx4S8Y2oQeD2b4L1rdikAipXFkcMrRFBI3ZSvpKYRcAb9Ww8+SHkv8PqGP0HNxUo9a41qsDH9dld/odQlFYpgNz29Qy0TdNGy3FK2xJmAE81ICzyh1/GrAb7URuJD0gL88M/x/PJrK7l+8uq3gAZfbrly/yHj/iQLX8WsNu6ZRs2rAn6zz10Zytjt7HdzQ1YSPv/Ic3HrhcnznHZcV7N9HHc35X388jpd9/hEt2F/TUY8fvesK/MU164wTDvW1wemUJH2/iPeTS9os/tMTWhlZJZXzAB4I+BPeA+AMgC8JIe4VQnxaCPFbxLfsHgHw96bbH0r8Z/aRxO3/jxDi4cT93Iv4lt0zmH9CQRZ5cQa/qr46oM1OLvU8/vEMTbuFkm35VigSM06AfALoaOCWXav0jJd7AX8pmnaFENq4PjsnxUPqWNdG+yeMl6yeP48/GIniyGBq9rUXMvyNNannark27WrlPJvLLyN55Qa9jj/mwiKodKSUaRt2kzYtbjJOkPtGZzE6HdKOrflsSb/9qrX43BsvKGjyTH2uf+OR48aYWSGAd169Fve97xpctKpN+54lLkwmK8WxLR/LW+uMhXsTcxFjAVqllfMAHgn4E1n+SwDcA2AHgA8AWI94oH65lNJSjUbidlcA+BKADYn72QHgbgAXJ34O2RSLSZwcSU1+8epM5x3rvDGPPxyNGfW/QqAoGbaVpuVbapbirJKd7WisKdsZwqWglj+51bgrpdSuFhRiTnUmTkZzzoQimA7FrwZU+31orrNfCXqZ0mOTbKo/OjiFSCKYW9Veb5T5lJJew19+JT3RmMQjZVq/n7R5cRMWNcaD1dGZcNpGSjecGpvFcOKkt6kmMK9UtTrgw1ZlatTeU+OuBfzF0JrmyvK6zgb85F1X4KM3n4O66vnZ6rb6amM60cRcBLMh61cBk4oxocdNQghsUev4E1cdK62cB/BIwA8AUsqTUsrbpZRLpZTVUsrVUsr3SynnrWeUUgopZdqiNynliJTyfYnvr07c3zuklH2F/7+oTBNz8fo/IP6H79VLxJd5pHHXPIM/3YZdt7XUVRmjUufCMS3IPzPBhl2nFhegcXdkOmQ8n5pqAznXv7vJyWjOocnU7RY1VjuqN75E2Ya95+QYgpGotnDrvOWlz+4DQGOZl/TsOzVu1G13NtVoE0jKhRACVyhlPY8XqKxH3aB73vKWtMvv1Mbd3x4axFw4/rxtrg0UZRhDPpYqiQSfAP7q2nX49R0vwsXK1TYzn0/oSQ4HdfzlVtIDpJ/UU2nlPICHAn7yruFpfWusV12yus2YTHBoYEIrqymmYjfsJmWq49dn8DPgt0NbvuVSSU+xJlik42Q0p3ryuMjh309Xc61xZTAYiWH/qQmjsRQo/cKtpOYyD/j/cDiV3b9mY/mM4zS7ShnP+fixwlyt3aOW86xM//e3fXmqzOe+/QPG+1YXO5XS265YjS1LmnDZmnb89N1X4u9evtVS0JpvHf9gmZX0AHrAHx8VXHnlPAADfrJgpEwC/qbaKqPxT0rgqRLN4x+fTf2+WorQsJu0sj39LH4u3XKuEBn+Ur4gaqM5LWb4z+ZZv5+k1/GPaBl+L9TvA6Ya/jIcy/mHI6n5++VYv5+kNu4+eWLEmPriJn3DbvpRs2qGXz2Oer2cBwA2Lm7C/e+/Bj961xW40FSrn82SPJMcAyUYSJAv82jOSiznARjwV7RQJKZtBnRqeCoVGHi94fOyNfNrhYttdDqV4W8r4mVfrXF3ONVzcVYt6eGEHlu6tOVb7mT4Bwu8dj4b82hOK9QJPYvyCPjVefy7TozgUH+qYdcLM/iB8h7LOTYTMmar+wTwog2LcnyHd63qqDeWCc6EotjT5+6+zFhMaiU96sAH1cauRtQE5odJTkdyloN8Fw4OlKg/KR8bFzcapbfdwzP48dOpCvBKKecBGPBXrLlwFC/9/B9w9b/+Dncp2/ScKJcMP2Bq3D1emsbdsVkPlPQoJ3qD2pbd8jgAe8XipvynVpipGbBizeBPanVQ0pPPSE6VunH3kSNnjbGgXU01nrnyVM5jOf94dAjJgTbnr2zVxjKWo6uULP9jL7hbx39ieNoYrNDRUI3lrXVpbxfw+9JefSqHDL9T+S4cLMeSntoqP9Z3psq0frCr13i/Usp5AAb8FeuJY8PGJamfPpNfv/LIdOoFv73R2y8il61pR7Jsdf/piZJclh9TAql0kxIKZWVb+tGc3LLrXCFKetRAu6OhuP8e7Q3qtl1rGWy1pGdRHs//9Z0NRsIgooxa9Ep2HwAaqgPG8WMmFEW0QCMhC6Fct+tmoo7ndLuOXy3n2b6iJWuvw/Y0+yG8OqnODWqSY8DmVc1gJKqNgM7neFFsalmPOqSkUsp5AAb8Fev5gdTl8nznh6tNu14v6Wmpr8KWJfEnbjQm8XQJynrGijyDP4lNu+5zY0ydmZo5VjPKxaCegI45yPA7bdoF4tNX1Gk9SV5p2AXiU0oalalJ5TKLX0pZcQH/FUrj7rO9o5gJufdvkW3Drlm6hXCVnOFXy3DsJjnUssfOpvIaAZ1uolUllfMADPgr1uGB1ASM4algXpkqvaTH+wHjDnXmdwkWcI0pTbvFLOlZ1lpnTCkamJhDMBIPTvUtu+VxidUr3BhTZ6YH/MUd7edk8daQ0sOTT9MuAFy6Zv5IQK+M5ExqLMNZ/If6J40rMa31VTmD2HLQ1VSLTYsbAQDhqMRT3fMmdDtmzvBnY/5d+n1CG3lZadSrmnZr+Mtt6ZZqa5qAv5LKeQAG/BVLzfDHZDzod2qkjDL8gB7w7zIF/LGYxL3PnsLb7noS/3Lf84hE3Z/+oGb4i1lHWx3wYWlLvBZVSuDU6CyiMan92+cbsC1Eek1r/o27ajNoc5Ez/G1aht9BSU+eV4guWePtDD9QnnX8v1em87xoY2dRdn8Ugzqt53GX6vgj0RgOnE4lxHKdHK3vbECdkuVd3lpXVplru7q0bbtBbYljLuU4oSfJHPBXWjkPwIC/IoWjMRw7O6V9Lp9ARZ3S4/WmXUBfwLW3bwyzoSiklPjd4TN4xZcfxft/+BweOXIWX//DMXziFwdsHdCsUAOpliJm+IH5ozmHp4JGI19bfZWxKp6sc7txV+0raSx2SY9Sw289w+/OlB4gHtzXVqX+BptrA8Y0Fq9orFFKespkNKc6f/+6CgpSrtqgLuByp47/yOAUgokxn8taanM2ogf8Pq3PpJLr94H41uHkCc5sOIoJGye9WsNumV0F6Wyq0f4WKq2cB2DAX5FODE0jHNWD2HxKEcppSg8AdDTWYENX6lLwt5/oxpv+cyduv/spHDKtaf/ezv+/vTuPc+us7z3+fSTNvnk8nhk73uI4tmOTBZKQkASSAIWWNGUpoQUKJGUp0BYKLaU7tL3ltuV12dvSS4FCc3kBZe8GIZS1hKWkoTRkcULs7PEyXmbfpOf+cY6k52g0M1qOpKNzPu/Xy6+ZkTTysUcj/fQ73+f3PKAPf/twqH9/INLTxEW70socfytHQMZFvVMrSrUy0jPQlVHG7/7OLWU1v7T2moSZhWXN+usWOjOpus9IdGZSevz2Ykf1wBmDkdscyv2ZtMNozqn5Jd16fzHu8pS97TuOs9QluzYWYoq3P3K64nUna7nnqDMOtkw+vxw3xx/nkZySt9bGLdarifW0c4dfUuC56drzz2jhkTQGBX8MuXGevFoX7lprg5GeNll178Z6/uKLdwWiPT0daV3g5Db/7F/v0M13HAnt7w5Eepq8/XpwNOdc4I1ePSMVk8x94ToWwm67gQ5/V3M7/MaYqkZzHi+Jg4VRnF+yq7gYM4pZ83aL9Nzyk4nC1KPHnTEYmRGnYRjq6dB5/mPEWum7IYxavu/YTOHzs0Yr2zH32Y8vFn/PODBe9zFEnTvcoZp0QDtn+CXpTc/cp6fs2aRXX3mWnr5/rNWHEzoK/hhyF+zm1bpp0PTCcmFEVXdHSr2dzS1QauXO48/LpIxe+qSd+sabr9YnX32ZLtxRfCF5/cdv0+0Pn17xPbU47U7p6WnuG6Tg5luz7LIbguCLX30dfmttoGvc7Ck9Uslozpm1O9jBOE84j+XrL9up87YOad/4gK6//MxQ7jNM7Vbwx206T6krnGk93763/oL/0HGn4N9UWcF/4Y5h/ftvXaUvveEpeuq++BWCpcYHa4sxtnOkR5L2bR7Qja+4VL93zf7InXkMAwV/DN1dtsNfW6HiFgTNnhlej8t3jwSywteev0Vf+c2r9L+ee67GBrrV3ZHW373s4kLmfW4pq1d89D/16Om51e6yIkvZXGFDl5RpfkEXKPhPzAbe6LHLbm3GSxax1WNhOVeI23WkTdldPButmg7/sSlnQk9IZ4hG+rv0z697sm5645WrbnjUSsFIT7QLfmttIL8fy4I/kOOvf+Hu4Yliwb9rU3/F37d7tL8w8jnu3Ek9j1UT6Zls70hP3FHwx1CYkZ4JZ9Ot4b7mxlPqsam/Sx++4Yn61at3659//cn6qxdfqDNLujkj/V36+xueWCjKj0wu6BUf+YFm6liod3ouuGA31eRpGaUZfjbdql+g4K9zLGdpfr8VXaSN1RT8IS7YbRfBRbvRzvDfPzGrh095TYqBrowuLLPPQbu7aOdwYdjAT47NBHLi1bLW6pAT6TlzU7zz+LVyn/MqzfBbawMNkXbs8McdBX/MTC8s66GTK7vUtRb87TaD33X57k1688+co/PWmLN89tiA/vYlFxUWMt7x6KRe//Hbat63ILjpVvPXO4z0daq305ssMLWwrHuOFKc1EempTXAudX0d/lbm9/OGq9ht9/hU8gr+dor03P5IMYb4hJ3D6ojhuMjujrQu2lF8I1NPl//49GLhDGx/V4Yxxauo5azmydklLS4Xd6ht1fMbVhe/Z4eEc+M8A84v3LEas8fttMtura44e5Pe9rxzC1//+11HdeN3Dtd0X+4UiWaP5JS8RZnbh4tdqx86G8yME+mpyVBPcZzp9MJyXaMaW53fl4Kz+Nfbn8Pt8Cdl0Xegwx/xgt+dOuaOjoyby50cvzuRqFpufn/Xpr5Y5rTDUMtZzeCEnmQ8V7QbCv6YcQv+JzlPksemq9tAI6/dRnLW6hefuEOvesquwtfuQrhqtHJCT56b4893XCQ6/LUyxtS1+6TLLSBb1QHb4uTmy50NdCWzw1/8va1mBnkr3OFsIFVup9C4eILT4f9vp4lRrcMlBT/Kc5/vjlQYoWr3BbtJQMEfM+6Ensdv31Do8i9l7bqn78tJSsEvSc9yttE+Pl3bvOdTc62N9EjBHL+LRbu1czffqmYRW6nJFs7gz9vubHT14InZNW/biCk9UefuNRD1DP8dTof/QIwLfjeWedejU+vuH7Ga+467+X0K/tUEMvxTC8pVEHFlwW70UfDHjLtg95zNAxp1O5M1LDh0d9mNa6Qnz81z1jpvvdWRHknasXHl5JOB7kzsdg1splrH1JVyIz31bmJVK/cM0Hod/kRGetokwz8xvVDIV3d3pGLdsR7q6SiM0FzO2cAbnWocOl5c01TpSM4k6u5IF16/lnNWJyrY8MyN9LTjDP4koOCPEWut7j5SLPj3bR4ITGapZcHhCWdKT9w7/G5BMzFTWVejVDDS05r/r3I7QTKhpz5hjeYMLNptUcG/dUOP8tHlR0/PBWJfpY47Yzk3JeQx1C5jOe981H2uH1S6yRPBmu0CZxfU/36wtljP4ePFM1pxfoMUhvEqdxgn0hN9FPwxcnRqoVBwDnRltHVDTyC3XcuknnbcZbdW3R3pQATKHbFZqVNzxf+vDS3K8JeL9JDfr8/mIWcudR1jAYNjOVtT8Hd3pAsRpZyVHjlVvss/s7CsOT860ZVJBYYAxFlwLGd0C/47Hi1O6IlznCfP3R29loI/l7M6NEGkp1LVntUk0hN9FPwx4k5s2Lt5QMaYYIe/lkhPG4/lrIXbxTy+zgSTck4GxnK2puDfNryy4GdqQn3CivQEx3K2bl+L7U7s68GT5XP8x0oW7CZloklwLOdSTcMOmsHt8B/YMtDCI2mO890O/0PV74r+iHM2a6Svs2WRy3ZR7VlNIj3RR8EfI+6Enn2bvReAsTpniCdp0a5Uf47/dIvn8EtST2d6Rd56jCfgurgvYPUs2o3CWE5JgdGtD54o3+EPLNhNSJxH8s5mdKS9NzdLWauFNSJPreRO6DkQ45GceQe2DBb2Szl0fCbwXFuJ0pGcWBuRnvih4I+Ru0sW7ErBd+nVFrDzS1nNLnqnx0wBrAAAIABJREFU9DvSpmWLDJtp00CxSD9WQ4c/EOlpYQepNNZDhr8+7gtYpWPqyolCpEeStrk7Mq/S4XcL/iRtUGSMiXyOf34pq3uPeQtQjfEy/HHX3ZEOjB790cPVxXoYyVmdajr880vZwtntdMokZoRvu6HgjxF3Qs++ca/gH60j0uPGeYZ7OxNxSr/eDv/JmdZHeqSVBX9SJqw0Si1j6sqJSsFfyWhO9/E/OhD/s3uuge5o5/jvPTpd2A1858bexOxqesH22nP8jOSsjrvua70Ov5seGO3viv0C8nZFwR8Ty9lcoeMjSef4HZ96Fu2emE5WnEcKFsa1dPhPR2AOv7RyUg+LduvT3ZEuvIFbzlkdn6ltUk9UMvw7Ah3+8pGeY87vf9I6dm4B7cawoiJpcZ6887cVc/w/fLC6HL8b6WEk5/oCZzXXKfgDC3aJ80QWBX9MHJ6YKSxI2jzYrSG/OBkryeFVswBtwilq4j6hJ88tbNyRhJVYyuYKBV3KqKVTTdwOrsSmW2FwN986crq2gj8yGX634F+lw388gTP48wYiPovfnUO/PwFxnrzHBxbunqrq9SwQ6Rml4F9PMMO/9vOdW/Bv5rUmsij4Y+KuMgt2Ja/o7O7wfszzSzlNVXF6+uRssib0SPV1+N0Z/EM9HUq18LRmaaSHMWn1cztXtS7cnZ53O/ytK/jHB7sLC1NPzCxqpszzQumUniRxz75EveBPUod/92i/+jq9DQSPTS1U/Hu4uJwLnMk6c4SCfz3eZC7v84mZBS1lV1+8foQJPW2Bgj8myi3YleSP5nRiPVVM6knSLrt5wQ5/dQX/6bngmodW2jFSLPj7OtOJyfg2ktu5qrXgd4vHwe7WRXrSKaOtG9YezRmY0pOwgn+wO7qRHmut7kxopCedMjqvhnn8D56cLax5OGOom13HK9CRTmnEb/RZu/aY6iNEetoCBX9MrNbhl1TzLP6kjeSUQuzwt3DBriRtGerRMw6MS5J+6Uk7W3osceF2ro7WUPDnclbTi63faTcvGOtZmeNPcqSnP8KLdh86OVc4U7uhtyNxHdULasjxHzpGnKcWlW44GIz0JOvx2E5o+8VEuRn8eW5+u5rJM27BP5yQgt9dq3BiZlHZnK144kBg060IbOrygZdepImZxcR1ZxslEOmpYTTnzOKy8pHj3s50yydZbBtePcdvrS2J9CTj9z8vyhn+QJxny2Aipqe5LnBy/D96qLIO/+EJRnLWYnygW7fLe7ytleM/QsHfFujwx8DMwrIe8F+w0ymjs8f6A9fXHOmZSV6kpyuTLuzAmM3ZwDqG9ZyajU6kR/LiXBT74al3862ojOTMc9d5PFBS8M8sZjW/5GV2uzKpxEXC3Ax/1Dr87oQedy59UgQL/tMVjcgNjOQkv1+xscA44so6/ER6oouCPwYOHil293dt6lNXJphPrHUWfxIjPVKwm1nNGRF3JGerIz0IX3AjmuoL/uBIztYX0Ns3FjP8D5Vk+I9PBeM8SesiD0Q4w1/a4U+aM4a6C42M6YVl3Xd8ep3vCEZ6ziLSU7FKdtu11ga6/3T4o4uCPwbWivNIpRn+2iI9SenwS8E3SGstVCrlng3Y0JOc/6+k2FxnpCc4krP1bwi3D6+e4T+W4AW7UrQjPXcmdEJPnjFGFwQW7q6f4w9GevrXuCVcgbOaq4wiPjm7VBgJPtCVUV8Emhkoj4I/BtwFu+eMlyn4B2ud0lO8bZI6/KNOBKqaDr+7aHe4r/UFHcK1sbezMMpycn5Zc4vZqr4/apGewKLdk7OBmebHEzySU4puwX96bkkP+eMlO9JGu0eTWbxeUDKPfy2zi8t61H+DnkkZbSvZowSrG68g0uM2P4jzRBsFfwxU1+GvrDO5lM1p0n+hM6a1u8Y2mxvpqabDf2ouOIcf8ZJKBUfcVpvjj1rBP9zbUZhpPruYDZzRS/KEHil4BiZKkR63u79nbECdmWS+hAcK/nVGcx4+Xoyrbd/Yq450Mv/PalG6cWc5LNhtHzzy25y1VncfcWfwrzzFW0uk5+RMcAFqqyeKNFNgNGdVHX4n0pOgN0hJ4mZaq431RC3Db4wp6fIXYz3u4340YRN6pODPJ0qLdpMe58k7f2sx0nPHo5NaWF79bNuh40zoqVVw3VL518LAgl0K/kij4G9zx6YXCp253s502dOVw04UYWp+WfNL60cRJhK6YFcq2XxrupopPU6kh0W7seTm+KtduBu1DL8UjPW4k3qOOY/7ZHb4oxnpSfqEnrzhvk7t9DcXXMpa3fXo1Kq3ZSRn7dwY4+m5pbK1g9v4cOf2I3oo+NucG+fZOz6gVJlOfCplNOoUsZXk+JM6oUeqp8PvzuFP1v9ZUozXMZpzej5aHX6pdOFuseBP8i67kjTgjuWMUsGf8Ak9LncDrrVy/Pcdo+CvVWmMsVyTg0hP+6Dgb3NuwX9Omfx+3miF83TzkjiDP2+0v7YpPW6kh7Gc8RScWlFdwT8ZsQy/tPpozsCmWwns8Ad22l1crmjWe6MtZXO650hxBGXiC/7t7o67qxf8h5yxnRT81Qvm+Fe+HhLpaR8U/G3uGwePFT4vt2A3r9oc/4mETuiRauvwLy7nNONPbUmnjAYjUtAhXG6kp5o9LaRgFjwyBf8qozkDi3YT2OFPp0xhQbO13i7JrfaTY9NazHrjD7du6El8UyE4mnP1gv/wRPGNLAV/9cbX6fAHIz0U/FFGwd/GfnD4hL51z3FJUspIV+0dXfW2gYK/gihCUmfwS94bnPw+QydmF7Xsv8iu5XTJhJ6kbVSUFON1dPijnuF/0O/wW2uDkZ4EdvilYJc/Cjl+8vtBjztjqDBM4r7jM5osM03p1Oxi4bWsuyNF5KQGa61bWs7m9MipYqOA/99oo+BvY+/48sHC5899/FadtcZMZjeHV1GHfza5Gf6OdErD/pQda4NvflYTmNDDSM7Y2lzB1IrVRG0spxSM9Dx8ck7ZnNX0wrLml7w3ud0dqUKnO2mCozlbX/AHJvRsWf1sblL0dKa1z993xlrp9odWbsDlTug5c6Sv7Bo3rG2t0Zw3fvf+QlRxqKdDIwk8G9hOKPjb1C33Htd37puQ5J1+/o2f2rPm7dfL4ZUKLNpN4C9xYJFzBW+Q3Bn8GxJ+qj3OSrtd1WS7ozaWU5J6OzOFfSeWc1aPnp4LTKYaHehK7Nmq4GjO1s/iv4ORnCsEcvxlFu4yoad+wUhP8bXw2NSC3uk0HV9z1e5Eje9uRxT8bchaq//z5bsLX//Cxdu0c2TtJ7NqN9+amE5upEeSNg1Ut/lWYEIPM/hjq7sjXdhUbTlnA4vb1xPs8EfnTeG2khx/0if05LlnYSZb3OG31gYiPQe2DK1x6+RYL8d/iAk9dQvO4i/WDm//0l2a8psYZ23q08uffGazDw1VouBvQ1+/+5j+6wHvya0zndKvP23t7r4UjPRUshA1yWM5pWCHv5L/r5NEehJj8yovgOuJYqRHWpnjD0zooeCX1PrRnEcmF3TSbyr0d2XK7reSRG6H/2t3HdN/+Gva8u5j0626ubP18893//XASX3q1ocKl7/12Y9TVyaZ0b92QsHfZkq7+y++dIe2blj/yd+N9FSU4U/wol2p+s23TtPhT4yxGnfbDS7ajVDB7xSPD52YDU7oSeiCXSk4i7/VGf47Hi3m0/dvKb/fShLtGx8ojCddzOb0Kzf+QLc9cLJwPbvs1m+sZN1SNmf11i/8uHDZMw+MrzkwBNFBwd9mbvrxY/qxf2q3uyOlX716d0XfN+JOnplZ1OLy6pNncjkb7FgnsICtdjTnqTn3/4sOf5xtrmHzrcXlnBb837l0yqinIzrdsGCHf07H6fBLKunwtzjDz4Se8lIpow+87CJt8dfWzC5mdcPf/6fufmxK1lodpuCv20BXpvB8NbeU1Qe/dZ/+52HvDWhXJqU/uvZAKw8PVaDgbyPZnNU7by4ukrn+sjMD777XkkmnNNJX2YZSp+aWlF+LONCdUWcmeQ8Tt9A5VkGG/+Qsi3aTYq0xdaspXbAbpYWwO5yC/4ETs4HH+2h/8t7s50VpLOdDJ4ujD88eW30aWxJtG+7Vja+4tBA9PT23pJd+6Hu69f6Thb1RBrsziYymhsEYo3HnrKY7HfC1V+8ONAwQbcmr5NrYv/zoER30d1rs60zr1VdV1t3Pq3TzrRMzxeuSGOeRgh3+4xV0+In0JEcts/jdDHhUJvTkBTffmtWxqeCUnqSK0lhO9+/n+WWls8f69dFfvqTwu3V0akHXf/j7het3jfZH6k12u3Gf8/Kbv20b7tFrqqxB0FoU/G1iOZvTu79yT+Hrlz95V9UdC/dd+lqbb7kTepLaFQlEeiqZ0jPHot2kCCzarXAn5smI5vclacuGbuUj4UenFvTQyeLOpImO9HRFp8Mf5cdPVJy3bUgfvP7iwhnpfHdfknaN0IWux3iZJMFbrj2g7ghFE7E+Cv428dnbHi4sQBrszuiVTzmr6vuodPOt4ISeZL7gBxftVhDpmSHSkxSBSE+lHf6FaE7okbyN5rYMFRfuHjwyVfg82R1+t+BvbYbfHQs6GLHHT5Q86awR/c2LL1wxD37XJmJQ9XCbhZJ05d5RPePAeIuOBrWi4G8Di8s5vcfp7v/KlWcVZoFXo9JJPRMJn9AjeWc28q8Zp2aX1lzkLHm50bxhTrnH2ngNi3ajOoM/z91x191LLMkd/v7Aot0WR3qc55fBCD5+ouSnDozrHS+4IHDZWaMs2K2H+5zXkTZ6688dICLVhij428C//s8jeviUt2hrY1+nbrhiV033MxaYPLN6oRLcZTeZxWs6ZQJnNyZm1u7yn3KmGg3R4Y+1kb5OdaS9F7vTc0uaX8qu8x3BKS9R6/BLwYW7eT0dafVFbL1BM0Upwz8Z8TeMUfPcJ2zVn//8eerrTOuczQN0o+v0xDM3Fj5/zVW7tXuUMybtKLnP5m3kORdsVUc6pXfdfFAvfOKOmhf9jbqRnsnKIj1J7fBLXpwhH+c5NrUQiD24FpdzhbxoOmUC2V/ETyplNDbQXXgT/tjpeZ25zsi/qQgv2pWCC3fzkhznkUrHcra64Hc6/D3Re/xE0Ysu2aHnPWGrujIputF1umD7Bv3jqy/TiZlF/fTjePPUrnjmaAOplNG155+hZ527RVn3fHuVaon0JHXRriRtcs5urD3GNLhglxeX+Bsb7CoW/JPVFfxR7NCWG623KaFn9/KCi3Zbl+FfWM4WIoVR28Mh6lhUGp5Ldm1c/0aINAr+NpJOmRWLkaoRHMu5VqSnWNwmueCvdPMtdyQncZ5kCEzqqSDHHyz4o/e062b485Kc35eiE+mZKlmwS0MBQC3I8CdIYLb89OKqZwtOOBNnRhI6pUeqvOAPbLrFSM5EqHYW/1TExyoS6VmpuyNVaLAsLOfWXbjfKJNz7mOH5xcAtaHgT5CuTLowMjKbs4GsvivQ4U/waf3R/uAbpNW4C3aZ0JMM7mjOSib1lO60GzWjA13qKtlRO+kdfmNMJEZzBjr85PcB1IiCP2HWi/VYa1m066u0w39qjkhP0tQX6YneY8QYsyLHvynhHX4p+OasVQt3A5tudUXvsQOgPUSm4DfGXG6M+TdjzAljzJwx5kfGmDcYYypedWOM2WqMeZ0x5ovGmMPGmAVjzIQx5mZjzM838vjbxdg6k3qmFpa1lPWiPr2d6UQvenI7nGvttut2+Df0JPcNUpJUG+mZjviUHknaPhzM8Y8mvMMvRSPHH/X1HwDaQyQKfmPMcyR9U9KVkj4n6a8kdUp6l6RPVHFXr5P0Xkn7JH1N0jsl3STpKZI+Y4x5Z4iH3ZbW6/CfmGZCT15gzcNaHf5Zd9MtOnBJENhtd40Rt3mTEc/wSysn9YwOJPv3XyrdbbdFHX530y3WCAGoUctfeYwxg5L+TlJW0tXW2h/4l/+RpK9Kus4Y80JrbSWF//f9+/hGyd+xX9J3Jb3RGPMxa+2tof4j2sioO5qzTKHCLrtFFXf4nRfkDRT8ieBGeo5OzSuXs0qtMUHLjYNEtuAvWbib9Ay/FI3RnHT4AYQhCh3+6ySNSvpEvtiXJGvtvKQ/9L98bSV3ZK39bGmx719+p6RP+l9eXdfRtrlApKdM1/oEM/gLNvR0KOMXcVPzy6vuqBrcZTfZ/2dJ0dOZ1qBffC1lrU7Mrr6oW4p+hl9aOZqTgj8am28FNt2K6GMHQPRFoeB/mv/xS2Wu+6akWUmXG2PqffXJP2u2dsvEFhsfXCfS40zoGU54wZ9KmWCXf5VYD5GeZApM6lkjx2+tjfyUHkna5nT4ezvT6ovocTZTfwQiPXT4AYQhCgX/Pv/jwdIrrLXLkg7Jix6dVetf4MeGni/JSvpyrfcTB+t1+In0BG0aWH+33VOBOfz8nyXFeIWTeuaWsoU9L7oyKXVmovC0u9LZY/2FN7gX7hhu8dFEQ3DRbmsiPYEOPxl+ADWKQrtgyP94epXr85dvqOXOjbct4QcljUv6Gz/eU8n3rZbzP6eW44iKwKLdMhn+4KJdTumPVtThd6b00OFPjMCknjUK/naI80hSd0daN77iEn3rnmN69gVbW304keCejZlqVaRnLrjTLgDUIpRnD2PMYUk7q/iWj1lrXxLG312Bd0h6gaRvSfrNJv2dkTU2GCxgrbWBrdqZwR+0qYLNt1i0m0yBWfxrRHraKZKxf8ug9m8ZbPVhRMZgJCI9ZPgB1C+sV5+fSFp/GHXRI87n+Q7+ULkbOpefqvagjDFvl/RGeWsBftZau/78PJ+19qJV7vNWSRdWeyxR0duZUX9XRtMLy1rM5nR6bkkbnIWmEyzaDVhv862F5axmF73FvJmUiWw+G+Ebr3C33ak2GMmJ8twzMtOtGsvZJmeIAERbKK8+1tqn1/Htd0u6WNJeSYEYjTEmI2mXvIW291Vzp8aYd0l6g7x5/Ndaa2frOMZYGRvs0vQx70Xkxu/cr9c9fU/husCUnn4K/mCHf2XBf3o22N13z5Yg3jYHIj2r9xLaYcEuyuuPxFhO3jACqF8UVo991f/4M2Wuu1JSr6RbKu3OG89fyyv2b5bX2afYd1xz7pbC5++4+aC+8MOHC18T6Qlar8PvxnmGWFCXKHGM9CAoEmM52XgLQAiiUPB/WtJxSS80xlycv9AY0y3pz/wv3+9+gzGm1xhzjjFmR8nlRtIHJP2qpC9Kera1dq6RB9+OXv/0Pbp890jh69/+1I/0vfsmJEkTzlhOIj3rd/gDE3qYwZ8o40PFx8aRMiNu89woSH8XBVs7afVYzlzOtsWmbQCir+XPHtbaSWPMq+QV/l83xnxC0glJz5Y3svPTKm6alXeJvKjONxTcSOstkl4paU7SDyX9bpmIxQ+ttZ8P+Z/RVjozKb3/JRfpuvffonuOTmsxm9Ov3HirPvbKSzW/lPNuk04RP1BJh79MwX/SmdDDDP5k2dTXpUzKaDlndWp2SfNLWXV3pFfcbpJIRtsaDIzlbH7BP7O4LH+iq3o60upIR6FHB6AdReLVx1r7eWPMVZL+QN68/G5J98qbqvNea62t8K52+R97JP3eKrf5qKREF/ySFz/5+19+op73N7fo2NSCTs8t6foPf79w/ca+TvLoWj/S42b4h5jBnyiplNHYQJce8eM8RybntXOkb8Xt6NC2r1Zn+ImDAQhLZNoF1tpvW2uvsdYOW2t7rLXnWWvfZa3Nlrnt1621xlp7dcnlN/iXr/Xnhmb9m6Ju23CvPnT9xerxu5JM6FlpsDujTr+rNruY1UxJjvfUHDP4k2y8gt12KdraV39Jhr/y3lM43McO+X0A9YhMwY/WOH/bBr33RU9QaTN/hAk9kiRjTKDL7+b4l7M5ff624oRZd1MzJMP4wPqjOcnwt6+OdErdHd7LZM6qMIK3WYiDAQgLBT/0jAPjeuu1BwKX0eEv2uS8+XEL/o/cclh3PDopSeruSOlnz9+y4nsRb5udDv+RVQr+qQWKtnY20MIcP5tuAQgLBT8kSTdcsUsvv2JX4evzt21o4dFES7kc/yOn5vTOmw8WLn/90/do23Bv048NrTXuzuI/XX5yMJGe9hYczdncHP/kHI8dAOHgGQQFf/Cz+/W4MwZ1em5JL750x/rfkBDuaM5j015m/0/++ceF0/t7x/v1qqec1ZJjQ2udsaFY8D94svx2HxT87W3AWbg72coOPxl+AHXg1QcF6ZTR8y/a1urDiJzSDv9X7jiim358pHDZ2553HuPyEmr3aH/h83uPTpe9TXCnVIq2duP+zKabXPBP8mYRQEh4BgHW4Rb8D0zM6DO3PlT4+hcv3q4nnrmxFYeFCNg92i9jJGul+ydmys7id8dysrdF+wmO5mx2wU+GH0A4aEsC63AjPf/034/o4VPe5s0b+zr1u886p1WHhQjo6Uxru792I2el+47NrLgNkZ72NtDduln8boZ/kMcOgDpQ8APrcDv8OWcM9+9fs1/DTDNKvL3jxVjPPUenAtdlc7aw1sMYqa+Toq3dlM7ibybiYADCQsEPrMPt8Oddumujnn/h1hYcDaJmz/hA4fN7jgRz/IEZ/J0ZpVLsXt1u3EK7+Yt23Y23eLMIoHYU/MA6Rks21OpIG73teefKlO5WhkTaM1bs8B88EuzwuzP4+4lktCU3StP8Rbt0+AGEg4IfWEdfZ1o9zkLMV1+5W2ePDazxHUiSvU6Hv3RSD/n99hdctNvcDH+gw0/BD6AOFPzAOowx+unHjUuS9m8Z1K8/7ewWHxGiJD+pR5IO+5N68pjQ0/4CYzmbnOGfnGOXZgDh4BkEqMA7fuHxevmTd+nssf4VYxeRbPlJPQ+cmC1M6jlwxqAkFl3GQX9368ZyBjP8PH4A1I4OP1CBdMro/G0b1MuUFZSx2qQeIj3tr1VjOZeyOc35Z4tSxosWAkCtKPgBoE7umg53Ug8Ff/sbcDP8TYz0uI+d/q4MQwIA1IWCHwDqtFqH3818E+lpT+7PrZmRHje/T5wHQL0o+AGgTntW7fA7YzlZtNuWBlo0ljN4doiCH0B9KPgBoE5njwUn9Swse9lrIj3tr7czrfx+aXNLWS1lc035e903i4M8dgDUiYIfAOqUn9QjqTCpRyrZaZcOf1syxgR+djNNyvGz6RaAMFHwA0AIyu24O0ksIxZakeOfDIzk5M0igPpQ8ANACPaU2XF3eoGNk+JgoAWz+AOLdnmzCKBOFPwAEIJyHX4y/PHgRnqaNYs/sOkWjx0AdaLgB4AQ7HU6/PcUOvxk+OOgJR1+MvwAQkTBDwAhcCf13D8xq4XlLKMVYyKQ4V9ofoefs0MA6kXBDwAh6OlMa9twjyQpm7M6dHwmMKWHoq19DTkbX52abU7Bz8ZbAMJEwQ8AIdnrbMB1+8OTWvRntnekjboyPN22q419nYXPT8wsNuXvpMMPIEy8AgFASM4eLy7cvfX+k4XPB7o7ZPJ5H7Sdkf5iwT/RrIJ/gSk9AMJDwQ8AIXE7/Lc9UCz4WbDb3gId/unmFPyTc3T4AYSHgh8AQuJO6rnbH80pUbC1O7fgn5hZaMrf6Y7/JMMPoF4U/AAQkt1jfYXPrS1eToe/vW3q7yp83oxIj7W2ZJdmHj8A6kPBDwAh6e3MaPvGnhWXM5KzvTV70e7cUlbZnPeOsSuTUlcm3fC/E0C8UfADQIj2ODn+PDq07W24t7Owx8Kp2SUt+9OXGiWY3+fNIoD6UfADQIj2OJN68ij421s6ZbTBydGfbPAs/kB+n8cOgBBQ8ANAiPaW6fCT4W9/zYz1TDoF/wALdgGEgIIfAEJUvsNP0dbuRtyFu9ONndTjLtilww8gDBT8ABCis8dWFvz9FG1tb6SveZtvTc6x6RaAcFHwA0CIejsz2jYcnNRDl7b9NTPSM8VITgAho+AHgJC5G3BJFG1x0MwOv1vws+kWgDBQ8ANAyPaUxHr6uyja2l2ww9/oDL+zaJcF3wBCQMEPACHbQ4c/djYGFu02usPvZPjp8AMIAQU/AIRs73hph5+Cv91tauqiXTL8AMJFwQ8AIds9Giz4mbTS/jb2N3PRLlN6AISLgh8AQtbXldFT941Kki7YNqTBHrq07a65G2/R4QcQLp5JAKAB/uaXLtKt95/UhTs3yBjT6sNBnYZ7iwX/ydlFZXNW6VRjfq5uh59N2wCEgQ4/ADRAT2daT96zSb2d9FXioCOd0pC/gNZa6dRs47r8boafs0MAwkDBDwBABUb6m7Nwlw4/gLBR8AMAUIHA5lsNGs25nM1pZjErSTKGOfwAwkHBDwBABZqxcHd6oRjn6e/MKNWgdQIAkoWCHwCACmzsK26+1ajddqfm3fw+cR4A4aDgBwCgAiNN2Hzr9Jyb3yfOAyAcFPwAAFSgGZGeQIefBbsAQkLBDwBABQJTehq0aHdyng4/gPBR8AMAUIERJ8M/0YQMPwU/gLBQ8AMAUIFmRHomnQw/i3YBhIWCHwCACriRnmZk+OnwAwgLBT8AABUY7g0W/LmcDf3vcHfZZdEugLBQ8AMAUIHOTEqDftc9Z6VTTvwmLMFFuxT8AMJBwQ8AQIVG+hu7+VZw4y0iPQDCQcEPAECF3IW7jRjNSYcfQCNQ8AMAUKFGT+oJbrxFhx9AOCj4AQCo0Ijb4W9Awe+O5aTDDyAskSn4jTGXG2P+zRhzwhgzZ4z5kTHmDcaYdJ33+4fGGOv/+amwjhcAkDx0+AG0o0gU/MaY50j6pqQrJX1O0l9J6pT0LkmfqON+L5T0FknTIRwmACDh3EW7E9PhLtq11gYy/Gy8BSAsLS/4jTGDkv5OUlbS1dbaV1hrf1vS4yV9R9J1xpgX1nC/3ZJulPSf8t5EAABQl0ZGehaWc1rKerP9O9JGXZmWv0QDiIkoPJtcJ2lU0iestT/IX2itnZf0h/6Xr63hfv9c0i5JN0jK1XmMAAA0NNIzWbLpljEm1PsHkFxRKPif5n92SbyJAAAQgklEQVT8UpnrvilpVtLlxpiuMteXZYx5mqTfkPR71tp76j9EAAAaXPDPFfP7A+T3AYQoCs8o+/yPB0uvsNYuG2MOSXqcpLMk3bnenRljhiR9RNK3JL231oMyxty6ylXn1HqfAID2NtLfuEjPFPl9AA0ShYJ/yP94epXr85dvqPD+3idpo7z1ALaeAwMAwOV2+E/OLCqXs0qlwoneTM7T4QfQGKE8oxhjDkvaWcW3fMxa+5Iw/u6S43i+pJdK+jVr7X313Je19qJV/o5bJV1Yz30DANpTVyatga6MphaWtZzzpups6O1c/xsrMFWS4QeAsITVQviJpPkqbv+I83m+gz9U7obO5afWukNjzEZJfyvp3yW9v4pjAQCgYhv7OzW14HXjJ2YWQyv4yfADaJRQnlGstU+v49vvlnSxpL2SArl5Y0xG3qSdZUnrdex3SNok6emScqtMN7jZv/yN1tp313HMAICE2tjXqfsnZiV5C3d3j4Zzv26Hn112AYQpCi2Er0r6JUk/I+njJdddKalX0jettevtcDIh6UOrXHelpD2Svijv7MLtNR8tACDRArP4p8NbuFs6lhMAwhKFgv/Tkv5S0guNMe/Lz+L3N876M/82gYiOMaZXXkd/1lr7gCRZax+U9Mpyf4Ex5iPyCv53Wmu/0oh/BAAgGRo1mnOKRbsAGqTlc/ittZOSXiUpLenrxpgPGmPeLumHki6T94bgkyXfdom8EZ3/0MxjBQBgpL+4LczE9HonnyvnFvyM5QQQppYX/JJkrf28pKvkbbT1fEmvk7Qk6TclvZDxmgCAqAhEekLs8E/OuRl+OvwAwhOZZxRr7bclXVPhbb8uqeLBx9baGyTdUMtxAQDgakakhww/gDBFosMPAEC7aFTBPzlPhx9AY1DwAwBQhZE+J8PfoA7/EBl+ACGi4AcAoAoj/W6HP7xFu2T4ATQKBT8AAFUojfSEMVdidnG5sHtvykj9XRT8AMJDwQ8AQBW6O9Lq60xLkpayVpNOFKdW9x6dLnx+5qY+ZdK8PAMID88oAABUaWN/uAt3735sqvD5vvGBuu8PAFwU/AAAVGmjs3A3jBz/PU6Hfy8FP4CQUfADAFClwOZb0yF3+DdT8AMIFwU/AABVGgl5Fv/BI8WCf+94f933BwAuCn4AAKrkZvjrncV/em5Jj56elyR1plPaOdJX1/0BQCkKfgAAqhRmpOfeo8Xu/lmjfepgQg+AkPGsAgBAlcJctHv3Y8UFu+T3ATQCBT8AAFUKdPjrjPQE8/sU/ADCR8EPAECVRkKcw+9O6KHgB9AIFPwAAFRpY4hTetwOP5tuAWgECn4AAKo04mT4J6YXZa2t6X6OTy8UIkE9HWltG+4J5fgAwEXBDwBAlXo60+rpSEuSFrM5TS8s13Q/pfP3UykTyvEBgIuCHwCAGoQR6zlIfh9AE1DwAwBQg5EQNt+6+wgjOQE0HgU/AAA1cEdznqhx8y030rOHDj+ABqHgBwCgBu7mWxM1bL5lrWVCD4CmoOAHAKAG9UZ6Hpuc19S8t9h3sDuj8cGudb4DAGpDwQ8AQA021hnpcTfc2rd5QMYwoQdAY1DwAwBQg3qn9ARHchLnAdA4mVYfAAAA7WiTE+n57G0P66YfP6a+roz6uzPq78pooDuja88/Qy+6ZEfZ77/7MSb0AGgOCn4AAGqwdUNv4OuZxaxmFrM6OlVcwPvteyd0YMugLti+YcX333PUmdAzRsEPoHGI9AAAUIN9mwf0sst2qr9r7d7ZR245vOKyXM6u2GUXABqFDj8AADX60+ecqz99zrnK5qxmF5c1vbCsmYVlHTwyrV/92H9Jkv7lR4/o96/Zr9GB4hSeB0/Oan4pJ0na1N+lkX4m9ABoHDr8AADUKZ0yGuju0JahHp09NqBrztuiJ+zwYjxLWauPf/+BwO2DE3ro7gNoLAp+AAAa4IbLzyx8/rHv3a+lbK7wNRN6ADQTBT8AAA3wrHO3FGI8RyYX9KXbHytcd/BIcUIPBT+ARqPgBwCgATozKb3YGcn5UWfxLh1+AM1EwQ8AQIP80qU7lEl5O+j+4P6Tuv3h01rK5vSTY26Hnww/gMai4AcAoEHGBrt1zXlbCl9/9JbDOnx8RktZK0nauqFHA90drTo8AAlBwQ8AQANd7yze/cJ/P6LvHjpR+JruPoBmoOAHAKCBLtyxQedtHZIkLS7n9J6vHCxct3cz+X0AjUfBDwBAAxljAl3+49OLhc/3jlHwA2g8Cn4AABrs2vO3aGNf54rL99HhB9AEFPwAADRYd0daL7pke+AyY6Szx8jwA2g8Cn4AAJrgJU/aqbQ/olOSzhzpU3dHuoVHBCApKPgBAGiCLUM9+unHjRe+ZkIPgGah4AcAoElee9XZ6sx4L73POnfLOrcGgHBkWn0AAAAkxXnbhnTzG6/U1PyyzvVHdQJAo1HwAwDQRDtH+lp9CAAShkgPAAAAEGMU/AAAAECMUfADAAAAMUbBDwAAAMQYBT8AAAAQYxT8AAAAQIxR8AMAAAAxRsEPAAAAxBgFPwAAABBjFPwAAABAjFHwAwAAADFGwQ8AAADEGAU/AAAAEGMU/AAAAECMUfADAAAAMUbBDwAAAMSYsda2+hjaijFmoqenZ+P+/ftbfSgAAACIsTvvvFNzc3MnrLUj9dwPBX+VjDGHJA1KOtzkv/oc/+NdTf57ER08BpKNn3+y8fNPNn7+yXWmpElr7a567oSCv00YY26VJGvtRa0+FrQGj4Fk4+efbPz8k42fP+pFhh8AAACIMQp+AAAAIMYo+AEAAIAYo+AHAAAAYoyCHwAAAIgxpvQAAAAAMUaHHwAAAIgxCn4AAAAgxij4AQAAgBij4AcAAABijIIfAAAAiDEKfgAAACDGKPgBAACAGKPgjzhjzDZjzIeNMY8YYxaMMYeNMe82xgy3+thQP2PMiDHmlcaYzxlj7jXGzBljThtj/sMY8wpjTNnfUWPM5caYfzPGnPC/50fGmDcYY9LN/jcgfMaYlxhjrP/nlavc5lpjzNf9x8u0MeZ7xpjrm32sCIcx5un+88Bj/nP9I8aYm4wx15S5Lb//MWOM+VljzJeNMQ/5P9P7jDGfMsZctsrteQygKmy8FWHGmN2SbpE0JukLku6SdImkp0q6W9IV1tqJ1h0h6mWMeY2k90t6VNLXJD0gaVzSz0sakvQZSS+wzi+qMeY5/uXzkj4p6YSkn5O0T9KnrbUvaOa/AeEyxmyX9D+S0pL6Jb3KWvvBktv8uqT3SZqQ9xhYlHSdpG2S3mGtfVNTDxp1Mca8XdJvS3pI0hclHZc0KukiSV+x1r7ZuS2//zFjjPlLSW+W9/v8eXk//7MlPVtSRtLLrLX/z7k9jwFUjYI/wowxN0l6pqTXW2vf51z+TklvlPR/rbWvadXxoX7GmKdJ6pP0r9banHP5Zknfl7Rd0nXW2s/4lw9Kulfem4ErrLU/8C/vlvRVSZdJepG19hNN/YcgFMYYI+lmSbskfVbSm1RS8BtjzpT35n9G0kXW2sP+5cOS/lPSbkmXW2u/08xjR22MMa+S9AFJH5X0K9baxZLrO6y1S/7n/P7HjP9c/7CkY5LOt9Yeda57qryf6yFr7Vn+ZTwGUBMiPRHld/efKemwpL8uufqt8l7sX2qM6WvyoSFE1tqvWmv/2S32/csfk/S3/pdXO1ddJ6/z94n8E71/+3lJf+h/+drGHTEa7PWSnibpl+X9jpfzckldkv4qX+xLkrX2pKT/7X9JI6ANGGO6JL1N3pm9FcW+JOWLfR+///GzU14t9j232Jcka+3XJE3J+5nn8RhATSj4o+up/scvlykGpyR9W1KvpCc1+8DQNPkX+mXnsqf5H79U5vbflDQr6XK/kEAbMcbsl/QXkt5jrf3mGjdd6zHwxZLbINqeIa94+6yknJ/j/h1jzG+skt3m9z9+7pEXybvEGLPJvcIYc6WkAUlfcS7mMYCaUPBH1z7/48FVrr/H/7i3CceCJjPGZCS9zP/SfWJf9XFhrV2WdEhe5vOshh4gQuX/vG+U1+n9/XVuvtZj4FF5Zwa2GWN6Qz1INMIT/Y/zkm6T9C/y3vS9W9ItxphvGGPc7i6//zFjrT0h6Xfkrd26wxjzAWPMnxtj/lHSl+VF/F7tfAuPAdSEgj+6hvyPp1e5Pn/5hiYcC5rvLySdK+nfrLU3OZfzuIint0h6gqQbrLVz69y20sfA0CrXIzrG/I+/LclKeoq8ju758oq9KyV9yrk9v/8xZK19t7xBDRlJr5L0u5JeIOlBSR8pifrwGEBNKPiBiDHGvF7Sb8lbmPnSFh8OGswYc6m8rv47WGibOPnX4GVJz7bW/oe1dtpa+z+Snidvas9Vq41mRDwYY94s6dOSPiJv0X2fvAlN90n6mD/FCagLBX90rdely19+qgnHgibxxy2+R9Idkp7qn+518biIET/K8w/yTs//UYXfVuljYLUOIKIj/3t6m7sAW5KstbOS8mf3LvE/8vsfM8aYqyX9paR/stb+prX2PmvtrLX2v+S96XtY0m8ZY/IRHR4DqAkFf3Td7X9cLaO/x/+4WsYfbcYY8wZ5s9Vvl1fsP1bmZqs+LvzicZe8buF9jTpOhKpf3s9yv6R5Z7MtK28alyT9nX/Zu/2v13oMbJHXHXzILxgRbfmf5WrF2Un/Y0/J7fn9j49r/Y9fK73C/x3+vrxa7Qn+xTwGUBMK/ujK//I/s3S3VWPMgKQr5K3G/26zDwzhM8b8jqR3SfqhvGL/6Co3/ar/8WfKXHelvMlNt1hrF8I/SjTAgqQPrfLnNv82/+F/nY/7rPUYeFbJbRBt/y4vu39glV21z/U/HvI/8vsfP/lpOqOrXJ+/PD+ylccAamOt5U9E/8g7nWslva7k8nf6l/9tq4+RP6H8nP/I/3n+QNLGdW47KG+DlgVJFzuXd8vbldlKemGr/038CeVx8cf+z/OVJZfvkjfVZULSmc7lw/I25LGSLmv18fOn4p/zF/yf2RtLLn+mpJy8Lv+Qfxm//zH7I+kX/J/bY5K2llz3LP8xMCdphMcAf+r5w067EeZvvnWLvEkOX5B0p6RL5c3oPyhvN82J1h0h6mWMuV7eQq2svDhPudz1YWvtR5zvea68BV7zkj4hb1v1Z8vfVl3SL1h+sdueMeaP5cV6Ajvt+te9TtJ75RX9n5TX/btO0jZ5i3/f1NyjRa2MMdvkPc9vl9fxv03em7rnqli8fca5Pb//MeKf2blJ0k/J22Trc/KK//3y4j5G0huste9xvofHAKpGwR9xxpjtkv5U3um7EUmPyntC+BPr7ayJNuYUdWv5hrX26pLvu0LSH8jbRr1bXmf3w5Lea63Nhn+kaLa1Cn7/+p+T9CZJF8qLZ94hb/fdjzbzOFE/f9b+W+QVbVskTUr6lqQ/t9Z+v8zt+f2PEWNMh6Rfk/RCSQfkxXJOyMvvv9da++Uy38NjAFWh4AcAAABijEW7AAAAQIxR8AMAAAAxRsEPAAAAxBgFPwAAABBjFPwAAABAjFHwAwAAADFGwQ8AAADEGAU/AAAAEGMU/AAAAECMUfADAAAAMUbBDwAAAMQYBT8AAAAQYxT8AAAAQIxR8AMAAAAxRsEPAAAAxBgFPwAAABBjFPwAAABAjP1/7S+PNXV2izsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_diff = ndi.convolve(mean_filter(signal_noisy, 5), diff)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(filtered_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: padding\n",
    "\n",
    "What is the shape of your mean-filtered signal?\n",
    "\n",
    "...\n",
    "\n",
    "Oops! We've shortened the signal, which means that our indices have changed: `signal_filtered[i]` does not correspond to the signal around `signal[i]`.\n",
    "\n",
    "Use `np.pad` to add some \"fake\" data around `signal` before filtering, so that the filtered result has the same shape as the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Gaussian filtering\n",
    "\n",
    "It turns out that mean filtering is not the \"optimal\" way to recover your signal, assuming certain properties of the noise. For that, we use *Gaussian* filtering, which uses a *weighted* mean of the sliding window elements. The weights are given by the famous Gaussian bell-shaped distribution. For example, here are the weights for a window size of 17 for a particular sigma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5861646ef0>]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight = np.exp(-(np.arange(-8, 9) / (8/3))**2)\n",
    "weight /= np.sum(weight)  ## ensure overall intensity of signal doesn't change\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function that uses sliding windows, broadcasting, and axis-wise operations to compute the Gaussian filter of a signal for a given window size. (You should also pad your input.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84, 17)\n",
      "(1, 17)\n"
     ]
    }
   ],
   "source": [
    "windows = sliding_window(signal_noisy, size=17)\n",
    "print(windows.shape)\n",
    "print(weight[np.newaxis, :].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5861534e48>]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted = windows * weight\n",
    "filtered_gaussian = np.sum(weighted, axis=1)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(filtered_gaussian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f58616eab00>]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff_gaussian = ndi.convolve(filtered_gaussian, diff)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(diff_gaussian)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fancy indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can index arrays with slicing, but also with boolean arrays (including broadcasting!), integer arrays, and individual indices along multiple dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 2]\n",
      " [1 1 0 0]\n",
      " [1 2 0 2]]\n",
      "[[ 0  0  5 99]\n",
      " [ 5  5  0  0]\n",
      " [ 5 99  0 99]]\n"
     ]
    }
   ],
   "source": [
    "values = np.array([0, 5, 99])\n",
    "selector = np.random.randint(0, 3, size=(3, 4))\n",
    "print(selector)\n",
    "print(values[selector])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5, 99, 99, 99,  0],\n",
       "       [ 5,  5,  5,  5,  5]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values[np.array([[1, 2, 2, 2, 0], [1, 1, 1, 1, 1]])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "values2d = np.array([[0, 5, 99],\n",
    "                     [8, 70, 80]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values2d[1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 80])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values2d[np.array([1]), np.array([0, 2])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "relabeled = values[selector]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  5, 99],\n",
       "       [ 5,  5,  0,  0],\n",
       "       [ 5, 99,  0, 99]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relabeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_large_cols = np.any(relabeled > 10, axis=1)\n",
    "has_large_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  5 99]\n",
      " [ 5 99  0 99]]\n"
     ]
    }
   ],
   "source": [
    "print(relabeled[has_large_cols])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Use boolean indexing and broadcasting to select the columns of `relabeled` that do not contain 99."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "no99 = ~np.any(relabeled == 99, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "large_cols_idx = np.nonzero(has_large_cols)[0][:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "no99_idx = np.nonzero(no99)[0][np.newaxis, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 5],\n",
       "       [5, 0]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relabeled[large_cols_idx, no99_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: quantile normalization\n",
    "\n",
    "Quantile Normalization (https://en.wikipedia.org/wiki/Quantile_normalization) is a method to align distributions. Here we implement it using NumPy axis-wise operations and fancy indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numexpr, numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = x + y + q ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qnorm(X):\n",
    "    \"\"\"Quantile normalize an input matrix.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X : 2D array of float, shape (M, N)\n",
    "        The input data, with each column being a\n",
    "        distribution to normalize.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    Xn : 2D array of float, shape (M, N)\n",
    "        The normalized data.\n",
    "    \"\"\"\n",
    "    ranks = \n",
    "    return Xn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logexpr = np.log(expr + 1)\n",
    "logrpkm = np.log(rpkm + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logexprn = qnorm(logexpr)\n",
    "logrpkmn = qnorm(logrpkm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_col_density(logexprn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_col_density(logrpkmn, xlim=(0, 0.25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fancy indexing along multiple dimensions\n",
    "\n",
    "Combining fancy indexing and slicing selects entire rows/columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relabeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relabeled[[1, 1, 2], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relabeled[:, [1, 3, 1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select individual elements for a new array shape, we must use as many fancy indices as the array has dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector_rows = [[0, 0],\n",
    "                 [1, 2]]\n",
    "selector_cols = [[0, 3],\n",
    "                 [1, 2]]\n",
    "\n",
    "arr = np.arange(12).reshape((3, 4))\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(arr[selector_rows, selector_cols])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to think about this is:\n",
    "- make a \"coordinate array\", of the shape that you want plus one more axis, to hold the coordinates of each point (see below),\n",
    "- transpose that final axis to the front, and\n",
    "- convert to tuple\n",
    "\n",
    "For the above example, perhaps you find this \"notation\", with the individual coordinates in the final axis, more intuitive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector_t = [[ [0, 0], [0, 3] ],\n",
    "              [ [1, 1], [2, 2] ]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is, we want element (0, 0) in the top left corner, (0, 3) in the top right, (1, 1) in the bottom left, and (2, 3) in the bottom right. However, *rows and columns* must be in the first dimension and presented as a tuple to index the original array. So, to use this notation, we can use np.transpose and cast the result to `tuple`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = tuple(np.transpose(selector_t, (2, 0, 1)))\n",
    "print(selector[0], selector[1], sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For technical reasons that one might grasp for fleeting moments, the \"tuple of index arrays\" format is most consistent with other forms of multi-dimensional indexing in NumPy. It is a widespread convention (see e.g. `scipy.ndimage.map_coordinates`), so it's worth practicing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "What happens when you make `selector_col`:\n",
    "- a single number?\n",
    "- a 1D array with two elements?\n",
    "- a 2D array of shape (1, 2)?\n",
    "- a 1D array with three elements?\n",
    "\n",
    "Repeat similar experiments with `selector_row`.\n",
    "\n",
    "Does this remind you of any other NumPy feature we may have seen?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced exercise: Jack's dilemma\n",
    "\n",
    "(If time permits.)\n",
    "\n",
    "```email\n",
    "Date: Wed, 16 Jul 2008 16:45:37 -0500\n",
    "From: Jack Cook\n",
    "To: <numpy-discussion@scipy.org>\n",
    "Subject: Numpy Advanced Indexing Question\n",
    "```\n",
    "\n",
    "Greetings,\n",
    "\n",
    "I have an I,J,K 3D volume of amplitude values at regularly sampled\n",
    "time intervals. I have an I,J 2D slice which contains a time (K)\n",
    "value at each I, J location. What I would like to do is extract a\n",
    "subvolume at a constant +/- K window around the slice. Is there an\n",
    "easy way to do this using advanced indexing or some other method?\n",
    "Thanks in advanced for your help.\n",
    "\n",
    "-- Jack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the command-line, run `python tools/generate-volume.py`\n",
    "\n",
    "data = np.load('geo.npz')  # npz file with multiple arrays, keyed like dict\n",
    "strata, density = data['strata'], data['density']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = 256\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(1, 2, sharey=True, figsize=(7.2, 3.6))\n",
    "ax0.imshow(strata[:, row, :], cmap='gray')\n",
    "ax0.set_ylabel('depth')\n",
    "ax0.set_xlabel('x')\n",
    "ax0.set_title(f'strata at row {row}')\n",
    "ax1.imshow(density[:, 256, :], cmap='magma')\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_title(f'fossil density at row {row}') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Exercise\n",
    "\n",
    "We want to quantify the apparent extinction event near a particular stratum. The strata, however, are not perfectly horizontal, and there is a large break in the rock, too. Therefore, we want to align the strata by using the solution to Jack's dilemma (which you must solve for him).\n",
    "\n",
    "Using a window size of 120 (so the half-width is K=60):\n",
    "- find the stratum of maximum intensity along the depth axis. This is a 2D slice of integers measuring the depth of the maximum intensity stratum at each (row, column) coordinate.\n",
    "- using broadcasting, create a volume of shape (2K, Nrow, Ncol) containing the depth coordinate at each (row, column) of your desired window.\n",
    "- create matching row and column index volumes to perform fancy indexing of the fossil density array.\n",
    "- extract the subvolume of fossil density around the stratum using fancy indexing\n",
    "- compute the mean fossil density at each depth.\n",
    "\n",
    "**-->Advice<--**: play around with a much tinier subset of the volume, say, a subset of size (10, 15, 20), and window size of 3. Evaluating the wrong expression can result in a giant dataset that blows up your memory and crashes your computer. (Yay Science!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear algebra with NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since version 3.5, Python supports the matrix multiplication operator, denoted by the `@` symbol. This makes it a new powerhouse for linear algebra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = np.array([[0, 1],\n",
    "              [1, 1],\n",
    "              [1, 0]])\n",
    "v = np.array([9, 2])\n",
    "\n",
    "print(M @ v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(M.T @ M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now consider the rotation matrix\n",
    "\n",
    "$\n",
    "R = \\begin{bmatrix}\n",
    "  \\cos \\theta &  -\\sin \\theta & 0 \\\\\n",
    "  \\sin \\theta & \\cos \\theta & 0\\\\\n",
    "  0 & 0 & 1\\\\\n",
    "\\end{bmatrix}\n",
    "$\n",
    "\n",
    "When $R$ is multiplied with a 3-dimensional column-vector $p =\n",
    "\\left[ x\\, y\\, z \\right]^T$, the resulting vector $R p$ is rotated\n",
    "by $\\theta$ degrees around the z-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_deg = 45\n",
    "theta = np.deg2rad(theta_deg)\n",
    "\n",
    "c = np.cos(theta)\n",
    "s = np.sin(theta)\n",
    "\n",
    "R = np.array([[c, -s, 0],\n",
    "              [s,  c, 0],\n",
    "              [0,  0, 1]]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis = np.array([1, 0, 0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "R @ x_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rotated = R @ x_axis\n",
    "R @ rotated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: rotation matrices\n",
    "\n",
    "If you know some linear algebra, try to answer the following questions about $R$ in your head, before trying them out in Python. If you're totally stuck, just give them a go!\n",
    "\n",
    "- What does the matrix $S = RR$ do? (Remember, `S = R @ R` in code.)\n",
    "- What does $R^4 = RRRR$ do?\n",
    "- The inverse $R^{-1}$ of $R$ is defined as the matrix such that $R^{-1}R = I$, where $I$ is the identity matrix. What does $Q = R^{-1}$ do? (Note: NumPy provides matrix inverse computation in `np.linalg.inv`.)\n",
    "- What does $R$ do to the vector [0, 0, 1]?\n",
    "- What does that make the vector [0, 0, 1]?\n",
    "- Verify this with some function in `np.linalg`. (Look at the online documentation for this module.)\n",
    "- What does $R^8$ do? What does that make $R^8$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: revisiting Gaussian filtering\n",
    "\n",
    "Above, we performed Gaussian filtering with a broadcast product and a axis-wise sum. Use a matrix multiplication instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural network with NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(128) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.random.random(size=(n, 3)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Make a vector y of length n that is 0 at position i if `X[i, 0] + X[i, 2] < 1`, and 1 otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Hint:* How do I get the 0th column of X?\n",
    "\n",
    "*Hint 2:* How can I convert an array of (True/False) to an array of float?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (X[:, 0] + X[:, 2] > 1).astype(float) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(X[:, 2], X[:, 0], color=plt.cm.viridis(y)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xb = np.column_stack((X, np.ones(X.shape[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = 2 * np.random.random(size=Xb.shape[1]) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = sigmoid(Xb @ W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dsigmoid(x):\n",
    "    return x * (1 - x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(X, y, W, n_iter=100):\n",
    "    W_history = np.empty((n_iter, W.size), dtype=float)\n",
    "    error_history = np.empty(n_iter, dtype=float)\n",
    "    W_out = np.copy(W)\n",
    "    for i in range(n_iter):\n",
    "        W_history[i] = W_out\n",
    "        predictions = sigmoid(X @ W_out)\n",
    "        derror = y - predictions\n",
    "        gradient = derror * dsigmoid(predictions)  # iamtrask\n",
    "        update = X.T @ gradient\n",
    "        error_history[i] = np.sum(np.abs(error))\n",
    "        W_out += update\n",
    "    return W_out, W_history, error_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Wout, Whist, errhist = train(Xb, y, W, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.semilogx(errhist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mgrid[0:1:0.3, 0:1:0.3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_space = np.mgrid[0:1:0.01, 0:1:1, 0:1:0.01, 1:2:1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_space.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_image = sigmoid(decision_space.T @ Wout).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_squeezed = np.squeeze(prediction_image) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.imshow(prediction_squeezed, cmap='magma', origin='lower',\n",
    "          extent=[0, 1, 0, 1])\n",
    "ax.scatter(X[:, 2], X[:, 0], color=plt.cm.viridis(y)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Why do some points appear misclassified?\n",
    "\n",
    "*Hint:* Look at `Wout`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References and sources\n",
    "\n",
    "In addition to original content, these notes use materials from the following sources:\n",
    "\n",
    "- [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/), compiled by Nicolas Rougier, used here under the terms of the MIT License.\n",
    "- [SciPy Lecture Notes](http://www.scipy-lectures.org/), compiled by Gaël Varoquaux, Emmanuelle Gouillart, and Olav Vahtras, used under the terms of the CC-BY license.\n",
    "- [Elegant SciPy](http://elegant-scipy.org), written by Juan Nunez-Iglesias, Stéfan van der Walt, and Harriet Dashnow, used with permission from O'Reilly.\n",
    "- [iamtrask blog](https://iamtrask.github.io/2015/07/12/basic-python-network/), for a neural network from scratch that inspired this one.\n",
    "\n",
    "They are all freely available online and worth checking out in their entirety!"
   ]
  }
 ],
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