tools_numpy.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tools - NumPy**\n",
    "\n",
    "*NumPy is the fundamental library for scientific computing with Python. NumPy is centered around a powerful N-dimensional array object, and it also contains useful linear algebra, Fourier transform, and random number functions.*\n",
    "\n",
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/tools_numpy.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: this notebook accompanies the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating arrays\n",
    "First let's make sure that this notebook works both in python 2 and 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, unicode_literals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's import `numpy`. Most people import it as `np`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.zeros`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `zeros` function creates an array containing any number of zeros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's just as easy to create a 2D array (ie. a matrix) by providing a tuple with the desired number of rows and columns. For example, here's a 3x4 matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some vocabulary\n",
    "\n",
    "* In NumPy, each dimension is called an **axis**.\n",
    "* The number of axes is called the **rank**.\n",
    "    * For example, the above 3x4 matrix is an array of rank 2 (it is 2-dimensional).\n",
    "    * The first axis has length 3, the second has length 4.\n",
    "* An array's list of axis lengths is called the **shape** of the array.\n",
    "    * For example, the above matrix's shape is `(3, 4)`.\n",
    "    * The rank is equal to the shape's length.\n",
    "* The **size** of an array is the total number of elements, which is the product of all axis lengths (eg. 3*4=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((3,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim  # equal to len(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N-dimensional arrays\n",
    "You can also create an N-dimensional array of arbitrary rank. For example, here's a 3D array (rank=3), with shape `(2,3,4)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0.,  0.]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array type\n",
    "NumPy arrays have the type `ndarray`s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.zeros((3,4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.ones`\n",
    "Many other NumPy functions create `ndarrays`.\n",
    "\n",
    "Here's a 3x4 matrix full of ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.full`\n",
    "Creates an array of the given shape initialized with the given value. Here's a 3x4 matrix full of `π`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.14159265,  3.14159265,  3.14159265,  3.14159265],\n",
       "       [ 3.14159265,  3.14159265,  3.14159265,  3.14159265],\n",
       "       [ 3.14159265,  3.14159265,  3.14159265,  3.14159265]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3,4), np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.empty`\n",
    "An uninitialized 2x3 array (its content is not predictable, as it is whatever is in memory at that point):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.68156159e+154,  -2.32035135e+077,   2.22457274e-314],\n",
       "       [  2.24336635e-314,   2.23817082e-314,   4.17203587e-309]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## np.array\n",
    "Of course you can initialize an `ndarray` using a regular python array. Just call the `array` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [10, 20, 30, 40]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2,3,4], [10, 20, 30, 40]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.arange`\n",
    "You can create an `ndarray` using NumPy's `range` function, which is similar to python's built-in `range` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also works with floats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1.0, 5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course you can provide a step parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1, 5, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, when dealing with floats, the exact number of elements in the array is not always predictible. For example, consider this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.33333333  0.66666667  1.          1.33333333  1.66666667]\n",
      "[ 0.          0.33333333  0.66666667  1.          1.33333333  1.66666667]\n",
      "[ 0.          0.33333333  0.66666667  1.          1.33333334]\n"
     ]
    }
   ],
   "source": [
    "print(np.arange(0, 5/3, 1/3)) # depending on floating point errors, the max value is 4/3 or 5/3.\n",
    "print(np.arange(0, 5/3, 0.333333333))\n",
    "print(np.arange(0, 5/3, 0.333333334))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.linspace`\n",
    "For this reason, it is generally preferable to use the `linspace` function instead of `arange` when working with floats. The `linspace` function returns an array containing a specific number of points evenly distributed between two values (note that the maximum value is *included*, contrary to `arange`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.33333333  0.66666667  1.          1.33333333  1.66666667]\n"
     ]
    }
   ],
   "source": [
    "print(np.linspace(0, 5/3, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.rand` and `np.randn`\n",
    "A number of functions are available in NumPy's `random` module to create `ndarray`s initialized with random values.\n",
    "For example, here is a 3x4 matrix initialized with random floats between 0 and 1 (uniform distribution):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.37454012,  0.95071431,  0.73199394,  0.59865848],\n",
       "       [ 0.15601864,  0.15599452,  0.05808361,  0.86617615],\n",
       "       [ 0.60111501,  0.70807258,  0.02058449,  0.96990985]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a 3x4 matrix containing random floats sampled from a univariate [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution) (Gaussian distribution) of mean 0 and variance 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.46947439,  0.54256004, -0.46341769, -0.46572975],\n",
       "       [ 0.24196227, -1.91328024, -1.72491783, -0.56228753],\n",
       "       [-1.01283112,  0.31424733, -0.90802408, -1.4123037 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To give you a feel of what these distributions look like, let's use matplotlib (see the [matplotlib tutorial](tools_matplotlib.ipynb) for more details):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8a1eYMEGzkLUQSgDSfFasgDFjYP361PLnnoM99ognJmmaG27IHE4awhSUq1er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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f84bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.rand(100000), normed=True, bins=100, histtype=\"step\", color=\"blue\", label=\"rand\")\n",
    "plt.hist(np.random.randn(100000), normed=True, bins=100, histtype=\"step\", color=\"red\", label=\"randn\")\n",
    "plt.axis([-2.5, 2.5, 0, 1.1])\n",
    "plt.legend(loc = \"upper left\")\n",
    "plt.title(\"Random distributions\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## np.fromfunction\n",
    "You can also initialize an `ndarray` using a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "        [  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.]],\n",
       "\n",
       "       [[  1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.],\n",
       "        [  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.]],\n",
       "\n",
       "       [[  2.,   2.,   2.,   2.,   2.,   2.,   2.,   2.,   2.,   2.],\n",
       "        [  2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.]]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_function(z, y, x):\n",
    "    return x * y + z\n",
    "\n",
    "np.fromfunction(my_function, (3, 2, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy first creates three `ndarrays` (one per dimension), each of shape `(2, 10)`. Each array has values equal to the coordinate along a specific axis. For example, all elements in the `z` array are equal to their z-coordinate:\n",
    "\n",
    "    [[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
    "      [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
    "    \n",
    "     [[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
    "      [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]\n",
    "    \n",
    "     [[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]\n",
    "      [ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]]\n",
    "\n",
    "So the terms x, y and z in the expression `x * y + z` above are in fact `ndarray`s (we will discuss arithmetic operations on arrays below).  The point is that the function `my_function` is only called *once*, instead of once per element. This makes initialization very efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Array data\n",
    "## `dtype`\n",
    "NumPy's `ndarray`s are also efficient in part because all their elements must have the same type (usually numbers).\n",
    "You can check what the data type is by looking at the `dtype` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 [1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(1, 5)\n",
    "print(c.dtype, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 [ 1.  2.  3.  4.]\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(1.0, 5.0)\n",
    "print(c.dtype, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of letting NumPy guess what data type to use, you can set it explicitly when creating an array by setting the `dtype` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complex64 [ 1.+0.j  2.+0.j  3.+0.j  4.+0.j]\n"
     ]
    }
   ],
   "source": [
    "d = np.arange(1, 5, dtype=np.complex64)\n",
    "print(d.dtype, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Available data types include `int8`, `int16`, `int32`, `int64`, `uint8`|`16`|`32`|`64`, `float16`|`32`|`64` and `complex64`|`128`. Check out [the documentation](http://docs.scipy.org/doc/numpy-1.10.1/user/basics.types.html) for the full list.\n",
    "\n",
    "## `itemsize`\n",
    "The `itemsize` attribute returns the size (in bytes) of each item:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = np.arange(1, 5, dtype=np.complex64)\n",
    "e.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `data` buffer\n",
    "An array's data is actually stored in memory as a flat (one dimensional) byte buffer. It is available *via* the `data` attribute (you will rarely need it, though)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<read-write buffer for 0x10f8a18a0, size 16, offset 0 at 0x10f9dbbb0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = np.array([[1,2],[1000, 2000]], dtype=np.int32)\n",
    "f.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In python 2, `f.data` is a buffer. In python 3, it is a memoryview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\xe8\\x03\\x00\\x00\\xd0\\x07\\x00\\x00'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if (hasattr(f.data, \"tobytes\")):\n",
    "    data_bytes = f.data.tobytes() # python 3\n",
    "else:\n",
    "    data_bytes = memoryview(f.data).tobytes() # python 2\n",
    "\n",
    "data_bytes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several `ndarrays` can share the same data buffer, meaning that modifying one will also modify the others. We will see an example in a minute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reshaping an array\n",
    "## In place\n",
    "Changing the shape of an `ndarray` is as simple as setting its `shape` attribute. However, the array's size must remain the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
      "Rank: 1\n"
     ]
    }
   ],
   "source": [
    "g = np.arange(24)\n",
    "print(g)\n",
    "print(\"Rank:\", g.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n",
      "Rank: 2\n"
     ]
    }
   ],
   "source": [
    "g.shape = (6, 4)\n",
    "print(g)\n",
    "print(\"Rank:\", g.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n",
      "Rank: 3\n"
     ]
    }
   ],
   "source": [
    "g.shape = (2, 3, 4)\n",
    "print(g)\n",
    "print(\"Rank:\", g.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `reshape`\n",
    "The `reshape` function returns a new `ndarray` object pointing at the *same* data. This means that modifying one array will also modify the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]]\n",
      "Rank: 2\n"
     ]
    }
   ],
   "source": [
    "g2 = g.reshape(4,6)\n",
    "print(g2)\n",
    "print(\"Rank:\", g2.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set item at row 1, col 2 to 999 (more about indexing below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5],\n",
       "       [  6,   7, 999,   9,  10,  11],\n",
       "       [ 12,  13,  14,  15,  16,  17],\n",
       "       [ 18,  19,  20,  21,  22,  23]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2[1, 2] = 999\n",
    "g2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding element in `g` has been modified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1,   2,   3],\n",
       "        [  4,   5,   6,   7],\n",
       "        [999,   9,  10,  11]],\n",
       "\n",
       "       [[ 12,  13,  14,  15],\n",
       "        [ 16,  17,  18,  19],\n",
       "        [ 20,  21,  22,  23]]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `ravel`\n",
    "Finally, the `ravel` function returns a new one-dimensional `ndarray` that also points to the same data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7, 999,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arithmetic operations\n",
    "All the usual arithmetic operators (`+`, `-`, `*`, `/`, `//`, `**`, etc.) can be used with `ndarray`s. They apply *elementwise*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + b  = [19 27 35 43]\n",
      "a - b  = [ 9 19 29 39]\n",
      "a * b  = [70 92 96 82]\n",
      "a / b  = [  2.8          5.75        10.66666667  20.5       ]\n",
      "a // b  = [ 2  5 10 20]\n",
      "a % b  = [4 3 2 1]\n",
      "a ** b = [537824 279841  32768   1681]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([14, 23, 32, 41])\n",
    "b = np.array([5,  4,  3,  2])\n",
    "print(\"a + b  =\", a + b)\n",
    "print(\"a - b  =\", a - b)\n",
    "print(\"a * b  =\", a * b)\n",
    "print(\"a / b  =\", a / b)\n",
    "print(\"a // b  =\", a // b)\n",
    "print(\"a % b  =\", a % b)\n",
    "print(\"a ** b =\", a ** b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the multiplication is *not* a matrix multiplication. We will discuss matrix operations below.\n",
    "\n",
    "The arrays must have the same shape. If they do not, NumPy will apply the *broadcasting rules*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, when NumPy expects arrays of the same shape but finds that this is not the case, it applies the so-called *broadcasting* rules:\n",
    "\n",
    "## First rule\n",
    "*If the arrays do not have the same rank, then a 1 will be prepended to the smaller ranking arrays until their ranks match.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 1, 2, 3, 4]]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = np.arange(5).reshape(1, 1, 5)\n",
    "h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to add a 1D array of shape `(5,)` to this 3D array of shape `(1,1,5)`. Applying the first rule of broadcasting!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[10, 21, 32, 43, 54]]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h + [10, 20, 30, 40, 50]  # same as: h + [[[10, 20, 30, 40, 50]]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second rule\n",
    "*Arrays with a 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is repeated along that dimension.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = np.arange(6).reshape(2, 3)\n",
    "k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to add a 2D array of shape `(2,1)` to this 2D `ndarray` of shape `(2, 3)`. NumPy will apply the second rule of broadcasting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 101, 102],\n",
       "       [203, 204, 205]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + [[100], [200]]  # same as: k + [[100, 100, 100], [200, 200, 200]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining rules 1 & 2, we can do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 201, 302],\n",
       "       [103, 204, 305]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + [100, 200, 300]  # after rule 1: [[100, 200, 300]], and after rule 2: [[100, 200, 300], [100, 200, 300]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And also, very simply:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1000, 1001, 1002],\n",
       "       [1003, 1004, 1005]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + 1000  # same as: k + [[1000, 1000, 1000], [1000, 1000, 1000]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Third rule\n",
    "*After rules 1 & 2, the sizes of all arrays must match.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operands could not be broadcast together with shapes (2,3) (2,) \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    k + [33, 44]\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting rules are used in many NumPy operations, not just arithmetic operations, as we will see below.\n",
    "For more details about broadcasting, check out [the documentation](https://docs.scipy.org/doc/numpy-dev/user/basics.broadcasting.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upcasting\n",
    "When trying to combine arrays with different `dtype`s, NumPy will *upcast* to a type capable of handling all possible values (regardless of what the *actual* values are)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8 [0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "k1 = np.arange(0, 5, dtype=np.uint8)\n",
    "print(k1.dtype, k1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 [ 5  7  9 11 13]\n"
     ]
    }
   ],
   "source": [
    "k2 = k1 + np.array([5, 6, 7, 8, 9], dtype=np.int8)\n",
    "print(k2.dtype, k2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `int16` is required to represent all *possible* `int8` and `uint8` values (from -128 to 255), even though in this case a uint8 would have sufficed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 [ 1.5  2.5  3.5  4.5  5.5]\n"
     ]
    }
   ],
   "source": [
    "k3 = k1 + 1.5\n",
    "print(k3.dtype, k3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditional operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The conditional operators also apply elementwise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.array([20, -5, 30, 40])\n",
    "m < [15, 16, 35, 36]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using broadcasting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False], dtype=bool)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m < 25  # equivalent to m < [25, 25, 25, 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is most useful in conjunction with boolean indexing (discussed below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, -5])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[m < 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mathematical and statistical functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many mathematical and statistical functions are available for `ndarray`s.\n",
    "\n",
    "## `ndarray` methods\n",
    "Some functions are simply `ndarray` methods, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -2.5   3.1   7. ]\n",
      " [ 10.   11.   12. ]]\n",
      "mean = 6.76666666667\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
    "print(a)\n",
    "print(\"mean =\", a.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this computes the mean of all elements in the `ndarray`, regardless of its shape.\n",
    "\n",
    "Here are a few more useful `ndarray` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min = -2.5\n",
      "max = 12.0\n",
      "sum = 40.6\n",
      "prod = -71610.0\n",
      "std = 5.08483584352\n",
      "var = 25.8555555556\n"
     ]
    }
   ],
   "source": [
    "for func in (a.min, a.max, a.sum, a.prod, a.std, a.var):\n",
    "    print(func.__name__, \"=\", func())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These functions accept an optional argument `axis` which lets you ask for the operation to be performed on elements along the given axis. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=np.arange(24).reshape(2,3,4)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 14, 16, 18],\n",
       "       [20, 22, 24, 26],\n",
       "       [28, 30, 32, 34]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=0)  # sum across matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 15, 18, 21],\n",
       "       [48, 51, 54, 57]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=1)  # sum across rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also sum over multiple axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  92, 124])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=(0,2))  # sum across matrices and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 92, 124)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0+1+2+3 + 12+13+14+15, 4+5+6+7 + 16+17+18+19, 8+9+10+11 + 20+21+22+23"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal functions\n",
    "NumPy also provides fast elementwise functions called *universal functions*, or **ufunc**. They are vectorized wrappers of simple functions. For example `square` returns a new `ndarray` which is a copy of the original `ndarray` except that each element is squared:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   6.25,    9.61,   49.  ],\n",
       "       [ 100.  ,  121.  ,  144.  ]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
    "np.square(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a few more useful unary ufuncs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original ndarray\n",
      "[[ -2.5   3.1   7. ]\n",
      " [ 10.   11.   12. ]]\n",
      "\n",
      " absolute\n",
      "[[  2.5   3.1   7. ]\n",
      " [ 10.   11.   12. ]]\n",
      "\n",
      " sqrt\n",
      "[[        nan  1.76068169  2.64575131]\n",
      " [ 3.16227766  3.31662479  3.46410162]]\n",
      "\n",
      " exp\n",
      "[[  8.20849986e-02   2.21979513e+01   1.09663316e+03]\n",
      " [  2.20264658e+04   5.98741417e+04   1.62754791e+05]]\n",
      "\n",
      " log\n",
      "[[        nan  1.13140211  1.94591015]\n",
      " [ 2.30258509  2.39789527  2.48490665]]\n",
      "\n",
      " sign\n",
      "[[-1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "\n",
      " ceil\n",
      "[[ -2.   4.   7.]\n",
      " [ 10.  11.  12.]]\n",
      "\n",
      " modf\n",
      "(array([[-0.5,  0.1,  0. ],\n",
      "       [ 0. ,  0. ,  0. ]]), array([[ -2.,   3.,   7.],\n",
      "       [ 10.,  11.,  12.]]))\n",
      "\n",
      " isnan\n",
      "[[False False False]\n",
      " [False False False]]\n",
      "\n",
      " cos\n",
      "[[-0.80114362 -0.99913515  0.75390225]\n",
      " [-0.83907153  0.0044257   0.84385396]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-c:5: RuntimeWarning: invalid value encountered in sqrt\n",
      "-c:5: RuntimeWarning: invalid value encountered in log\n"
     ]
    }
   ],
   "source": [
    "print(\"Original ndarray\")\n",
    "print(a)\n",
    "for func in (np.abs, np.sqrt, np.exp, np.log, np.sign, np.ceil, np.modf, np.isnan, np.cos):\n",
    "    print(\"\\n\", func.__name__)\n",
    "    print(func(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary ufuncs\n",
    "There are also many binary ufuncs, that apply elementwise on two `ndarray`s.  Broadcasting rules are applied if the arrays do not have the same shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  6,  2, 11])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, -2, 3, 4])\n",
    "b = np.array([2, 8, -1, 7])\n",
    "np.add(a, b)  # equivalent to a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.greater(a, b)  # equivalent to a > b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 8, 3, 7])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.maximum(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., -3.,  4.])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.copysign(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Array indexing\n",
    "## One-dimensional arrays\n",
    "One-dimensional NumPy arrays can be accessed more or less like regular python arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
    "a[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 19, 13])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 19, 13,  7])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 13,  3])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 13, 19,  3,  5,  1])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, you can modify elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   5,   3, 999,  13,   7,   3])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3]=999\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also modify an `ndarray` slice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   5, 997, 998, 999,   7,   3])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5] = [997, 998, 999]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences with regular python arrays\n",
    "Contrary to regular python arrays, if you assign a single value to an `ndarray` slice, it is copied across the whole slice, thanks to broadcasting rules discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5, -1, -1, -1,  7,  3])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5] = -1\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, you cannot grow or shrink `ndarray`s this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot copy sequence with size 6 to array axis with dimension 3\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a[2:5] = [1,2,3,4,5,6]  # too long\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You cannot delete elements either:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot delete array elements\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    del a[2:5]\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last but not least, `ndarray` **slices are actually *views*** on the same data buffer. This means that if you create a slice and modify it, you are actually going to modify the original `ndarray` as well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    5,   -1, 1000,   -1,    7,    3])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_slice = a[2:6]\n",
    "a_slice[1] = 1000\n",
    "a  # the original array was modified!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1, 2000,   -1,    7])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] = 2000\n",
    "a_slice  # similarly, modifying the original array modifies the slice!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want a copy of the data, you need to use the `copy` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    5,   -1, 2000,   -1,    7,    3])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "another_slice = a[2:6].copy()\n",
    "another_slice[1] = 3000\n",
    "a  # the original array is untouched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1, 3000,   -1,    7])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] = 4000\n",
    "another_slice  # similary, modifying the original array does not affect the slice copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-dimensional arrays\n",
    "Multi-dimensional arrays can be accessed in a similar way by providing an index or slice for each axis, separated by commas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(48).reshape(4, 12)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, 2]  # row 1, col 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, :]  # row 1, all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 25, 37])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:, 1]  # all rows, column 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Caution**: note the subtle difference between these two expressions: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1:2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first expression returns row 1 as a 1D array of shape `(12,)`, while the second returns that same row as a 2D array of shape `(1, 12)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fancy indexing\n",
    "You may also specify a list of indices that you are interested in. This is referred to as *fancy indexing*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3,  4],\n",
       "       [26, 27, 28]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[(0,2), 2:5]  # rows 0 and 2, columns 2 to 4 (5-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  2, 11],\n",
       "       [23, 14, 23],\n",
       "       [35, 26, 35],\n",
       "       [47, 38, 47]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:, (-1, 2, -1)]  # all rows, columns -1 (last), 2 and -1 (again, and in this order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you provide multiple index arrays, you get a 1D `ndarray` containing the values of the elements at the specified coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41, 33, 37, 33])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[(-1, 2, -1, 2), (5, 9, 1, 9)]  # returns a 1D array with b[-1, 5], b[2, 9], b[-1, 1] and b[2, 9] (again)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Higher dimensions\n",
    "Everything works just as well with higher dimensional arrays, but it's useful to look at a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3,  4,  5],\n",
       "        [ 6,  7,  8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15, 16, 17],\n",
       "        [18, 19, 20, 21, 22, 23]],\n",
       "\n",
       "       [[24, 25, 26, 27, 28, 29],\n",
       "        [30, 31, 32, 33, 34, 35]],\n",
       "\n",
       "       [[36, 37, 38, 39, 40, 41],\n",
       "        [42, 43, 44, 45, 46, 47]]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = b.reshape(4,2,6)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1, 4]  # matrix 2, row 1, col 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 33])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, :, 3]  # matrix 2, all rows, col 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you omit coordinates for some axes, then all elements in these axes are returned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 31, 32, 33, 34, 35])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1]  # Return matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ellipsis (`...`)\n",
    "You may also write an ellipsis (`...`) to ask that all non-specified axes be entirely included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, ...]  #  matrix 2, all rows, all columns.  This is equivalent to c[2, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 31, 32, 33, 34, 35])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1, ...]  # matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 33])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, ..., 3]  # matrix 2, all rows, column 3.  This is equivalent to c[2, :, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  9],\n",
       "       [15, 21],\n",
       "       [27, 33],\n",
       "       [39, 45]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[..., 3]  # all matrices, all rows, column 3.  This is equivalent to c[:, :, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean indexing\n",
    "You can also provide an `ndarray` of boolean values on one axis to specify the indices that you want to access."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(48).reshape(4, 12)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_on = np.array([True, False, True, False])\n",
    "b[rows_on, :]  # Rows 0 and 2, all columns. Equivalent to b[(0, 2), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  7, 10],\n",
       "       [13, 16, 19, 22],\n",
       "       [25, 28, 31, 34],\n",
       "       [37, 40, 43, 46]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_on = np.array([False, True, False] * 4)\n",
    "b[:, cols_on]  # All rows, columns 1, 4, 7 and 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.ix_`\n",
    "You cannot use boolean indexing this way on multiple axes, but you can work around this by using the `ix_` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  7, 10],\n",
       "       [25, 28, 31, 34]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[np.ix_(rows_on, cols_on)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0],\n",
       "        [2]]), array([[ 1,  4,  7, 10]]))"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ix_(rows_on, cols_on)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you use a boolean array that has the same shape as the `ndarray`, then you get in return a 1D array containing all the values that have `True` at their coordinate. This is generally used along with conditional operators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[b % 3 == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iterating\n",
    "Iterating over `ndarray`s is very similar to iterating over regular python arrays. Note that iterating over multidimensional arrays is done with respect to the first axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2, 3, 4)  # A 3D array (composed of two 3x4 matrices)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item:\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "Item:\n",
      "[[12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n"
     ]
    }
   ],
   "source": [
    "for m in c:\n",
    "    print(\"Item:\")\n",
    "    print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item:\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "Item:\n",
      "[[12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(c)):  # Note that len(c) == c.shape[0]\n",
    "    print(\"Item:\")\n",
    "    print(c[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to iterate on *all* elements in the `ndarray`, simply iterate over the `flat` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item: 0\n",
      "Item: 1\n",
      "Item: 2\n",
      "Item: 3\n",
      "Item: 4\n",
      "Item: 5\n",
      "Item: 6\n",
      "Item: 7\n",
      "Item: 8\n",
      "Item: 9\n",
      "Item: 10\n",
      "Item: 11\n",
      "Item: 12\n",
      "Item: 13\n",
      "Item: 14\n",
      "Item: 15\n",
      "Item: 16\n",
      "Item: 17\n",
      "Item: 18\n",
      "Item: 19\n",
      "Item: 20\n",
      "Item: 21\n",
      "Item: 22\n",
      "Item: 23\n"
     ]
    }
   ],
   "source": [
    "for i in c.flat:\n",
    "    print(\"Item:\", i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking arrays\n",
    "It is often useful to stack together different arrays. NumPy offers several functions to do just that. Let's start by creating a few arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = np.full((3,4), 1.0)\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2 = np.full((4,4), 2.0)\n",
    "q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.,  3.]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = np.full((3,4), 3.0)\n",
    "q3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `vstack`\n",
    "Now let's stack them vertically using `vstack`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 3.,  3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.,  3.]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4 = np.vstack((q1, q2, q3))\n",
    "q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was possible because q1, q2 and q3 all have the same shape (except for the vertical axis, but that's ok since we are stacking on that axis).\n",
    "\n",
    "## `hstack`\n",
    "We can also stack arrays horizontally using `hstack`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.,  3.,  3.,  3.,  3.],\n",
       "       [ 1.,  1.,  1.,  1.,  3.,  3.,  3.,  3.],\n",
       "       [ 1.,  1.,  1.,  1.,  3.,  3.,  3.,  3.]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5 = np.hstack((q1, q3))\n",
    "q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 8)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is possible because q1 and q3 both have 3 rows. But since q2 has 4 rows, it cannot be stacked horizontally with q1 and q3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all the input array dimensions except for the concatenation axis must match exactly\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    q5 = np.hstack((q1, q2, q3))\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `concatenate`\n",
    "The `concatenate` function stacks arrays along any given existing axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 2.,  2.,  2.,  2.],\n",
       "       [ 3.,  3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.,  3.]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7 = np.concatenate((q1, q2, q3), axis=0)  # Equivalent to vstack\n",
    "q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you might guess, `hstack` is equivalent to calling `concatenate` with `axis=1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `stack`\n",
    "The `stack` function stacks arrays along a new axis. All arrays have to have the same shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.]],\n",
       "\n",
       "       [[ 3.,  3.,  3.,  3.],\n",
       "        [ 3.,  3.,  3.,  3.],\n",
       "        [ 3.,  3.,  3.,  3.]]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q8 = np.stack((q1, q3))\n",
    "q8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q8.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting arrays\n",
    "Splitting is the opposite of stacking. For example, let's use the `vsplit` function to split a matrix vertically.\n",
    "\n",
    "First let's create a 6x4 matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = np.arange(24).reshape(6,4)\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's split it in three equal parts, vertically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1, r2, r3 = np.vsplit(r, 3)\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a `split` function which splits an array along any given axis. Calling `vsplit` is equivalent to calling `split` with `axis=0`. There is also an `hsplit` function, equivalent to calling `split` with `axis=1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 4,  5],\n",
       "       [ 8,  9],\n",
       "       [12, 13],\n",
       "       [16, 17],\n",
       "       [20, 21]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r4, r5 = np.hsplit(r, 2)\n",
    "r4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3],\n",
       "       [ 6,  7],\n",
       "       [10, 11],\n",
       "       [14, 15],\n",
       "       [18, 19],\n",
       "       [22, 23]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transposing arrays\n",
    "The `transpose` method creates a new view on an `ndarray`'s data, with axes permuted in the given order.\n",
    "\n",
    "For example, let's create a 3D array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 3,  4,  5]],\n",
       "\n",
       "       [[ 6,  7,  8],\n",
       "        [ 9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14],\n",
       "        [15, 16, 17]],\n",
       "\n",
       "       [[18, 19, 20],\n",
       "        [21, 22, 23]]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = np.arange(24).reshape(4,2,3)\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create an `ndarray` such that the axes `0, 1, 2` (depth, height, width) are re-ordered to `1, 2, 0` (depth→width, height→depth, width→height):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  6, 12, 18],\n",
       "        [ 1,  7, 13, 19],\n",
       "        [ 2,  8, 14, 20]],\n",
       "\n",
       "       [[ 3,  9, 15, 21],\n",
       "        [ 4, 10, 16, 22],\n",
       "        [ 5, 11, 17, 23]]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = t.transpose((1,2,0))\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `transpose` reverses the order of the dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  6, 12, 18],\n",
       "        [ 3,  9, 15, 21]],\n",
       "\n",
       "       [[ 1,  7, 13, 19],\n",
       "        [ 4, 10, 16, 22]],\n",
       "\n",
       "       [[ 2,  8, 14, 20],\n",
       "        [ 5, 11, 17, 23]]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = t.transpose()  # equivalent to t.transpose((2, 1, 0))\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy provides a convenience function `swapaxes` to swap two axes. For example, let's create a new view of `t` with depth and height swapped:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 6,  7,  8],\n",
       "        [12, 13, 14],\n",
       "        [18, 19, 20]],\n",
       "\n",
       "       [[ 3,  4,  5],\n",
       "        [ 9, 10, 11],\n",
       "        [15, 16, 17],\n",
       "        [21, 22, 23]]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = t.swapaxes(0,1)  # equivalent to t.transpose((1, 0, 2))\n",
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4, 3)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear algebra\n",
    "NumPy 2D arrays can be used to represent matrices efficiently in python. We will just quickly go through some of the main matrix operations available. For more details about Linear Algebra, vectors and matrics, go through the [Linear Algebra tutorial](math_linear_algebra.ipynb).\n",
    "\n",
    "## Matrix transpose\n",
    "The `T` attribute is equivalent to calling `transpose()` when the rank is ≥2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = np.arange(10).reshape(2,5)\n",
    "m1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 5],\n",
       "       [1, 6],\n",
       "       [2, 7],\n",
       "       [3, 8],\n",
       "       [4, 9]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `T` attribute has no effect on rank 0 (empty) or rank 1 arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = np.arange(5)\n",
    "m2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the desired transposition by first reshaping the 1D array to a single-row matrix (2D):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2r = m2.reshape(1,5)\n",
    "m2r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2r.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix dot product\n",
    "Let's create two matrices and execute a matrix [dot product](https://en.wikipedia.org/wiki/Dot_product) using the `dot` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = np.arange(10).reshape(2, 5)\n",
    "n1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11],\n",
       "       [12, 13, 14]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2 = np.arange(15).reshape(5,3)\n",
    "n2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 90, 100, 110],\n",
       "       [240, 275, 310]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1.dot(n2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Caution**: as mentionned previously, `n1*n2` is *not* a dot product, it is an elementwise product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix inverse and pseudo-inverse\n",
    "Many of the linear algebra functions are available in the `numpy.linalg` module, in particular the `inv` function to compute a square matrix's inverse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 5,  7, 11],\n",
       "       [21, 29, 31]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy.linalg as linalg\n",
    "\n",
    "m3 = np.array([[1,2,3],[5,7,11],[21,29,31]])\n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
       "       [-0.04545455,  0.29545455, -0.06818182]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.inv(m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also compute the [pseudoinverse](https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_pseudoinverse) using `pinv`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
       "       [-0.04545455,  0.29545455, -0.06818182]])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.pinv(m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identity matrix\n",
    "The product of a matrix by its inverse returns the identiy matrix (with small floating point errors):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,  -1.11022302e-16,  -6.93889390e-18],\n",
       "       [ -1.33226763e-15,   1.00000000e+00,  -5.55111512e-17],\n",
       "       [  2.88657986e-15,   0.00000000e+00,   1.00000000e+00]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.dot(linalg.inv(m3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create an identity matrix of size NxN by calling `eye`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QR decomposition\n",
    "The `qr` function computes the [QR decomposition](https://en.wikipedia.org/wiki/QR_decomposition) of a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.04627448,  0.98786672,  0.14824986],\n",
       "       [-0.23137241,  0.13377362, -0.96362411],\n",
       "       [-0.97176411, -0.07889213,  0.22237479]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q, r = linalg.qr(m3)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-21.61018278, -29.89331494, -32.80860727],\n",
       "       [  0.        ,   0.62427688,   1.9894538 ],\n",
       "       [  0.        ,   0.        ,  -3.26149699]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   2.,   3.],\n",
       "       [  5.,   7.,  11.],\n",
       "       [ 21.,  29.,  31.]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.dot(r)  # q.r equals m3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determinant\n",
    "The `det` function computes the [matrix determinant](https://en.wikipedia.org/wiki/Determinant):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.999999999999972"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.det(m3)  # Computes the matrix determinant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eigenvalues and eigenvectors\n",
    "The `eig` function computes the [eigenvalues and eigenvectors](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors) of a square matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 42.26600592,  -0.35798416,  -2.90802176])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvalues, eigenvectors = linalg.eig(m3)\n",
    "eigenvalues # λ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.08381182, -0.76283526, -0.18913107],\n",
       "       [-0.3075286 ,  0.64133975, -0.6853186 ],\n",
       "       [-0.94784057, -0.08225377,  0.70325518]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvectors # v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8.88178420e-15,   2.49800181e-15,  -3.33066907e-16],\n",
       "       [  1.77635684e-14,  -1.66533454e-16,  -3.55271368e-15],\n",
       "       [  3.55271368e-14,   3.61516372e-15,  -4.44089210e-16]])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.dot(eigenvectors) - eigenvalues * eigenvectors  # m3.v - λ*v = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Singular Value Decomposition\n",
    "The `svd` function takes a matrix and returns its [singular value decomposition](https://en.wikipedia.org/wiki/Singular_value_decomposition):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 2],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4 = np.array([[1,0,0,0,2], [0,0,3,0,0], [0,0,0,0,0], [0,2,0,0,0]])\n",
    "m4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., -1.],\n",
       "       [ 0.,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U, S_diag, V = linalg.svd(m4)\n",
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.        ,  2.23606798,  2.        ,  0.        ])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_diag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `svd` function just returns the values in the diagonal of Σ, but we want the full Σ matrix, so let's create it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  2.23606798,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  2.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = np.zeros((4, 5))\n",
    "S[np.diag_indices(4)] = S_diag\n",
    "S  # Σ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.        ,  0.        ,  1.        , -0.        ,  0.        ],\n",
       "       [ 0.4472136 ,  0.        ,  0.        ,  0.        ,  0.89442719],\n",
       "       [-0.        ,  1.        ,  0.        , -0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  1.        ,  0.        ],\n",
       "       [-0.89442719,  0.        ,  0.        ,  0.        ,  0.4472136 ]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  0.,  2.],\n",
       "       [ 0.,  0.,  3.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  2.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.dot(S).dot(V) # U.Σ.V == m4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagonal and trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  7, 31])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(m3)  # the values in the diagonal of m3 (top left to bottom right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.trace(m3)  # equivalent to np.diag(m3).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving a system of linear scalar equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `solve` function solves a system of linear scalar equations, such as:\n",
    "\n",
    "* $2x + 6y = 6$\n",
    "* $5x + 3y = -9$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.,  2.])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs  = np.array([[2, 6], [5, 3]])\n",
    "depvars = np.array([6, -9])\n",
    "solution = linalg.solve(coeffs, depvars)\n",
    "solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6., -9.]), array([ 6, -9]))"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs.dot(solution), depvars  # yep, it's the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good! Another way to check the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(coeffs.dot(solution), depvars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorization\n",
    "Instead of executing operations on individual array items, one at a time, your code is much more efficient if you try to stick to array operations. This is called *vectorization*. This way, you can benefit from NumPy's many optimizations.\n",
    "\n",
    "For example, let's say we want to generate a 768x1024 array based on the formula $sin(xy/40.5)$. A **bad** option would be to do the math in python using nested loops:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "data = np.empty((768, 1024))\n",
    "for y in range(768):\n",
    "    for x in range(1024):\n",
    "        data[y, x] = math.sin(x*y/40.5)  # BAD! Very inefficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sure, this works, but it's terribly inefficient since the loops are taking place in pure python. Let's vectorize this algorithm. First, we will use NumPy's `meshgrid` function which generates coordinate matrices from coordinate vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       ..., \n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_coords = np.arange(0, 1024)  # [0, 1, 2, ..., 1023]\n",
    "y_coords = np.arange(0, 768)   # [0, 1, 2, ..., 767]\n",
    "X, Y = np.meshgrid(x_coords, y_coords)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  1,   1,   1, ...,   1,   1,   1],\n",
       "       [  2,   2,   2, ...,   2,   2,   2],\n",
       "       ..., \n",
       "       [765, 765, 765, ..., 765, 765, 765],\n",
       "       [766, 766, 766, ..., 766, 766, 766],\n",
       "       [767, 767, 767, ..., 767, 767, 767]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, both `X` and `Y` are 768x1024 arrays, and all values in `X` correspond to the horizontal coordinate, while all values in `Y` correspond to the the vertical coordinate.\n",
    "\n",
    "Now we can simply compute the result using array operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.sin(X*Y/40.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot this data using matplotlib's `imshow` function (see the [matplotlib tutorial](tools_matplotlib.ipynb))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DaMFr9UuaR2jhrauLYoMMH9MfMGtvTI1+BLW7KwlOCCM6rI5GBYxzNMR7jRW2/kuRrNxQ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\nE03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H77qMLn+e+2fEVNrFjBQPGHzSxd1j4yh1R3iMb1E+PM+acIru1BNs9VXmTC4Oc30c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AgVyw0QUSCqftQTWs1roa0eJ26ZI+xBpPsjhHqXEJrzoqiDJJwkSZzXRYjyvkJo\nvwjlKgPRA8QcXUmoL/kIrfajUN4LFStMJDFQjij40Ulo6y4K5cdJ4oK/gRgmRzwZ4CSUmUuEMnmj\neD7aWUy1Rhzp6CCKEjoLZdxhEf+Xh/jDw1ys7uIg8nYHicrb+0TCzWBxw9tVzBvmKPIn+Ijsmz+J\n+H9iREZHGzFzTJBIGOArsi91EQ/P/yUqZsrEsjER4sTIIFHzt764fv6oqNxgKo6O9RbLx/UULVPk\n4vb5reLbqc7i/TA7MWtcb1EaZSheXpgkki8vElXf24u5Y8PEgzAXkXApUHy7vFcU7LMXf/byE38F\n2ouEnVai/PoRkfcyVJyM9BPz/HXEi1EuourFH6I6bZZ42t9fTLKRxDVfK1H1aqLQFB0QefP8xQ9m\nkljhIBNVVzoL0fJANFwKEz+aIgbLJFH1u6kQ6pdCmz9EnLFEeEiSyOmIEOrjQqj3icxghB2IezoI\nbcMwodWmiJaFMhEBYrGE0D70EUKbKVqve4v5uohBMklU/m4jtK13hSp5hDjhKRN9ZYg3Q62EOm+D\nUH9eL54MtBPjjBGnO1mJ8k/9hebdAZGwyl387KYn1nayEnknI0TzvYMi8Z8AccLLUSzoay3yZgeJ\nwrsrRcLdIeJDqKOYMcpdJPcPFNk3Bojn59eJ/Am64qdR4eLSkCBRdtxU3Dp/RJStUYg9IwPE6rHd\nhHK+XNw8v1tkHA4Tz0fZidljB4jqATLx9PxckXRxuqgeai1mjY0SLzrZiYTzHcSniztE8QZr8dt3\nQWJvWxeReMhMFFw8JvJiPcWxyCCxONxIvJniLCoebBKln8eIh72CxBR3xL9BDqL85Qyhyt4oMicH\ni4mWiPVuOqL8Rk+hbbgsKo62FfOtJDFOgSjbaC+E+pHQpPYTh6wQASCyB0hCqM8JrXKD+OTzn+yf\nGSFEy3ghRKJQxUjiPyz97w2R/hffDv5fX5IkCQdgoiSx8aIu0qjPiNZD5HfdytBEiQ5y2L3KCr0V\nxxB1X0hbvIYVp5uxk8HSJXZ4/LwUoW7i474DXNpaQoscBs3yJvqH78BIl3cX43l97jnaSgm7mW0Y\n5dsDWUgBr65Z8/XzKazeySmeFMo4vRBMuz4hNm4EJQ3H6HSnngejuxGtsSfU6yJX8+ejFC8Y+fQd\nJ6NHYivkjC67xGnHCUgIxpee4ZJ1DBWiiUlx17jcoxPGUiTD3PaSlBHDc3ke/c495/VQU5yMp9Kz\n5xVqn/bhYmsi9ic/U9lJTUDgFDqNKkH73pXLaQ8pP5AFDhJdx3Uj4vu2UKfmyfmb3D2QhQESYxfb\nETJ/Pki6ZG3bxJatlVQI2DTJgDbb/wTjNqj+nMKcddW8FxK3Ogqcnv6KpDcZcSGEpT+0cgGIc5Lw\nTB+PZLAVEefLzh41/CXgvINE9Jt+CJd9iNejOTXiPVtLBcs8JMYdGIhOj0VoHp/iwp8XOPdOTV8/\nOVNmDcN4ckdU/5Zw8c4hPlxoxjFcxtAeY3Cdr4Zb7pzJvYLYX0h9Jy2BIROIGpCFMtmHS1W5uF15\nSokfaHynEeP4kuYaO860ehP+5TCtCj3eu8zge+6CXMkFaQLdlbuxqlFz0WYBUaoTuJZVctH1R/qK\nF3jI0zihmo51zVmCk8t52GMEgyRDXIIecC9hLAUNhwm5rCF5jgcjZQOxGPmYr6e7Elt0FOPLGjTT\ndBnnNguTKUU0HjTnVPxh0u9rCOyqx4ThP2A0IQTVvRQuHDzJxTgVA+wlpq3rhsEPyxAZV3g67QRL\nXmv5TgGr9zoin3wHoTxBQZed9Pkg0VkIjuyQIfsxEbTPqeu/iO6xYC8ENxbJ0d1xHainefoERh3T\noJIkTsfoYntkLeBO2aZFrNtcSl6r4JcYYzr9Nh/J2Y6Svy9ydlci6TVquk82ZVTfqUhdJLKOVXAv\n9gJN8WAy1ZIYhxgU378j82hHHtfuwfu4nNS5lvSTRuI9+CIf7k8ggdv0PZvJ/bG+uBPNQPd9PCuc\nzBeplknPr3K9UwcaZB2YWraPhw5R5NKOKbW7eGfYlXiZDz/EH+ZdmC3liilMsjhJSWU4V4QFEY+u\nUOQpofGcyvDAVCi15kJpC9LtB8hkYDy0D4MjHMGwldjYfNLOxaFqlAic5c2A/kPAU8nXfYlcOvKG\nwioNY6YZ0nv2IpCm9z0AACAASURBVGTe3lQf3MGuVZ943ADrusmIOjkdXGaivf4D2yZ8Y28TnLWX\n6JrUC2F3EelLW86F5rFALTglh4HZ3uCcCEV9eOwXT0yDYKUBzL3li9TzJiJ/Dc+HXOTHZMFoI8GP\n2wJRTF6PNv05sRv2cPRyC35WMibP7YTb3H5o05Xcu3Gcd/sqMDGHTpOi6TTSFanIlBvvPlJ54Q1q\nhRbz7wYw3F8f9FRc+2yBLOkMJuUyMnsPZ4JuAwaOXzhbOBLLqiP4fa3ldodp9NZ+xVcWz3H5fDzE\nfXp9/cwB7zm4kEj/lLccC52Mv2iku+EVLtbPpU77jEE3UrgxqiNtRQfadTtGUuwUXnGN9ofL+TBd\nQU/dGXhNvUXV6X5crjmD7pEG1P3UDAibivtUNZr7Jpx/d5KPJ2txc5UzbmJvHBZEo/lSQey+Axw5\n00CAvox5q12xW7ABGjNJn7+WeZdU2EhwZIEBhttOA40op01h8AkNTZJE7HCB4vIJJFkbtKu6Il8r\nEEL8b7uV/5/iFQpogBvO4JkUDNYPES37qJ+/nl+Oa3mqhRXWgtG7XTEYth3RqqTm5GZO7v3Eg68S\n7e1kjJ5lT8DguUhO1lR8Osfb9fnczM3CSqXguyhLbGdH46HqRYXLDip/MueoNh3j4lZGKAxoXm2H\nZe1PmLgcxOBoKfurbSnRL2fxxyrql0Ch0QacScDnwxHOZ4fzQu5GjPIJHQLqeRa0Gi0yolNWk/jZ\nmDMGPYjU5BPjlkRG28nk0RHH5t8w/wu2Bpti22rPPLNKmmda0Jg3lwqzXRj8WcT1JiW1jnKmyvyw\n2V6FddFP5MqfUnLoCbfjqiiXKxni4U7kclesw2KgoJrUu3u4sreI12WCfj4wZU4A5lOXIekboby1\nhKtzsvmzHKJkEtsmSJgdXAr6P0NlP3LbJjEkD5Ak7tkKHFPDwDIOtFtonbCemecgFthuKDHqlD3S\n8JOgrqf5+E8c+SOP08XQywJmzLPFc8ISsHen5v1+Xq5K5mpyFYYGEjFdHHBf2At702gqdM9R9VMj\nB1s+olcIQ82skG+yxLPqZ1R+J5HvreZ4vpxC/VympchR/VFLs7QNJ/kHbDKOcyOhLU/1jRlXmURY\nRDXxwSvRQUFU+mrefzLlhE4POpDGFLPPJEXHUEg0AfW/ojjRwnZ3fyy1hixUfKNpjDN5tfPRGu3E\neks+59GhytqE2U02mG4uxTj3V3KNn9F05hFPHzWT6VDLEEUQEb+YYO0/AnVxM6kvD/JgbyWfSpVE\nBMuY8EM41hOmIekYUx27lnuLM9lZoKGDPqwdZ4jlzp/AbDIicyZZ/Z8wJvM/Q+9uuoJzUluwfILQ\nbkI7diMTrvwn9326MPKOIVLvJyAqUJ2I4fCPtexogOEGsGSRKTaLViJZhNH0ah3Plr/mSHwLJmYS\ns6JtCVjcHxPP7tQ0XCdvaT5H8pLQlsFQewdsttgS0LiI2qDLtGyt4kxaLfkih+9zLNFZq8RAZw02\nxl9QfDvHg38DeGTRyJjMUnz6lZMe8BsGGBJWsZzPT505pteediQzXZ7B24E/UCr1pF3tMnT/qWWL\nVTfMgeXa11QMCyCFhZhKB/Hek8x+c3PKDW1ZVCVh8GcO6vz1lBq9QnbtDq8f65LsV82QplDClzRj\n7jyO5ko1GUmHeLerkQ/KGoJ8TPh+SDDWEwaj02JDfvxOnv+SwYkCJYEWEkuG2uO0YQKSzRC0H9eQ\nNPUxC5O0yIDDIRJ+9zuDw3VQb0E95i/GX4NHwBG5xNBYA+j+FkQJ4tQA9k1Xs1YNE2SwZqkCw1Ub\nwaArmndzeDw7kS0fBFYGsKyXGcG/j0QnuD/NRdfI/jmRPZ++0VgnMdHWBucd3vgYz6DW6TFNW7I4\n8TmbwrpSBtbaY7lehaPeSgxsvyGSb/LkmjOPbXIY+hE8xpZR6bcUU2GJi2YF2We9OWzhTmjrJ6bI\n8kkYNppKqS8da1cju1fKZqP+GAklv2ifUDnEh4+yX3HUHsb35Ft2m7lQquvBwpoSTBYWUlq9iQa9\neMweXOLdC1NeBqgYXeZD8KJiFNZTqK0XFKfvIW2nxEujUkKsXRg5wAWLmM4YlrYhrXgzH5ZUc62w\nBidHWNTFB6c/B6HjEIUq/hCJPz5ldZISHbVgW4SE39l+SG32QuMBGqdtY+YlLU+Agzow+LEBRCUC\nRYgTfZFN0f7/j9dI4CEwUpL4OxxMbviA00VAjfgyn5zF8Wx7ouVxq6CbLszsLcd/UXcUkRNBR07r\n01jiY6/z9lQzCcpW7Byge7An0d2C0Z2gwPBbZ3LzC0itPIh8vYyP6lYKurYSRjdGWcvRLH2L5vVi\nUpRG1OvvwudwGSlKBXEhHthpPZjc+gWL6DTSpGmkEoEN14h+8YDUVgWXjL5Dg5xhTXdor2ngee8o\nivmeNnwhUHcPrQ+8OGIQTJ6UT2RaFh2bm/nyowKj1mUE6gl0Ov+NfH8wN7Ml3snjsH+lQ5DQR75Y\ng6/rRLwc/Gn2j6f1WjVv73/lQUoaRTmCEENduo40ouPgfuj3GIiEPi0fjpG95TEH/tXwoEUQqQMr\nOkh47feH4INI2ELVOFpGfGDpM8EpoIskccoaLF8rkLweAx6I+hhaJj9m/TXBUQHhwJoAifCNfsj7\n/gb6HvDpDBnnznDvUAM36wWuehJ9o/XpH90Ng2m+6Gn8aH5XQnzpPqrWaXhV34jSXY2fhx9jLP3Q\nWZ6OXu5wCrMs+Wq0F/PDpVSk6BDXW8JKimB8ixarH+Joqh5HgrorWukOwfG3aEpRcM43nBbJi+80\nr4jWz+Rjuw58YhJWZNGtejvSB10OGfYjG33a1T1lZHo1CXOMqJJ+wRM93Hx2onfPnDuZbXhhnILT\npzI658up+bEFC9eJtBWhqLrfRftIxcebKu42PkGZIHDWGhI4WJ/AwZ1x8xsEjloa02Mp2faBEwnZ\nvM4CRwOYFayg028uyPv9hqQbhqhYS9Oym+w9rWKPVsJVCPabQdBVfaSeZ0EagNDOhh/PsHcPrBcC\ne2C3iUSXQwZIo7eCbDiibisNf+/n2rYm9tdLGAvB+BAYMSoEw7k9kBl3g89f+ZK8i7QNDdyuaESh\nBc9oY2KsumK4XItCtx2q13a80z1O86lcil9p+RzSgLl9AGNUnjhOTULoRJKV15NC3VhsPv2D+WUj\nzvcwpF4nkgEtVfT0eEmZhz+vNTPQUkTHqr+wfazhsHMXvkk+hIm3TMpKJW2YIZ8Nf8EMUzoa7MI4\npYI7ed14aqDBtjqeka81FM5ooMVjIj6qjliF3UDKKObbFXeu68TT+qWENoUmOPSVsB0ZSnv5GDSR\nRTQXJ1G7pYGTpS/Jfq5BMhSMcDcmbJkNjh0XITn7o626SsO2e5w9n8/xIgldIVhhBf02GKEzdSVC\nPhNJuwPx93puLBMsVgu0wHaZxPAtwOKVSNKvoD2AOPULj+ZpWNYkaAIWm0HMbHMMlo0Bs+lQ/Zra\ne1t5s7GM4zkttDRDRDsZkwLCsfjFHUOHSEhR8Ln0EjlnP/HluYpSwwaM25syRNYNrx9qkQXaoHre\njw8Gz1B9voLlIUPut9NQ4+JH73p7eoYkoAw15lPJHEqpwK3pL4JPajkR7EiKThRBlDC58TF17fV4\nbrqMVizpqD2A+8c07jW156GOD5ba90yIz6FugJJsnxjMRBQh5nfRFc/Ju9CZC0ZVNNcm0eWlApNo\nNZpxPoQ3TMa4XS6t8ucoN9pxpvktuQml6DXoEmVviPOvprR1no0swobWpiSadn/katwb7r3SUi3B\nREuJAYtMsZ00G5wmIrU8Rn1pHS+WlrKs7D/1DksRzJsL0s7xSDoHgVjEi+HIov4L8FoPHAFqgP7A\nCkuJwBVymDwcyXIxAitoPov69k3SLyRz67mGuEpokkt01IfIzgb4DrfFx34sUkc7tMbNtJZ8RX1c\nw6OKND5UJaF6DyaV+rQx1qFpYismnj3wbe6Bu3kdms4XkL03pOTfIO7r65Crk4F93Sf6vdKgcG8l\nZaAV5bKJ6OOID28Ja7yELFvwUD+YN7KOqNAhXHxkaNNbZB5avpr05zM9qacOC+kSPnHpmL4x4E4X\nGTl2nthqAuij1MO7Yzra/lnI42MoLXDii+ItlRU30DuhQ26plgaDVjRttQQ4+zBIEYbeJAV6Pt7I\n1EaQWEtBzjU+/5tJ0v1mYpVa0EB3BQzoKiNstCe64/ogGc9FICE170bcOEberyq250tcFAIJGAes\nt5AwOA3SwAUg/Q60guYPxKVTZKxp4VC6xC0h0JMkhurB4C5yQmMc0Os6EbzCoEWLquQ+yuP5xOa9\nIOFdC+VZINOXaG9ogPEEcPeNJkzbHVnbelrbPIEHNpQ8MONfWQG5+i+xiYeINB007VspHmmATD0V\nB60TwfJ4DN0uofPGlkfVHXmtY4eacsJEHINTq2gOhHfe/cijNzo04M1ZIp5lUFUt51aAMzmySKy1\nRvRtLcIv7Dkqb13qvkwhRThRppOAVHsFl+N6lFfI+BjWgtrfmKCWSPoa2qI/oRSZrwH6yR2p/1rH\nR65T+G82hffUvNe2YKGS4RQs6BlqTmjHLuiP8UFuNAghL4JPcTTcv0TS4XqO5gieasFSkpgsCSZH\ngukuYwhfiyTNAnJAOwux5QVpq+A3leApYAvMkmBitITlHw7QeT6SfCRCk4fIO4Ty2AsePC0lLl7L\nRxVYKiT6mejiMUGPUP/uWPtGQbAKFUWoY5spul/DA+LJyyrE7IMOtip9TNsLVCMNsBMxBKjdMPOJ\nR3hcQ+d0JM/KXHlt0EgryYRmFdL/UyvfRmjI8OxFg+iDFQ20FRdxy/lKQbMOt/V6kSW1wYYa+qqf\nE2KUS7OzBYlMJBt3BAnY114m6Cx8UejxMlKiRactoS0e9NdtRD7hPZKRO/JX/UhTNZKhfwnlkxxa\n78rI1raictBgHiGju2EkIW090RmtQE8/BFHZgvbVZz7nXeHr8Rbu5LWQpgQXCQa5wPBeOpjN7ois\n7SyQ90GQgFSwEu2aDzw4BZs0Eu+FwBdYBowaIcEBGyTrQyD1AxGHKFpG6/pk7p8THKmTSBUCP7nE\nJDMIn6jAq2cfpC49wMgbbctHWp+kkn83lXulHyl4AZXNAle5Pp5tdTEbY4ifzvd4m7jR0vk9Gu0X\nOOHPq3zBG0UKLQ0phDzXJ6BekD1RSa1XNAaavniJZnwsLqLbmkZFQig3dYPJkgywIY0+tW8Iq1Hx\nrZspH3SnUoUbFnwgpPo0Pve1PHVT8NI2jBbJjyCVhiHSV2RD4tHWdCe3pB+ZUhM1issYP8vC9JYe\n6aZaciKbsbLwp5s6mKAwXaQfitFtCUIn3pWc8o+kVl2j4piad4VKylq1uCtk+HeGIV6+mMaEoxfa\nG0nhjWh8i3h1kfxTycT+08oRJeQCHSSJJYaCzvNBtiYMoX8YSQoFLkLeDCQ35f//eD0DTksSz4Bi\nIbAEOksS3xkIegSA2RgLFN85Idl+D6YRoGMEjeVQ9IHa2ltUPqml4VYziWV1JKkbqagTyFSgsAZn\nYzMisMPPWYEY3IosqhW9ll4YFrdHNJhRryii0ewpgjsYPjTE/JYJH1HwxlGHbBMFSkyxkVQEKWvo\nXleOh3UlTeFQ6uVBvjScGoLRosCYbBx4gFP1M0wSBcqvRjw2ciTByIh8yQitpMShuY7wYuhYr0Lq\n1EjD4Bq0jgEo6oZgUe+Lng40OqTQYh6HKqMUbhpR+VbNKypIbymjoQTUWgkDE4GvsS7RuuZY9TDE\nYrA+Vo49kBy6gYULIIOGr4jaq2ifJVJ/pZTkOImbVYJYIAcwACIkiZHAeDOBzmJgjjHCahHwA/+Z\nH5qPUP+DVHCWlmv5lF+vIy5NEFcHX5XQBNjKwNsOIm10aefgiP53phj0MMZYpxcKmRuS0oTWljLK\ndRKpUz5GdV8P+V0dUmrVZMiV5NrW0WTejGWDI4G1ZnRSCRx9amgaVESrmz2q8tFUijCaUaBLLpbE\nYtPwCJuXAlUWPLW25J3Mj1xhhxoZ9lQQKjLpUV6JnW0zZd2g1iGYGs0gWghEDxnm0ld0bO4gb0pC\n/44DypcWvEMi2aiOIpMipGYNDmVmuAsTItBDt6eE7ndqTB3DsJE6omfoijBQQ1M59eI5TclZNN+o\nI/NTEU8qWkkphWwNSICPBFEmEoPsBU4jdNAbb43kMwp0JwPBQDVCXAexDulsAdq/4NEnOC9JvAAq\nhMAaiJIk+pkLugWB8Qh7jPo5IlmNAdMw0JdBdQ2iOInK+jtUP2qi6ZaS+Op6Pok6qusEQgIjc3DR\nt6U91nh56KAd3IQUqUWvrj+K4naoWo1p0stDafoYmfYBxv8aYHrPjHiFAe8cdcjWN0eFMbaSkhBV\nGT3rinGyakAZAgVtfClgKLUEATqYkIEj97Gtfo3Je0HTZ0seG9uRaKxHoVwXSWrGoa6FdkUy2raq\n0XZtpGlwLZJ9KEZ1A7Cq9UGuUNPgmILS8Dmq1ErETQWlSc28pIz0hhqaikElkzA2EQSZyInWN8Oy\nrzFWg/QxdeqP5BAFJl4g1ND8EVFzHhH7gYZz1bx/KXGlQfAYyAdMgE6SxGhgtP3/PA+TbRBW84Cx\nSJIjkIlovoWUd56GG2VU3KjlSbaGF3WQ1QgqwFYPPByhs5kRIS4O6A02RBFlhLGsJ4ZqN6RGUxpF\nIRX6b2iof4n2vj7y+3qkNmj4qtdCjkM9SlMV1nX2BNcZ0UWpxjqwguZ+ZbTYu9NYM4JKQmhBHz2y\nsOYBjv+DuvMMk6Lo2/2vuiduzjknYAnLLrDkqAiKgEQDimIAFDH7GFAQs48JRBHEhAqCKIqCSJS0\nhAV2YWEDG9icc5yd0F3nw3I877fzfDnX+56a677+1WF6prvqrrv+VdVVzSfwOwO2SjgcGMRF0Y9K\nGYBEEkIjqa5yptS14RHXRdNE6AxMo8sxAxf9MANehnwI+QOl6RrmvWF0ZnqSiUaupYU691rULghp\n8yZWeJGmmjHdLDDP0PEJGkmgko7iF4w0adBRT492lK6L5fTuaSEvr5ZjdZKcJigFDMAgYLKHYEa4\nJPROBeWBAET0/aDcD/QDGpByNzjfQjHX/c8XL7snXO2E80AOUATUAB1CoAHmG4IWISBRQLg3+MYq\nBMQIgsJVwgzx+FtiIDwQwr0Q4QLCesHbAZgAO7QbEOUBiFJ39GojtZqkRthoFB20q03YlGo0UYFR\nr8KjXse/GoKrIKJawdMGtZGSumhBQ4RKsyGONuLolsH04o1ERcGOmRY8qMabYvy76gmsgJAKCK2E\nBl9JTSQ0hOu0RkhswaCpkRj1WNz1UPz0IIKkN+HShL9VQUY7kTFdENUKJiugAjZoV5E1KtQ4kdXt\nyKpqWmzXqe2upb5C0lQOreWSYgcUaZJqoF0I7IBRSvyBSCHoBwwB0qVkgD+IccBEYKyAhCjwTuvb\nKQYiiQS8EDhANoK8jrQVIyvakGVNyLJa6u1lVDuraaiVtFZLeqpBq5RozZI2oFf0vY9hBdzM0BOp\n44jRIRqEJQSTHoeHHoaX7os/ZgKkkxDZToioRQ0uRQaVgwThkMhehUZnHFUiijoiaMGfLnxwYUYB\n3HDiQwdBsplwWUOYWoUSVYr07AGDRLQHQ1U8oiaMZrsfdZhpFhpNtNOm1tOplNHrKkWvBlkOxjKw\ndUvaBTiQKFLggUKADt4eAmukgncEBIZDWBiEewdgiIxDREcgooMQPvGgpCCJBnwRQgLNSFkEXAD+\nRnQfg3ManAR5AlqvQlEzXJCQK/vIXy8EnYAuJVbAX/R5F/EKeEUIfGIV/KIgyM+XUOIJVsJRQ/0h\n3AxhEsI6kUGtfQmhO6EmCFHuhyj1oqvVTA2Cehy0iE461SYcogEhKjCLMrybegmslYTVCCJqJEZd\n0hAmqI1SqA/wpolEWmUknQRixw0NiaAbEw24UY43RfjW2QmsFISUgVcjtPpAXbikKULSEa7hCNcR\nliDMejReeiT+MphQ6U2EdMMSpCJjNIjqgSAH4N3HCdGNtHcga4DqXmRlA1RX0tVaTE1DN3Xl0Fgu\nqa6DPF1yXUKNELQDDikxA4FCEA0MAFKkZDR9AqSMu8GLEW4QmQiB/UBJR4r+IMIRwgTSAbIBKYuR\nTc3Isk4ob8NeU0MlldTKUhobnHTWCOxVEqrBUCvp1gUuQCBwF6D56nTF6vTGSPQQBUQ8Jj0Gqx6I\nF174SoVAaSNYbyJMVuDmWwLWdtAkOMGuCKpMoVSLWBqIoJ0gbPiiY8WEwAMn/nQTorcSIRvw9q5C\nxpZBYCOgQF0CojIGURVMV6cX9UKlHhvNSjNtoopOSujpaMNZTl/tt1rSJCUdQuIUYETgqUMQglCz\ngkcY+EUIQsIFEeECvxh3REwsIiYKERUGhuFIMQSIRggPwIWUjUABcA7BIYSS8T9fvORcwNHHJ10X\naE6Jrgt0DTSXxCWhVwX7DfSq4DAIHIADAw4MOG/AhYoTA5ow4kJFw4COgo7aB3kjLlRcqoKmKP9Y\n3aDiVEBXFDRVoAsVDSM6BnQMIAxoUiCFii4BlBtrMQn6uuB1QEcIiZQuhNBRcCGlE4ETBUeflU4U\nTUfVJUJzoWgaqqah6q4bVgOpIYSGwIWChorrxjVcqLqOQQODBqouMLr6ZoY3aGDWBaYb2+YbsGhg\ndvVZqy4wOyVWXWCSElUFYQRpBMygW0CzguYm0Kyyz5oFulFFM9+ASUE3/W+r9B03gW4W6EbQTaCZ\nJLpJIk2gqwKJGR0jUprQhQGJipQGpG5FamakbkJqZtD+j0U3oEkzOgY0TP+kg44RieEGVPgHCgIV\ngYKCigBUFJT/cobhBlTACP9czYiOQWgoqo6q6qiKhqJqqKqrD4oLVdFQhUSR9J2LjlBcoLpA1fve\ncFWdYHCC6gC158a+XjD0gNqLULtAtSHVbgR2pKYjNCe4+jiAg74qvB3oVZE2I6JHRbcb0RwGdLuK\n7lDR7AZ0h4puV5EOFd1pQPYqSEcfSaRDgEMFh4JLU3AKFQfKP7nIiUBD4JJ9P60h0IXAJSW6AIcC\nLoPApUgcqsSpgEuVOFWJrkicQuASKpoEl1D6LKDR13ekod/4aEgh0aQTKTQkLjRpR+IEHOjYb9x0\nL0I4kJqDG4fACcqNZ6I4QHEKFCfoGn0jvG5Ad0kUDYSrjw/c2DZoYNAFilPeiPfp9v+GChiEwCAl\nBtHXL2a4kS9MN6AqYFDAYBCoisRoEKgKGBV5gy8C3WRAmlR0k4q0GJAmgW5SkCaBNCtgFkgTfTAL\nMEswSTDpYJR9JDX9F5h1hNHVp80qCGlGaGaEZkFo1r64bgZpQWJGYkFiBCz//HuBGTChYERgQsGA\nghEV9R9r+McaMKJgQMV44xtGVIxCoKjinzLin4fyX+Mm/cZ9AKq4QS5xg3DyBi90UDVQNYTq6uOI\n0G6Ul9qNXOPqS3/pQoi+PCHElv9IvAz/idD8PwvTQQ4VKPE+CLehGMzJwEAgDggB3JEoCLrA1Q6O\nRrBXoLmKcNpLcZbpuEo1tOsassRBR5OdCuGgRnbTJGy00U0P3XTjwnHjeRntKlabGd8eI0E2AyE2\nhSBdEoyG1cdFb5wde5wDe7jEESZwqhH0yv70EIedMJz4o+GBRKDgxEA7JhqxUoEbJVhFLua2Lky1\nYKkE83Uz5jIzTS4DtQaVaqugzqrTYnXR6e6ky2pHM7hQLQKLy4C70wNP4YGf9CZYuBGOhQjVhBpt\nRMSpqHEqhhiJMdQNk+iHYogFcxiYfMHgjsSAQEPSiqAaKYsQvcVIWx6iNR8KepFZArKBi5LmSrgC\n5ApBkZRUCKiV0IakG4lEx4QTTyHwQxJgglATRJohRhjx97egxhkxxCgY4xWM0RI3czgm4rHooVj0\nICwOf6x2DxQBDqsTp7UDl2c9LmsFurUYLAWIji7UMjcMpe4Yr7ujllqoxY0Kk4lqs5k6g5EW4U6H\nNGPDiBQqAg2LdOCJDX86CRYdhDu6iXLaiHJ1Q3gXjmgHzjBwBYPm7YHs7YfoSUDYolBtweD0RpNm\nNAl2pYsetRW7qRaHWomTcpy91bjKDGhlAr1EIq9rtDe7KMdOjW6n3gEtduhyQI/sK3sVwCoEvlIS\nKgSRUhKnCmLNkgR3QVCYRB0CMk1AKogUCd7JwGgkQxEiCWQkCD8UVBQcIFtAq0faqxCOEnRHLo76\ndpzXXbiuO9FKHbga7VQ6u6kUvdTJLpqVLrocEptN4nAJdB0UDSwOFS+bGT+bCR9dxQ+FQCRBqo4z\n1okjzo4jxo4zUuD0cUNzDMCux2MnGieBuPClr84tUenCRDMWanCjDHcK8eitwFwrsdYIzJUSa6mJ\nrg4zFW5GKq0qNW6CJqtOq1WjxyJxWHWEMGPCAw/pjrdmwdvgRoBqItRqJlwY8fJVIV5FxIESSx8H\nLJGYRCyqGg5mfzC5gdHQR3S6QTYhbU0IWwXSVgg9l6G0HfJ64RLo2dBdKMl3CnKBHCm5DlQBTVLQ\n6ZK4NBCyryTyA0KBYBNEWSDG6CTGJDHFqBhjFUxxKqZYBXOwBxYSsRCNlSAs0geh+IBqAEXitHbh\nsLbislajWUvRLYXIniZEuRvKdXcMJRaMpRa6W8xUqSaqLCZqjAqNZpV2aaJHmHFIgRBglA7c6cVH\nNBNIB6GymShXF9GOLrysLmSgxBEicIbKPg5osUhbP7DF4HKF4cCfXox0oyCFA4exFYepEae5DJda\nhotStBoneqkBWaZAiUSr0KlyOanB3uexO6DTDja77Ctn6dM0LyAQCFYg1F0QaZHEuQliPSTmgQLD\nYAmpAgariIhQJGOAEf+xfPz3el6lKjJ6IbAEGA6oCC4jXT9Dbgby6BWKT2gUXtA43yTI1qFJ6xP+\nWCSxISrBwyAh1odB6gi84iMRaUZEcgeoHog2A8qlYBz5CrlKI+VKHi3KOSjqxjfLSOhVgZSC8mSd\nmqE63T5JW7HdMgAAIABJREFUCD0ZH/yJ0l0ka2Ukul0AnxZ0ATUyjjwxkioS6cYDMz2EUEo/eZFE\nV27fdCW9Zso7hpGrJlGmuNFCF5ooxM1xmZCrkJAtMDigIUmnIc2Fa6ABL0YRqSXTXwvFN1KgpzUi\nw9uQ0gGFXsgsgT2vlhx5meL6CmqzJHUlkiKgU0q8FRiowEhviB9uYOBwBePURBg6EuExF0Q6YAZy\nQf6IbPoWfuiCjZJjxfATkCEEVfRlukRgkpRMDhakpIH3DC+UESNRkpPBNA5ECIgmZGcB8lIzjqvX\nyHWdpfRaBy3ZAlueRpXUcSLxBfQEiZoCfr5xROgp9JPBxHq6kGlV6GkXEDYPRNZImqpjKRSeVCk2\nbJRjFlkEdeWTeFknMV+nMkold6CZUsMIGsUAXHhilW2Eco0kZyZD8ux4dkFhf0FRPzfqlHTsDMaN\nQCJ0QT/ZSEBEHnLoOei1oFwcjj0/knxNUKI0UGO4RHdLKUqOwHRN0IWCFQjwUFFSJX5pEBcYT7Ka\nhnFwDCLFHeEeB9ID9DJk+yW0rFL085mUne0k96LOiSY4g6BUShQgHpgE3Clg8FgBy0Dc5QOGZ4H5\nSBkHtAJ/Q9cOyDqJc1cHuZku8q5I/nbBNR16pMBX9KV7ZIJC1FAYEBJDgmcKIjUYkSoRAeY+j7Ta\nisj2oLm6hwKljEolm86OAoyXVIIuqBiaBZoFKoZqNA2WuIypWGUCIdKHeFcXg/VCPGOzkNZeZK+F\nQscoCsVg6gjFgcCDJiK5ykD9NKHtnSgIuu1+XGEEhWoUtdJEj2hB5Qp+bUVEZwtiCgS9XlA+TKM1\n1QV+sfi5hhOjxZOMB4bkHvTUCqR3N7QpkB0AWS4qWkvJk1mUl7TSehEqG3UqASkgVBWkKZLERJV+\nwxTixrihDB+DGDgMDDORJCBEN8iToH0NWcdgi0bnD4If7fCHlFyibwCZnxCkArcJGBsHSRNAmRmF\nkpaGCBkPynAQJiQlyJoSZFYFrWWXyG+7SkW2oPOiTnWzRpMqEbpOoEEgB4HbEIVg8xDi9AEMkj64\nJfWgDy9G9s9BXBuKkjWE8o5gioWgQWnEIQpwE2cJL+6gfzb4NSlcSRFci/OlmpF0EoOKET/RSDSX\nGdR+hYRiid0MeSkKJX5JNDEcnVi8cSNGtzGAUqz9LyDj8hGliShZQ6luCaIAJ+VqJU1qJlplK9Zs\nFWoUdKHgg0RJAFMqBA20kKiOIM4jGSU1EJFsQhAD0oUkB/1aHfrFArqzs7hy2UlBtuR4ryBblzQD\n3kIwWEpuBWb5QOB8EMtBpk0CVqAoC/7nNxvqrnRQPkWIoSD/gqZ/4Xz3Gme3whfNktOADzDVA+5J\nVAlfEoXvtJEogfPBXUVWVNDUtpXm7zspONDKEVczba1g9JT09w7gNkswpoc0TBNC8G6Yg7kpkLrA\nTHrU7zB/507LMSuHfTUKI3rwcHkwvtHMDL2FrsU1dIWMo9ZxJz344cUZYnq+JHR/L5d73NjjNoTr\nMgJvOpkoc5jVUYdtvKQsaTJ13Al4ECxO4Kt8g/fvJq7lRbI3UKXUoxv/DsnESjOpgU66l7agxI8h\noHEOVqy0RR/B1nIMNntxMbeT065KWhtdGCyQ6u7FpAg/vJdbCE26FVP4KKTZAo2X6SreQdvGav7M\nbGdvNeQBkcBdboI7R4PPam/EuMdBWQkIpFwHme+iP6yz8SpsEYJyYJCUPK7C7Gng9nowctAzCPMc\nkM1Qt5Pu47up3tTAL4W9nG6EbhdEBMC0UAsjU6NwX+aOr9/tmNV+6D2d1HOE1uxsXFtUMpp7uGZu\npNXHQVxXBBNdPsSO7MHxQBNq9724WsfipBl8tuNefAb/bwP4092HU4G+dAkfEvUG7mirZEB4C42T\n3LnmtpRm0jHTQCzfkZCdhX5eYad3NKfVOHqBZGcVcys78BlVT9MMDxzdy3DpaXgqVciIbzGcr4Wt\nIRx2alz0q8ahd5PQEMxYPAiYAaZFBoLFArz0ZDS3KlrkYbr21dC4o5nfumrIL5PYHRAbIJgboJI8\nJ5DQuxIRccvBPQ20a9CwDudXZ8n/rJcNdZJ99DWa3AasNguiXwPx3FAwfI6UacBRRPtTaO8UcP4L\n2NjWV7Fwl5JpVlgcL4h4KAKf20YjgheCuwWqymlp/J7GbY0U/tXKIWcrTU0Soyf09/dlpjEU00Ng\nmRyMV8ss3BpCqAm4QJe6DeN3Jlr+tnDMW6Mgsg13pzdjG925jXZ6FlfQEzGChp576SYQT84Raf+C\n0D9t5La5s8czmWIRhTc2xuuXmN1Rg2u0oDh5AjUsROJDMCcIUr/Ce5+Ra7kR/B5optTNiX+PnYnl\nJob5Ouha2gzxo/FrnIebdKM9+ii9XYfhcx8uXbVxRlbS2GLHZIDBFm8mh/nh+aiZ0H43YwkZi7Ca\nkE25dFzfRfumGg5kNvNnJeRJCAfmuMNdIyDgFT+YsBxhXAEYQH4GWW+hLdXZkg2f3/C8BgnB4wLm\n3SSxvBUAQ58F411I2YJo2kbv0V1Ubarnl3wHJ5qgXYNYH5gaYWTs0CjcH/HCN3gmJksisstGg+tv\nWq5cwLVJcLKpi2tqI21eTmLsoYzX/ElId+JcUo9qvxsax+IQ7bh8d+JWeoqALwI5aPHkeIg7HcKX\nRNnOnNZaBkTU0TTZSpH1ERoZiYlmYthGv6xzaBfhZ59YMpQkuqVggFbOnKoWglIbaZhlxda9HKcc\njoeoRQ3bivFyGcrWcI44INO/ll7ZSVJNIGPwIOBWMC6CQLEQX8dgnF6VtCqH6dpfQ9P2dv7oqezj\ngV0Q4S+Z42ci+Y4gwhbGosYvA68UcF6Hxg3Yt56l8LMuPq8R7AWcUjINeMUECa+AfDEFDJ+jKGP+\nPxAv/Q2EeB7kDuSh5eQu7uWFBkG2lMxV4KmbFOJXj4IxKxF6BNr1L2j89ADbDzRyokhisghmxFoY\nvCyAlLgHEWOsOFylODb3sLcikysFRZhzDMRZjbBcEBN0L+lqLK7pOxFH/LhwOpIj1jzcanOYfkyh\nYU4vtUNux1sfz0j1BH7uv1OROZxdpmHYqGdq537GNNs5NSWFAu7Bm0rGN36OT6bG9/0HUSbGkKY3\nM8/rEI6BgpyGlRQKMImNDNncQ65RJWu8B1HOCSxUjJiePIVaOYP63BiyrDuw/VpIfYakzceOdaLC\nLOstxN0ZhHFEBGqFDy3nznHm3D4u/NjLmV6NAUa4darKpDvGYV44D2EdiWzchePrzfz5QSfvtEAT\nsFLAikUC0+eTweN7oA1absUxvYJ/XRB8BwyQks88IOVrC2Le20hxH8L1Na5t/+bo281sKIJKIZhr\nkcxcZGHIHbNRbp4ELjfsmUcp2nGW3y4XUnlZ4GlQSBltJnb+ANIt82FyNc7u6/R8FsgvjnzKG8/T\n/6iVoHBoXG4lWF/OMCGwjtmE8mcM+2oHkmVuIqr9BAtO2ShYKCgMehgz0YyWPxJbfomTWgIHxU24\n0ckCbQ/xbj1khs/kKpOwcpJhOb9hyjbx09hgusQ4ptph5IgTuKJ9qcpdxFW1gt72Lwhfb6HAXaNy\nqmSIuJmZIf7IJ0oxl0+g/bzgjP4FLRs6yax2YJY6UTfDPRET8H4iHlP4zciOanoP7ub0tovsPG4n\n0w7jVVg2TpC8eijqxNWgpIFtDfra79jxkWS1S2IHXgMefAzEJ3cgla+BQkTpHJrn1PJSDuyhbyTc\nqwmStLcjETOfQxgnItt+oHvjHn75vZgDFyXNKsz0NpKywoP0/osxTonAZW7C+WsDZ07ncbwiE/2s\nQhgWPO6UhKTdxGjHBNQpGcj6Wqq3J/OrqZJuWwZT/lIxDLFTOmsAFtc9pMlKwsM34zgZxzZGUy5c\nDHcd4I78Jq5M8yfb8hgKgvTez+l/sond8aFcYBpR0sk94gjW1EaK2h4mR8QglZ/o92cOtmtmjtxi\nxKqO4w6HPxELs8AnBlvGJM5aTtJy+QCu7Qp1Rge9EzUm+E9gzKhYDPMtGJtj6T1RxemKbyj8xMH+\nTjtBGkwYJ5g/MQXrE5NRvBcie0+i/f4x51+r451iyNYlc4C1w8FnVzRE/4wQ0eC6D7nsAJ9/DW/R\n19z7roB5r0rES0vB/DroZ9BPvEDeC4WsOw+nJIwzwv2TTIy+Pw11xl1gjsZZtZ/mjZfYmXeRwhMS\nu0EyNtKN8GW+jPZ4ALcxArv/JbTNgeyvrOCS7Sjhh8zESJWWxzS8/O8nzRWD7/CdqEXdXDwzikOW\nLqzOU8w/1knXGDtXU2aiyQkM5SSD2/ZQVh/AT8bb6MbIVLmfCbZq8gcN4Tz3oFHJkMYvSNqt8+NE\nT0rNU0l1uDEr6ALaTTW0ZK0gG4V2ZRPhm7uobxDk3GInym0ccy1xeDx2HYM6AHkkhgzlZ6p/zeNa\nhoNup4uA0YJZSUNJWjAES3o66DquQ6e4cGgPB7fZ2NsNsQKWpMDNK6MxLnoMaboHoX+L3P4mmSvs\nPN0JRULwgJS8OxrUg9HgcQghkv5/EK/DCBGFvDSOi+MaubdbEgh8mSJI2j0aYr+Azr9oWbeWzR92\n8lunYGqA5Im5cfivmYXqNRL78SNk7PiZfXs76dQ00ib7cOfICZgfCcR8fhiZ3dto+yiPnGIHNWNc\n3CYmM/LuekTwMEqLB1NteIfo13v4vZ+B9uBUHuxxErYog+LGV7iOoF/961j+MvFl0ng8pQdPd++l\nfbQ/xz1exJ88bsrbwvHmeH4zjGag8xSLKss4c28aPfJhRlp/wafsArtyJ3DOXMRNp+sISrfRects\nBrsmYJ2wDueHCXxfX0t1xRVSSj0JWAGDBz5CeHIA3Zyg5o0uvik9Tm0e3OTpzrC1/gwa8wxEBdBz\nZhun3z/MpqNOugS8NEhlwvqhKOM/AVcRzk3P8NULrbzeK5kuBBvngmXnI6CuA+0ebDft4a7jkrNC\nsE6R3PWjFeb/hiAIeWoOGXPLeaJJEiIEb41XGLJhMuqAV5H15yjd/i6/vN/GkVadYYmwfOxwAtYM\nwWIYQfO5S5zfv4sLv/TS4NFD/7QE7h44APVRDdPRmZw274DNlyi+Jrg6RWF651jGzcrB6TuOnKZR\nOHifwR81sm2INw3uo1ncW0bi2EucM71IHZ6MblyNOCP5PGAmZqHxVN0fNE7257zPS0RSQXrLp2Rd\nSmWHJZqErlPMK23j8ooEzK7HGRr4F8brufyZkcxJyxlGHtYIHetCXTiZdG06zsk/4Pi3P9+X5lB2\nqZzoLk8GPWggfcwSfNOj6e4+S92aPDadu0x+BczyMTB9dTBRc19C+CfiyHyXc8/9zZrMvrEXHybC\nyJ/7w+DfEDIbuXkxG1e6WK1Jbge+nQXsfhyhvo+034vt5l+4L0OQCfzbKlm4xR/lzq8QwhvXgZUc\nfzSP1yp1QhV4ZpqJlNVTsQxcgiwuJefY+5x4p4MzHb3EDVV4OHEi/q9549E5idKiQvIzfqRyq05B\nUgeJoWncGxeAfLSF7hMPcsXyGz4/nqS02MyZUaFM7o5n2tiTdPrcxFnnFLy0DaR/U8QX/WKoNIzm\nbtcphkaVcTj6GToIZXLnq+gXNTaa5yBlAysLMqi9w0qh3yr600ic5ROKfhvD994K4VWXmV5tp/TF\nQCJ6nyO6/3FE3nWO/unPUQ4Tf8qNfiNU/B8azQi3WXQPP0bvuh6+v3ySa8fbCTIZmLLIg7G3PYBx\nfBqO5mPUvXqI9XsrONsC9/rCXWsC8V36GsKcjsx6jKx7zvNocd9Iv62hkkEZkRB9BsFp+PxONqyQ\nrBGC2VKyZb7AsP1xpGEtdC6l/s5fePKAJAfBy8GSBW8mYb5nNShWev94h2OvXOXTsl5CVbh3agDD\nnhmD+6BbkJe7OXPtY6693s1Feyd+o0zc73MzAWs6caueRX59NdVXvsS22UjGKBvRbqN5MNCJ6/4W\nKq88TqXhILF/7qWgxpO/k4YyzmlkbsIRyiOnc0G/nSjXF6T/donNoUMpFINY6NzHMO9WDg59FI0E\nbpKrcWZY+UydiE0rYvnpYqoelrT6vcxggw2fiHVUfTaBbz2r8c8vZ3y7oH2NB4MdL+CTnoUrq4wT\nu10cqT+FV4GJkSluxK8YQXLcXOyR1+jemMcPJw5x6phOmIfg7vlepD+0GGXE7eiNu2l46Ufe/bGD\nAw5YaYVH3vLEuPJTpDoaCmZROKGA+c19GvWLrySpwA8C8xAi5H++eElZBq4vaZ/wJlPO9HXu7Vps\nxOOrjxGiH7Z1i1jzcgPHnYKPBqmM+u5OlOTFdJ/+gN3PHWPXJSfTwy3c+n4ysSNW0in2U7K8gE0l\nOYT1ejB1qTcD0p+C2Gt0bsjji/xyRE0XM5PMqA/NIkJLgLYP2bcjiYue13nimoPmV6LRXY/Rz/g8\nJd+H8rVnNPc2nCXm9h4uBm6gv+s7Av7M4A1lJgOo4OGgSxxLXws0Mva3z/iEfrQbIlllyqTh9imU\nOSYRVPYC+T/7cTjJm6dbvPB6qxl78UvUmN+m51kbu2IamaIPZsK/wN9rCaXaEepePMdP5fUExhpZ\nNn04gY/MRFRqZG/dwOc76tA0eGl6OEkfPIjwS6Htp6fZ8GQluzvg3f6CW3ZPRfTfhChdzLGxGdxb\nJ3lIEby23YS48wrov9M14zluPiBQpeTwDLD+/hGIyehbR7PmETs/uiRfRcOEv6Yj+r2N8+Iq9j30\nFx9dgdsCFe7/IJ6Q21fhbM+m6Om9fHymGDdF5Z67Qxl8x6OIqC5avzjFFydy6azqYkFUAO6vjiPW\nNYbOno2c+MSPY1453F/gjXzbCw/HCwSGvUTbW2GsD7RwW2Up/W9vpzjyAxLZR/Bfh3lDm0owlTxh\nzOHYLc8hMTHp+Dts6kimTMTyKodpmTGWAjGfpPqnKf3Fk1+ikni01Uboc9epq/+IHtOHGNc08018\nL2O6BnDT0nqsvo9SQQ6dHx3mj8I2jFFWlo5IxveJYShlYVz5awM/fVtHh9R4IjWBxI1TMHuNpe7A\nv/nxmXx+r9N4dLBgzqapmEa9iSx6k3Mz9rLkumSmAd75yB1lxWGEnkXv3SuY8TO0ScmxceB57A2k\nci/s7c/bs+1skLDVH6ZljEIm7YTyRzh260GeuSa4zV3y/IcheN/3KbQXUPTC57y9pwYNycoFwaQu\nXYGI9KDx+91s+TWL6uJe5kUHEf3+MOIMt9No+4rTb8NBPZs7SoPw+Vgl3LkWY/x6OldbWOfZwbji\nTlLuaKYp4S2ijRcJPvgn73QPw6RV86yjkDMLHkYnmomXVvNdZTKXRRyvOPbSPmMYV82PMKzpaer+\nUPgycCz39ZQxcF4deep7qMYtBL9bwvo4NwZ2xzBnViF68EqqlHrkzp3sv+SiJ8KN5XFReD6TgChJ\noyD3PU6s7+G6tYMlcUMY8Gk83mIWZTmfc+jZq/xZ0cPtQ40sfGc0XhNeQMvbzdnl3/Ov806GmyVv\nveKH+wtbQdZjW/EYd29xUCEER1MkPuefBfUZxN8DWH9TB68D35kFM64kQOJZaLuX/MH7uaMKphsF\n77zpg/Xp78BeTt2zq3nhu1badMlrs3xJXb0SIpNo+vlTtmzKJCdfZ0GCP8M+SCc6bD6NPVs5968W\n/mjOZUxbAP3+rdDf9Bb2gd/S9rLOZ65SBhVIht/RiUx9gWC3OjxO/8rGoji6ZQ1PddWR/+As0Ecy\nrPYF9p1L5pghgpebD9JzVxRXzKsY2bqa9oNNbPC4jdnOy4wfVcPpkA/xUH5l8LqTvB4bR5jDj4dS\nL9GStIJaCV5HP+PQBXeqo7x5ws0X0/MBaMUzKGtZQ8HrgrNRDdztMYJB73sTaLuHgo7PubyylL11\nTYxMdufu59Lwn7wUZ3Em5179jnePtRNrhVeXRxL0ylugOml5/Ume+aCLXE2wYwTEH3kY4bEamTGS\nryfV8IIm2ChgYa4f9C9CCL//SLz+W5dEAW8o+paNZ6ET2DZV4Pn1VoTDRe7kWxjxXANJXnD67G2M\nzT6A7VQmz3pNY8Wth5h4cxx72j7hka1LOfldLvcnL+HnNXsY8MRkPv3recYu68/pjxvYsuZlqrdq\nBL7owZJJdxM80kHh+U7Ony3BreM05t5p9I5zY8mFNvZP80bPHskw43Psz19GU3QtiwxHuTB6Cb4t\nVryK93KiPArfXkiIDyImGup7rFwpsTOkaBd5SeNR+qWwouwo38Wm0ZTvz9jedznT8yBENjNZlpI/\ndjQ+LhOO/MucPtpBY3cr6RP6c8fScfiZB7P7z6/5ffY+Lru1sOrFpbyy7zF0cwTvTV7F01NeRfdy\n58uid/nqxPPkdzcwNfoNdoy+E6+U6bzatJ+jr4expUiyePBBXBtGImM3M/HsVD50E3ypS0rW2MG1\nG8QgNAu0SslkwLo2BJT7kCfnsPrhXva5JGfuNDGxZDsEjiVj0nDGjNhPew/8fepeXqj8AVFt5tmo\nB3g2ZT3WQX5sLl7Lu58+zuWSBlZNf5UdH3xGQHoqq9bdzfglw8i61szBdXsoPXENnzQr/SeNIyXU\nRa3ayqVLA4m3fQ0XbubQhFCevHyRkvHh1HYsZFLn8xwpSeFygInZPqcgbipODzPdJfnUlZSjoCAS\nJjM17Aq1Vo1910cwvmkVPU3TyZk8lH9lZ3JytJX2swtIMvyba9kjKfZpZZilh/Dx6XinedJ6spID\n237lyvlWBi2O5dWX7ydo5hD2btjD67e+zLnT9bzyxoNsKnqdoHHxvDFmC0vDF9N+tZOnSz/lyNUn\nMLsrjB17kD2po8AUyajCA5x/xpNsp+SRJ7uRP8xAKjOxfJDKPaqkDmjtBghEcIGWj+18LeEZFW45\n3B+SdiJ/m8xz/Q6y9hrsfdSTN5t24nXLEg5Ov4tJ4a9wtqCOL/csY2vNJ/hEhvLS1Nd4Ne15Onrc\neOmPx3nvnScpimzjp9sP8v2et/FrG8HtL6UxccpoGvQ2zvzWTlPOZXyMvdSMnMgInyp8LE0cc97D\nyLYvcRXpfBk5kUdKjuGZFMXZgbNJv76DvJJurtm9SA5vISo+Gm8DZFYFIEtOENzg5Pyoh5jtl4nq\nXcauskdIta/F41J/fp0ew0Nnm3CM6cXVNQdPw5/kZtaRe6mTwH49LBwzj6BVbnT94MlXn71Exust\nJD4cyGfb/sWw1ZM5sP4YywY9yF/vZ7Po/UXs7viM2XOHs3rBSR7ynEHDvr2MyfiRk9ceYFK0Qvor\nLZwYMhM68nHb/Bc7lpoxSckrlwUc2YQQZkjwRgrwAEaMkBC/Gly7qZyxn5lV8JQF1mWl4/b8cZzb\nV/Ck/+Ms/6qFtx8PZk/b96S88gA/P/kuM4PuZf/uLJ778DG2Va4lan44Xy04xBvzltH8ZzC37Ezn\ntQefomNUF1nL2vjlwid45MQRNX8AKSPiCe9p51iXOzKnCX/PPzhuvZf4wFwmOWr4ceSDpDWcwL3k\nDN/2zGJqUw5JCR5cGzEe/7xGKkqKoaiXjkEJJMQFMKbrOju6JiFKzjKs6jw/TH2U8eZS+rtl85u+\njGDTTjyutLHfJ5CU2g5iU73wHDcMzxqFI1m7yXmjA1tKBy/MeJwxP8TT/UcU7z+1jB8WZOO30I9v\ns1az7JOlHN17noWh97FtxSZGvrCQvW3befWF/ry0vpIH/BbTuX4Vfm/t49v8+bwYKLklU5I7bAuy\n50vEmO+4c4FKiJSc02XfukD8XzXrn/DfKl5SdtLzTQPfI1jjJfDfuRKpaVwe9QxzT0p2TbPwUPVP\nKF7ufBRxM4ufLuLF1xP4tvlXzP08WR79JG8u/Yzbn5zKN1e2kOIexGMPfcKeB3eRdncMi/c+RYoa\nwoaD31P2TDO+ky2MXzgas3sA1y8W09qQS7P/eMaFnuVYZBBuV3yIG/Yb5/XXmVq+nsP1SXg2hnBr\nv2tc7L+YxAt/kZ3bTauHwoBBYcQmS662+tKYewpLWzP1gxfxkO9BtoenYzlbhkfIAXLcX2SWcycn\nDf0IK/InOrWUlo7xuM6foKTIxaDBfkwdNZ/mxKvsWpvJ+d35TH42jEff/AQZmc2aKRv5YuPvLHpn\nDOsLPyek28o9MS+yfd6nzHj7UQ7VriMk0MrEtC1kz52H79Pr2VVwLyPdBVOfasLx7wkQtZkFW4NJ\nFYLN10BWbwci0SyCHmBKsEAOegbp/IpT88rZocGBu8wEbP8bPWcTL8Wu5tMzkmPfJ7I49zANp0+z\nwOdutn1dwJtH7mN9zmc05FawKGwNGZt+576PHmfV0QdxP+nGEy99Qvm3Z5l2y1zGb07B7Zovu0/t\nofGEg8AhSYyJ9eBSkB8BGWW0xDRQFTWImVmn+GRgMjMvtRA42EiO52zGntjIjuqxDHH2MnhQOI1D\nfejOKaDqagndIUZiBqUw0dzAju6xxJ37lir3aGxDhnF/7QU+GJLK5KOFdPYT1HslMfDsRTJ8fBlj\n9CJmcCxNFSoHjh3Cdt7AiLdCmD/2SWrKDrPqmW+p39PB0j038diXb1B3JIPFMavJ+uk8rx5cxcYr\nL3L1eCO3eT9G/vptzD60i4zT0zhfprOg32fYv38U9/dPs++9IIolrF/agmh4HBn5PlNHgxSCdhuA\nL9iOcvS0wCwETz5thJTf0Y7fwkMLS+lEcOjvZMLW76N6zeNMjX+HmgY4kv809+36kNOrdjDf50na\ns1p59+p7PPfTXA7/mMnK9PdprT3B0lfWM3tjMpW/dvDJR3todGUxLe0eUuZ5U1Wq0JBxkqbeIPyH\nSoYXubMnLJJZBX9zPWg2nTGNDDmYyScx41jiOoVz8DQ63DW4vJcTtVEkRvbQb1A/8IELVyUR5/aS\nlzSJYQMVUhudbO1NZ/r5z8h2X4znmDxSMjW2pYYyra2Atqg0Wmz1NJ7Oo1v4MXJhFPHpSdRtaeDD\nA1sJK/Vj3p+3ccvCBWS/vp9l89YTdtWNTzLf4t5N97P9/e3cHbSCZlsNH1dt4+Pdo3jz4wYe952H\nvShVOyE7AAAgAElEQVSPOXlHOL3Sj38VCH5K+gjZ8geWTzezKVmwR0qq3+kBmYd0T0Gnb1quwAVW\npLgZbffLLDsjWGAWLM8eB8nvUfrACEY+UMEtyQZ+q36X8CeWsz/1IWamfczQqGD+qN3KjGdu4p37\nN/Nk6lpCI4ysPbOZ2csT+OLr39ky6yCkX+G+pW8ycloQ2X8XcHnzVdoSuhiVHotncAAltQZCWvZS\n0bqC6KT9qJe9OGVM4V7fbZwPXkuYtofmy6Uc8k3mzvgMHIOnYarppCT3Kj3XeyF5GJMG9FBv8eZS\nnkZq/g5ORq5iZsouelqSON8Zwhjz5+TbXyYw7Fc6c91o9Atg9CCN3ugo6n4v42zmOWKCgrj1+bvx\nnt1Jxj15vPvNN0yMCWX1oQ9JvTWZdQvXsfL2daSkxvNT9ffctnwgyxZs4bX4RXhMiOer5p95baEP\nN79cx98Jk8B/BPOuvc1nsYJ5RYKWZW8iCcR9bQrThSAPkC2Cvsb2/yz8N3te5ZT+7ECTkgWrfJA+\nK6l/fAl3XtE5MNdK/32H6fz2FW5J/omBgSq/NGzCe0QSzwTPYftrl/nsz+dZ9dtSvlq9n9XjVhA5\nYTQbzzyNLVhj3YjfKc49y4TvbmXhgAn8fiqXkyd3EWcdQNL04Xjam7hc3Yaxsxq/ihRaE3RSPfMo\nbFpJWsXbfGqcyFOtFyma9RDR1Sc4dqIRpwqTJ8XjSojg6tFMOk7WYJ4ygVnDOvizORbX4W10WoaQ\ndJMPUVozJ86PpJ/+KRmxC7izuIRTw/qTaD5PaXkgTZXFBAR4EbEkFkOjkXOvXSRXKeXph25l8ISl\n/PHdGj54IpP7J41nTcZqqq9W82C/R6mu72Z7xSdMXpPE/SM/4cc5LzBx5xscPX4XX/7dw9q4heBu\n4vGiN1jkAXetakGenY+Yu5XV/eEPwPFrJRIvNJOKBIYMFwjzHLRt7/NEk+SrGEHAD98gL7/MklEn\n8bHA9uKnsY6fxwdJU1mztoTvdt/O0xkf8NdLv3H/gBX43RTFtop3sEZ68ezQj7m09xjzdi3jyVkz\n2HLgEgc2v00is5i3bAR2j06u/FBBT0sjsVGR6HG+hLQWUlA0j/7mDewMmMiSkkKyZ08hvDODq8dt\nNFgFt441UJMyAsfR7Zw8E0JKqjdpk+MorDRReGwfTg8ryRPTSbE3sut8Eikta8kY/iwvVV5mR7/+\nxFYdpblsNN0h1/Hx0nGLTcO7t4mLB6pp7L3OvDETSR4xg6P57/HDqgJuTh7KsqP30FHZy6qRqznd\n1cDXOY8z9pU7eHvCO7w3+z1mbV7EvuxVZPzVxsOBc9FaFd5o2Mqy4SYmPVBM90fTMT5/kJ8Wm9nQ\nK2l5eC8QScRDVvyAjh4BeCGrz7DTCQ9YJKbVT0DrN6y7vRC7lHx+ahKG9Fc4NHwS9/+7kZ3rB/HA\n5V1c+fd33Br/FO7DLPxS/xWBaWE8PPB5Mj48wLKfXuWdF2ezaedltq94jgjPuTy9YSI9Ib0cefoa\nVc2FxN2dRGx8It3lJVy9Ekq0dwa50aO4uayRrHGReGtXaTtuIC/MwMxBVeTFLCHq1Ht8c20Ed8QW\nETRlNqYsG+eOncFhFgyfPIFE7052nfYi8eLXZE9+nid8L/FtaDyRJ/fTVTsZObaRUNlBR+NkAk2n\nuNiq4l1bQejwoST0T+RC9QEOflFK/8RIZu0ZhLweyo+TP+WAo4hPflrEsFUP8/2iN1k7/SvmP3kb\nP+ZuIP9CCwv8F+HI6ebT6l9ZeY8XU2aco/T+6Xh/9Dt/fx7Ehy2S4+PWgfBm6A9jGaUIfjoH2H8D\n42AQghiAaUNAZvK/iHur2Lq2Zt22jWlPTzMzM8V2YogdBgccZs4KrDAzO8zoMDMzM8cBJ3ZiJ8Y4\nZmZmmnOch7W2dK7Oubr/lba066nrU68aj02qUb2+L8uKyRdFNh80B5fDpE7swaBLjTz5W5P+399Q\nfvEUfWw2IjWX8LT4JBb9PVlgP4kbi9+x5uYqdl+fwvlTv9jVex5mJp3Y+nIiUmuBawO/8DvjPa7b\n/ent1ImfiX/48SUJKxUFMj8/7CuqiDarRLWiHP0IXeLcJQwy/kW0+nLa/VzPkcJeTFXEoeg1Hu2U\nMsI+xFIrl9CpW2t03FUJ/ViM9vsX5PYay+Q2idxXskPp7UOay71RD9LAs7qUr8l9cFQ/RLgkiC6J\nWeT6O+FoWkV6bjFJrzIwMtSj3UYXVHPteTX3Aa8bktiydBg+c+by6MBmNg17yPge/hz/tZ+iwnzG\nWE8kJ6yCc1nnmb7WndHdn/K473hsT+/i86v+XMoSCXFdBfJK+nxdyhQ1WHBdAfELwWEtfWz+eVsn\nlgmIYs1/TI//UXgJinDuFcIIKajMXYqYOIu/z7Rw0EvA5tZDKo6NoNesZI7PNaP3j8d8n76MMb2e\nse5IbxaFhnBh5kGCB5xm3r5prD4/neNLH3B29iVG7BjM8JCOvFz7ndCtMXRY6USrxUZEX8sg60ku\n1p1d8XGW8CNPRFb1mgLzIAanFPPEoC0+God5rTGJ8VEfeOA4mG6KG7xU7UeH0jfEGvti25RMoUwb\nVRpRr1FQojChVU06SRq++BaEES4E0js/glAHfzrWPiOhfAzt/vzko5MhrU0Tqa4agNK3GJIkAgHt\nrFF2NOLd6+fUfJczatI4dAIV7N97gJo3Naw/tQ6jjoas7ryGos9FnPu1B4vW1kwwX0hhXD7Xii/g\n6KjGIPMl1OcmcjTvHLZ6AjMczkFdMtOi56AnhStDI0Fei88OF6RAyf1yBJqQy6SoALr9tEEs5t32\nUsyALs97Q0Uqa7p+xE0HVqbuoCkpliGOO/F0UeNk0XWKvv5miMUi3AOsuJRxiuRPacx0WYNLR0cO\nP99I+osCtk7ZjVU/TeZsmEPu+yYu3L6Iev9megz3JztVIPnbVxRGbvh5VhFpIsEs6TtZ5RPopPKW\nJ22c6Vr2ik8tY+mT9ZhQi550qgknSuiEa0k8RSo2qCtqUW6RUymxwq7uDxGyANrLn/PcsR8DUp/x\nRJxFT5VDXPIKYmzMd37K/sK1+RxvlSzpQgMtLu1pjI4kPqISjyBTrCdbE3M4lZgduXTa0J4uW524\ntfQZz5aGM+/YRMZuGcq+gce5tvQKG9+sZeH2YczodIY38/YxI+Ei29faEzTwOXlrltAz9DlHAmX0\nX56L/NVkTI6fYpkerH+hgNJzCJ2H4SyKVNeIiKjT/LWcGIXIpCApguY0smbt5VKtyIWzdkg8l3DD\newKXkuS8ThiJfq8gVpgMJTSiiqdFG7D382ayxRR+J6VyNmMXRl4WzO66jIK0KrZfW4e6gzp71m+i\nMUuJOeMXoawv8PD5VahvxL9zByQqkBFWAkqNqLVrQiJRoByvQ4uoQrNdCs3KauiXa9CoUEOluZgW\ntFBvkdEkSmkW/tlj06hQR1WspkzbEIOWQjJV9DFTiiNfyRO3/CKiHYwwNnxFVm5bfNOaKGhvjqr2\nB+IjdDHXBOcubVHEVvL+yWv0OqswYewYKs6pcnrTKcwnGbPq5CJiLv5m1citdJjhz87IDTw8/4Ll\nXovot2A012MWsPt4HMdchuOycSuhjzoz+3oj33sGojb1Lq8XGTInUaR6+xSENgdZ6yJwt0FEEROB\noGyHiIiLkoBgPgQSTrM1Gw7YCkinXaf+QH+GX5XzbooO5qeeETWqL2OC03lwqS09b+3hbJd5BC98\nzt7Qv5lycAab++/gwuarrLw7j8EburN3zgU+74pi8q6huC+w5dXWl+Qeb6bruI6Y9VLj++s0WuKy\n0PTzpnUL/Mi2w0D3BrEubQmKLuIr/fHSO8tLywDa5f4g0rQTHuInEmS2mLakUaxtgkysAUFOo5IO\nOjUVFGCGU0Ul2RpOWDb8JF3uhXtOGckWBphaRFJe0wbt6Ery9MDBtxkEI/6E/aRUEOjTqxdKprVc\nfHKIxkoFK+avQKrfQvD8YJrr4eDrbTRpwBT3RUgadLmec5S6ZjmjDaagJjXkUclBiipF5hnOQmJi\ny/nUqaRWwwXPXaAdwLJrLvxWwJ9lH0HwoF1XJWqBxmoRQaz4j/nxP9s2zE/lc73AqO5SUB/E1wnv\n0FcW6P1yLc2vdtB/YQG3V1ngsOMgRz0G8CG8jnu5x6nKL2Kky3z6LQtg68slbJt0lrdbbrL2bQht\nTI0JDjyOnoU+s6535WV0HAn7ntHRfw4+Zk58ex5OiVYNXn56VNYKFMszMTLPJFbVhW4pKSSoLqbj\nz3NcNXeht3cG1RoGRH/Kw0WlDrXAkWh+DeXbr2a8O1lj7KTGl49/aGpUpksXayIEcwxfX+Kn2wIm\nK//gQlMrLFTvUdDWFKVKAfsUGZUSNyiIpNTQjDZuxmQkpxCZGknXWdZY2thxd3o4JmVqDL+3kOyI\nK6xZeovF+zrQc+sE1nRbTVxYEpfTz6BIEJlmNRnvxSO58WIIEydFk7hqLZN+PaCXpTKr2lwEAy+O\nHHZidyHUn1+MctAaBssEQv8AYgkKmRJagMQvALHgFkdSBLZ3liC47CBs+EYyGmDlj8U0hb+hT9Br\njs83IOjecU76T+Lm6Szupx5AW1eJsXbT8RxoyqFvCziy7xEPVh9hypmtjLS0Y9Pfj1EYpjH0fEfS\nk4qI2ZtEa+veONvbkfUzirxGQ1prqpIu1QX9dFS0akhLc6BHVgHJ9nPxSD7AMQ1vRrfKptjYm5T3\nX1DVUODerRs1iQUkf0hCr6MP7ZxqeR7Rgm5+Ci7dXMlW08Lo+Qt+a81mkiSUc6buuBqeIsnGHp3M\nWozlFiip61IQ9QuUDAkY4EZ67mduR39i8FQPvFr34UG/Z1RnN7Dw8yqKU78wv98ZJi8KYtLNpazq\nv523p95xPvMUMnN1phhPQCeoH6/ejWf6oVL+zB6Bz/OnzHYXWDH0FzTVMm2/G+/k0HD3FNj2w10T\nauoAVGgIL0YVMNrgjlh5lCX3mjkboIR0wnVigoZxP13OpfhZtBSW0b/VPqaPt2JB6AWud97FwY2v\nORu/HbcAaybYr8TI1JADH1dx78l3bs07xoD5qxnS2ZN9+55SLv1B982dqCtQEHclD1tzczTdbJHG\nJpFZ3AHflnQ+WZjhaBNGraoJBd+06JRVTmnAODwSTnCqzIPx3r8p83Eh7cNLmhsktO/mS1Ue5L5/\nSU1nf8a4JXIlxQqHpOeodO6GXq0An3LJlw+kq1Yo950taS37RlpJV2QJeeg72aBnpMOv+5kol+vR\nZX4gmZWh7H/4jHFjveg2eBYXh58l+lcWu8M2oqio5m+vTfT+24dt35axZvp53ky+wK64GwR0MGCU\n3QIkthY8+TGI9Z+byPirFzr77rLXSWDVjgrEhnd47LKkBGj+8AeUTREAU0NAM4DKq68pEAQ6n+qA\nWPyG6SvyCOkixfDkU74P6cPOZ/U8jVyAwsyeEWbz8e1hTEjCSa7Pv07IpFNseLuFfn39meFzCFoK\nWPdlAVExGbwccYvOPaYxuI8HF168J6/5MwFTXFE0ycj5lIyuoSpq2raYJhaRrNwVX+Wr3LRyYoDB\nJ3Ikg5C/SEYDBZpdAzH//Z47sUb0da9C2c+bP+/Cqc1vom1XfypFKYVvH5Pl1odJ9imcqG+Na+5x\nilqPpFVeI00RmjQoW2Oi8YMoMyPa6JeQWWJLWVw8Lu42OJi3InJ7Ho1p0O9ET4pU3rFh7Qumd3Nj\nRPBKLk3ey53HYZyMXouxpYSJ1nNxDbThSux69q7+wu1+a5j69SrzhmgyzO8YLaX1HP06gZtFAgl/\nj0Np0CV2uQrsfaOA6ofIhrdCRRBorAEURf8xP/5n4ZWSRY4o4jnVFjHrKuuiYP9SQ1CzZ+7w9xwe\nqI7V2mPs8RqDuarA8uRr3Bu7kPuHY7ideYz0j6lsDtjLxpvBOPRwZ43/Qrz7dWbJ4hFcGP6Q6gRT\n1s8fQejbQqI+fqf7LBdq9av48TERA3tLnAwFvuVaYEAU9V4Scu21ca2+wW2DLnTRKEavWMa7JGdG\nt8SQ5dKN4tgodEUFOh5+FCWk01jchKunDQmNSpjHPaTcfyBaqkWoJIQRaz6KkRUx/EnxRVGgRUBt\nAQm27TEo/0CWWI6Ztz16zY2EP8/E1dMSo0EePL57gFrLCsac7Mmbq5d5dD+bfXfmk5lUz7oBJ1l9\nYQYeI1szxWYathM7c+TeeMZ3OENJVB73/6xn25V84hZOZ1TkFURl+Bg0C/W/z7LcVuDcpkxEmSMD\nOygRWgmIacilAgYCCO7u1H24S6Yo4nm0B82RwSwKlXP2nCOiqMLIvm+4stAAs7WbWWU2ESd3FZb/\nOcbxPit4+iKJa3l7+HariP1Dj7PuxUL0vPXZPHwZ1jN7MXZiF65Ofw3pDkwbOZTId3nkRSTiMswH\nzZYykqMK0VRxx49avrc4YlwXToOVglR/W1pXHOex1JcAyyr0aqt4lm5GH5NESmzbkBYTj55+I3bO\nJsQlVaBdmYeXiwHvKtzx+H2NYr9+iKrVGCW+44vZGMbkx1P6uxMN0Xb4FRdR6RqAPD+B9PwKWndo\ni0qRGT/2J+Hd0wnbES7c3rabKk8Vpl8axr1Np3n/IIsT79eREJbKzkG72PZ8NV7ezkw2n0H7aWPY\nf3IgwzocRVFYzIOwOSy4UE5FyGRGf9xObgukT1uKyvi9DNMU+RFSgSC442UAZSIIgoT82DoCJCBx\nWkP55itUysH31jKaLk9j1sdmrr7oh1iYy4Dubzl30B/HRaOYbDMe9542rP+ymQ291xHzMZ/LSVt5\nczuCy4tPsOLRfBR2qhybF0zriZ3oMLg9L1a+RrPUgn6BvUh7XUJZagZGHX3RqyunMKmRBlzpVp3P\nD7EvusXf0NDPIbJPAB6Fx3gsdcfTTQXDwjLeJNjSwb4CubUmyTGJSKx18HEq51OcNrIqgXbuVdwW\nvGhVeJHMbl7UGFVgUnWXMP0gOjTm0dRoQXaqDNvacjT9/ChN+UP2rzJ69+6JWooeb7Z8o/eg7jj1\n6sjRBRvQ8tdi2s0pnJq7m6/fEjifFMynOwnsGrGPPTEb0fbTY6bNGHz2TmLfNi+GtLkJaPLkQRfG\n32qg/uFy+j6dRWyDSPneXSh1W4svkP2tBKSaiAJYG4mAHp9uNTFGX0TosZm46buQSgUCHx+keMtg\n1r+s4/r3JdT9/MXwPje5dH0Irv37M950Bj36t2H5vRWs77qWzKxCTr9fz6MTv3m6+zpLb/1Ftq2c\nG3s24TrXC7feJnzb/RWNakf8fP34k1gO+Uk0tfbDvqIYRaw6+dm9mZD9hyj1kejEx2GoXERa26F4\nx5/iS7MjTm5WGOWnEZnRgKuLAaoaErJjf1Hu7EsPvUw+JGkga1Gmt2EJ95o74118mdI2ZuTYybCs\nfcE31Va0llah3WjLn8hmDIUKbAZ6kf4jhvjYQqaOGEBdjCn350Ywa0IQRv26sn3YGpy72LLw+nR2\nB+0k4UsDl9L2cudEOJdH72Zbyhm0dVvYaDMKl8MHODRFk786Xkc0cOXmEWum3WpGHreXbuc6Ei6H\nxrcXUO7UBn1EmmsEUOT/x/z4n4VXcgoGgNB9EjnnTmKkImK0YQ+/pizAWF2Cz60bPO8yDBOJyNCv\nNwnxm4CgqsnCuENsabMQKRI2fdjN6qBtqDS1sOL5XFYuPU2DZiGTrwzlbMg1mrVT6bEukKdPXlKj\nLKfDSBNiwhOoRo825hrkp1VRInjSKa+AymJt8tQC6Zj8mqpmY1IdeuKecocsHVPq7QOwiXpHlYoE\nPVsHqrLzaKqV42Yr5WeBI0p1OXhb1vBS4YtJRSzehtk80vdFX/MnpjbhPDO1xrLVL5oVuSQ0a+PR\nWo/quGwKkpXoFtSL5GeplL5vYfScRUQfjiH1USVLTm/g85YXJJ9NZe/HEG7tukzotW+czdnNhVUv\n+HrqCTeST7BndzhJIVe4FL+JjRcKKbm0hR2fphEcLtL4fjXjTvTlYh4Qd5U240z40wBifSoKmRIG\nKoBKJ3JOFDNcXUDiupYnU14wz0mC+qgT7O26mzk9NDHfsp/VtnOZMkqHbju3MtVqOn1HtWHc/slM\nsllB+zEuTNs0grm+IXj38GTcikCCpx/HNtCQXoutOXb8Mkbd6/EaZMWTD2+RumribK9D4bdEmrTc\nsBZFNBJKSFMfgG1GIQ5ZNfzUnIb999+0Ka4lwWIo6qmfMFKCShd3GpMSkGkoMLLUJT2rhgqJDZ1M\nM/herke+pj59TEJ5I7WixESkh8p7Xhg60mCuhh3P+Gmsh1ZrNWpywilW08LDpxUZt2KRNKjSo+8o\nXm+NRbdUyqRFczk39wHSRjnzri4jZMQB1CsrWftxC6sHb6K6sZbTsXOZMugY1c3p3Pk0nRF/vaK5\nKJuHd/owNDgHRV4Wxw/aM/teHWJNPNMm63IqUwFNWfgaSCkHECGqHDo7SUDdgpOXK9nUQwXBqD2L\nZ8dzY4ohSu5BjOr+hHO7W2PU052RLvs4dDkA2+6+jHMMZs3hv2nd05bZHuv5a99kvHuYsnrQLgYv\nG4yPlwUhi+/SdUwH3F00uX/6PZ4dndFw1uJ3aDi6rraoqGmiFRdBg543TbUatM2OJEOzO/nFGvRo\njiRBtzuF6c0MkseRoteZ/Kw8XFXl1Njqk5+eRbODJvaqEooyc0g386NnUwElqY3Ea/UhqDGGqnwl\nUtT64V0WRWOtBtW6/lhERqKmooGGgyMpXyPQtdHGzd+KOzc+4uClTbshbTm4+gYdPczpP3MI6wce\npl0XB8buGcpszx34DTNhccgk/rJah0e39uw72p3h9nsw7t2FYyHujA24hsRvMBdn6bL4rwiw6sm+\nLipc3F2KoOFObzf4lgwIqsiVwMYQaCjgXg5MHmYI1VEse9HEvn0OiDnxjNpaxsN73WjIzGLk9FAe\nfJhLXcVvZg4+zfk/CyiqrWDZyG1sjNyIJKeBkJEHWHZjNcolzVyedYMpq5YgLVXl2Zaf9BswCVmp\nJumXo2kd0JZSZQ0qUn6i56NOjIEeBrKnNNkKPDO1oqvkHaWGAq9U7AnS/0qOkRJfK03oZRlDlcKE\nlEwRS2s1ZFKoS/pDg50HmpotqCd/5I9Nf2wUVVhGR/JDfyrO2Q14xWaRoj4YzZRSrFugwtCd8vAU\nHJw0UNia8DbsFW2CrNHqXMeZszfousQZ456qbFh0nNGbA3Dp4sLidkeYuWks7YZaMcV5GeNP/U2n\n0e5MtfuboMNbGdvXnOWOU7HZc4ypndU50C4Y7SlnmO4K9ybdRtp2EyM0BbIOJiKoDsNGGZqqQZRn\n/cf8+P+ElyAIMkEQwgVB+CkIQrwgCNv/1fUEQXglCMIfQRBeCoKg87/lrBYEIVkQhN+CIPT+f6td\nUJxJKx0Q9Nvz4Egl6/trI1bksvhBNRvujybvxErupcqZGHWZ013H4uquzcBjq5lkM49ZB4Zi1M6W\nZb2Xs+vdLsL/pPDy8FX2PF3CuV3hVOfVM2N1L86siMTU2oe2Tm58uhaKvXs3ZLIWfv9pwMrIBqv8\nfMrLzUnWdMRXmoB+bQaP1NzQ766CU9I5CmoMkHcZRu2Hh5hZllLv35Xwdz9w76iLqkyFkpdhOPTq\nREK2CbZRT+jcQZXzVXYoCTX83ZRCapIe2fVDCCgpxjZegySJHkrqGlibVJL4Iw87R2d0lTWJuJzE\n6L/ak1f6nadh8cy8NJo3x/aRpVLOqFsr2NllJd0D2xK4sDPTXZaz+O5qWjTg0NDFHP59hlM30kg/\nf4frn8czflECKFtzfLoO+8d+QRq4iB56AqnXLqPSeSK1gJhVgVwmwUJVAMGEu8kNDBwng8JvhMSK\n/HVhPAWHZpBaqiDowWlOtptG735aOK7ZxDivxey51h8VXS3WdTvF+cgQXl+L59O1pxx6G8y2JXdR\naNWzfG5fzo+4g6f7GDyd7Pi89RVtxvvQ3NREUtoPrANaoZSXTVmLSLmBBw6VWRjxna/GFij89PCK\nvUKdegt/eo5H/uIVQS1V1Lu34uObAjq2V0XeKCU5PBnfHu2ISxdQT8igT7cG7scaIW+2ZYxmPH8i\nRVKMhuFdWYJpQRTxJq7UmEixbimhoCQTNXdHREHOj9hE2k/0JjvtB+E5mfQ9PJgnpw8gVVdlSPAM\ndvTazcDp3tj2DWR5u2C2Pl1LVmkJZyac4tLvM+xZl0DOpzBuvZzGyGGPkXr5EzLJmL19TqA3/RDu\nepC5cxc2s6bypxHkpY9RDTCiEhBpIrFMIKCLEmL2Jx6ViPgf7UfxrqUIoojN0dOc6LqQVX00MR8z\njnEeF7n6qA/NiTK2/XWLqxnbeLD1AUl3Ujn8dRmrhp9B39KWFRP7sK/PCXwnj6GVsRYf112m3YZA\nMhqL+fPnGwFD7ShOSkdRVwsuTtTVFqNv8p4/VsbkOdhim3cPLeUGMm0nUfk2mYEa6WTbtuX5zwYG\nttJCbMgl8rsSHQJ9+R1dS3NpDUG+Wrz41kKxqRd9TBPJDUsk1W4yBs0tmObep9jakzRjAyyE75Ro\n5lLl7oRY10BFUipOw+1JTEkhvSmHLguCeHf4ChZGhnjNHM/e0cf5e44/JrburO55g4Ofp5P0oJp7\na+9wJfkAG+bfprQolysPpjDO9wgWo3qxargpBwOW4RhyCTUZFK1eiO/pidysFRHzb9DP35DkFBFo\nQqEMFnr6iDVvSWgBk6U9qNp2BAdVEYOpBznV5xh7B+ghbdeV8UPvcv/hGCqi37J2ZTIXM45yb9Q5\nCn5WsjtsIYvbb8Cupx8jF3sT3G8LfTbOQVNNl8e7DjP84GB+5qaSmhBJwBJ3PsemIMhbMPZwJis7\nCxuKqDPWJEP0wrjxM3YNNSSaDCMzpoFB2r+pU3bgwXdH+nsrIUkv4Usx+HYPIDM8nhpJC+3bmvPx\nUwaN9q3prl5G+etPZAf8TYNWAzbpZ2hoa88bC2sM1F5hIRZQrulGQ7USkrJsrNtak5qaQnNdM0Xl\n2XQAACAASURBVJ6THfi2+yteFsZ4eA/jwl+PWDkuEImKEZtX3mT359nEXP3A82MpnE7Yys5u59Gu\nU+fwzYGMc1uO07ppTOihz1GvSfR6eJHCCpH8TVOZeH88B36KiBnvGD9VxvlEBajo4i6F5moBsf6/\nsW0oimIj0F0URW/+sYIKFAShI7AKeCOKogvwDlj9L7jcgVH8Y5PTFzgmCML/dXg/M0Okm4WA2JjC\n3Upw3zCVHxs2M81FBWXvAGYs+83Rz5OImbMcabMKQYdXM6XVco6+nkjG6zg+nPzN7rANrOy4gn6z\n+mJpaMWlhdcJvjGPl8Gv0Jbp4TvWmTvHbtBnXEcqIptQxJTh292ImK+FyAxd0JAq0Cn4gtRWhW+G\nrrTUCXSsiUe/WMIXpQ7kOFrgXPoFx4ZEirXcyKg2RF9HgVhTjShRYKDUgFheRqGlJ3EaprhUJ9Kp\nMI/4JhfCLdujrVqErfAAUVAh2c6bkhgVvNUFBOVCfuc24NHbj2/vfqDnro5+LwmPQj4xad0EPt+I\no7C8kYlrxrNq1DqmnhlDYVERd0LecjpsD7N6rSdgbE/6BJhyoOc89sacZvXeGMTqZo4vMuZory14\nhBwlrEqk5f1lps0x4sWpanD0xlgCYmYxcqmApVQEoYSwYvAaMoS0q7vpb6yExK8f89ensv/OQDJ3\nrKWyVKT76RCm+Szi6M1+lH8q4trhMPYk7GJtwDJ6TeiMq6M3ByceYvuz1bxe8AO5qoQu8zpybPcp\nBs4OpDCxmcqXjXQZ6krEsxxUnWzR0JYjiYtF1kaPT6Y6qBVJ8W9KQaVIxjv9dhTY6uNd8BEDtVh+\n27kTV+WKqXYl+s2JNDVqoaYupaG8klJzGwrUBBzyC3FvLuWXwoF01e5YNqdjx1Mq9DRI0AuAeCl+\n9S2gm0lKrjL2/m5kfo/FVFsNiy5KvLn2lOF/DybmTTYlSdX8tbEPW2ZtZ+z6ATTVKbhw4Db7Inax\netgOfHp40H+gF+u6z+RA5FlC9sXTXBjHpW1eBLffivehEyTXitRf3svare4cP1CK4NwDTxk0P0lD\n1k6bGkCggbxSEVuvjpR+O4ufuoCy/XTWHkxjZ0h7Sm9vIiVHgt+14yz2W0XICT+qMuXs2feVPen7\n2eS7Ft+/3HDr15HNPQ+y/8MKXq4NpUCpnvFLBrN/4T56b+hNSV4t6XcqGTymF6H3w9G2dUPHRImy\n7/HotLcmTiJHK1ObNtIMWgolREt602AgpW3BO3R0okk0aU9ilSN2GulYKSKpaNBCoWVFfVUFooYR\n9So1mFZlYCmtI6bFinIVH2zFOOwbP1BmYEqctD366bW0q81BLq0hsV4DPS8bKpJi0FeSYNZGj89v\nXtKrb18yvpdTFF3MqOBAjqzfQ/8p3UBNnfM773Dg9VZ2jj6Lrb8ZQQvbsjhgKSHhxzl9KpWipA9c\nPteHGW4H8Tl2kDJRQvHupWy70Jtlh3OQWA+ir65A6bE3aI7zJqERaKlFoSQgsTOhPikSS0SUbCdw\n6EoWW6Y7U/ftIKF5Ir5Xj7K9w1b2rrZDbBEJXvaHE4mH2Re0AMf+VnQc15cVPoc5ELmG50efk5td\nzvI1E9k2dhcDd3SlKa+FqBtxTJ08jjcXX2FsY42RnRZpoeE4dPAgvaQR5doGPLSrqMgWyWnpg1y/\nkTbF79DQSyRZO5C0enPc1X/hXhVPDmpU6HnSUlWKjkY9TSoKZA156KtLiG8yp0zfCmvpL6yqoijW\n9+C7hic2OeW0q0tGXm5AlIYRyp5qqKTGodfSgpqjI9+//qRtNx+Ko9TJiitm8OxAzh8/hPcYf1rc\ndLi6+il7r6zj2LKLaDroM2BTBxa2WcuWL3u48T6SP2+/cenRBP5qvQmvg7tAW5n4pbPZ+noqM3dl\noWQSxFQHJZI2n8B20iS+F4qIzRk46yjTUgOKQvl/H7z+BVjdv0fZvznlwGDg4r/6RWDIv+dBwA1R\nFFv+WaFBMuD/f6tbkCbi72hGU9g7nJUBp27sO9fIyNtjeTs5mOWjtJA31HDwSQGTQw+xxGUZhx5M\n4M/uL2RXlzF68yQWdN3C7rC97F5xHScfVxwDNAmbd4cJtyZwYOsD2gX1oaWgltyqt3iPsObdpx94\neXpQnl5JrZohtVomNDY14lZWglVxEZU6xnyU2IC9D/WJUYyQFNMippBVZYykrTcxkckEtDWg4kcN\njYYK7M0VlP8Mxc3fhnepWuSb9KLFoAGnxLf4WeWQlyenQHk4CZq6GOkXol30CyszBflVFchlmhiZ\nqxPz/QcBf3cjOjiJdp3tUWkuJexjNGM3LWXd5MME75jCj4fvEf80Mn7lCFZ1WMOJ6N1snHAPk1FO\n+LfX5MGcRVz5PJPJg25hHxxCRlULtS9vs2+iDbc2X8dhwlIeVonQCA4mIGbmo5BJsJVIEat/U9cC\nkp5dubuvgkn77Cnct532llJUfXoyf3sqyyL2cqz9fNYu9qc+uZF7t+NZ8mIn01qtYNOrZTy7+AZ5\ncwWD17pxbOAxpr5fyIldL7Ftb4OnXIvw58/pubQTt599xbl1e2ryoaRWRMfOhKa0DJw06lHOayLX\nxI3YGlt0XZsxzXxN++ZiSszlhJa4oe7XmeJPEXSxkaIcLVBYr4SdmyXJ4T+xDPDna5YhTcrGOFrk\n4/L8OVZ+qnwpNidXLxBFozI22q8xVfqNXE1Kcb0DxQ0aWFsYkf7lF66DW5F6sxJrFVPMbI15eucT\no/bM4uSiG0wZ3ImaxiQir/xmwfV5LG27hi0v5/B613fKZHUsmd6VTYEzOPb9ELMnf0NnwDhc7WQk\nr19OyNE+rF/2Af3xW4hrAXn+W3p3kJDxPQpVHzUaBQFRqKWkESQ9XcnaWcTIUaq0hD2kskVEc8oC\nFs2LZvvjKYTOXMxwTz30PNuyeuVrdv7ex4o2C5h7rislyal8+vyGZbf/YlmHPUx9uobXJ6MQtGsZ\n0r4Vdzfep++usdx6Eoaxlx2acm0KYtJwCjSkODIeSztlSmqVyDI3oqXIBCPbn7gXfkS3Wkq6lQ9v\nc5yx8nRC7eszuiiLtFQbkZCtg61va35/i8Xc243MEk2Uqovo5JFBc+gXVDt68a7YkQo9EwIKK7Es\neYeOcSOJUkMqFB0oL5fgrC+lKCUOa09rCn9LkOTLcPdz5f7lL/Tb253Xp5/gZ2eCsZ/AlZ0fWXl3\nA9t6bmX2+mFUfa3ky7MEtp9ZyHTfmWx5dZz9u7OQKxkw/W8rXgZOYeOLFczYnoxaj/GYawuUf1zH\nXzPNefAiCVW/TpQKAtQ0opAKSBz0aXhSTicHCZSV8L5IRH/9fI5Ne8Ph9d7k39iJrAEcF8xj0uib\nnPyzgitBSwgca4eDrg0hO66w/8NO5rbbzriQeWRFFJEe+YvZq/tydPR1Bu+dx4uXcShbV+HuZkLM\n/a/4zjQi5lc0+jb6VAka5KrIcSrURU+IxkqRQ2GTAcm2PYjJNMDfvBmjjLt4FkvJM29NWIIBru1c\nKfz+BUsPgWYJ1OX/pmNbOYnfkmjw60dSvg1KzSUMas5EPT6SWg8XPkssqFdxQrWkEu+GOsok8ajp\nGVGt0KI4txLvLtZ8ufIV/+X2pEREIKtUp/UAHw6tecTyq0u4uOgYffzaYGRqwoWl7zn4bjfTOy9m\n/qElhH+vJeP1d0KuBLAxYCZzQ/ez/nwxEkMDZnpqEr15GeP2DGLfk1KU3PugoipAXBqaraXIq0FR\n2PzfCy9BECSCIPwECoAPoigmACaiKBb+C7cC/nnjB/8scs7+39Jz/9X+jyhPFVEboU/e7QiG9FCn\nNvYsdpoCKoadOfK4ji4ndrOo9x1OvZjBxaHzWLy4HZlhf0ipqabf2BFsWHiAQ59XEBywnA135nPm\n0D1cfNuR16qR1OfpjB/UnTvnjzNkox8/NmbjHNCBksxKlBqUMddSpbAwDxUnIxIFJbINjNFCF42K\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91DCMvAsRk4uxtKqhqjYXdRM1ymtakMl0KVBrQKIwogFllOs0sEvWQblCBSWtWpQ1f6Op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\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DfR2p6RLryX0s3+jE8uY87o1ZXu1rcNOiZsCa576IAnNlP/5T76BjpIqn8zmagoj1rTtJ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f84b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "fig = plt.figure(1, figsize=(7, 6))\n",
    "plt.imshow(data, cmap=cm.hot, interpolation=\"bicubic\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving and loading\n",
    "NumPy makes it easy to save and load `ndarray`s in binary or text format.\n",
    "\n",
    "## Binary `.npy` format\n",
    "Let's create a random array and save it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.41307972,  0.20933385,  0.32025581],\n",
       "       [ 0.19853514,  0.408001  ,  0.6038287 ]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.rand(2,3)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"my_array\", a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done! Since the file name contains no file extension was provided, NumPy automatically added `.npy`. Let's take a peek at the file content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\x93NUMPY\\x01\\x00F\\x00{'descr': '<f8', 'fortran_order': False, 'shape': (2, 3), }          \\n\\xa8\\x96\\x1d\\xeb\\xe5o\\xda? \\x06W\\xa1s\\xcb\\xca?*\\xdeB>\\x12\\x7f\\xd4?x<h\\x81\\x99i\\xc9?@\\xa4\\x027\\xb0\\x1c\\xda?<P\\x05\\x8f\\x90R\\xe3?\""
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"my_array.npy\", \"rb\") as f:\n",
    "    content = f.read()\n",
    "\n",
    "content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load this file into a NumPy array, simply call `load`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.41307972,  0.20933385,  0.32025581],\n",
       "       [ 0.19853514,  0.408001  ,  0.6038287 ]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_loaded = np.load(\"my_array.npy\")\n",
    "a_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text format\n",
    "Let's try saving the array in text format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"my_array.csv\", a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the file content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.130797191668116319e-01 2.093338525574361952e-01 3.202558143634371968e-01\n",
      "1.985351449843368865e-01 4.080009972772735694e-01 6.038286965726977762e-01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"my_array.csv\", \"rt\") as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a CSV file with tabs as delimiters. You can set a different delimiter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"my_array.csv\", a, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load this file, just use `loadtxt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.41307972,  0.20933385,  0.32025581],\n",
       "       [ 0.19853514,  0.408001  ,  0.6038287 ]])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_loaded = np.loadtxt(\"my_array.csv\", delimiter=\",\")\n",
    "a_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zipped `.npz` format\n",
    "It is also possible to save multiple arrays in one zipped file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]], dtype=uint8)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(24, dtype=np.uint8).reshape(2, 3, 4)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez(\"my_arrays\", my_a=a, my_b=b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's take a peek at the file content. Note that the `.npz` file extension was automatically added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'\"PK\\\\x03\\\\x04\\\\x14\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00x\\\\x94cH\\\\xb6\\\\x96\\\\xe4{h\\\\x00\\\\x00\\\\x00h\\\\x00\\\\x00\\\\x00\\\\x08\\\\x00\\\\x00\\\\x00my_b.npy\\\\x93NUMPY\\\\x01\\\\x00F\\\\x00{\\'descr\\': \\'|u1\\', \\'fortran_order\\': False, \\'shape\\': (2,[...]'"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"my_arrays.npz\", \"rb\") as f:\n",
    "    content = f.read()\n",
    "\n",
    "repr(content)[:180] + \"[...]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You then load this file like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<numpy.lib.npyio.NpzFile at 0x10fa4d4d0>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays = np.load(\"my_arrays.npz\")\n",
    "my_arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a dict-like object which loads the arrays lazily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['my_b', 'my_a']"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.41307972,  0.20933385,  0.32025581],\n",
       "       [ 0.19853514,  0.408001  ,  0.6038287 ]])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays[\"my_a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What next?\n",
    "Now you know all the fundamentals of NumPy, but there are many more options available. The best way to learn more is to experiment with NumPy, and go through the excellent [reference documentation](http://docs.scipy.org/doc/numpy/reference/index.html) to find more functions and features you may be interested in."
   ]
  }
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