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  {
   "cell_type": "markdown",
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   "source": [
    "# Graded Exercise Session 1: Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To make sure you already have necessary libraries installed, please execute the following import statements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import cv2\n",
    "import scipy\n",
    "import skimage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you have any missing libraries\n",
    "\n",
    "You can install libraries individually using conda OR if you want to create a new environment with all the necessary libraries,\n",
    "\n",
    "- Create a new conda environment: ```conda create -n cv2019 python=3.7 matplotlib opencv scikit-image jupyter```\n",
    "- Activate it: ```conda activate cv2019```\n",
    "- Launch jupyter notebook: ```jupyter-notebook```\n",
    "\n",
    "#### If you haven't installed python yet\n",
    "- Install python: https://www.anaconda.com/distribution/ (download python 3.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To find the documentation of a function, you can use ```help``` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function sum in module numpy.core.fromnumeric:\n",
      "\n",
      "sum(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>)\n",
      "    Sum of array elements over a given axis.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    a : array_like\n",
      "        Elements to sum.\n",
      "    axis : None or int or tuple of ints, optional\n",
      "        Axis or axes along which a sum is performed.  The default,\n",
      "        axis=None, will sum all of the elements of the input array.  If\n",
      "        axis is negative it counts from the last to the first axis.\n",
      "    \n",
      "        .. versionadded:: 1.7.0\n",
      "    \n",
      "        If axis is a tuple of ints, a sum is performed on all of the axes\n",
      "        specified in the tuple instead of a single axis or all the axes as\n",
      "        before.\n",
      "    dtype : dtype, optional\n",
      "        The type of the returned array and of the accumulator in which the\n",
      "        elements are summed.  The dtype of `a` is used by default unless `a`\n",
      "        has an integer dtype of less precision than the default platform\n",
      "        integer.  In that case, if `a` is signed then the platform integer\n",
      "        is used while if `a` is unsigned then an unsigned integer of the\n",
      "        same precision as the platform integer is used.\n",
      "    out : ndarray, optional\n",
      "        Alternative output array in which to place the result. It must have\n",
      "        the same shape as the expected output, but the type of the output\n",
      "        values will be cast if necessary.\n",
      "    keepdims : bool, optional\n",
      "        If this is set to True, the axes which are reduced are left\n",
      "        in the result as dimensions with size one. With this option,\n",
      "        the result will broadcast correctly against the input array.\n",
      "    \n",
      "        If the default value is passed, then `keepdims` will not be\n",
      "        passed through to the `sum` method of sub-classes of\n",
      "        `ndarray`, however any non-default value will be.  If the\n",
      "        sub-class' method does not implement `keepdims` any\n",
      "        exceptions will be raised.\n",
      "    initial : scalar, optional\n",
      "        Starting value for the sum. See `~numpy.ufunc.reduce` for details.\n",
      "    \n",
      "        .. versionadded:: 1.15.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    sum_along_axis : ndarray\n",
      "        An array with the same shape as `a`, with the specified\n",
      "        axis removed.   If `a` is a 0-d array, or if `axis` is None, a scalar\n",
      "        is returned.  If an output array is specified, a reference to\n",
      "        `out` is returned.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    ndarray.sum : Equivalent method.\n",
      "    \n",
      "    cumsum : Cumulative sum of array elements.\n",
      "    \n",
      "    trapz : Integration of array values using the composite trapezoidal rule.\n",
      "    \n",
      "    mean, average\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Arithmetic is modular when using integer types, and no error is\n",
      "    raised on overflow.\n",
      "    \n",
      "    The sum of an empty array is the neutral element 0:\n",
      "    \n",
      "    >>> np.sum([])\n",
      "    0.0\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> np.sum([0.5, 1.5])\n",
      "    2.0\n",
      "    >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)\n",
      "    1\n",
      "    >>> np.sum([[0, 1], [0, 5]])\n",
      "    6\n",
      "    >>> np.sum([[0, 1], [0, 5]], axis=0)\n",
      "    array([0, 6])\n",
      "    >>> np.sum([[0, 1], [0, 5]], axis=1)\n",
      "    array([1, 5])\n",
      "    \n",
      "    If the accumulator is too small, overflow occurs:\n",
      "    \n",
      "    >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)\n",
      "    -128\n",
      "    \n",
      "    You can also start the sum with a value other than zero:\n",
      "    \n",
      "    >>> np.sum([10], initial=5)\n",
      "    15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.sum)"
   ]
  }
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