notebooks/Section0_1-IPython_and_Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPython\n",
    "\n",
    "**IPython** (Interactive Python) is an enhanced Python shell which provides a more robust and productive development environment for users. There are several key features that set it apart from the standard Python shell.\n",
    "\n",
    "### History\n",
    "\n",
    "In IPython, all your inputs and outputs are saved. There are two variables named `In` and `Out` which are assigned as you work with your results. All outputs are saved automatically to variables of the form `_N`, where `N` is the prompt number, and inputs to `_iN`. This allows you to recover quickly the result of a prior computation by referring to its number even if you forgot to store it as a variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727500169043067"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.sin(4)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727500169043067"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'import numpy as np\\nnp.sin(4)**2'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_i1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14318750422607668"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_1 / 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output is asynchronous\n",
    "\n",
    "All output is displayed asynchronously as it is generated in the Kernel. If you execute the next cell, you will see the output one piece at a time, not all at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "import time, sys\n",
    "for i in range(8):\n",
    "    print(i)\n",
    "    time.sleep(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introspection\n",
    "\n",
    "If you want details regarding the properties and functionality of any Python objects currently loaded into IPython, you can use the `?` to reveal any details that are available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mType:\u001b[0m        dict\n",
      "\u001b[0;31mString form:\u001b[0m {}\n",
      "\u001b[0;31mLength:\u001b[0m      0\n",
      "\u001b[0;31mDocstring:\u001b[0m  \n",
      "dict() -> new empty dictionary\n",
      "dict(mapping) -> new dictionary initialized from a mapping object's\n",
      "    (key, value) pairs\n",
      "dict(iterable) -> new dictionary initialized as if via:\n",
      "    d = {}\n",
      "    for k, v in iterable:\n",
      "        d[k] = v\n",
      "dict(**kwargs) -> new dictionary initialized with the name=value pairs\n",
      "    in the keyword argument list.  For example:  dict(one=1, two=2)\n"
     ]
    }
   ],
   "source": [
    "some_dict = {}\n",
    "some_dict?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If available, additional detail is provided with two question marks, including the source code of the object itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m \u001b[0mcholesky\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSource:\u001b[0m   \n",
      "\u001b[0;34m@\u001b[0m\u001b[0marray_function_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_unary_dispatcher\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mcholesky\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"\u001b[0m\n",
      "\u001b[0;34m    Cholesky decomposition.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Return the Cholesky decomposition, `L * L.H`, of the square matrix `a`,\u001b[0m\n",
      "\u001b[0;34m    where `L` is lower-triangular and .H is the conjugate transpose operator\u001b[0m\n",
      "\u001b[0;34m    (which is the ordinary transpose if `a` is real-valued).  `a` must be\u001b[0m\n",
      "\u001b[0;34m    Hermitian (symmetric if real-valued) and positive-definite. No\u001b[0m\n",
      "\u001b[0;34m    checking is performed to verify whether `a` is Hermitian or not.\u001b[0m\n",
      "\u001b[0;34m    In addition, only the lower-triangular and diagonal elements of `a`\u001b[0m\n",
      "\u001b[0;34m    are used. Only `L` is actually returned.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Parameters\u001b[0m\n",
      "\u001b[0;34m    ----------\u001b[0m\n",
      "\u001b[0;34m    a : (..., M, M) array_like\u001b[0m\n",
      "\u001b[0;34m        Hermitian (symmetric if all elements are real), positive-definite\u001b[0m\n",
      "\u001b[0;34m        input matrix.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Returns\u001b[0m\n",
      "\u001b[0;34m    -------\u001b[0m\n",
      "\u001b[0;34m    L : (..., M, M) array_like\u001b[0m\n",
      "\u001b[0;34m        Upper or lower-triangular Cholesky factor of `a`.  Returns a\u001b[0m\n",
      "\u001b[0;34m        matrix object if `a` is a matrix object.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Raises\u001b[0m\n",
      "\u001b[0;34m    ------\u001b[0m\n",
      "\u001b[0;34m    LinAlgError\u001b[0m\n",
      "\u001b[0;34m       If the decomposition fails, for example, if `a` is not\u001b[0m\n",
      "\u001b[0;34m       positive-definite.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    See Also\u001b[0m\n",
      "\u001b[0;34m    --------\u001b[0m\n",
      "\u001b[0;34m    scipy.linalg.cholesky : Similar function in SciPy.\u001b[0m\n",
      "\u001b[0;34m    scipy.linalg.cholesky_banded : Cholesky decompose a banded Hermitian\u001b[0m\n",
      "\u001b[0;34m                                   positive-definite matrix.\u001b[0m\n",
      "\u001b[0;34m    scipy.linalg.cho_factor : Cholesky decomposition of a matrix, to use in\u001b[0m\n",
      "\u001b[0;34m                              `scipy.linalg.cho_solve`.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Notes\u001b[0m\n",
      "\u001b[0;34m    -----\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    .. versionadded:: 1.8.0\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Broadcasting rules apply, see the `numpy.linalg` documentation for\u001b[0m\n",
      "\u001b[0;34m    details.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    The Cholesky decomposition is often used as a fast way of solving\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    .. math:: A \\\\mathbf{x} = \\\\mathbf{b}\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    (when `A` is both Hermitian/symmetric and positive-definite).\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    First, we solve for :math:`\\\\mathbf{y}` in\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    .. math:: L \\\\mathbf{y} = \\\\mathbf{b},\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    and then for :math:`\\\\mathbf{x}` in\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    .. math:: L.H \\\\mathbf{x} = \\\\mathbf{y}.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Examples\u001b[0m\n",
      "\u001b[0;34m    --------\u001b[0m\n",
      "\u001b[0;34m    >>> A = np.array([[1,-2j],[2j,5]])\u001b[0m\n",
      "\u001b[0;34m    >>> A\u001b[0m\n",
      "\u001b[0;34m    array([[ 1.+0.j, -0.-2.j],\u001b[0m\n",
      "\u001b[0;34m           [ 0.+2.j,  5.+0.j]])\u001b[0m\n",
      "\u001b[0;34m    >>> L = np.linalg.cholesky(A)\u001b[0m\n",
      "\u001b[0;34m    >>> L\u001b[0m\n",
      "\u001b[0;34m    array([[1.+0.j, 0.+0.j],\u001b[0m\n",
      "\u001b[0;34m           [0.+2.j, 1.+0.j]])\u001b[0m\n",
      "\u001b[0;34m    >>> np.dot(L, L.T.conj()) # verify that L * L.H = A\u001b[0m\n",
      "\u001b[0;34m    array([[1.+0.j, 0.-2.j],\u001b[0m\n",
      "\u001b[0;34m           [0.+2.j, 5.+0.j]])\u001b[0m\n",
      "\u001b[0;34m    >>> A = [[1,-2j],[2j,5]] # what happens if A is only array_like?\u001b[0m\n",
      "\u001b[0;34m    >>> np.linalg.cholesky(A) # an ndarray object is returned\u001b[0m\n",
      "\u001b[0;34m    array([[1.+0.j, 0.+0.j],\u001b[0m\n",
      "\u001b[0;34m           [0.+2.j, 1.+0.j]])\u001b[0m\n",
      "\u001b[0;34m    >>> # But a matrix object is returned if A is a matrix object\u001b[0m\n",
      "\u001b[0;34m    >>> np.linalg.cholesky(np.matrix(A))\u001b[0m\n",
      "\u001b[0;34m    matrix([[ 1.+0.j,  0.+0.j],\u001b[0m\n",
      "\u001b[0;34m            [ 0.+2.j,  1.+0.j]])\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mgufunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcholesky_lo\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_makearray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0m_assert_stacked_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0m_assert_stacked_square\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_commonType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFile:\u001b[0m      ~/miniforge3/envs/bios8366/lib/python3.9/site-packages/numpy/linalg/linalg.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "from numpy.linalg import cholesky\n",
    "cholesky??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This syntax can also be used to search namespaces with wildcards (\\*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plt.Subplot\n",
      "plt.SubplotSpec\n",
      "plt.SubplotTool\n",
      "plt.boxplot\n",
      "plt.eventplot\n",
      "plt.get_plot_commands\n",
      "plt.matplotlib\n",
      "plt.plot\n",
      "plt.plot_date\n",
      "plt.plotting\n",
      "plt.stackplot\n",
      "plt.streamplot\n",
      "plt.subplot\n",
      "plt.subplot2grid\n",
      "plt.subplot_mosaic\n",
      "plt.subplot_tool\n",
      "plt.subplots\n",
      "plt.subplots_adjust\n",
      "plt.triplot\n",
      "plt.violinplot"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pylab as plt\n",
    "plt.*plot*?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tab completion\n",
    "\n",
    "Because IPython allows for introspection, it is able to afford the user the ability to tab-complete commands that have been partially typed. This is done by pressing the `<tab>` key at any point during the process of typing a command.\n",
    "\n",
    "**Place your cursor after the partially-completed command below and press tab:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.ar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can even be used to help with specifying arguments to functions, which can sometimes be difficult to remember:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### System commands\n",
    "\n",
    "In IPython, you can type `ls` to see your files or `cd` to change directories, just like you would at a regular system prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34mAIS\u001b[0m/                          microbiome_missing.csv\n",
      "baseball-archive-2011.sqlite  mushroom.csv\n",
      "baseball.csv                  nashville_precip.txt\n",
      "\u001b[01;32mbaseball.dat\u001b[0m*                 occupancy.csv\n",
      "besx97e.dta                   pima-indians-diabetes.data.txt\n",
      "bikeshare.csv                 pima-indians-diabetes.metadata.txt\n",
      "\u001b[01;32mbodyfat.dat\u001b[0m*                  pitches.csv\n",
      "brasil_capitals.txt           pitches.md\n",
      "cancer.csv                    prostate.data.txt\n",
      "cdystonia.csv                 radon.csv\n",
      "concrete.csv                  salmon.txt\n",
      "credit.csv                    \u001b[01;32msrrs2.dat\u001b[0m*\n",
      "\u001b[01;32mcty.dat\u001b[0m*                      survey.db\n",
      "\u001b[01;34mebola\u001b[0m/                        test_scores.csv\n",
      "heart_rate.csv                titanic.html\n",
      "heart_rate.txt                titanic.xls\n",
      "measles.csv                   TNNASHVI.txt\n",
      "measles.xlsx                  vlbw.csv\n",
      "melanoma_data.py              walker.txt\n",
      "\u001b[01;34mmicrobiome\u001b[0m/                   \u001b[01;32mwine.dat\u001b[0m*\n",
      "microbiome.csv                wisconsin_breast_cancer.csv\n"
     ]
    }
   ],
   "source": [
    "ls ../data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Virtually any system command can be accessed by prepending `!`, which passes any subsequent command directly to the OS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!touch test.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can even use Python variables in commands sent to the OS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/baseball.csv\t../data/microbiome_missing.csv\n",
      "../data/bikeshare.csv\t../data/mushroom.csv\n",
      "../data/cancer.csv\t../data/occupancy.csv\n",
      "../data/cdystonia.csv\t../data/pitches.csv\n",
      "../data/concrete.csv\t../data/radon.csv\n",
      "../data/credit.csv\t../data/test_scores.csv\n",
      "../data/heart_rate.csv\t../data/vlbw.csv\n",
      "../data/measles.csv\t../data/wisconsin_breast_cancer.csv\n",
      "../data/microbiome.csv\n"
     ]
    }
   ],
   "source": [
    "file_type = 'csv'\n",
    "!ls ../data/*$file_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of a system command using the exclamation point syntax can be assigned to a Python variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_files = !ls ../data/microbiome/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['metadata.xls',\n",
       " 'MID1.xls',\n",
       " 'MID2.xls',\n",
       " 'MID3.xls',\n",
       " 'MID4.xls',\n",
       " 'MID5.xls',\n",
       " 'MID6.xls',\n",
       " 'MID7.xls',\n",
       " 'MID8.xls',\n",
       " 'MID9.xls']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Qt Console\n",
    "\n",
    "If you type at the system prompt:\n",
    "\n",
    "    $ ipython qtconsole\n",
    "\n",
    "instead of opening in a terminal, IPython will start a graphical console that at first sight appears just like a terminal, but which is in fact much more capable than a text-only terminal.  This is a specialized terminal designed for interactive scientific work, and it supports full multi-line editing with color highlighting and graphical calltips for functions, it can keep multiple IPython sessions open simultaneously in tabs, and when scripts run it can display the figures inline directly in the work area.\n",
    "\n",
    "![qtconsole](images/qtconsole.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebook\n",
    "\n",
    "Over time, the IPython project grew to include several components, including:\n",
    "\n",
    "* an interactive shell\n",
    "* a REPL protocol\n",
    "* a notebook document fromat\n",
    "* a notebook document conversion tool\n",
    "* a web-based notebook authoring tool\n",
    "* tools for building interactive UI (widgets)\n",
    "* interactive parallel Python\n",
    "\n",
    "As each component has evolved, several had grown to the point that they warrented projects of their own. For example, pieces like the notebook and protocol are not even specific to Python. As the result, the IPython team created Project Jupyter, which is the new home of language-agnostic projects that began as part of IPython, such as the notebook in which you are reading this text.\n",
    "\n",
    "The HTML notebook that is part of the Jupyter project supports **interactive data visualization** and easy high-performance **parallel computing**. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f97e081ffd0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UwMaRsRifxLXprgQDiYjoMuw59/iI1uvSdVHLsSU7Dtd2CZWoMv/HQCIi6gBBEPDfP9dh0bfVomcZAUC6Vol3b4pDYiQPqZ3B3iMiuoTqBifm7jXhH8ctLttuTgzDhpFaRIVw0nJndagH9+7di+nTp2PAgAHQaDTYsmWLaLsgCMjLy0Nqaiq6du2K8ePH48iRI6J9bDYbcnJy0KdPH3Tv3h3Tp09HSUmJ+z4JEZEHfH+2ASP+r7zNMMq5OgrvZMcyjNykQ71YV1eHtLQ0LFu2DGq168W6tWvXYt26dVi+fDl27twJnU6HyZMno7a2tnmf3NxcbN++HRs3bsTHH3+M2tpaTJs2DQ6Hw+XPIyKSmlMQ8GaxEjd/dNZlgdToUBn+Z0wsFl4TzaWA3KhDgTRu3DgsWrQIkyZNglwufokgCFi/fj3mzp2LSZMmIS0tDevXr4fZbMbWrVsBANXV1XjrrbewePFijB49GhkZGXjllVfw008/4csvv3T7hyIi6ozjtY247ZMK/PV4KBrbuF705YQuuLUnZ9K5W6fHmSdOnEBZWRmys7Ob29RqNYYPH44DBw4AAAoLC2G320X7JCQkoH///s37EBFJzSkI2HDEjOv/txz7yhpctj84IAL/vK0L+kTz8rsndLpXy8rKAAA6nU7UrtPpUFpaCgAoLy+HQqFAXFycyz7l5eWdLYGIqNMM1Xb8eZ8Ju8+4BpFWJcPfrtfy/iIPc1vMt14eQxCESy6Zcal9DAZDp2rq7OsDEftEjP0hFoz9YXMCm0+F4I1iJeyC6/FomMaBp1MaoG+oQxB2j4vO/BtJSUm56PZOB5JerwfQNApKSEhobq+oqGgeNXXp0gUOhwNGoxHx8fGifYYPH97un32p4i/GYDB06vWBiH0ixv4QC7b+EAQBnxfb8NQ31ThW0+iyPVIpw2O9rMi5oTfXozvH0/9GOn0NKSkpCXq9HgUFBc1tVqsV+/fvR1ZWFgAgIyMDISEhon1KSkpw9OjR5n2IiLzlF5MdUz834q4vjG2G0ejuKuyb3AWTuzoYRl7UoRGS2WxGUVERAMDpdKK4uBiHDh2CVqtFYmIiZs+ejdWrVyMlJQXJyclYtWoVIiIiMHXqVABATEwM7rnnHixatAg6nQ5arRYLFy5Eeno6Ro0a5bEPR0TUUrG5Eat+rMVbhnqX1RYAQBcmR15WDKb0VkMmk4Fn6LyrQ4F08OBBTJgwofn7vLw85OXlYcaMGVi/fj0ef/xxWCwW5OTkwGQyITMzE/n5+YiKimp+zdKlS6FQKDBz5kxYrVaMGDECL7/8MhQKhfs/FRFRC6X1Dqz5sRabf61Dg9N1uwzAH/qH49nMGGhUvMlVKjKTydTGzwn+L9jOh3cE+0SM/SEWiP1RVu/A2n/VYtMvdS4Pzzvv+q6hyBsag0FxrouiBmKfdIan+4OT6Yko4Pyr0o7//smMrUX1bY6IAKB3lALPDYnBhKQwXifyEQwkIgoITkHAZ8VW/PdPddhVamt3v8RIBXKujsKM5HCEcNkfn8JAIiK/dqK2Ee/+Vo//OVaP47Xtr43ZPVyOJ66Owj0pEQhVMIh8EQOJ/JKlUUBNgxM1didqGs7/XkCDQ0CDU4DdCTQ4BNjPXSFVyAClDFDKZVDImr6vKFfgl1ALIpUyRIbIERkiQ0SIDFHnfs+fnn1Xnd2JbSeseMdQ1+bKCi31ilJgTlok7u0XgTAl/059GQOJfI7dKeBkrQNFtY0oqmnECXMjyuqdOGNxoKzeiTKLA7V2d8zFUQG/Vra7NVIpQ1yYvOlLde7XMAXiW7R1j1CgW7gCujA5V332sEqrA5+esuLDk1YUlNhgaWvedgvD9aGYkx6JWxPD+HfjJxhIJBlBEHDC7MCPRjsOVzZ9HTXZccrsaPMeEW8zNwowmx0ujx5oi0IG6NVydAtvCqhuEQp0P//7cAV6RDSFV7iSU4o7yikIOFLViK9KbfjkpAX7yhou+e9CpQAm9VJjTlokMuL5KHF/w0AirzHbnfi2vAH7yhqwv8yGQ0Y7atwy0pGeQwBO1ztxut4JwN7uflqVDN3DFUiIUKD7udDqEdH0df77iCB92JtTEPBbTSP2nmnArlIbdpXaUGFtZ4pcK0N0Ibg7OQKTe6t5H5EfYyCRx9idAvaXNeDzYiv2nrHhR6PdbSMfpQzQqOSIDpEhOlSO6FA5okJkUCtlUMqAUIUMoXIZzg9IHM6m0GgUBDQ6AYcgoKq6Fgp1JMx2J+oaBZjtAursAmobnTDbBTg9kJVVNgFVtkb8VOW6XM15mlAZukecC63wpqBqDq1zAebvoeUUBJw8Nzo+WNGAHyrsKDQ2oKah452eEKHAlN5q/D4lHP01IR6slryFgURuVWF14PNiGz47ZcU/S6xXPALqFi5H7ygl+kQr0TtKiW7hcnQNV0CvVqBruBxalRzyTt47YjAYkZIS1+Y2pyCgpkGA0epEhdUBo82JCqsTlVZn8+/LLQ6U1jd9Vdncl16mBgGmhkb8fJHQigmVNYVUi8Dqfu57fbgCsSo5YlVySWeT2Z0CSusdOF3nQHGdA4bqRhiqG/FrdSOOVdvbvVH1YtI0SoxPUuO2pDAMig3h/UMBhoFEnVbT4MT2ExZ8UGTBrlLbZY0sYkJlGBgb0vx1VWwI+kYrJR8ByGUyaFQyaFRy9I259P8mlkYBZ+odOH0uoErrHCi1OFBa58TpegdK6hw4U+9wefrolapuEFB9idACmiZmaMPkzQEVq2oK88hzo8lwpQwRSjnUShlqKhT4d5gVchkgkzWtvCyTNS2r0/RfAVYHYHUIsDQKsDkE1DUKMDU4UWVzwmRr+tVoc6K0zoEyixOd/bhRITJc31WFEd1UuCUxjA/GC3D826UrYncK+LzYivd/s+DTU5YO/7SbHK3EdfpQDO+qwnX6UCRFKgLip1y1Uobe0Ur0vsgB0+EUcNbqREldU0CdD6rT574vORdk7got4MLEjFMdmJgBqIBfjO578ysQHSpDZnwobuzWFEIZcSFQcoZc0GAg0WUprXfgjaN1eONoHcosl77gHB8mx9iEMIztocL1XVXQhwfvYroKuQxdwxXoGq5Apq7tfZyCgHKLsymkWgTW+fAqqWsagdk7dq3fp2lVMqRqQnBNfCgGxzf92jsqMH5AoSvDQKJLEgQB+8oa8NqROnx4wnLJn+DTtUrc2lONWxLDcE18SKev9QQTuexCaF3Tzj5OQcBZS9OpwOLzgdUiwM5f66pqcHpkYsbl6KKWN0/GSIpSoH9MCPpplOgXo0RcWPD+cEJtYyBRu5yCgA9PWLHqx1ocqmx/KjMA9IxU4K4+4bizr5oznjxMLpNBf27ywuD49vc7PzGjyuZEZYuvKpsT9Y0C6u0C6h3Opl8bBZRX10IRFgFBAAQ0fTmFph9IBDRdRQpTyBCmlDX9eu73mtCm61JNXzJoQ+XQhzeFEJfoocvBQCIXDqeAfxy3YPWPtThiav+ieaRShjv7qjGtbziyuoTyVIuPaTkxo3cH9m+adZjk8bqI2sNAomYOp4D3iyxY9WMNfqtp/yJ4/xglZg2IwLS+4YgO9e/7YYjIdzCQCADw1Wkbnv62GofbOTUnA/C7nmF4aEAkRnTjaIiI3I+BFOR+NdnxzHc12HHK2uZ2uQyY2luNP18dhVReGyIiD2IgBSmTzYkXfqjBpqN1bS7no5QB05LD8eeBUR26MZSIqLN4pAkygiBga5EFT31TjbPtLFw5pbcaz2RGo1cU/3kQkffwiBNEii0y5HxmxJen2368c1aXULwwNAZDdFy2n4i8j4EUBJyCgA1H6rDoYBisTtcw6hWlwHNDYjAxKYyTFYhIMgykAHeithF/3FN17jHP4rAJlQN/HhSFuQOj+GhnIpIcAymAvfdbPZ7YZ4K5jbV+RnRTYc11MUiO4cw5IvINDKQAVGt3Ime/Ce/+ZnHZFh0iwwtDY/AfKeE8PUdEPoWBFGB+NDbg/i8r21xpIUvjwIax3ZEYyb92IvI9PDIFkC2GOjyx3+TybKIwBbDk2hiMUJQyjIjIZ/HoFABsDgELDpjw+tF6l20DNEpsHBWLNG0IDAYJiiMi6iAGkp87U+/Af+w04ruzrmvQzewfjheGxiBcyQVQicj3MZD82CFjA2Z8UYmSevE5OrVChpeu12Ba33CJKiMiunwMJD/18UkLHvyqCnWtpnT3ilLgrew4DIzldG4i8i8MJD+0/icznvqmGq3vLhrTQ4WNI2OhUfEUHRH5HwaSHxEEAf/1XQ3W/svssu3hARF4YWgMlHLeW0RE/omB5CfsTgF/2lPlcrOrQgYsz4rBrAGRElVGROQeDCQ/UN/oxH07K/F5iXhh1EilDJuzYzGmR5hElRERuQ8DyceZ7U5M/8KIPWcaRO3xYXJsHRuHjHg+KoKIAgMDyYdVNzhx52dGfHNWHEa9ohTIHxePPtH86yOiwMEjmo+qtDpwx2dGFBrFN7yma5X4x83x6KJWSFQZEZFnMJB8kMnmxO07jDhUKQ6jwfEhyB8XDy2ndRNRAOKRzcfUNDgx5bMKlzDK6hKK/72ZYUREgcstR7e8vDxoNBrRV79+/Zq3C4KAvLw8pKamomvXrhg/fjyOHDnijrcOKGa7E3d+bsT3FeIwuqFrKP4+Lg4xoQwjIgpcbjvCpaSk4OjRo81f+/bta962du1arFu3DsuXL8fOnTuh0+kwefJk1NbWuuvt/Z61UcD0L4w4UC6ewHCdPhTv3RSHyBCGEREFNrcd5ZRKJfR6ffNXfHw8gKbR0fr16zF37lxMmjQJaWlpWL9+PcxmM7Zu3equt/drjU4BD3xV6TK1+1pdCN4fG4cIhhERBQG3HemOHz+OAQMGYNCgQbj//vtx/PhxAMCJEydQVlaG7Ozs5n3VajWGDx+OAwcOuOvt/ZYgCPjPfSZ8dNIqah8cH4IPxsYjimFEREFCZjKZWq/Redk+//xzmM1mpKSkoKKiAitXroTBYMDXX38Ng8GAm2++GYcPH0ZiYmLzax599FGUlpYiPz+/3T/XEARPlPvr8RC8WSxembt3uBOvDrRCwwW7iSiApKSkXHS7W6Z9jx07VvT9kCFDkJGRgXfeeQfXXnstAEAmEy/6KQiCS1trlyr+YgwGQ6de7w0v/2zGm8XVoraECAU+HN8VPSLcf5+RP/SJN7E/xNgfrtgnYp7uD4+cD4qMjERqaiqKioqg1+sBAOXl5aJ9KioqoNPpPPH2fuHjkxbkHhCHUZxKjn/cHOeRMCIi8nUeCSSr1QqDwQC9Xo+kpCTo9XoUFBSItu/fvx9ZWVmeeHufV1jRgFlfVYmeZxShlGHruDikxPA8HREFJ7ecsnv66adxyy23ICEhofkaUn19PWbMmAGZTIbZs2dj9erVSElJQXJyMlatWoWIiAhMnTrVHW/vV06aGzHtCyPqWzzpVSED3hgdi8FcKJWIgphbAun06dOYNWsWjEYj4uPjMWTIEHz++efo2bMnAODxxx+HxWJBTk4OTCYTMjMzkZ+fj6ioKHe8vd84v3J3mcUpal85TIOxCXyEBBEFN7cE0qZNmy66XSaTITc3F7m5ue54O7/kFAQ8sqsKP1c1itr/dFUk7k+NkKgqIiLfwZtcvGRFYS0+bHWv0cSkMDw3JFqiioiIfAsDyQu2HbdgWaF4maSBsSFYf6MW8ktMfSciChYMJA87UmXH7N1Vorb4MDm2jInlkkBERC3wiOhBZrsT9xVUoq7FjDqlDHhzdCx6RvJRVERELTGQPEQQBMzdZ8Kv1eJJDCuGaTC8q0qiqoiIfBcDyUNeP1qPrUUWUdvdKeGcUUdE1A4GkgcUVjRgwQGTqC1Nq8TKYTHSFERE5AcYSG5msjVdN2poce9rpFKGzaNjEa5kdxMRtYdHSDcSBAFz9lThhNkhav/L9RquUUdEdAkMJDd6+ec6fNzq5tcHUyNwR59wiSoiIvIfDCQ3+anSjv/6Xvw4icHxIVgylNeNiIg6goHkBtZGAQ/uqoStxZm66FAZXh8VC5WCKzEQEXUEA8kNlvxQ47Jo6ovXadArije/EhF1FAOpk746bcO6n8yitrv6qDGF142IiC4LA6kTTDYn5uwWP/k1IUKBFcM0UpVEROS3GEhXSBAE/Hm/CSX1Fy4cyQCsv1ELjYrdSkR0uXjkvEIfFFmQ/2/x0kCPXRWJG7txnToioivBQLoC5RYHnvzaJGobGBuCp67hw/aIiK4UA+kK5HxtgqnhwpUjlQJ4dYSWU7yJiDqBgXSZth234P+Oi1djWDg4GgO0XBqIiKgzGEiXwWRzIqfVqbqMuBDMSY+UpiAiogDCQLoMC7+tRpnlwjLeShnwtxu0UMp5qo6IqLMYSB1UUGLFFkO9qO0/B0XhqlieqiMicgcGUgeY7U48ts8kakvVKDHv6ihpCiIiCkAMpA54/vsanDKLb4D96/WcVUdE5E4MpEsorGjAa7/UidoeSYvAtV1CJaqIiCgwMZAuwikImPe1Cc4Wi9UlRSrwNG+AJSJyOwbSRbxtqMd3Z+2itpXDNIgIYbcREbkbj6ztqLQ68F/f1YjaxvcMw7jEMIkqIiIKbAykdiz+vgaVtgv3HKkVMuRl8XHkRESewkBqw/dnG7D5V/E9R/OujkLPSD4BlojIUxhIrTicAp7YbxI9dC85Wok/XsXlgYiIPImB1MrmX+tRaGw9kSGG9xwREXkYA6mFKpsTi7+vFrXd3kuN0T04kYGIyNMYSC0sL6wRPecoQinDC0M5kYGIyBsYSOccq7ZjwxHxigzzro5CjwiFRBUREQUXBtI5i76rQWOLmQyJkQrMTuNEBiIib2EgAdhVasPHJ8VPgX0uMxphSk5kICLylqAPJIdTwMJvxBMZrtWFYHJvtUQVEREFJ68H0oYNGzBo0CDo9XqMHDkS+/bt83YJIu/+Vo/DleJp3kuHaiCTcXRERORNXg2k/Px8LFiwAE888QR27dqFoUOH4s4778SpU6e8WUazOrsTz38vXq9uSm81Hy1BRCQBrwbSunXr8Pvf/x733Xcf+vfvj5UrV0Kv12PTpk3eLKPZX/5lxhnLhfXqVApgUSYfLUFEJAWvBVJDQwMKCwuRnZ0tas/OzsaBAwe8VUaz0noH/nLYLGqbkxaJpCiuV0dEJAWvHX2NRiMcDgd0Op2oXafToby8vM3XGAyGTr3nxV6/7FgILI6Q5u+1IQImRpTDYGi7lkDR2T4NNOwPMfaHK/aJWGf6IyUl5aLbvT4caD1ZQBCEdicQXKr4izEYDO2+/t81jfi/vWWitqeHaDA4NeGK388fXKxPghH7Q4z94Yp9Iubp/vDaKbu4uDgoFAqX0VBFRYXLqMnT8g6Kb4LtFaXAvf0ivFoDERGJeS2QQkNDkZGRgYKCAlF7QUEBsrKyvFUGfqq044Mii6jtqcHRCJFzmjcRkZS8esru0UcfxcMPP4zMzExkZWVh06ZNOHPmDGbOnOm1Gpb8UCN61lGaVompfXgTLBGR1LwaSHfccQcqKyuxcuVKlJWVYcCAAXj//ffRs2dPr7z/N+U2fHJKvETQ09dEQ86bYImIJOf1SQ2zZs3CrFmzvP22EAQBi1vdBHutLgS3JvJZR0REviBo1rL78rQNe840iNqeyYzhEkFERD4iKAJJEAQs/kE8OhrdXYUR3VQSVURERK0FRSB9dNKKgxXiBVSfuYZLBBER+ZKADyRBELCisFbUNiEpDNfouIAqEZEvCfhA2lFsxaFWj5fIHczRERGRrwnoQGprdDQxKQxp2pB2XkFERFIJ6ED6Z4kNP7S6djTv6iiJqiEioosJ2EASBGB5oXhm3e96hmFQHK8dERH5ooANpG+q5fj2rHh09CRHR0REPisgA0kQBGw4Kb5OdHOCChnxHB0REfmqgAykPWcaUFijELU9mcGZdUREviwgA2lFq2tHN/VQIZP3HRER+bSAC6QfjQ3Y3WrNuiczeO2IiMjXBVwgXR0Xig9vjceQGAcAYFR3FYZ24Zp1RES+zuuPn/CGG7qqsH6gDRXRPRGu5GreRET+ICAD6bzr9BwZERH5i4A7ZUdERP6JgURERD6BgURERD6BgURERD6BgURERD6BgURERD6BgURERD5BZjKZBKmLICIi4giJiIh8AgOJiIh8AgOJiIh8AgOJiIh8AgOJiIh8QkAG0oYNGzBo0CDo9XqMHDkS+/btk7okSaxZswajR49GYmIi+vbti2nTpuHnn3+WuiyfsXr1amg0GuTk5EhdiqTOnDmDRx55BH379oVer0dWVhb27NkjdVmScDgcWLJkSfPxY9CgQViyZAkaGxulLs1r9u7di+nTp2PAgAHQaDTYsmWLaLsgCMjLy0Nqaiq6du2K8ePH48iRI25574ALpPz8fCxYsABPPPEEdu3ahaFDh+LOO+/EqVOnpC7N6/bs2YMHHngAO3bswLZt26BUKnH77bejqqpK6tIk9+2332Lz5s1IT0+XuhRJmUwm3HzzzRAEAe+//z4OHDiAFStWQKfTSV2aJF566SVs2LABy5cvxzfffINly5bhtddew5o1a6QuzWvq6uqQlpaGZcuWQa1Wu2xfu3Yt1q1bh+XLl2Pnzp3Q6XSYPHkyamtrO/3eAXcf0pgxY5Ceno6//OUvzW3XXHMNJk2ahGeffVbCyqRnNpvRs2dPbNmyBbfeeqvU5UimuroaI0eOxNq1a7FixQqkpaVh5cqVUpclicWLF2Pv3r3YsWOH1KX4hGnTpkGr1eLll19ubnvkkUdQVVWF9957T8LKpNGjRw+sWLECd999N4Cm0VFqaioefPBBzJs3DwBgsViQkpKC559/HjNnzuzU+wXUCKmhoQGFhYXIzs4WtWdnZ+PAgQMSVeU7zGYznE4nNBqN1KVIau7cuZg0aRJGjhwpdSmS++ijj5CZmYmZM2ciOTkZN9xwA1599VUIQkD9nNphw4YNw549e/Drr78CAH755Rfs3r0bY8eOlbgy33DixAmUlZWJjrFqtRrDhw93yzE2oJ4YazQa4XA4XE436HQ6lJeXS1SV71iwYAEGDhyIoUOHSl2KZDZv3oyioiK88sorUpfiE44fP46NGzdizpw5mDt3Lg4fPoz58+cDAB566CGJq/O+uXPnwmw2IysrCwqFAo2NjZg3bx5mzZoldWk+oaysDADaPMaWlpZ2+s8PqEA6TyaTib4XBMGlLdg89dRT+Prrr/Hpp59CoVBIXY4kDAYDFi9ejE8++QShoaFSl+MTnE4nBg8e3Hw6++qrr0ZRURE2bNgQlIGUn5+Pd999Fxs2bEBqaioOHz6MBQsWoGfPnrj33nulLs9neOoYG1CBFBcXB4VC4TIaqqioCNqLtACQm5uL/Px8bN++Hb169ZK6HMl88803MBqNuO6665rbHA4H9u3bh02bNuH06dNQqVQSVuh9er0e/fv3F7X169cPxcXFElUkrUWLFuGPf/wjpkyZAgBIT0/HqVOn8OKLLzKQ0PTvBQDKy8uRkJDQ3O6uY2xAXUMKDQ1FRkYGCgoKRO0FBQXIysqSqCppzZ8/H1u3bsW2bdvQr18/qcuR1Pjx47Fv3z7s3r27+Wvw4MGYMmUKdu/eHZSjpmHDhuHYsWOitmPHjiExMVGiiqRVX1/vcgZBoVDA6XRKVJFvSUpKgl6vFx1jrVYr9u/f75ZjbECNkADg0UcfxcMPP4zMzExkZWVh06ZNOHPmTKdnf/ijefPm4b333sPbb78NjUbTfP43IiICkZGRElfnfRqNxmVCR3h4OLRaLdLS0qQpSmJz5szBuHHjsGrVKtxxxx04dOgQXn31VTzzzDNSlyaJW265BS+99BKSkpKQmpqKQ4cOYd26dZg+fbrUpXmN2WxGUVERgKZTusXFxTh06BC0Wi0SExMxe/ZsrF69GikpKUhOTsaqVasQERGBqVOndvq9A27aN9B0Y+zatWtRVlaGAQMGYOnSpbj++uulLsvr2ptNN3/+fOTm5nq3GB81fvz4oJ72DQA7duzA4sWLcezYMSQkJODBBx/Eww8/HJTXXWtra/HCCy/gww8/REVFBfR6PaZMmYInn3wSYWFhUpfnFbt378aECRNc2mfMmIH169dDEAQsW7YMb7zxBkwmEzIzM7Fq1Sq3/FAXkIFERET+J6CuIRERkf9iIBERkU9gIBERkU9gIBERkU9gIBERkU9gIBERkU9gIBERkU9gIBERkU9gIBERkU/4f0mgx4B8Fww3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "def f(x):\n",
    "    return (x-3)*(x-5)*(x-7)+85\n",
    "\n",
    "import numpy as np\n",
    "x = np.linspace(0, 10, 200)\n",
    "y = f(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook lets you document your workflow using either HTML or Markdown, providing a complete and self-contained record of a computation that can be exported to various formats and shared.\n",
    "\n",
    "The Jupyter Notebook consists of three interacting components:\n",
    "\n",
    "* A notebook web application: An interactive web application for writing and running code interactively and authoring notebook documents.\n",
    "* Kernels: Separate processes started by the notebook web application that runs notebook code and returns output to the web application, as well as secondary features like interactive widgets, tab completion and introspection.\n",
    "* Notebook documents: JSON documents that contain a representation of all content visible in the notebook web application, including inputs and outputs of the computations, narrative text, equations, images, and rich media representations of objects. They are stored on your filesystem with an `.ipynb` extension.\n",
    "\n",
    "The Notebook can be used by starting the Notebook server with the command:\n",
    "\n",
    "    $ jupyter notebook\n",
    "    \n",
    "This opens a Jupyter notebook dashboard that acts as a home page for your Jupyter instance. It displays the notebooks and other files in your current directory.\n",
    "\n",
    "The notebook web application provides a rich computing environment for data science work. For example, you can embed images, videos, or entire websites into notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fonnesbeck/miniforge3/envs/bios8366/lib/python3.9/site-packages/IPython/core/display.py:724: UserWarning: Consider using IPython.display.IFrame instead\n",
      "  warnings.warn(\"Consider using IPython.display.IFrame instead\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe src=http://fonnesbeck.github.io/Bios8366 width=700 height=350></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML(\"<iframe src=http://fonnesbeck.github.io/Bios8366 width=700 height=350></iframe>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/rl5DaFbLc60\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f97e084c5b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"GExKsQ-OU78\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remote Code\n",
    "\n",
    "Use `%load` to add remote code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load http://matplotlib.org/mpl_examples/shapes_and_collections/scatter_demo.py\n",
    "\"\"\"\n",
    "Simple demo of a scatter plot.\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = np.pi * (15 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mathjax Support\n",
    "\n",
    "Mathjax ia a javascript implementation $\\alpha$ of LaTeX that allows equations to be embedded into HTML. For example, this markup:\n",
    "\n",
    "    \"\"\"$$ \\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right). $$\"\"\"\n",
    "    \n",
    "becomes this:\n",
    "\n",
    "$$\n",
    "\\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SymPy Support\n",
    "\n",
    "SymPy is a Python library for symbolic mathematics. It supports:\n",
    "\n",
    "* polynomials\n",
    "* calculus\n",
    "* solving equations\n",
    "* discrete math\n",
    "* matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Never import like this!\n",
    "from sympy import *\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "init_printing()\n",
    "x, y = symbols(\"x y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left(x + 1\\right) \\left(x + y\\right)^{2}$"
      ],
      "text/plain": [
       "               2\n",
       "(x + 1)⋅(x + y) "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq = ((x+y)**2 * (x+1))\n",
    "eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle x^{3} + 2 x^{2} y + x^{2} + x y^{2} + 2 x y + y^{2}$"
      ],
      "text/plain": [
       " 3      2      2      2            2\n",
       "x  + 2⋅x ⋅y + x  + x⋅y  + 2⋅x⋅y + y "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand(eq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + O\\left(x^{6}\\right)$"
      ],
      "text/plain": [
       "     2      4        \n",
       "    x    5⋅x     ⎛ 6⎞\n",
       "1 + ── + ──── + O⎝x ⎠\n",
       "    2     24         "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1/cos(x)).series(x, 0, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle - \\frac{1}{6}$"
      ],
      "text/plain": [
       "-1/6"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit((sin(x)-x)/x**3, x, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle - \\frac{4 x \\sin{\\left(x^{2} \\right)} \\cos{\\left(x^{2} \\right)}}{x + 1} - \\frac{\\cos^{2}{\\left(x^{2} \\right)}}{\\left(x + 1\\right)^{2}}$"
      ],
      "text/plain": [
       "         ⎛ 2⎞    ⎛ 2⎞      2⎛ 2⎞\n",
       "  4⋅x⋅sin⎝x ⎠⋅cos⎝x ⎠   cos ⎝x ⎠\n",
       "- ─────────────────── - ────────\n",
       "         x + 1                 2\n",
       "                        (x + 1) "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(cos(x**2)**2 / (1+x), x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Magic functions\n",
    "\n",
    "Jupyter has a set of predefined ‘magic functions’ that you can call with a command line style syntax. These include:\n",
    "\n",
    "* `%run`\n",
    "* `%edit`\n",
    "* `%debug`\n",
    "* `%timeit`\n",
    "* `%paste`\n",
    "* `%load_ext`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %conda  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timing the execution of code; the `timeit` magic exists both in line and cell form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.48 ms ± 54.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.linalg.eigvals(np.random.rand(100,100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.36 ms ± 146 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit a = np.random.rand(100, 100)\n",
    "np.linalg.eigvals(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n",
    "\n",
    "These are all equivalent to `%%script <name>`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello from Ruby 2.7.4\n"
     ]
    }
   ],
   "source": [
    "%%ruby\n",
    "puts \"Hello from Ruby #{RUBY_VERSION}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello from /bin/bash\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "echo \"hello from $BASH\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the above generates an error, it is likely that you do not have the `rpy2` module installed. You can install this now via:\n",
    "\n",
    "    !pip install rpy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = rnorm(10) ~ rnorm(10))\n",
      "\n",
      "Coefficients:\n",
      "(Intercept)  \n",
      "     0.5598  \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <span>ListVector with 11 elements.</span>\n",
       "        <table>\n",
       "        <tbody>\n",
       "        \n",
       "          <tr>\n",
       "            <th>\n",
       "            coefficients\n",
       "            </th>\n",
       "            <td>\n",
       "            \n",
       "        <span>FloatVector with 1 elements.</span>\n",
       "        <table>\n",
       "        <tbody>\n",
       "          <tr>\n",
       "          \n",
       "            <td>\n",
       "            0.559818\n",
       "            </td>\n",
       "          \n",
       "          </tr>\n",
       "        </tbody>\n",
       "        </table>\n",
       "        \n",
       "            </td>\n",
       "          </tr>\n",
       "        \n",
       "          <tr>\n",
       "            <th>\n",
       "            residuals\n",
       "            </th>\n",
       "            <td>\n",
       "            \n",
       "        <span>FloatVector with 10 elements.</span>\n",
       "        <table>\n",
       "        <tbody>\n",
       "          <tr>\n",
       "          \n",
       "            <td>\n",
       "            -0.539487\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            -0.752117\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            -0.765536\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            ...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            0.317830\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            -0.495214\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            0.527881\n",
       "            </td>\n",
       "          \n",
       "          </tr>\n",
       "        </tbody>\n",
       "        </table>\n",
       "        \n",
       "            </td>\n",
       "          </tr>\n",
       "        \n",
       "          <tr>\n",
       "            <th>\n",
       "            effects\n",
       "            </th>\n",
       "            <td>\n",
       "            \n",
       "        <span>FloatVector with 10 elements.</span>\n",
       "        <table>\n",
       "        <tbody>\n",
       "          <tr>\n",
       "          \n",
       "            <td>\n",
       "            -1.770300\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            -0.622504\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            -0.635922\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            ...\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            0.447443\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            -0.365601\n",
       "            </td>\n",
       "          \n",
       "            <td>\n",
       "            0.657495\n",
       "            </td>\n",
       "          \n",
       "          </tr>\n",
       "        </tbody>\n",
       "        </table>\n",
       "        \n",
       "            </td>\n",
       "          </tr>\n",
       "        \n",
       "          <tr>\n",
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       "            ...\n",
       "            </th>\n",
       "            <td>\n",
       "            ...\n",
       "            </td>\n",
       "          </tr>\n",
       "        \n",
       "          <tr>\n",
       "            <th>\n",
       "            call\n",
       "            </th>\n",
       "            <td>\n",
       "            \n",
       "Call:\n",
       "lm(formula = rnorm(10) ~ rnorm(10))\n",
       "\n",
       "Coefficients:\n",
       "(Intercept)  \n",
       "   0.003158  \n",
       "\n",
       "\n",
       "            </td>\n",
       "          </tr>\n",
       "        \n",
       "          <tr>\n",
       "            <th>\n",
       "            terms\n",
       "            </th>\n",
       "            <td>\n",
       "            rnorm(10) ~ rnorm(10)\n",
       "attr(,\"variables\")\n",
       "list(rnorm(10))\n",
       "attr(,\"factors\")\n",
       "          rnorm(10)\n",
       "rnorm(10)         1\n",
       "attr(,\"term.labels\")\n",
       "[1] \"rnorm(10)\"\n",
       "attr(,\"order\")\n",
       "[1] 1\n",
       "attr(,\"intercept\")\n",
       "[1] 1\n",
       "attr(,\"response\")\n",
       "[1] 1\n",
       "attr(,\".Environment\")\n",
       "<environment: R_GlobalEnv>\n",
       "attr(,\"predvars\")\n",
       "list(rnorm(10))\n",
       "attr(,\"dataClasses\")\n",
       "rnorm(10) \n",
       "\"numeric\" \n",
       "\n",
       "            </td>\n",
       "          </tr>\n",
       "        \n",
       "          <tr>\n",
       "            <th>\n",
       "            model\n",
       "            </th>\n",
       "            <td>\n",
       "            \n",
       "        <span>R/rpy2 DataFrame (10 x 1)</span>\n",
       "        <table>\n",
       "          <thead>\n",
       "            <tr>\n",
       "              \n",
       "              <th>rnorm(10)</th>\n",
       "              \n",
       "            </tr>\n",
       "          </thead>\n",
       "          <tbody>\n",
       "          \n",
       "          <tr>\n",
       "            \n",
       "            <td>\n",
       "              ...\n",
       "            </td>\n",
       "            \n",
       "          </tr>\n",
       "          \n",
       "          </tbody>\n",
       "        </table>\n",
       "    \n",
       "            </td>\n",
       "          </tr>\n",
       "        \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<rpy2.robjects.vectors.ListVector object at 0x7f97ded4e880> [RTYPES.VECSXP]\n",
       "R classes: ('lm',)\n",
       "[FloatSexp..., FloatSexp..., FloatSexp..., IntSexpVe..., ..., IntSexpVe..., LangSexpV..., LangSexpV..., ListSexpV...]\n",
       "  coefficients: <class 'rpy2.robjects.vectors.FloatVector'>\n",
       "  <rpy2.robjects.vectors.FloatVector object at 0x7f97cc232a40> [RTYPES.REALSXP]\n",
       "R classes: ('numeric',)\n",
       "[0.559818]\n",
       "  residuals: <class 'rpy2.robjects.vectors.FloatVector'>\n",
       "  <rpy2.robjects.vectors.FloatVector object at 0x7f97cc225d00> [RTYPES.REALSXP]\n",
       "R classes: ('numeric',)\n",
       "[-0.539487, -0.752117, -0.765536, 0.842866, ..., 0.287228, 0.317830, -0.495214, 0.527881]\n",
       "  effects: <class 'rpy2.robjects.vectors.FloatVector'>\n",
       "  <rpy2.robjects.vectors.FloatVector object at 0x7f97cc233080> [RTYPES.REALSXP]\n",
       "R classes: ('numeric',)\n",
       "[-1.770300, -0.622504, -0.635922, 0.972480, ..., 0.416841, 0.447443, -0.365601, 0.657495]\n",
       "  rank: <class 'rpy2.robjects.vectors.IntVector'>\n",
       "  <rpy2.robjects.vectors.IntVector object at 0x7f97cc23b100> [RTYPES.INTSXP]\n",
       "R classes: ('integer',)\n",
       "[1]\n",
       "...\n",
       "  assign: <class 'rpy2.robjects.vectors.IntVector'>\n",
       "  <rpy2.robjects.vectors.IntVector object at 0x7f97cc23ba00> [RTYPES.INTSXP]\n",
       "R classes: ('integer',)\n",
       "[9]\n",
       "  qr: <class 'rpy2.robjects.language.LangVector'>\n",
       "  Rlang( lm(formula = rnorm(10) ~ rnorm(10)) )\n",
       "  df.residual: <class 'rpy2.robjects.Formula'>\n",
       "  <rpy2.robjects.Formula object at 0x7f97ded8f200> [RTYPES.LANGSXP]\n",
       "R classes: ('terms', 'formula')\n",
       "<rpy2.robjects.vectors.ListVector object at 0x7f97ded4e880> [RTYPES.VECSXP]\n",
       "R classes: ('lm',)\n",
       "[FloatSexp..., FloatSexp..., FloatSexp..., IntSexpVe..., ..., IntSexpVe..., LangSexpV..., LangSexpV..., ListSexpV...]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = np.arange(10), np.random.normal(size=10)\n",
    "%R print(lm(rnorm(10)~rnorm(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = y ~ x)\n",
      "\n",
      "Residuals:\n",
      "    Min      1Q  Median      3Q     Max \n",
      "-0.7938 -0.6169 -0.2493  0.6764  1.1001 \n",
      "\n",
      "Coefficients:\n",
      "            Estimate Std. Error t value Pr(>|t|)\n",
      "(Intercept) -0.05839    0.45573  -0.128    0.901\n",
      "x           -0.03019    0.08537  -0.354    0.733\n",
      "\n",
      "Residual standard error: 0.7754 on 8 degrees of freedom\n",
      "Multiple R-squared:  0.0154,\tAdjusted R-squared:  -0.1077 \n",
      "F-statistic: 0.1251 on 1 and 8 DF,  p-value: 0.7327\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i x,y -o XYcoef\n",
    "lm.fit <- lm(y~x)\n",
    "par(mfrow=c(2,2))\n",
    "print(summary(lm.fit))\n",
    "plot(lm.fit)\n",
    "XYcoef <- coef(lm.fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05838551, -0.03019335])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XYcoef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LaTeX\n",
    "\n",
    "In addition to MathJax support, you may declare a LaTeX cell using the `%latex` magic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{align}\n",
       "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
       "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
       "\\end{align}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\\begin{align}\n",
    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Javscript\n",
    "\n",
    "Jupyter also enables objects to declare a JavaScript representation. At first, this may seem odd as output is inherently visual and JavaScript is a programming language. However, this opens the door for rich output that leverages the full power of JavaScript and associated libraries such as D3 for output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "\nalert(\"Hello world!\");\n",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "\n",
    "alert(\"Hello world!\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting and Converting Notebooks\n",
    "\n",
    "In Jupyter, one can convert an `.ipynb` notebook document file into various static formats via the `nbconvert` tool. Currently, nbconvert is a command line tool, run as a script using Jupyter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook Section0_1-IPython_and_Jupyter.ipynb to html\n",
      "[NbConvertApp] Writing 747185 bytes to Section0_1-IPython_and_Jupyter.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to html Section0_1-IPython_and_Jupyter.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, `nbconvert` supports HTML (default), LaTeX, Markdown, reStructuredText, Python and HTML5 slides for presentations. Some types can be post-processed, such as LaTeX to PDF (this requires [Pandoc](http://johnmacfarlane.net/pandoc/) to be installed, however)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook Section2_1-Introduction-to-Pandas.ipynb to pdf\n",
      "[NbConvertApp] Writing 96264 bytes to notebook.tex\n",
      "[NbConvertApp] Building PDF\n",
      "[NbConvertApp] Running xelatex 3 times: ['xelatex', 'notebook.tex', '-quiet']\n",
      "[NbConvertApp] Running bibtex 1 time: ['bibtex', 'notebook']\n",
      "[NbConvertApp] WARNING | bibtex had problems, most likely because there were no citations\n",
      "[NbConvertApp] PDF successfully created\n",
      "[NbConvertApp] Writing 98609 bytes to Section2_1-Introduction-to-Pandas.pdf\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to pdf Section2_1-Introduction-to-Pandas.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very useful online service is the [Jupyter Notebook Viewer](https://nbviewer.org/) which allows you to display your notebook as a static HTML page, which is useful for sharing with others:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=https://nbviewer.org/2352771 width=700 height=300></iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<iframe src=https://nbviewer.org/2352771 width=700 height=300></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, GitHub supports the [rendering of Jupyter Notebooks](https://github.com/fonnesbeck/Bios8366/blob/master/notebooks/Section1_2-Programming-with-Python.ipynb) stored on its repositories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproducible Research\n",
    "\n",
    "> reproducing conclusions from a single experiment based on the measurements from that experiment\n",
    "\n",
    "The most basic form of reproducibility is a complete description of the data and associated analyses (including code!) so the results can be *exactly* reproduced by others.\n",
    "\n",
    "Reproducing calculations can be onerous, even with one's own work!\n",
    "\n",
    "Scientific data are becoming larger and more complex, making simple descriptions inadequate for reproducibility. As a result, most modern research is irreproducible without tremendous effort.\n",
    "\n",
    "*** Reproducible research is not yet part of the culture of science in general, or scientific computing in particular. ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scientific Computing Workflow\n",
    "\n",
    "There are a number of steps to scientific endeavors that involve computing:\n",
    "\n",
    "![workflow](http://f.cl.ly/items/3B0l063n2T0H1p041U3L/workflow.png)\n",
    "\n",
    "\n",
    "Many of the standard tools impose barriers between one or more of these steps. This can make it difficult to iterate, reproduce work.\n",
    "\n",
    "The Jupyter notebook eliminates or reduces these barriers to reproducibility."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Links and References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[IPython Notebook Viewer](http://nbviewer.ipython.org) Displays static HTML versions of notebooks, and includes a gallery of notebook examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[NotebookCloud](https://notebookcloud.appspot.com) A service that allows you to launch and control IPython Notebook servers on Amazon EC2 from your browser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data](http://ged.msu.edu/papers/2012-diginorm/) A landmark example of reproducible research in genomics: Git repo, iPython notebook, data and scripts.\n",
    "\n",
    "Jacques Ravel and K Eric Wommack. 2014. [All Hail Reproducibility in Microbiome Research](http://www.microbiomejournal.com/content/pdf/2049-2618-2-8.pdf). Microbiome, 2:8.\n",
    "\n",
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