{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd0c99c6",
   "metadata": {},
   "source": [
    "Lecture 19 - jupyter notebooks\n",
    "----\n",
    "\n",
    "Things look a little different right now\n",
    "\n",
    "We're going to start by introducing a new programming environment: Jupyter Notebooks\n",
    "\n",
    "[Link to notes on Jupyter Notebooks](https://muzny.github.io/csci1200-notes/01/1/intro_jupyter_notebooks.html)\n",
    "\n",
    "There are a few things that are really nice about Jupyter Notebooks:\n",
    "- I can have text cells (like this one)\n",
    "- I can also have code cells (like the following one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3627442",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a code cell\n",
    "# write python code as normal\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7121c1b",
   "metadata": {},
   "source": [
    "Awesome things about notebooks\n",
    "---\n",
    "\n",
    "- They are convenient ways to visualize data and graphs in the context of code\n",
    "- Cells can be used to help indicate what pieces of the code goes together sematically\n",
    "- Easy to mix text (notes) together with code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a33c423c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.plot([1, 2, 3, 4], [2, 3, 0, 5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a1ba164",
   "metadata": {},
   "source": [
    "Confusing things about notebooks\n",
    "---\n",
    "\n",
    "- They do not __enforce__ top-to-bottom code execution\n",
    "   - I can run cells in any order I want to\n",
    "- The kernel (your program's memory) *continuously runs*\n",
    "   - This means that when you run a cell, any *variables* and *functions* defined are accessible in cells that you run in the future\n",
    "- they make keeping track of scope complex!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "def4bed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scope and continuous kernel examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38bf7bb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f050f60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7e19e14d",
   "metadata": {},
   "source": [
    "Prof. Felix's Notebook recommendations\n",
    "---\n",
    "- If you use them, always double check your work by doing \"Kernel\" > \"Restart & Run All\"\n",
    "- You can always also save your notebook as a .py file and run it \"normally\" if you'd like to\n",
    "    - \"File\" > \"Download As\" > \"Python (.py)\"\n",
    "- If you save a notebook as `.html`, then that's an easy way to make it easy to show the contents of the notebook to people who don't have Jupyter Notebook installed.\n",
    "    - Gradescope *does* know how to render `.ipynb` files\n",
    "    - \"File\" > \"Download As\" > \"HTML (.html)\"\n",
    "    \n",
    "    \n",
    "__DO NOT USE JUPYTER NOTEBOOKS FOR HW 8 IN DS 2000__\n",
    "\n",
    "(Consult with your DS 2001 instructor for their preference if you are interested in using jupyter notebooks for your DS 2001 project.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92b8be9",
   "metadata": {},
   "source": [
    "Notebook structure\n",
    "----\n",
    "\n",
    "In general, you'd like your notebooks structured as follows:\n",
    "\n",
    "In each cell, from the top to the bottom\n",
    "1. Your header comment\n",
    "2. Your imports\n",
    "3. One function per cell (in general)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}