{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Jupyter Notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Interactive computing\n",
    "\n",
    "The Jupyter Notebook is an **interactive computing environment** that enables users to author notebook documents that include: \n",
    "- Live code\n",
    "- Interactive widgets\n",
    "- Plots\n",
    "- Narrative text\n",
    "- Equations\n",
    "- Images\n",
    "- Video\n",
    "\n",
    "These documents provide a **complete and self-contained record of a computation** that can be converted to various formats and shared with others using email, [Dropbox](https://www.dropbox.com/), version control systems (like git/[GitHub](https://github.com)) or [nbviewer.jupyter.org](https://nbviewer.jupyter.org)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Multiple languages\n",
    "\n",
    "Through Jupyter's kernel and messaging architecture, the Notebook allows code to be run in a range of different programming languages. For each notebook document that a user opens, the web application starts a kernel that runs the code for that notebook. There are kernels available in the following languages:\n",
    "* Python(https://github.com/ipython/ipython)\n",
    "* C++ (https://github.com/root-project/root/)\n",
    "* Julia (https://github.com/JuliaLang/IJulia.jl)\n",
    "* R (https://github.com/IRkernel/IRkernel)\n",
    "* Ruby (https://github.com/minrk/iruby)\n",
    "* Scala (https://github.com/Bridgewater/scala-notebook)\n",
    "* node.js (https://gist.github.com/Carreau/4279371)\n",
    "* Go (https://github.com/takluyver/igo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The notebook document\n",
    "\n",
    "Notebooks consist of a **linear sequence of cells**. There are three basic cell types:\n",
    "\n",
    "* **Code cells:** Input and output of live code that is run in the kernel\n",
    "* **Markdown cells:** Narrative text with embedded LaTeX equations\n",
    "* **Raw cells:** Unformatted text that is included, without modification, when notebooks are converted to different formats using nbconvert\n",
    "\n",
    "Internally, notebook documents are [JSON](https://en.wikipedia.org/wiki/JSON) **data** with **binary values** [base64](https://en.wikipedia.org/wiki/Base64) encoded. This allows them to be **read and manipulated programmatically** by any programming language. Because JSON is a text format, notebook documents are version control friendly.\n",
    "\n",
    "Tip: **shift + enter** to run a cell!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Write code and execute it, produce text output\n",
    "print(\"This is code!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Display an image\n",
    "from IPython.display import Image\n",
    "Image(filename='images/cern-logo.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "widgets.Dropdown(\n",
    "    options=['1', '2', '3'],\n",
    "    value='2',\n",
    "    description='Number:',\n",
    "    disabled=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Write _formatted_ **markdown text**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Use cases\n",
    "\n",
    "* Programming and Computer Science\n",
    "* Statistics, Machine Learning and Data Science\n",
    "* Mathematics, Physics, Chemistry, Biology\n",
    "* Earth Science and Geo-Spatial data\n",
    "* Linguistics and Text Mining\n",
    "* Signal Processing, Engineering Education\n",
    "* and even Psychology and Neuroscience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "For more information, please visit: https://jupyter-notebook.readthedocs.io\n",
    "\n",
    "Notebook examples available at: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
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