ipynb-slideshow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Custom cell metadata in Jupyter notebooks via org-mode\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One feature of Jupyter notebooks that is sometimes used is custom cell metadata. This can, for example be used to put names on a cell, or tags for future analysis or conversion. One of the main uses is for annotating cells as part of a slide show. A cell can be a slide, subslide, fragment, note or skip.\n",
    "\n",
    "ox-ipynb mostly supports this now. You can add metadata to a heading via properties or paragraphs via an attr\\_ipynb line or to a code block. The syntax is lispy, basically cons and list cells. These are read by emacs-lisp and converted to json at export time. Here is an example:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "name": "cell-range",
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "range",
     "print"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Out[2]:\n",
      "# output\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "unique-cell",
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "tag1",
     "tag2"
    ]
   },
   "source": [
    "You can also use it to put a name and list of tags on a markdown cell.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For convenience you can call `ox-ipynb-insert-slide` to insert the metadata on a paragraph or src-block. It will offer completion on the types of slides you can insert. This is a subslide.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "This paragraph is just for notes. It won't show in the slide show.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "No one should probably see this in the slide show.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "These features were requested in these issues:\n",
    "\n",
    "[https://github.com/jkitchin/ox-ipynb/issues/6](https://github.com/jkitchin/ox-ipynb/issues/6)\n",
    "\n",
    "[https://github.com/jkitchin/ox-ipynb/issues/11](https://github.com/jkitchin/ox-ipynb/issues/11)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Export as slide show\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a new export option `ox-ipynb-export-to-ipynb-slides-and-open`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "#### Or if you like keybindings\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C-c C-e n s.\n",
    "\n",
    "*This was a slide fragment.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### You can have equations in your slides\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A spiral is a parametric plot in $t$:\n",
    "\n",
    "$x = \\cos(t) e^{-a t}$\n",
    "\n",
    "and\n",
    "\n",
    "$y = \\sin(t) e^{-a t}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### You can put in code and figures in your slides\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Out[2]:\n",
      "# text/plain\n",
      "[<matplotlib.lines.Line2D at 0x11c0e22e8>]\n",
      "\n",
      "\n",
      "\n",
      "# image/png"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "t = np.linspace(0, 8 * np.pi, 500)\n",
    "x = np.cos(t) * np.exp(-0.1 * t)\n",
    "y = np.sin(t) * np.exp(-0.1 * t)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Not bad for something quick and easy!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [https://github.com/jkitchin/ox-ipynb](https://github.com/jkitchin/ox-ipynb).\n",
    "\n",
    "Probably there are issues. Report them at [https://github.com/jkitchin/ox-ipynb/issues](https://github.com/jkitchin/ox-ipynb/issues)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.0"
  },
  "org": {}
 },
 "nbformat": 4,
 "nbformat_minor": 1
}