{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How to generate slides from a Jupyter notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# JupyterLab Cell tag editor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In order to export your notebooks as Reveal.js slides, follow these steps:\n",
    "\n",
    "- Open a notebook by double clicking it in the file browser.\n",
    "- Select Cell tools in the left sidebar.\n",
    "- Select the slide type (Slide, Subslide, Fragment, Skip, Notes)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cell tag\n",
    "\n",
    "![](https://github.com/NordicESMhub/NEGI-Abisko-2019/raw/master/content/images/exporting_slide_type.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Type of slides\n",
    "\n",
    "- **Slide**: New slide\n",
    "- **Sub-Slides**: New section within a topic. Cells with this marker will be placed on a new slide underneath the previous slide.\n",
    "- **Fragments**:  Additional information that adds to an existing slide or sub-slide.\n",
    "- **-**: additional information that appears at the same time than the previous cell.\n",
    "- **Notes**: Speaker notes. These can be viewed on a separate browser window out of the view from the audience. (see the ‘Installation’ -> ‘Full Setup’ section of the REVEAL.js Github repo).\n",
    "- **Skip**: Cells that are set to skip do not appear in the presentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Generate your presentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Exporting Notebooks\n",
    "\n",
    "JupyterLab allows you to export your jupyter notebook files (.ipynb) into other file formats. For creating slides, we can use:\n",
    "\n",
    "- Reveal.js Slides .html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To access these options, while a notebook is open, browse the File menu:\n",
    "\n",
    "![](https://github.com/NordicESMhub/NEGI-Abisko-2019/raw/master/content/images/export_slides.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Customize your slides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## How to hide code to show cell output only?\n",
    "\n",
    "- In the cell tool, click on \"Advanced Tools\" to edit metadata\n",
    "- Paste the following in \"Cell Metadata\"\n",
    "\n",
    "```\n",
    "{\n",
    "    \"slideshow\": {\n",
    "        \"slide_type\": \"fragment\"\n",
    "    },\n",
    "    \"tags\": [\n",
    "        \"to_remove\"\n",
    "    ]\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Advanced cell editor\n",
    "\n",
    "![](https://github.com/NordicESMhub/NEGI-Abisko-2019/raw/master/content/images/hide_input_cell.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Example Hide code and show outputs only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-19T09:32:54.381701Z",
     "start_time": "2019-10-19T09:32:53.719603Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "to_remove"
    ]
   },
   "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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# Turn interactive mode on\n",
    "plt.ion()\n",
    "\n",
    "data = np.random.randn(2, 100)\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(*data, c=data[1], s=100*np.abs(data[0]));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure: scatterplot with random data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Generate your slides\n",
    "\n",
    "- Create a new cell and execute the command:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook presentation.ipynb to slides\n",
      "[NbConvertApp] Writing 323700 bytes to presentation.slides.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert presentation.ipynb --to slides --no-prompt --TagRemovePreprocessor.remove_input_tags={\\\"to_remove\\\"} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A new file was created. Its name is the same as your jupyter notebook but its extension is `html`.\n",
    "- Double click on your file to show your presentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Visualize in slideshow mode\n",
    "\n",
    "- Switch to standard jupyter notebook e.g. change `lab` to `tree` in the Jupyterhub address.\n",
    "\n",
    "For instance, if you adress is \n",
    "\n",
    "`https://abisko.uiogeo-apps.sigma2.no/user/df0bb829-8d2d-487e-9748-cf1b58c1ab4b/lab` \n",
    "\n",
    "then the corresponding standard jupyter noteboo view is \n",
    "\n",
    "`https://abisko.uiogeo-apps.sigma2.no/user/df0bb829-8d2d-487e-9748-cf1b58c1ab4b/tree`\n",
    "\n",
    "- Click on your html file to visualize. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Skip cells\n",
    "\n",
    "You can hide cells, for instance to hide the generation of your slides.\n",
    "\n",
    "- In the cell tool, select \"Skip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-19T09:32:57.296729Z",
     "start_time": "2019-10-19T09:32:55.582397Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook presentation.ipynb to slides\n",
      "[NbConvertApp] Writing 323700 bytes to presentation.slides.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert presentation.ipynb --to slides --no-prompt --TagRemovePreprocessor.remove_input_tags={\\\"to_remove\\\"} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Tips and ticks\n",
    "\n",
    "- if you add images (not generated by python plotting), it is important to download these images too (for instance to the github repositoy). \n",
    " \n",
    "- In presentation mode, you can know more about the reveal-specific shortcuts just pressing the help button at the slideshow left bottom corner.\n",
    "- Darkish themes have css conflict with the notebook css, so it need customization to make them work (not provided by default).\n",
    "- Markdown Images get left aligned by default. Enclose the image like `<center>![noimg](path/to/image.png)</center>` to center it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Customize the style of your slides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Change the default theme\n",
    "\n",
    "- By default `simple` theme is used\n",
    "- Try out for instance `sky`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-19T21:18:52.400228Z",
     "start_time": "2019-10-19T21:18:50.528517Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook presentation.ipynb to slides\n",
      "[NbConvertApp] Writing 319216 bytes to presentation.slides.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert presentation.ipynb --to slides --no-prompt --TagRemovePreprocessor.remove_input_tags={\\\"to_remove\\\"} --SlidesExporter.reveal_theme=sky"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of available theme at [https://github.com/hakimel/reveal.js/tree/master/css/theme](https://github.com/hakimel/reveal.js/tree/master/css/theme)"
   ]
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "title_cell": "Table of Contents",
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