{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2902c90",
   "metadata": {},
   "source": [
    "(authoring/jupyter-notebooks)=\n",
    "# Jupyter Notebooks\n",
    "\n",
    "This notebook is a demonstration of directly-parsing Jupyter Notebooks into\n",
    "Sphinx using the MyST parser.[^download]\n",
    "\n",
    "[^download]: This notebook can be downloaded as **{nb-download}`jupyter-notebooks.ipynb`** and {download}`jupyter-notebooks.md`\n",
    "\n",
    "## Markdown\n",
    "\n",
    ":::{seealso}\n",
    "For more information about what you can write with MyST Markdown, see the\n",
    "[MyST Parser syntax guide](myst:syntax/syntax).\n",
    ":::\n",
    "\n",
    "### Configuration\n",
    "\n",
    "The MyST-NB parser derives from [the base MyST-Parser](myst:intro/get-started), and so all the same configuration options are available.\n",
    "See the [MyST configuration options](myst:sphinx/config-options) for the full set of options, and [MyST syntax guide](myst:syntax/core) for all the syntax options.\n",
    "\n",
    "To build documentation from this notebook, the following options are set:\n",
    "\n",
    "```python\n",
    "myst_enable_extensions = [\n",
    "    \"amsmath\",\n",
    "    \"colon_fence\",\n",
    "    \"deflist\",\n",
    "    \"dollarmath\",\n",
    "    \"html_image\",\n",
    "]\n",
    "myst_url_schemes = (\"http\", \"https\", \"mailto\")\n",
    "```\n",
    "\n",
    ":::{note}\n",
    "Loading the `myst_nb` extension also activates the [`myst_parser`](myst:index) extension, for enabling the MyST flavour of Markdown.\n",
    "It is not required to add this explicitly in the list of `extensions`.\n",
    ":::\n",
    "\n",
    "### Syntax\n",
    "\n",
    "As you can see, markdown is parsed as expected. Embedding images should work as expected.\n",
    "For example, here's the MyST-NB logo:\n",
    "\n",
    "```md\n",
    "![myst-nb logo](../_static/logo-wide.svg)\n",
    "```\n",
    "\n",
    "![myst-nb logo](../_static/logo-wide.svg)\n",
    "\n",
    "By adding `\"html_image\"` to the `myst_enable_extensions` list in the sphinx configuration ([see here](myst:syntax/images)), you can even add HTML `img` tags with attributes:\n",
    "\n",
    "```html\n",
    "<img src=\"../_static/logo-wide.svg\" alt=\"logo\" width=\"200px\" class=\"shadow mb-2\">\n",
    "```\n",
    "\n",
    "<img src=\"../_static/logo-wide.svg\" alt=\"logo\" width=\"200px\"  class=\"shadow mb-2\">\n",
    "\n",
    "Because MyST-NB is using the MyST-markdown parser, you can include rich markdown with Sphinx in your notebook.\n",
    "For example, here's a note admonition block:\n",
    "\n",
    ":::::{note}\n",
    "**Wow**, a note!\n",
    "It was generated with this code ([as explained here](myst:syntax/admonitions)):\n",
    "\n",
    "````md\n",
    ":::{note}\n",
    "**Wow**, a note!\n",
    ":::\n",
    "````\n",
    "\n",
    ":::::\n",
    "\n",
    "If you wish to use \"bare\" LaTeX equations, then you should add `\"amsmath\"` to the `myst_enable_extensions` list in the sphinx configuration.\n",
    "This is [explained here](myst:syntax/amsmath), and works as such:\n",
    "\n",
    "```latex\n",
    "\\begin{equation}\n",
    "\\frac {\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y} = - \\, \\frac{\\partial w}{\\partial z}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{align*}\n",
    "2x - 5y &=  8 \\\\\n",
    "3x + 9y &=  -12\n",
    "\\end{align*}\n",
    "```\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac {\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y} = - \\, \\frac{\\partial w}{\\partial z}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{align*}\n",
    "2x - 5y &=  8 \\\\\n",
    "3x + 9y &=  -12\n",
    "\\end{align*}\n",
    "\n",
    "Also you can use features like **equation numbering** and referencing in the notebooks:\n",
    "\n",
    "```md\n",
    "$$e^{i\\pi} + 1 = 0$$ (euler)\n",
    "```\n",
    "\n",
    "$$e^{i\\pi} + 1 = 0$$ (euler)\n",
    "\n",
    "Euler's identity, equation {math:numref}`euler`, was elected one of the\n",
    "most beautiful mathematical formulas.\n",
    "\n",
    "You can see the syntax used for this example [here in the MyST documentation](myst:syntax/math).\n",
    "\n",
    "## Code cells and outputs\n",
    "\n",
    "You can run cells, and the cell outputs will be captured and inserted into\n",
    "the resulting Sphinx site.\n",
    "\n",
    "### `__repr__` and HTML outputs\n",
    "\n",
    "For example, here's some simple Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "574169c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.59086323, 79.83624738, 61.49789431, 21.56557308,  5.33604509,\n",
       "        28.8372061 , 38.78924803, 46.09940332, 13.98756901, 31.83678898],\n",
       "       [ 4.41928439, 16.44770346, 66.76097525, 82.32408083, 44.4966367 ,\n",
       "        98.48406574, 94.04469258, 32.29938187, 20.74176917, 79.04972576],\n",
       "       [43.45002701, 89.16815436, 15.02240151, 73.63002647,  8.80796986,\n",
       "        45.56375942,  3.32037668, 63.74157117, 52.56561704, 94.42854793]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "data = np.random.rand(3, 100) * 100\n",
    "data[:, :10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a21d9a3",
   "metadata": {},
   "source": [
    "This will also work with HTML outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1dfe6e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.590863</td>\n",
       "      <td>4.419284</td>\n",
       "      <td>43.450027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.836247</td>\n",
       "      <td>16.447703</td>\n",
       "      <td>89.168154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.497894</td>\n",
       "      <td>66.760975</td>\n",
       "      <td>15.022402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.565573</td>\n",
       "      <td>82.324081</td>\n",
       "      <td>73.630026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.336045</td>\n",
       "      <td>44.496637</td>\n",
       "      <td>8.807970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           a          b          c\n",
       "0   3.590863   4.419284  43.450027\n",
       "1  79.836247  16.447703  89.168154\n",
       "2  61.497894  66.760975  15.022402\n",
       "3  21.565573  82.324081  73.630026\n",
       "4   5.336045  44.496637   8.807970"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(data.T, columns=['a', 'b', 'c'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17fa236",
   "metadata": {},
   "source": [
    "as well as math outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f963e79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\sum_{i=0}^n i^2 = \\frac{(n^2+n)(2n+1)}{6}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Math\n",
    "Math(r\"\\sum_{i=0}^n i^2 = \\frac{(n^2+n)(2n+1)}{6}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e26382",
   "metadata": {},
   "source": [
    "This works for error messages as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "402589c8",
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This will be properly printed...\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'thiswont' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-8f5b3a77886c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This will be properly printed...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthiswont\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'thiswont' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"This will be properly printed...\")\n",
    "print(thiswont)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24aa28cf",
   "metadata": {},
   "source": [
    "### Images\n",
    "\n",
    "Images that are generated from your code (e.g., with Matplotlib) will also\n",
    "be embedded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f54474f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fdcdf6e0f40>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(*data, c=data[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c19219b9",
   "metadata": {},
   "source": [
    "## Raw cells\n",
    "\n",
    "The [raw cell type](https://nbformat.readthedocs.io/en/latest/format_description.html#raw-nbconvert-cells) can be used to specifically render the content as a specific [MIME media type](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/MIME_types).\n",
    "\n",
    "````markdown\n",
    "```{raw-cell}\n",
    ":format: text/html\n",
    "\n",
    "<p>My cat is <strong>very</strong> grumpy.</p>\n",
    "```\n",
    "````"
   ]
  },
  {
   "cell_type": "raw",
   "id": "dd1cb8d5",
   "metadata": {
    "format": "text/html"
   },
   "source": [
    "<p>My cat is <strong>very</strong> grumpy.</p>"
   ]
  }
 ],
 "metadata": {
  "file_format": "mystnb",
  "kernelspec": {
   "display_name": "python3",
   "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": "3.8.6"
  },
  "source_map": [
   5,
   124,
   129,
   133,
   137,
   141,
   144,
   148,
   153,
   160,
   163,
   177
  ]
 },
 "nbformat": 4,
 "nbformat_minor": 5
}