{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8bda6fb4",
   "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": "e34d5156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[26.59947279, 98.03821766, 58.23609027, 70.26947523, 59.31895406,\n",
       "        21.1227099 , 83.59051317,  2.12201407, 94.44876671, 11.69044867],\n",
       "       [97.39468064, 19.7389163 , 88.338811  ,  7.06922797, 56.30006951,\n",
       "        18.09139558, 12.88842663, 64.42585041, 94.85442285, 38.4760182 ],\n",
       "       [ 1.74988383, 91.59758352, 49.93034764, 66.56360849, 84.35349458,\n",
       "        88.72709499, 58.3334899 , 70.98011815, 28.87184062, 86.10161235]])"
      ]
     },
     "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": "e5177bba",
   "metadata": {},
   "source": [
    "This will also work with HTML outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09b37348",
   "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>26.599473</td>\n",
       "      <td>97.394681</td>\n",
       "      <td>1.749884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.038218</td>\n",
       "      <td>19.738916</td>\n",
       "      <td>91.597584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.236090</td>\n",
       "      <td>88.338811</td>\n",
       "      <td>49.930348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.269475</td>\n",
       "      <td>7.069228</td>\n",
       "      <td>66.563608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59.318954</td>\n",
       "      <td>56.300070</td>\n",
       "      <td>84.353495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           a          b          c\n",
       "0  26.599473  97.394681   1.749884\n",
       "1  98.038218  19.738916  91.597584\n",
       "2  58.236090  88.338811  49.930348\n",
       "3  70.269475   7.069228  66.563608\n",
       "4  59.318954  56.300070  84.353495"
      ]
     },
     "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": "2fbb5663",
   "metadata": {},
   "source": [
    "as well as math outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e34c281",
   "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": "5aa1bf67",
   "metadata": {},
   "source": [
    "This works for error messages as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae8bac80",
   "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": "ff842138",
   "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": "efbd525e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fefa0aadbe0>"
      ]
     },
     "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": "a44cb4bb",
   "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": "6b0e4698",
   "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",
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