{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example Jupyter Notebook\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\n",
    "            **{jupyter-download:notebook}`basic`** and {download}`basic.md`\n",
    "\n",
    "## Markdown\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",
    "![](../_static/logo.png)\n",
    "\n",
    "because MyST-NB is using the MyST-markdown parser, you can include rich markdown with Sphinx\n",
    "in your notebook. For example, here's a note block:\n",
    "\n",
    "`````{note}\n",
    "Wow, a note! It was generated with this code:\n",
    "\n",
    "````\n",
    "```{note}\n",
    "Wow, a note!\n",
    "```\n",
    "````\n",
    "`````\n",
    "\n",
    "Equations work as expected:\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",
    "And some MyST-specific features like **equation numbering** can be used in notebooks:\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](https://myst-parser.readthedocs.io/en/latest/using/syntax.html#roles-an-in-line-extension-point).\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,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 7.65856217, 77.50683455, 76.6292053 , 75.50042516,  9.53204748,\n",
       "        20.57801603, 78.64515099, 71.24094754, 35.09063661, 78.64697143],\n",
       "       [81.67000198, 15.85152669, 35.30333755,  5.43947639, 44.94160698,\n",
       "        90.28811921, 82.06552136, 47.91661398, 19.41788208,  2.98159657],\n",
       "       [44.36282524, 34.64668099, 22.23518987, 49.44878042, 66.11661212,\n",
       "        15.05490797, 71.07759266,  8.03645309, 63.13802588, 73.9903403 ]])"
      ]
     },
     "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",
   "metadata": {},
   "source": [
    "This will also work with HTML outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>7.658562</td>\n",
       "      <td>81.670002</td>\n",
       "      <td>44.362825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77.506835</td>\n",
       "      <td>15.851527</td>\n",
       "      <td>34.646681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.629205</td>\n",
       "      <td>35.303338</td>\n",
       "      <td>22.235190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.500425</td>\n",
       "      <td>5.439476</td>\n",
       "      <td>49.448780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.532047</td>\n",
       "      <td>44.941607</td>\n",
       "      <td>66.116612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           a          b          c\n",
       "0   7.658562  81.670002  44.362825\n",
       "1  77.506835  15.851527  34.646681\n",
       "2  76.629205  35.303338  22.235190\n",
       "3  75.500425   5.439476  49.448780\n",
       "4   9.532047  44.941607  66.116612"
      ]
     },
     "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",
   "metadata": {},
   "source": [
    "as well as math outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\sum_{i=0}^n i^2 = \f",
       "rac{(n^2+n)(2n+1)}{6}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Math\n",
    "Math(\"\\sum_{i=0}^n i^2 = \\frac{(n^2+n)(2n+1)}{6}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works for error messages as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f89d9970160>"
      ]
     },
     "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": {
      "filenames": {
       "image/png": "/home/docs/checkouts/readthedocs.org/user_builds/myst-nb/checkouts/v0.8.5/docs/_build/jupyter_execute/examples/basic_9_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(*data, c=data[2])"
   ]
  }
 ],
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