{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86d5467d",
   "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": "35541ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17.55023754, 60.71836065, 83.63124212, 94.57853266, 11.55365737,\n",
       "        14.40606359, 54.95556313, 26.55211275, 54.00923226, 32.3895385 ],\n",
       "       [51.91167592, 84.94591374, 59.84076814, 89.32355344, 65.20479655,\n",
       "        70.07585952, 96.59882797, 37.57030003, 22.16282137, 26.67618965],\n",
       "       [68.47662715, 42.10190426, 37.98816813, 35.55986652, 99.09055172,\n",
       "        66.03512501, 99.73095719, 97.55038414, 53.27250829,  3.68223054]])"
      ]
     },
     "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": "1ff7621b",
   "metadata": {},
   "source": [
    "This will also work with HTML outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26e8a983",
   "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>17.550238</td>\n",
       "      <td>51.911676</td>\n",
       "      <td>68.476627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.718361</td>\n",
       "      <td>84.945914</td>\n",
       "      <td>42.101904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.631242</td>\n",
       "      <td>59.840768</td>\n",
       "      <td>37.988168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.578533</td>\n",
       "      <td>89.323553</td>\n",
       "      <td>35.559867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.553657</td>\n",
       "      <td>65.204797</td>\n",
       "      <td>99.090552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           a          b          c\n",
       "0  17.550238  51.911676  68.476627\n",
       "1  60.718361  84.945914  42.101904\n",
       "2  83.631242  59.840768  37.988168\n",
       "3  94.578533  89.323553  35.559867\n",
       "4  11.553657  65.204797  99.090552"
      ]
     },
     "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": "c3e25dde",
   "metadata": {},
   "source": [
    "as well as math outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd466d8b",
   "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": "cdc9fae6",
   "metadata": {},
   "source": [
    "This works for error messages as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "487c2d18",
   "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": "63b76cde",
   "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": "36a4891a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc4251c3be0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OYNaRB7dQLREMT7uADbUv0N4EtCStsdaLJpwMSLsxBpH1XL1yQLWvM7QkBqWez5isWxiefnXCJXaAiwdNw623/ilkUFI2Bd741vHpTr9+fT67A0GCTdNVbSkJmGH+Nm8hFfXtz4hKNMmOAoaknIUuIi8KEtgkCz+G0NCEm2GZfyTZOS7GUfYsKrkrPdKZBYcy0JuJI8JmF27dwd0TzolDVLFhWhZfbdmBHaGZqgvBR+s2xyGq+JuScwcj0i5GFy4M4UFgoAsnAkjVJDkOL3nJF3NI/zfJST473uHGneqWUXokl+7giSNmc/+6d3lz53IEgrC0GJs6gJ+MPYOx6d1bPjmepKTV7gcJEZN+XyCExiHZP2BMxlXsbPiEoFWF28hiQNJxODS1FeGBVHJXeiyv4eJn48/h1jGnUx7YTbLDndD97Hs4DZ1DB/VnybbiFvfZUnLsyL5d8dOpJzMk9Yx4h9HjqW4Zpcdz6w4GJmX1icS+x/+deQJJTgeGtu9P1OMw+N6MKfRLU5uuKO1TLXdF6YFG9svmtR9cyeMLl7BoaxG5KUl898jJfb7VrnScSu6K0kMNyEjjF2fNjHcYSi+lumUURVESkEruPVggYLJ8xXZWri7CNKNfhtdf37KMgaIoiaFLyV0I8WMhxGohxCohxFwhhFsIMUQI8ZUQYqMQ4nkhRPxLDPZC5eW7+e7Vj/Dz//sfP/3ZC1x/05PUN7Tcab2zPnx2IWenzeK1f74btXMqitJzdDq5CyEGAD8Epkopx9NYoecS4E/AfVLK4UA1cE00Au1r/nLfu1RW1ePzhfD7TYp2VvPIYx9H7fxOjxMhBC6v2lKvp2gIrqS8/kUq6l8hGC6JdzhKL9fVAVUD8AghTMALlAAzgcua7p8D/Ar4dxev0+ds216Jbe9brGKaFps2l0ft/EeddzjvmXP7dGGlnqIuuIzNFbcTskoAAQikNEl1H8mw7Htx6D2jUqfSu3Q6uUspdwoh7gW2A37gfWAJUCOl3FO6rQiIuJRQCDEbmA0waNCgzoaRsAoHZVFRUbc3wTscOsOH5Ub1GvFM7L6gydMLlvDWknUYmsZ508ZxyYxJOIz47wAVS3XBpawrvQJb+lvcVxv4jFUl5zCh/1sYelocolN6s650y2QA5wBDgHwgCTi1o4+XUj4spZwqpZyak5PT2TAS1u23nkZ2VgperxOPx8HAgkyuu/rYeIe1V319gG3bKggEWt+wuzVm2GLWg8/z6LxFbC2rZuOuSh58+3NufPiVqO4729NJKdlUcWvExN7IxLTK2Vn7j5jGpSSGrnTLnAhskVKWAwghXgZmAOlCCKOp9V4A7Ox6mH1PTnYKTz1+Ld9uKEXXBSNH9MPoIa3aD+at4q9/eRtd1xAC7vnjRUyc2PFPX/NWbGB7Rc3eiocAATPMyu27WLRxB9NG9JxPclJKAnYIp+ZAb2ff3IPVEFqOabXd1SYJUVY/l4EZdxxUrX5F6Upy3w5MF0J4aeyWOQFYDHwEfAd4DpgFvNbVIPsql8vBhPEF8Q6jmcrKev5279uEQvs2Tbj7Zy/yyms/Rtc7lvy+WL8Nf6hliz8QMlm6eWePSO5+K8izW+fz6s7P8VtBBIJjciZw9dDTGODNjso1fKG1SDryScXGtMpwdWB/XEXZo9NNESnlV8BLwFJgZdO5HgZ+CtwqhNgIZAGPRSFOpYcoKa5u8QkiFAxTV9da10JLOalJGBHeCFwOg4zk+Ff381tBblr8IC/s+IT6sB9L2oSlxcdl3zD767+xpX5XdC4kDDoy6iGxm7aMU5SO69LnTCnlL6WUo6WU46WUV0opg1LKzVLKw6WUw6WUF0opozc5W4m7/PwMwuHmC6pcLoOUFE+Hz3HetPHoWstfPSEEp04a2eUYu+r5bR+z01dOyG6+pZuNxG8F+f2aZ6NynVTXdKS02z3O0DJw6NEdTFc6L2zb+MNmjx8fUitUlYOSmZXMT356Jk6ngcfjICnJxT1/uKjDXTIAA7PT+ePlp+J1OUhyO0lyOUnzuvnHteeQntTxN4nu8urOzwjJyHt1SmCHr4ztDaVdvo7bMYhk1yTa2sRZEx7yU69XU1Z7gKK6Wm784DVGP3kf4+Y8wIznH+aF9St7bJIXPSGwqVOnysWLF8c7DOUg+HxBKivryc1NxeXq3EBf0AyzfGsxuqZxSGF/HHr8B4wt2+Kkj+9osyfcIWzO6O/hO4MuJd8zvkuJNxQuZdWuszGtaqD5OIQmPKS4pjEq91GEiP/Ppi8raajj1JefZHcwiL3fb4fHMJg94TBunXJUXOISQiyRUk6NdJ9quSud4vW6GDgwq9OJHRr72KeNGMTUYQU9IrEDaELDobX9nGwkVaFVvLrjLuaV/KVDXSutcRp5TOj/FrnJF6MJD5pIQhNuHHoOBWm3qcTeQzyw9HPqQs0TO4A/HOahFYuo8Pe8fW3VKI2i7EcIwYl5k3mv5GssIidth7DIcPgIS9hQ9wmZrsFMzbq409d06NkMyfotgzPvJhQuRggHTj0fEeWpl0rnvbl5PVYrvRy60Phg+yYuGTUxxlG1Tf32KMoBZg05Ca/hQkSYy6Jjc0j6Tvb0xIRlkCWVz2PLrlft1IQLt2MILqNAJfYexrRbf30tKQlakcdo4kn9BinKAXLdGfx76o8YlzYYXUgMEUYXFh49xGGZW+nv3t3seAuT8sDGOEWrxMLkvPxW7xPAtH4DYxdMB6luGUWJYIA3mwen3Mx9ay+hMhTAoVmkGX4ijZ0KNMIyFPsglZi5dfIMlpYWEzighe7UdCbn5jM6s+eVUFEtd6VHazBDlPnqsezOD1p2xaCkAnJc9aQ7Iid2AEuapDvV6tFEdli/Au4/7nRSnS6SHU6SHU5cus5RAwbzyEnnxTu8iPpcy11KSTi8DtsqR9MyMBxdm8qmdI8ttVX86qv5fF6yDU1ouHWD68YfxvcnTkeL4es1OfNCSv3rMGXkXasEggLvISQZmTGLSYmP04aM4sTBw/myeAd1ZpAJ2f0YmNJzq3X2qeTu871O3e4/YNvlND51CyFSSEm9Ha/30m5N8lXBeipD9WS7UshwJnXbdRJBUX0t57z5NHWhYNPEM5ugFeaf33zB1t3V3Hv06TGLpTDpMAqSDmVHw1LCByy2FgicWhLH590cs3iU+HJoOkcXFMY7jA7pM8m9vu7f1NXdizygvKqUDeyu/QXh8CbS0n4R9etub6jg96tf45vqbTiEgSktJmcUctf4cyjwqtZeJH9f/jkNZqjFQiK/FeaNLev4/sTpDE2Lzc9OCI0zB/yKL8qf4Jvq15oaAAJLmvT3jOWEfreS5mx9sO1gSSnVJ0klKvrECtWwuZGyspOB1jeEFsJDVtZzOF2HRe26Rb4qrvjsn9SHg82q/wkEKQ43z874Af096VG7XqKY8N8HqDMjlyRyaBq3Hno0N06cFuOoIGyH2BVYiyVNMp2DSHFEr97L599u40+vf8Km0kqS3U4uO3ISN548vccs7lJ6pj6/QrW+/lGg7XmoUgaor4/uboAPrn+PhgMSO4BE0mAGeGjDB1G9XqJoa05x2LYJxWlOsaE5KfAewuCkqVFN7F9vKuKHT77OptJKAOoDIZ76dCl3P/9+1K7RnRa9v5J7rn6Yz95cFu9QlP30ieQeDH5Ee8kdJMHg51G7pmmH+aR0bYvlyntYSN4rWYHdhaXriWpKbuszTzyGg2n9e96c4q544J2FBMzmv58BM8y8lRvYVVMXp6g6pnJXDfdc8zAL31jKn298nJKt0dvnV+mahOtz37mziq1bKsgfkMGQIY1zTyUdXT0YvUTrt9rffs6ybUzbwnUQFRX7glsPPYolZTtbzCl2aBqFqRlMy4tOci+qreW55SvZWl3D2LwcLp44gayk2NeT37CrIuLtTkNnw64K+qWnxDiijjOD4WZVEUOd2HZR6R4Jldzfe3cFD9z/HrquY1kWl19xJJdfMQOHYzxBqwTa2fXGMEZELZZkw4VTNzDDrb+xJDvcOLWEegmiYkreAO4/9kzuWPgOdlPiCNs2E7P78Z+Z50VlwPHtdeu54+33m95gbT7avJmHvvyaJy48jykFsZ2znpuWzJay6ha3hy2b/IzUmMZysPoNzuaa/zufd55eyAkXTWPw6OgNLitdkzADqoGAyXnn3EcotC+ZOp0GTz41m/T0tVRVXoGUvlYfL4SX9Iy/4/FEb5rdA+ve5bltX7TY9AHApRnMGnoM1484IWrXSzSmbfF5yXZqgwHGZuYyPD0rKuet9vs5+t+PEgi3fF0yPG6+uOl6jAibiXSXN5as5df/+6BZ14yha4wbkMszN18asziU3qdPDKjW1vpatOgcDp2Kijqczmm4XMcCrW0E4cbhmIjbfXJUY5o9fCaFSdm4Digh69YcDE/J46qhx0T1eonGoekcO2AIZw8dE7XEDvD2um9bvS9k2XyxbUfUrtURZ00Zw40nTcfjNEhyOXEZOlOGDOCfV58b0ziUxJIwfQLZ2Sl4PE6CwX2tHyklgwZlIYQgI/MhamvvxtfwAkJoTfPd3YDE7TmNjIx7EaJzPw5p14FdDsINWv+9bzIew8kTR9zA/7Yv4rltX1AdqifLmcIlhUdw/sDDcOlqN/t4qPb7CUZotQMgJdX+ju8HGy3XHH8Yl82YxNbyajKTveSlJcc8BiWxJExy13WNP997KT+78wWqqupJSnLxm99esHdvTyEcpKf/idSUn+IPvIVtlaFpGbg9Z6J3cn9KGd6AXXc/MvgRCAfIMOg5CO+NaN4LEULDrTu4fMgMLh8yI5pPV+mCQm8awgIZYQq5JSXj8+KzX6nH6WDMALVXqhIdCZPcAYYNy+X5F39AMGjidBoRB940PZOkpCu7fC0ZWoxVfTXIAGDDnqqAVhGy7nfYoc/Q0u9Xdbl7oM+e/QbdbWN7BWj7fkecus5hBQMYmqVWDiu9X0JmHpfL0a1LuKX0Y1VfC9JH5OmTfmTwI2zf3G6LQem8rVsq6PdFEHeVjbAkmikxhOCE4UP557lnxTs8RYmKhGq5x4r0vwHtLj7yIxseQnovU7VCephhw3Kp+rqefl+FCLsFWqrBL28/g2OnjYp3aIoSNQnZcu9utv81oPVplfsOrAFrW3eHoxyk2+44nYIBmTidBu6w4LunTVOJXUk4quXeGbKDO50LvanrRukO4XAxtQ1z8PnfRkoTp2M0aSnX43ZOb/PTUkZmMo88eR1VlfV4vE6SklwxjFpRYkMl904QRiEyvJr2VrwiQ6DnxSSmvqau4WUqam5rWvreWEEybG3HH/wUj+tI8rIeQwhnq4/XNEF2Ts9d1q8oXaWSeydo3iuxgh+23yp3Ho7Qorf4Jl4qgsV8UfE2Rb5vcWhuJmUcw8T0o3Fq8Wnx+oNfUFFzO7LF7kgSKX34Awspr76d3My/xyU+RWmPbds89c/5fDpvNQMGZ/PjX51LRlZ01zYkTPmBWJJSYlVdCuYKoLWNkd3oWXMRjgmxDC3qPix9nk/KXsGWFnZTATan5sYQDq4d9lvy3INiHlNx2bkEQl+1c5SLQf2/xND7xSQmRdlja8M2FlV9jd8KUJg0mOmZh+PSmzeEXpqzkKf/9SHBgImuawwd1Y8H59540Ndqq/yAarl3ghACPePRxnnu4XUg/ezrovGAAC39H70+sS+vXsCCslcJy+ZvYCE7QIgAj276BbePfgiX3lpZh+izrAoCoY7VDa/3vUp6yg3dHJGiNPJbfu5b/3e2+LZi2iYSiUtz8cy2uVw/7FqmZEzee+zyrzYTbKqgaVk2m9aXRD0eNVumk4SWjJ75PHrGIwjXSaCPAGMiIuVW9JzP0FzHxjvELpFS8kHpXEwZeUckgLBt8k3NghhGBZZd2WZf+j5Bwtaubo9HUfb42/oH2NSwmZAd2rtBT9AOErSDPLTpYTbWbdx77LhDB+FyN5Yf0TTBoKHRX5msWu5dIIQA5zR0Z+y3fOtuNWY5dWbLMrT7C8kAy6o/4fCsU2IUFWhaOsiO1Ax3YOg53R2O0klSSvyWiVt3oCXAOpBN9ZvZ6ttGWEauWRSyTV4qeoU7x/wEgIu+dzTVFfV8/uFa8gdl8pPffyfqMankrkRk2iE0obU7Ici0W2/ZdwdDz8PhGEXIXNHmcQKNJM/ZMYpK6Sh/2OSBlQt5ZsMy/OEQTs3gwmETue2QY0h1ujt8nqAZZvGmIizbZuqwAryujnya6z5fVS7CtNtudKyv+5aQbeLUHOiGzvfvOpPv33Vmt8XUpeQuhEgHHgXG05gGrgbWA88DhcBW4CIpZdtNQKXHSXdmt7sFoIZOvmdojCLaJzP1Dkqrrmuq7NmSwIXHfRwOI7G24+vtQpbFJfOeYX1NGcGmfXL9lslzG5fzackW3jjteyQ52k/Sn63bym1PvQUCBI2bmvzywhM5c8qYbn4GrfNZ/hZ7JR9IExqmHcKpxaYabFf73B8A3pVSjgYOAdYCdwLzpZQjgPlN3yu9jFNzMyF9BhoRSic20YXOkdlnxDCqRl7PCWSm3oUQHg5snwiRhNM5jtzMf8U8rkiklNQHPmNr+VWsK57O+pJjKKn5PWa4ON6hxdxb29eysbZ8b2LfI2RblPh2M3dD+wPlFXUN/OjJN2gIhmgIhKgPhAiYYX794gdsbtpgPB4Gewfh1Np+Y3JqTjwxnHzQ6eQuhEgDjgEeA5BShqSUNcA5wJymw+YA53YtRCVeTuv/XZKNtIgJ3iFcTMs6jX6ewtgHBqSlXMeA3LdJ9n4HTctGE+m4HJPJzXyA/JzX0LTY74V6IFuG2Foxi20V36MuMB/TKiIU3kxl3aOs33UMNQ2vxzvEmHp6/Uf4rMh90gErzNyN7Sf3d5atJ9L0bdOyeOnLlV2OsbOOzD4iYlx7OISDE/JmNnZ1xkhXumWGAOXAE0KIQ4AlwC1AnpRyz7yeXUDEJZpCiNnAbIBBg2I/VzrR1ZtBttZVkeRwUZic0aniZUlGGjeNvJd3ip9kVe2X6ELHljYePYmZeRczNfPEboi845yO0eRm3hfXGNpSXHUnDYHPkTRfbCUJgYSi6ltxGgPwuqbEKcLOk1KyrrqcMn89Q1MzGZiS3ubxwdAKqv3bgdbLKdcGI28Uvr/qej/BCPsSW7akfHcHy4J0gyTDy9VDZvHE1qcI2c2nDjuEgzx3Lmf1j94Wnh3RleRuAJOBm6WUXwkhHuCALhgppRRCRHw7k1I+DDwMjYuYuhCHsp+gFea3S9/n5W0rcQiNsLTp50nld1NP44i8woM+X7KRzoWDfsRZlo/qUCmGcJLtyleVLtthWuXU+F5D0vqAs5QBSmv/xpDcZ2IYWdetqizl5gWvsctXjyE0QrbFIdn9+Mcx55DrjbzKsrr2T4xNFWz3pWNH6DAQ2IxILiIc3olhtL5B+dRhBTzz6TJ8oeaDlx6ng6NGF3bpeXXVkdlHkOHM4KWil9nSsBUNDUMzOCH3eM7OP7PFQqbu1pXkXgQUSSn3LBV8icbkXiqE6C+lLBFC9AfKuhqk0jFSSq5e8BzLKnYStMN708rW+iqu/fR5njz2Ug7L6dynJLfupb9nSPSCTXC1vjdpHO5raVXRQN7+ZjJV9SmMK9jJj04pJT+jd9QgKqqv5eL3nqXBbN46XVK2k/Pf+S/zz70Wl948rdj2bgLBhVxYkMz8spEE7ZbJ3alZXDZoGfW+l0hPvaXV608fMYixA/NYtX3X3g3FnYZOfkYqp06Kf2XPMamj+cXYn+G3/Ji2SbKRHNOumP11+qpSyl3ADiHEnp/oCcAa4HVgVtNts4DXuhSh0mGLK3awoqqYoN2yXzNghfndsnlxiKp3k9JmZ8NCPii6kZc2n8TLW07jy9J7qA1tbvNxll3VojsG4M1lU/jLW+exdOswtlbk8e6KiZz/wItsq+gdE8oeXrWIUIT9Zy0pqQ74eXdby83HLascIRwUJlXxk5Ef4NRMnKKx5e0QYZxamOuHLmRC2nbC4bZLZGua4D+zz+OmU49gaG4mg7LTuXbmYTzzw0twOXrOzG6P7iHVkRq3xA5dn+d+M/CMaFwyuBn4Ho1vGC8IIa4BtgEXdfEaSge9vm01/nDrc23X15ZTHfSR4Yr/YGNvYEuLhbvuYpdvEeH9pl1uqXuLbfXvMTXnDoalRp6nbGjZCOFuVtysPuDm+a+OwrT2/dlZtkF90OQvby3gH7PO6b4nEyUfFG3EbGWKbEM4xDvb1nPO0LHNbte0NGTTwrMT8jYwOaOId3eNYbsvg3zPbk7tt5YcVz2go+ntF9pzGgZXHTeVq46LWFJFadKl5C6lXA5E+gmf0JXzKp3jC4fanGmrC0GwldkKSksrqx6hxPcVljxwQNTCkhaLy/9MunMYWe6W86vTvGdQUvObZret3jkQQ7OaJXcAKeGzDb1jUxetla6mvfdHGIvR9WycjpGEzFUAZDj9XDpoaYvjhHCS7Dk3KnEqqrZMQjkybwheo/W5tl7DSa5H1TBvTU11AyXFNdi2xJIh1tc83yKx78+SIdZUz4l4n6Fnk550AULsW3Xp0Ft/YzX03vGneHrhKBxa5LUPXsPBmYWjI96XnvqTpnUJrXHgcIzF6RwXhSgVUMk9oZwxcAxuPfKHMY/u4PtjZkSljsfmTWXMeXwBL8z9ktra5jXtpZRsKq9kdXEpptVyylpPJKXkX/e/z2XnP8h1V/6H6696hC1lS2ltQHS/R7LT91mr9w7IuIcU93EI4QUE4wu2R/xk5dA1Tj8kclLsaa4ZOxWvYbT4yTg0nf7eFE4aNCLi47yek0lL+WFTgm/+aIEHQy8gL+vJbom5r1L13BPMhtpyrvz4GXxhk4ZwCIemoaFx8dBJ/N/kk7s8hXH1yiLuuO1ZQkETw9BJTfPwyJOzSU31sLq4lB8//xYV9Q1oQqBpGneddiznHdqzW2OLvtjIb3/xMoGmEqyGQ+Owo3OYMGsupqxv87ECjUuHf9Hq/VJKfKGlVNT9h4C5jmVbB/Dntw5HSo2QZeN1OshLS+aZGy8hzdvx2irxtKm2klsXvsW66nKcmk7IDnNM/hD+MuN00l1tr8AMBBdTW/dPAsFPkNLE0AeQmnwDyUkX9YiFZ72Nqufeh4xIy2HhWT9kfvG3rKoqIdnh5oxBYyhISo/K+Z949OO9dahN06Jud4AP563i6FPGcdUTL1EfbD5F7jdvfkheajJHDhsclet3h6IdVVjWvkHCsGlTvDXEONqvPuk12i7VKoQgyTWFJNfDAIzqDyeMq+e1pWspra1j6pACZo4bhkNvvcxDTzMsLYvXzvguRfW1lPsbGJSSTpa7Y4nZ7ZqK2/VEN0eogEruCcnQNE4pGM0pBdH/qB8INk94tm0TClm8tGRlxG6YgBnmXx992aOT+7DheWi6BmZj/A6nzrjxg0l3DqcyuLrVx+nCxej0yw76ejmpyVx73GGdjrenKEhOoyA5Ld5hKK1Qfe49iC1tttaX8m3dTgJWa9v3xde55x+Gy7Wvqp3h0Jlx9EjW7iqPuCwcYHMPn8N9yOTBXDHrKHRDQ9cFY8cXcMMPTuLw3DsxWhkE1HCQZOQz0D2cquqfUlZxMRVVN+APzEe2U01TUWJBtdx7iLeKF/HIpnfxW0EEAlvanJF/ODcMPwOXHpsSoR1x4snj0XXBm68txet1cdW1xzKgIJNhOZk4dZ1QhNZ7QUZqHCI9OJdceSQXXjqdkBnG42mcceRhJCcO+Defl/6KhvAuBILGhfJh+nsOZ4SjiKqqC5EyBE37ywYCH6BpGeRmv4hhFMbt+SiKGlDtAZ7d+hFPbplH4IBi/07NYEzqIO6ffD16HFe6dUTp7npOe+BJ/OYBNT8cBvdeeDozRw+LU2TRURVYR01oI5pwkOueQn3NDQRDiyFi7RgNTcukX+4n6HrrhbIUpavaGlDt2RmjD6gzfTy+5f0WiR0gZIf5tq6ILyvWxSGyg5OXmsxDV5xLptdDktNBssuJ2zC4eeaRvT6xA2S6RzM09UwKU05Bt7cQMpcRObED2Nh2HfUNkefAK0osqG6ZOPuobEWbq/78VoiXiz5jRs7YVo/pKQ4fUsCCO2bzzY4S/GaYSQP7kxTn7c+6Q33DY63uArVPkPqGx0lL/XFMYlKUA6nkHmfVoXqC7ey9WBHcHaNouk7XNCYPbr1kayIww5tod3NZwLYrkVKq8shKXKhumTjr787ArbfeuhVAgTc7dgEp7RIics3ylpwqsStxo5J7nB2TO6HNNqBLd3LhwKNjFo/SviTvd5pKCrRFw+NW9fOU+FHJPc7cupO7xlyEK8KO6G7NwfG5EzkkXW2S0ZN4PecBbU9PFcJFSsrNsQlIUSLo9cm9dGc1q77esndJfG90fN4h3DvpWg5JH4KGQBca/dwZ3DzybO4cc5H6aN/DaJqXnOxnECIJImweLoSHtNS7cTknxTw2RdmjV89z/3zeKv5861w0XSM9K4kHX/0RSSm9o/hSa0w7TFjauDWHSupdZNkNWHYDhp6GJqK/f2U4vI3ddQ/S4PsfjYuYbFyuo0lLuQWXa3rUr6coB0rYwmFz/vbe3ha7lJKvPlzDzHMmxzmqrnFoRjsf+JX21AYWsaPmfuqCixFCR0pJlvcUBqbfgscxNGrXMYzBZGbcS0b6n5CyHiE8NG5K1nFVwRrqLR9ZzgySjLYrKirKwejVyT0tMwmhCaQtQUJKuioZ2teV1j3PlupfYzdtsiFl4wYZFb63qPLPZ1zeU6S4otsAEEJHiIMroLW6dgOPb/kfO3wlGJpO2LaYmjmea4ZcSJYrParxKX1Tr+5zv/WPFzFwWC5uj5OTLzyMqcfEf/fz9hTX7GZ9aQWhVopsKZ3nNzc3S+zN2djSx5qyq7FlaytLY2NZ9Rp+s+afbG7YgSnD+K0gpgzzVeUKbvvmj1SFauMan5IYenXLvd/ATP7z9m3xDqNDtlfV8OMX32JjeSWGpiGE4JaZR3LltEPjHVrCKN79BLZse49YKS0qfe+SkxSfzagtafPAhjmEIixcs7GpMxv479bX+OHI78YhOiWR9OqWe2/hD5lc+tjze8viNoRM6oMh/vbBQt5csTbe4SWMav+HQNvJ3ZYNVPnei01AEayq/ZaQ1frMLhubhRVLIyb/7rR2Synf/8OLnHTjv7jut8+xYkNxTK+vRJ9K7lFgWdVU7v4X23adzpaSmRRX/gB/cPne+99Z/S3+kIl9wMwkvxnmgY9a36JNOThSdiwh2jJ+tfLLg1XIdkoXCAH1ZkOMIoLtJdXc8PsXWLJ2B7sbAqzYUMzNf3qJb7eVxSwGJfp6dbdMT1Dvn09x5WyAvcWkQuFvqfe/Q5J7JvlZ/2JNSRk+M3LiKapR/avR4nWOojZQ0eYxAhfJzokxiqilVCMZ0U75ZlvaeGM4c2buu0swzeafeIJmmDlvLOKeH5x50OcL2SG+qFzM2t3f4hAODsucxPi0MWg9vGx1olHJvQsCoRUUV86OUCHQRko/DYEP2VX1UwZlXoHbMAiEW3YZZHjV9LdoyU+9jrrgMmzpa/O4vJRLYxRRS4dmjGnzfgEcmj4Wtx79efmt2VFag2U3/zQhJRSV1Rz0udbu/pa/rP8HUkoCduPA9cKKL8lwpnP32FvJdGZEI2SlA/rMW2lRSTVPv/IVz772NWWVdVE5Z0XtvciIMzMaSemnzvcKp4/PRtNaLkhyOwyumj4lKrEokO4+ilTXNDQReSGbJjwUpN2EU8+JcWT7ODQHVxWeF7HcBIBLc3FlYWwHe484pBCXo3k7z2HoTJ9QeFDnKQ2U8ad1D+K3AnsTO0DADlIaKOfXq/+CJdUssVjpE8n9k6++5bu3zeGR5z7j4bkLufSHj7F8TVGXzmnb9fgCC+hI6VdNvsPDl59LqttFkstJktOBy9A5fdworpmhknu0CKExOvchcpIuQAgXmkhC4EIXyegimUHpP2Fg+g86fL7yQA0rajaxtaGEaK7kPrnfUVw39GKSDS8e3Y1Hd+PSnBR4+nHPhB8z0Ns/atfqiPNnHsKAvDQ8TXvjelwOcjOTufz0iAsfW/Vm8fuEWxkItrHZbdaxrHpll+NVOqZXlx/oCNuWnHnNv6ita951MnhAJs8+cHWnz2uGd7Bl1/HIdroAANKTryUv4zeYlsUXm7ezOxBk8sB88tN7/t6ivVXY3k2170PCcjcuvT/pnmPROrh6tNhfwV/XPce6um04hIElbTKdKdw88jtMzRwdtRgtabGmdhP1lo/+7hwKk+JXBz9khvnw6w2s3bKLEYNyOXHaSNzOg1srPXvxrdSF69s8ZnrWVG4ZMbsroSr7SdjyAx1RW+fH5285O2J7cVWXzqtpKR2cneHA0BrrsTt0nWNGqAqPsWBoqeQkn3vQjysLVPODJfdRH/YjkYSaplYWByr51arH+b9x3+PwrLb7zTtKFzoT0kdG5Vxd5XQYnHrkGE49svPPLdzOGgOAoBXfBWR9ScJ3y6Qmu3G7Wr6H9c89uOXiB9K1dDzO9hcgCTRSks5u9X4pJet2b+RfG+fw+zV/Z87WFyj2l3YpNqXz5mx5h4ZwIOJ0xaBtcv+3z0e1iyaRDPS2/cnDKRyMTOn9++n2Fgmf3HVd40dXn4DLaaAJga4LXE6D2687qcvnzkq7HSFan+0icOJxz8BpFEa8P2SF+N2a+/n92r+zoPxLvqldw3sln3DHN7/jmW0v98kkIqVk/rxVPP7Ixyz4eG1MfwaWtPmobBk2dqvH1IcDrK/bHrOYepOz80/FpbXe9SWBmblq45lYSfhuGYBTjx3L0EHZvL9gDYZD5/TjxjEoP7PL501yH0VO2v9RXvvrpi6afTMBhPDiNIYzIOuhVh//701Psb5uM+Z+3TsWFpa0eG/Xx/Rz53BCXt/6Y/jLH95gwcfrCARM3G4Hq1bu4Ps3nxyTawetUJuJHUAgqA5Fnm1lhi0MXeuzpZonp09kRvY0Pqv4iqC9rytUIHBoDm4YOotUR0ocI+xb+kRyBxg5JJeRQ3Kjft6MlFl43dOp3v0w9YH3kdLEYRSSmXojKZ7TESLyoFR1qJavq5ZjttJPGbRDvFT0FjNzj9qbLKrqfHywfAP1gRAzxhYyakD8pvR1h+qqej6avwbTbHyTDARMXnt5Cd+79jg8noMrpdsZbt2JU3Pgb6Nf2JI2ee7mDYMla7bzh0c/YGd5DalJbm686CjOnRm/hVLxIoTg2iFXMCFtDK/tfIcd/p1oaExMH8d5A85gWHLh3mNXVm/nqS0LWVu7E6/h5NyCqZxdMIVkR+/ej6En6XJyF0LowGJgp5TyTCHEEOA5IAtYAlwpZRzXe8eAyzGKfll/PajHrKxdiy70VpM7QEPYR2mwgn7uHD5dvYXbH38TCYQtm/+8+yVnHjaGuy8+IWFaiqGQ1WI9gBBghdtuTUeLJjRO7TeNN4o/I9zKfOxcVzpDk/P3fr+zrIbb7n2VQKjxdaytD3D/fz8mKz2Joyf3vf5lIQTTs6YyPav1aZRPbPqYRzd9TNAK7x3b+OeGefx362fMOeIGctxqFlk0RKPP/RZg/+pXfwLuk1IOB6qBa6JwjYRjSav9GiMILGkRCIW548m3CJhhgmYYy7YJmGHeWryOL9YlTv9vbl4qhUNycDgat65zOg0mTBxEcgx317qi8GSynKkYouX2eW7NyU/HXN7stjc/WU3Yav7mEwiFeeat7pna29utqtnBoxs/JmCZzX7/A5ZJRaCOn3/zQhyjSyxdSu5CiALgDODRpu8FMBN4qemQOcC5XblGohqWXNhuckcIcl1ZfL1hR8TWuT9k8saiNZ26vhVaga/6x9SXnU5D+XkE6x9D2vGtcyOE4C/3X86ppx/CmLEDOOPsQ/ntHy+KaQypjiT+NfU2Tup3GC7NgUtzYAidqRmjeGDyLYxKHdTs+DpfAMtq+cmi3qem/EXy3y0LCdqRP61a2Kyq2UGxrzrGUSWmrnbL3A/cAewZJckCaqTc29dQBEScHyWEmA3MBhg0aFCkQxLaIO8ACjz5bG3YEXEQzykcnJR7NA7N0eabQLtvEAceL4P4q28iHPwUZBCarm2F1xCs+zOejH/jcM88qHNGk9fr4pbbTovb9aExwd866mJ+MOICdpv1eHU3XiPyp4fjDxvJmwtWEwjuS1gup8HJXZgvnsjW7i5u83fWoelsqi8l36tq0HRVp1vuQogzgTIp5ZLOPF5K+bCUcqqUcmpOTmINDHbUbaOuJ9WRjPOAQVeX5qQweSAXD2qcH3/YiIHYdss/CI/TwVmHjT2oa/qrf0w4sACkH/Z/U5F+kH78VTcQDi076OfSk5RX1/PQCwu564E3ePXDFQRD7S+uicSpGWS70ltN7ACTxxTwnRMn4XToeD1O3E6DSaMGcMmpahOWSJKNtguiSQnJbfy8lY7rdPkBIcQfgCtp3B3BDaQCrwCnAP2klGEhxBHAr6SUp7R1ru4sP9DT1YcbmLdrAfPLPqUh7CfblckZ/U/gqOxpGNq+ft9PVm3mjifeahpQtXDoOqdNHc0vLzmxwwOqVngLDWUnA213GejOI0jKfr4Lzyp+tu+q5upfPEPQDGOGbdwugyH5WTzyy0swjJb96NGyq2I367aUMiAvnRGD+mZjpSNe2vYV9617h0ArNWjSHB7en3lXs999pXVtlR+ISm0ZIcRxwO1Ns2VeBP4npXxOCPEQsEJK+a+2Ht+Xk/vBqNzdwPvLN9AQCDFjTCFjBh7c1M7a6t+xteYlwCLfUY9La61Cn4vkvM/Q9OhPHe1udz/4Jh8u2tBsYxSP28EvZp/CzMN7xlL/vswfDnHxwgcpDdQQls27I92agzvHnc1ZBdHdwDyRxbq2zE+B54QQvwOWAY91wzX6pKzUJC49ZhK1Zi0Lyz9n4eYSUh2pzMg6goI2ln6bdogXdzzHZxUb0BkHgCUFU727ODttIw5xQJ+/cCKtEuiFyX31pl0td7wKmKzdXKqSew/gMZzMOeIGfrHiRZZUbcGp6dgSnJrOrWNO54wBqjsrWqKS3KWUHwMfN329GTg8GuftwHXZWFvJ7mCQ4elZpLkSv6/unZJ3+V/RqwCY0kRD44PSD5iQNoEbh83GcUCdcEta3PftX9jWsAVTgrnfS/61rx8lZhI3Zi9HF/snRAtEUgyeTfQNG5jNrordzYbsPG4HQwuy2nycbVfjb5hL0DcXW+5G0zJxe7+Hx3sBQuudP4ueKsOVxD8Ou4qyQC2b6spIMlyMSy9AVzs1RVWvXaG6aNcObl/4DuX+egyhEbItzhk6lt9MPwm30WufVps+r/iCl3e+1qxcgY1NyLZZWbOSx7c8yfXDrmv2mGXVi9nh29bsMXuE0SkOJ7PCn8Oh3n37ZQqRimb0zgU41184gyVrdhAyw1i2xOnQyUlPbrPVboaWUlt5WVMJicbNVyy7gobdv8VXfy/p2S9jGMNj9Az6jlx3GrnurhXwU1rXK98q11SVMWvei2yvq8EfDlNnhghaFq9vXssNH70a7/C6hZSSl4peJmRHXuwbkiaLqhZTHWo+R3he6XsE7dYHUEPS4JOGgftuEB6cyTf12lWvIwbl8ORvL+f0o8cxcWQ+3ztnGo//5jJczshv+Ja1i9rKS5Gyjj2JfR8f0q6ipuICpN1+3X5F6Ul6ZRP3r0s/jbgfacAK82XJdtZVlTM6M7FmLJQGStvdCEETGstrvuH43OP23lYZLG/33NXWnu4sD4brJJxJV3Yh0vgbnJ/Jz6/rWLExf8OTtF0dQyJtHwH/y3iSrohOgIoSA72y5b6weFuryyDCts3HOzfHNJ5YCNrBdvskbWk1q8YH4DXa7y/2iDCaMRx3+u/xZDyI6EN9nwHfXKC90kc+/A1zYhGOokRNr/wrbqvDQIi27++tclw5hO22Nxc2hIMCT/NZM8fkHI+zje3lHMLBcf0uIzn3Q5zeC3ptd0xndbTkgm1XdHMkihJdvTK5HzOgsNUErguN4wt652BgW7yGl6mZk9FpfXGHW3cxNrX5svcZ2cfgNZLQIrzUGhpew8tROW2uMUtoQiR36DhNS+/eQJSYktIkEPwcv/89QqEVCbkxTq9M7rdNPhqP0bJOuls3OCq/kJEZ2XGIqvtdPuhS0p3pLSoWCgQuzcXNI25CO6BLxaN7uHPML8j3DMCpOdGFjo6OU3PR35PPnaN/idfwxvJp9Chu73eAdjaCFh7cXtXfngiktKndfR87S8ZTUTmLyuqbKas4j5LS6fh8b8c7vKiKygrVrurMCtUlZTu5Y+E77KzfjUPTMO0w3xk+nl9MOxGX3ivHiTukIdzA68Vv8nHZAkwZAgSHpk/igoJzyffkt/nYrQ1b+LausTrzyJTRFCYNjUHEPZsVLqKq/HiQrc+GESKNzLwv0TRVZ7w3k1JSWXU9/sAHgL/F/UJ4SEv9FSnJ3419cJ3U7eUHuqqz5QektPi24r8EAo+B3AqAyxhGbur3yUg6HxGhJneisKVN0A7ubY0rnRcKfkpt1dUgwzQfXHUhhIu07BdxOMbHK7w+o7x8N/94cB7FxdVMmDiQ2bNn4na386nqIPj8b1NVfTOyjTdycJHf7yt0PS9q1+1OsS4/EBNSmmwuv4pQcHGzVlcwvJGd1XdT3fAKFeF7+KSoCEMTnDxkBOOze8cL1hGa0PDorW/OrXSc03U0mTkf4294goD/RaRdj6al4066Ao/3SjQ9Mbv5ehKfL8j3b5xDTU0Dti0pKqqiqKiaP//5kqhdo67uH+0k9kb1DXNIS70jateNl16b3Etq/kpDcBFSHrjwBGzpo8r3Je9tv53H1x2BhuCRFYs5uqCQf554Fg5dtXSV5nRjAMlpd5Ocdne8Q+mTVq4sIhAI7S1tHQpZLF+2jfr6AMnJXS8rIqUkZK7owJFB/IGPEiK598oBVVsGqKx/MmJi38Opm5w8cBkOLYyNxB8Os2DHVu5f/HkMI1UUpSN0XSNSD7GuRzNFdbQLuu0px71Fr0zuvuBSOhK6LQWj00v2fh+wwsxZvYywHZsNlxVF6ZiJEweSk5Oyd/9cl8vBzJlj8XhaX6NxMIQQGB2ql+TA5TwsKteMt17ZLWPLliPdrXHpzQtmmbZNdcBPjldV+lOUnsLpNHjwH9/lmWc+Z2dRNRMPGch550UcJ+y01OSbqK75GZK2+t01kpOvjep146VXJnenMRhJ5J1c9qdrNrt86c1uk1KS4oxOa0BRlOhJTnZz/fXdt3+v13se9Q1PEzJXEWk3MiE8JCd9D4cxpNtiiKVe2S3jdgzHZbQ/R7ukIZ2ihsy93xtC45Qhw3FHWAClKEpiE8JJTvYLeD1n0jjN1Qs4ESIZIZJITbmVtNTEGVDvlS13gAEZv2Fz+RWtDqoGLYMn1u1rBbh1gyyPl1/O6L6WgaIoPZumecnK/Afp1q/wB+Yh5W50PR+P+ySESKzNfnptck92T6Mw+xG2VdwEWNiyAQBNJAE6AzL/yszCVEJyA7qmcf7IsVw4ajwpzrZ3X1cUJfHpejbJSZfGO4xu1atXqALYMkSt710agl8CkOSaTpr3VLQ2KiEqiqIkgoRcoQpQU17LhiWbmXrKWWQknR3vcPqsqtIaXv3X+4w/chSHn3JIvMNRFIVeOqC6xxN3z+Vnp/+e7WuL4h1Kn/bXGx7lxfvf4TeX/Z2q0pp4h6MoCr285X7RT85h+KFDKRjVdjVEpXtl5KThcBlomobTpWYiKUpP0Ov73JX4M0NhFr27nCHjB5I/NHGKsynxYduSmpoGvF5XVKtCJqKE7XNXegaH02DG2dFdTaj0DvNXbuSxDxdRVtvAoUPyufHkIxial9n+A1tRXFLDrXfMpbrah23bzLpiBldcdmQUI44NW0rWV1Vg2TajMrPjUqxQJXdFUTrlsQ+/5qH3vyRghgF4/5sNLFizhf/+8BJG9O9cmeS77n6RsrK6vdvePTP3S0aO7MfhU3vPxjJvblzHrz77EF/YRCDQhODWw2Zw1fhDY7pHca8eUFWUnsaybUwrMaoKtqUhEGqW2KGxteoPmdz/1sJOndM0LXYUVTXbzzQQNFm9ZmeX442V+ds2cfvH71Lh9+EzTRrMEHWhIH/+agFzVi2LaSyq5a4ctKL6Wubv2EhY2hzRbzBjM3PjHVLcra8q557PP+GznduQEsZk53DX9GM5qmBwvEPrFuuLyzF0jQNLPElg6ZbOJWPD0HC7nfj9+3bDcrkMsjJTuhBpbN3z+ccEwuEWt/vDYe5b/BmXjz0kZl00KrkrHWbZNj/74j1e3bwahEBKiSYEk3PyeXjm+SQ7+ubq33WV5Zz/yrP4wvsy3eqKMq595xUeOOF0Thk6Mo7RdY+MZA9hK3Lp7FRP4zL+sGXzxbLNFJftZtLoAYxqZ7BdCMFPbzuNP/zlLaQETRMMLMjk1JN7xxaHtcEA2+tqW70/bNtsqK5kbHZsGkMquSsd9vcVn/P6ljUE7ebdDkvKd3LLgjd57IQL4hRZfP3284+aJfY9AlaYuz/9gJOGjECLYV9rLAzJzWRIbgbfllRg2fu6UdwOg+8eO5l6X5Drfv4s5VV1mJaNLgSnHTuOn1x7YpvnPfaY0QwcmMXKVUWkpLg5esbIvTXeezpdaG3uByIlOLTY9YSrPnelQ4JWmEfXfI3favmRM2hZLCzZQlF9662WRGVaFl8W72j1fl/YZE1lWQwjip0HrzmXwpxMPE4HyW4nTkPnzCmjuXTGJJ55/WuKy2vxBUxM0yIQCvPOJ6tZt7m03fMOHZLDOWcdyszjxvSaxA6Q7HQyPqf1TycpLheDk9J48r73+NnVj/HOi4vozqnoquWudMiO+loi7oPWxKHprKzcRUFyWgyjij9LyjY3bxMIzFa6L3q7vLRkXvnJlawpKqWstoGxA/PIS0sG4IvlWzDN5p/wLNvmm7VFjE7gtRC/nDGTy954Hv8B/e5uw+A3R53AQ79/k/mvLSUUDLNm+TbcHifHnzmpW2LpdMtdCDFQCPGREGKNEGK1EOKWptszhRDzhBAbmv7PiF64SrwkGQ7Csu0k5e2DdfLdhsHw9NbnddtSMiarc9MCewMhBOMG9uP48cP2JnaAQf0zWkz7cxg6/XJTYx1iTB2a15+5Z13M5Lx8dCEwhMaozGz+c8o5nDJkBMu/2Ego2Jj4g36TZZ9v7LZYutJyDwO3SSmXCiFSgCVCiHnAVcB8KeUfhRB3AncCP+16qEo89U9KZUhqJuuqy1s9Znq/QTGMqOe4a/qx3Djv9RazJDyGwfcPPbxPbg4z67zpLFyymWAojJQSh6GTnZHMjEN7z3z1zpqU15+Xz7sMv2liI0ly7KtQO25KIZVluwkFw7g8DiYc1n27PkWt/IAQ4jXgH03/jpNSlggh+gMfSylHtfVYVX4ALBlmW/2XVAW3oWtOCpOnk+EcGO+wmllStpMr3n+uRb+7Wze4Z/opXDC8d8xq6A5vbFzH/y2cT8gKIxDYUnLDpMO5ecr0mC5c6Uk2ba/gyVe+ZHtxNdMnFXLF2YeRkpRYG2IcrGDA5PG/vsOG1Ts5+uQJnDtrRpd+P9oqPxCV5C6EKAQWAOOB7VLK9KbbBVC95/vW9PXkvqX+Cz4o/gO2tDBlEA0dIQT9PeM5dcAvces9Z57vyspd/H7xRywq3YEERqZn89PJx3J8QUd2lk9slm2zqqIU07IZl52Lx9H3WuxKbHVrchdCJAOfAPdIKV8WQtTsn8yFENVSyhb97kKI2cBsgEGDBk3Ztm1bl+LorXY0LOWtop8Tli037NUwSHcO5OIhD6GLnpUoTNvClhKXrsbkFSVe2kruXZoKKYRwAP8DnpFSvtx0c2lTdwxN/0ecByalfFhKOVVKOTUnJ6crYfRqn+x6IGJiB7AJU2eWsKluQYyjap9D01ViV5QerCuzZQTwGLBWSvm3/e56HZjV9PUs4LXOh5fYKoNbqA+3PkAJYMoAy6v+F6OIFEVJFF1pes0ArgRWCiGWN932M+CPwAtCiGuAbcBFXYowgdWbZWhCb3NVG0BDO28AiqIoB+p0cpdSLgRaG+Y9obPn7UvceiqS9he4uLSOD6hKKVlWVMK8dRvQhOCUsSOZmN+vK2EqihJl/nApO+rexBfeiUvPpCD5dFKc0Z0mqjpN4yjXPQqH8GDib/UYQ7gZn35Wh85n2TY/+t9bfLppGwHTRADPLP6GU8eO5A9nndxnp+QpSk8hpcWKij+yvf41kGATQqCzsfZpstxTODzvXgzNG5VrqdoycSSExvScazFE5GqKAoFDczM67eQOne+lZav4dNNW/KaJBGzAb4Z5d80G3l79bfQCV5RuIKVkzrtfM/NH/2bq7Pu48nfPsmbrrniHFVXfVPyBHfVvYMsQNo2ljSUWtgxS6V/M5yU3Rq3ejErucTY2/VQOz56FLpzoYt9KNofwkGRkc8HgB3HqSR0619NfL8dvRqglbZo8/XVsNwpQlIP1+NuLePiNL6ltCCAlrNlWyux7X6Kkcne8Q4sKf7iUHfWvY8lAxPttQuwObaAisCgq11PdMj3A5KxLGJV2Eqtr3qI88C0OzcOIlOMZnDytccC1g2r9kX9pAKp9rXf9KEq8NbbaFxMINW+cmGGLFz9ewQ8vOCpOkUXP9rrX2yy+B2BJH5tr55Ljmdbl66nk3kMkGVkcnv3dLp1jyqABvLd2A/YBv0CGJphW2LNKGSjK/sywhS/YsiZ+2LLZUVYdh4iir8HcgX3g1lUR+MLFUbme6pZJIDcdPQ2X0fL92mUYXHdkxEVsitIjOB0GBTkty0W7nQbTxiRGQTqXnklHUq5Ti07ZbJXcD4Jph/mkbBlPb32PV4oWUBHsWZtTjMjN5skrLmB0XjYOXcOhaUzIz+O/372IgRnp8Q5PUdr0sytOwO000LXGWV1uh0H/rFTOOGJsnCOLjoLk05uNq0WiCy+DU6Ozo1nUqkJ2RW8oHLa4ah2/Wz0HG4nfCuIUBgg4rd90vj/iPDTRs94na/0BhIBUd9+uwqf0LpuLK5k7fxk7K2o5asIQzjt6Ah5Xz6qr1BULi6+lOrBi70yZ5jTceg4nDXoTrYO1pLq9KmRX9fTkvql+J7csfYCg3bK/zKU5OWfAUVw3rGNz0RVF6btMu47Pi6+nztyKJX17bzeEF0NL4aj8x0lyDOjw+dpK7mpAtQOe3voeIbvlFEOAoB3itZ2fcvngk/AaqpWsKErrHFoKxwz4L2X+L9hSOxdfuBinnk5h6nfon3Riu902B0Ml9w74qnINbe2UqQuNZdUbmJEzIYZRKYrSGwmhkeedQZ53Rrdep2d1FPdAUkosabV9DETsslGURCWljNpKSqV7qJZ7O4QQFHhy2eGPWJYeAEvajEgpiGFUihIf/kCIex+ax4efrUcTgjNOnMAPrz4ew+j4YjslNlTLvQMuHXwibi1yX5iGxrDkAQz05sY4KkWJvT//630+/vxbwmGbkGnx9vxV/Oe/n8Y7LCUCldw74MS8qczImdgiwbs0BxnOZH4xdlYrj1SUxGFZNh9//i0hc183ZTAU5s0PVsYxKqU1qlumA4QQ/HT0ZSzKncQL2z9ih6+MJMPNaf2P4Iz86SQZnniHqCixEaFqtCol3TOp5N5BQgimZY1jWta4eIeiKHGh6xonHjWaDz9fTyjU2Hp3uQzOOXlinCNTIukzyb08sItPyt9na8MGDOFkauaRHJY5A5eu5qYrSkfdfsNJOJ0G73+yFk0TnHPKIVxzWe+v2JiI+sQK1fmlb/FW8YvY0saiscXh1Fw4hJNbRt5Nf4+a6aIovV3Q3EzIKkHX0vA4xiJ6WEmQ7tCnV6iurl3OW8UvYcrm89BDdpAQQf6+4Xf8evzfcbYyG0ZRlJ5tt/9jiqt/S8jajsABWGgimby0H5OVfHmfHRNI+Le2t0tewpSRivQ0Mm2TZdVfxjAiRVGipar+ZbZWXEcw/C1SBrBlHbb0EbbLKK75DcU1v4l3iHGT0Mk9ZIco8m1r85igHWBx1Rcxiqht5ZV1PP7sQu77zwd8tmhjvMNRlB4tbFVTVP1TZCvb1knpp7L+GRqCPbcoYXdK6G4ZS4YbP5K1M6wQbqNlHyvFpTVc++On8PlD2LbknfkrufyCacy6+Mh4h6YoPVJV/dx2j5EyQNnu/zAkp+9tVpPQLXe35sGrJ7d5jCEMhiWPilFErXv4qU/3JnaAQDDMnBe+oK6+9X1RFaUv2x34pNVW+z6ShuBXEe8xbYuwbUc/sB4ioVvuQghm5p7O2yX/a7XfXSA4OvukGEfWUmn57r2JfQ+HoVOz209KspquqSgtdTQxN/+7Wli8jT9+/TGrK8tAwLR+A/n5YccxIbtf9EOMo4RuuQMcl3sqg5OG4YhQJ9khnFw08HukOTPiEFlz40bl43Q0L74khCA3OyVOESlKdFRU1/PQ8wu5677XeXX+N4TMyHsjHKwk13QErnaP8zj3LbJ6Z8t6rpn3P1ZWlmIjsaXki5LtXPj2sywti87G1D1Fn5jnbskwn5Z/wPzSt6g1G3dSH5E8htPyL2B48uhuu+7BCARNfnT382zZXoGmCWxbcs9d5zJ1UmG8Q1OUTispr2XWXU8TCIYxwxZul8HwQTk89KtL0LWutS1Nq5S1O2cgCbZ6jCa8FOY8Sor7aCzbZsrcf1Id9Ec8dmxmLu+ce1WXYoq1Pj3PHUAXBsflnspxuadi2ia60Hvcnqdul4N//PEy1qwvpsEXYuSwXLIy2h4vUJSe7vGXv6ThgLGkTTsq+HL5VmZMHtqlczv0PPqn30lJ7Z+RsmXCFsJDquckkl2NK2iXV5Rg2q3vzbCxtpJSXz153sT4u+vVyd1n7qQhvBNDeEhzjUET7T8dh9ZzN9s1dI2JY9Vq2XiRMgSBD5CBd0A2gDES4b0EYRTGO7Rea+2mXS3GkoKhMBu3l3c5uQPkpF6LrmVQUnMPtvSxf2WznJTryEv70d5FTP5wOFLds70MoREIJ86mO70yudcE17Ki4o/Uhtah4QAkQhiMSL+a4Wnf7dCKtIb6AOtWFpGa7mXEmPzuD1rp0WRoObJ6NmA2JnaA0FdI3zNI1wmI9D8jori/ZV8xfkR/tu6sxNovwbucBiMLG/c/CIctPlqwjsrKesaMzueQCQMP+hqZyReQkXQeDcGvMa1d6Foqye4j0UTz/vjxWbmE2mi5OzSdAclpB339nqrXJfeqwDd8XnI9VtMUKHtPf5uE9dX/pi60iUNzft1mgq8s381Nl/+HYMDEsmxOPWcy37/j9FiEr/RAMrwRWX0V7LcbfaOmVlzwQ2TNrYiMf8Q6tF7ve+dP5+NFG/AHTUJmY5/76MI8pk0sJBy2+NHtz7JpSznhsIWua1x1xVFcctG0g76OEBrJ7rYfl+7ycP6w8byyaTUBq/mgrscw+P7EaRhdHAfoSXrVM5FS8nXpHXsT+4EsGaC4YR4Vga/bPM9zj3/K7hofvoYgwYDJO68sobSkphsiVnoDufsvEKHPdp8ABBcgzTUxiylR5GWlMvev3+Pq86dz6lFj+Ok1J/H3n38HTRN88ul6Nm0pJxAwCYdtgsEwjz/1KfUNrQ+QdtVvjjiRUwaPwKXreA0HSQ4nLl3nytGHcv2Ew7vtuvHQLS13IcSpwAOADjwqpfxjNM5bEfga065r8xhL+tlY8yQ5ntZfKL8vhGXtmyOr6YKAP/6rVJXYk3YVhD6j3WXMhJC+pxBpUflV7lMyUr3MOnd6i9urqhua/R1CY8342lofyUntT3HsDKeu8/fjzqK4fjefl2xH1wTHDhhCptvbLdeLp6i33IUQOvBP4DRgLHCpEGJsNM5dE1yD3YFSATXBtW3ef9ZFh+NyOxACnC6DwUNzGViYHY0Qld7GKoIO9aXbYK7v9nD6ktGj+qNpzbtPHQ6d3JzUbr92fnIq3xkxnvOGjUvIxA7d03I/HNgopdwMIIR4DjgH6PJnWoFOxH2+DjyunQHVUeMGcN8T1/LZh2tIS/dy2nlT0BKor005GI0D8h2iBlSjasK4Aq6ZdTQPP74Aw9BwOnT+dM9FOA5YzKd0Tnck9wHAjv2+LwJajHQIIWYDswEGDRrUoRNnew5DEzqWbH26kkAjx9PyI+CBho3sx7CRibXcWOkEYzgd+wDrBtfJ3R1Nn3PhBYdzxmmHULvbT052CoahEnu0xK25KqV8WEo5VUo5NScnp0OPSXeNJskxiLbC1oST4WnfjVKUSqITwgHeK6ADy9iF94LuD6gP8npd9O+XrhJ7lHVHct8J7D9ZtaDptqg4PO9vOLVURIQPHbpwMyrjRtJcPaOkgNI7iOTvN7XgW0vwbkj7I0JLj2FUitI13ZHcvwZGCCGGiMZVH5cAr0fr5EmOAo4veJHC1AvQhQdNuBAYZLgmcnje3xiRPital1L6CCFciKy54L0EhBdEMogUwA3GaETGQ2getQ5C6V26pXCYEOJ04H4ap0I+LqW8p63jO1s4zJYmIWs3hubB0BJzxFuJLSkDEFoOBEAfiDCGxTskRWlVzAuHSSnfBt7ujnPvTxMO3EZWd19G6UOEcIOr/QF5Renp1Pw/RVGUBKSSu6IoSgJSyV1RFCUB9YidmIQQ5cC2Tjw0G6iIcji9QV983uo59x198Xl39jkPllJGXCjUI5J7ZwkhFrc2UpzI+uLzVs+57+iLz7s7nrPqllEURUlAKrkriqIkoN6e3B+OdwBx0heft3rOfUdffN5Rf869us9dURRFiay3t9wVRVGUCFRyVxRFSUC9NrkLIU4VQqwXQmwUQtwZ73i6gxBioBDiIyHEGiHEaiHELU23Zwoh5gkhNjT9nxHvWKNNCKELIZYJId5s+n6IEOKrptf7+aaKowlFCJEuhHhJCLFOCLFWCHFEor/WQogfN/1urxJCzBVCuBPxtRZCPC6EKBNCrNrvtoivrWj096bnv0IIMbkz1+yVyb0792ntYcLAbVLKscB04Kam53knMF9KOQKY3/R9orkF2H8z3D8B90kphwPVwDVxiap7PQC8K6UcDRxC4/NP2NdaCDEA+CEwVUo5nsYqspeQmK/1k8CpB9zW2mt7GjCi6d9s4N+duWCvTO7st0+rlDIE7NmnNaFIKUuklEubvq6j8Y99AI3PdU7TYXOAc+MSYDcRQhQAZwCPNn0vgJnAS02HJOJzTgOOAR4DkFKGpJQ1JPhrTWNlWo8QwgC8QAkJ+FpLKRcAVQfc3Nprew7wlGz0JZAuhOh/sNfsrck90j6tA+IUS0wIIQqBQ4GvgDwpZUnTXbuAvHjF1U3uB+4A7Kbvs4AaKWW46ftEfL2HAOXAE03dUY8KIZJI4NdaSrkTuBfYTmNSrwWWkPiv9R6tvbZRyW+9Nbn3KUKIZOB/wI+klLv3v082zmVNmPmsQogzgTIp5ZJ4xxJjBjAZ+LeU8lCggQO6YBLwtc6gsZU6BMgHkmjZddEndMdr21uTe7fu09qTCCEcNCb2Z6SULzfdXLrnY1rT/2Xxiq8bzADOFkJspbG7bSaNfdHpTR/dITFf7yKgSEr5VdP3L9GY7BP5tT4R2CKlLJdSmsDLNL7+if5a79HaaxuV/NZbk3u37tPaUzT1NT8GrJVS/m2/u14H9mwWOwt4LdaxdRcp5V1SygIpZSGNr+uHUsrLgY+A7zQdllDPGUBKuQvYIYQY1XTTCcAaEvi1prE7ZroQwtv0u77nOSf0a72f1l7b14HvNs2amQ7U7td903FSyl75Dzgd+BbYBPw83vF003M8isaPaiuA5U3/TqexD3o+sAH4AMiMd6zd9PyPA95s+noosAjYCLwIuOIdXzc830nA4qbX+1UgI9Ffa+DXwDpgFfA04ErE1xqYS+O4gknjp7RrWnttAUHjbMBNwEoaZxMd9DVV+QFFUZQE1Fu7ZRRFUZQ2qOSuKIqSgFRyVxRFSUAquSuKoiQgldwVRVESkEruiqIoCUgld0VRlAT0/0bh+5esuxzUAAAAAElFTkSuQmCC\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": "95c018bc",
   "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": "b2cf579f",
   "metadata": {
    "format": "text/html"
   },
   "source": [
    "<p>My cat is <strong>very</strong> grumpy.</p>"
   ]
  }
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