{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample Filtering Notebook\n",
    "\n",
    "This cell should remain in the output since it has no tag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a code cell\n",
    "# with no plot in the output\n",
    "# so it should be gone\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This markdown cell should also remain. **Look at the plot** of this function:\n",
    "\n",
    "$$\\Large\n",
    "f(x) = cos(2x) + e^x$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "ignore"
    ]
   },
   "source": [
    "This cell should be _gone_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f36964118d0>]"
      ]
     },
     "execution_count": 2,
     "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": [
    "# this cell should remain because it has a plot\n",
    "x = np.linspace(0,10,1000)\n",
    "y = np.cos(2*x) + np.exp(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "include"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.02002002 0.04004004 0.06006006 0.08008008]\n"
     ]
    }
   ],
   "source": [
    "# this code cell should remain, since it has the \"include\" tag\n",
    "y = 2*x\n",
    "print(y[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "include"
    ]
   },
   "source": [
    "This MD cell should also remain, because it's included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "ignore"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3695d45c88>]"
      ]
     },
     "execution_count": 4,
     "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": [
    "# this plot should be excluded, since it's tagged as \"ignore\"\n",
    "plt.plot(x,y**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def negate(x):\n",
    "    return not x\n",
    "\n",
    "def square(x):\n",
    "    return x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import otter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>q1</strong></p>\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<p><strong>q2</strong></p>\n",
       "    \n",
       "    \n",
       "        <p>0 of 1 tests passed</p>\n",
       "        \n",
       "        \n",
       "        <p> <strong>Tests failed: </strong>\n",
       "            <ul>\n",
       "            \n",
       "                <li> \n",
       "    <p><strong style='color: red;'>tests/q2.py</strong></p>\n",
       "    <p><strong>Test code:</strong><pre><div class=\"highlight\" style=\"background: #f8f8f8\"><pre style=\"line-height: 125%\"><span></span><span style=\"color: #000080; font-weight: bold\">&gt;&gt;&gt; </span><span style=\"color: #666666\">1</span> <span style=\"color: #666666\">==</span> <span style=\"color: #666666\">1</span>\n",
       "<span style=\"color: #888888\">False</span>\n",
       "</pre></div>\n",
       "</pre></p>\n",
       "    <p><strong>Test result:</strong><pre>Trying:\n",
       "    1 == 1\n",
       "Expecting:\n",
       "    False\n",
       "**********************************************************************\n",
       "Line 2, in tests/q2.py 1\n",
       "Failed example:\n",
       "    1 == 1\n",
       "Expected:\n",
       "    False\n",
       "Got:\n",
       "    True\n",
       "</pre></p>\n",
       "     </li>\n",
       "            \n",
       "            </ul>\n",
       "        \n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><strong>q3</strong></p>\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><strong>q4</strong></p>\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<p><strong>q5</strong></p>\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = otter.Notebook(\"tests\")\n",
    "n.check_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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