{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#should not be included in tags, but everything else should except last cell\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- BEGIN QUESTION -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $$y = mx + b $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f30c21270f0>]"
      ]
     },
     "execution_count": 8,
     "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": [
    "plt.plot(a,a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"end\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## END CELL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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