notebook1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# This is a Markdown Head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "write codes for computing an equation:\n",
    "\\begin{equation*}\n",
    "f(x)  = 2x^2 + x + 1\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f(2) = 2*2^2 + 2 + 1 = 11\n"
     ]
    }
   ],
   "source": [
    "def f(x):\n",
    "    return 2*x**2 + x + 1\n",
    "\n",
    "x = 2\n",
    "print('f({:d}) = 2*{:d}^2 + {:d} + 1 = {:d}'.format(x, x, x, f(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data  visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ARBIBXI+FnsjgKWP7JdG1bSR//WorVbV6WItS3mbywhwOHq3i12O6IiK241jhzlU98SIS4/o6DBgJbAFmARNc3zYBmOmuDDYEBgiPX96VvOJjTFumRzQq5U0OHq1i0oKdXNYjgQEdYm3HscadI/5E4BsRWQ+spGGO/zPgGWCUiGwHRrme+5WLOscztFMcL329XbdtVsqL/O3f26isreeXo7vajmJVUGO+SUTaAEOBdsAxIBvIMsbUn+pnjDHrgX4nef0QMKJJaX2EiPDEmG5c+Y9F/PObHTwxppvtSEo53o7CMqavyGP8oPZ09ONDVhrjtCN+EblERL4EPgcup2EU3x34LbBBRJ4SkSj3x/Q9PdpFM65/Mm8v3k1ecYXtOEo53tNfbCE8OJCH/fyQlcY401TPGOAeY8x5xpiJxpjfGmMeM8ZcDfQB1tAwXaNO4rFLuxAQAM/O3WI7ilKOtmTHQeZvKeSB4Z0csxHb6Zy2+I0xvzDGnPQKpTGm1hjzL2PMJ+6J5vvaRocycVg6n60vYHXuYdtxlHKkunrDnz7fTFJMGHecn2o7jldo1MVdEXlXRKKPe54qIvPdF8t/3HtRR+IjW/CnzzZhjN7UpZSnzVi9l00FpfxydBfHbc1wKo1d1bMIWC4iY0TkHuAr4G/ui+U/IloE8YtLu7A69wiz1ulNXUp50tGqWp77cit9U2K4qnc723G8RqNW9RhjXheRjcA3wEGgnzFmv1uT+ZFxA5KZunQ3z8zZwqXd2xIWoqMOpTzhlW92UFRWxaTbBhAQ4MybtU6msVM9twFTgNuBt4EvRKSPG3P5lcAA4fdX9aCgpJLXF+j5vEp5Ql5xBZMX5XBtvyT6tff53d+bVWOnesYBFxhjphtjfg38hIZ/AFQjDUyL5Ypeibz23U4KSo7ZjqOU33t6zmYCRfiVw2/WOplGFb8xZqwxpvC45yuAQW5L5acev7wr9QaemaPLO5Vyp2W7DvHFhv3cd3FH2kaH2o7jdc50A9dvReSkG1oYY6pFZLiIXOmeaP4nJTacicPSmbk2n6zdxbbjKOWXauvq+cOsjSTFhHHPsHTbcbzSmUb8G4DZIjJfRP4iIr8UkSddyzs3AFcBy90f03/cf0lHEqNDeXLmRup0z36lmt37K3LZsr+M317RTRdSnMKZiv96Y8xQ4EtgIxAIlALvAQONMY8aY3zjlBQvER4SxBNjurGpoJTpK3T3TqWaU3F5Nc9/tY3zO8Yxumdb23G81pmWcw4QkQ7AeOCSE34vjIYN29RZurJ3ItOW7+GvX23lyt6JxISH2I6klF/461dbOVpVyx+u7uHYvfYb40wj/teAuUBXIOu4X6tcj6oJRIQ/XN2Dsspanv9qm+04SvmF7H0lTF+Ry4QhqXROiLQdx6udaa+el4wx3YApxpj0436lGWP0qsk56No2itsGd2Da8j1k7yuxHUcpn1Zfb/jtv7KJiwjhkVG6++aZNHY5533uDuJEj47qTGxEC377r2w9nF2pc/BRVh5r847wxJhuRIUG247j9Zx3vLwXiQ4L5jdXdGVt3hE+zMqzHUcpn1RcXs0zc7cwMC2Wa/sl2Y7jE7T4LRvbN4lBabE8O3cLxeXVtuMo5XOem7uFsspa/nhNT72g20ha/JaJCH8c25OjlbU8M2ez7ThK+ZTVuYf5YGUedw5NpUtbvaDbWFr8XqBzQiR3XZDGR1l7Wal39CrVKDV19TwxYwNto0J5eGRn23F8iha/l3h4ZAZJMWE8MWMD1bWnPMNeKeXy1uIctuwv4w9X96Bli0btMK9ctPi9RHhIEP9zTQ+2Fx7ljYW7bMdRyqvlFVfw4rztjOyWwGU9EmzH8Tla/F5kRLcExvRqy0vzt7P7YLntOEp5JWMMT87MRgSeukbv0G0KLX4v8/urehASGMDvZmbrGb1KncSc7P18s7WIn43qTFJMmO04PkmL38skRIXyy9FdWLj9IDNW77MdRymvUlJRw+9nbaRnUhR3nJ9qO47P0uL3QuMHdWBAh1b88fNNHDxaZTuOUl7jz19sbrhh67reBAVqfTWV/s15oYAA4dlxvaioquOp2Ztsx1HKKyzZcZAPs/K4Z1g6PZOibcfxaVr8XqpTm0geHN6J2evymb/5gO04Sll1rLqOx2dsIK11BI+M1E3YzpXbil9EUkTkGxHZLCIbReRh1+uxIjJPRLa7Hlu5K4Ov+8lFHemSEMlvPs2mtLLGdhylrHnx39vILa7g6et6ERqsp2qdK3eO+GuBn7u2dR4MPCAi3YHHgfnGmAxgvuu5OomQoACevb43hWWV/Plz3c5BOdPq3MNMXriLWwa1Z3B6nO04fsFtxW+MKTDGrHZ9XQZsBpKAa4Cprm+bCox1VwZ/0DclhokXduSDlXks2KanXCpnqayp47GP15EYHcavL+9qO47f8Mgcv4ikAv1oOJg9wRhTAA3/OABtPJHBlz0yMoNObVry+CfrdcpHOcqL87axq6icZ8b1IlL32W82bi9+EWkJfAI8YowpPYufmygiWSKSVVTk7JFuaHAgf7m+N/tLdcpHOcfq3MO8sXAXNw9sz7CMeNtx/Ipbi19Egmko/WnGmBmulw+ISKLr9xOBwpP9rDFmkjEm0xiTGR+v/9H7tW/FPRem88HKPL7detK/MqX8xvFTPE+M0Sme5ubOVT0CvAlsNsa8cNxvzQImuL6eAMx0VwZ/8+jIznROaMmvPlnPkQo9tEX5r2fnbmFXUTnPXd9bp3jcwJ0j/qHAbcBwEVnr+jUGeAYYJSLbgVGu56oRQoMDeeHGvhw6Ws2TMzfajqOUWyzZcZC3Fu/mjvNTGdqpte04fsltm1gbYxYBp9o2b4S73tff9UyK5uERGTw/bxujuidwVZ92tiMp1WxKK2t47ON1pLeO4FejdYrHXfTOXR9038Ud6ZMSw+9mZnOgtNJ2HKWazVOzNnGgrIoXbupLWIjeqOUuWvw+KCgwgBdu7PPDBbD6et2+Wfm+z9bn88nqvTxwcUf6psTYjuPXtPh9VMf4ljx5ZQ8Wbj/IlMU5tuModU7yjxzjiRkb6JsSw0MjdC8ed9Pi92E3D0xhVPcEnpu7lU35jb5FQimvUldvePTDtdTVG/7+o74E63bLbqd/wz5MRHh2XG+iw4N5+IM1VNbU2Y6k1FmbtGAXy3OK+cPVPegQF2E7jiNo8fu42IgQnr+hD9sLj/LHz3TvfuVb1uQe5vmvtjKmV1uuH5BsO45jaPH7gQs7x3PvhelMW57LFxsKbMdRqlFKjtXw0PQ1JESF8vR1vfXQdA/S4vcTj13Whb4pMfzqk/XkFVfYjqPUaRljeGLGBgpKKvnHLf2IDtO7cz1Ji99PBAcG8I+b+wHw0PQ11NTVW06k1KlNX5HH5xsKeOzSLvRvr2cxeZoWvx9JiQ3n2XG9WZt3hOfmbrEdR6mT2pRfylOzN/4wRak8T4vfz4zplcjtQzrwxsIc5mbvtx1Hqf9QWlnD/dNWERMezAs39iEgQOf1bdDi90O/uaIbfZKj+cXH69hzqNx2HKWAhnn9X368nrzDx/jnLf1p3bKF7UiOpcXvh1oEBfLP8f0JCBDue2+1ru9XXuHNRTnM3bifx0d3JTM11nYcR9Pi91PJrcJ58aY+bCoo5Xf/ysYY3c9H2bMip5hn5mzh0u4J3D0szXYcx9Pi92PDuybw0PBOfLxqL9OW59qOoxxqf0kl909bTUpsOH+9sY+u1/cCWvx+7pGRnbmkSzxPzd7Iqj2HbcdRDlNVW8d901ZxrLqWSbcNIEpP0/IKWvx+LjBA+NtN/WgXE8Z9762iUPfvVx701OxNrMk9wl9v6ENGQqTtOMpFi98BosODmXRbJmWVtdz73iq92Ks84r1le3h/eS73XdyRy3sl2o6jjqPF7xBd2kby4k19WJN7hCc+3aAXe5VbLd15iD/M2sjwrm147NIutuOoE2jxO8jonok8OrIzM1bv442Fu2zHUX4q91AF901bRWrrCP7+o74E6k1aXsdth60r7/TTEZ3YdqCMp+dsoVOblgzvmmA7kvIjZZU13P3OSoyBybdnEqkXc72SjvgdRkT46w196NEuiofeX6Mnd6lmU1tXzwPvr2FXUTmvjO9Pams9VMVbafE7UFhIIG9OOI+osGDufHsl+0t0pY86N8YYnpy1kQXbivjT2J4M7dTadiR1Glr8DpUQFcqbE86jrLKGu6aupLyq1nYk5cMmL8z5YQXPjwa2tx1HnYEWv4N1bxfFy+P7s7mglIemr6FW9/BXTTBnQwF/nrOZK3ol8gtdweMTtPgd7pIubfjj2J58vaWQ33yqe/qos7Ns1yEe/mAt/du34nndZtln6KoexfhBHThQWsVL87eTENWCn+moTTXClv2l3PNOFu3jwnlzQiahwYG2I6lG0uJXADw6MoPC0kpe+noH8VGh3Da4g+1IyovtO3KMCVNWEBESxNQ7BxITHmI7kjoLWvwKaFjm+aexPTl4tIonZ2YTFRrENX2TbMdSXqiorIpbJy+norqOj38yhKSYMNuR1Fly2xy/iEwRkUIRyT7utVgRmSci212PesqyFwkKDODlW/ozMDWWn3+0jvmbD9iOpLxMSUUNt09Zwf6SSt7+8Xl0bRtlO5JqAnde3H0bGH3Ca48D840xGcB813PlRUKDA5k8IZPu7aK4f9pqlu48ZDuS8hIV1bX8+O0V7Cw8yqTbBzCgg56i5avcVvzGmAVA8QkvXwNMdX09FRjrrvdXTRcZGszbPx5I+9hw7p66klV7TvzPqJzmWHUdd0/NYm3eEV66uS/DMuJtR1LnwNPLOROMMQUArsc2p/pGEZkoIlkiklVUVOSxgKpBbEQI0+4eRJuoUCZMWcmaXD3Exakqa+qY+G4WS3cd4vkb+zC6p26x7Ou8dh2/MWaSMSbTGJMZH6+jCxvaRIXy/j2DiI0I4fYpK1i/94jtSMrDqmrruPfdVSzacZDnxvXm2n7JtiOpZuDp4j8gIokArsdCD7+/OkuJ0WFMnziY6LBgbp28nLV5Wv5OUVlTx0/eXcV324p4+tpe3JCZYjuSaiaeLv5ZwATX1xOAmR5+f9UESTFhfDBxMNHhDeWftVvn/P1dRXUtd0/N4tttRTx9XS/df8fPuHM553RgKdBFRPaKyF3AM8AoEdkOjHI9Vz4guVU4H907hPjIFtw+ZYWu9vFjR6tqueOtlSzZeZC/XN+Hm7X0/Y74wt4smZmZJisry3YMBRSWVjJ+8nJyiyt4ZXx/RnTTg1z8yeHyau54eyXZ+0p48aa+XN2nne1I6hyIyCpjTOaJr3vtxV3lndpEhfLBxMF0aRvJxHdXMWP1XtuRVDMpKDnGDa8vZXNBKa+O76+l78e0+NVZi2vZgvfvGcygtFh+9tE6pizKsR1JnaOdRUe5/tWl7C+p5J07B3Jpj7a2Iyk30uJXTdKyRRBT7jiP0T3a8j+fbeJ/P99Efb33Txuq/5a1u5jrX11CZU0dH0wczOD0ONuRlJtp8asmCw0O5J/j+zNhSAfeWJjDg9NXU1lTZzuWOgufry/glsnLiQkPYcb959MzKdp2JOUBujunOieBAcIfru5BSmw4f/p8MwdKl/P6bQNo3bKF7WjqNIwxTFqwi6fnbCGzQyveuD2TVhG6tbJT6IhfnTMR4e5h6bwyvj/Z+0q45uXFbMovtR1LnUJlTR0//3gdT8/ZwhW9E3nv7kFa+g6jxa+azZheiXz8kyHU1Ruuf20Jc7P3246kTlBYWsnNbyxjxup9PDqyMy/f3E9PznIgLX7VrHonxzDrwaFkJETyk/dW8dcvt1KnF329wqo9xVz18iK2FJTx6vj+PDwyAxE9I9eJtPhVs2sTFcqHEwdzY2YyL3+zgwlTVnDoaJXtWI5ljOGtxTnc9PoyWgQF8sl953N5L91h08m0+JVbhAYH8tz1fXjmul6s2F3MVf9YpPv6W1BaWcND09fw1OxNXNwlntkPXUD3dnpqltNp8Su3+tHA9nzyk/MJDBRufH0Z/5i/Xad+PGRN7mGueGkhc7L384vLujDptkyiw4Jtx1JeQItfuV2v5Gg+/+kwxvRK5Pl52xg/eRn5R47ZjuW36uoNr3y7gxteW0p9PXw4cabF5J4AAAjdSURBVDAPXNKJgACdz1cNtPiVR0SFBvPSj/ryl+t7s35vCZe9uID/t2ovvrBJoC/JOVjOja8v5bm5W7m0RwJf/HQYmal6Nq76T3oDl/IYEeGGzBQGpsXyi4/X89jH65ibvZ8/X9uTNlGhtuP5tPp6w7vL9vD0nM2EBAbw4k19GNs3SVftqJPSbZmVFXX1DStNnvtyKy2CAnj88q7cfF57nY5ogq37y/j1jPWszj3CRZ3jeXZcb9pG6z+k6tTbMmvxK6t2FR3lN59ms3TXITI7tOJ/r+1Fl7aRtmP5hGPVdbz8zXZe/24XkaFB/O7K7lzbT0f56v9o8SuvZYzh/63ay/9+sZmyylpuHdSeR0d1JiZctxE4GWMMs9cX8PQXmykoqeS6/kn89oruxOq2C+oEpyp+neNX1n0/9z+yWwIvzNvGu8v2MHNdPg+PyOCWQe1pEaRbCnxvTe5hnv5iCyt2F9OjXRQv3dyP8/TirTpLOuJXXmdzQSn/M3sTS3cdIrlVGD+/tDNX90ki0MHz/zsKy/jLl1v5cuMB4iJCeOyyLtyYmeLovxN1ZjrVo3yKMYYF2w/y7JwtbCoopVObljx4SSeu7J1IUKBzViFvO1DGP7/Zwex1+YSHBHHPsHTuGpZGyxb6P+vqzLT4lU+qrzd8tqGAl7/ezrYDR0mNC+feizpybb8kv95VcnXuYSZ9t4u5G/cTHhLIrYM7cO+F6cTpOQfqLGjxK59WX2/4atMBXv5mO9n7SmkVHswtg9pz6+AOJEaH2Y7XLKpr65m7cT9TFuWwNu8IkaFB3HF+Kj8emqYXblWTaPErv2CMYXlOMVMW5TBv8wEEuLhLG246L4XhXdsQ7IPTQDsKy/hwZR4zVu/jUHk1aa0juOP8VMYNSNYpHXVOdFWP8gsiwuD0OAanx5FXXMEHK3P5OGsvX28pJC4ihNE923Jl73YMTIv16gufew9X8MWGAj5bX8D6vSUEBQgjuyVw08AULsqI1xvZlFvpiF/5vNq6er7dWsS/1u5j/uZCjtXU0bplCBd1bsPwrm24IKO19V0p6+oN6/ce4ZsthXyztYgN+0oA6J0czVW92zG2XxLxkTp/r5qXTvUoR6ioruXrLYV8tfEA320rouRYDQEC3dtFMTA1jvNSW9ErOZqkmDC33uF6tKqWTfmlrM49zIqcYlbuLqasspYAgf7tWzGiWwJX9EqkfVy42zIopcWvHKe2rp61eUdYsP0gK3IOsSb3CFW19QBEhwXTo10U6fERpMY1/GobHUqbyBbEtWxxxmkiYwxlVbUUllZRWFbJ3uJj5BwqZ/fBcrbsLyPnYPkP35seH8GgtDgGp8dyYUa8HmyuPEbn+JXjBAUGkJka+8O2xFW1dWzML2Vjfimb8kvYlF/KrLX5lFbW/sfPiUDLkCDCWwQSERL0wz8Chob9cSqqaymvqqO6rv4/3y9AaB8bTkabllzbL4meSVH0SorRKRzldawUv4iMBv4OBAKTjTHP2MihnKVFUCD927eif/tWP7xmjOFwRQ27D5VTWFpFUVklRWVVlFXVUlFVx9Hq2v84MyA0uOEfg/AWgcRFhNAmMpT4yBYktwojKSbMUTeXKd/l8eIXkUDgn8AoYC+wUkRmGWM2eTqLUiJCbESIrpNXjmJjeDIQ2GGM2WWMqQY+AK6xkEMppRzJRvEnAXnHPd/reu0/iMhEEckSkayioiKPhVNKKX9no/hPtlziv5YWGWMmGWMyjTGZ8fHxHoillFLOYKP49wIpxz1PBvIt5FBKKUeyUfwrgQwRSROREOBHwCwLOZRSypE8vqrHGFMrIg8CX9KwnHOKMWajp3MopZRTWVnHb4z5AvjCxnsrpZTT6d0mSinlMD6xV4+IFAF7bOdogtbAQdshPMyJnxmc+bmd+JnBtz53B2PMfy2L9Ini91UiknWyDZL8mRM/MzjzczvxM4N/fG6d6lFKKYfR4ldKKYfR4nevSbYDWODEzwzO/NxO/MzgB59b5/iVUsphdMSvlFIOo8WvlFIOo8XvISLymIgYEWltO4u7ichfRGSLiKwXkU9FJMZ2JncRkdEislVEdojI47bzeIKIpIjINyKyWUQ2isjDtjN5iogEisgaEfnMdpZzocXvASKSQsOJY7m2s3jIPKCnMaY3sA34teU8bnHcaXKXA92Bm0Wku91UHlEL/NwY0w0YDDzgkM8N8DCw2XaIc6XF7xkvAr/kJOcO+CNjzFfGmO9PMF9Gw9bb/siRp8kZYwqMMatdX5fRUIT/dZiSvxGRZOAKYLLtLOdKi9/NRORqYJ8xZp3tLJbcCcyxHcJNGnWanD8TkVSgH7DcbhKP+BsNA7h620HOlZXdOf2NiPwbaHuS3/oN8ARwqWcTud/pPrMxZqbre35Dw7TANE9m86BGnSbnr0SkJfAJ8IgxptR2HncSkSuBQmPMKhG52Haec6XF3wyMMSNP9rqI9ALSgHUiAg1THqtFZKAxZr8HIza7U33m74nIBOBKYITx35tFHHuanIgE01D604wxM2zn8YChwNUiMgYIBaJE5D1jzK2WczWJ3sDlQSKyG8g0xvjKzn5NIiKjgReAi4wxRbbzuIuIBNFw8XoEsI+G0+Vu8feDhaRhFDMVKDbGPGI7j6e5RvyPGWOutJ2lqXSOX7nDy0AkME9E1orIa7YDuYPrAvb3p8ltBj7y99J3GQrcBgx3/fdd6xoJKx+hI36llHIYHfErpZTDaPErpZTDaPErpZTDaPErpZTDaPErpZTDaPErpZTDaPErpZTDaPEr1QQicp7rvIFQEYlw7Uvf03YupRpDb+BSqolE5E807NsSBuw1xjxtOZJSjaLFr1QTiUgIDfvzVALnG2PqLEdSqlF0qkepposFWtKwL1Go5SxKNZqO+JVqIhGZRcOpW2lAojHmQcuRlGoU3Y9fqSYQkduBWmPM+66zd5eIyHBjzNe2syl1JjriV0oph9E5fqWUchgtfqWUchgtfqWUchgtfqWUchgtfqWUchgtfqWUchgtfqWUcpj/D0fCl/2LUIB9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "    \n",
    "x_range = np.arange(-5, 5, 0.1)\n",
    "fx_range = f(x_range)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(x_range, fx_range)\n",
    "plt.xlabel('x'); plt.ylabel('f(x)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "435e503b046145af8f7a26eef5c7dcf2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), Output()), _dom_classes=('widget-…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(x)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "interact(f, x=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4dddb9f9f204091b07af01b79fa54fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='a', max=10, min=-10), IntSlider(value=25, description='b…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def f(a, b):\n",
    "    x = np.arange(0, 10, 0.1)\n",
    "    y = a*x + b\n",
    "    plt.plot(x, y)\n",
    "    plt.legend(['f(x)={:d}x+{:d}'.format(a, b)]) \n",
    "    plt.ylim(-100, 150)\n",
    "    plt.show()\n",
    "\n",
    "interactive_plot = interactive(f, a=(-10, 10, 1), b=(0, 50, 5))\n",
    "output = interactive_plot.children[-1]\n",
    "# output.layout.height = '350px'\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py36]",
   "language": "python",
   "name": "conda-env-py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
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