nn_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-07T11:36:10.733433Z",
     "start_time": "2020-10-07T11:36:10.253432Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from MLlib.quark.layers import *\n",
    "from MLlib.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-07T11:13:59.612797Z",
     "start_time": "2020-10-07T11:07:57.429610Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "             Input  Output Shape Activation  Bias  Parameters\n",
      "Layer Name                                                   \n",
      "Input Layer      1             1     linear  True           2\n",
      "Total Parameters: 2\n",
      "\n",
      "Total 999 samples.\n",
      "Training samples: 900 Validation samples: 99.\n",
      "Total 90 batches, most batch has 10 samples.\n",
      "\n",
      "Epoch: 0:\n",
      "Time: 0.283sec\n",
      "Train Loss: 2.6015 Train Accuracy: 0.0%\n",
      "Val Loss: 3.0542 Val Accuracy: 0.0% \n",
      "\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-ad3bb6fb2d6b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.001\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"sgd\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"mse\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_every\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;31m# m.visualize()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# m.save_model()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\Desktop\\projects\\ML-DL-implementation\\MLlib\\quark\\Stackker.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, X, Y, epochs, show_every, batch_size, shuffle, val_split, val_x, val_y)\u001b[0m\n\u001b[0;32m    290\u001b[0m                 \u001b[0mbatch_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurr_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurr_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m                     \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeedforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m                     \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m                     \u001b[1;31m#loss = loss.mean(axis=0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\Desktop\\projects\\ML-DL-implementation\\MLlib\\quark\\Stackker.py\u001b[0m in \u001b[0;36mfeedforward\u001b[1;34m(self, x, train)\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m                 \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m                 \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan_to_num\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_activation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m                 \u001b[1;31m#print(l.name, x.shape)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mnan_to_num\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\type_check.py\u001b[0m in \u001b[0;36mnan_to_num\u001b[1;34m(x, copy, nan, posinf, neginf)\u001b[0m\n\u001b[0;32m    454\u001b[0m     \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m222222.\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m111111.j\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m111111.\u001b[0m     \u001b[1;33m+\u001b[0m\u001b[1;36m0.j\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m111111.\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m222222.j\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m     \"\"\"\n\u001b[1;32m--> 456\u001b[1;33m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    457\u001b[0m     \u001b[0mxtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "x = np.arange(1, 1000).reshape(-1, 1)/1000\n",
    "y = x*2 -3\n",
    "m = Sequential()\n",
    "m.add(FFL(1, 1, activation='linear'))\n",
    "m.compile_model(lr=0.001, opt=\"sgd\", loss=\"mse\")\n",
    "m.summary()\n",
    "m.train(x, y, epochs=2000, show_every=100, batch_size=10)\n",
    "# m.visualize()\n",
    "# m.save_model()\n",
    "# load_model()\n",
    "print(m.predict(np.array([10]))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-07T11:14:01.924795Z",
     "start_time": "2020-10-07T11:14:01.912794Z"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([[1.99922196]]), array([-2.99958381]))"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "m.layers[0].weights, m.layers[0].biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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tynQZBY9tiybT2Bj9zrowAACQAoSwyVRWSosXM6YCAACkBCFsKoypAAAAKUIImwpjKgAAQIoQwqYSDktHj0p9fZmuBAAA5BlC2FTiYyr2789sHQAAIO8QwqYydkwFAADAHCKETWXswFYAAIA5RAibSl2dVFtLJwwAAMw5Qth0GFMBAABSgBA2naYmOmEAAGDOEcKmEw5Lhw5JQ0OZrgQAAOQRQth0wmFpZEQ6fDjTlQAAgDxCCJsOYyoAAEAKEMKmw5gKAACQAoSw6SxeLFVU0AkDAABzihA2HTPGVAAAgDlHCEsGYyoAAMAcI4QlIxyObuIdiWS6EgAAkCcIYcloapL6+6XjxzNdCQAAyBOEsGTEn5DkliQAAJgjhLBkMKYCAADMMUJYMpYvl4qL6YQBAIA5QwhLRnGxtHIlnTAAADBnCGHJYkwFAACYQ4SwZMUHtjqX6UoAAEAeIIQlq6lJ6uqSOjszXQkAAMgDhLBkMaYCAADMIUJYshhTAQAA5hAhLFmNjdHNvOmEAQCAOUAIS1Z5udTQQCcMAADMCULYTDCmAgAAzBFC2EzEx1QAAABcJELYTDQ1SSdPSj09ma4EAADkOELYTPCEJAAAmCOEsJkghAEAgDlCCJsJBrYCAIA5QgibiepqKRikEwYAAC4aIWymGFMBAADmACFsphhTAQAA5gAhbKaamqQjR6SBgUxXAgAAchghbKbCYSkSkQ4ezHQlAAAghxHCZooxFQAAYA4QwmaqqSn6ncX5AADgIhDCZioUkubPpxMGAAAuCiFspswYUwEAAC4aIWw2GFMBAAAuEiFsNpqapAMHpJGRTFcCAAByFCFsNsJhaXBQam3NdCUAACBHEcJmgzEVAADgIhHCZoMxFQAA4CIRwmajoUEqLaUTBgAAZo0QNhtFRVJjI50wAAAwa4Sw2WJMBQAAuAiEsNmKD2x1LtOVAACAHEQIm61wWOrtlU6dynQlAAAgBxHCZiv+hCS3JAEAwCwQwiY4cPqANj+9WS+fennqC+OzwlicDwAAZoEQNsFwZFgPbH1Avzz6y6kvXLlSCgTohAEAgFkhhE2wqnaVyorK1NLeMvWFpaXS8uV0wgAAwKwQwiYoDhRrdf1q7WzbOf3FjKkAAACzRAhLwAt503fCpPNjKgAAAGaIEJaAH/R14PQB9Q31TX1hOCx1dEhdXekpDAAA5A1CWAJeyJOT0+6O3VNfyJgKAAAwS4SwBPyQL0lqaZvmliRjKgAAwCwRwhK4tO5SBSww/eL8xsbodzphAABghghhCZQVl6mprmn6xfnz50uLFtEJAwAAM0YIm4QX9BhTAQAAUoYQNgk/5Gtv514NjQxNfSFjKgAAwCwQwibhBT0NR4a1r3OagBUOS0ePSn3TjLMAAAAYgxA2idEnJKdbFxYfU3HgQIorAgAA+YQQNom1wbWSNP26MMZUAACAWUhZCDOzZWb2UzNrMbOXzewDCa653sy6zGx77OvjqapnpipLK7W8evn0nbB4CGNxPgAAmIHiFH72sKQ/dc5tM7MqSVvN7F+dcxNbS//hnPutFNYxa37In35ga12dVFNDJwwAAMxIyjphzrnjzrltsZ/PSmqR1JCq35cKXtDTrvZdirjI5BeZMaYCAADMWFrWhJnZSkm/Jun5BKevMbMXzOxHZnbZJO+/28yazay5ra0thZWO54d89Q336dCZQ1NfyJgKAAAwQykPYWY2X9JTku5xznVPOL1N0grn3HpJ/yDpu4k+wzn3oHNuk3NuUygUSm3BY3hBT1KSi/MPHZKGh9NQFQAAyAcpDWFmVqJoAHvCOfftieedc93OuZ7Yzz+UVGJmwVTWNBNeKBrCkhpTMTwsHT6chqoAAEA+SOXTkSbpYUktzrnPTHLNoth1MrOrYvV0pKqmmaqrqNMllZcwpgIAAMy5VD4d+RpJd0h60cy2x459TNJySXLObZH0dknvNbNhSX2S3uWccymsacb8kM+YCgAAMOdSFsKcc89Jsmmu+YKkL6SqhrngBT09/uLjcs4p1rS70OLFUkUFnTAAAJA0JuZPwwt56h7o1vGe45NfFAhIjY10wgAAQNIIYdOI7yE57bowxlQAAIAZIIRNIz6mYtrJ+eGwtH+/lF1L2gAAQJYihE1j0fxFqimvSW5MRV+fdHyK25YAAAAxhLBpmJm8oMeYCgAAMKcIYUlgTAUAAJhrhLAkeEFPp3pPqePcFHNkV6yQiovphAEAgKQQwpIQf0Jyym5YcXE0iNEJAwAASSCEJSG+hyRjKgAAwFwhhCVhefVyzSuZl9yYin37GFMBAACmRQhLQsACWhtcq53tSXTCurqkzs70FAYAAHIWISxJXtBLrhMmsS4MAABMixCWJD/k60j3EZ0dODv5RU1N0e+EMAAAMA1CWJLi2xftat81+UWrVkW/szgfAABMgxCWpKTGVFRUSA0NdMIAAMC0CGFJCteFVRIoYUwFAACYE4SwJBUHirW6fnVy2xfRCQMAANMghM2AF0piI++mJunECamnJz1FAQCAnEQImwE/6Gv/6f3qH+6f/KL4mIr9+9NTFAAAyEmEsBnwQp4iLqI9HXsmv4gxFQAAIAmEsBkYfUJyqqGt8U4Yi/MBAMAUCGEzsLp+tQIWmHpdWHW1VF9PJwwAAEyJEDYD5cXlWlWzavonJBlTAQAApkEImyE/5E//hCRjKgAAwDQIYTPkBT3t6dij4cjw5Bc1NUmHD0uDg+krDAAA5BRC2Az5IV9DkSHtPz3FCIpwWIpEpIMH01YXAADILYSwGfJC0Y28p7wlyZgKAAAwDULYDHnBaAhjTAUAALgYhLAZqiqr0tIFS7WzfYpO2MKFUmUlnTAAADApQtgs+CF/6k6YGWMqAADAlAhhs+AFPbW0tyjiIpNfxJgKAAAwBULYLPghX+eGzulI15HJL2pqim7iPTKSvsIAAEDOIITNQnxx/pRPSIbD0TlhR4+mqSoAAJBLCGGzEB9TMeX2RYypAAAAUyCEzUJwXlCheaHpO2ESi/MBAEBChLBZ8kLe1J2wpUulkhI6YQAAICFC2Cz5weiYCudc4guKiqTGRjphAAAgIULYLHkhT6f7T+tk78nJL2JMBQAAmAQhbJb8kC9pmu2L4gNbJ+uWAQCAgkUIm6Wkx1T09EhtbWmqCgAA5ApC2CwtqVqiBWULGFMBAABmhRA2S2YmL+gxpgIAAMwKIewi+CF/6k7YypXRzbzphAEAgAkIYRfBC3o60XNCp/tOJ76grExavpxOGAAAuAAh7CIktX0RYyoAAEAChLCLEB9TMeW6sPiYCgAAgDEIYRdhRfUKlReXTz0rLByW2tul7u70FQYAALIeIewiFAWKtDa4ljEVAABgxghhF4kxFQAAYDYIYRfJD/k61HVIvYO9iS9obIx+pxMGAADGIIRdpPj2RbvadyW+oKpKuuQSOmEAAGAcQthFGt3ImzEVAABgBghhF6mprknFgWLGVAAAgBkhhF2kkqISXVp36fSdsNZWqa8vfYUBAICsRgibA15omick42MqDhxIT0EAACDrEcLmgBf09ErnKxocGUx8QXxMBevCAABADCFsDvghXyNuRHs79ia+gIGtAABgAkLYHIiPqZj0lmRdnVRdzeJ8AAAwihA2B9YE18hkky/ON2NMBQAAGIcQNgfmlczTypqVjKkAAABJI4TNET/kTz+m4uBBaXg4bTUBAIDsRQibI17Q0+723RqJjCS+oKkpGsAOH05vYQAAICsRwuaIH/I1MDKgA2cmmQXGmAoAADAGIWyOeKFpnpBkTAUAABiDEDZH4mMqWtomWRe2eLFUXs7ifAAAIIkQNmeqy6u1pGqJdrZP0gkLBKTGRjphAABAEiFsTnlBb/JOmMSYCgAAMIoQNofiYyqcc4kviA9snew8AAAoGISwOeQFPfUM9qi1uzXxBU1NUl+fdPx4egsDAABZhxA2h/yQL0mTD21lTAUAAIghhM0hxlQAAIBkpSyEmdkyM/upmbWY2ctm9oEE15iZ3W9m+8xsh5ltTFU96RCaF1J9Rf3ki/OXL5eKilicDwAAVJzCzx6W9KfOuW1mViVpq5n9q3NubJvozZIujX29WtIXY99zkpnJC3mTj6koKZFWrKATBgAAUtcJc84dd85ti/18VlKLpIYJl90s6Z9d1H9LqjGzxamqKR38oK+dbTsnf0KSMRUAAEBpWhNmZisl/Zqk5yecapB0ZMzrVl0Y1GRmd5tZs5k1t7W1parMOeGFPHX2dart3CR1xsdUAACAgpbyEGZm8yU9Jeke51z3xNMJ3nJBC8k596BzbpNzblMoFEpFmXNm2u2Lmpqk06elzs40VgUAALJNSkOYmZUoGsCecM59O8ElrZKWjXm9VNKxVNaUavExFZM+IcmYCgAAoNQ+HWmSHpbU4pz7zCSXfV/SnbGnJK+W1OWcy+lJpksXLNX80vmTzwpjTAUAAFBqn458jaQ7JL1oZttjxz4mabkkOee2SPqhpN+QtE/SOUl3pbCetDCz6B6Sk4WwxsbodxbnAwBQ0FIWwpxzzynxmq+x1zhJf5KqGjLFC3n6t/3/lvhkRYW0ZAmdMAAAChwT81PAD/o6dvaYuvq7El/AmAoAAAoeISwF4tsXTbmHJJ0wAAAKGiEsBUY38p5qTMXx41JvbxqrAgAA2YQQlgKralaprKhs+jEV+/enrygAAJBVCGEpUBQo0prgGsZUAACASRHCUsQLetN3wlicDwBAwSKEpYgX9HTwzEGdGzp34cmaGqmujk4YAAAFjBCWIn7Il5PT7vbdiS9gTAUAAAWNEJYijKkAAABTIYSlyKV1l6rIiqYeU3HokDQ4mN7CAABAViCEpUhZcZnCdWHtbJ9icX4kEg1iAACg4BDCUsgP+VN3wiRuSQIAUKAIYSnkBT3t7dyroZGhC08ypgIAgIJGCEshP+RrODKsfZ0JgtYll0iVlXTCAAAoUISwFPKC0SckEw5tNYt2w+iEAQBQkAhhKbQ2uFYSYyoAAMCFCGEpVFlaqRXVKybfvqipKbqJdySS3sIAAEDGEcJSzAt5U3fCBgako0fTWxQAAMg4QliK+UFfu9p3aSQycuHJ+JgK1oUBAFBwCGEp5oU89Q/361BXgqGs8TEVrAsDAKDgEMJSzA/5kpR4aOuyZVJJCSEMAIACRAhLsSnHVBQVSatWcTsSAIACRAhLsdqKWi2av4gxFQAAYBxCWBp4QW/qMRX79knOpbcoAACQUYSwNPBDvlraW+QSBa1wWDp7VmpvT39hAAAgYwhhaeAFPXUPdOvY2WMXnmRMBQAABYkQlgajT0gmWhfGmAoAAAoSISwNvNAUT0iuWhXdzJsQBgBAQSGEpcEllZeoprwm8aywsrLovDBuRwIAUFAIYWlgZvJDvna2T/KEJGMqAAAoOISwNPGCXuJOmHR+TAUAACgYhLA08UO+2s61qf1cglEU4bDU1iZ1d6e/MAAAkBGEsDSJb1+UsBsWH1PBLUkAAAoGISxNGFMBAADGIoSlybLqZZpXMi/xmApCGAAABYcQliYBC0QX5yfqhFVVSQsXsjgfAIACQghLIy80xUbejKkAAKCgEMLSyA/6au1uVfdAgqcgGVMBAEBBIYSlUXz7ol3tuy48GQ5Lra1Sf3+aqwIAAJlACEujacdUOCcdOJDmqgAAQCYQwtIoXBdWSaCEJyQBAAAhLJ2KA8VaXb868ROSDGwFAKCgEMLSzA/5iUNYfb20YAGL8wEAKBCEsDTzgp72n96v/uEJC/DNGFMBAEABIYSlmR/yFXER7enYc+FJxlQAAFAwCGFpFh9TMeni/IMHpeHh9BYFAADSjhCWZqvrVytggcnHVAwNSUeOpL8wAACQVoSwNCsvLldjbaN2tjOmAgCAQkYIywA/5E/eCZMIYQAAFABCWAZ4QU97OvZoODJh7deSJVJZGYvzAQAoAISwDPCCnoYiQ3qlc0LHKxBgTAUAAAWCEJYBfsiXNMUTknTCAADIe4SwDFgbXCtJiSfnxzthzqW5KgAAkE6EsAyoKqvSsgXLJt9D8tw56cSJ9BcGAADShhCWIV7Im/x2pMS6MAAA8hwhLEP8oK9d7bsUcZHxJ+JjKlgXBgBAXiOEZYgX8mohJ9cAACAASURBVHRu6JwOdx0ef2LFCqmoiE4YAAB5jhCWIfEnJC8Y2lpSEg1ihDAAAPIaISxDvOA0G3lzOxIAgLxGCMuQ+nn1Wli5cOoxFQAAIG8RwjLIC07yhGRTk9TZKZ0+nf6iAABAWhDCMsgLemppb5GbOJiVMRUAAOQ9QlgG+SFfZ/rP6ETPhMGsjKkAACDvJR3CzOxaM7sr9nPIzFalrqzC4IWii/MvWBfW2Bj9TicMAIC8lVQIM7O/lPQRSX8WO1Qi6fFUFVUoJh1TMW+etGQJIQwAgDyWbCfsrZJuktQrSc65Y5KqUlVUoVg8f7EWlC1gTAUAAAUo2RA26KKrx50kmVll6koqHGYmP+QzpgIAgAKUbAj7hpk9IKnGzP5Q0r9J+lLqyiocU46pOHZMOncu/UUBAICUSyqEOef+XtK3JD0laY2kjzvn/iGVhRUKP+TrZO9JdfZ1jj8RH1Oxf3/6iwIAACmX7ML8Skk/cc59SNEOWIWZlaS0sgIR377ogsX5jKkAACCvJXs78ueSysysQdFbkXdJeixVRRWSScdUMLAVAIC8lmwIM+fcOUlvk/QPzrm3SvJTV1bhWFG9QhXFFReuC6utlerqCGEAAOSppEOYmV0j6TZJP4gdK57mDY+Y2Skze2mS89ebWZeZbY99fTz5svNHUaBIa4JrJn9CktuRAADkpWRD2AckfVTSt51zL8em5f9kmvc8JulN01zzH865DbGv+5KsJe/4IX/yWWF0wgAAyEvJhrBzkiKSbjGzHZK+L+l1U73BOfdzSZ1TXYMoL+jpcNdh9Qz2jD/R1CQdOiQNDWWmMAAAkDLJhrAnJD2i6Jqwt0j6rdj3i3WNmb1gZj8ys8smu8jM7jazZjNrbmtrm4Nfm13i2xftbt89/kQ4LI2MRIMYAADIK8mGsDbn3L845w445w7Fvy7yd2+TtMI5t17SP0j67mQXOucedM5tcs5tCoVCF/lrs098TMUFtyQZUwEAQN6acnH9GH9pZg9J+ndJA/GDzrlvz/YXO+e6x/z8QzP7JzMLOufaZ/uZuaqprknFgWLGVAAAUECSDWF3SVorqUTRtWFSdB/JWYcwM1sk6aRzzpnZVYp25Tpm+3m5rKSoRJfWXXphJ2zRImnePEIYAAB5KNkQtt45d/lMPtjMvibpeklBM2uV9JeKhjg557ZIeruk95rZsKQ+Se+KbRJekPyQrxdPvTj+oBljKgAAyFPJhrD/NjPfOZdgjkJizrlbpjn/BUlfSPbz8p0X9PSdXd/RwPCAyorLzp8Ih6U9ezJXGAAASIlkF+ZfK2m7me02sx1m9mJsVAXmiBfyFHER7e3cO/5EU1P0dmQkkviNAAAgJyXbCZtu6CouUnxMxc62nVq3cN35E+GwNDAgHTsmLV2aoeoAAMBcSyqEzcE4CkxjTf0amUwtbROekBw7poIQBgBA3kj2diRSrKKkQqtqV2ln+4Rld4ypAAAgLxHCsogX9C7shC1bJpWUEMIAAMgzhLAs4od87enYo+HI8PmDxcXSypWMqQAAIM8QwrKIF/Q0MDKgA6cPjD8Rf0ISAADkDUJYFok/IZlw+6J9+6TCnWULAEDeIYRlkbXBtZISbOQdDkvd3VJHQe7qBABAXiKEZZHq8mo1VDVc2AkbO6YCAADkBUJYlvFCXuJOmMS6MAAA8gghLMvEx1SM28t81aroZt6EMAAA8gYhLMv4IV+9Q7060n3k/MHy8ui0fG5HAgCQNwhhWcYLepKUePsiOmEAAOQNQliWmXZMBQAAyAuEsCwTqgypvqI+8eL8U6eks2czUxgAAJhThLAs5If8ycdUcEsSAIC8QAjLQl4wOqZi3BOSjKkAACCvEMKykB/y1dnXqbZzbecPxkMY68IAAMgLhLAs5IWiT0iOWxe2YIEUCtEJAwAgTxDCstDoE5KMqQAAIG8RwrJQQ1WDqkqrEj8hye1IAADyAiEsC5mZ1gbXJp4VduSINDCQmcIAAMCcIYRlKT/kX9gJa2qSnJMOHMhMUQAAYM4QwrKUF/R0vOe4uvq7zh9kTAUAAHmDEJalEm5fFB/YyrowAAByHiEsSyUcUxEMSlVVdMIAAMgDhLAstapmlcqKysaPqTBjTAUAAHmCEJaligJFWhNco53tjKkAACAfEcKymB/yLxzYGg5Hn44cGclMUQAAYE4QwrKYF/R08MxBnRs6d/5gU5M0NBSdFwYAAHIWISyL+SFfTk6723efP8iYCgAA8gIhLIt5wQRPSDKmAgCAvEAIy2KX1l+qIisaPyusoUEqK6MTBgBAjiOEZbHSolI11TWN74QFAlJjIyEMAIAcRwjLcl7IS7yRN7cjAQDIaYSwLOcHfe3r3KfBkcHzB8PhaCfMucwVBgAALgohLMt5IU/DkWHt6xzT+Wpqknp7pZMnM1cYAAC4KISwLDe6kffYoa2MqQAAIOcRwrLcmvo1khhTAQBAviGEZbnK0kqtrFk5fnH+ihXRpyTphAEAkLMIYTnAC3rjO2GlpdEgRggDACBnEcJygB/ytbtjt0YiYzbtZkwFAAA5jRCWA7ygp/7hfh08c/D8waYmOmEAAOQwQlgO8ELRPSTHrQsLh6WODunMmQxVBQAALgYhLAck3MibMRUAAOQ0QlgOqK2o1aL5i8Z3whhTAQBATiOE5Qg/5I8f2NrYGP1OJwwAgJxECMsR8TEVLr5fZGWltHgxIQwAgBxFCMsRfsjX2cGzOnb22PmDjKkAACBnEcJyRMLF+YypAAAgZxHCcsToRt4Tx1QcPSr19WWoKgAAMFuEsByxsHKhastrE4+p2L8/M0UBAIBZI4TlCDOLPiHJmAoAAPICISyHXLCRNwNbAQDIWYSwHOKFPLWfa1dbb1v0QF2dVFtLCAMAIAcRwnLIpIvzuR0JAEDOIYTlkPiYinGT8xlTAQBATiKE5ZBl1ctUWVJ5YSfs4EFpaChjdQEAgJkjhOWQgAW0Nrj2wsX5IyPS4cOZKwwAAMwYISzHMKYCAID8QAjLMV7QU2t3q7oHuqMHGFMBAEBOIoTlmPgTkrvad0UPLF4sVVTQCQMAIMcQwnKMF5qwkbdZtBtGJwwAgJxCCMsxjbWNKi0qZUwFAAA5jhCWY4oDxVpdv1o72yc8IfnKK1IkkrnCAADAjBDCcpAX9MZ3wsJhqb9fOn48c0UBAIAZIYTlID/ka//p/eob6oseYEwFAAA5hxCWg7ygJyenPR17ogcYUwEAQM4hhOWgCzbyXr5cKi6mEwYAQA4hhOWg1fWrFbDA+TEVxcXSypV0wgAAyCGEsBxUVlymcG34wu2LCGEAAOSMlIUwM3vEzE6Z2UuTnDczu9/M9pnZDjPbmKpa8pEX8i7cyHvfPsm5zBUFAACSlspO2GOS3jTF+TdLujT2dbekL6awlrzjB33t7diroZGh6IFwWOrqkjo7M1sYAABISspCmHPu55KmSgQ3S/pnF/XfkmrMbHGq6sk3XsjTUGRIr5yO3YJkTAUAADklk2vCGiQdGfO6NXbsAmZ2t5k1m1lzW1tbWorLdl4wuofk6NBWxlQAAJBTMhnCLMGxhAuanHMPOuc2Oec2hUKhFJeVG9YG10oas5F3Y2N0M286YQAA5IRMhrBWScvGvF4q6ViGask5VWVVWrZg2fknJMvLpYYGOmEAAOSITIaw70u6M/aU5NWSupxzbH44A37IH/+EJGMqAADIGcWp+mAz+5qk6yUFzaxV0l9KKpEk59wWST+U9BuS9kk6J+muVNWSr7ygp58f+rkiLqKABaLrwp5+OtNlAQCAJKQshDnnbpnmvJP0J6n6/YXAD/nqG+7T4a7DWlmzMtoJO3lS6umR5s/PdHkAAGAKTMzPYV4o+oTk6C1JnpAEACBnEMJyGGMqAADIXYSwHFY/r14LKxde2AljTAUAAFmPEJbj/JB/fkxFdbUUDNIJAwAgBxDCcpwXjG7k7eIbdzOmAgCAnEAIy3Fe0FPXQJdO9JyIHgiHuR0JAEAOIITlOD/kSxrzhGRTk3TkiDQwkMGqAADAdAhhOS4+pmJ0XVg4LEUi0sGDmSsKAABMixCW4xbPX6zqsmpmhQEAkGMIYTnOzOSFvPOdsKam6HfWhQEAkNUIYXnAD/rnB7aGQtEti+iEAQCQ1QhhecALeTrZe1KdfZ2SGWMqAADIAYSwPBB/QnLc9kXcjgQAIKsRwvJAfA/JcWMqDhyQRkYyWBUAAJgKISwPrKhZoYriivFjKgYHpdbWzBYGAAAmRQjLAwELaG1wLWMqAADIIYSwPMGYCgAAcgshLE/4QV+Huw6rZ7BHamiQSkvphAEAkMUIYXkivn3RrvZdUlGR1NhICAMAIIsRwvLEBRt5M6YCAICsRgjLE+HasIoDxednhcUHtjqX2cIAAEBChLA8UVJUotX1q8ePqejpkU6dymxhAAAgIUJYHvGCHmMqAADIEYSwPOKHfL1y+hUNDA8wpgIAgCxHCMsjXtBTxEW0p2OPtHKlFAjQCQMAIEsRwvLI6Ebe7S3ROWHLl9MJAwAgSxHC8sjq+tUy2fh1YXTCAADISoSwPFJRUqFVtavGb19ECAMAICsRwvKMH/LHd8La26WurswWBQAALkAIyzNe0NOejj0ajgwzpgIAgCxGCMszfsjX4Mig9p/ez5gKAACyGCEsz3jB6EbeLW0t0U28JTphAABkIUJYnvFCsRDW3iLNny8tWkQnDACALEQIyzMLyhaooaqBMRUAAGQ5Qlge8kM+YyoAAMhyhLA85AU9tbS1KOIi0U5Ya6vU15fpsgAAwBiEsDzkh3z1DvWqtbv1/JiKAwcyWxQAABiHEJaH4ovzd7btZEwFAABZihCWh8aNqWBgKwAAWYkQlodClSEF5wWjnbC6Oqmmhk4YAABZhhCWp7ygF31C0owxFQAAZCFCWJ6Kb+TtnGNMBQAAWYgQlqe8oKfT/ad1qvdUtBN28KA0PJzpsgAAQAwhLE/5IV9SbPuipqZoADt8OMNVAQCAOEJYnho3piL+hCSL8wEAyBqEsDzVUNWgqtIqxlQAAJClCGF5yszkhTztbN8pLV4sVVTQCQMAIIsQwvKYH/KjnbBAQGpspBMGAEAWIYTlMS/o6XjPcZ3pP8OYCgAAsgwhLI9dsH3RK69IzmW4KgAAIBHC8lp8TMXoRt59fdLx4xmuCgAASISwvLayZqXKisqis8IYUwEAQFYhhOWxokCR1gbXjg9hrAsDACArEMLynBfyorcjV6yQiovphAEAkCUIYXnOD/o6dOaQeiMD0SBGJwwAgKxACMtzXsiTk9Pujt2MqQAAIIsQwvLc6Ebe8TEV3I4EACArEMLyXFNdk4qs6PyYijNnpM7OTJcFAEDBI4TludKiUl1afyljKgAAyDKEsALgBWNPSDKmAgCArEEIKwBe0NO+zn0aXLE0eoBOGAAAGUcIKwB+yNeIG9Hec61SQwOdMAAAsgAhrAB4odhG3u0t0cX5dMIAAMg4QlgBWBtcK5OdH1NBJwwAgIwjhBWAeSXztKJmhXa2x8ZUnDgh9fZmuiwAAAoaIaxA+CH/fCdMohsGAECGEcIKhBf0tKt9l0ZWrYweIIQBAJBRhLAC4Yd8DYwM6HCwJHqAxfkAAGQUIaxAeMHoE5IvDbZK9fV0wgAAyDBCWIFgTAUAANmFEFYgasprtHj+4vPbF9EJAwAgowhhBcQLeec7YYcPS4ODmS4JAICCldIQZmZvMrPdZrbPzD6a4Py7zazNzLbHvv4glfUUOj8YHVPhGhulSEQ6eDDTJQEAULBSFsLMrEjSP0p6syRf0i1m5ie49OvOuQ2xr4dSVQ+inbCzg2fVtnhB9AC3JAEAyJhUdsKukrTPObffOTco6UlJN6fw92EafiiagVuqh6IHWJwPAEDGpDKENUg6MuZ1a+zYRL9jZjvM7FtmtizRB5nZ3WbWbGbNbW1tqai1IMTHVGyPHJMqK+mEAQCQQakMYZbgmJvw+l8krXTOXSHp3yR9OdEHOecedM5tcs5tCoVCc1xm4VhYuVB1FXVq6djFmAoAADIslSGsVdLYztZSScfGXuCc63DODcRefknSlSmsp+CZmbygx5gKAACyQCpD2P9IutTMVplZqaR3Sfr+2AvMbPGYlzdJaklhPVBsI+/4mIr9+6WRkUyXBABAQUpZCHPODUt6n6RnFA1X33DOvWxm95nZTbHL3m9mL5vZC5LeL+ndqaoHUV7QU/u5dp1dujA6J+zo0UyXBABAQSpO5Yc7534o6YcTjn18zM9/JunPUlkDxotvX3QgWKQrpOgtyeXLM1oTAACFiIn5BSY+pmLH/N7oARbnAwCQEYSwArNswTJVllSq2U5IJSUszgcAIEMIYQXGzOSFPL18epfU2EgnDACADCGEFSAv6KmlrYUxFQAAZBAhrAD5IV9Hzx7VwKrl0RDmJs7QBQAAqUYIK0Dx7YuOL6yQzp6V2AoKAIC0I4QVoPgTkntrYx0wbkkCAJB2hLACtKp2lUqLSrW98mz0AIvzAQBIO0JYASoOFGtN/Rr9V/ExyYxOGAAAGUAIK1BeyNOOM7uj0/LphAEAkHaEsALlBT0dOH1AI42r6IQBAJABhLAC5Yd8OTmdbqgnhAEAkAGEsAIVH1PRGiyNjqjo7s5wRQAAFBZCWIFaXb9aAQuopWYoeoBuGAAAaUUIK1BlxWUK14bVXHE6eoDF+QAApBUhrID5IV8/L26NvqATBgBAWhHCCpgX9LS99xW5Sy6hEwYAQJoRwgqYH/I1HBlW3/IldMIAAEgzQlgB80LRJyTbllQTwgAASDNCWAFbG1wrSTpYF5BaW6X+/gxXBABA4SCEFbD5pfO1vHq5Xqrul5yTDhzIdEkAABQMQliB84Keni9rj75gcT4AAGlDCCtwfsjXTwOHoy9YFwYAQNoQwgqcF/TUWtqvkQVVdMIAAEgjQliB80O+ZFLPsoV0wgAASCNCWIGLj6k4fsl8OmEAAKQRIazA1VXU6ZLKS/RKrZMOHpSGhzNdEgAABYEQBnkhT9ureqIB7MiRTJcDAEBBIIRBftDXf5WcjL7gliQAAGlBCIO8kKcX5vdGX7A4HwCAtCCEQX7I17EqaaSslE4YAABpQgiDvKAnF5DOLKmjEwYAQJoQwqBF8xepuqxarQvL6YQBAJAmhDDIzOSHfO2uHpL2749u5g0AAFKKEAZJ0VuSWyu7pHPnpBMnMl0OAAB5jxAGSdHF+dsre6IvuCUJAEDKEcIgKTqmYl9d7AWL8wEASDlCGCRFO2GHq6VIUYBOGAAAaUAIgyRpefVylZbPU2eoSnr2WamtLdMlAQCQ1whhkCQFLKC1wbX6/nULpV/8Qlq5UvrIRwhjAACkCCEMo7ygp09c3S+9/LL01rdKf//3hDEAAFKEEIZRfsjXke4jOruqQXr8ccIYAAApRAjDKC/oSZJ2te+KHli7ljAGAECKEMIwygtFQ1hLe8v4E4QxAADmHCEMo8K1YZUESrSzbWfiCyYLYx/+sHTqVFprBQAg1xHCMKqkqERrgmv05Re+rE//16fVca4j8YXxMLZzp/S2t0mf/rS0ahVhDACAGSCEYZwtv7lFTXVNuvdf71XDZxp0x3fu0H8e/k+5RJt6r1kjfeUrhDEAAGbBEv4/1yy2adMm19zcnOky8t5Lp17SA80P6J93/LO6B7q1buE6bb5ys26/4nZVl1cnftPu3dJf/7X01a9K5eXSn/yJdO+90sKF6S0eAIAsYWZbnXObEp4jhGEqvYO9evKlJ/XF5i9q6/Gtqiyp1K2X36rNmzZr4+KNid9EGAMAQBIhDHOk+ViztjRv0Vdf/Kr6hvv0qiWv0uZNm/XOy96pytLKC98wMYz98R9LH/oQYQwAUDAIYZhTZ/rP6PEdj+uLzV/Uzradqi6r1p3r79QfXflHumzhZRe+gTAGAChQhDCkhHNO/3nkP7WleYu+ufObGhwZ1P9a/r/03k3v1du8t6msuGz8G/bsiYaxJ54gjAEACgIhDCnX1tumx7Y/pge2PqBXTr+i4Lyg3rPhPbr7yrsVrguPv5gwBgAoEIQwpE3ERfTv+/9dW7Zu0fd2fU8jbkRvCL9Bm6/crLeseYuKA8XnLx4bxsrKogv4CWMAgDxCCENGHDt7TA9ve1gPbntQrd2tWlK1RH+48Q/1Bxv/QEsXLD1/IWEMAJCnCGHIqOHIsH6494fa0rxFP973Y5mZ3rL6Ldq8abPeEH6DAhabGTwxjMVvU15ySWb/AAAAZokQhqxx4PQBfWnbl/Twrx7Wqd5TWlWzSndfebfe82vv0cLKWOdrzx7pf//v6NZIhDEAQA4jhCHrDI4M6jst39GWrVv07MFnVRIo0e/4v6PNV27WdSuuk5lJe/dGO2OEMQBAjiKEIavtat+lB5of0GMvPKYz/We0NrhWm6/crDvX36nailrCGAAgZ00VwtjAGxm3NrhWn33TZ3Xsg8f02M2Pqaa8Rvc8c4+WfGaJ7vreXXq+olPuscekXbukd7xD+uxnoxuF33uvdPJkpssHAGBW6IQhK/3q+K/0wNYH9PiOx9U71KsNizZo85Wbdevlt6rq8Ak6YwCAnMDtSOSs7oFuffXFr+qLzV/UjpM7VFVapduvuF2bN23WFd0V48PYe98bDWOLFmW6bAAAJBHCkAecc3r+6PPa0rxFX3/56+of7tc1S6/R5k2b9bslv6byv/209JWvEMYAAFmFEIa80tnXqS9v/7K2bN2iPR17VFteq3dveLfeV/NGNX7xa4QxAEDWIIQhLznn9OzBZ7Vl6xZ9u+XbGo4M64ZVN+je4M16w9ebVfT4E4QxAEBGEcKQ9070nNCjv3pUD2x9QIe6DumSykv04dBbdfczbZr/je9KpaWEMQBA2hHCUDBGIiN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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "m.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4-final"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "nbTranslate": {
   "displayLangs": [
    "*"
   ],
   "hotkey": "alt-t",
   "langInMainMenu": true,
   "sourceLang": "en",
   "targetLang": "fr",
   "useGoogleTranslate": true
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}