plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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92ZgxYxQYGGjfwsLCbu+LAAAAbuPWIJOZmam6detq9OjRqlOnjnr37q1evXrpgw8+cLrPYcOGKTU11b4dOXLEhRUDAICCxK1BJjQ0VJGRkQ5tEREROnz4sCQpJCREkpSSkuJwTEpKin3fn3l7eysgIMBhAwAAhZNbg0yTJk2UmJjo0LZ3716Fh4dLur7wNyQkRKtWrbLvT0tL0+bNmxUdHX1HawUAAAWPW69aGjx4sBo3bqzRo0frySef1JYtWzRt2jRNmzZNkmSz2TRo0CCNGjVKVatWVaVKlfT666+rXLlyat++vTtLBwAABYBbg8wDDzygL774QsOGDdPIkSNVqVIlTZgwQV27drUf8+qrr+rixYvq3bu3zp07p6ZNmyo+Pl4+Pj5urBwAABQENmOMcXcRt1NaWpoCAwOVmprq8vUyFYcudWl/hd3Bsa3dXQIAwCLy+vfb7V9RAAAA4CyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsKyi7nzyESNGKC4uzqGtevXq2rNnjyTp8uXLevnllzV//nylp6crNjZWU6ZMUXBwsDvKRQFRcehSd5dgKQfHtnZ3CQBw27h9Rua+++5TcnKyfduwYYN93+DBg/X1119r4cKFWrt2rY4dO6aOHTu6sVoAAFCQuHVGRpKKFi2qkJCQLO2pqamaMWOG5s2bpxYtWkiSZs6cqYiICG3atEmNGjW606UCAIACxu0zMvv27VO5cuVUuXJlde3aVYcPH5Ykbdu2TVeuXFFMTIz92Bo1aqhChQpKSEjIsb/09HSlpaU5bAAAoHBya5Bp2LChZs2apfj4eE2dOlVJSUl68MEHdf78eR0/flxeXl4qUaKEwznBwcE6fvx4jn2OGTNGgYGB9i0sLOw2vwoAAOAubv1oqVWrVvafo6Ki1LBhQ4WHh+uzzz6Tr6+vU30OGzZMQ4YMsT9OS0sjzAAAUEi5/aOlPypRooSqVaum/fv3KyQkRBkZGTp37pzDMSkpKdmuqbnB29tbAQEBDhsAACicClSQuXDhgg4cOKDQ0FDVq1dPnp6eWrVqlX1/YmKiDh8+rOjoaDdWCQAACgq3frT0yiuvqE2bNgoPD9exY8f05ptvqkiRInr66acVGBionj17asiQISpVqpQCAgLUv39/RUdHc8USAACQ5OYg89tvv+npp5/W6dOnVaZMGTVt2lSbNm1SmTJlJEnvvfeePDw81KlTJ4cb4gEAAEhuDjLz58/Pdb+Pj48mT56syZMn36GKAACAlRSoNTIAAAD5QZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACW5dR9ZJKSkrR+/XodOnRIly5dUpkyZVSnTh1FR0fLx8fH1TUCAABkK19BZu7cuXr//fe1detWBQcHq1y5cvL19dWZM2d04MAB+fj4qGvXrnrttdcUHh5+u2oGAACQlI8gU6dOHXl5ealHjx5avHixwsLCHPanp6crISFB8+fPV/369TVlyhR17tzZ5QUDAADckOcgM3bsWMXGxua439vbW82bN1fz5s311ltv6eDBg66oD0ABUXHoUneXYBkHx7Z2dwnAXSPPQSa3EPNnQUFBCgoKcqogAACAvHLqqqXt27fr559/tj/+8ssv1b59e/3f//2fMjIyXFYcAABAbpwKMi+99JL27t0rSfr111/11FNPqVixYlq4cKFeffVVlxYIAACQE6eCzN69e1W7dm1J0sKFC9WsWTPNmzdPs2bN0uLFi11ZHwAAQI6cCjLGGGVmZkqSVq5cqccee0ySFBYWplOnTrmuOgAAgFw4FWTq16+vUaNGac6cOVq7dq1at76+Qj8pKUnBwcEuLRAAACAnTgWZCRMmaPv27erXr5+GDx+uKlWqSJIWLVqkxo0bu7RAAACAnDj1FQVRUVEOVy3d8M4776hIkSK3XBQAAEBeOBVkcsL3LAEAgDspz0GmZMmSstlseTr2zJkzThcEAACQV3kOMhMmTLD/fPr0aY0aNUqxsbGKjo6WJCUkJGj58uV6/fXXXV4kAABAdvIcZLp3727/uVOnTho5cqT69etnbxswYIAmTZqklStXavDgwa6tEgAAIBtOXbW0fPlyPfroo1naH330Ua1cufKWiwIAAMgLp4JMUFCQvvzyyyztX375JV8WCQAA7hinrlqKi4vTiy++qDVr1qhhw4aSpM2bNys+Pl7Tp093aYEAAAA5cSrI9OjRQxEREZo4caI+//xzSVJERIQ2bNhgDzYAAAC3m9P3kWnYsKHmzp3ryloAAADyxekgk5mZqf379+vEiRP2L5C8oVmzZrdcGAAAwM04FWQ2bdqkZ555RocOHZIxxmGfzWbTtWvXXFIcAABAbpwKMn/9619Vv359LV26VKGhoXm+4y8AAIArORVk9u3bp0WLFtm/9RoAAMAdnLqPTMOGDbV//35X1wIAAJAvTs3I9O/fXy+//LKOHz+uWrVqydPT02F/VFSUS4oDAADIjVNBplOnTpKkF154wd5ms9lkjGGxLwAAuGOcCjJJSUmurgMAACDfnAoy4eHhrq4DAAAg35y+Id6BAwc0YcIE/fLLL5KkyMhIDRw4UPfee6/LigMAAMiNU1ctLV++XJGRkdqyZYuioqIUFRWlzZs367777tO3337r6hoBAACy5dSMzNChQzV48GCNHTs2S/trr72mhx9+2CXFAQAA5MapGZlffvlFPXv2zNL+wgsvaPfu3bdcFAAAQF44FWTKlCmjnTt3ZmnfuXOnypYte6s1AQAA5IlTQaZXr17q3bu33n77ba1fv17r16/X2LFj9dJLL6lXr15OFTJ27FjZbDYNGjTI3nb58mX17dtXQUFBKl68uDp16qSUlBSn+gcAAIWPU2tkXn/9dfn7++vdd9/VsGHDJEnlypXTiBEjNGDAgHz398MPP+jDDz/MckfgwYMHa+nSpVq4cKECAwPVr18/dezYURs3bnSmbAAAUMg4NSNjs9k0ePBg/fbbb0pNTVVqaqp+++03DRw4MN/fhH3hwgV17dpV06dPV8mSJe3tqampmjFjhsaPH68WLVqoXr16mjlzpr7//ntt2rTJmbIBAEAh41SQSUpK0r59+yRJ/v7+8vf3l3T9W7EPHjyYr7769u2r1q1bKyYmxqF927ZtunLlikN7jRo1VKFCBSUkJDhTNgAAKGScCjI9evTQ999/n6V98+bN6tGjR577mT9/vrZv364xY8Zk2Xf8+HF5eXmpRIkSDu3BwcE6fvx4jn2mp6crLS3NYQMAAIWTU0Fmx44datKkSZb2Ro0aZXs1U3aOHDmigQMHau7cufLx8XGmjGyNGTNGgYGB9i0sLMxlfQMAgILF6TUy58+fz9Kempqa52++3rZtm06cOKG6deuqaNGiKlq0qNauXauJEyeqaNGiCg4OVkZGhs6dO+dwXkpKikJCQnLsd9iwYfZ1O6mpqTpy5Ei+XhsAALAOp4JMs2bNNGbMGIfQcu3aNY0ZM0ZNmzbNUx8tW7bUzz//rJ07d9q3+vXrq2vXrvafPT09tWrVKvs5iYmJOnz4sKKjo3Ps19vbWwEBAQ4bAAAonJy6/Prtt99Ws2bNVL16dT344IOSpPXr1ystLU3fffddnvrw9/dXzZo1Hdr8/PwUFBRkb+/Zs6eGDBmiUqVKKSAgQP3791d0dLQaNWrkTNkAAKCQcWpGJjIyUj/99JOefPJJnThxQufPn1e3bt20Z8+eLOHkVrz33nt6/PHH1alTJzVr1kwhISH6/PPPXdY/AACwNqdmZKTrN8AbPXq0K2vRmjVrHB77+Pho8uTJmjx5skufBwAAFA5OzchI1z9KevbZZ9W4cWMdPXpUkjRnzhxt2LDBZcUBAADkxqkgs3jxYsXGxsrX11fbt29Xenq6pOtXLbl6lgYAACAnTgWZUaNG6YMPPtD06dPl6elpb2/SpIm2b9/usuIAAABy41SQSUxMVLNmzbK0BwYGZrnvCwAAwO3iVJAJCQnR/v37s7Rv2LBBlStXvuWiAAAA8sKpINOrVy8NHDhQmzdvls1m07FjxzR37ly98sor6tOnj6trBAAAyJZTl18PHTpUmZmZatmypS5duqRmzZrJ29tbr7zyivr37+/qGgEAALLlVJCx2WwaPny4/v73v2v//v26cOGCIiMjVbx4cVfXBwAAkCOn7yMjSV5eXoqMjFSNGjW0cuVK/fLLL66qCwAA4KacCjJPPvmkJk2aJEn6/fff9cADD+jJJ59UVFSUFi9e7NICAQAAcuJUkFm3bp39yyK/+OILZWZm6ty5c5o4caJGjRrl0gIBAABy4lSQSU1NValSpSRJ8fHx6tSpk4oVK6bWrVtr3759Li0QAAAgJ04FmbCwMCUkJOjixYuKj4/XI488Ikk6e/asfHx8XFogAABATpy6amnQoEHq2rWrihcvrvDwcDVv3lzS9Y+catWq5cr6AAAAcuRUkPnb3/6mBg0a6MiRI3r44Yfl4XF9Yqdy5cqskQEAAHeMU0FGkurXr6/69evLGCNjjGw2m1q3bu3K2gAAAHLl9H1kPv74Y9WqVUu+vr7y9fVVVFSU5syZ48raAAAAcuXUjMz48eP1+uuvq1+/fmrSpImk618Y+de//lWnTp3S4MGDXVokAABAdpwKMv/+9781depUdevWzd7Wtm1b3XfffRoxYgRBBgAA3BFOfbSUnJysxo0bZ2lv3LixkpOTb7koAACAvHAqyFSpUkWfffZZlvYFCxaoatWqt1wUAABAXjj10VJcXJy6dOmidevW2dfIbNy4UatWrco24AAAANwOTs3IdOrUSVu2bFHp0qW1ZMkSLVmyRKVLl9aWLVvUoUMHV9cIAACQrXzPyFy5ckUvvfSSXn/9dX3yySe3oyYAAIA8yfeMjKenpxYvXnw7agEAAMgXpz5aat++vZYsWeLiUgAAAPLHqcW+VatW1ciRI7Vx40bVq1dPfn5+DvsHDBjgkuIAAABy41SQmTFjhkqUKKFt27Zp27ZtDvtsNhtBBgAA3BFOBZmkpCRX1wEAAJBvTn9pJAAAgLs5fR+Zt99+O0v7uHHj1Llz51suCgAAIC+cCjLr1q3TY489lqW9VatWWrdu3S0XBQAAkBdOBZkLFy7Iy8srS7unp6fS0tJuuSgAAIC8cCrI1KpVSwsWLMjSPn/+fEVGRt5yUQAAAHnh1FVLr7/+ujp27KgDBw6oRYsWkqRVq1bp008/1cKFC11aIAAAQE6cCjJt2rTRkiVLNHr0aC1atEi+vr6KiorSypUr9Ze//MXVNQIAAGTLqSAjSa1bt1br1q1dWQsAAEC+cB8ZAABgWXmekSlVqpT27t2r0qVLq2TJkrLZbDkee+bMGZcUBwAAkJs8B5n33ntP/v7+kqQJEybcrnoAAADyLM9Bpnv37tn+DAAA4C5OL/aVpBMnTujEiRPKzMx0aI+KirqlogAAAPLCqSCzbds2de/eXb/88ouMMQ77bDabrl275pLiAAAAcuPUVUsvvPCCqlWrpu+//16//vqrkpKS7Nuvv/6a536mTp2qqKgoBQQEKCAgQNHR0Vq2bJl9/+XLl9W3b18FBQWpePHi6tSpk1JSUpwpGQAAFEJOzcj8+uuvWrx4sapUqXJLT16+fHmNHTtWVatWlTFGs2fPVrt27bRjxw7dd999Gjx4sJYuXaqFCxcqMDBQ/fr1U8eOHbVx48Zbel4AAFA4OBVkWrZsqR9//PGWg0ybNm0cHr/11luaOnWqNm3apPLly2vGjBmaN2+e/WsQZs6cqYiICG3atEmNGjW6pecGAADW51SQ+c9//qPu3btr165dqlmzpjw9PR32t23bNt99Xrt2TQsXLtTFixcVHR2tbdu26cqVK4qJibEfU6NGDVWoUEEJCQkEGQAA4FyQSUhI0MaNGx3Ws9yQ38W+P//8s6Kjo3X58mUVL15cX3zxhSIjI7Vz5055eXmpRIkSDscHBwfr+PHjOfaXnp6u9PR0++O0tLQ81wIAAKzFqcW+/fv317PPPqvk5GRlZmY6bPm9Yql69erauXOnNm/erD59+qh79+7avXu3M2VJksaMGaPAwED7FhYW5nRfAACgYHMqyJw+fVqDBw9WcHDwLRfg5eWlKlWqqF69ehozZozuv/9+vf/++woJCVFGRobOnTvncHxKSopCQkJy7G/YsGFKTU21b0eOHLnlGgEAQMHkVJDp2LGjVq9e7epaJEmZmZlKT09XvXr15OnpqVWrVtn3JSYm6vDhw4qOjs7xfG9vb/vl3Dc2AABQODm1RqZatWoaNmyYNmzYoFq1amVZ7DtgwIA89TNs2DC1atVKFSpU0Pnz5zVv3jytWbNGy5cvV2BgoHr27KkhQ4aoVKlSCggIUP/+/RUdHc1CXwAAIOkWrloqXry41q5dq7Vr1zrss9lseQ4yJ06cULdu3ZScnKzAwEBFRUVp+fLlevjhhyVd/6JKDw8PderUSenp6YqNjdWUKVOcKRkAABRCTgWZpKQklzz5jBkzct3v4+OjyZMna/LkyS55PgAAULg4tUYGAACgIMhzkBk7dqx+//33PB27efNmLV261OmiAAAA8iLPQWb37t2qUKGC/va3v2nZsmU6efKkfd/Vq1f1008/acqUKWrcuLG6dOkif3//21IwAADADXleI/Pxxx/rxx9/1KRJk/TMM88oLS1NRYoUkbe3ty5duiRJqlOnjl588UX16NFDPj4+t61oAAAAKZ+Lfe+//35Nnz5dH374oX766ScdOnRIv//+u0qXLq3atWurdOnSt6tOAACALJy6asnDw0O1a9dW7dq1XVwOAABA3nHVEgAAsCyCDAAAsCyCDAAAsCyCDAAAsKxbCjL79+/X8uXL7TfKM8a4pCgAAIC8cCrInD59WjExMapWrZoee+wxJScnS5J69uypl19+2aUFAgAA5MSpIDN48GAVLVpUhw8fVrFixeztXbp0UXx8vMuKAwAAyI1T95FZsWKFli9frvLlyzu0V61aVYcOHXJJYQAAADfj1IzMxYsXHWZibjhz5oy8vb1vuSgAAIC8cCrIPPjgg/r444/tj202mzIzMzVu3Dg99NBDLisOAAAgN059tDRu3Di1bNlSW7duVUZGhl599VX973//05kzZ7Rx40ZX1wgAAJAtp2Zkatasqb1796pp06Zq166dLl68qI4dO2rHjh269957XV0jAABAtpyakZGkwMBADR8+3JW1AAAA5IvTQeby5cv66aefdOLECWVmZjrsa9u27S0XBgAAcDNOBZn4+Hh169ZNp06dyrLPZrPp2rVrt1wYAADAzTi1RqZ///7q3LmzkpOTlZmZ6bARYgAAwJ3iVJBJSUnRkCFDFBwc7Op6AAAA8sypIPPEE09ozZo1Li4FAAAgf5xaIzNp0iR17txZ69evV61ateTp6emwf8CAAS4pDgAAIDdOBZlPP/1UK1askI+Pj9asWSObzWbfZ7PZCDIAAOCOcCrIDB8+XHFxcRo6dKg8PJz6dAoAAOCWOZVCMjIy1KVLF0IMAABwK6eSSPfu3bVgwQJX1wIAAJAvTn20dO3aNY0bN07Lly9XVFRUlsW+48ePd0lxAAAAuXEqyPz888+qU6eOJGnXrl0O+/648BcAAOB2cirIrF692tV1AAAA5BurdQEAgGXleUamY8eOmjVrlgICAtSxY8dcj/38889vuTAAAICbyXOQCQwMtK9/CQwMvG0FAQAA5FWeg8zMmTM1cuRIvfLKK5o5c+btrAkAACBP8rVGJi4uThcuXLhdtQAAAORLvoKMMeZ21QEAAJBv+b5qifvEAACAgiLf95GpVq3aTcPMmTNnnC4IAAAgr/IdZOLi4rhqCQAAFAj5DjJPPfWUypYteztqAQAAyJd8rZFhfQwAAChI3HrV0pgxY/TAAw/I399fZcuWVfv27ZWYmOhwzOXLl9W3b18FBQWpePHi6tSpk1JSUlxaBwAAsKZ8BZnMzEyXfqy0du1a9e3bV5s2bdK3336rK1eu6JFHHtHFixftxwwePFhff/21Fi5cqLVr1+rYsWM3/YoEAABwd3Dq269dJT4+3uHxrFmzVLZsWW3btk3NmjVTamqqZsyYoXnz5qlFixaSrt9hOCIiQps2bVKjRo3cUTYAACggCtS3X6empkqSSpUqJUnatm2brly5opiYGPsxNWrUUIUKFZSQkJBtH+np6UpLS3PYAABA4VRggkxmZqYGDRqkJk2aqGbNmpKk48ePy8vLSyVKlHA4Njg4WMePH8+2nzFjxigwMNC+hYWF3e7SAQCAmxSYINO3b1/t2rVL8+fPv6V+hg0bptTUVPt25MgRF1UIAAAKGreukbmhX79++uabb7Ru3TqVL1/e3h4SEqKMjAydO3fOYVYmJSVFISEh2fbl7e0tb2/v210yAAAoANw6I2OMUb9+/fTFF1/ou+++U6VKlRz216tXT56enlq1apW9LTExUYcPH1Z0dPSdLhcAABQwbp2R6du3r+bNm6cvv/xS/v7+9nUvgYGB8vX1VWBgoHr27KkhQ4aoVKlSCggIUP/+/RUdHc0VSwAAwL1BZurUqZKk5s2bO7TPnDlTPXr0kCS999578vDwUKdOnZSenq7Y2FhNmTLlDlcKAAAKIrcGmbzcKdjHx0eTJ0/W5MmT70BFAADASgrMVUsAAAD5RZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACW5dYgs27dOrVp00blypWTzWbTkiVLHPYbY/TGG28oNDRUvr6+iomJ0b59+9xTLAAAKHDcGmQuXryo+++/X5MnT852/7hx4zRx4kR98MEH2rx5s/z8/BQbG6vLly/f4UoBAEBBVNSdT96qVSu1atUq233GGE2YMEH/+Mc/1K5dO0nSxx9/rODgYC1ZskRPPfXUnSwVAAAUQAV2jUxSUpKOHz+umJgYe1tgYKAaNmyohISEHM9LT09XWlqawwYAAAqnAhtkjh8/LkkKDg52aA8ODrbvy86YMWMUGBho38LCwm5rnQAAwH0KbJBx1rBhw5Sammrfjhw54u6SAADAbVJgg0xISIgkKSUlxaE9JSXFvi873t7eCggIcNgAAEDhVGCDTKVKlRQSEqJVq1bZ29LS0rR582ZFR0e7sTIAAFBQuPWqpQsXLmj//v32x0lJSdq5c6dKlSqlChUqaNCgQRo1apSqVq2qSpUq6fXXX1e5cuXUvn179xUNAAAKDLcGma1bt+qhhx6yPx4yZIgkqXv37po1a5ZeffVVXbx4Ub1799a5c+fUtGlTxcfHy8fHx10lAwCAAsStQaZ58+YyxuS432azaeTIkRo5cuQdrAoAAFhFgV0jAwAAcDMEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFlF3V0AACBnFYcudXcJlnFwbGt3lwA3YEYGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFveRAQDgT7h/T965+/49lpiRmTx5sipWrCgfHx81bNhQW7ZscXdJAACgACjwQWbBggUaMmSI3nzzTW3fvl3333+/YmNjdeLECXeXBgAA3KzAB5nx48erV69eev755xUZGakPPvhAxYoV00cffeTu0gAAgJsV6CCTkZGhbdu2KSYmxt7m4eGhmJgYJSQkuLEyAABQEBToxb6nTp3StWvXFBwc7NAeHBysPXv2ZHtOenq60tPT7Y9TU1MlSWlpaS6vLzP9ksv7LMxc9Ttg3POHcb/zXPnfG8Y97xh397gdf1//2K8xJtfjCnSQccaYMWMUFxeXpT0sLMwN1eCPAie4u4K7E+N+5zHm7sG4u8ftHvfz588rMDAwx/0FOsiULl1aRYoUUUpKikN7SkqKQkJCsj1n2LBhGjJkiP1xZmamzpw5o6CgINlstttab0GQlpamsLAwHTlyRAEBAe4u567BuLsH4+4ejLt73G3jbozR+fPnVa5cuVyPK9BBxsvLS/Xq1dOqVavUvn17SdeDyapVq9SvX79sz/H29pa3t7dDW4kSJW5zpQVPQEDAXfFGL2gYd/dg3N2DcXePu2ncc5uJuaFABxlJGjJkiLp376769eurQYMGmjBhgi5evKjnn3/e3aUBAAA3K/BBpkuXLjp58qTeeOMNHT9+XLVr11Z8fHyWBcAAAODuU+CDjCT169cvx4+S4Mjb21tvvvlmlo/XcHsx7u7BuLsH4+4ejHv2bOZm1zUBAAAUUAX6hngAAAC5IcgAAADLIsgAAADLIshYTPPmzTVo0CB3l3FXuB1jPW3aNIWFhcnDw0MTJkxwad+FhTFGvXv3VqlSpWSz2bRz5053lwTcEt7TtxdBBrhD0tLS1K9fP7322ms6evSoevfu7e6SCqT4+HjNmjVL33zzjZKTk1WzZs1b6s9ms2nJkiWuKQ5wAu/p28sSl18DhcHhw4d15coVtW7dWqGhoe4up8A6cOCAQkND1bhxY3eXAiddu3ZNNptNHh78f2Wp4L6nr1y5Ik9PT3eXcct4l1nY2bNn1a1bN5UsWVLFihVTq1attG/fPodjpk+frrCwMBUrVkwdOnTQ+PHj78qvbLhVtzrWs2bNUq1atSRJlStXls1m08GDB+/wqyj4evToof79++vw4cOy2WyqWLGi4uPj1bRpU5UoUUJBQUF6/PHHdeDAAfs5GRkZ6tevn0JDQ+Xj46Pw8HCNGTNGklSxYkVJUocOHez93W1yG7+DBw/KZrNp/vz5aty4sXx8fFSzZk2tXbvWfv6aNWtks9m0dOlSRUVFycfHR40aNdKuXbvsx8yaNUslSpTQV199pcjISHl7e+vw4cO5/rs5efKkQkJCNHr0aHs/33//vby8vLRq1ao7NDq33518T3/55ZeqW7eufHx8VLlyZcXFxenq1av2/TabTVOnTlXbtm3l5+ent956646MwW1nYCl/+ctfzMCBA40xxrRt29ZERESYdevWmZ07d5rY2FhTpUoVk5GRYYwxZsOGDcbDw8O88847JjEx0UyePNmUKlXKBAYGuu8FWIgrx/rSpUtm5cqVRpLZsmWLSU5ONlevXnXTKyu4zp07Z0aOHGnKly9vkpOTzYkTJ8yiRYvM4sWLzb59+8yOHTtMmzZtTK1atcy1a9eMMca88847JiwszKxbt84cPHjQrF+/3sybN88YY8yJEyeMJDNz5kx7f3eb3MYvKSnJSDLly5c3ixYtMrt37zYvvvii8ff3N6dOnTLGGLN69WojyURERJgVK1aYn376yTz++OOmYsWK9vf/zJkzjaenp2ncuLHZuHGj2bNnj7l48eJN/90sXbrUeHp6mh9++MGkpaWZypUrm8GDB7ttrG6HO/WeXrdunQkICDCzZs0yBw4cMCtWrDAVK1Y0I0aMsNciyZQtW9Z89NFH5sCBA+bQoUN3fkBuA4KMxdz447p3714jyWzcuNG+79SpU8bX19d89tlnxhhjunTpYlq3bu1wfteuXQkyeeTqsd6xY4eRZJKSku5E+Zb13nvvmfDw8Bz3nzx50kgyP//8szHGmP79+5sWLVqYzMzMbI+XZL744ovbUKk1/XH8bgSZsWPH2vdfuXLFlC9f3rz99tvGmP8fZObPn28/5vTp08bX19csWLDAGHM9yEgyO3futB+Tl383xhjzt7/9zVSrVs0888wzplatWuby5cu37bW7y514T7ds2dKMHj3aoW3OnDkmNDTU4bxBgwY59yIKMD5asqhffvlFRYsWVcOGDe1tQUFBql69un755RdJUmJioho0aOBw3p8f4+YYa/fat2+fnn76aVWuXFkBAQH2qfTDhw9Luj51v3PnTlWvXl0DBgzQihUr3FhtwXOz8ZOk6Oho+89FixZV/fr17e/t7I4pVaqUw/tfkry8vBQVFWV/nJd/N5L0r3/9S1evXtXChQs1d+7cu+L2+7fjPf3jjz9q5MiRKl68uH3r1auXkpOTdenSJftx9evXvy2vyZ1Y7AugQGvTpo3Cw8M1ffp0lStXTpmZmapZs6YyMjIkSXXr1lVSUpKWLVumlStX6sknn1RMTIwWLVrk5soLhpuNn6v4+vrKZrPl+7wDBw7o2LFjyszM1MGDB+1ryQqz2/GevnDhguLi4tSxY8cs+3x8fOw/+/n5uf4FuRkzMhYVERGhq1evavPmzfa206dPKzExUZGRkZKk6tWr64cffnA478+PcXOMtfvcGOd//OMfatmypSIiInT27NksxwUEBKhLly6aPn26FixYoMWLF+vMmTOSJE9PT127du1Ol14g5HX8Nm3aZP/56tWr2rZtmyIiInI85uzZs9q7d2+WY/4oL/9uMjIy9Oyzz6pLly765z//qRdffFEnTpxw+vVawe16T9etW1eJiYmqUqVKlq2wXz3GjIxFVa1aVe3atVOvXr304Ycfyt/fX0OHDtU999yjdu3aSZL69++vZs2aafz48WrTpo2+++47LVu2zKn/13Q3Y6zdp2TJkgoKCtK0adMUGhqqw4cPa+jQoQ7HjB8/XqGhoapTp448PDy0cOFChYSE2K8Yq1ixolatWqUmTZrI29tbJUuWdMMrcY+8jJ8kTZ48WVWrVlVERITee+89nT17Vi+88ILDMSNHjlRQUJCCg4M1fPhwlS5dWu3bt8/xufPy72b48OFKTU3VxIkTVbx4cf33v//VCy+8oG+++cal41CQ3K739BtvvKHHH39cFSpU0BNPPCEPDw/9+OOP2rVrl0aNGuWGV3oHuXuRDvLnj1fSnDlzxjz33HMmMDDQ+Pr6mtjYWLN3716H46dNm2buuece4+vra9q3b29GjRplQkJC3FC59bh6rFnsmzd/Xhj57bffmoiICOPt7W2ioqLMmjVrHBY7Tps2zdSuXdv4+fmZgIAA07JlS7N9+3b7+V999ZWpUqWKKVq0aK4LLgur3MbvxmLfefPmmQYNGhgvLy8TGRlpvvvuO/v5Nxb7fv311+a+++4zXl5epkGDBubHH3+0HzNz5sxsLyLI7d/N6tWrTdGiRc369evtxyclJZmAgAAzZcqU2zcgbnCn3tPx8fGmcePGxtfX1wQEBJgGDRqYadOm2ferkC58txljjBtzFO6wXr16ac+ePVq/fr27Syn0GGsUdAcPHlSlSpW0Y8cO1a5dO9tj1qxZo4ceekhnz57lHlQokPhoqZD717/+pYcfflh+fn5atmyZZs+erSlTpri7rEKJsQaAO48gU8ht2bJF48aN0/nz51W5cmVNnDhRL774orvLKpQYawC48/hoCQAAWFbhviYLAAAUagQZAABgWQQZAABgWQQZAABgWQQZAC4za9asu/ZeIz169Mj1TrcAbg+CDHCX4A+tozVr1shms+ncuXP5Ou/gwYOy2WzauXOnQ/v777+vWbNmuaw+AHnDfWQAwAUCAwPdXQJwV2JGBrgLNW/eXP3799egQYNUsmRJBQcHa/r06bp48aKef/55+fv7q0qVKlq2bJn9nBszGEuXLlVUVJR8fHzUqFEj7dq1K9fn+vLLL1W3bl35+PiocuXKiouL09WrV+37bTabPvzwQz3++OMqVqyYIiIilJCQoP3796t58+by8/NT48aNdeDAgXz3+5///EcdOnRQsWLFVLVqVX311VeSrs+qPPTQQ5Kuf4mfzWZTjx49JEnx8fFq2rSpSpQooaCgID3++OMOz12pUiVJUp06dWSz2dS8eXNJWWe80tPTNWDAAJUtW1Y+Pj5q2rSpwzei3xjPVatWqX79+ipWrJgaN26sxMTEm/36APwBQQa4S82ePVulS5fWli1b1L9/f/Xp00edO3dW48aNtX37dj3yyCN67rnndOnSJYfz/v73v+vdd9/VDz/8oDJlyqhNmza6cuVKts+xfv16devWTQMHDtTu3bv14YcfatasWXrrrbccjvvnP/+pbt26aefOnapRo4aeeeYZvfTSSxo2bJi2bt0qY4z69euX737j4uL05JNP6qefftJjjz2mrl276syZMwoLC9PixYslSYmJiUpOTtb7778vSbp48aKGDBmirVu3atWqVfLw8FCHDh2UmZkp6fodnCVp5cqVSk5O1ueff57ta3/11Ve1ePFizZ49W9u3b1eVKlUUGxurM2fOOBw3fPhwvfvuu9q6dauKFi2a5VunAdyEW7+yEsAd0717d9OuXTtjzPVv9m7atKl939WrV42fn5957rnn7G3JyclGkklISDDG/P9vQZ4/f779mNOnTxtfX1+zYMECY0zWb0Fu2bKlGT16tEMdc+bMMaGhofbHksw//vEP++OEhAQjycyYMcPe9umnnxofH59b6vfChQtGklm2bJnD6zl79mwOI3bdyZMnjSTz888/G2OM/Rujd+zY4XDcH8f3woULxtPT08ydO9e+PyMjw5QrV86MGzfO4flXrlxpP2bp0qVGkvn9999zrQnA/8caGeAuFRUVZf+5SJEiCgoKUq1atextwcHBkqQTJ044nBcdHW3/uVSpUqpevbp++eWXbJ/jxx9/1MaNGx1mSq5du6bLly/r0qVLKlasWJZabjzvn2u5fPmy0tLSFBAQ4FS/fn5+CggIyPJ6/mzfvn164403tHnzZp06dco+E3P48GHVrFkz13NvOHDggK5cuaImTZrY2zw9PdWgQYMsY/XHGkNDQyVdH/MKFSrk6bmAux1BBrhLeXp6Ojy22WwObTabTZLsf8idceHCBcXFxaljx45Z9vn4+GRby43nza0WZ/q90c/NXk+bNm0UHh6u6dOnq1y5csrMzFTNmjWVkZGR63nOcvWYA3cbggyAfNm0aZN9tuDs2bPau3evIiIisj22bt26SkxMVJUqVVxagyv69fLyknR9JueG06dPKzExUdOnT9eDDz4oSdqwYcNNz/uze++9V15eXtq4caPCw8MlSVeuXNEPP/ygQYMGOV0zgKwIMgDyZeTIkQoKClJwcLCGDx+u0qVL53h/mjfeeEOPP/64KlSooCeeeEIeHh768ccftWvXLo0aNcrpGlzRb3h4uGw2m7755hs99thj8vX1VcmSJRUUFKRp06YpNDRUhw8f1tChQx3OK1u2rHx9fRUfH6/y5cvLx8cny6XXfn5+6tOnj/7+97+rVKlSqlChgsaNG6dLly6pZ8+eTr9uAFlx1RKAfBk7dqwGDhyoevXq6fjx4/r666/tsxR/Fhsbq2+++UYrVqzQAw88oEaNGum9996zz1I4yxX93nPPPYqLi9PQoUMVHBysfv36ycPDQ/Pnz9e2bdtUs2ZNDR48WO+8847DeUWLFtXEiRP14Ycfqly5cmrXrl22/Y8dO1adOnXSc889p7p162r//v1avny5SpYseUuvHYAjmzHGuLsIAAXfmjVr9NBDD+ns2bN37dcQACh4mJEBAACWRZABAACWxUdLAADAspiRAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlvX/AGPuzwC4lspxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "y = [65.5, 50.7, 46.8, 2.5, 1.1] \n",
    "x = list(range(len(y)))\n",
    "\n",
    "plt.bar(x, y)\n",
    "plt.xticks(x, ['log', 'logf', 'fast', 'approx', 'faster'])\n",
    "\n",
    "plt.xlabel('Implementation')\n",
    "plt.ylabel('Time (microseconds)')\n",
    "plt.title('Time to compute log')\n",
    "plt.savefig('log.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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