Test_magnetic_data_100hz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pysindy as ps\n",
    "from scipy.integrate import odeint\n",
    "from math import pi\n",
    "from pysindy.differentiation import FiniteDifference\n",
    "fd = FiniteDifference(order=2, d=1)\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import glob\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import timeit\n",
    "\n",
    "from scipy.io import loadmat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'ac100.txt'\n",
    "data = np.loadtxt(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.83956238e+03, 1.59964649e+00],\n",
       "       [2.84138034e+03, 1.59970508e+00],\n",
       "       [2.84410727e+03, 1.59972187e+00],\n",
       "       ...,\n",
       "       [2.82138281e+03, 1.59854542e+00],\n",
       "       [2.82865464e+03, 1.59974460e+00],\n",
       "       [2.83410851e+03, 1.59905279e+00]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = data[:,1]\n",
    "#x = x*100\n",
    "y = data[:,0]\n",
    "#plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(0, 10, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi\n",
    "x = 20*np.sin(pi*t/5 - 3*pi/2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f769c1ac8d0>]"
      ]
     },
     "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(t, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f769c0b6410>]"
      ]
     },
     "execution_count": 9,
     "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(t, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal import savgol_filter\n",
    "yhat = savgol_filter(y, 51, 3) # window size 51, polynomial order 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f769c045d90>]"
      ]
     },
     "execution_count": 12,
     "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(t, y)\n",
    "plt.plot(t, yhat)\n",
    "plt.plot(t, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f769bfd4110>]"
      ]
     },
     "execution_count": 13,
     "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": [
    "dx = fd._differentiate(x, t)\n",
    "plt.plot(t, dx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f769bf4c4d0>]"
      ]
     },
     "execution_count": 14,
     "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": [
    "mdx = np.abs(dx)\n",
    "plt.plot(t, mdx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f769becc6d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tkWZOr4qroaCUS4ZnE3QUORTg8hLaqvQuTi6QTOc4WMT+BJu9Ku5cIlP0915OQ0EpFyymMswlM0VvPoJ8v8JwTJuPyoETncw2e1CB08uaaCgo5YLRufxN24magk5gKx+vDs9R4/Wwu4hzFGxujTTTUFDKBfZs5mL3KUD+ZhFLpFlIOdusoK7t1ZE59raH8RdpeYvlOu2RZg6viquhoJQLRqxf5E4Hmo/s2oeugVRaxhhODs9xsKP4TUcALXUBV1bF1VBQygX2xLXWSPEmrtm6XGprVlc3NpdienGJQ53FH3kEl1fFHXF4+LGGglIuGI4l2VJXQ9BfvIlrNruteUQ7m0vq1ZEYgCMjj2wdDUHtU1BqMxiNJehoLH5/AkBrfQCvR7SzucRODuVHHu1vL96aR1dyY1CBhoJSLhiJJWmPFL8/AcDn9dAeCWrzUYmdGJxlZ0sd9UG/Y9foagw5viquhoJSLsgvceFMTQHyN4tBl7ZrVK9njOF4/wxv3O7sZpGdLqyKq6GglMPiSxliibRjzUcAO6N1nHdhXRy1svMTi8zE0xzpcTYU3BhppqGglMPskUdO1hR2RcNMLS4xs7jk2DXU6o735xd0fuP2Zkev0xbJ/wyNzWlNQamKZY8KcqpPASjMoL0wqbWFUjjWN0NjrZ+dLXWOXqfdCgV7hrwTNBSUcpgdCp0ONh/tiuZD4fz4omPXUKt7tm+aI9ub8HiKu1z2lRpr/dT4PIxrKChVuUat2cx21d8JXU0hanweV9bbV6/VP7VI/1Sct+xucfxaIkJbJKA1BaUqmZMT12xej7ArGubM6Lxj11Are+rcJABv2xt15Xpt9UHGNBSUqlxObK6zkkOdkcLSzco9/3B2gq7GEDsc7k+wtTUEtaNZqUrm9BwF28GOCJMLKUfbm9VrpTJZfnh+irftjRZ9+83V2DUFp3Zg01BQymEjsYQjm+tcyd7YRWsL7nny9ATzqQx3HWpz7ZrtDQHiS1nmHVoqXUNBKQcllrLMxtOuNB8dKIRCzPFrqbyvvjBISzjAW13oZLbZAxacqhFqKCjlICd3XLtSJOhnZ7SOFy7NOn4tBTOLS/z96XHue0MnPgc21VmNHQqjMWf6FTQUlHKQvRyBG81HAEd7mjnWP0Mu50x7s7rs/z57iXTW8BNv7Hb1updnNWtNQamKM+LCEhfLHelpJpZIc3Zch6Y6KbGU5ZF/vMg79kU54NBOa6tpszZqcmquQtWGwqe/28uXjw2Uuhhqk7N/cd3oU4B8TQHg2YvTrlyvWn3xuUtMLy7x0Dt3u37t2hof9UGf9ikU2zdOjPD4K6OlLoba5IZnEzQ7PHFtua3NIXq21PKdU+OuXK8azSXTfOrJ8xztaebWHmcXwFtNeySoNYVia40EGHdwTXKlwJq45uDyFlcSEe461M4Pzk8yn0y7dt1q8tvfOs3UYopfv/dgycrQFnFuAlvVhkI0HGBCQ0E5zK2Ja8vddbCNdNbwxKtjrl63Gnz1hUH+/OlLfPiOHdzY3VCycuRDQWsKRdUaCTC5kNJRGspRIw7uzbyaW7Y1sStaxyP/eNGxWa/V6FuvjPLL/+8l3rSjmV+5Z39Jy9JmtXQ4cf+q2lCIhgNkcoaZuG5KopyRTGeZiaddG45q83iEf/XWnZwcnuMH56dcvfZmlM0ZPv3dXn7hL5/npu4GHvnQrfhdnJewkhu7Gnj3gVYS6WzR37t6Q6E+/9fbxII2ISlnjBY213G3pgDw/pu7aIsE+PW/eYVZ/cNn3XrHF/iZzz7Db3/rDG/bE+XPfvYo4YCv1MXinhs7+JMPHKHOgbJUbSi0WmN9xx1cbVBVt+GYPXHN/VAI+r380U/dwuBMgp/638/w/KUZ18tQqZLpLF967hL3P/xD3v273+PE4Cy//eM38ciHbqUh5C918RxX+sgrkWg4Hwra2aycMjJrTVxrdLf5yHZrTzOf+Zlb+MRfv8yPffoHvO9wJw/cvp1DnQ2uDZGtFLPxJZ6/NMMzF6f5q+ODTC4ssbOljn//nn38s1u30mLdL6pB2YWCiNwN/AHgBT5rjPktJ64TrbdqChoKyiFurnu0mncdaOPJX97CH3/vPA8/dYFHTwzj9woHOiJs31LH7miYm7c1Eq0P0N0UIhzwubYEdCnkcob5ZIbeiXnOjy9yvH+GY/3TnJ/Ib2Pq8whv2dPCv37bLm7b2byp/1+spqxCQUS8wKeAHwEGgedE5FFjzKvFvlZdwEddjVdrCsoxbk9cW01dwMdH79rHh27v4Vj/DMf7Z3hpcJbn+2f4xkvDLB+gVFvjZWe0jsZQDc11NeyM1hXK3xDy45H8XAiPCJGgj8baGmprvNaXj1CNl7lEmrqAj4aQH++yPYuNMWu+yS5/TSabYyGVwef1sJTJsZTJkc7mSGVyJNNZUpksybT9OMdiKsPYXJL5ZIa5ZJqzYwv0Ty0yuXC5j6Uh5OeN25v4sVu6eeP2Jg53NxKqqe5aVFmFAnAU6DXGXAAQkS8C9wFFDwXI1xY2Q0dzLJFmIZUhnspQ4/OwkMowvbjEYirDfDJDNmfweoRYIs18MsPAdJxwMP8LbA9pS2Vy5IxhIZlhIZUlnc1RW+PF5/WQzeUwBowBv89DOOAjvpQhkzPkcgaPR2iq9ZPOGOLpLF2NIWprvAT9HjwihGq81Hg91Pjy3wd8HtJZw67WOiLB/EbkTbU1r7mBbAYjLk9cu5Yt4QDvOdTOew61F47NJdOcGJhlenGJwZkEE/MpLk4uMp9M0z+9yNevCI218nsFv9dT+BkMB3wspjI0hPykMjkS6Sxej9BY6yeVzjEbT9NU5yeZzj+3lMlRV+MlnTUYDOns2gsT8HmoD/rpagpx284t7G+vZ0dLmH3tYXa2hPFssp+7jSq3UOgCli9INAi86cqTRORB4EGAbdu2rftirfXBst6lKpPNcWFykQsTC0wtLjG1sMRILMn4XJLx+VThr6C1DEsTgY5IkPlUhlQmh9f6K6zG50Ekv/yyzyv4PR4WlzJ4PflfarFem8rkWEhmqAv48HkFn0eYT2ZIprP4vB6S6SzzybVv/hHye9nbXs+R7U10NoZoiwS4eVsTnQ3Biq3CD88m6G4qTX/C9YoE/bx1z+p7C8eXMuRM/i/2hVT+cS5nyBlT+CNjMZX/GVxMZVlIpYkE/STS+X0klrI50pkcHo+wlMkxl8w/P5/MEKrxEPR5yeQMs/ElAj4v4aCP+WSakN9LsMaL3+MhvpSlxpcfE9MSriFnDDVeD36fB7/HQ8DvIej35r98lx+H/F5aI4GS19QqTbmFwnUxxjwMPAxw5MiRdf8dE60PcGq0fHapmlpI8b2zEzx9YYoXB2YZmkmwuPTaG35LuIbW+iCtkQAHOuoJB/y01NcQCfqprcn/gkWCPprrAtQFvNQH8qMl0rkcTVZV3+lfkmzOkM0ZEktZxJNfUdKu6seXsmRyBo9A/1ScuWSaZDrH0EyCE4OzfOGHfa/5azDk99LdFGJ/R4R7bmjnzv2tFfNLPhJLlmxtnGKprbl8i6gPbv6RN6r8QmEI2Lrs+27rmCOi9QGeOlva5iNjDN89M8GfPHWepy/kV7ZsrPVz89ZGbtu5hRs6GzjYGaElHKCpzk/AV/43RK9H8Hqk8NddZJWbyU3dja87ZoxhNp6mb2qRFwdmGZxJMDAd5we9k3z9xDAAh7sb+Ge3buO+N3Q6Mk67GOJLGWKJtOuzmZXaqHL7jXoO2CMiO8iHwf3ATzl1sWh9gPlUhsRStiSdS8l0lgf/z3GeOjtBR0OQX7xzNz9ysJ1DnZGqbecUEZrqamiqq+HmbU2F49mc4fu9kxzrn+HxV0b51a++zH977BQ/8+btPPTO3WUxoWg5ex+FTpdnMyu1UWX1m2SMyYjIzwOPkx+S+jljzEmnrmcPS51cSLG1udapy6wolzP8/P99nn84N8Fv3HuQD7x5e8mnzpczr0d4294ob9sb5d+9ew/PX5rhT7/fx2e+e55HXxzmt3/iJu5wcZ/ca7HnKLi1j4JSxVJ2dyFjzGPGmL3GmF3GmN908lqthbkK7nc2f+nYAN8+Nc5v3HuQf/GWHRoIayAivHF7M3/0U7fwlX/zZgI+Dz/92Wf4wg/7Sl20Ans2s9YUVKWp6juRXVNwe65CLmf4k++d5/DWRj50e4+r195sjvQ087e/+FZ+5GAbv/G1k/yv75wri5VB7ZpCW0P1zIRVm0NVh0KrtSie27Oav3d2gr6pOB9+y46KHW5ZTkI1Xj7z07fwYzd38T+fOMv/ebq/1EViJJagJRyoiIEBSi1X1aHQXFeDR9yvKXz9xDCNtX7uuaH92ier6+LzevidnzzMO/dF+S/fOMWpkdIONR6JJenUkUeqAlV1KHg9wpZwwNWVUtPZHN85Pc6d+1u1H6HIPB7hd37yMA21fn75/50o6QZKI7FESdc8Umq9qv6u1OryUhfPXZwmlkhz10GtJThhSzjAf/jRA5wcnuPrLw2XrBwjs0nXN9dRqhiqPhSi9e7u1fz985N4rZUYlTP+yU2dHOiI8D//7ixLmZzr159PpplPZbSmoCpS1YdCa33A1SGpz16c5oauhrKbbLWZeDzCx+7ex6XpOH/zomMT4ldlT1wr1T4KSm1E1YdCtD7A5MKSK+3PyXSWEwMxbttR2evhVIJ37I2yty3MF37Y5/oQ1cuzmbWmoCqPhkI4QDZnmHZhH9sTA7MsZXMc1VBwnIjwgTf38MrQHC8Nxly99sistQ2n1hRUBar6ULCXIbA3WXfSyeH8MMmVFoJTxfe+w534vcI3XO5wHo4lEbk8Y16pSlL1odBmbYIy5sK+CqdG5mgJBwozqZWzGkJ+3rYnyt++NOJqE9LIbILW+oAOOVYVqep/ags1BTdCYXSOAx31jl9HXfbeGzsYjiV5eci9JqSRmA5HVZWr6kMhGg7gERhzuPkok81xdmyBAx0RR6+jXuvt+/K7ij11dsK1aw7NJugq8x3XlFpN1YeCz+shWh8ojBhxysXJRZYyOa0puKwlHODGrga+51Io5HKGodkE3drJrCpU1YcCQHsk6Hjz0avWWjz727Wm4La3743y/KVZYom049eaXEyxlMlpTUFVLA0F8p3NTnc0nx2bx+sRdkXDjl5Hvd7b90XJ5gw/6J10/FpDM/nhqF1aU1AVSkOBfGez00NS+6bibG0KFfYtVu453N1I0O/hmYvTjl9ryJqj0KmhoCqU3qHIh8JcMkN8KePYNfqnFtm+pc6x91erq/F5uHlrE8/1uRAKdk1Bm49UhdJQIN+nAM5NYDPG0D8Zp2eLu/tAq8tu3dHMqZE55pLO9isMzSaoD/qIBP2OXkcpp2gosCwUHOpXmInnV83UmkLpHO1pJmfgeP+Mo9cZmklof4KqaBoKQFuDs7Oa+6YWAehp0ZpCqdy8rRGvR3jO4X6FodkE3dp0pCqYhgKXawpOzVXot0JhW7PWFEqlLuBjX1u94zObtaagKp2GAvkbRn3Q59is5r7JOCKwtVlvFqV0Q1eEk8Nzjq2DFEvkmwm1k1lVMg0Fi5MT2PqnFulsCBHweR15f3V9DnU2ML245Ni/8+U5CtpMqCqXhoKlvSHI6Jwz23L2TcW1P6EM3NCVn03+ytCcI+8/PKvDUVXl01CwtEeCjMYSjry3zlEoD/vbI4jAKw71K9gT17RPQVUyDQVLe0OQifkUmWxxN3qfT6aZiafZ1qw1hVKrC/jY2VLHyWHnQiHg89ASrnHk/ZVyg4aCpS0SJGdgcqG423LaE+I6dL/esnCws4FTI/OOvLc98khEHHl/pdygoWC5PCy1uE1Iw/Ym7tqkUBb2t9czNJtwZGbzoO6joDYBDQWLfdMu9lyFwibuWlMoC/vb8/tZnB0tfm1hcDqu/Qmq4mkoWOxfZntYYbGMWJu423tBq9LaZ4XC6SKHwkIqw9TiEtt0fStV4TQULJGQj3DAVxhBUiwjsQTRsG7iXi66GkPUB3ycKXIoDEzHAdius9ZVhdM7lUVE6GwMOhAKSTq0SaFsiAh72+uLHgr9U/lQ0FFmqtJtKBRE5H+IyGkReUlEvioijcue+4SI9IrIGRF5z7Ljd1vHekXk48uO7xCRZ6zjXxIR18f1dTWGit58NDyboFP7E8rKvvZ6To8Wd7kLu6agoaAq3UZrCk8ANxhjbgLOAp8AEJGDwP3AIeBu4NMi4hURL/Ap4B7gIPDPrXMB/jvwe8aY3cAM8OENlm3NuppCDBdx9JExhpFYknYNhbKyv72euWSmqMtd9E8v0hDy01Cr+yioyrahUDDG/J0xxt6u7Gmg23p8H/BFY0zKGHMR6AWOWl+9xpgLxpgl4IvAfZIf2H0n8BXr9Z8H3r+Rsq1HZ2OI2XiaxVRxdmDL7+aWpbNBm4/Kyb624nc2X5pOaC1BbQrF7FP4F8A3rcddwMCy5watY6sd3wLMLgsY+/iKRORBETkmIscmJiaKVPzLI5CGi9SvYM956GjUmkI52d+eXwOpmP0KA9NxHXmkNoVrhoKIfFtEXlnh675l5/wakAH+wsnC2owxDxtjjhhjjkSj0aK9rx0Kg0ULBZ3NXI4aav20R4JFC4VszjA4E9eagtoUfNc6wRjz7qs9LyIfAu4F3mUu99wNAVuXndZtHWOV41NAo4j4rNrC8vNdY89GLVpNYdYOBW0+Kjd72sL0ji8U5b1GYgnSWaOhoDaFjY4+uhv4GPA+Y0x82VOPAveLSEBEdgB7gGeB54A91kijGvKd0Y9aYfIk8BPW6x8AvraRsq1Ha30Qn0eKNgJpJJbAI9BaHyjK+6ni2dtWT+/4ArncxkcgXZqy5yhoKKjKt9E+hT8C6oEnRORFEfljAGPMSeDLwKvAt4CHjDFZqxbw88DjwCngy9a5AL8C/JKI9JLvY3hkg2VbM69HaG8IFrFPIZkPGp24Vnb2tIZJpLNFmZdyyRqOulVDQW0C12w+uhpr+Ohqz/0m8JsrHH8MeGyF4xfIj04qqc7GUNEmsI3EEtrJXKb2tIUBODc+v+Gb+aXpOD6P6KKHalPQP2Gv0N0YYni2OOPXR2aTOhy1TO2O5oelnhvbeL9C/3Sc7qYQXo8uma0qn4bCFbqaQozOJTe82Y49cU1HHpWnhlo/rfUBzhWhs7lvcpGeFl3zSG0OGgpX6GwMkc0ZxuY3tl9zLJEmkc7qbOYytretnnNjGxuWaozh4uQiOzQU1CahoXCFYi2hbTdBaTtz+drdGubc+MKG1kAan08RX8qyU0NBbRIaClewb+JDs/FrnHl1hdnMWlMoW3vawsSXsoXd8dbjwsQiADtawsUqllIlpaFwhW5rAtvA9MZqCpdnM2tNoVztabU7m9ffhHRx0gqFqNYU1OagoXCFoN9LeyRYGHu+XiOxBD6PENWJa2VrT6s1LHUDI5AuTi4Q8Hno0J311CahobCCbc21hVmq6zUym6QtEtRhimWsqa6GlnCAc+MbqynsaKnDo//OapPQUFjB1ubaItQUdB+FSrDH6mxerws68khtMhoKK9jWXMvoXJJkOrvu9xiJJbSTuQLsaQvTO7a+EUiZbI5LU3ENBbWpaCisYNsWawntdQ5LtSeu6XDU8renNcx8KsPY3NrnpQzOJMjkjIaC2lQ0FFawrTn/Sz6wziakmXiaVCanNYUKsNsagXR2HSOQ7KW3d0Z1OKraPDQUVmCvi7/efgV7lVUNhfK3t7Aw3tr7Fc5YQWK/h1KbgYbCClrCNYT83nWHgs5RqBxbwgGa62roXccIpLNj83Q1hqgP+h0omVKloaGwAhHJD0tdZyiM6t7MFWV3a3hdcxXOjM5rLUFtOhoKq9i6gbkKw7Ekfq/QUqcT1yrBntYwZ8fm1zQCKZPNcWFikb1t9Q6WTCn3aSiswq4prGeo4shsgrZIUCc0VYg9rWHmkhkm1rAybt9UnKVsTkNBbToaCqvY1hwikc4yubC05tcOx3RznUqyx7qxr6Wz+Wyhk1lDQW0uGgqr2LZl/SOQRmNJ7U+oIIWtOdcwLPXs2Dwi+f4IpTYTDYVVrHeuQi5nGNUlLipKNBygIeTnzBo6m8+OzbO9uZZQjdfBkinlPg2FVdhLaK+1pjC5kGIpm6NbZzNXDBHhUGeEk8Ox637NyeE59rdHHCyVUqWhobCK9S6hPWAtjdHdVOtEsZRDbuxq4PTIPEuZa+/NHUuk6Z+Kc2N3gwslU8pdGgpXsZ4ltAdn8ud3NWlNoZLc0NXAUjZ3XctdnBzK1yhu7NJQUJuPhsJVbN9Sy8WpxTW9Zsha4qJLm48qin2Df2Xo2k1IL2soqE1MQ+EqdrWGmZhPMZdMX/drBmcSNNfVUBfwOVgyVWzbmmupD/gKN/yreWkwRldjiKa6GhdKppS7NBSuYqe1JLK9Ofv1GJpJaC2hAnk8wqGuyDVrCsYYnu2b5khPk0slU8pdGgpXscsag35+DZOaBmfihZFLqrLc2NXAqdF50tnVO5svTceZmE9xa0+ziyVTyj0aClexrbkWn0e4MHl9oWCMYWhWawqV6g1bm1jK5K7ahPTsxWkAju7QUFCbk4bCVfi9HrZtqeX8+PU1H00tLpFM57SmUKFu25m/0f+gd3LVc57rm6ax1s9u3VhHbVIaCtewKxq+7pqCvX1nl85RqEhbwgEOdkT4x1VCwRjDMxenObK9SRc7VJuWhsI17IzW0TcZJ5u79mqp9hwFrSlUrrfsaeH5/lniS5nXPXd+YpH+qThv3xstQcmUcoeGwjXsioZZyuYKN/yrGSrUFDQUKtUdu1tYyuZ45sL065779qkxAN51oM3tYinlGg2Fa9gVzQ9L7b2OEUh9U3Gaav1EdHvGivWmHc1Egj6+9uLQ6577zqkxDnZE6NSBBGoTK0ooiMhHRcSISIv1vYjIH4pIr4i8JCK3LDv3ARE5Z309sOz4G0XkZes1fygiZdFoa6+1f+Y6lj+4MLHATu2ArGhBv5d7D3fyrZOjLKQuNyGNxpIc75/h3QdaS1g6pZy34VAQka3AXcClZYfvAfZYXw8Cn7HObQY+CbwJOAp8UkTsWUCfAf7VstfdvdGyFUMk6KerMcTpkWuHwsXJRXZYE95U5frxW7pJpnP87UvDhWN/+v2LiAg/eWRrCUumlPOKUVP4PeBjwPKe2PuAL5i8p4FGEekA3gM8YYyZNsbMAE8Ad1vPRYwxT5v8/pdfAN5fhLIVxYGOek6NzF31nPlkmvH5FDujGgqV7pZtjexvr+dTT54nlckyPJvgz5/u5703drC1WUeWqc1tQ6EgIvcBQ8aYE1c81QUMLPt+0Dp2teODKxxf7boPisgxETk2MTGxgU9wfQ50RLgwuUgynV31nL7JfEf0Tq0pVDwR4dd+9ACXpuN85C9f5EN/+iwG+Pd37St10ZRy3DVXbRORbwPtKzz1a8Cvkm86cpUx5mHgYYAjR45ce6zoBu1vj5DNGc6NLay6hr49l0H7FDaHt+6J8gt37uaPv3cer0f43x88UtiiVanN7JqhYIx590rHReRGYAdwwuoT7gaeF5GjwBCwvPG12zo2BLzjiuPftY53r3B+WbjJCoIXBmZWD4WJRUTyy22rzeGjd+3jgdt7MAai9YFSF0cpV6y7+cgY87IxptUY02OM6SHf5HOLMWYUeBT4oDUK6TYgZowZAR4H7hKRJquD+S7gceu5ORG5zRp19EHgaxv8bEXT3RSiLRLgWN/MqudcmFykuylEwKd79m4mLeGABoKqKk4t+v8Y8F6gF4gDPwtgjJkWkf8MPGed95+MMfYsoZ8D/gwIAd+0vsqCiHCkp5nj/auHwqmROfZZw1eVUqpSFS0UrNqC/dgAD61y3ueAz61w/BhwQ7HKU2xHtjfxty+NMDybeN3kpflkmvMTC7zvcGeJSqeUUsWhM5qv05Ht+RU0j61QW3h5KIYxl/selFKqUmkoXKcDHfXU1ng51vf6NXFeGsyvv39Td6PLpVJKqeLSULhOPq+Hm7c1rtjZfGJglm3NtTTrnr1KqQqnobAGR7Y3c3p0jlg8XThmjOHEwKw2HSmlNgUNhTV45/5WcgYef3W0cOz06DzDsSS372opYcmUUqo4NBTW4HB3A1ubQ3zjpZHCsW++PIJH4K5Dusa+UqryaSisgYjwT27q5Pu9k/RNLpLLGR49MczRHc20hHWCk1Kq8mkorNGHbu/B5xF+94mzfO3EEH1TcX76TdtLXSyllCoKp2Y0b1qtkSD/8q07+NST53n0xDCHuxt4740dpS6WUkoVhYbCOvzyXfvY2lTL2bEF/vXbd+L1lMUmcUoptWEaCusgItx/dFupi6GUUkWnfQpKKaUKNBSUUkoVaCgopZQq0FBQSilVoKGglFKqQENBKaVUgYaCUkqpAg0FpZRSBZLfTrlyicgE0L/Ol7cAk0UsTiXQz1wdqu0zV9vnhY1/5u3GmOiVBys+FDZCRI4ZY46Uuhxu0s9cHartM1fb5wXnPrM2HymllCrQUFBKKVVQ7aHwcKkLUAL6matDtX3mavu84NBnruo+BaWUUq9V7TUFpZRSy2goKKWUKqjKUBCRu0XkjIj0isjHS10ep4nIVhF5UkReFZGTIvKRUpfJLSLiFZEXROQbpS6LG0SkUUS+IiKnReSUiLy51GVymoj8O+vn+hUR+UsRCZa6TMUmIp8TkXEReWXZsWYReUJEzln/bSrGtaouFETEC3wKuAc4CPxzETlY2lI5LgN81BhzELgNeKgKPrPtI8CpUhfCRX8AfMsYsx84zCb/7CLSBfwicMQYcwPgBe4vbakc8WfA3Vcc+zjwHWPMHuA71vcbVnWhABwFeo0xF4wxS8AXgftKXCZHGWNGjDHPW4/nyd8oukpbKueJSDfwo8BnS10WN4hIA/A24BEAY8ySMWa2pIVyhw8IiYgPqAWGS1yeojPGPAVMX3H4PuDz1uPPA+8vxrWqMRS6gIFl3w9SBTdIm4j0ADcDz5S4KG74feBjQK7E5XDLDmAC+FOryeyzIlJX6kI5yRgzBPwOcAkYAWLGmL8rbalc02aMGbEejwJtxXjTagyFqiUiYeCvgH9rjJkrdXmcJCL3AuPGmOOlLouLfMAtwGeMMTcDixSpSaFcWe3o95EPxE6gTkR+prSlcp/Jzy0oyvyCagyFIWDrsu+7rWObmoj4yQfCXxhj/rrU5XHBHcD7RKSPfBPhnSLy56UtkuMGgUFjjF0L/Ar5kNjM3g1cNMZMGGPSwF8Dt5e4TG4ZE5EOAOu/48V402oMheeAPSKyQ0RqyHdKPVriMjlKRIR8O/MpY8zvlro8bjDGfMIY022M6SH/b/z3xphN/RekMWYUGBCRfdahdwGvlrBIbrgE3CYitdbP+bvY5J3ryzwKPGA9fgD4WjHe1FeMN6kkxpiMiPw88Dj5kQqfM8acLHGxnHYH8AHgZRF50Tr2q8aYx0pXJOWQXwD+wvqD5wLwsyUuj6OMMc+IyFeA58mPsnuBTbjkhYj8JfAOoEVEBoFPAr8FfFlEPkx++4B/WpRr6TIXSimlbNXYfKSUUmoVGgpKKaUKNBSUUkoVaCgopZQq0FBQSilVoKGglFKqQENBKaVUwf8HJehGj1M8+HIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dy = fd._differentiate(yhat, t)\n",
    "plt.plot(t, dy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "my = np.abs(y)\n",
    "my = my.reshape(-1,)\n",
    "mx = np.abs(x)\n",
    "mx = mx.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.reshape(-1,)\n",
    "dy = dy.reshape(-1,)\n",
    "mdy = np.abs(dy)\n",
    "mdy = mdy.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.stack((y, x, dx, mdx, my), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pysindy.feature_library import FourierLibrary, CustomLibrary\n",
    "from pysindy.feature_library import ConcatLibrary\n",
    "lib_fourier = FourierLibrary(n_frequencies=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(x0)' = -13.088 x0 + 8.786 x1 + 84.012 x2 + -14.989 x3 + -0.020 x4 + 0.122 x0 x3 + 0.179 x1^2 + 0.071 x1 x2 + -2.867 x1 x3 + 0.656 x1 x4 + 0.597 x2^2 + -6.131 x2 x3 + 0.685 x2 x4 + 0.597 x3^2\n",
      "(x1)' = 1.000 x2\n",
      "(x2)' = -0.395 x1\n",
      "(x3)' = -0.053 x1 x2\n",
      "(x4)' = -0.259 1 + 0.836 x1 + 4.439 x2 + 869.120 x3 + 3.058 x4 + -0.068 x0 x1 + 0.747 x0 x2 + -11.498 x1^2 + -0.514 x1 x2 + -0.076 x1 x3 + -36.425 x2^2 + -0.341 x2 x3 + -36.425 x3^2 + -0.163 x3 x4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhishek/anaconda3/envs/sindy/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.3076e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/home/abhishek/anaconda3/envs/sindy/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.3076e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/home/abhishek/anaconda3/envs/sindy/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.3076e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/home/abhishek/anaconda3/envs/sindy/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.3076e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/home/abhishek/anaconda3/envs/sindy/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=3.3076e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/home/abhishek/anaconda3/envs/sindy/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=6.60156e-18): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ]
    }
   ],
   "source": [
    "opt = ps.STLSQ(threshold=0.01)\n",
    "model = ps.SINDy(optimizer=opt)\n",
    "model.fit(X,t)\n",
    "model.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=t.reshape(-1,)\n",
    "y=y.reshape(-1,)\n",
    "my=my.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c1 = 0.640\n",
    "#c2 = -100.365\n",
    "#c3 = 78.573\n",
    "#c4 = -1.219\n",
    "#c5 = 0.280\n",
    "#c6 = -0.339\n",
    "#c7 = -101.503\n",
    "#c8 = 0.873\n",
    "#c9 = -609.448\n",
    "#c10 = 1.108\n",
    "#def test_model(y, t):\n",
    " #   t1 = c1*(y)\n",
    " #   t2 = c2*(.20*np.sin(pi*t/5 - 3*pi/2))\n",
    "  #  t3 = c3*(0.04*pi*np.cos(pi*t/5 - 3*pi/2))\n",
    "   # t4 = c4*(np.abs(0.04*pi*np.cos(pi*t/5 - 3*pi/2)))\n",
    "    #t5 = c5*(y)*(0.04*pi*np.cos(pi*t/5 - 3*pi/2))\n",
    "  #  t6 = c6*(y)*(np.abs(0.04*pi*np.cos(pi*t/5 - 3*pi/2)))\n",
    "   # t7 = c7*(.20*np.sin(pi*t/5 - 3*pi/2))*(np.abs(0.04*pi*np.cos(pi*t/5 - 3*pi/2)))\n",
    "   # t8 = c8*(.20*np.sin(pi*t/5 - 3*pi/2))*(np.abs(y))\n",
    "#    t9 = c9*(0.04*pi*np.cos(pi*t/5 - 3*pi/2))*(np.abs(0.04*pi*np.cos(pi*t/5 - 3*pi/2)))\n",
    " #   t10 = c10*(0.04*pi*np.cos(pi*t/5 - 3*pi/2))*(np.abs(y))\n",
    "  #  dydt = t1 + t2 + t3 + t4 + t5 + t6 + t7 + t8 + t9 + t10\n",
    "   # return dydt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(y, t):\n",
    "    x0 = y\n",
    "    x1 = 20*np.sin(pi*t/5 - 3*pi/2)\n",
    "    x2 = 4*pi*np.cos(pi*t/5 - 3*pi/2)\n",
    "    x3 = np.abs(x2)\n",
    "    x4 = np.abs(y)\n",
    "    dydt = -0.814*x0 + 0.651*x1 + 2.983*x2 + 0.063*x0*x4 - 0.026*x1*x3 - 0.058*x1*x4 - 0.132*x2*x3 - 0.061*x2*x4\n",
    "    return dydt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c1 = 1.175\n",
    "#c2 = -1.324\n",
    "#c3 = 0.697\n",
    "#c4 = -0.092\n",
    "#c5 = 0.066\n",
    "#def test_model2(y, t):\n",
    " #   dydt = c1*y + c2*20*np.sin(pi*t/5 - 3*pi/2) + c3*4*pi*np.cos(pi*t/5 - 3*pi/2) + c4*y*np.abs(4*pi*np.cos(pi*t/5 - 3*pi/2)) + c5*(20*np.sin(pi*t/5 - 3*pi/2))*(np.abs(4*pi*np.cos(pi*t/5 - 3*pi/2)))\n",
    " #   return dydt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2839.562381"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest_0 = 17.46060934\n",
    "y_test = odeint(test_model, ytest_0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7699d35e10>]"
      ]
     },
     "execution_count": 27,
     "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(t, y_test)\n",
    "plt.plot(t, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.25, -0.2 , -0.15, -0.1 , -0.05,  0.  ,  0.05,  0.1 ,  0.15,\n",
       "         0.2 ,  0.25]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 28,
     "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.grid(linestyle='dotted')\n",
    "plt.plot(y, x/100, 'r')\n",
    "plt.plot(y_test, x/100, linewidth=2, linestyle=':')\n",
    "plt.xlabel('Voltage', fontsize = 20)\n",
    "plt.ylabel('Displacement', fontsize = 20)\n",
    "plt.legend(['Ground truth' , 'Learned relation'], loc='upper left', prop={'size': 12})\n",
    "plt.xticks(fontsize=20, rotation='45')\n",
    "plt.yticks(fontsize=20)\n",
    "#plt.savefig(\"Pics/Slide14_loop.png\", dpi=1000,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2 = r2_score(y, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01610560271534811"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test:  98.38934058520643 %\n"
     ]
    }
   ],
   "source": [
    "y = y.reshape(-1,1)\n",
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = np.mean((y_test - y)**2)/np.mean(y**2)\n",
    "print(\"Relative Error Test: \", relative_error_test*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7699bf0710>]"
      ]
     },
     "execution_count": 33,
     "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": [
    "err_t = np.abs(y-y_test)\n",
    "plt.plot(t, err_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2788.89404052846"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "rmse = np.sqrt(mean_squared_error(y, y_test))\n",
    "nrmse = rmse*2.5\n",
    "nrmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename2 = 'data_file_Loops.txt'\n",
    "data = np.loadtxt(filename2, skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19.99 , 17.24 , 17.359],\n",
       "       [19.99 , 17.651, 17.354],\n",
       "       [19.98 , 17.517, 17.347],\n",
       "       ...,\n",
       "       [19.98 , 17.625, 17.347],\n",
       "       [19.99 , 17.622, 17.354],\n",
       "       [19.99 , 17.564, 17.359]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_ax = data[:,0]\n",
    "y_hystlab = data[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(988,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ax.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7699b82c10>]"
      ]
     },
     "execution_count": 39,
     "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(x_ax, y_sind)\n",
    "plt.plot(y_hystlab, x_ax/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.195, 0.196, 0.197, 0.198, 0.199, 0.2  , 0.201, 0.202, 0.203]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 40,
     "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.grid(linestyle='dotted')\n",
    "plt.plot(y, x/100, 'r', linewidth=2, linestyle=':')\n",
    "plt.plot(y_test, x/100, 'y', linewidth=2, linestyle=':')\n",
    "plt.plot(y_hystlab, x_ax/100, 'b', linewidth=2, linestyle=':')\n",
    "plt.xlabel('H [A/m]', fontsize = 15)\n",
    "plt.ylabel('B [T]', fontsize = 15)\n",
    "plt.xlim([17.5, 17.72])\n",
    "plt.ylim([0.195, 0.203])\n",
    "plt.legend(['Experimental Data' ,'SINDy', 'Hystlab'], loc='upper left', prop={'size': 12})\n",
    "plt.xticks(fontsize=20, rotation='45')\n",
    "plt.yticks(fontsize=20)\n",
    "#plt.savefig(\"Results/pic6.png\", dpi=1000,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_limited = y[0:988,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test:  98.38065911133712 %\n"
     ]
    }
   ],
   "source": [
    "y_hystlab = y_hystlab.reshape(-1,1)\n",
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = np.mean((y_hystlab - y_limited)**2)/np.mean(y_limited**2)\n",
    "print(\"Relative Error Test: \", relative_error_test*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 17.24    ],\n",
       "       [ 17.651   ],\n",
       "       [ 17.517   ],\n",
       "       [ 17.425   ],\n",
       "       [ 17.334   ],\n",
       "       [ 17.291   ],\n",
       "       [ 17.239   ],\n",
       "       [ 17.226   ],\n",
       "       [ 17.156   ],\n",
       "       [ 17.115   ],\n",
       "       [ 17.052   ],\n",
       "       [ 17.016   ],\n",
       "       [ 16.949   ],\n",
       "       [ 16.907   ],\n",
       "       [ 16.854   ],\n",
       "       [ 16.803   ],\n",
       "       [ 16.726   ],\n",
       "       [ 16.682   ],\n",
       "       [ 16.614   ],\n",
       "       [ 16.55    ],\n",
       "       [ 16.473   ],\n",
       "       [ 16.422   ],\n",
       "       [ 16.352   ],\n",
       "       [ 16.296   ],\n",
       "       [ 16.222   ],\n",
       "       [ 16.152   ],\n",
       "       [ 16.068   ],\n",
       "       [ 16.011   ],\n",
       "       [ 15.919   ],\n",
       "       [ 15.857   ],\n",
       "       [ 15.775   ],\n",
       "       [ 15.698   ],\n",
       "       [ 15.606   ],\n",
       "       [ 15.542   ],\n",
       "       [ 15.443   ],\n",
       "       [ 15.363   ],\n",
       "       [ 15.262   ],\n",
       "       [ 15.184   ],\n",
       "       [ 15.076   ],\n",
       "       [ 14.982   ],\n",
       "       [ 14.892   ],\n",
       "       [ 14.799   ],\n",
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      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hystlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_limited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib notebook"
   ]
  }
 ],
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