{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import csv\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "import scipy.signal as sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "712481c28d9d4e1d874a66d31c3e8bff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0, 64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "a = np.array([-00.000732, -00.000352, -00.000666, -00.000202, -00.000706, -00.000006, -00.000597, -00.002039, 000.050663, -00.644566, 004.456614, -16.817095, 034.654587, -39.021217, 024.007816, -08.070650, 001.478795, -00.150260, 000.006110, -00.002328, -00.002322, -00.002426, -00.002177, -00.002452, -00.002333, -00.002438, -00.002342, -00.002396, -00.001979, -00.003049, -00.001720, -00.002686, -00.002168, -00.002507, -00.001868, -00.002899, -00.002017, -00.001952, -00.003255, -00.001080, -00.003335, -00.001575, -00.002704, -00.001872, -00.002735, -00.001983, -00.002191, -00.002478, -00.002155, -00.002203, -00.002328, -00.002206, -00.002443, -00.001770, -00.002718, -00.002004, -00.002378, -00.002112, -00.002122, -00.002691, -00.001679, -00.002690, -00.001946, -00.002232])\n",
    "b = np.array([-00.002734, -00.001325, -00.002220, -00.003693, -00.004907, -00.006454, -00.007737, 000.004823, -00.363143, 004.688968, -33.795303, 130.992630, -274.092651, 309.377991, -188.427826, 061.912941, -10.974002, 001.053608, -00.048927, 000.007710, 000.007010, 000.006493, 000.007234, 000.006725, 000.006938, 000.006694, 000.006356, 000.006173, 000.006333, 000.005684, 000.005697, 000.005575, 000.005101, 000.005693, 000.004319, 000.005344, 000.004673, 000.003566, 000.006213, 000.002719, 000.004850, 000.003755, 000.004243, 000.003419, 000.003960, 000.003498, 000.003297, 000.003877, 000.002836, 000.003487, 000.003144, 000.002824, 000.003355, 000.002528, 000.002975, 000.003012, 000.002137, 000.003112, 000.002416, 000.002512, 000.002084, 000.003008, 000.001837, 000.002351])\n",
    "ax.plot([3.906250*i for i in range(len(a))], np.sqrt(a**2 + b**2))\n",
    "a2 = ax.twiny()\n",
    "a2.set_xlim([0, len(a)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4024377df494eac935fd487026edc8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2eb410f280>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "d = [50.000839,50.000839,50.000832,50.000824,50.000839,50.000832,50.000839,50.000824,50.000847,50.000824,50.000824,50.000839,50.000832,50.000839,50.000824,50.000824,50.000839,50.000824,50.000824,50.000835,50.000816,50.000832,50.000847,50.000832,50.000835,50.000824,50.000824,50.000832,50.000832,50.000843,50.000824,50.000832,50.000832,50.000832,50.000828,50.000832,50.000832,50.000824,50.000816,50.000835,50.000843,50.000824,50.000824,50.000832,50.000832,50.000847,50.000824,50.000824,50.000824,50.000835,50.000835,50.000851,50.000824,50.000824,50.000832,50.000828,50.000828,50.000824,50.000832,50.000835,50.000835,50.000832,50.000847,50.000824,50.000832,50.000839,50.000839,50.000824,50.000832,50.000832,50.000832,50.000835,50.000816,50.000820,50.000824,50.000832,50.000824,50.000832,50.000835,50.000832,50.000816,50.000820,50.000839,50.000839,50.000824,50.000839,50.000820,50.000820,50.000839,50.000832,50.000835,50.000828,50.000824,50.000839,50.000839,50.000839,50.000816,50.000832,50.000824,50.000832,50.000832,50.000839,50.000824,50.000832,50.000828,50.000832,50.000828,50.000835,50.000832,50.000843,50.000839,50.000820,50.000832,50.000835,50.000824,50.000824,50.000828,50.000820,50.000820,50.000828,50.000832,50.000832,50.000828,50.000835,50.000839,50.000820,50.000832,50.000832,50.000824,50.000832,50.000832,50.000839,50.000839,50.000816,50.000828,50.000832,50.000839,50.000824,50.000824,50.000824,50.000835,50.000824,50.000832,50.000839,50.000835,50.000832,50.000828,50.000835,50.000828,50.000828,50.000824,50.000824,50.000839,50.000832,50.000824,50.000832,50.000832,50.000820,50.000851,50.000824,50.000824,50.000839,50.000824,50.000839,50.000832,50.000835,50.000820,50.000832,50.000839,50.000832,50.000832,50.000824,50.000832,50.000824,50.000832,50.000839,50.000839,50.000832,50.000816,50.000835,50.000854,50.000824,50.000816,50.000832,50.000832,50.000835,50.000816,50.000832,50.000824,50.000832,50.000832,50.000832,50.000824,50.000832,50.000824,50.000835,50.000832,50.000835,50.000832,50.000832,50.000828,50.000839,50.000824,50.000839,50.000824,50.000824,50.000839,50.000816,50.000839,50.000816,50.000832,50.000839,50.000839,50.000832,50.000824,50.000832,50.000820,50.000824,50.000835,50.000824,50.000835,50.000832,50.000824,50.000824,50.000820,50.000839,50.000816,50.000832,50.000832,50.000832,50.000824,50.000847,50.000824,50.000839]\n",
    "\n",
    "ax.plot(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('impl_test_out.json') as f:\n",
    "    impl_measurements = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd23cf23221e4e14aaafdd58bb9416d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(len(impl_measurements), figsize=(8, 20), sharex=True)\n",
    "fig.tight_layout()\n",
    "axs = axs.flatten()\n",
    "\n",
    "for (label, data), ax in zip(impl_measurements.items(), axs):\n",
    "    ax.set_title(label)\n",
    "    ax.plot(data[1:-1])\n",
    "    mean = np.mean(data[1:-1])\n",
    "    rms = np.sqrt(np.mean(np.square(data[1:-1] - mean)))\n",
    "    ax.text(0.2, 0.2, f'mean={mean:.3}Hz, rms={rms*1e3:.3}mHz', ha='center', va='center', transform=ax.transAxes,\n",
    "            bbox=dict(boxstyle=\"square\", ec=(0,0,0,0), fc=(1,1,1,0.8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.35232554e-18,  4.70465108e-18,  2.35232554e-18,\n",
       "         1.00000000e+00, -1.97549493e+00,  9.75650918e-01],\n",
       "       [ 1.00000000e+00,  2.00000000e+00,  1.00000000e+00,\n",
       "         1.00000000e+00, -1.97916324e+00,  9.79319515e-01],\n",
       "       [ 1.00000000e+00,  2.00000000e+00,  1.00000000e+00,\n",
       "         1.00000000e+00, -1.98597735e+00,  9.86134166e-01],\n",
       "       [ 1.00000000e+00,  2.00000000e+00,  1.00000000e+00,\n",
       "         1.00000000e+00, -1.99495144e+00,  9.95108965e-01]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig.butter(8, 20e-3, output='sos', fs=10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "655ce5c77d2a4047905245df39a095b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4609ce6a90>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot([0-0.00012937261, 0-0.00022784119 , 0-0.00039295876 , 0-0.00066361829 , 00-0.0010971602 , 00-0.0017754816 ,\n",
    "    00-0.0028116399 , 00-0.0043560231 , 00-0.0066005666 , 00-0.0097788338 , 000-0.014159188 , 000-0.020027947 ,\n",
    "    000-0.027659611 , 000-0.037272236 , 000-0.048968014 , 0000-0.06266222 , 0000-0.07800759 , 000-0.094325546 ,\n",
    "    0000-0.11055938 , 0000-0.12526666 , 0000-0.13666715 , 0000-0.14275811 , 0000-0.14149973 , 00000-0.1310612 ,\n",
    "    0000-0.11010384 , 000-0.078063987 , 000-0.035389599 , 00000.016317957 , 00000.074297836 , 000000.13478363 ,\n",
    "    000000.19331697 , 000000.24519242 , 000000.28597909 , 000000.31204596 , 000000.32101141 , 000000.31204596 ,\n",
    "    000000.28597909 , 000000.24519242 , 000000.19331697 , 000000.13478363 , 00000.074297836 , 00000.016317957 ,\n",
    "    000-0.035389599 , 000-0.078063987 , 0000-0.11010384 , 00000-0.1310612 , 0000-0.14149973 , 0000-0.14275811 ,\n",
    "    0000-0.13666715 , 0000-0.12526666 , 0000-0.11055938 , 000-0.094325546 , 0000-0.07800759 , 0000-0.06266222 ,\n",
    "    000-0.048968014 , 000-0.037272236 , 000-0.027659611 , 000-0.020027947 , 000-0.014159188 , 00-0.0097788338 ,\n",
    "    00-0.0066005666 , 00-0.0043560231 , 00-0.0028116399 , 00-0.0017754816 , 00-0.0010971602 , 0-0.00066361829 ,\n",
    "    0-0.00039295876 , 0-0.00022784119 , 0-0.00012937261 ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt('/tmp/foo.csv', delimiter=',')[1000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c771472882e4ceeb8aeddfa5c08ca17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, figsize=(15, 9), sharex=True)\n",
    "axs = axs.flatten()\n",
    "axs[0].set_title('corr')\n",
    "axs[1].set_title('cwt')\n",
    "#axs[2].set_title('iir')\n",
    "\n",
    "axs[0].plot(data[:,0], label='corr')\n",
    "axs[1].plot(data[:,1], label='cwt')\n",
    "axs[0].plot(data[:,2], label='avg')\n",
    "axs[1].plot(data[:,2], label='avg')\n",
    "\n",
    "for ax in axs:\n",
    "    ax.legend()\n",
    "    ax.grid()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "labenv",
   "language": "python",
   "name": "labenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}