ook_decoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "sample_rate = 32e3\n",
    "\n",
    "with open('signal.bin', 'r') as file:\n",
    "    signal = sp.fromfile(file, dtype=sp.uint8)\n",
    "\n",
    "time = np.arange(0, len(signal)) / sample_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1800x1200 with 1 Axes>",
      "image/png": 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GvBq1WGzWdW0vIj7blhfYIeNu1md1y3EAZrttebwt7A7Qtb8veH7UxU6bc45x4F4W2FV115JHaw92Hwb6pu7a+Tkedje5LKW8LaXcRvyjCdHbaL8/nWU/NCiCJwQBAAAA4JnqQuGsLcTvI+L9hk8HYO1KKdfRPiF/o4AC2LbM/BjNYuC9nZkAFvpzy+OHuisCG7Jol5PnFISt+l7P7UILsCnTu5Z0eU0EWLvMvImHXTsjIj6UUk6nX1dKuS+lHEXTiGjZXfDGDYr2F74SBkIQAGD3tE6yd7X1LwAAq6tFEbO2Jr+PiHfGbMArNq9I4krRGLAtdeFv3MXazkwAi827b/2XjZ0FMGSLdmRvCyytom2nKIF2YOfULtfTna7tBgD0QmbuZebneDzOOqqNhlrV538fEXNfN+Vq8UtgGAQBABZTyAUAwKSreNjGcuwuhACAV65uQd628LgXJt6B7Rlffy7qtQqA+eYV4B5t7CyAIVs0h9ZWvL+K1mteLbgF2CVPOmZHt+EogHX6JR6HmY5KKUvtalJKGZVSPkRzT7rMWPAgM2c1boPBEQQAWGzehLhuhwAAA5KZ5/G0W9hdRLwXAgAG4uc5zx1mpo6KwEZl5sdoFhjvSynzdi4B4MG8+1fjOWATFu0I0GUQYJ7vN/Q+AMs6mfGYwDuw8zLzKh6HAM6WDQFMKqXcllLeRsTFEi8/qTuFwqAJAgAs9mXbJ1BtasILAIAZMvM4Ij5OPXxbSrETADAkiybuzzdyFgARURf6xtedD9s8F4Ce0QAJ2LZFc2ldzrXNO5bCMWBn1DWIJ0opakWAnVavX5PXsLtSyjKF/K1qw493sbheTmMQBk8QAGCxXSnq2pXzAAAYnLpN+NXUw9ellKNtnA/AtpRSFnUgO9CBB9ig8fbfl0tcnwBYUmYKAwDrtmhHgEXPd8X1Dtgls3YpUScC9MFPUz//0MVBSyl3dXeAyzkvO3EPy9AJAgAsNi9Z+O0a3q/tmFLeAABbUAtaf5l6+KKUoussMFSL7k8PN3IWwKBl5kk015tRKeV02+cD0KXM3MvMT5n5W/26ycwux1iLCsrWsfYB8A9L7K7Z5XVoXqjA9Q7YJbN2BNhUMApgJXWObrIQ/7brhh117m/eDgPWJBg0QQCAxTa9RW7bMQUBAAA2rHaQ+ByPx2indTtKgKFaVLBhtxRgreoYbbwbgHAm8Bp9johxMcVeNEUNN7XAoguL1jZ0UwQ24XbbJxCud8COmBP6VCcC7LrpBh2fZr7qherabNv6rDUJBk0QAGCBBSnFTXaJ+PMG3wsAYPBaQgAfSinztp8E2LrMPKhdY9fVQXZRJ7L9Dt8LYJar+v2ylLILBWQAncnM82gfT51v6DQUnAGbMG8NdlMF+ouC7gCb0lbEalwG7Ky6lnow+Vgp5Xpd71dKuYjZYVJrEgyaIADActomojqdhKoDpOeeAwAA6/FLPEwcjSLi3TonrwC6UAv+P0fTNXa6g2xXhWOLCiV0VATWJjOPo7mujaK9CxhAn80LcO5l5sGc55c1t8lRKUVhLLAJm2qCNu+atyjoDrApxy2PG5cBu+y7qZ83EV6a3oEgQhCAgRMEAFhOW2exf+74fVoHJqUUSW8AgA3JzJt46GAxDgEIZgI7rYbLr+a85GNHhWMK/YFt+r5+34uI3zKzdPEVEb+1vN/BEv8tQJcWjbW62Kl43nsoNgM2ZRd2Zbf+CmxdndNrqxX5sslzAXim6WvX2sdWtX7ODqEwQRAAYDltHSm6ThS2HU/RGQDAhmTmp3jowHgfEb9/SSgzMw8z83jB7k8AXfiXWFw49v2C55exqCBDIQUAwOoWFeL/pYP3mDeeU1ABbMSC+bYu12Dn3ScrsAV2wXRHbYC+mB5nbSpYfrOl94Wd9LttnwBAT7RNfG8qCPBzx+8DAMAMmXkeESf1x/todgJ46eTReDLqzQuPA7DI2yVes4kdAQQBgHW6iYivazjuYcyemxtFxJ/W8H4Abe6jfcx20cE9asT8ceN0QQXAOl1HxPGMx5e5v+2C8BOwC+bN163j/hegK9P3p5tqija9BmFNgkETBABYQilllJl38fQGrOsgQNuk1nXH7wMAwJTMPImIj/XHu4h4/9ICi8wcjxdHHRVrAMzz9yVe08WE+KIdARSPAWtTSrlcx3HrrlAnM566L6WcruM9AVrMGq+NIuKsw2vgvGIz4Sdgkz7F7CBAl2uwbfewo1KKXdmBXfCHbZ8AwIqmw0pd19G1mV5zFQRg0L7Z9gkA9Misrvx7mdllmvFwxmN3C7bGBADghTLzOJqFx4iOQgDVuLjCeA7YhGWuNS8q0q/3wHPvg0spwuwAAKubXIt4ExFvSilvOg5Cfdfy+J0QO7BJpZTbeFrIFdFtEVlb+EnwCdgV80KaxmbALptek9hUEGD6fWbV9MFgCAIALK9tkn1W8f6qZg2IPs14DACAjmTmYURc1R/vSinvOix8GHfy+UtHxwOYZ1Enw9sOivQX3QPfvvD4AACDVrtTj+9JD7suzK8717UFO8+6fC+AJc1agz3oohnbgmNYgwV2xaYKZwE6NXX/GhH/aL62bu8m/n1vlyeGThAAYEl1sn3WRNT3XRy/FqDNohsFAMCaZOZBPHTHvi2lvJv3+hWMx3hfOj4uwBN1N7m2XQEuSylHHbzNoq3KFY8BALzceC3ij2s49mnL47e1MzfAprUV5HfRjK3tGLcKxoBdsETo6etGTgRgddO1dG33nF36l4l/C3cyeIIAAM9zPuOxrpKMswZCZ7bhBQBYj9oF8Zf6421HBbKTx9+Lhy19LSwCmzJ533odEW8j4k0ppavJ95M5z10rpAAA6MSP9fvBGropto3nBDqBraih9osZT3UxV9d2L+yaB+wKuwEAfTddiH84pxnui2XmSTzscndfSpk1joRBEQQAeIa2iag6yFhZLRKbnsw3WAEAWJM6/rqJZqLouusQQDU5RvzLGo4P8EQp5TIedgU4joi9rgLmtQhtXpeyH7p4HwCAoZvaofiqBtlfLDM/xuzx3JlAJ7BNpZSzeLrD3ckSnbJb1WvnrCK0C9c8YId8u+0TAHiJWks3HbJcS5f+OjacbIa0id0HYOdlKWXb5wDQO5n5JR4ns0cR8ftViysy8yqeBgGObMML7KLM/BwPHa4nndbCM4CdN3EtG0UzObXK9rrTE/R7EfHP9ft3MXGtLKXkamcK8HyZeRARn+uPo4h430WRw4x74UnuYYFea5mfi4i4K6W82/T5AEQ8Gn/dR8S7lwQ8p8aIk65LKR9WPS5AV2rn2Juph1fexTMzb+JpEMDYDtgptfHG1ZyXvBNeAvpgRh1Jp+OuGgL4ZeI9LjvcCRl67XfbPgGAnjqKZsJ83IViLyJ+iohnT5bXSa3pRcYzBRTADmvrwLNyZx6ATaqLgONJor1YU1eKCSbpgY0qpdxl5mk017e9iPglMz+85D4zMz9Fewjg1D0s8Iq51wW26SgixmGAz5m5UhigdsX+ZcZTd0IAwK4opdxm5kVEfJx4+DAzPz53F/W6m/t0CGAUEe9feJoAAMz2PppauvE6wkFdk/3w0l2Lawhg8tjXQgDw4JttnwBAH9VtjaYnio4z83zW69vUDjzTnS0unzuZBbBhbQVgbzd6FgArqJ1eZ20Jvk7T25oDrF3dqWk8Eb4XETeZeVUnzJ+l3uuetDz9wa5QwCvRdq87vQsUwMbUtYhxJ+z9iPhrbS60tImdAKbHgde6YgO7ppRyFhHT95jntbB/KbW79nTjj/t4we7uAGvknhN4Feo46108bpB2GCvcx06q48Df4nEIQKAdJggCAKyobr/2LpruEWMfa5fEheok1PQ2vBcSi8CuysxxYrvNSWaerFJcBrAJtZB1eiemTfjzFt4TYBwGOIqH+9bjiPitBgIWXg8zc7+O/z7OePo+mq3Jrzs7YYAtycyP8Xjr8kl7mXnuXhfYlrrz0jgMMA543tQC/1aZuVfXK2aFAM4UTgC7qq6VTocBPi0Kt09c966mnrqN5v5VCADYRV8XPO/aBfRGKWVUA+eTY7nxfeznZQMBdVx3kplf4nHA89S9LDyVpZRtnwNAr9UJp5/icVHZKCJ+jCaFeD/1+uOI+GM8XlwcRdNF8XbNpwswU72W/bH+OJ5I/3bi39/F0wXDee7r13hyajyJNYqIL7rGAptWx2DTi4CbcmScB2xbDUPNKui/jaZDz9/jYQeT/WiKzdom5S9ql0aA3qjdw95Gc2877ri4H+0BgFnuorm/HU18/7vdPYFNyMz9aAogJsdoo4j4U0R8iWYs920017qDmD2Wu42mcMLOdcDOq/N5P8XTtYnbaHZcX3QPO4om+GQ9AthZdY32t5anR6WUN5s8H4Cu1PD6VczehXO8LvElHmpJ9qO5n/0uns7XuZeFOQQBADpSU4tnMXtyfTwBPz1RNYqIHy0WAtu2YJKpa7ellKPFLwPoTmZ+jucVeXXprYkpYBfUMd9JNAHQ53a2HkXTxedHXRSBPsrMq1jP7lB3tdMZwEa0NBta5DYizoXUgT6qwfaTWP4+dhQRlwLsQF/MuV89U0sC9F2tpzuN1eblrqNZk7jr9qzgdREEAOhY7cpzGE3nif36tRfNpNN9NEnGu4i4MekO7IqpIMCsLv6rmtxdYPyzIACwcXXryFkdJ9aulJLbeF+AeWbcu+7Fw3Vy8v71JmbsdgfQN1OFFZP3uV8n/j3r/ney4OzbGY8LAgBbUcdzp9EEAvbjoRnReJfO+4j4OZq5OEFOoPdqEdmHeNjBeD8edmq6j2b99WeFYkAfTYWe7qMJcdrRBHg1ak3Kd9GsSUzfx06O6e4j4kpNHSxPEAAAAAAAAAAAAAAAAHrkm22fAAAAAAAAAAAAAAAAsDxBAAAAAAAAAAAAAAAA6BFBAAAAAAAAAAAAAAAA6BFBAAAAAAAAAAAAAAAA6BFBAAAAAAAAAAAAAAAA6BFBAAAAAAAAAAAAAAAA6BFBAAA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     },
     "metadata": {
      "image/png": {
       "width": 1537,
       "height": 1032
      },
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(dpi=300)\n",
    "plt.axvline(1.31, color='orange')\n",
    "plt.axvline(1.44, color='orange')\n",
    "plt.plot(time, signal)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1800x1200 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 1537,
       "height": 1032
      },
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from dsp import select_time_window\n",
    "\n",
    "t, s = select_time_window(time, signal, 1.28, 1.46)\n",
    "\n",
    "plt.figure(dpi=300)\n",
    "plt.axvline(1.309, color='orange')\n",
    "plt.axvline(1.313, color='orange')\n",
    "plt.plot(t, s)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1800x1200 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 1541,
       "height": 1032
      },
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from dsp import select_time_window\n",
    "\n",
    "t, s = select_time_window(time, signal, 1.309, 1.313)\n",
    "\n",
    "plt.figure(dpi=300)\n",
    "plt.axvline(1.3093, color='orange')\n",
    "plt.axvline(1.3096, color='orange')\n",
    "plt.axvline(1.3107, color='orange')\n",
    "plt.axvline(1.3117, color='orange')\n",
    "plt.scatter(t, s, s=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1800x1200 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 1537,
       "height": 1032
      },
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "t, s = select_time_window(time, signal, 1.30, 1.35)\n",
    "\n",
    "plt.figure(dpi=300)\n",
    "plt.axvline(1.313, color='orange')\n",
    "plt.axvline(1.323, color='orange')\n",
    "plt.plot(t, s)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bits_to_uint(bits):\n",
    "    return np.dot(bits, 2**np.arange(len(bits))[::-1])\n",
    "\n",
    "\n",
    "class Parser:\n",
    "\n",
    "    def __init__(self, sample_rate=32e3, sequence_timeout=5e-3, transmission_timeout=20e-3, on_width=1.0e-3, off_width=0.01e-3):\n",
    "        self.bits = []\n",
    "        self.values = []\n",
    "        self.median = None\n",
    "\n",
    "        self.on_width = on_width\n",
    "        self.off_width = off_width\n",
    "        self.time_step = 1 / sample_rate\n",
    "\n",
    "        self.sequence_timeout = sequence_timeout\n",
    "        self.transmission_timeout = transmission_timeout\n",
    "\n",
    "        self.low_time = 0\n",
    "        self.high_time = 0\n",
    "\n",
    "    def parse(self, samples):\n",
    "        for sample in np.nditer(samples):\n",
    "            self.parse_sample(sample)\n",
    "\n",
    "    def parse_sample(self, sample):\n",
    "        if self.high_time == 0:\n",
    "            if sample == 0:\n",
    "                self.low_time += self.time_step\n",
    "\n",
    "                if self.low_time >= self.sequence_timeout:\n",
    "                    if len(self.bits) == 0:\n",
    "                        self.low_time = 0\n",
    "                        return\n",
    "\n",
    "                    self.values.append(bits_to_uint(self.bits))\n",
    "                    self.bits = []\n",
    "\n",
    "                    if len(self.values) == 10:\n",
    "                        self.median = np.median(self.values).round().astype(np.uint)\n",
    "                        self.low_time = 0\n",
    "                        self.values = []\n",
    "                        return\n",
    "\n",
    "\n",
    "            if sample == 1:\n",
    "                self.low_time = 0\n",
    "                self.high_time += self.time_step\n",
    "\n",
    "        else:\n",
    "            if sample == 0:\n",
    "                if self.high_time >= self.on_width:\n",
    "                    self.bits.append(1)\n",
    "                else:\n",
    "                    if self.high_time >= self.off_width:\n",
    "                        self.bits.append(0)\n",
    "\n",
    "                self.high_time = 0\n",
    "                self.low_time += self.time_step\n",
    "\n",
    "\n",
    "            if sample == 1:\n",
    "                self.high_time += self.time_step\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "([], [2], None)"
     },
     "metadata": {},
     "execution_count": 98
    }
   ],
   "source": [
    "_, s = select_time_window(time, signal, 1.309, 1.32)\n",
    "\n",
    "parser = Parser()\n",
    "parser.parse(s)\n",
    "parser.bits, parser.values, parser.median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "([], [], 2)"
     },
     "metadata": {},
     "execution_count": 99
    }
   ],
   "source": [
    "_, s = select_time_window(time, signal, 1.30, 1.45)\n",
    "\n",
    "parser = Parser()\n",
    "parser.parse(s)\n",
    "parser.bits, parser.values, parser.median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "([], [], 2)"
     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "source": [
    "parser = Parser()\n",
    "parser.parse(signal)\n",
    "parser.bits, parser.values, parser.median"
   ]
  }
 ],
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