Queqiao/Queqiao Reed-Solomon bug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "701c6273",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ctypes\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 5)\n",
    "plt.rcParams['figure.facecolor'] = 'w'\n",
    "\n",
    "libfec = ctypes.CDLL('libfec.so')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d9320d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def strhex(x):\n",
    "    if type(x) == np.ndarray:\n",
    "        x = bytes(x)\n",
    "    h = x.hex()\n",
    "    s = ''\n",
    "    count = 0\n",
    "    per_row = 16\n",
    "    for a, b in zip(h[::2], h[1::2]):\n",
    "        s += f'{a}{b} '\n",
    "        if count % per_row == per_row - 1:\n",
    "            s += '\\n'\n",
    "        count += 1\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de76217c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rs_size = 252\n",
    "padding = 255 - rs_size\n",
    "frames = np.fromfile('queqiao_rs_frames.u8', dtype='uint8').reshape((-1, rs_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "80111519",
   "metadata": {},
   "outputs": [],
   "source": [
    "rs_decode_errors = np.array([libfec.decode_rs_ccsds(bytes(f), 0, 0, padding) for f in frames])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9cea3277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rs_decode_errors, '.')\n",
    "plt.title('Reed-Solomon corrected bytes (-1 = decode error)')\n",
    "plt.xlabel('Reed-Solomon codeword')\n",
    "plt.ylabel('Corected bytes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "273fe606",
   "metadata": {},
   "outputs": [],
   "source": [
    "good_frames = rs_decode_errors == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ecfe2d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "decodes = []\n",
    "for f in frames[good_frames]:\n",
    "    frame = bytes(f)\n",
    "    libfec.decode_rs_ccsds(frame, 0, 0, padding)\n",
    "    decodes.append(np.frombuffer(frame, 'uint8'))\n",
    "decodes = np.array(decodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "860ba1f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03 79 16 0a 32 98 02 81 27 55 9a aa bb 02 c5 00 \n",
      "00 f9 6b 00 00 00 00 a4 09 00 00 00 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 \n",
      "21 00 28 00 3e a7 02 20 ee 58 01 04 08 24 00 00 \n",
      "63 00 00 00 62 00 00 00 07 92 92 00 00 db cf 25 \n",
      "53 0f 00 00 08 c6 02 3b 12 95 0c 0c 30 23 fe f0 \n",
      "58 28 ca 53 1a 10 1f 35 ac 4f da f8 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 0f 38 2a 29 d9 49 07 07 \n",
      "f9 0d 00 00 00 00 00 00 80 49 04 43 00 00 00 00 \n",
      "ae 7f af ae ac af 00 00 00 00 04 04 ff c1 00 ff \n",
      "11 00 96 55 ff 01 01 01 01 01 01 01 01 01 01 76 \n",
      "8f 80 02 01 01 01 01 01 01 01 01 01 01 01 01 01 \n",
      "01 01 aa aa aa aa aa aa aa aa aa aa aa aa aa aa \n",
      "aa aa ee ee ee ee ee ee ee ee ee ee d2 1e ec e9 \n",
      "a3 5c ae bf 0f 1e 66 47 e3 46 82 ff bf de d4 a9 \n",
      "87 1e b9 26 67 58 f2 e9 4c f9 62 ab \n"
     ]
    }
   ],
   "source": [
    "print(strhex(frames[good_frames][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7cac0ca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03 79 16 0a 32 98 02 81 27 55 9a aa bb 02 c5 00 \n",
      "00 f9 6b 00 00 00 00 a4 09 00 00 00 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 \n",
      "21 00 28 00 3e a7 02 20 ee 58 01 04 08 24 00 00 \n",
      "63 00 00 00 62 00 00 00 07 92 92 00 00 db cf 25 \n",
      "53 0f 00 00 08 c6 02 3b 12 95 0c 0c 30 23 fe f0 \n",
      "58 28 ca 53 1a 10 1f 35 ac 4f da f8 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 0f 38 2a 29 d9 49 07 07 \n",
      "f9 0d 00 00 00 00 00 00 80 49 04 43 00 00 00 00 \n",
      "ae 7f af ae ac af 00 00 00 00 04 04 ff c1 00 ff \n",
      "11 00 96 55 ff 01 01 01 01 01 01 01 01 01 01 76 \n",
      "8f 80 02 01 01 01 01 01 01 01 01 01 01 01 01 01 \n",
      "01 01 aa aa aa aa aa aa aa aa aa aa aa aa aa aa \n",
      "aa aa ee ee ee ee ee ee ee ee ee ee d2 1e ec e9 \n",
      "a3 5c ae bf 0f 1e 66 47 e3 46 82 ff bf de d4 a9 \n",
      "87 1e b9 26 67 58 f2 e9 4c f9 62 b1 \n"
     ]
    }
   ],
   "source": [
    "print(strhex(decodes[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a4cf2cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(decodes != frames[good_frames], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a843301a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0,    1,    2, ..., 2957, 2958, 2959]),\n",
       " array([251, 251, 251, ..., 251, 251, 251]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(decodes != frames[good_frames])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a9c00c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "parity = []\n",
    "for f in frames[good_frames]:\n",
    "    d = bytes(f[:-32])\n",
    "    p = b'\\x00'*32\n",
    "    libfec.encode_rs_ccsds(d, p, padding)\n",
    "    data.append(np.frombuffer(d, 'uint8'))\n",
    "    parity.append(np.frombuffer(p, 'uint8'))\n",
    "data = np.array(data)\n",
    "parity = np.array(parity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58bafb75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03 79 16 0a 32 98 02 81 27 55 9a aa bb 02 c5 00 \n",
      "00 f9 6b 00 00 00 00 a4 09 00 00 00 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 \n",
      "21 00 28 00 3e a7 02 20 ee 58 01 04 08 24 00 00 \n",
      "63 00 00 00 62 00 00 00 07 92 92 00 00 db cf 25 \n",
      "53 0f 00 00 08 c6 02 3b 12 95 0c 0c 30 23 fe f0 \n",
      "58 28 ca 53 1a 10 1f 35 ac 4f da f8 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 0f 38 2a 29 d9 49 07 07 \n",
      "f9 0d 00 00 00 00 00 00 80 49 04 43 00 00 00 00 \n",
      "ae 7f af ae ac af 00 00 00 00 04 04 ff c1 00 ff \n",
      "11 00 96 55 ff 01 01 01 01 01 01 01 01 01 01 76 \n",
      "8f 80 02 01 01 01 01 01 01 01 01 01 01 01 01 01 \n",
      "01 01 aa aa aa aa aa aa aa aa aa aa aa aa aa aa \n",
      "aa aa ee ee ee ee ee ee ee ee ee ee d2 1e ec e9 \n",
      "a3 5c ae bf 0f 1e 66 47 e3 46 82 ff bf de d4 a9 \n",
      "87 1e b9 26 67 58 f2 e9 4c f9 62 ab \n"
     ]
    }
   ],
   "source": [
    "print(strhex(frames[good_frames][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "93db2baf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03 79 16 0a 32 98 02 81 27 55 9a aa bb 02 c5 00 \n",
      "00 f9 6b 00 00 00 00 a4 09 00 00 00 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 \n",
      "21 00 28 00 3e a7 02 20 ee 58 01 04 08 24 00 00 \n",
      "63 00 00 00 62 00 00 00 07 92 92 00 00 db cf 25 \n",
      "53 0f 00 00 08 c6 02 3b 12 95 0c 0c 30 23 fe f0 \n",
      "58 28 ca 53 1a 10 1f 35 ac 4f da f8 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 0f 38 2a 29 d9 49 07 07 \n",
      "f9 0d 00 00 00 00 00 00 80 49 04 43 00 00 00 00 \n",
      "ae 7f af ae ac af 00 00 00 00 04 04 ff c1 00 ff \n",
      "11 00 96 55 ff 01 01 01 01 01 01 01 01 01 01 76 \n",
      "8f 80 02 01 01 01 01 01 01 01 01 01 01 01 01 01 \n",
      "01 01 aa aa aa aa aa aa aa aa aa aa aa aa aa aa \n",
      "aa aa ee ee ee ee ee ee ee ee ee ee a1 e7 33 a8 \n",
      "2a cb ce 5e 76 b4 10 5c 6e e4 84 07 6b f6 50 ac \n",
      "c3 d5 f4 18 52 3a e4 18 cc 7f 4b ec \n"
     ]
    }
   ],
   "source": [
    "print(strhex(bytes(data[0]) + bytes(parity[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc213c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(data == frames[good_frames, :-32])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "096bb623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f09d7e68e50>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sum(parity == frames[good_frames, -32:], axis=1), '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf2f1723",
   "metadata": {},
   "outputs": [],
   "source": [
    "decodes = []\n",
    "errors = []\n",
    "for f in frames[good_frames]:\n",
    "    frame = b'\\x00'*3 + bytes(f)\n",
    "    errors.append(libfec.decode_rs_ccsds(frame, 0, 0, 0))\n",
    "    decodes.append(np.frombuffer(frame, 'uint8'))\n",
    "decodes = np.array(decodes)\n",
    "errors = np.array(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff03b854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f09d7e05850>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(errors, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "490da61b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cf fc 1d 03 79 16 0a 32 98 02 81 27 55 9a aa bb \n",
      "02 c5 00 00 f9 6b 00 00 00 00 a4 09 00 00 00 00 \n",
      "00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 \n",
      "00 00 00 21 00 28 00 3e a7 02 20 ee 58 01 04 08 \n",
      "24 00 00 63 00 00 00 62 00 00 00 07 92 92 00 00 \n",
      "db cf 25 53 0f 00 00 08 c6 02 3b 12 95 0c 0c 30 \n",
      "23 fe f0 58 28 ca 53 1a 10 1f 35 ac 4f da f8 00 \n",
      "00 00 00 00 00 00 00 00 00 00 00 0f 38 2a 29 d9 \n",
      "49 07 07 f9 0d 00 00 00 00 00 00 80 49 04 43 00 \n",
      "00 00 00 ae 7f af ae ac af 00 00 00 00 04 04 ff \n",
      "c1 00 ff 11 00 96 55 ff 01 01 01 01 01 01 01 01 \n",
      "01 01 76 8f 80 02 01 01 01 01 01 01 01 01 01 01 \n",
      "01 01 01 01 01 aa aa aa aa aa aa aa aa aa aa aa \n",
      "aa aa aa aa aa ee ee ee ee ee ee ee ee ee ee d2 \n",
      "1e ec e9 a3 5c ae bf 0f 1e 66 47 e3 46 82 ff bf \n",
      "de d4 a9 87 1e b9 26 67 58 f2 e9 4c f9 62 b1 \n"
     ]
    }
   ],
   "source": [
    "print(strhex(decodes[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d0533e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "decodes = []\n",
    "errors = []\n",
    "for f in frames[good_frames]:\n",
    "    frame = b'\\x00'*4 + bytes(f[:-1])\n",
    "    errors.append(libfec.decode_rs_ccsds(frame, 0, 0, 0))\n",
    "    decodes.append(np.frombuffer(frame, 'uint8'))\n",
    "decodes = np.array(decodes)\n",
    "errors = np.array(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "891fcbbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f09d7db6df0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(errors, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e0ca343",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b1 cf fc 1d 03 79 16 0a 32 98 02 81 27 55 9a aa \n",
      "bb 02 c5 00 00 f9 6b 00 00 00 00 a4 09 00 00 00 \n",
      "00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 \n",
      "00 00 00 00 21 00 28 00 3e a7 02 20 ee 58 01 04 \n",
      "08 24 00 00 63 00 00 00 62 00 00 00 07 92 92 00 \n",
      "00 db cf 25 53 0f 00 00 08 c6 02 3b 12 95 0c 0c \n",
      "30 23 fe f0 58 28 ca 53 1a 10 1f 35 ac 4f da f8 \n",
      "00 00 00 00 00 00 00 00 00 00 00 00 0f 38 2a 29 \n",
      "d9 49 07 07 f9 0d 00 00 00 00 00 00 80 49 04 43 \n",
      "00 00 00 00 ae 7f af ae ac af 00 00 00 00 04 04 \n",
      "ff c1 00 ff 11 00 96 55 ff 01 01 01 01 01 01 01 \n",
      "01 01 01 76 8f 80 02 01 01 01 01 01 01 01 01 01 \n",
      "01 01 01 01 01 01 aa aa aa aa aa aa aa aa aa aa \n",
      "aa aa aa aa aa aa ee ee ee ee ee ee ee ee ee ee \n",
      "d2 1e ec e9 a3 5c ae bf 0f 1e 66 47 e3 46 82 ff \n",
      "bf de d4 a9 87 1e b9 26 67 58 f2 e9 4c f9 62 \n"
     ]
    }
   ],
   "source": [
    "print(strhex(decodes[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "668b67df",
   "metadata": {},
   "outputs": [],
   "source": [
    "decodes = []\n",
    "errors = []\n",
    "for f in frames[good_frames]:\n",
    "    frame = bytes.fromhex('00cffc1d') + bytes(f[:-1])\n",
    "    errors.append(libfec.decode_rs_ccsds(frame, 0, 0, 0))\n",
    "    decodes.append(np.frombuffer(frame, 'uint8'))\n",
    "decodes = np.array(decodes)\n",
    "errors = np.array(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "361aec00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f09d7cd1610>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(errors, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3e72a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(decodes[:, 1:4] == np.frombuffer(bytes.fromhex('cffc1d'), 'uint8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a91260bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V1am4uHjCurlz52r27NmSpFAopJs3b+rq1asJtxMOhxWLxRSLxVRUVJRtWQAAALbKOmS1t7cn/ajwo48+kmVZkqRoNKovvvhC99xzT7a7BAAAcLysnvj+73//W8eOHdMrr7wSX7Z//35JUmtrq1577TW9/PLLcrvduvvuu9XR0SGXy5VdxQAAAHkgq5A1a9Ysffzxx+OWtba2xn9ua2tTW1tbNrsAAADISzzxHQAAwAAmiEbaTE+OmmuZ7seJE4/aPXnqnRPGOrGP7DbZSX/vfF1oE7Rz7piXqo8zPVcZu8njThYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGOC2uwA4BzOrI1c4l9KXrM8KrS8L7XgzkWkfOblvnVxbNriTBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGBAViGrtLRU1dXVCgQCCgaDE9ZblqWnn35afr9fNTU1On36dDa7AwAAyBtZT6tz/PhxLViwIOG67u5u9ff3q7+/X6dOndK2bdt06tSpbHcJAADgeEY/Luzs7NSmTZvkcrm0YsUKXbt2TcPDwyZ3CQAA4AhZhSyXy6XVq1dr6dKlikQiE9YPDg5q0aJF8dc+n0+Dg4PZ7BIAACAvZPVx4cmTJ+X1enXlyhU1NDRoyZIlqq+vj6+3LGvCe1wuV8JtRSKReFAbGRnJpiwAAADbZXUny+v1SpI8Ho+ampoUjUbHrff5fBoYGIi/vnTpUvw9dwqHw4rFYorFYioqKsqmLAAAANtlHLKuX7+u0dHR+M9Hjx5VVVXVuDaNjY06dOiQLMvSu+++q3nz5qmkpCS7igEAAPJAxh8XXr58WU1NTZKkW7du6bvf/a7Wrl2r/fv3S5JaW1sVCoXU1dUlv9+vWbNm6dVXX81N1QAAAA6XccgqKyvTmTNnJixvbW2N/+xyubRv375MdwEAAJC3eOI7AACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwwG13AQDyT+nOI3aXAIfgXACS404WAACAAYQsAAAAAzIOWQMDA3rooYdUUVGhyspKvfTSSxPanDhxQvPmzVMgEFAgENDzzz+fVbEAAAD5IuPvZLndbr344ouqq6vT6Oioli5dqoaGBt1///3j2j344IM6fPhw1oUCAADkk4zvZJWUlKiurk6SNGfOHFVUVGhwcDBnhQEAAOSznHwn6+LFi3r//ff1wAMPTFj3zjvvqLa2VuvWrdPZs2dzsTsAAADHy/oRDp999pnWr1+vPXv2aO7cuePW1dXV6cMPP9Ts2bPV1dWlxx9/XP39/Qm3E4lEFIlEJEkjIyPZlgUAAGCrrO5k3bx5U+vXr9fGjRv1xBNPTFg/d+5czZ49W5IUCoV08+ZNXb16NeG2wuGwYrGYYrGYioqKsikLAADAdhmHLMuytHXrVlVUVOiZZ55J2Oajjz6SZVmSpGg0qi+++EL33HNPprsEAADIGxl/XHjy5En95je/UXV1tQKBgCTpZz/7mf75z39KklpbW/Xaa6/p5Zdfltvt1t13362Ojg65XK6cFA4AAOBkGYesVatWxe9SJdPW1qa2trZMdwEAAJC3eOI7AACAAYQsAAAAA7J+hAMAAE5WuvOI3SWgQHEnCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGJBVyOrp6dHixYvl9/u1e/fuCesty9LTTz8tv9+vmpoanT59OpvdAQAA5I2MQ9bY2Ji2b9+u7u5u9fX1qb29XX19fePadHd3q7+/X/39/YpEItq2bVvWBQMAAOSDjENWNBqV3+9XWVmZZs6cqQ0bNqizs3Ncm87OTm3atEkul0srVqzQtWvXNDw8nHXRAAAATpdxyBocHNSiRYvir30+nwYHB9NuAwAAMB25M32jZVkTlrlcrrTb3BaJRBSJRCRJf//73xUMBjMtbVKskREVFRUZ3QcmZ4SxcAzGwjkYC+dgLJwj3bEIBn9ssJr/unjxYsLlGYcsn8+ngYGB+OtLly7J6/Wm3ea2cDiscDicaTlpCwaDisViU7Y/JMdYOAdj4RyMhXMwFs6Rb2OR8ceFy5YtU39/vy5cuKAbN26oo6NDjY2N49o0Njbq0KFDsixL7777rubNm6eSkpKsiwYAAHC6jO9kud1u7d27V2vWrNHY2JhaWlpUWVmp/fv3S5JaW1sVCoXU1dUlv9+vWbNm6dVXX81Z4QAAAE6WcciSpFAopFAoNG5Za2tr/GeXy6V9+/ZlswtjpvKjSXw5xsI5GAvnYCycg7FwjnwbC5eV6NvpAAAAyArT6gAAABhQcCEr1VRAMKu0tFTV1dUKBALxx3R88sknamhoUHl5uRoaGvTpp5/aXOX01dLSIo/Ho6qqqviyL+v/Xbt2ye/3a/HixfrjH/9oR8nTVqKxeO6557Rw4UIFAgEFAgF1dXXF1zEWZgwMDOihhx5SRUWFKisr9dJLL0niurBLsvHI22vDKiC3bt2yysrKrPPnz1uff/65VVNTY509e9busgrKvffea42MjIxb9uyzz1q7du2yLMuydu3aZf3oRz+yo7SC8Je//MV67733rMrKyviyZP1/9uxZq6amxvrPf/5j/eMf/7DKysqsW7du2VL3dJRoLH784x9bv/jFLya0ZSzMGRoast577z3LsizrX//6l1VeXm6dPXuW68ImycYjX6+NgrqTNZmpgDD1Ojs7tXnzZknS5s2b9cYbb9hb0DRWX1+v+fPnj1uWrP87Ozu1YcMGfeUrX9E3vvEN+f1+RaPRqS552ko0FskwFuaUlJSorq5OkjRnzhxVVFRocHCQ68ImycYjGaePR0GFLKb5sZ/L5dLq1au1dOnS+BP+L1++HH9+WklJia5cuWJniQUnWf9zvdhj7969qqmpUUtLS/wjKsZialy8eFHvv/++HnjgAa4LB/jf8ZDy89ooqJBlpTHND8w4efKkTp8+re7ubu3bt09//etf7S4JSXC9TL1t27bp/Pnz6u3tVUlJiXbs2CGJsZgKn332mdavX689e/Zo7ty5SdsxFlPjzvHI12ujoEJWOtP8wIzb/e3xeNTU1KRoNKri4mINDw9LkoaHh+XxeOwsseAk63+ul6lXXFysGTNm6K677tJTTz0V/9iDsTDr5s2bWr9+vTZu3KgnnnhCEteFnZKNRz5eGwUVsiYzFRDMuX79ukZHR+M/Hz16VFVVVWpsbNTBgwclSQcPHtRjjz1mZ5kFJ1n/NzY2qqOjQ59//rkuXLig/v5+LV++3M5Sp73bf9Ql6fXXX4//z0PGwhzLsrR161ZVVFTomWeeiS/nurBHsvHI22vDxi/d2+LIkSNWeXm5VVZWZr3wwgt2l1NQzp8/b9XU1Fg1NTXW/fffH+//q1evWg8//LDl9/uthx9+2Pr4449trnT62rBhg/X1r3/dcrvd1sKFC61f/vKXX9r/L7zwglVWVmbdd999VldXl42VTz+JxuJ73/ueVVVVZVVXV1vf+ta3rKGhoXh7xsKMt99+25JkVVdXW7W1tVZtba115MgRrgubJBuPfL02eOI7AACAAQX1cSEAAMBUIWQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABvwfpXPGX1G9grEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(decodes[:, 0], bins=np.arange(0, 257));"
   ]
  }
 ],
 "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": "3.9.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}