renewed_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.fft import fft, fftfreq, rfft, rfftfreq\n",
    "from scipy.io.wavfile import write, read \n",
    "from scipy.io import wavfile\n",
    "import math\n",
    "from time import sleep\n",
    "from math import floor\n",
    "import pyaudio\n",
    "import wave\n",
    "import random\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ALSA lib pcm_dsnoop.c:601:(snd_pcm_dsnoop_open) unable to open slave\n",
      "ALSA lib pcm_dmix.c:1032:(snd_pcm_dmix_open) unable to open slave\n",
      "ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear\n",
      "ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe\n",
      "ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side\n",
      "ALSA lib pcm_oss.c:397:(_snd_pcm_oss_open) Cannot open device /dev/dsp\n",
      "ALSA lib pcm_oss.c:397:(_snd_pcm_oss_open) Cannot open device /dev/dsp\n",
      "ALSA lib confmisc.c:160:(snd_config_get_card) Invalid field card\n",
      "ALSA lib pcm_usb_stream.c:482:(_snd_pcm_usb_stream_open) Invalid card 'card'\n",
      "ALSA lib confmisc.c:160:(snd_config_get_card) Invalid field card\n",
      "ALSA lib pcm_usb_stream.c:482:(_snd_pcm_usb_stream_open) Invalid card 'card'\n",
      "ALSA lib pcm_dmix.c:1032:(snd_pcm_dmix_open) unable to open slave\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n"
     ]
    }
   ],
   "source": [
    "#ЗАПИСЬ\n",
    "filename = \"recorded.wav\"\n",
    "chunk = 1024\n",
    "FORMAT = pyaudio.paInt16\n",
    "channels = 1\n",
    "sample_rate = 44100\n",
    "record_seconds = 12\n",
    "p = pyaudio.PyAudio()\n",
    "stream = p.open(format=FORMAT,\n",
    "                channels=channels,\n",
    "                rate=sample_rate,\n",
    "                input=True,\n",
    "                output=True,\n",
    "                frames_per_buffer=chunk)\n",
    "frames = []\n",
    "print(\"Recording\")\n",
    "for i in range(int(sample_rate / chunk * record_seconds)):\n",
    "  data = stream.read(chunk)\n",
    "  frames.append(data)\n",
    "stream.stop_stream()\n",
    "stream.close()\n",
    "p.terminate()\n",
    "wf = wave.open(filename, \"wb\")\n",
    "wf.setnchannels(channels)\n",
    "wf.setsampwidth(p.get_sample_size(FORMAT))\n",
    "wf.setframerate(sample_rate)\n",
    "wf.writeframes(b\"\".join(frames))\n",
    "wf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "class modulator:\n",
    "\n",
    "    def __init__(self):\n",
    "        self.sample_rate, self.freq0, self.freq1, self.change_time = 44100, 1000, 2000, 0.5\n",
    "\n",
    "    def set_values(self, sample_rate, freq0, freq1, text, change_time):\n",
    "        self.sample_rate, self.freq0, self.freq1, self.text, self.change_time = sample_rate, freq0, freq1, text, change_time\n",
    "    \n",
    "    def set_freq0(self, freq0):\n",
    "       self.freq0 = freq0\n",
    "    \n",
    "    def set_freq1(self, freq1):\n",
    "       self.freq1 = freq1\n",
    "    \n",
    "    def set_sample_rate(self, sample_rate):\n",
    "       self.sample_rate = sample_rate\n",
    "    \n",
    "    def set_text(self, text):\n",
    "       self.text = text\n",
    "    \n",
    "    def set_change_time(self, change_time):\n",
    "       self.change_time = change_time\n",
    "\n",
    "    @staticmethod\n",
    "    def generate_sine_wave(freq, sample_rate, duration):\n",
    "      x = np.linspace(0, duration, int(sample_rate*duration), endpoint=False)\n",
    "      frequencies = x * freq\n",
    "      y = np.sin((2 * np.pi) * frequencies)\n",
    "      return x, y\n",
    "    \n",
    "    def signal_modulation(self): #Следует доработать, т.к неэффективно работает. Но это мало на что влияет, на этом этапе скорость не нужна\n",
    "      change_time, text, freq0, freq1, sample_rate = self.change_time, self.text, self.freq0, self.freq1, self.sample_rate\n",
    "      duration = change_time * len(text)\n",
    "      num = int(duration / change_time)\n",
    "      time = np.array([], dtype = np.int16)\n",
    "      signal = np.array([], dtype = np.int16)\n",
    "      for i in range(num):\n",
    "        if text[i] == \"0\":\n",
    "            freq = freq0\n",
    "        if text[i] == \"1\":\n",
    "            freq = freq1\n",
    "        time_diff, data = self.generate_sine_wave(freq, sample_rate, change_time)\n",
    "        signal = np.concatenate((signal, data), axis = None)\n",
    "        time = np.concatenate((time, time_diff), axis = None)\n",
    "      return time, signal\n",
    "    \n",
    "class wav_manager:\n",
    "   \n",
    "   def __init__(self):\n",
    "    self.recorded_name = \"recorded.wav\"\n",
    "    self.writing_name = \"tone_ex\"\n",
    "\n",
    "   def set_recorded_name(self, name):\n",
    "    self.recorded_name = name\n",
    "\n",
    "   def set_writing_name(self, name):\n",
    "    self.writing_name = name\n",
    "\n",
    "   def get_data_sample_rate(self):\n",
    "    sample_rate, data = wavfile.read(self.recorded_name)\n",
    "    duration = len(data) / sample_rate \n",
    "    return data, sample_rate\n",
    "   \n",
    "   def write_sound(self, sample_rate, data):\n",
    "      write(self.writing_name, sample_rate, data)\n",
    "\n",
    "def record(record_seconds, filename):\n",
    "  chunk = 1024\n",
    "  FORMAT = pyaudio.paInt16\n",
    "  channels = 1\n",
    "  sample_rate = 44100\n",
    "  p = pyaudio.PyAudio()\n",
    "  stream = p.open(format=FORMAT,\n",
    "                channels=channels,\n",
    "                rate=sample_rate,\n",
    "                input=True,\n",
    "                output=True,\n",
    "                frames_per_buffer=chunk)\n",
    "  frames = []\n",
    "  print(\"Recording\")\n",
    "  for i in range(int(sample_rate / chunk * record_seconds)):\n",
    "    data = stream.read(chunk)\n",
    "    frames.append(data)\n",
    "  stream.stop_stream()\n",
    "  stream.close()\n",
    "  p.terminate()\n",
    "  wf = wave.open(filename, \"wb\")\n",
    "  wf.setnchannels(channels)\n",
    "  wf.setsampwidth(p.get_sample_size(FORMAT))\n",
    "  wf.setframerate(sample_rate)\n",
    "  wf.writeframes(b\"\".join(frames))\n",
    "  wf.close()\n",
    "\n",
    "def recording_no_stop(duration_array): # Многократная запись, которая синхронизируется с помощью текстового файла\n",
    "  num = len(duration_array)\n",
    "  sleep(3)\n",
    "  print(\"RECORDING\")\n",
    "  i = 0\n",
    "  for i in range(num):\n",
    "    duration = duration_array[i]\n",
    "    for l in range(10000000):\n",
    "      flag = \"\"\n",
    "      while len(flag) == 0:\n",
    "        f = open(\"flag.txt\", \"r\")\n",
    "        flag = f.read()\n",
    "        f.close()\n",
    "      flag = bool(int(flag[len(flag) - 1]))      \n",
    "      if flag:\n",
    "        break\n",
    "      sleep(0.001)\n",
    "    record(duration, \"recorded\" + str(i))\n",
    "    print(\"MAKING PROGRESS\")\n",
    "  print(\"DONE\")\n",
    "    \n",
    "\n",
    "def get_code_array(max_length):\n",
    "  code_array = []\n",
    "  for i in range(4, max_length + 4, 4):\n",
    "    for z in range(100):\n",
    "      code = \"\"\n",
    "      for j in range(i):\n",
    "        code += str(random.randint(0,1))\n",
    "      code_array.append(code)\n",
    "  return code_array\n",
    "\n",
    "def get_duration_array(code_array, change_time):\n",
    "  duration_array = []\n",
    "  for i in range(len(code_array)):\n",
    "    code = code_array[i]\n",
    "    duration = change_time * len(code) + 2.5\n",
    "    duration_array.append(duration)\n",
    "  return duration_array\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class decoder:\n",
    "   \n",
    "   def __init__(self):\n",
    "      self.sample_rate, self.freq0, self.freq1, self.change_time, self.index_range, self.draw_graph, self.max_param, self.amplitude_threshold, self.print_progress, self.check_yf, self.print_amp = 44100, 1000, 1500, 0.5, 2, False, 8, 0.45, True, False, False\n",
    "   \n",
    "   def set_models(self, model_x, model_param):\n",
    "    self.model_x = model_x\n",
    "    self.model_param = model_param\n",
    "   \n",
    "   def set_print_yf(self, flag):\n",
    "     self.check_yf = flag\n",
    "   \n",
    "   def set_print_amp(self, flag):\n",
    "     self.print_amp = flag\n",
    "   \n",
    "   def set_print_progress(self, flag):\n",
    "     self.print_progress = flag\n",
    "   \n",
    "   def set_max_param(self, n):\n",
    "     self.max_param = n\n",
    "   \n",
    "   def set_amplitude_threshold(self, param):\n",
    "     self.amplitude_threshold = param\n",
    "   \n",
    "   def set_draw_graph(self, flag):\n",
    "     self.draw_graph = flag\n",
    "    \n",
    "   def set_freqses(self, freq0 = 1000, freq1 = 2000):\n",
    "      self.freq0, self.freq1 = freq0, freq1\n",
    "\n",
    "   def get_freqs_indexes(self, xf):\n",
    "     index_freq0, index_freq1 = int(7.5 * len(xf) / 330750 * self.freq0), int(7.5 * len(xf) / 330750 * self.freq1)\n",
    "     return index_freq0, index_freq1\n",
    "\n",
    "   def fourier_abs(self, data):\n",
    "    sample_rate = self.sample_rate\n",
    "    duration = len(data) / sample_rate\n",
    "    xf = rfftfreq(len(data), 1 / sample_rate)\n",
    "    yf = rfft(data)\n",
    "    yf = yf.flatten()\n",
    "    return np.array([xf, np.abs(yf)])\n",
    "   \n",
    "   def get_index_start_end_of_longest_ones(self, lt):\n",
    "     m = 0\n",
    "     index = 0\n",
    "     cnt = 0\n",
    "     end_index = 0\n",
    "     length = 0\n",
    "     for i in range(len(lt)):\n",
    "       a = bool(lt[i])\n",
    "       if a:\n",
    "         cnt += 1\n",
    "       if not a:\n",
    "         if cnt > m:\n",
    "           m = cnt\n",
    "           index = i - cnt\n",
    "           end_index = index + cnt -1\n",
    "           length = end_index - index + 1\n",
    "         cnt = 0\n",
    "     if cnt > m:\n",
    "       index = i + 1 - cnt\n",
    "     return index, end_index, length\n",
    "   \n",
    "   def fourier_for_array(self, data_array, sample_rate):\n",
    "    _, ans = self.fourier_abs(data_array[0])\n",
    "    ans = [ans]\n",
    "    for i in range(1, len(data_array)):\n",
    "      data_window = data_array[i]\n",
    "      xf_for_array, yf_window = self.fourier_abs(data_window)\n",
    "      ans = np.concatenate((ans, [yf_window]), axis = 0)\n",
    "    return xf_for_array, ans\n",
    "   \n",
    "   def abs_diff(self, yf_window, xf, freq0, freq1, range_index):\n",
    "    absolute_difference = abs(self.freq0_sum(yf_window) - self.freq1_sum(yf_window)) ** 2\n",
    "    return absolute_difference\n",
    "   \n",
    "   def calc_index(self, freq, xf):\n",
    "     index = int(len(xf) / (self.sample_rate / 2) * freq)\n",
    "     return index\n",
    "   \n",
    "   def freq_sum(self, yf, xf, freq):\n",
    "     index_range = self.index_range\n",
    "     index_freq = self.calc_index(freq, xf)\n",
    "     s = 0\n",
    "     for i in range(index_freq - index_range, index_freq + index_range + 1):\n",
    "       amplitude = yf[i]\n",
    "       s += abs(amplitude)\n",
    "     return s\n",
    "   \n",
    "   def set_change_time(self, change_time):\n",
    "     self.change_time = change_time\n",
    "\n",
    "   \n",
    "   def fill_in(self, yf_array, xf, freq0, freq1, amplitude_threshold, length, N0):\n",
    "     data_for_prediction = pd.DataFrame(index = [0])\n",
    "     data_for_prediction[[f\"f{i}_freq0\" for i in range(64)]] = 0\n",
    "     data_for_prediction[[f\"f{i}_freq1\" for i in range(64)]] = 0\n",
    "     amplitude_array = np.zeros((2, N0))\n",
    "     freq0, freq1 = self.freq0, self.freq1\n",
    "     bool_array = np.zeros(len(yf_array), dtype = bool)\n",
    "     model_param = self.model_param\n",
    "     model_param.compile(optimizer = \"adam\", loss = \"mean_squared_error\")\n",
    "     model_x = self.model_x\n",
    "     model_x.compile(optimizer = \"adam\", loss = \"mean_squared_error\")\n",
    "     normalized_yf = self.normalize_data_array(yf_array, xf, freq0, freq1)\n",
    "     for i in range(len(bool_array)):\n",
    "       window = normalized_yf[i]\n",
    "       amplitude_freq0, amplitude_freq1 = self.freq_sum(window, xf, freq0), self.freq_sum(window, xf, freq1)\n",
    "       amplitude_array[0,i], amplitude_array[1,i] = amplitude_freq0, amplitude_freq1\n",
    "     if N0 > length:\n",
    "       for i in range(64):\n",
    "         data_for_prediction[f\"f{i}_freq0\"][0] = amplitude_array[0,i]\n",
    "         data_for_prediction[f\"f{i}_freq1\"][0] = amplitude_array[1,i]\n",
    "     data_for_prediction = pd.DataFrame(data_for_prediction).to_numpy()\n",
    "     predictions = model_param.predict(data_for_prediction)[0]\n",
    "     max_param, amplitude_threshold = predictions[0], predictions[1]\n",
    "     max_param = int(1 + max_param)\n",
    "     print(\"PREDICTED\", amplitude_threshold, max_param)\n",
    "     data_predicted = model_x.predict(data_for_prediction)[0]\n",
    "     for i in range(len(amplitude_array[0])): \n",
    "       amplitude_freq0, amplitude_freq1 = amplitude_array[0,i], amplitude_array[1,i]\n",
    "       if self.print_amp:\n",
    "         print(amplitude_freq0, amplitude_freq1)\n",
    "       if amplitude_freq0 + amplitude_freq1 > amplitude_threshold:\n",
    "        bool_array[i] = True\n",
    "     return bool_array, max_param, data_predicted\n",
    "   \n",
    "   def normalize_data_array(self, yf_array, xf, freq0, freq1):\n",
    "    normalized_array = yf_array\n",
    "    medium_amplitude_freq0, medium_amplitude_freq1 = self.calc_medium(yf_array, xf, freq0), self.calc_medium(yf_array, xf, freq1)\n",
    "    for i in range(len(yf_array)):\n",
    "      window = yf_array[i]\n",
    "      normalized_array[i] = self.normalize(window, xf, medium_amplitude_freq0, medium_amplitude_freq1)\n",
    "    return normalized_array\n",
    "   \n",
    "   def decoder_1(self, data):\n",
    "     x = 0\n",
    "     freq0, freq1 = self.freq0, self.freq1\n",
    "     sample_rate, change_time, range_index = self.sample_rate, self.change_time, self.index_range #Включаем все параметры\n",
    "     duration = len(data) / sample_rate      #whole time\n",
    "     N0 = int(duration / change_time)        #number of windows\n",
    "     data_array = self.secure_split(data, N0)         #array of arrays with sound data\n",
    "     points_per_window = int(change_time * sample_rate) #Количество отсчетов за такт\n",
    "     xf_for_array, yf_array = self.fourier_for_array(data_array, sample_rate)  \n",
    "     yf_for_array_bool, max_param, data_predicted = self.fill_in(yf_array, xf_for_array, freq0, freq1, self.amplitude_threshold, 64, N0) #В этом массиве отображается длина сигнала\n",
    "     rough_beginning_index, rough_end_index, N = self.get_index_start_end_of_longest_ones(yf_for_array_bool)#index where signal  starts (rough estimation)\n",
    "     actual_sound = 0\n",
    "     definite_sound = 0\n",
    "     if N % 4 == 0: #По идее, такое должно происходить не очень часто. Смещаем сигнал пока не определится как N + 1\n",
    "       if self.print_progress:\n",
    "         print(\"Смещаем сигнал\")\n",
    "       while N % 4 == 0:  \n",
    "         data = np.concatenate((data[int(points_per_window / 2):], np.zeros(int(points_per_window / 2))), axis = None)#Само смещение\n",
    "         N0 = int(duration / change_time)       \n",
    "         data_array = self.secure_split(data, N0)   \n",
    "         points_per_window = int(change_time * sample_rate)\n",
    "         xf_for_array, yf_array = self.fourier_for_array(data_array, sample_rate)  \n",
    "         max_param, yf_for_array_bool, data_predicted = self.fill_in(yf_array, xf_for_array, freq0, freq1, self.amplitude_threshold, 64, N0)\n",
    "         rough_beginning_index, rough_end_index, N = self.get_index_start_end_of_longest_ones(yf_for_array_bool)\n",
    "     if rough_beginning_index == 0 or rough_end_index == len(yf_for_array_bool) - 1:\n",
    "       return 0, 0\n",
    "     if N % 4 == 1:\n",
    "       if self.print_progress:\n",
    "         print(\"Сигнал определился нормально\")\n",
    "       definite_sound_array, supposed_sound_array, p = self.sound_array_by_index(data, rough_beginning_index, points_per_window, N) #Здесь мы достаем два массива: в одном полностью содержится сигнал (включая шумы),  а во второй - должен превратиться в сигнал (находится внутри первого)\n",
    "       x, actual_sound, F_max, data_exp = self.find_sound_1(N - 1, definite_sound_array, supposed_sound_array, max_param, data_predicted) #Находим сам сигнал\n",
    "       code = self.decoding(actual_sound, N - 1)#Декодируем сигнал\n",
    "       code_exp = self.decoding(data_exp, N - 1)\n",
    "       if self.print_progress:\n",
    "         print(\"Определенная длина равна\", N - 1)\n",
    "       if self.check_yf:\n",
    "         print(yf_for_array_bool)        \n",
    "       if self.draw_graph:\n",
    "         _, definite_yf_array = self.fourier_for_array(self.secure_split(definite_sound_array, N), sample_rate) #Эта часть необязательна: она нужна лишь для построения функции (наглядное представление)\n",
    "         definite_sound, _, _ = self.sound_array_by_index(data, rough_beginning_index, points_per_window, N)\n",
    "         _, m, act = self.plotting_function(definite_yf_array, definite_sound_array, supposed_sound_array, freq0, freq1, xf_for_array, range_index, N, sample_rate)\n",
    "         print(m - F_max)\n",
    "       definite_sound = 0\n",
    "     if N % 4 == 2:\n",
    "       if self.print_progress:\n",
    "         print(\"You done goofed up\")#Этого не должно быть ни при каких обстоятельствах. Почти наверняка накосячили с параметрами\n",
    "       code = \"rofl\"\n",
    "     if N % 4 == 3:\n",
    "       definite_sound_array, supposed_sound_array, p = self.sound_array_by_index_special(data, rough_beginning_index, points_per_window, N)#На эту часть забейте, практически до этого не доходит, я вообще не уверен что здесь нет ошибок в коде\n",
    "       actual_sound = self.find_sound(N + 1, definite_sound_array, supposed_sound_array)\n",
    "       code = self.decoding(actual_sound, N + 1)\n",
    "       _, definite_yf_array = self.fourier_for_array(self.secure_split(definite_sound_array, N + 1), self.sample_rate)\n",
    "     return x, code, code_exp\n",
    "   \n",
    "   def sound_array_by_index_special(self, data, index_left, points_per_window, N):\n",
    "     definite_sound_array = data[int(points_per_window * (index_left - 1)): int(points_per_window * (N + index_left + 1))]\n",
    "     supposed_sound_array = data[int(points_per_window * (index_left - 1)): int(points_per_window * (N + index_left - 1))]\n",
    "     return definite_sound_array, supposed_sound_array, points_per_window\n",
    "   \n",
    "   \n",
    "     \n",
    "   \n",
    "   def decoding(self, sound, N):\n",
    "     code = \"\"\n",
    "     xf, yf_array = self.fourier_for_array(self.secure_split(sound, N), self.sample_rate)\n",
    "     for i in range(len(yf_array)):\n",
    "       window = yf_array[i]\n",
    "       amp1, amp2 = self.freq_sum(window, xf, self.freq0), self.freq_sum(window, xf, self.freq1)\n",
    "       if amp1 > amp2:\n",
    "         code += \"0\"\n",
    "       if amp1 < amp2:\n",
    "         code += \"1\"\n",
    "     return code\n",
    "\n",
    "\n",
    "   def find_max(self, x, sound_data, N, l, definite_sound_data):\n",
    "    next_x = x\n",
    "    while len(next_x) > 4:\n",
    "      x_array = self.secure_split(next_x, 3)\n",
    "      next_x = np.array([x_array[0], x_array[1]])\n",
    "      x1, x2, x3, x4 = x_array[0][0], x_array[1][0], x_array[2][0], x_array[2][len(x_array[0]) - 1]\n",
    "      F1, F2, F3, F4 = self.calc_funcions(sound_data, N, x1, x2, x3, x4, definite_sound_data, l)\n",
    "      if F2 > F3:\n",
    "        next_x[0], next_x[1] = x_array[0], x_array[1]\n",
    "        next_x = next_x.flatten()\n",
    "      else:\n",
    "        next_x[0], next_x[1] = x_array[1], x_array[2]\n",
    "        next_x = next_x.flatten()    \n",
    "    ans_x = np.max(next_x)\n",
    "    return ans_x, F2\n",
    "     \n",
    "   \n",
    "   def find_sound(self, N, definite_sound_array, supposed_sound_array):\n",
    "    target_sound_data, definite_sound_data = supposed_sound_array.flatten(), definite_sound_array.flatten()\n",
    "    l, l2 = len(target_sound_data), len(definite_sound_data)\n",
    "    X = np.linspace(0, l2 - l, l2 - l)\n",
    "    X1, X2= self.secure_split(X,2)[0], self.secure_split(X,2)[1]\n",
    "    X1, X2 = X1.astype(int), X2.astype(int)\n",
    "    ans_x1, F1 = self.find_max(X1, target_sound_data, N, l, definite_sound_data)\n",
    "    ans_x2, F2 = self.find_max(X2, target_sound_data, N, l, definite_sound_data)\n",
    "    if F2 > F1:\n",
    "      ans = ans_x2\n",
    "    else:\n",
    "      ans = ans_x1\n",
    "    return self.sound_data_by_x(definite_sound_data, ans, l)\n",
    "   \n",
    "   def find_sound_1(self, N, definite_sound_array, supposed_sound_array, n, data_predicted):\n",
    "    target_sound_data, definite_sound_data = supposed_sound_array.flatten(), definite_sound_array.flatten()\n",
    "    l, l2 = len(target_sound_data), len(definite_sound_data)\n",
    "    X = np.linspace(0, l2 - l, l2 - l)\n",
    "    x = np.array(self.secure_split(X, n))\n",
    "    ans_array = np.zeros(n)\n",
    "    F_array = np.zeros(n)\n",
    "    x, ans_array = x.astype(int), ans_array.astype(int)\n",
    "    for i in range(n):\n",
    "      Xc = x[i]\n",
    "      if len(Xc) < 2:\n",
    "        break\n",
    "      ans_x, F = self.find_max(Xc, target_sound_data, N, l, definite_sound_data)\n",
    "      ans_array[i], F_array[i] = ans_x, F\n",
    "    index = np.argmax(F_array)\n",
    "    max_F = np.max(F_array)\n",
    "    ans = ans_array[index]\n",
    "    model_x = self.model_x\n",
    "    \n",
    "\n",
    "    return ans, self.sound_data_by_x(definite_sound_data, ans, l), F, self.sound_data_by_x(definite_sound_data, data_predicted, l)\n",
    "\n",
    "   def sound_data_by_x(self, definite_sound_data, x, l):\n",
    "     ans_sound_data = definite_sound_data[int(x):int(x + l - 1)]\n",
    "     return ans_sound_data\n",
    "   \n",
    "   def F(self, sound_array, N):\n",
    "     data_array = self.secure_split(sound_array, N)\n",
    "     xf, yf_array = self.fourier_for_array(data_array, self.sample_rate)\n",
    "     func = self.calc_function(yf_array, xf)\n",
    "     return func\n",
    "\n",
    "\n",
    "       \n",
    "\n",
    "   def calc_funcions(self, sound_data, N, x1, x2, x3, x4, definite_sound_data, l):\n",
    "     sound_data1, sound_data2, sound_data3, sound_data4 = self.sound_data_by_x(definite_sound_data, x1, l), self.sound_data_by_x(definite_sound_data, x2, l), self.sound_data_by_x(definite_sound_data, x3, l), self.sound_data_by_x(definite_sound_data, x4, l)\n",
    "     F1, F2, F3, F4 = self.F(sound_data1, N), self.F(sound_data2, N), self.F(sound_data3, N), self.F(sound_data4, N)\n",
    "     return F1, F2, F3, F4\n",
    "\n",
    "   def sound_array_by_index(self, data, index_left, points_per_window, N):\n",
    "    definite_sound_array = data[int(points_per_window * (index_left)): int(points_per_window * (N + index_left))]\n",
    "    supposed_sound_array = data[int(points_per_window * (index_left)): int(points_per_window * (N - 1 + index_left))]\n",
    "    return definite_sound_array, supposed_sound_array, points_per_window\n",
    "     \n",
    "\n",
    "   def secure_split(self, d_array, N):\n",
    "     if len(d_array) % N !=0:\n",
    "       d_array = np.concatenate((d_array, np.zeros(N - len(d_array) % N)), axis = None)\n",
    "     d_array = np.split(d_array, N)\n",
    "     l = np.shape(d_array)\n",
    "     if len(l) == 3:\n",
    "        d_array = np.reshape(d_array, (l[0], l[1]))\n",
    "     return np.array(d_array)\n",
    "   \n",
    "\n",
    "  \n",
    "\n",
    "   def normalize(self, array, xf, medium_amplitude_freq0, medium_amplitude_freq1):\n",
    "    freq0_amplitude, freq1_amplitude = 0, 0\n",
    "    index_freq0, index_freq1 = self.calc_index(self.freq0, xf), self.calc_index(self.freq1, xf)\n",
    "    freq0_amplitude, freq1_amplitude = abs(self.freq_sum(array, xf, self.freq0)), abs(self.freq_sum(array, xf, self.freq1))\n",
    "    array[index_freq0 - self.index_range: index_freq0 + self.index_range + 1] = 0\n",
    "    array[index_freq1 - self.index_range: index_freq1 + self.index_range + 1] = 0\n",
    "    array[index_freq0], array[index_freq1]  = abs(freq0_amplitude / medium_amplitude_freq0), abs(freq1_amplitude / medium_amplitude_freq1)\n",
    "    return array\n",
    "\n",
    "   def calc_medium(self, yf_array, xf, freq):\n",
    "    medium_amplitude = 0\n",
    "    sum = 0\n",
    "    N = len(yf_array)\n",
    "    for i in range(N):\n",
    "      window = yf_array[i]\n",
    "      amplitude = abs(self.freq_sum(window, xf, freq))\n",
    "      sum += abs(amplitude)\n",
    "    medium_amplitude = sum / N\n",
    "    return medium_amplitude\n",
    "   \n",
    "   def make_step(self, definite_sound_array):\n",
    "     step = len(definite_sound_array) * 0.0001\n",
    "     return int(step)\n",
    "   \n",
    "   def apply_step(self, definite_sound_array, supposed_sound_array, N, step, number_of_steps, sample_rate):\n",
    "     changed_sound_array = definite_sound_array[int(step * number_of_steps + step):int(step * number_of_steps + len(supposed_sound_array) + step)]\n",
    "     changed_sound_data_array = self.secure_split(changed_sound_array, N)\n",
    "     xf, changed_yf_array = self.fourier_for_array(changed_sound_data_array, sample_rate)\n",
    "     return xf, changed_yf_array, changed_sound_array\n",
    "   \n",
    "   def plotting_function(self, definite_yf_array, definite_sound_array, supposed_sound_array, freq0, freq1, xf, range_index, N, sample_rate):\n",
    "     number_of_steps = 0\n",
    "     y_array = np.array([], dtype = float)\n",
    "     step = self.make_step(definite_sound_array)\n",
    "     x_array= np.array([], dtype = float)\n",
    "     while int(step * number_of_steps) + len(supposed_sound_array) + step < len(definite_sound_array) - 1 - step:\n",
    "        xf, yf_array, changed_sound_array = self.apply_step(definite_sound_array, supposed_sound_array, N, step, number_of_steps, sample_rate)\n",
    "        func = self.calc_function(yf_array, xf)\n",
    "        y_array = np.concatenate((y_array, [func]), axis = None)\n",
    "        step = self.make_step(definite_sound_array)\n",
    "        supposed_sound_array = changed_sound_array\n",
    "        x_array = np.concatenate((x_array, [number_of_steps]), axis = None)\n",
    "        number_of_steps += 1\n",
    "     plt.plot(y_array)\n",
    "     m = np.argmax(y_array)\n",
    "     return x_array, np.max(y_array), step*(x_array[m])\n",
    "   \n",
    "     \n",
    "   \n",
    "   def abs_diff(self, yf_window, xf, freq0, freq1):\n",
    "    absolute_difference = (self.freq_sum(yf_window, xf, freq0) - self.freq_sum(yf_window, xf, freq1))** 2\n",
    "    return absolute_difference\n",
    "   \n",
    "   def calc_function(self, yf_array, xf):#Все функции снизу нужны для собирания данных\n",
    "    freq0, freq1 = self.freq0, self.freq1\n",
    "    func = 0\n",
    "    for i in range(len(yf_array)):\n",
    "      yf_window = yf_array[i]\n",
    "      func += self.abs_diff(yf_window, xf, freq0, freq1)\n",
    "    return func\n",
    "   \n",
    "   def testing_decoder(self, data, true_N):#Этот код очень (прям совсем) не хорошо оптимизирован, но это не важно.\n",
    "     freq0, freq1 = self.freq0, self.freq1\n",
    "     sample_rate, change_time, range_index = self.sample_rate, self.change_time, self.index_range #Включаем все параметры\n",
    "     duration = len(data) / sample_rate      #whole time\n",
    "     N0 = int(duration / change_time)        #number of windows\n",
    "     data_array = self.secure_split(data, N0)         #array of arrays with sound data\n",
    "     points_per_window = int(change_time * sample_rate) #Количество отсчетов за такт\n",
    "     amplitude_threshold = 1.5\n",
    "     xf_for_array, yf_array = self.fourier_for_array(data_array, sample_rate)  \n",
    "     yf_for_array_bool = self.fill_in(yf_array, xf_for_array, freq0, freq1, amplitude_threshold, 64, N0) #В этом массиве отображается длина сигнала\n",
    "     _, _, N = self.get_index_start_end_of_longest_ones(yf_for_array_bool)\n",
    "     rare_flag = 0\n",
    "     while True:\n",
    "       if amplitude_threshold < 0:\n",
    "         return 1\n",
    "       if N < true_N - 1:\n",
    "         amplitude_threshold -= 0.06\n",
    "       if N > true_N + 1:\n",
    "         amplitude_threshold += 0.8\n",
    "       if N % 4 == 0:\n",
    "         j = 0\n",
    "         data_backup = data\n",
    "         while N % 4 == 0:\n",
    "           if j > 2:\n",
    "             break\n",
    "           data = np.concatenate((data[int(points_per_window / 2):], np.zeros(int(points_per_window / 2))), axis = None)#Само смещение\n",
    "           N0 = int(duration / change_time)       \n",
    "           data_array = self.secure_split(data, N0)   \n",
    "           xf_for_array, yf_array = self.fourier_for_array(data_array, sample_rate)  \n",
    "           yf_for_array_bool = self.fill_in(yf_array, xf_for_array, freq0, freq1, amplitude_threshold, 64, N0)\n",
    "           _, _, N = self.get_index_start_end_of_longest_ones(yf_for_array_bool)\n",
    "           if yf_for_array_bool[0] == True:\n",
    "             break\n",
    "           j += 1\n",
    "         data = data_backup\n",
    "       if N % 4 == 1:\n",
    "         break\n",
    "       if N % 4 == 3:\n",
    "         if rare_flag > 10:\n",
    "           break\n",
    "         rare_flag += 1\n",
    "         amplitude_threshold -= 0.08\n",
    "       if N % 4 == 0:\n",
    "         break\n",
    "       yf_for_array_bool = self.fill_in(yf_array, xf_for_array, freq0, freq1, amplitude_threshold, 64, N0)\n",
    "       _, _, N = self.get_index_start_end_of_longest_ones(yf_for_array_bool)\n",
    "       \n",
    "     return amplitude_threshold\n",
    "\n",
    "def get_max_param(data, true_code, amplitude_threshold):\n",
    "  a = decoder()\n",
    "  a.set_amplitude_threshold(amplitude_threshold)\n",
    "  a.set_freqses(1000, 1500)\n",
    "  a.set_change_time(0.1)\n",
    "  a.set_draw_graph(False)\n",
    "  a.set_print_progress(False)\n",
    "  code = \"\"\n",
    "  i = 1\n",
    "  while code != true_code:\n",
    "    a.set_max_param(i)\n",
    "    _, code = a.decoder_1(data)\n",
    "    i += 1\n",
    "    if i >= 15:\n",
    "      break\n",
    "  return i\n",
    "\n",
    "\n",
    "  \n",
    "def get_max_param_threshold(data_array,code_array):\n",
    "  a = decoder()\n",
    "  amplitude_threshold_array = []\n",
    "  max_param_array = []\n",
    "  a.set_print_progress(False)\n",
    "  for i in range(0, len(code_array)):\n",
    "    data = data_array[i]\n",
    "    code = code_array[i]\n",
    "    N = len(code)\n",
    "    amplitude_threshold = a.testing_decoder(data, N)\n",
    "    max_param = get_max_param(data, code, amplitude_threshold)\n",
    "    amplitude_threshold_array.append(amplitude_threshold)\n",
    "    max_param_array.append(max_param)\n",
    "    print(\"MAKING PROGRESS\")\n",
    "  return max_param_array, amplitude_threshold_array\n",
    "\n",
    "def correction_array(code_array):\n",
    "  b = wav_manager()\n",
    "  a = decoder()\n",
    "  a.set_print_yf(False)\n",
    "  a.set_freqses(1000, 1500)\n",
    "  a.set_change_time(0.1)\n",
    "  a.set_max_param(8)\n",
    "  a.set_draw_graph(False)\n",
    "  a.set_print_progress(False)\n",
    "  true_data_array = []\n",
    "  true_code_array = []\n",
    "  amp_array = []\n",
    "  max_array = []\n",
    "  x_array = []\n",
    "  for i in range(1610):\n",
    "    b.set_recorded_name(\"recorded\" + str(i))\n",
    "    data, _ = b.get_data_sample_rate()\n",
    "    code = code_array[i]\n",
    "    l = len(code)\n",
    "    amp = a.testing_decoder(data, l)\n",
    "    if amp < 0:\n",
    "      raise ValueError(\"????\")\n",
    "    a.set_amplitude_threshold(amp)\n",
    "    max_param = get_max_param(data, code, amp)\n",
    "    a.set_max_param(max_param)\n",
    "    x, codi = a.decoder_1(data)\n",
    "    if codi == code:\n",
    "      true_data_array.append(data)\n",
    "      true_code_array.append(code)\n",
    "      print(i, \"-th RECORDED FILE DECODED CORRECTLY\")\n",
    "      amp_array.append(amp)\n",
    "      max_array.append(max_param)\n",
    "      x_array.append(x)\n",
    "    else:\n",
    "      print(\"ERROR IN DECODING FOR\", i, \"-th\")\n",
    "    \n",
    "  return true_data_array, true_code_array, amp_array, max_array, x_array\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "       \n",
    "       \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_array = get_code_array(600)#Здесь рандомным (почти) образом идет генерация двоичного кода, который кодируется в сигнал\n",
    "duration_array = get_duration_array(code_array, 0.1)\n",
    "code_array = ['1011',\n",
    " '0011',\n",
    " '0100',\n",
    " '0100',\n",
    " '1101',\n",
    " '0001',\n",
    " '0010',\n",
    " '1000',\n",
    " '1011',\n",
    " '1000',\n",
    " '0000',\n",
    " '1010',\n",
    " '1001',\n",
    " '1011',\n",
    " '1111',\n",
    " '1101',\n",
    " '0010',\n",
    " '1010',\n",
    " '0011',\n",
    " '1001',\n",
    " '0101',\n",
    " '0000',\n",
    " '0100',\n",
    " '0101',\n",
    " '1110',\n",
    " '1111',\n",
    " '1110',\n",
    " '1110',\n",
    " '1010',\n",
    " '0010',\n",
    " '1011',\n",
    " '1111',\n",
    " '1110',\n",
    " '0111',\n",
    " '1011',\n",
    " '1001',\n",
    " '1010',\n",
    " '0110',\n",
    " '0111',\n",
    " '1110',\n",
    " '0110',\n",
    " '0001',\n",
    " '0100',\n",
    " '1111',\n",
    " '0101',\n",
    " '1110',\n",
    " '0100',\n",
    " '1111',\n",
    " '1101',\n",
    " '0001',\n",
    " '1011',\n",
    " '1000',\n",
    " '0011',\n",
    " '1110',\n",
    " '1011',\n",
    " '1111',\n",
    " '0010',\n",
    " '0011',\n",
    " '0000',\n",
    " '1011',\n",
    " '0011',\n",
    " '1000',\n",
    " '0101',\n",
    " '1110',\n",
    " '0000',\n",
    " '0101',\n",
    " '1001',\n",
    " '1000',\n",
    " '1101',\n",
    " '1000',\n",
    " '0001',\n",
    " '1101',\n",
    " '0111',\n",
    " '1110',\n",
    " '0010',\n",
    " '0100',\n",
    " '1011',\n",
    " '0000',\n",
    " '1011',\n",
    " '0110',\n",
    " '1011',\n",
    " '0011',\n",
    " '1001',\n",
    " '0000',\n",
    " '0000',\n",
    " '0011',\n",
    " '1011',\n",
    " '0001',\n",
    " '1111',\n",
    " '1010',\n",
    " '0000',\n",
    " '1011',\n",
    " '0010',\n",
    " '0100',\n",
    " '0110',\n",
    " '0010',\n",
    " '0110',\n",
    " '0010',\n",
    " '0010',\n",
    " '1001',\n",
    " '00111111',\n",
    " '10010101',\n",
    " '00000111',\n",
    " '10001001',\n",
    " '01010011',\n",
    " '11101010',\n",
    " '01001110',\n",
    " '11001100',\n",
    " '01001000',\n",
    " '01011001',\n",
    " '11100010',\n",
    " '01111101',\n",
    " '01101100',\n",
    " '00001011',\n",
    " '01100010',\n",
    " '10010111',\n",
    " '10000010',\n",
    " '01100100',\n",
    " '00100000',\n",
    " '10100100',\n",
    " '00001110',\n",
    " '00010001',\n",
    " '10001111',\n",
    " '11101101',\n",
    " '10011111',\n",
    " '10111011',\n",
    " '10101001',\n",
    " '11001011',\n",
    " '11000111',\n",
    " '00000011',\n",
    " '11001000',\n",
    " '11101001',\n",
    " '01001001',\n",
    " '01101011',\n",
    " '01100011',\n",
    " '00011111',\n",
    " '11101000',\n",
    " '10011101',\n",
    " '01000010',\n",
    " '10001100',\n",
    " '10010000',\n",
    " '10110010',\n",
    " '00110100',\n",
    " '01010010',\n",
    " '01100100',\n",
    " '11111000',\n",
    " '10110011',\n",
    " '01001000',\n",
    " '11111111',\n",
    " '11110001',\n",
    " '11000001',\n",
    " '00001101',\n",
    " '00011010',\n",
    " '01100111',\n",
    " '00000000',\n",
    " '00111111',\n",
    " '10100100',\n",
    " '10110111',\n",
    " '00000101',\n",
    " '10111001',\n",
    " '10011001',\n",
    " '01010010',\n",
    " '00000100',\n",
    " '11011111',\n",
    " '10001011',\n",
    " '10011001',\n",
    " '00000101',\n",
    " '11010010',\n",
    " '00101111',\n",
    " '00101110',\n",
    " '00100010',\n",
    " '10000110',\n",
    " '00010100',\n",
    " '00000100',\n",
    " '10011100',\n",
    " '11001001',\n",
    " '00001000',\n",
    " '01000110',\n",
    " '00001110',\n",
    " '00000001',\n",
    " '11110110',\n",
    " '00111010',\n",
    " '00100010',\n",
    " '00000101',\n",
    " '10100000',\n",
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    " '00101100110111000101001011110111010011011100000111111000010000000000011100101101110101101100101000000001101101111100101000111101',\n",
    " '00001000111001010001011000001000110000010001110100111110111010010101110100111110011000001110001011000101101000010101111000100000']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_max_x(data_array, amplitude_array):\n",
    "    a = decoder()\n",
    "    a.set_freqses(1000, 1500)\n",
    "    a.set_change_time(0.1)\n",
    "    a.set_max_param(8)\n",
    "    a.set_print_amp(False)\n",
    "    a.set_draw_graph(False)\n",
    "    a.set_print_progress(False)\n",
    "    x_array = []\n",
    "    for i in range(len(data_array)):\n",
    "        data = data_array[i]\n",
    "        amp = amplitude_array[i]\n",
    "        a.set_amplitude_threshold(amp)\n",
    "        x, code = a.decoder_1(data)\n",
    "        x_array.append(x)\n",
    "        print(i)\n",
    "    return x_array\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 72ms/step\n",
      "PREDICTED 3.9149325 4\n",
      "1/1 [==============================] - 0s 114ms/step\n",
      "0.37914291676235007 0.060707364732695945\n",
      "0.664938725096724 0.11664995538904269\n",
      "0.6945765054960396 0.1064572401206843\n",
      "0.602321766267209 0.10003080543686842\n",
      "0.609501815735965 0.12273512760352438\n",
      "0.5640197510910465 0.0734289293063296\n",
      "0.486278042461493 0.11151551640091312\n",
      "0.6163196686481015 0.09001920867268132\n",
      "0.4897936450097911 0.1051816031670269\n",
      "0.7849897611039877 0.1349102819873607\n",
      "0.43076744986712484 0.10647092447115582\n",
      "0.48790911196794545 0.09992502079441895\n",
      "0.6166804466625622 0.09860061872713952\n",
      "0.5344940004943278 0.11672292932919419\n",
      "0.5513488726752158 0.13299304541156012\n",
      "0.4621449837833402 0.09404630584750118\n",
      "0.3864436186733927 0.15057974379828665\n",
      "0.3649640008689159 0.057930568025658255\n",
      "0.5392835240189452 0.11605258650517179\n",
      "0.5129320815368477 0.14371725459081094\n",
      "0.46940401454849573 0.08622038717922766\n",
      "0.6590059709042869 0.10920426021790533\n",
      "0.8381467522213482 0.10677572811436205\n",
      "0.6084295466280676 0.14446186452594018\n",
      "0.6346530786544438 16.1695867265703\n",
      "28.075399367320802 20.95501439693539\n",
      "30.21481528815082 16.044134610203315\n",
      "2.3597404934660133 29.032360284177603\n",
      "0.703163769031709 22.812925725527112\n",
      "0.4581600331756111 2.0576942343119042\n",
      "0.41794377343282374 0.5730202132886184\n",
      "0.6544279962174802 0.15806213839865568\n",
      "0.5126797678013246 0.0938048885443695\n",
      "0.4744413358688399 0.09708532266592702\n",
      "0.49790725628377686 0.09322699946725223\n",
      "0.5225427558682197 0.09041749212442317\n",
      "0.39056734804719756 0.10186097617322874\n",
      "0.6024559073547976 0.062477194085255164\n",
      "0.4733643356118107 0.07638690003633765\n",
      "0.4360747651166112 0.07188320937567191\n",
      "0.3890585784652371 0.06878276886719753\n",
      "0.5164991769152224 0.14247454106932286\n",
      "0.42211104980333836 0.08115078153935694\n",
      "0.5622489706534856 0.1180601375335647\n",
      "0.45166304469671004 0.11281025729240084\n",
      "0.3627001474838558 0.10260647120493967\n",
      "0.4737008052681145 0.13026796582025607\n",
      "0.9352219930663699 0.34837673481889586\n",
      "0.8189493350682262 0.19814331641076582\n",
      "0.623815041377379 0.08393184913819697\n",
      "0.43461413989993175 0.09676225380386996\n",
      "0.2374882183980122 0.08207879161124984\n",
      "0.38442224944705666 0.07301775531723048\n",
      "0.3481963499743643 0.07799572002215627\n",
      "0.45562856960474357 0.11811198090468611\n",
      "0.3781288780782141 0.0982830858860375\n",
      "0.5481147413883883 0.08303615389386887\n",
      "0.4556801518649497 0.11490278444599361\n",
      "0.5256097106608869 0.09299780981321613\n",
      "0.47814538342844726 0.09929353951223283\n",
      "0.7286767110447775 0.05425898316489498\n",
      "0.32877537824735287 0.1013632468923591\n",
      "0.4748512088905865 0.05987532434761073\n",
      "0.4723575821334149 0.10555481262062426\n",
      "0.5299293440630006 0.09031428637417228\n",
      "0.570689380149584 0.08007929062095658\n",
      "0.4484125955201471 0.11694734718375814\n",
      "0.4864545734062103 0.0981172530233939\n",
      "0.3388184819051577 0.11290955825349264\n",
      "0.5289173059359593 0.08262118104871288\n",
      "0.47761802575872003 0.0799746943720822\n",
      "0.5073279117054523 0.10596055339544044\n",
      "0.4949396859064607 0.08276179973716527\n",
      "0.6207933699029996 0.04489673664495601\n",
      "0.3653389097853164 0.09759595101462192\n",
      "0.4943355137416251 0.1098437844246621\n",
      "0.4648961145188844 0.10779412864749531\n",
      "0.47037497689485036 0.07193488346843378\n",
      "0.5605835200961069 0.06797178840919665\n",
      "0.4369209241998882 0.1216333540866517\n",
      "0.4531111197074933 0.09332348467254763\n",
      "0.4384810470074696 0.07485046083298573\n",
      "0.5084115250595799 0.10114176761407785\n",
      "0.4307965184256516 0.14951683029765747\n",
      "0.5303076300052346 0.11720353687118144\n",
      "0.475829200370094 0.13329840523174416\n",
      "0.5317590460223504 0.10494726834384799\n",
      "0.31143867261150243 0.11143331588258323\n",
      "0.4249432508260359 0.12495961520734855\n",
      "0.4734982537840164 0.11897898932437974\n",
      "0.4690713306440444 0.15011801542955852\n",
      "0.49411982984351005 0.07451003546514887\n",
      "0.5802532746818074 0.08687042449703598\n",
      "0.544794729479628 0.08421702691819778\n",
      "0.49257571693076013 0.10598032754760238\n",
      "0.46180400362871266 0.06242979782913584\n",
      "0.5601503025724619 0.10043608792280678\n",
      "0.5560364341354348 0.07219968369100897\n",
      "0.5525938943597538 0.07081213794248009\n",
      "0.42952562575481057 0.07706705904185226\n",
      "0.5444338743295677 0.06380441766299984\n",
      "0.48975157140720804 0.08638352240707088\n",
      "0.5906849157029437 0.08180919220025312\n",
      "0.3953808305372578 0.12899813963793172\n",
      "0.410150622207162 0.10030814534748621\n",
      "0.4397953931218025 0.08229641965522237\n",
      "0.4320252532037284 0.08684461296935779\n",
      "0.4658906651081463 0.08061283521481098\n",
      "0.5360225639915767 0.0822167133699238\n",
      "0.4748863133027564 0.07281089565894179\n",
      "0.4417819094221673 0.1325284875668234\n",
      "0.28923217897399095 0.06122241654199562\n",
      "0.4306038872861003 0.11895383476722386\n",
      "0.6526039180421728 0.09528614064664558\n",
      "0.40556016309409026 0.11723487667721394\n",
      "0.5151288466452141 0.0999281589972803\n",
      "0.5248878032992009 0.13838478911834393\n",
      "0.42155210026071016 0.08042718316759485\n",
      "0.4349434909386031 0.0848827089527095\n",
      "Сигнал определился нормально\n",
      "Определенная длина равна 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2863, '1011', '1111')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_x = tf.keras.models.load_model(\"x_position_0.h5\")\n",
    "model_param = tf.keras.models.load_model(\"Threshold_Param_Prediction_1.h5\")\n",
    "a = decoder()#Запустите эту ячейку, чтобы декодировать сигнал. Ниже идет собирание, обработка данных\n",
    "b = wav_manager()\n",
    "b.set_recorded_name(\"recorded.wav\")\n",
    "data, _ = b.get_data_sample_rate()\n",
    "a.set_models(model_x, model_param)\n",
    "a.set_freqses(1000, 1500)\n",
    "a.set_change_time(0.1)\n",
    "a.set_max_param(8)\n",
    "a.set_print_amp(True)\n",
    "a.set_draw_graph(False)\n",
    "a.set_amplitude_threshold(5)\n",
    "_, codi, exp = a.decoder_1(data)\n",
    "_, codi, exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_2 (Conv1D)           (None, 126, 32)           128       \n",
      "                                                                 \n",
      " max_pooling1d_2 (MaxPooling  (None, 63, 32)           0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 2016)              0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 256)               516352    \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 2)                 514       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 516,994\n",
      "Trainable params: 516,994\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_param.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -th RECORDED FILE DECODED CORRECTLY\n",
      "1 -th RECORDED FILE DECODED CORRECTLY\n",
      "2 -th RECORDED FILE DECODED CORRECTLY\n",
      "3 -th RECORDED FILE DECODED CORRECTLY\n",
      "4 -th RECORDED FILE DECODED CORRECTLY\n",
      "5 -th RECORDED FILE DECODED CORRECTLY\n",
      "6 -th RECORDED FILE DECODED CORRECTLY\n",
      "7 -th RECORDED FILE DECODED CORRECTLY\n",
      "8 -th RECORDED FILE DECODED CORRECTLY\n",
      "9 -th RECORDED FILE DECODED CORRECTLY\n",
      "10 -th RECORDED FILE DECODED CORRECTLY\n",
      "11 -th RECORDED FILE DECODED CORRECTLY\n",
      "12 -th RECORDED FILE DECODED CORRECTLY\n",
      "13 -th RECORDED FILE DECODED CORRECTLY\n",
      "14 -th RECORDED FILE DECODED CORRECTLY\n",
      "15 -th RECORDED FILE DECODED CORRECTLY\n",
      "16 -th RECORDED FILE DECODED CORRECTLY\n",
      "17 -th RECORDED FILE DECODED CORRECTLY\n",
      "18 -th RECORDED FILE DECODED CORRECTLY\n",
      "19 -th RECORDED FILE DECODED CORRECTLY\n",
      "20 -th RECORDED FILE DECODED CORRECTLY\n",
      "21 -th RECORDED FILE DECODED CORRECTLY\n",
      "22 -th RECORDED FILE DECODED CORRECTLY\n",
      "23 -th RECORDED FILE DECODED CORRECTLY\n",
      "24 -th RECORDED FILE DECODED CORRECTLY\n",
      "25 -th RECORDED FILE DECODED CORRECTLY\n",
      "26 -th RECORDED FILE DECODED CORRECTLY\n",
      "27 -th RECORDED FILE DECODED CORRECTLY\n",
      "28 -th RECORDED FILE DECODED CORRECTLY\n",
      "29 -th RECORDED FILE DECODED CORRECTLY\n",
      "30 -th RECORDED FILE DECODED CORRECTLY\n",
      "31 -th RECORDED FILE DECODED CORRECTLY\n",
      "32 -th RECORDED FILE DECODED CORRECTLY\n",
      "33 -th RECORDED FILE DECODED CORRECTLY\n",
      "34 -th RECORDED FILE DECODED CORRECTLY\n",
      "35 -th RECORDED FILE DECODED CORRECTLY\n",
      "36 -th RECORDED FILE DECODED CORRECTLY\n",
      "37 -th RECORDED FILE DECODED CORRECTLY\n",
      "38 -th RECORDED FILE DECODED CORRECTLY\n",
      "39 -th RECORDED FILE DECODED CORRECTLY\n",
      "40 -th RECORDED FILE DECODED CORRECTLY\n",
      "41 -th RECORDED FILE DECODED CORRECTLY\n",
      "42 -th RECORDED FILE DECODED CORRECTLY\n",
      "43 -th RECORDED FILE DECODED CORRECTLY\n",
      "44 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 45 -th\n",
      "46 -th RECORDED FILE DECODED CORRECTLY\n",
      "47 -th RECORDED FILE DECODED CORRECTLY\n",
      "48 -th RECORDED FILE DECODED CORRECTLY\n",
      "49 -th RECORDED FILE DECODED CORRECTLY\n",
      "50 -th RECORDED FILE DECODED CORRECTLY\n",
      "51 -th RECORDED FILE DECODED CORRECTLY\n",
      "52 -th RECORDED FILE DECODED CORRECTLY\n",
      "53 -th RECORDED FILE DECODED CORRECTLY\n",
      "54 -th RECORDED FILE DECODED CORRECTLY\n",
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      "60 -th RECORDED FILE DECODED CORRECTLY\n",
      "61 -th RECORDED FILE DECODED CORRECTLY\n",
      "62 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 63 -th\n",
      "64 -th RECORDED FILE DECODED CORRECTLY\n",
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      "177 -th RECORDED FILE DECODED CORRECTLY\n",
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      "179 -th RECORDED FILE DECODED CORRECTLY\n",
      "180 -th RECORDED FILE DECODED CORRECTLY\n",
      "181 -th RECORDED FILE DECODED CORRECTLY\n",
      "182 -th RECORDED FILE DECODED CORRECTLY\n",
      "183 -th RECORDED FILE DECODED CORRECTLY\n",
      "184 -th RECORDED FILE DECODED CORRECTLY\n",
      "185 -th RECORDED FILE DECODED CORRECTLY\n",
      "186 -th RECORDED FILE DECODED CORRECTLY\n",
      "187 -th RECORDED FILE DECODED CORRECTLY\n",
      "188 -th RECORDED FILE DECODED CORRECTLY\n",
      "189 -th RECORDED FILE DECODED CORRECTLY\n",
      "190 -th RECORDED FILE DECODED CORRECTLY\n",
      "191 -th RECORDED FILE DECODED CORRECTLY\n",
      "192 -th RECORDED FILE DECODED CORRECTLY\n",
      "193 -th RECORDED FILE DECODED CORRECTLY\n",
      "194 -th RECORDED FILE DECODED CORRECTLY\n",
      "195 -th RECORDED FILE DECODED CORRECTLY\n",
      "196 -th RECORDED FILE DECODED CORRECTLY\n",
      "197 -th RECORDED FILE DECODED CORRECTLY\n",
      "198 -th RECORDED FILE DECODED CORRECTLY\n",
      "199 -th RECORDED FILE DECODED CORRECTLY\n",
      "200 -th RECORDED FILE DECODED CORRECTLY\n",
      "201 -th RECORDED FILE DECODED CORRECTLY\n",
      "202 -th RECORDED FILE DECODED CORRECTLY\n",
      "203 -th RECORDED FILE DECODED CORRECTLY\n",
      "204 -th RECORDED FILE DECODED CORRECTLY\n",
      "205 -th RECORDED FILE DECODED CORRECTLY\n",
      "206 -th RECORDED FILE DECODED CORRECTLY\n",
      "207 -th RECORDED FILE DECODED CORRECTLY\n",
      "208 -th RECORDED FILE DECODED CORRECTLY\n",
      "209 -th RECORDED FILE DECODED CORRECTLY\n",
      "210 -th RECORDED FILE DECODED CORRECTLY\n",
      "211 -th RECORDED FILE DECODED CORRECTLY\n",
      "212 -th RECORDED FILE DECODED CORRECTLY\n",
      "213 -th RECORDED FILE DECODED CORRECTLY\n",
      "214 -th RECORDED FILE DECODED CORRECTLY\n",
      "215 -th RECORDED FILE DECODED CORRECTLY\n",
      "216 -th RECORDED FILE DECODED CORRECTLY\n",
      "217 -th RECORDED FILE DECODED CORRECTLY\n",
      "218 -th RECORDED FILE DECODED CORRECTLY\n",
      "219 -th RECORDED FILE DECODED CORRECTLY\n",
      "220 -th RECORDED FILE DECODED CORRECTLY\n",
      "221 -th RECORDED FILE DECODED CORRECTLY\n",
      "222 -th RECORDED FILE DECODED CORRECTLY\n",
      "223 -th RECORDED FILE DECODED CORRECTLY\n",
      "224 -th RECORDED FILE DECODED CORRECTLY\n",
      "225 -th RECORDED FILE DECODED CORRECTLY\n",
      "226 -th RECORDED FILE DECODED CORRECTLY\n",
      "227 -th RECORDED FILE DECODED CORRECTLY\n",
      "228 -th RECORDED FILE DECODED CORRECTLY\n",
      "229 -th RECORDED FILE DECODED CORRECTLY\n",
      "230 -th RECORDED FILE DECODED CORRECTLY\n",
      "231 -th RECORDED FILE DECODED CORRECTLY\n",
      "232 -th RECORDED FILE DECODED CORRECTLY\n",
      "233 -th RECORDED FILE DECODED CORRECTLY\n",
      "234 -th RECORDED FILE DECODED CORRECTLY\n",
      "235 -th RECORDED FILE DECODED CORRECTLY\n",
      "236 -th RECORDED FILE DECODED CORRECTLY\n",
      "237 -th RECORDED FILE DECODED CORRECTLY\n",
      "238 -th RECORDED FILE DECODED CORRECTLY\n",
      "239 -th RECORDED FILE DECODED CORRECTLY\n",
      "240 -th RECORDED FILE DECODED CORRECTLY\n",
      "241 -th RECORDED FILE DECODED CORRECTLY\n",
      "242 -th RECORDED FILE DECODED CORRECTLY\n",
      "243 -th RECORDED FILE DECODED CORRECTLY\n",
      "244 -th RECORDED FILE DECODED CORRECTLY\n",
      "245 -th RECORDED FILE DECODED CORRECTLY\n",
      "246 -th RECORDED FILE DECODED CORRECTLY\n",
      "247 -th RECORDED FILE DECODED CORRECTLY\n",
      "248 -th RECORDED FILE DECODED CORRECTLY\n",
      "249 -th RECORDED FILE DECODED CORRECTLY\n",
      "250 -th RECORDED FILE DECODED CORRECTLY\n",
      "251 -th RECORDED FILE DECODED CORRECTLY\n",
      "252 -th RECORDED FILE DECODED CORRECTLY\n",
      "253 -th RECORDED FILE DECODED CORRECTLY\n",
      "254 -th RECORDED FILE DECODED CORRECTLY\n",
      "255 -th RECORDED FILE DECODED CORRECTLY\n",
      "256 -th RECORDED FILE DECODED CORRECTLY\n",
      "257 -th RECORDED FILE DECODED CORRECTLY\n",
      "258 -th RECORDED FILE DECODED CORRECTLY\n",
      "259 -th RECORDED FILE DECODED CORRECTLY\n",
      "260 -th RECORDED FILE DECODED CORRECTLY\n",
      "261 -th RECORDED FILE DECODED CORRECTLY\n",
      "262 -th RECORDED FILE DECODED CORRECTLY\n",
      "263 -th RECORDED FILE DECODED CORRECTLY\n",
      "264 -th RECORDED FILE DECODED CORRECTLY\n",
      "265 -th RECORDED FILE DECODED CORRECTLY\n",
      "266 -th RECORDED FILE DECODED CORRECTLY\n",
      "267 -th RECORDED FILE DECODED CORRECTLY\n",
      "268 -th RECORDED FILE DECODED CORRECTLY\n",
      "269 -th RECORDED FILE DECODED CORRECTLY\n",
      "270 -th RECORDED FILE DECODED CORRECTLY\n",
      "271 -th RECORDED FILE DECODED CORRECTLY\n",
      "272 -th RECORDED FILE DECODED CORRECTLY\n",
      "273 -th RECORDED FILE DECODED CORRECTLY\n",
      "274 -th RECORDED FILE DECODED CORRECTLY\n",
      "275 -th RECORDED FILE DECODED CORRECTLY\n",
      "276 -th RECORDED FILE DECODED CORRECTLY\n",
      "277 -th RECORDED FILE DECODED CORRECTLY\n",
      "278 -th RECORDED FILE DECODED CORRECTLY\n",
      "279 -th RECORDED FILE DECODED CORRECTLY\n",
      "280 -th RECORDED FILE DECODED CORRECTLY\n",
      "281 -th RECORDED FILE DECODED CORRECTLY\n",
      "282 -th RECORDED FILE DECODED CORRECTLY\n",
      "283 -th RECORDED FILE DECODED CORRECTLY\n",
      "284 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 285 -th\n",
      "286 -th RECORDED FILE DECODED CORRECTLY\n",
      "287 -th RECORDED FILE DECODED CORRECTLY\n",
      "288 -th RECORDED FILE DECODED CORRECTLY\n",
      "289 -th RECORDED FILE DECODED CORRECTLY\n",
      "290 -th RECORDED FILE DECODED CORRECTLY\n",
      "291 -th RECORDED FILE DECODED CORRECTLY\n",
      "292 -th RECORDED FILE DECODED CORRECTLY\n",
      "293 -th RECORDED FILE DECODED CORRECTLY\n",
      "294 -th RECORDED FILE DECODED CORRECTLY\n",
      "295 -th RECORDED FILE DECODED CORRECTLY\n",
      "296 -th RECORDED FILE DECODED CORRECTLY\n",
      "297 -th RECORDED FILE DECODED CORRECTLY\n",
      "298 -th RECORDED FILE DECODED CORRECTLY\n",
      "299 -th RECORDED FILE DECODED CORRECTLY\n",
      "300 -th RECORDED FILE DECODED CORRECTLY\n",
      "301 -th RECORDED FILE DECODED CORRECTLY\n",
      "302 -th RECORDED FILE DECODED CORRECTLY\n",
      "303 -th RECORDED FILE DECODED CORRECTLY\n",
      "304 -th RECORDED FILE DECODED CORRECTLY\n",
      "305 -th RECORDED FILE DECODED CORRECTLY\n",
      "306 -th RECORDED FILE DECODED CORRECTLY\n",
      "307 -th RECORDED FILE DECODED CORRECTLY\n",
      "308 -th RECORDED FILE DECODED CORRECTLY\n",
      "309 -th RECORDED FILE DECODED CORRECTLY\n",
      "310 -th RECORDED FILE DECODED CORRECTLY\n",
      "311 -th RECORDED FILE DECODED CORRECTLY\n",
      "312 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 313 -th\n",
      "314 -th RECORDED FILE DECODED CORRECTLY\n",
      "315 -th RECORDED FILE DECODED CORRECTLY\n",
      "316 -th RECORDED FILE DECODED CORRECTLY\n",
      "317 -th RECORDED FILE DECODED CORRECTLY\n",
      "318 -th RECORDED FILE DECODED CORRECTLY\n",
      "319 -th RECORDED FILE DECODED CORRECTLY\n",
      "320 -th RECORDED FILE DECODED CORRECTLY\n",
      "321 -th RECORDED FILE DECODED CORRECTLY\n",
      "322 -th RECORDED FILE DECODED CORRECTLY\n",
      "323 -th RECORDED FILE DECODED CORRECTLY\n",
      "324 -th RECORDED FILE DECODED CORRECTLY\n",
      "325 -th RECORDED FILE DECODED CORRECTLY\n",
      "326 -th RECORDED FILE DECODED CORRECTLY\n",
      "327 -th RECORDED FILE DECODED CORRECTLY\n",
      "328 -th RECORDED FILE DECODED CORRECTLY\n",
      "329 -th RECORDED FILE DECODED CORRECTLY\n",
      "330 -th RECORDED FILE DECODED CORRECTLY\n",
      "331 -th RECORDED FILE DECODED CORRECTLY\n",
      "332 -th RECORDED FILE DECODED CORRECTLY\n",
      "333 -th RECORDED FILE DECODED CORRECTLY\n",
      "334 -th RECORDED FILE DECODED CORRECTLY\n",
      "335 -th RECORDED FILE DECODED CORRECTLY\n",
      "336 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 337 -th\n",
      "338 -th RECORDED FILE DECODED CORRECTLY\n",
      "339 -th RECORDED FILE DECODED CORRECTLY\n",
      "340 -th RECORDED FILE DECODED CORRECTLY\n",
      "341 -th RECORDED FILE DECODED CORRECTLY\n",
      "342 -th RECORDED FILE DECODED CORRECTLY\n",
      "343 -th RECORDED FILE DECODED CORRECTLY\n",
      "344 -th RECORDED FILE DECODED CORRECTLY\n",
      "345 -th RECORDED FILE DECODED CORRECTLY\n",
      "346 -th RECORDED FILE DECODED CORRECTLY\n",
      "347 -th RECORDED FILE DECODED CORRECTLY\n",
      "348 -th RECORDED FILE DECODED CORRECTLY\n",
      "349 -th RECORDED FILE DECODED CORRECTLY\n",
      "350 -th RECORDED FILE DECODED CORRECTLY\n",
      "351 -th RECORDED FILE DECODED CORRECTLY\n",
      "352 -th RECORDED FILE DECODED CORRECTLY\n",
      "353 -th RECORDED FILE DECODED CORRECTLY\n",
      "354 -th RECORDED FILE DECODED CORRECTLY\n",
      "355 -th RECORDED FILE DECODED CORRECTLY\n",
      "356 -th RECORDED FILE DECODED CORRECTLY\n",
      "357 -th RECORDED FILE DECODED CORRECTLY\n",
      "358 -th RECORDED FILE DECODED CORRECTLY\n",
      "359 -th RECORDED FILE DECODED CORRECTLY\n",
      "360 -th RECORDED FILE DECODED CORRECTLY\n",
      "361 -th RECORDED FILE DECODED CORRECTLY\n",
      "362 -th RECORDED FILE DECODED CORRECTLY\n",
      "363 -th RECORDED FILE DECODED CORRECTLY\n",
      "364 -th RECORDED FILE DECODED CORRECTLY\n",
      "365 -th RECORDED FILE DECODED CORRECTLY\n",
      "366 -th RECORDED FILE DECODED CORRECTLY\n",
      "367 -th RECORDED FILE DECODED CORRECTLY\n",
      "368 -th RECORDED FILE DECODED CORRECTLY\n",
      "369 -th RECORDED FILE DECODED CORRECTLY\n",
      "370 -th RECORDED FILE DECODED CORRECTLY\n",
      "371 -th RECORDED FILE DECODED CORRECTLY\n",
      "372 -th RECORDED FILE DECODED CORRECTLY\n",
      "373 -th RECORDED FILE DECODED CORRECTLY\n",
      "374 -th RECORDED FILE DECODED CORRECTLY\n",
      "375 -th RECORDED FILE DECODED CORRECTLY\n",
      "376 -th RECORDED FILE DECODED CORRECTLY\n",
      "377 -th RECORDED FILE DECODED CORRECTLY\n",
      "378 -th RECORDED FILE DECODED CORRECTLY\n",
      "379 -th RECORDED FILE DECODED CORRECTLY\n",
      "380 -th RECORDED FILE DECODED CORRECTLY\n",
      "381 -th RECORDED FILE DECODED CORRECTLY\n",
      "382 -th RECORDED FILE DECODED CORRECTLY\n",
      "383 -th RECORDED FILE DECODED CORRECTLY\n",
      "384 -th RECORDED FILE DECODED CORRECTLY\n",
      "385 -th RECORDED FILE DECODED CORRECTLY\n",
      "386 -th RECORDED FILE DECODED CORRECTLY\n",
      "387 -th RECORDED FILE DECODED CORRECTLY\n",
      "388 -th RECORDED FILE DECODED CORRECTLY\n",
      "389 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 390 -th\n",
      "391 -th RECORDED FILE DECODED CORRECTLY\n",
      "392 -th RECORDED FILE DECODED CORRECTLY\n",
      "393 -th RECORDED FILE DECODED CORRECTLY\n",
      "394 -th RECORDED FILE DECODED CORRECTLY\n",
      "395 -th RECORDED FILE DECODED CORRECTLY\n",
      "396 -th RECORDED FILE DECODED CORRECTLY\n",
      "397 -th RECORDED FILE DECODED CORRECTLY\n",
      "398 -th RECORDED FILE DECODED CORRECTLY\n",
      "399 -th RECORDED FILE DECODED CORRECTLY\n",
      "400 -th RECORDED FILE DECODED CORRECTLY\n",
      "401 -th RECORDED FILE DECODED CORRECTLY\n",
      "402 -th RECORDED FILE DECODED CORRECTLY\n",
      "403 -th RECORDED FILE DECODED CORRECTLY\n",
      "404 -th RECORDED FILE DECODED CORRECTLY\n",
      "405 -th RECORDED FILE DECODED CORRECTLY\n",
      "406 -th RECORDED FILE DECODED CORRECTLY\n",
      "407 -th RECORDED FILE DECODED CORRECTLY\n",
      "408 -th RECORDED FILE DECODED CORRECTLY\n",
      "409 -th RECORDED FILE DECODED CORRECTLY\n",
      "410 -th RECORDED FILE DECODED CORRECTLY\n",
      "411 -th RECORDED FILE DECODED CORRECTLY\n",
      "412 -th RECORDED FILE DECODED CORRECTLY\n",
      "413 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 414 -th\n",
      "415 -th RECORDED FILE DECODED CORRECTLY\n",
      "416 -th RECORDED FILE DECODED CORRECTLY\n",
      "417 -th RECORDED FILE DECODED CORRECTLY\n",
      "418 -th RECORDED FILE DECODED CORRECTLY\n",
      "419 -th RECORDED FILE DECODED CORRECTLY\n",
      "420 -th RECORDED FILE DECODED CORRECTLY\n",
      "421 -th RECORDED FILE DECODED CORRECTLY\n",
      "422 -th RECORDED FILE DECODED CORRECTLY\n",
      "423 -th RECORDED FILE DECODED CORRECTLY\n",
      "424 -th RECORDED FILE DECODED CORRECTLY\n",
      "425 -th RECORDED FILE DECODED CORRECTLY\n",
      "426 -th RECORDED FILE DECODED CORRECTLY\n",
      "427 -th RECORDED FILE DECODED CORRECTLY\n",
      "428 -th RECORDED FILE DECODED CORRECTLY\n",
      "429 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 430 -th\n",
      "431 -th RECORDED FILE DECODED CORRECTLY\n",
      "432 -th RECORDED FILE DECODED CORRECTLY\n",
      "433 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 434 -th\n",
      "435 -th RECORDED FILE DECODED CORRECTLY\n",
      "436 -th RECORDED FILE DECODED CORRECTLY\n",
      "437 -th RECORDED FILE DECODED CORRECTLY\n",
      "438 -th RECORDED FILE DECODED CORRECTLY\n",
      "439 -th RECORDED FILE DECODED CORRECTLY\n",
      "440 -th RECORDED FILE DECODED CORRECTLY\n",
      "441 -th RECORDED FILE DECODED CORRECTLY\n",
      "442 -th RECORDED FILE DECODED CORRECTLY\n",
      "443 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 444 -th\n",
      "445 -th RECORDED FILE DECODED CORRECTLY\n",
      "446 -th RECORDED FILE DECODED CORRECTLY\n",
      "447 -th RECORDED FILE DECODED CORRECTLY\n",
      "448 -th RECORDED FILE DECODED CORRECTLY\n",
      "449 -th RECORDED FILE DECODED CORRECTLY\n",
      "450 -th RECORDED FILE DECODED CORRECTLY\n",
      "451 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 452 -th\n",
      "453 -th RECORDED FILE DECODED CORRECTLY\n",
      "454 -th RECORDED FILE DECODED CORRECTLY\n",
      "455 -th RECORDED FILE DECODED CORRECTLY\n",
      "456 -th RECORDED FILE DECODED CORRECTLY\n",
      "457 -th RECORDED FILE DECODED CORRECTLY\n",
      "458 -th RECORDED FILE DECODED CORRECTLY\n",
      "459 -th RECORDED FILE DECODED CORRECTLY\n",
      "460 -th RECORDED FILE DECODED CORRECTLY\n",
      "461 -th RECORDED FILE DECODED CORRECTLY\n",
      "462 -th RECORDED FILE DECODED CORRECTLY\n",
      "463 -th RECORDED FILE DECODED CORRECTLY\n",
      "464 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 465 -th\n",
      "466 -th RECORDED FILE DECODED CORRECTLY\n",
      "467 -th RECORDED FILE DECODED CORRECTLY\n",
      "468 -th RECORDED FILE DECODED CORRECTLY\n",
      "469 -th RECORDED FILE DECODED CORRECTLY\n",
      "470 -th RECORDED FILE DECODED CORRECTLY\n",
      "471 -th RECORDED FILE DECODED CORRECTLY\n",
      "472 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 473 -th\n",
      "474 -th RECORDED FILE DECODED CORRECTLY\n",
      "475 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 476 -th\n",
      "477 -th RECORDED FILE DECODED CORRECTLY\n",
      "478 -th RECORDED FILE DECODED CORRECTLY\n",
      "479 -th RECORDED FILE DECODED CORRECTLY\n",
      "480 -th RECORDED FILE DECODED CORRECTLY\n",
      "481 -th RECORDED FILE DECODED CORRECTLY\n",
      "482 -th RECORDED FILE DECODED CORRECTLY\n",
      "483 -th RECORDED FILE DECODED CORRECTLY\n",
      "484 -th RECORDED FILE DECODED CORRECTLY\n",
      "485 -th RECORDED FILE DECODED CORRECTLY\n",
      "486 -th RECORDED FILE DECODED CORRECTLY\n",
      "487 -th RECORDED FILE DECODED CORRECTLY\n",
      "488 -th RECORDED FILE DECODED CORRECTLY\n",
      "489 -th RECORDED FILE DECODED CORRECTLY\n",
      "490 -th RECORDED FILE DECODED CORRECTLY\n",
      "491 -th RECORDED FILE DECODED CORRECTLY\n",
      "492 -th RECORDED FILE DECODED CORRECTLY\n",
      "493 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 494 -th\n",
      "495 -th RECORDED FILE DECODED CORRECTLY\n",
      "ERROR IN DECODING FOR 496 -th\n",
      "497 -th RECORDED FILE DECODED CORRECTLY\n",
      "498 -th RECORDED FILE DECODED CORRECTLY\n",
      "499 -th RECORDED FILE DECODED CORRECTLY\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[86], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m td, tc, amp_array, param_array, x \u001b[39m=\u001b[39m correction_array(code_array)\u001b[39m#Обработка (и выбраковка) записанных данных. В идеале, чем больше это времени займет, чем лучше. Плохо, что на 300 сигналов понадобилось 2 часа...\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mlen\u001b[39m(td)\n",
      "Cell \u001b[0;32mIn[84], line 586\u001b[0m, in \u001b[0;36mcorrection_array\u001b[0;34m(code_array)\u001b[0m\n\u001b[1;32m    584\u001b[0m   \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m????\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    585\u001b[0m a\u001b[39m.\u001b[39mset_amplitude_threshold(amp)\n\u001b[0;32m--> 586\u001b[0m max_param \u001b[39m=\u001b[39m get_max_param(data, code, amp)\n\u001b[1;32m    587\u001b[0m a\u001b[39m.\u001b[39mset_max_param(max_param)\n\u001b[1;32m    588\u001b[0m x, codi \u001b[39m=\u001b[39m a\u001b[39m.\u001b[39mdecoder_1(data)\n",
      "Cell \u001b[0;32mIn[84], line 539\u001b[0m, in \u001b[0;36mget_max_param\u001b[0;34m(data, true_code, amplitude_threshold)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[39mwhile\u001b[39;00m code \u001b[39m!=\u001b[39m true_code:\n\u001b[1;32m    538\u001b[0m   a\u001b[39m.\u001b[39mset_max_param(i)\n\u001b[0;32m--> 539\u001b[0m   _, code \u001b[39m=\u001b[39m a\u001b[39m.\u001b[39;49mdecoder_1(data)\n\u001b[1;32m    540\u001b[0m   i \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    541\u001b[0m   \u001b[39mif\u001b[39;00m i \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39m15\u001b[39m:\n",
      "Cell \u001b[0;32mIn[84], line 306\u001b[0m, in \u001b[0;36mdecoder.decoder_1\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[39mif\u001b[39;00m N \u001b[39m%\u001b[39m \u001b[39m4\u001b[39m \u001b[39m==\u001b[39m \u001b[39m3\u001b[39m:\n\u001b[1;32m    305\u001b[0m   definite_sound_array, supposed_sound_array, p \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msound_array_by_index_special(data, rough_beginning_index, points_per_window, N)\u001b[39m#На эту часть забейте, практически до этого не доходит, я вообще не уверен что здесь нет ошибок в коде\u001b[39;00m\n\u001b[0;32m--> 306\u001b[0m   actual_sound \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfind_sound(N \u001b[39m+\u001b[39;49m \u001b[39m1\u001b[39;49m, definite_sound_array, supposed_sound_array)\n\u001b[1;32m    307\u001b[0m   code \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdecoding(actual_sound, N \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m)\n\u001b[1;32m    308\u001b[0m   _, definite_yf_array \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfourier_for_array(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msecure_split(definite_sound_array, N \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msample_rate)\n",
      "Cell \u001b[0;32mIn[84], line 356\u001b[0m, in \u001b[0;36mdecoder.find_sound\u001b[0;34m(self, N, definite_sound_array, supposed_sound_array)\u001b[0m\n\u001b[1;32m    354\u001b[0m X1, X2 \u001b[39m=\u001b[39m X1\u001b[39m.\u001b[39mastype(\u001b[39mint\u001b[39m), X2\u001b[39m.\u001b[39mastype(\u001b[39mint\u001b[39m)\n\u001b[1;32m    355\u001b[0m ans_x1, F1 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfind_max(X1, target_sound_data, N, l, definite_sound_data)\n\u001b[0;32m--> 356\u001b[0m ans_x2, F2 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfind_max(X2, target_sound_data, N, l, definite_sound_data)\n\u001b[1;32m    357\u001b[0m \u001b[39mif\u001b[39;00m F2 \u001b[39m>\u001b[39m F1:\n\u001b[1;32m    358\u001b[0m   ans \u001b[39m=\u001b[39m ans_x2\n",
      "Cell \u001b[0;32mIn[84], line 338\u001b[0m, in \u001b[0;36mdecoder.find_max\u001b[0;34m(self, x, sound_data, N, l, definite_sound_data)\u001b[0m\n\u001b[1;32m    336\u001b[0m next_x \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([x_array[\u001b[39m0\u001b[39m], x_array[\u001b[39m1\u001b[39m]])\n\u001b[1;32m    337\u001b[0m x1, x2, x3, x4 \u001b[39m=\u001b[39m x_array[\u001b[39m0\u001b[39m][\u001b[39m0\u001b[39m], x_array[\u001b[39m1\u001b[39m][\u001b[39m0\u001b[39m], x_array[\u001b[39m2\u001b[39m][\u001b[39m0\u001b[39m], x_array[\u001b[39m2\u001b[39m][\u001b[39mlen\u001b[39m(x_array[\u001b[39m0\u001b[39m]) \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m]\n\u001b[0;32m--> 338\u001b[0m F1, F2, F3, F4 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcalc_funcions(sound_data, N, x1, x2, x3, x4, definite_sound_data, l)\n\u001b[1;32m    339\u001b[0m \u001b[39mif\u001b[39;00m F2 \u001b[39m>\u001b[39m F3:\n\u001b[1;32m    340\u001b[0m   next_x[\u001b[39m0\u001b[39m], next_x[\u001b[39m1\u001b[39m] \u001b[39m=\u001b[39m x_array[\u001b[39m0\u001b[39m], x_array[\u001b[39m1\u001b[39m]\n",
      "Cell \u001b[0;32mIn[84], line 397\u001b[0m, in \u001b[0;36mdecoder.calc_funcions\u001b[0;34m(self, sound_data, N, x1, x2, x3, x4, definite_sound_data, l)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcalc_funcions\u001b[39m(\u001b[39mself\u001b[39m, sound_data, N, x1, x2, x3, x4, definite_sound_data, l):\n\u001b[1;32m    396\u001b[0m   sound_data1, sound_data2, sound_data3, sound_data4 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msound_data_by_x(definite_sound_data, x1, l), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msound_data_by_x(definite_sound_data, x2, l), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msound_data_by_x(definite_sound_data, x3, l), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msound_data_by_x(definite_sound_data, x4, l)\n\u001b[0;32m--> 397\u001b[0m   F1, F2, F3, F4 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mF(sound_data1, N), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mF(sound_data2, N), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mF(sound_data3, N), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mF(sound_data4, N)\n\u001b[1;32m    398\u001b[0m   \u001b[39mreturn\u001b[39;00m F1, F2, F3, F4\n",
      "Cell \u001b[0;32mIn[84], line 388\u001b[0m, in \u001b[0;36mdecoder.F\u001b[0;34m(self, sound_array, N)\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mF\u001b[39m(\u001b[39mself\u001b[39m, sound_array, N):\n\u001b[1;32m    387\u001b[0m   data_array \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msecure_split(sound_array, N)\n\u001b[0;32m--> 388\u001b[0m   xf, yf_array \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfourier_for_array(data_array, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msample_rate)\n\u001b[1;32m    389\u001b[0m   func \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcalc_function(yf_array, xf)\n\u001b[1;32m    390\u001b[0m   \u001b[39mreturn\u001b[39;00m func\n",
      "Cell \u001b[0;32mIn[84], line 203\u001b[0m, in \u001b[0;36mdecoder.fourier_for_array\u001b[0;34m(self, data_array, sample_rate)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfourier_for_array\u001b[39m(\u001b[39mself\u001b[39m, data_array, sample_rate):\n\u001b[0;32m--> 203\u001b[0m  _, ans \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfourier_abs(data_array[\u001b[39m0\u001b[39;49m])\n\u001b[1;32m    204\u001b[0m  ans \u001b[39m=\u001b[39m [ans]\n\u001b[1;32m    205\u001b[0m  \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m, \u001b[39mlen\u001b[39m(data_array)):\n",
      "Cell \u001b[0;32mIn[84], line 177\u001b[0m, in \u001b[0;36mdecoder.fourier_abs\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    175\u001b[0m duration \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(data) \u001b[39m/\u001b[39m sample_rate\n\u001b[1;32m    176\u001b[0m xf \u001b[39m=\u001b[39m rfftfreq(\u001b[39mlen\u001b[39m(data), \u001b[39m1\u001b[39m \u001b[39m/\u001b[39m sample_rate)\n\u001b[0;32m--> 177\u001b[0m yf \u001b[39m=\u001b[39m rfft(data)\n\u001b[1;32m    178\u001b[0m yf \u001b[39m=\u001b[39m yf\u001b[39m.\u001b[39mflatten()\n\u001b[1;32m    179\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39marray([xf, np\u001b[39m.\u001b[39mabs(yf)])\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/scipy/fft/_backend.py:25\u001b[0m, in \u001b[0;36m_ScipyBackend.__ua_function__\u001b[0;34m(method, args, kwargs)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[39mif\u001b[39;00m fn \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m     24\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mNotImplemented\u001b[39m\n\u001b[0;32m---> 25\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/scipy/fft/_pocketfft/basic.py:62\u001b[0m, in \u001b[0;36mr2c\u001b[0;34m(forward, x, n, axis, norm, overwrite_x, workers, plan)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39minvalid number of data points (\u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m) specified\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     59\u001b[0m                      \u001b[39m.\u001b[39mformat(tmp\u001b[39m.\u001b[39mshape[axis]))\n\u001b[1;32m     61\u001b[0m \u001b[39m# Note: overwrite_x is not utilised\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m \u001b[39mreturn\u001b[39;00m pfft\u001b[39m.\u001b[39;49mr2c(tmp, (axis,), forward, norm, \u001b[39mNone\u001b[39;49;00m, workers)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "td, tc, amp_array, param_array, x = correction_array(code_array)#Обработка (и выбраковка) записанных данных. В идеале, чем больше это времени займет, чем лучше. Плохо, что на 300 сигналов понадобилось 2 часа...\n",
    "len(td)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "td_backup = td\n",
    "x_array = x\n",
    "x_array_backup = x_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9f1f1a7e80>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GzBKdzTSu6eAd7h99P0IKACD3eTuMGJntM+L0mqzaCwDIZ54OI1bP8nj7jBhH/i8VyBwAgHzm6TBilvDQ3gw00xBSAAD5wNNhJHTV3PQ/2p1eM1oHVlbtBQDkA4+HkbBld5VIM00810/s3EQRAEA+8HYYsdqWUDNNauIBQ3sBAPnM02HELPEVeOOZ9CyxHckoAIB84OkwYjUDa8ZuwAZ9RgAA+czbYSTk98Ov4p8O3n11xfk8I4kdDwBANvN2GLF4yic0HXyK0kGUdfIAAMgLng4jZokGCcNwP+mZ89E0lEYAAPnLdRhZt26dZs+erdraWvl8Pq1cuTLmMQ888IAmTpyofv36afjw4briiiu0d+/eeO43ZTLTZcThqr1kEQBAHnMdRjo6OjRx4kTdfffdjvb/29/+prlz5+prX/ua3nzzTT3yyCPasGGDrrrqKtc3m2xWD3lznxE3HUTjmYHV+bmJHQCA/FXk9oCGhgY1NDQ43n/9+vUaPXq0rrnmGknSmDFj9I1vfEM/+tGP3F46pfoe+OEjbJx2aDWUwg6s0bYTUgAAeSDlfUamTp2q5uZmrVq1SoZhaNeuXXr00Ud1/vnnRz2ms7NTra2tIT+pYO6LkcUje6MP7U3ivQAAkCkpDyPTpk3TAw88oK9+9asqKSlRTU2NKisrbZt5GhsbVVlZGfypq6tLyb2FPOMtpoN387A34hhOk/DQXtIIACAPpDyMvPXWW7r22mu1dOlSbdq0SU8//bQ+/PBDzZ8/P+oxS5YsUUtLS/Cnubk5JfcWazp4V31GUrjUHkN7AQD5zHWfEbcaGxs1bdo0XX/99ZKkk08+Wf3799eZZ56pH/7whxo+fHjEMX6/X36/P9W3FsIqSritjLieDt7xAdGaaUgpAIDcl/LKyIEDB1RQEHqZwsJCSZnvgJnx6eAdCgSst2fzPQMA4JTrMNLe3q6mpiY1NTVJkrZs2aKmpiZt3bpV0uEmlrlz5wb3nz17th577DHdc8892rx5s/72t7/pmmuu0eTJk1VbW5ucTxEnq8pC6NDexM+XDFRAAAD5zHUzzcaNG/W5z30u+Hrx4sWSpHnz5mnZsmXasWNHMJhI0mWXXaa2tjb98pe/1Le+9S0NHDhQZ599dlYM7bWqjIRscxECDCOOVXudzsDKpGcAgDzmOoxMnz7dtnll2bJlEduuvvpqXX311W4vlVaxOrM6Od79PCNOV+21uSgAADmOtWmOyET/FeeVETqwAgDyl6fDiNVD3umMq5HnSuF08Kk5LQAAWcHTYcSs74Ef7wgbI46ZRpxPehalMkJKAQDkAU+HkUwP7XU+HXyU45N3KwAAZIy3w4jFq9Dp4N2t2us2HTg9PwvlAQDymbfDiMWzPP5mmtRVKqItlAcAQD7wdBgxswwmLo93W6lIcDZ4mmkAAHnB02HE3EySzQ/2aJURCiYAgHzg7TBi8TAPnQ7eVW0kdUN7CR0AgDzm6TBiZj0dfN+22GnAMGJXV8LP4zTsMLkZACCfeTqMGCG/Rz7wrQJK1HM5mPQs/P1Eh/YePgdBBQCQ2zwdRsxpICPzjDjdz+bmyCIAgFzn6TAS8zluhPxPjF1jz8Aab24gcAAA8pmnw4iZ5aq9R7Y67jMSs5kmvM9I/Pfm5D0AAHKBp8NI6ARnGVi112GUsJv0jD4jAIBc5+0wYhEGQof29u3n5FzJ2cfyOPIGACCPeTuMxJoO3mY/q+NiVSniHU1DMw0AIJ95OoyYWQeTI31GHDzyY3dfjTwPo2kAAPB4GIk1z0jqbyAJfUaojQAAcpy3w0is0S8O9+vb2e2kZ05R/QAA5DNPhxEzy+ngXYQAQ+6rFM6baeJ7DwCAXODpMBKyaq/FyJmj84w4OJcRe6G8+KeDJ3EAAPKXp8NIrnS3yJHbBAAgLp4OI4bF74bF2F5no2kcrNobMZom8bG9FE0AALnO02HEzGr4rPt5RmLvY/c6GkbTAADymafDiDmAZOKR7vSa9tPBJ+deAADIFI+HEftt7qeDT9GqvXEeBwBALvB2GLF4ETLCxtWqvU5G08S5aq9dnxFnpwAAIGt5OoyYWQ3jdVsZcTqJWvg1Y56bVXsBAHnM02Ek3gnOkncDznYLUBkBAOQxb4cRi0e59XBfRyeLWemIezp4IgcAII95OoyYWc0zYrhopzEUu89I+HmYDh4AAI+HkVj9Q45uc9KBNY5Jzxyv2mt7UgAAcpqnw0iuoJMqACCfeTqMhE56Fr1JJtMzsNqek9IIACDHeTqMmNk30zg4/sj/2e9j/zoaZmAFAOQz12Fk3bp1mj17tmpra+Xz+bRy5cqYx3R2durGG2/UMcccI7/fr9GjR+t3v/tdPPebVFYjZ9J6fYb2AgCgIrcHdHR0aOLEibriiit04YUXOjrmK1/5inbt2qXf/va3Gjt2rHbs2KFAIOD6ZpPNejr4eGdgVcxkEG/fD/qMAADymesw0tDQoIaGBsf7P/3003r++ee1efNmDR48WJI0evRot5dNPSNydRnXM7A62Cf0tcMZWO3eI6gAAHJcyvuM/PGPf9SkSZP04x//WCNGjNDxxx+vb3/72zp48GDUYzo7O9Xa2hrykwqh69Ac+V/zcF+LbVHPZcQOBvF2YGVtGgBAPnNdGXFr8+bNeuGFF1RaWqrHH39ce/bs0Te/+U3t3btX999/v+UxjY2NuvXWW1N9aznT+ZPqBwAgn6W8MhIIBOTz+fTAAw9o8uTJOv/883XXXXfpv//7v6NWR5YsWaKWlpbgT3Nzc0ruLdYEZ0aw6cbpeJpE94h2nM175BQAQI5LeWVk+PDhGjFihCorK4PbTjzxRBmGoY8++kjHHXdcxDF+v19+vz/VtxYiGDwsmmkcTQfvYJ6RiOngHc/AajO0l4YaAECOS3llZNq0adq+fbva29uD29577z0VFBRo5MiRqb68rVj9Q1LfgdUZ24FHZBEAQI5zHUba29vV1NSkpqYmSdKWLVvU1NSkrVu3SjrcxDJ37tzg/nPmzNGQIUN0+eWX66233tK6det0/fXX64orrlBZWVlyPkXceJIDAJBprsPIxo0bVV9fr/r6eknS4sWLVV9fr6VLl0qSduzYEQwmkjRgwACtXr1a+/fv16RJk3TJJZdo9uzZ+sUvfpGkjxA/qypI2B4274Wfy0jhaBq7ZhoAAHKb6z4j06dPt304Llu2LGLbuHHjtHr1areXSqujzTTmDqx97zmY9ExOmmkM29fR2M7AShoBAOQ4T69NE1oZSf9T3fl08HRgBQDkL2+HEYsHudV6NU4nPYs9HbzTO4t+TwAA5BtPhxEriY2mSc2qvbYzsJJUAAA5ztNhxDp4xLtQnhEzGISfhw6sAAB4PYxk+gYcypX7BAAgHt4OIyGTnlnMwGpE7uf0fM7ed7hqr11lhHYaAECO83QYMbPqH+LmOW8Y7ke2OB9Nk/g5AADIVp4OI6H9Q478b4xqid253FZGkjG0FwCAXOfpMJIznTFy5T4BAIiDp8OIdZNMnDOwGqmbgdVuL4omAIBc5+kwYmbXJON00rNMNNMwAysAINd5OozkykiUHLlNAADi4u0wYv7dZjSNsxlYY88HH/cMrDZ7ElQAALnO02HESsiqva5mYHXSTBPfDKy2Q3udnQIAgKxVlOkbyCRzGNjVekhvbW9Vj+nJv2VPh4oKCvTRJwdinqujqydmuPhgd7sOdQeCr1sOdjm6z4BNGvlgd7sOdvU6Og+A+PQrKVRRoU+tB3uSfu7qCr92t3WquqJUAcNQb8BQ26FudfXE90+NmspS7Ww5FPf9DO5fovZO59ev7Feszu7ekL/b4jV0QIkKC3wqLirQR/sOSjr6eUqKfCovLdbe9i4NHVCi1kPdGtSvRLtaOy3PVVNZKunwPwJ9Pp8G+Iv00ScHVDuwTB+3daqrN6Bjh/ZXb8DQlj0dGty/JHjsoZ6AunsCqigr1v4DXaooK1ZPr6HWQ90aO2yA9h/s1s6WQxrYr1i1A8v0z70dKi8tVnGhT509Afl0+M9xX0eXdrd1amzVABUXhv7bf297pwb3L1HAkFoPdmuQ6fpe5DNyoONEa2urKisr1dLSooqKiqSd9+oHX9WTr21P2vkAALnju58fp9ea9+vpN3c6Pubck6r1/Hsfq6vncPhqGF+jP78Refyn6wbqHx/tV8CQpp8wTMsunxx876l/bNfC5a/qstNH663trdrw4T795bqzdHx1eeIfKss4fX57ujJSWXb041eV+4O/727r1NABfhX4Qvff3XY0gdePGqhtnxxU66FuHeoOBI/f3dapAt/RppWq8sP/6jGf32xgv2KVFBVod2tnyPmHDijRnvYulfuLVFZSqMH9D/+L4c3trSHXB5BaHZ096jhSfSwrLlR5afL+2vy4vTNqRbW8tEhlxYWuzmf+O2TIkb8z3Nh/sDv4kHVy/ZaD3eo8sv8Af5H6lbi7X7NPDnSpu9f9v419PmnYgNC/X/d2dKnXrn37iPd3tbkKIpK0+q1dIa+tgogkNTXvN12nPeS9O/78jiRp2YsfBrc9uukjfe/8E13dSz7xdBj54QUT9MMLJmT6NgBksd/8dbN++Ke3JUn/8ZlRunHWSUk796Qf/l/tabduZlj6hZN00aQ6V+c7/vt/DoaJJ68+Q7UDy1wdb64W3/6lCZo9sdZ2/+88+poe3viRJGnRjON05ZnHurqe2Zxfv6QX/3dv8HVFaZFaD8VuFhvSv0QbbpwRsu3cu57X+7vboxxxVLqaBeLtL+gldGAFABv9/Uf/zdavJLn/fispjF65KPC5q2pIUpGpElLksioiSf1NlY3+/thVDnM/iPA+EW6Ff94ih+ez+nNyWhFK11IbZI/YCCMAYKOfywe0G8VF0f8KLojjb2fzg9ltE40UGracBC9zACmyCVZOFITdr9MwZvU5nd5LuioUrC8WG2EEAGyYH8plSa6M2FUv4qmMmMUXRgotf4+m2PTQT7QyEp4fnJ7OsjLi8M8ubZWRiNm3CSfhCCMAYCOkMpJAB00rPpuHZjxhxHxEXGHEbw4j7iojJclupnFYGrLazelnT1ufkTRdJ5cRRgDAhttqQbIkWhlx+jA361fstjKSxD4j4c00Dk9nVQVx+tnTVaGIqIyk5aq5hTACADbc9qNIlgSf7XFVRsx9WPo7+KwlReYwkmCzUtyVkfg7sPbEMZQ4HjTLxEYYAQAbqezAaseuCceJeEbT+EwNPWUOKiPmayReGQl77fD2LSsjDoNRV2/is8Y6EbEuGdkkAmEEAGyYw4i/KHeaaawqBm6U2Iz06ZPKob1OqxtW+zk9tm9Olni46T9EZSQ2wggA2DDPM5JgPnAlnmd7uh95xclspikIDyPxzzPitCqUSBipKCt2vG/4ZLB2K7F7FWEEAGz4TQ/cYVGWdUiFRJtp4jFuuLu1UcyTttnNmeJEZGXE2XFWf0yOKyMJNNO4WRaAykhsnp4OHgBi8fl8enT+VLV39qiqvDRt1010aG88Thk1SPdccoqOGdLf0f7mTqbF8czSZhIRRhx+fqvnvNMw0pnASsMD/C7CSNxX8Q7CCADEMGn04LRf0+nDONkaJgx3vG9IM01Rgn1cIiY98+mYIf30z70HXJ/LaRNPZ0+v63P3KS913kwTnkYolESimQYAMsTu8Z1g/9O0KEnmDKwRfUZ8+u28STpnXJXuuNDdgqbp6DPiZmQV08HHRhgBgAyxe0Rlos+IW0mdgdUijIytKtdvLztNpxwzyNW53PYZqSr3a0vj+RHv//tp0VdNLi12MZomxmsQRgAgK8UzaVm6JXdob+hr8+c3/+7kj8VpZaTzSGWkuLDAMvzZfaYyN2GE9BETYQQAslA8WSTdzzzzQz/RVXvD+8iErEBssxpxIqNp+sJItDlV3IQRu6HN4c00hJNIhBEAyEK50Exj7vSS7LVpiqJURpwEDbd9RqLtb9cpN3ztHrtmqsjsQRoJ5/q/nnXr1mn27Nmqra2Vz+fTypUrHR/7t7/9TUVFRfr0pz/t9rIAkHeS3YE1k/El2av2FkapujhZs8bpaJo+0YKU3Wfyh1VGbGesJXvE5Pq/no6ODk2cOFF33323q+P279+vuXPn6pxzznF7SQDwnFzoM2KW/BlYE6iMuLyXaBO2uWmmKbKtjMRupsmtbzv5XM8z0tDQoIaGBtcXmj9/vubMmaPCwkJX1RQAyFd2/2BOdG2atDB9gETDk21lxFTpcHIZt392JVHCi121w18c+l6xzY2FTwePSGnpM3L//fdr8+bNuvnmmx3t39nZqdbW1pAfAPCSXMgiZon2cQl/lhfYdFo1s6oyuF2xOFoFxK4yEj7jrG1lJLwDq4t784qUh5H3339fN9xwg/7whz+oqMhZIaaxsVGVlZXBn7q66GO9ASBX/eTfTlZZcaEG9YuczTMXmmlOHT1Iowb305nHDU34XOGfN2Skjul3J6HH7Z9dtCARrWJy+Jjw0T/Rz+8kfHg9oKQ0jPT29mrOnDm69dZbdfzxxzs+bsmSJWppaQn+NDc3p/AuASAz6kcN0uu3zNSVZx4b8V4uNNP4iwr13Len6/dXTE74XOGftyBKPxEni865rYxECx22lREXHXYNQ/pgd5u+9/jr2rb/IAvnWUjp2jRtbW3auHGjXn31VS1cuFCSFAgEZBiGioqK9Je//EVnn312xHF+v19+f/pWxwSATCkqLLB8eOZAYURS8io44WEk2tBeJ4/xQrcdWONopnEbeC781YtqPdSj1z9qsXw/R77ulElpGKmoqNDrr78esu1Xv/qVnn32WT366KMaM2ZMKi8PADnB6oGeC5WRZAp/7oeMoHH5ZxEtKFx99lj917MfRGyPGkZsOrCGN+3ECkmth3okSa9va7FslvM612Gkvb1dH3xw9MvcsmWLmpqaNHjwYI0aNUpLlizRtm3b9Pvf/14FBQUaP358yPFVVVUqLS2N2A4AXmUVPLwWRsL7gpg/v7nJprayTPsPdNuey2qekcljBmtgvxLL/eOZZ8RtZcSMRppIrvuMbNy4UfX19aqvr5ckLV68WPX19Vq6dKkkaceOHdq6dWty7xIA8lhPIHL1WK+FEbsOrGYXTRqpy04fbXuuaMeW+63//R1tjpQSmxlYc6GDcS5xXRmZPn26beebZcuW2R5/yy236JZbbnF7WQDIW53dFmHEY4t1hD/aw6eH71NUWKBbvvgpLXvxw8PHWewW7djy0mhhJI4+IwlM8kb/1Uge+88dALJP34JtZl6rjIRz20/ELGplpNS6r0Z8HVjD+owQMBJCGAGADOvs6Y3Y5vkw4rDyYBUCojWhDIhWGYnSHGMXRhJppmFobyTCCABkmGVlxON/O6eiMjIgWp+RKH/Ydp1U6cCaXB7/zx0AMq+LZpoIiTzso1Ut+pUUWm6PVgGxq36Evxe+GF4iNv1zn97YZj0fidmGLfv09o7oy6U89+5ubd17IGn3lUqEEQDIsFFD+kVsiyeMfHrUQEnRH7q5JFon1JqK0pjHhvfn6FNRFqXPSJRmGruvIJEOrHa5ZV9Hl758z3p94b9esD3FrtZD+sr/u14NP/+r5fvr/3evLr//FZ31k+fiv880IowAQIZdMW2MvvHZY/X5T9UEt8XTTPHTr0zU184Yoz8uPCOZt5cR4Z//t/Mm6dpzjtOME6tiHltWYv1oG+Av0h0XTtAXTh4esj3afCIFPp+WNIyzfC+Ryo2d7fsPBn+361vy0ScHbPdrat6f1PtKNcIIAGRYaXGhljScqEmjBwW3+eL427mqvFQ3feEkja0akMS7y4zwDqznnFit68493tFCef1Kos9a8e+TR2nu1NEh2+yaaS6oHxF8bQ4t4ROruemTardrwHSigM2O5j+HXosdo82dkq0IIwCQhbzeZySRDqyxmqnCixrRmlwKfKFNNeYHvLkykswiiTlYBGwSjvm/jx6LMJKqyk2qEEYAIAsl8jDOB4kMnbWrjEiRfUGiVUZ8Pl/I92Bej8YcYAoLfO4qIzY7m8OIVcWjj/mPp6s3sgN0+No52S637hYAPMLjWSShf9lbVUbmTB4V/D28qaev+eXM44aGbC/0+UIqEMVhASTa+RJhDiB2Acd8X90Wo7FyrTKS0lV7AQDx8XozTWlx/COC+psqI40XTtCn6wZqXE15cFv4n2xfZeQ38ybp9y/+U7etelvS4e/A/D2YQ4d5xI7b535HV+Qkd32cNtOY3+vutWimMVVGegNG1q+lQ2UEALJQtj88Us1fHP/jqcxUGTEM6cThFSFBIjzo9VU8/EWF+lRtRXC7zxd98rnCAp+GlfslSaf/y1DrneLQawoZvTZhxNxPpNuimcZcxbGaxybbEEYAIAt5PIvIXxR/ZaSk6Oij7VB3ZBXCrs+IeX6TwoKwyojpmKICn/5n/um65uyxuvOiiUmb4j2kmcYmQ/TGCCPmyk0uhBGaaQAgCyWzH0Iu8hcl59/Khxys+xMSRsIqKObX5rhRWODTqCH9tHjmCUm5zz5Om2l6eu2bacxh9vDaR9YTvmULKiMAgKyTSJ8Rs87u2FUBc5OG+SFeENZMY84Gqeogag4Wds00sSoj5oE4VmsfZRvCCAAg66S1MlJk3UxTEN5MYzosVX16egJHg4NtZcS0n9XQXvOxVu9nG8IIACDrxOoz0jD+8NT53/jssZbvn1B9ePTMFybURrwX3gJmNx18SDONKRuEN6Mla5k8c/OLXTeUkNE0FpUP8/tOqkOZRp8RAEDWKY0xmuaXc07Rtk8OWi4yKElPLJymPe2dGjko9iKEdjOwpq0j8ZHrmKsYdpOemUOL1Qys5iBDZQQAgDjEqoz0dSCNprS40DKISPajacxvFRT40teR+Eh4MPf/sGumMQcVq7AR8j59RgAAcC+ReUZiCa92mJtpjJD9woNI9HCQ6MjevuBhbnKxOmdvwNDu1kMh1ZCWA91qPdStnS2HZBiGenoD2t3WGXw/F8IIzTQAgIwLn8K9NIF5RmKLPrTXLJ1zvRjByoj92jTz/7BJq9/apa9OqgtuW/RQU/D3OVNG6c1tLXrto5bgtk6LTrzZhsoIACDjLjI9XKX0VkbMQ3tDmmlS0ERzzrgqy+19uaM7xmia1W/tkiQ9tLHZ8jzLX94aEkSk3KiMEEYAABlXWlyo+y87Lfg62giXZAjvBxJthVs3YcRwMJ6msMCnOy+aGPX43oChHz/9bnCbTf9VV+jACgCAQ+aHZkEK20js+ozY7ZeowgJfyLo5ZoYhvbk9tKJh14HVDSY9AwDAIauZRFPBF9FnxDp1JHtis0KfL+pkboZhRMw6m6ww0mMxXXy2IYwAALLCMYP7p+U65tYXny966IiY2Mzmme4kNxT4oq85ZFicI5CkbNaTrBOlEGEEAJAVJoys1C8urtfKBdNSeh1zHiguLEjKXCJOag99TU8Pff0zEe919QT0vx+3h2xLVmUkFzqwMrQXALJERWl2r6yaDl+cGDl9e7KZO6YWh1VF7CZbqx1Ypr0dXXFft68CM3nM4Ij3VrzSrBWvhI6QSVozTbJ6wqYQYQQAssQF9SP07Du7dfrYIZm+lbwWUhkJ68Nx4vByfXVSnaorS4Pbbjz/RL320X7dfuEEXbeiSQ0Thkec01kzje/I9Z1VYpKVIXpyYDQNYQQAskRJUYHuvfTUTN9G3gupjISNpPH5fPrRv50csu2qs44uxvdb0/DjRK7rRG/A0C1/fFNv72jVA1dOiToEOZZuOrACAJBdzJkglfOZhHM7OMcwDC178UO9vGWfXt6yL+7rpmuUUiIIIwAATzEP7Y02rDcV3A4VNs+74pO06Z/xBZJc6DNCGAEAeIo5E8Tb9BEp9gPfbTNN+6Ge4O/FRQX68j3rXd+VRGUEAICsY+5AWpTG1fAKXD5x20xhJBFMegYAQJYJrYyksZnGZWWk7VB38PfuBOYKycvKyLp16zR79mzV1tbK5/Np5cqVtvs/9thjOvfcczVs2DBVVFRo6tSpeuaZZ+K9XwAAEmLuM1LotlwRhaOhvS6rMObKSGcCgSIvR9N0dHRo4sSJuvvuux3tv27dOp177rlatWqVNm3apM997nOaPXu2Xn31Vdc3CwBAonymJ1/4pGep5LbPSKupMnL5/a/Efd1cmA7e9TwjDQ0NamhocLz/z372s5DXt99+u5544gk9+eSTqq+vd3t5AAASYo4EyV4Mz477Zhrv9BlJ+6RngUBAbW1tGjw4cjrcPp2dners7Ay+bm1tTcetAQA8wFyhSFafESePe7dL4CQrjHTlY5+RRN15551qb2/XV77ylaj7NDY2qrKyMvhTV1eXxjsEAOQzcyhIVp+RL58yIuY+bqsw5maaROTCdPBpDSPLly/XrbfeqocfflhVVVVR91uyZIlaWlqCP83NzVH3BQDADXNlpCRJlZFvn3eC7rv0VD119RlafuUUy33MYeTZb3025uyvrTaVkT8unKZ7/8PZ0gG5MOlZ2pppVqxYoSuvvFKPPPKIZsyYYbuv3++X3+9P050BALwqfG2aePmLCjXzUzW2+5jnNzl22AB95l+GaN17H0fdv82mMnLyyIE6eaSze8vLob3xePDBB3X55ZfrwQcf1KxZs9JxSQAALNktlJdKboswyeozkgtDe11XRtrb2/XBBx8EX2/ZskVNTU0aPHiwRo0apSVLlmjbtm36/e9/L+lw08y8efP085//XFOmTNHOnTslSWVlZaqsrEzSxwAAwBlzn5G0hpGwPiOxsoldZcSNvOwzsnHjRtXX1weH5S5evFj19fVaunSpJGnHjh3aunVrcP/77rtPPT09WrBggYYPHx78ufbaa5P0EQAAcC60MpK+ob0+l8NpDnUnJ0TkZWVk+vTpMmymmlu2bFnI67Vr17q9BAAAKWOOBOltpklf8DHLhUnPWJsGAOApWdNME2c28Re5u+dcqIwQRgAAnmJuLikuSmczTXLO88J3z3a1f99omkPdvVrz9i4d6OpR874Dennz3uTcUBKkfQZWAACyRXGSJj1zwm0HVis+nzSs3N3UF33Twd/65Jt6cEOzZp5Urb+8tUuS9KdrztCnajM/mITKCADAs9LZTON2oTwrTlYHDtc36dmDGw5PINoXRCTp9Y9aEr6nZCCMAAA8K53NNOGzwbsdXSO57y8iSb02HVizpTcJYQQA4FmpaqZZfO7xGjGwLGRbMmZlv+ac40JeL7v8tJjH9FVGrLJPPJWWVKDPCADAs1I1z8g15xynq88eqzFLVgW3BcKe/E6v/NrSmaooO/y4Dq+mTD+hSp87YZieezf6tPK9R8JIUYEvYmSNkSW1EcIIAMCziuNo9nAqPDjEWxkpKSqwbdKJtRpwXxg53GclO8JHOJppAACelc4OrOEThjrtMhKrehOrY2xfGLEKLdnSTEMYAQB4Vjqngw9vpnHC54td+YgVRnoChgzDiBJGsiONEEYAAJ6VzspIb0Q7TewgVFxg30QjxQ4r0uEmoiKrMBLzyPQgjAAAPKusuDBt1wrPIk6aaZxUbpycpycQUGEaJ3hziw6sAADPmTv1GL21vVVnHT8sbdeMp0nESQdbJ5WR3oAhqyJQlrTSEEYAAN7z//zr+LRfM6Iy4uAYJ81ITmZ27QkYlqsG02cEAAAPiacDa0mSwkggYKjQosknO6IIYQQAgLRIVZ8RB6006gkYKsriPiPZe2cAAOSRQFgamTAi9mq5vQ6qKc77jDDPCAAAnhbeTHPVWcfqtNGDgq+/cdax+vbM4/V/F58V3HaoO/oid32cLLj3v7vb1dMbea54mo5SgTACAEAahM8z4i8q1Denjw2+Hls1QAvPPk5jq8qD27otAkQ4J1OlzPnNy/pw7wHnN5tmhBEAANLAqggxYtDRlX2tOqL29DpopnE6r3wWI4wAAJAGVivkjh02IPj75j3tEe93OaiMOGmmiXpP2dFKQxgBACAdIqeDlwoKfJo2dogkqWH88Ij3nTXTJBBGsmRwL5OeAQCQBtGqEPdfNll72jtVO7As4j0nlYsEsogChvT/vfRP1dcN1HgHo3tShTACAEAaRBu5UlJUYBlEnCpIII08+dp2vbm9VZL04R2z4j5PomimAQAgDSxaaZLCyQys0fQFkUwjjAAAkAapmtPDbjRNaXFuPOZz4y4BAMhx4TOwJotdK01pcWFKrplshBEAANIgZc00NmmkjDACAIC3zZkyKvj7979wouPjbvvSeEnS984fF3PfE6oPz9g6sF9xxHu5EkZ8hpEtU55E19raqsrKSrW0tKiioiLTtwMAgCOGYWj/gW4Zkgb3L3F17L6OLsfHbNt/UBWlRZpwy19Ctp80vEJv7XDWSTUVo2mcPr8Z2gsAQIr4fD4NchlC+rgJLyOiDA2mAysAAMioIier6GWB3LhLAADgWq4sokcYAQAgTxUVEkYAAABiIowAAJAnSsL6iGT/eNnDXIeRdevWafbs2aqtrZXP59PKlStjHrN27Vqdcsop8vv9Gjt2rJYtWxbHrQIAADsVZaFzjVhNQZ/IKr+p4jqMdHR0aOLEibr77rsd7b9lyxbNmjVLn/vc59TU1KRFixbpyiuv1DPPPOP6ZgEAQHThE58VWiSPbBxh43qekYaGBjU0NDje/95779WYMWP005/+VJJ04okn6oUXXtB//ud/6rzzznN7eQAAEEVlWGXkhOpy/fX9PSHboo2wMQxDvgyNvkl5PFq/fr1mzJgRsu28887T+vXrox7T2dmp1tbWkB8AAGBvXE15yOspxw6J2CdaM02q1s5xIuUzsO7cuVPV1dUh26qrq9Xa2qqDBw+qrCxy1rjGxkbdeuutqb41AADyyncbxml3W6c2/fMT3f6lCTpnXJUuO320Rg3up7ZDPQoYhh56pVkdXb0Rx/YGDMtmnXTIyunglyxZosWLFwdft7a2qq6uLoN3BABA9qsoLdav504K2XbLFz8V8vqhV5otj7Xq7JouKQ8jNTU12rVrV8i2Xbt2qaKiwrIqIkl+v19+vz/VtwYAAI7ozWA7Tcr7jEydOlVr1qwJ2bZ69WpNnTo11ZcGAABhovVR7c1gZcR1GGlvb1dTU5OampokHR6629TUpK1bt0o63MQyd+7c4P7z58/X5s2b9Z3vfEfvvPOOfvWrX+nhhx/Wddddl5xPAAAAEhbIpcrIxo0bVV9fr/r6eknS4sWLVV9fr6VLl0qSduzYEQwmkjRmzBj96U9/0urVqzVx4kT99Kc/1W9+8xuG9QIAkAHRuqhmspnGdZ+R6dOny7Ap5VjNrjp9+nS9+uqrbi8FAACSLNpcIjnVTAMAAPJPIJC5axNGAAAAlREAAJBZOdWBFQAA5J+8nmcEAABkP5ppAABAWkSb9IxmGgAAkBZ5MQMrAADIP/QZAQAAGcU8IwAAIC18USaEp5kGAABkFM00AAAgowJURgAAQDpEHU1DZQQAAGQS84wAAIC0iFIYUQ9hBAAApIMvSjsNo2kAAEBG0UwDAAAyig6sAAAgLaL1GWFoLwAAyKhepoMHAACZRAdWAACQHlHaacYM6Z/e+zAhjAAA4CFWWeSqM8dowsjKtN9LH8IIAAAel8GBNJIIIwAAeF4mR9JIhBEAADzFagbWDGcRwggAAF5nUBkBAADpctZxwyRJA/sVB7dNGDkwQ3dzWFFGrw4AANLq+vNO0Jhh/XX2uCod6OzRxn9+ogvrR2T0nggjAAB4SFlJoS79zDHB18dVl2fwbg6jmQYAAGQUYQQAAGQUYQQAAGQUYQQAAGQUYQQAAGRUXGHk7rvv1ujRo1VaWqopU6Zow4YNtvv/7Gc/0wknnKCysjLV1dXpuuuu06FDh+K6YQAAkF9ch5GHHnpIixcv1s0336y///3vmjhxos477zzt3r3bcv/ly5frhhtu0M0336y3335bv/3tb/XQQw/pe9/7XsI3DwAAcp/rMHLXXXfpqquu0uWXX66TTjpJ9957r/r166ff/e53lvu/+OKLmjZtmubMmaPRo0dr5syZuvjii2NWUwAAgDe4CiNdXV3atGmTZsyYcfQEBQWaMWOG1q9fb3nM6aefrk2bNgXDx+bNm7Vq1Sqdf/75Ua/T2dmp1tbWkB8AAJCfXM3AumfPHvX29qq6ujpke3V1td555x3LY+bMmaM9e/bojDPOkGEY6unp0fz5822baRobG3Xrrbe6uTUAAJCjUj6aZu3atbr99tv1q1/9Sn//+9/12GOP6U9/+pN+8IMfRD1myZIlamlpCf40Nzen+jYBAECGuKqMDB06VIWFhdq1a1fI9l27dqmmpsbymJtuukmXXnqprrzySknShAkT1NHRoa9//eu68cYbVVAQmYf8fr/8fr+bWwMAADnKVWWkpKREp556qtasWRPcFggEtGbNGk2dOtXymAMHDkQEjsLCQkmSYRhu7xcAAOQZ16v2Ll68WPPmzdOkSZM0efJk/exnP1NHR4cuv/xySdLcuXM1YsQINTY2SpJmz56tu+66S/X19ZoyZYo++OAD3XTTTZo9e3YwlMTSF1royAoAQO7oe27HKj64DiNf/epX9fHHH2vp0qXauXOnPv3pT+vpp58OdmrdunVrSCXk+9//vnw+n77//e9r27ZtGjZsmGbPnq3bbrvN8TXb2tokSXV1dW5vFwAAZFhbW5sqKyujvu8zcqCtJBAIaPv27SovL5fP50vaeVtbW1VXV6fm5mZVVFQk7bxIHr6j7Md3lP34jrJfvn5HhmGora1NtbW1ln1E+7iujGRCQUGBRo4cmbLzV1RU5NWXn4/4jrIf31H24zvKfvn4HdlVRPqwUB4AAMgowggAAMgoT4cRv9+vm2++mTlNshjfUfbjO8p+fEfZz+vfUU50YAUAAPnL05URAACQeYQRAACQUYQRAACQUYQRAACQUZ4OI3fffbdGjx6t0tJSTZkyRRs2bMj0LXlCY2OjTjvtNJWXl6uqqkoXXHCB3n333ZB9Dh06pAULFmjIkCEaMGCAvvzlL0esFr1161bNmjVL/fr1U1VVla6//nr19PSk86N4xh133CGfz6dFixYFt/EdZd62bdv0H//xHxoyZIjKyso0YcIEbdy4Mfi+YRhaunSphg8frrKyMs2YMUPvv/9+yDn27dunSy65RBUVFRo4cKC+9rWvqb29Pd0fJS/19vbqpptu0pgxY1RWVqZ/+Zd/0Q9+8IOQdVr4jo4wPGrFihVGSUmJ8bvf/c548803jauuusoYOHCgsWvXrkzfWt4777zzjPvvv9944403jKamJuP88883Ro0aZbS3twf3mT9/vlFXV2esWbPG2Lhxo/GZz3zGOP3004Pv9/T0GOPHjzdmzJhhvPrqq8aqVauMoUOHGkuWLMnER8prGzZsMEaPHm2cfPLJxrXXXhvczneUWfv27TOOOeYY47LLLjNefvllY/PmzcYzzzxjfPDBB8F97rjjDqOystJYuXKl8dprrxlf/OIXjTFjxhgHDx4M7vP5z3/emDhxovHSSy8Zf/3rX42xY8caF198cSY+Ut657bbbjCFDhhhPPfWUsWXLFuORRx4xBgwYYPz85z8P7sN3dJhnw8jkyZONBQsWBF/39vYatbW1RmNjYwbvypt2795tSDKef/55wzAMY//+/UZxcbHxyCOPBPd5++23DUnG+vXrDcMwjFWrVhkFBQXGzp07g/vcc889RkVFhdHZ2ZneD5DH2trajOOOO85YvXq18dnPfjYYRviOMu+73/2uccYZZ0R9PxAIGDU1NcZPfvKT4Lb9+/cbfr/fePDBBw3DMIy33nrLkGS88sorwX3+/Oc/Gz6fz9i2bVvqbt4jZs2aZVxxxRUh2y688ELjkksuMQyD78jMk800XV1d2rRpk2bMmBHcVlBQoBkzZmj9+vUZvDNvamlpkSQNHjxYkrRp0yZ1d3eHfD/jxo3TqFGjgt/P+vXrNWHChOBq0ZJ03nnnqbW1VW+++WYa7z6/LViwQLNmzQr5LiS+o2zwxz/+UZMmTdJFF12kqqoq1dfX69e//nXw/S1btmjnzp0h31FlZaWmTJkS8h0NHDhQkyZNCu4zY8YMFRQU6OWXX07fh8lTp59+utasWaP33ntPkvTaa6/phRdeUENDgyS+I7OcWCgv2fbs2aPe3t6QvyQlqbq6Wu+8806G7sqbAoGAFi1apGnTpmn8+PGSpJ07d6qkpEQDBw4M2be6ulo7d+4M7mP1/fW9h8StWLFCf//73/XKK69EvMd3lHmbN2/WPffco8WLF+t73/ueXnnlFV1zzTUqKSnRvHnzgn/GVt+B+TuqqqoKeb+oqEiDBw/mO0qCG264Qa2trRo3bpwKCwvV29ur2267TZdccokk8R2ZeDKMIHssWLBAb7zxhl544YVM3wpMmpubde2112r16tUqLS3N9O3AQiAQ0KRJk3T77bdLkurr6/XGG2/o3nvv1bx58zJ8d5Ckhx9+WA888ICWL1+uT33qU2pqatKiRYtUW1vLdxTGk800Q4cOVWFhYUTP/127dqmmpiZDd+U9Cxcu1FNPPaXnnntOI0eODG6vqalRV1eX9u/fH7K/+fupqamx/P763kNiNm3apN27d+uUU05RUVGRioqK9Pzzz+sXv/iFioqKVF1dzXeUYcOHD9dJJ50Usu3EE0/U1q1bJR39M7b7e66mpka7d+8Oeb+np0f79u3jO0qC66+/XjfccIP+/d//XRMmTNCll16q6667To2NjZL4jsw8GUZKSkp06qmnas2aNcFtgUBAa9as0dSpUzN4Z95gGIYWLlyoxx9/XM8++6zGjBkT8v6pp56q4uLikO/n3Xff1datW4Pfz9SpU/X666+H/D/p6tWrVVFREfEXNNw755xz9Prrr6upqSn4M2nSJF1yySXB3/mOMmvatGkRQ+Lfe+89HXPMMZKkMWPGqKamJuQ7am1t1csvvxzyHe3fv1+bNm0K7vPss88qEAhoypQpafgU+e3AgQMqKAh9zBYWFioQCEjiOwqR6R60mbJixQrD7/cby5YtM9566y3j61//ujFw4MCQnv9Ijf/zf/6PUVlZaaxdu9bYsWNH8OfAgQPBfebPn2+MGjXKePbZZ42NGzcaU6dONaZOnRp8v2/Y6MyZM42mpibj6aefNoYNG8aw0RQyj6YxDL6jTNuwYYNRVFRk3Hbbbcb7779vPPDAA0a/fv2MP/zhD8F97rjjDmPgwIHGE088YfzjH/8w/vVf/9Vy2Gh9fb3x8ssvGy+88IJx3HHH5d2w0UyZN2+eMWLEiODQ3scee8wYOnSo8Z3vfCe4D9/RYZ4NI4ZhGP/1X/9ljBo1yigpKTEmT55svPTSS5m+JU+QZPlz//33B/c5ePCg8c1vftMYNGiQ0a9fP+NLX/qSsWPHjpDzfPjhh0ZDQ4NRVlZmDB061PjWt75ldHd3p/nTeEd4GOE7yrwnn3zSGD9+vOH3+41x48YZ9913X8j7gUDAuOmmm4zq6mrD7/cb55xzjvHuu++G7LN3717j4osvNgYMGGBUVFQYl19+udHW1pbOj5G3WltbjWuvvdYYNWqUUVpaahx77LHGjTfeGDK0ne/oMJ9hmKaCAwAASDNP9hkBAADZgzACAAAyijACAAAyijACAAAyijACAAAyijACAAAyijACAAAyijACAAAyijACAAAyijACAAAyijACAAAyijACAAAy6v8H6mAyc76Jr/4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(amp_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216064"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Training_XF_Data, fourier_padded_data = a.fourier_for_array(a.secure_split(padded_data[:3065], 64), 44100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "392490.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(code_array[1504])\n",
    "duration_array[1504]\n",
    "8.9  * 44100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = decoder()\n",
    "True_Training_Data = np.zeros(shape = (856, 64, 3065))#Преобразование фурье для собранных данных\n",
    "for i in range(856):\n",
    "    data = np.array(td[i])\n",
    "    increment = int((392490 - len(data)) / 2)\n",
    "    padded_data = np.concatenate((np.zeros(increment), data, np.zeros(increment)))\n",
    "    Training_XF_Data, fourier_padded_data_1 = a.fourier_for_array(a.secure_split(padded_data, 64), 44100)\n",
    "    fourier_padded_data = np.zeros((64, 3065))\n",
    "    for j in range(len(fourier_padded_data)):\n",
    "      fourier_padded_data[j] = fourier_padded_data_1[j][:3065]\n",
    "    True_Training_Data[i] = fourier_padded_data[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2373389.879721985"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(True_Training_Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_freq0_array = np.zeros((856, 64))\n",
    "Train_freq1_array = np.zeros((856, 64))\n",
    "a = decoder()\n",
    "a.set_freqses(1000, 1500)\n",
    "for i in range(856):\n",
    "    yf_array = True_Training_Data[i]\n",
    "    yf_array = a.normalize_data_array(yf_array, Training_XF_Data, 1000, 1500)\n",
    "    for j in range(64):\n",
    "        yf = yf_array[j]\n",
    "        amp_freq0, amp_freq1 = a.freq_sum(yf, Training_XF_Data, 1000), a.freq_sum(yf, Training_XF_Data, 1500)\n",
    "        Train_freq0_array[i, j], Train_freq1_array[i, j] = amp_freq0, amp_freq1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51.84026190283571, 64)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(Train_freq1_array), len(yf_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0_freq0</th>\n",
       "      <th>f1_freq0</th>\n",
       "      <th>f2_freq0</th>\n",
       "      <th>f3_freq0</th>\n",
       "      <th>f4_freq0</th>\n",
       "      <th>f5_freq0</th>\n",
       "      <th>f6_freq0</th>\n",
       "      <th>f7_freq0</th>\n",
       "      <th>f8_freq0</th>\n",
       "      <th>f9_freq0</th>\n",
       "      <th>...</th>\n",
       "      <th>f57_freq1</th>\n",
       "      <th>f58_freq1</th>\n",
       "      <th>f59_freq1</th>\n",
       "      <th>f60_freq1</th>\n",
       "      <th>f61_freq1</th>\n",
       "      <th>f62_freq1</th>\n",
       "      <th>f63_freq1</th>\n",
       "      <th>max_param</th>\n",
       "      <th>amp_threshold</th>\n",
       "      <th>x_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851.0</th>\n",
       "      <td>0.021017</td>\n",
       "      <td>0.022497</td>\n",
       "      <td>0.024656</td>\n",
       "      <td>0.012635</td>\n",
       "      <td>0.019980</td>\n",
       "      <td>0.015657</td>\n",
       "      <td>0.022395</td>\n",
       "      <td>0.022664</td>\n",
       "      <td>0.018345</td>\n",
       "      <td>0.014537</td>\n",
       "      <td>...</td>\n",
       "      <td>2.241751</td>\n",
       "      <td>1.251971</td>\n",
       "      <td>0.255289</td>\n",
       "      <td>0.038765</td>\n",
       "      <td>0.010654</td>\n",
       "      <td>0.007782</td>\n",
       "      <td>0.010189</td>\n",
       "      <td>3</td>\n",
       "      <td>1.10</td>\n",
       "      <td>4065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852.0</th>\n",
       "      <td>0.020910</td>\n",
       "      <td>0.012653</td>\n",
       "      <td>0.012645</td>\n",
       "      <td>0.023136</td>\n",
       "      <td>0.015172</td>\n",
       "      <td>0.014234</td>\n",
       "      <td>0.019621</td>\n",
       "      <td>0.019511</td>\n",
       "      <td>0.019912</td>\n",
       "      <td>0.022529</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065808</td>\n",
       "      <td>0.022676</td>\n",
       "      <td>0.012070</td>\n",
       "      <td>0.013299</td>\n",
       "      <td>0.011748</td>\n",
       "      <td>0.009476</td>\n",
       "      <td>0.016354</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853.0</th>\n",
       "      <td>0.027006</td>\n",
       "      <td>0.023018</td>\n",
       "      <td>0.023370</td>\n",
       "      <td>0.022168</td>\n",
       "      <td>0.025780</td>\n",
       "      <td>0.033111</td>\n",
       "      <td>0.034665</td>\n",
       "      <td>0.035115</td>\n",
       "      <td>0.027016</td>\n",
       "      <td>0.019135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033709</td>\n",
       "      <td>0.021132</td>\n",
       "      <td>0.005710</td>\n",
       "      <td>0.005947</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.009577</td>\n",
       "      <td>2</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854.0</th>\n",
       "      <td>0.021773</td>\n",
       "      <td>0.021561</td>\n",
       "      <td>0.016379</td>\n",
       "      <td>0.022514</td>\n",
       "      <td>0.017173</td>\n",
       "      <td>0.011759</td>\n",
       "      <td>0.021688</td>\n",
       "      <td>0.016461</td>\n",
       "      <td>0.022318</td>\n",
       "      <td>0.019087</td>\n",
       "      <td>...</td>\n",
       "      <td>2.375190</td>\n",
       "      <td>1.335513</td>\n",
       "      <td>0.255109</td>\n",
       "      <td>0.041774</td>\n",
       "      <td>0.009767</td>\n",
       "      <td>0.007232</td>\n",
       "      <td>0.011165</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>3687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855.0</th>\n",
       "      <td>0.019375</td>\n",
       "      <td>0.016972</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>0.018738</td>\n",
       "      <td>0.014707</td>\n",
       "      <td>0.018736</td>\n",
       "      <td>0.010965</td>\n",
       "      <td>0.014651</td>\n",
       "      <td>0.019024</td>\n",
       "      <td>0.016987</td>\n",
       "      <td>...</td>\n",
       "      <td>2.076996</td>\n",
       "      <td>0.331482</td>\n",
       "      <td>0.052900</td>\n",
       "      <td>0.018386</td>\n",
       "      <td>0.006319</td>\n",
       "      <td>0.010369</td>\n",
       "      <td>0.015440</td>\n",
       "      <td>2</td>\n",
       "      <td>0.94</td>\n",
       "      <td>3634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>856 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       f0_freq0  f1_freq0  f2_freq0  f3_freq0  f4_freq0  f5_freq0  f6_freq0  \\\n",
       "0.0    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1.0    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2.0    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "3.0    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "4.0    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "851.0  0.021017  0.022497  0.024656  0.012635  0.019980  0.015657  0.022395   \n",
       "852.0  0.020910  0.012653  0.012645  0.023136  0.015172  0.014234  0.019621   \n",
       "853.0  0.027006  0.023018  0.023370  0.022168  0.025780  0.033111  0.034665   \n",
       "854.0  0.021773  0.021561  0.016379  0.022514  0.017173  0.011759  0.021688   \n",
       "855.0  0.019375  0.016972  0.016206  0.018738  0.014707  0.018736  0.010965   \n",
       "\n",
       "       f7_freq0  f8_freq0  f9_freq0  ...  f57_freq1  f58_freq1  f59_freq1  \\\n",
       "0.0    0.000000  0.000000  0.000000  ...   0.000000   0.000000   0.000000   \n",
       "1.0    0.000000  0.000000  0.000000  ...   0.000000   0.000000   0.000000   \n",
       "2.0    0.000000  0.000000  0.000000  ...   0.000000   0.000000   0.000000   \n",
       "3.0    0.000000  0.000000  0.000000  ...   0.000000   0.000000   0.000000   \n",
       "4.0    0.000000  0.000000  0.000000  ...   0.000000   0.000000   0.000000   \n",
       "...         ...       ...       ...  ...        ...        ...        ...   \n",
       "851.0  0.022664  0.018345  0.014537  ...   2.241751   1.251971   0.255289   \n",
       "852.0  0.019511  0.019912  0.022529  ...   0.065808   0.022676   0.012070   \n",
       "853.0  0.035115  0.027016  0.019135  ...   0.033709   0.021132   0.005710   \n",
       "854.0  0.016461  0.022318  0.019087  ...   2.375190   1.335513   0.255109   \n",
       "855.0  0.014651  0.019024  0.016987  ...   2.076996   0.331482   0.052900   \n",
       "\n",
       "       f60_freq1  f61_freq1  f62_freq1  f63_freq1  max_param  amp_threshold  \\\n",
       "0.0     0.000000   0.000000   0.000000   0.000000          2           1.50   \n",
       "1.0     0.000000   0.000000   0.000000   0.000000          2           2.30   \n",
       "2.0     0.000000   0.000000   0.000000   0.000000          2           1.50   \n",
       "3.0     0.000000   0.000000   0.000000   0.000000          2           1.50   \n",
       "4.0     0.000000   0.000000   0.000000   0.000000          2           1.50   \n",
       "...          ...        ...        ...        ...        ...            ...   \n",
       "851.0   0.038765   0.010654   0.007782   0.010189          3           1.10   \n",
       "852.0   0.013299   0.011748   0.009476   0.016354          2           0.85   \n",
       "853.0   0.005947   0.006579   0.008594   0.009577          2           1.16   \n",
       "854.0   0.041774   0.009767   0.007232   0.011165          2           0.95   \n",
       "855.0   0.018386   0.006319   0.010369   0.015440          2           0.94   \n",
       "\n",
       "       x_position  \n",
       "0.0          2374  \n",
       "1.0          4395  \n",
       "2.0          3725  \n",
       "3.0          3801  \n",
       "4.0          3601  \n",
       "...           ...  \n",
       "851.0        4065  \n",
       "852.0        4076  \n",
       "853.0        3982  \n",
       "854.0        3687  \n",
       "855.0        3634  \n",
       "\n",
       "[856 rows x 131 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TrainingDataset = pd.DataFrame(index = np.linspace(0, 855, 856))\n",
    "TrainingDataset[[f\"f{i}_freq0\" for i in range(64)]] = 0\n",
    "TrainingDataset[[f\"f{i}_freq1\" for i in range(64)]] = 0\n",
    "TrainingDataset[\"max_param\"] = 0\n",
    "TrainingDataset[\"amp_threshold\"] = 0\n",
    "TrainingDataset[\"x_position\"] = 0\n",
    "for i in range(64):\n",
    "    for j in range(856):\n",
    "        TrainingDataset[f\"f{i}_freq0\"][j] = Train_freq0_array[j,i]\n",
    "        TrainingDataset[f\"f{i}_freq1\"][j] = Train_freq1_array[j,i]\n",
    "        TrainingDataset[\"max_param\"][j] = param_array[j]\n",
    "        TrainingDataset[\"amp_threshold\"][j] = amp_array[j]\n",
    "        TrainingDataset[\"x_position\"][j] = x_array[j]\n",
    "TrainingDataset#Создание конечного датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = TrainingDataset.drop(columns = [\"max_param\", \"amp_threshold\", \"x_position\"])\n",
    "y = TrainingDataset[[\"max_param\", \"amp_threshold\", \"x_position\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0_freq0</th>\n",
       "      <th>f1_freq0</th>\n",
       "      <th>f2_freq0</th>\n",
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       "      <th>f57_freq1</th>\n",
       "      <th>f58_freq1</th>\n",
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       "      <th>f60_freq1</th>\n",
       "      <th>f61_freq1</th>\n",
       "      <th>f62_freq1</th>\n",
       "      <th>f63_freq1</th>\n",
       "      <th>max_param</th>\n",
       "      <th>amp_threshold</th>\n",
       "      <th>length</th>\n",
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       "      <td>0.105942</td>\n",
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       "      <td>0.140358</td>\n",
       "      <td>0.065110</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.042877</td>\n",
       "      <td>0.045239</td>\n",
       "      <td>0.061720</td>\n",
       "      <td>0.063699</td>\n",
       "      <td>0.052829</td>\n",
       "      <td>0.095235</td>\n",
       "      <td>0.051877</td>\n",
       "      <td>0.066920</td>\n",
       "      <td>0.222249</td>\n",
       "      <td>0.052747</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.54</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.119038</td>\n",
       "      <td>0.119468</td>\n",
       "      <td>0.125734</td>\n",
       "      <td>0.149360</td>\n",
       "      <td>0.096273</td>\n",
       "      <td>0.237518</td>\n",
       "      <td>0.151898</td>\n",
       "      <td>0.142295</td>\n",
       "      <td>0.185614</td>\n",
       "      <td>0.133377</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.76</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.120825</td>\n",
       "      <td>0.159579</td>\n",
       "      <td>0.097711</td>\n",
       "      <td>0.101082</td>\n",
       "      <td>0.151825</td>\n",
       "      <td>0.186910</td>\n",
       "      <td>0.087471</td>\n",
       "      <td>0.367049</td>\n",
       "      <td>0.190753</td>\n",
       "      <td>0.132302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.82</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.184287</td>\n",
       "      <td>0.141417</td>\n",
       "      <td>0.193877</td>\n",
       "      <td>0.103230</td>\n",
       "      <td>0.048362</td>\n",
       "      <td>0.163119</td>\n",
       "      <td>0.209220</td>\n",
       "      <td>0.182319</td>\n",
       "      <td>0.122975</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.63</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>0.008657</td>\n",
       "      <td>0.006718</td>\n",
       "      <td>0.008872</td>\n",
       "      <td>0.003921</td>\n",
       "      <td>0.005415</td>\n",
       "      <td>0.008062</td>\n",
       "      <td>0.006881</td>\n",
       "      <td>0.006145</td>\n",
       "      <td>...</td>\n",
       "      <td>1.622959</td>\n",
       "      <td>0.073616</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>0.003677</td>\n",
       "      <td>0.005632</td>\n",
       "      <td>0.003502</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0.006634</td>\n",
       "      <td>0.004733</td>\n",
       "      <td>0.008560</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>0.005768</td>\n",
       "      <td>0.007504</td>\n",
       "      <td>0.009368</td>\n",
       "      <td>0.008457</td>\n",
       "      <td>0.009200</td>\n",
       "      <td>0.008119</td>\n",
       "      <td>...</td>\n",
       "      <td>1.762585</td>\n",
       "      <td>0.093952</td>\n",
       "      <td>0.012084</td>\n",
       "      <td>0.005941</td>\n",
       "      <td>0.003292</td>\n",
       "      <td>0.005011</td>\n",
       "      <td>0.004733</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0.007051</td>\n",
       "      <td>0.008490</td>\n",
       "      <td>0.010663</td>\n",
       "      <td>0.009003</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.009136</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>0.007322</td>\n",
       "      <td>0.009069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.181647</td>\n",
       "      <td>0.036029</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.003514</td>\n",
       "      <td>0.004506</td>\n",
       "      <td>0.004664</td>\n",
       "      <td>0.004358</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>0.007917</td>\n",
       "      <td>0.006686</td>\n",
       "      <td>0.006220</td>\n",
       "      <td>0.005961</td>\n",
       "      <td>0.006623</td>\n",
       "      <td>0.010409</td>\n",
       "      <td>0.005074</td>\n",
       "      <td>0.009230</td>\n",
       "      <td>0.010030</td>\n",
       "      <td>0.006652</td>\n",
       "      <td>...</td>\n",
       "      <td>2.291145</td>\n",
       "      <td>1.125921</td>\n",
       "      <td>0.036541</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>0.004069</td>\n",
       "      <td>0.005200</td>\n",
       "      <td>0.002881</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>0.008300</td>\n",
       "      <td>0.011232</td>\n",
       "      <td>0.014047</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.008683</td>\n",
       "      <td>0.007945</td>\n",
       "      <td>0.010412</td>\n",
       "      <td>0.010926</td>\n",
       "      <td>0.007778</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>...</td>\n",
       "      <td>2.022166</td>\n",
       "      <td>1.045472</td>\n",
       "      <td>0.032046</td>\n",
       "      <td>0.006925</td>\n",
       "      <td>0.003895</td>\n",
       "      <td>0.005066</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>283 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     f0_freq0  f1_freq0  f2_freq0  f3_freq0  f4_freq0  f5_freq0  f6_freq0  \\\n",
       "0    0.122921  0.154449  0.229447  0.409696  0.167682  0.105942  0.107506   \n",
       "1    0.042877  0.045239  0.061720  0.063699  0.052829  0.095235  0.051877   \n",
       "2    0.119038  0.119468  0.125734  0.149360  0.096273  0.237518  0.151898   \n",
       "3    0.120825  0.159579  0.097711  0.101082  0.151825  0.186910  0.087471   \n",
       "4    0.126829  0.184287  0.141417  0.193877  0.103230  0.048362  0.163119   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "278  0.003451  0.004447  0.008657  0.006718  0.008872  0.003921  0.005415   \n",
       "279  0.006634  0.004733  0.008560  0.003606  0.005768  0.007504  0.009368   \n",
       "280  0.007051  0.008490  0.010663  0.009003  0.008198  0.009136  0.007512   \n",
       "281  0.007917  0.006686  0.006220  0.005961  0.006623  0.010409  0.005074   \n",
       "282  0.008300  0.011232  0.014047  0.009256  0.008683  0.007945  0.010412   \n",
       "\n",
       "     f7_freq0  f8_freq0  f9_freq0  ...  f57_freq1  f58_freq1  f59_freq1  \\\n",
       "0    0.140358  0.065110  0.139592  ...   0.000000   0.000000   0.000000   \n",
       "1    0.066920  0.222249  0.052747  ...   0.000000   0.000000   0.000000   \n",
       "2    0.142295  0.185614  0.133377  ...   0.000000   0.000000   0.000000   \n",
       "3    0.367049  0.190753  0.132302  ...   0.000000   0.000000   0.000000   \n",
       "4    0.209220  0.182319  0.122975  ...   0.000000   0.000000   0.000000   \n",
       "..        ...       ...       ...  ...        ...        ...        ...   \n",
       "278  0.008062  0.006881  0.006145  ...   1.622959   0.073616   0.010136   \n",
       "279  0.008457  0.009200  0.008119  ...   1.762585   0.093952   0.012084   \n",
       "280  0.007514  0.007322  0.009069  ...   0.181647   0.036029   0.003480   \n",
       "281  0.009230  0.010030  0.006652  ...   2.291145   1.125921   0.036541   \n",
       "282  0.010926  0.007778  0.006662  ...   2.022166   1.045472   0.032046   \n",
       "\n",
       "     f60_freq1  f61_freq1  f62_freq1  f63_freq1  max_param  amp_threshold  \\\n",
       "0     0.000000   0.000000   0.000000   0.000000          2           0.99   \n",
       "1     0.000000   0.000000   0.000000   0.000000          2           0.54   \n",
       "2     0.000000   0.000000   0.000000   0.000000          2           0.76   \n",
       "3     0.000000   0.000000   0.000000   0.000000          2           0.82   \n",
       "4     0.000000   0.000000   0.000000   0.000000          2           0.63   \n",
       "..         ...        ...        ...        ...        ...            ...   \n",
       "278   0.003677   0.005632   0.003502   0.002715          2           0.99   \n",
       "279   0.005941   0.003292   0.005011   0.004733          2           0.99   \n",
       "280   0.003514   0.004506   0.004664   0.004358          2           0.99   \n",
       "281   0.007514   0.004069   0.005200   0.002881          2           0.99   \n",
       "282   0.006925   0.003895   0.005066   0.002092          2           0.99   \n",
       "\n",
       "     length  \n",
       "0         4  \n",
       "1         4  \n",
       "2         4  \n",
       "3         4  \n",
       "4         4  \n",
       "..      ...  \n",
       "278      64  \n",
       "279      64  \n",
       "280      64  \n",
       "281      64  \n",
       "282      64  \n",
       "\n",
       "[283 rows x 131 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TrainingDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "TrainingDataset.to_csv(\"Dataset2_correct.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: (56, 128), (56,)\n",
      "Test dataset size: (227, 128), (227,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8)#Т.к данных собрано (пока что) не очень много, выбран достаточно большой коэффициент\n",
    "\n",
    "print(f'Train dataset size: {X_train.shape}, {y_train.shape}')\n",
    "print(f'Test dataset size: {X_test.shape}, {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.40344778956331157\n",
      "Mean Squared Error: 0.2722166229155917\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))"
   ]
  }
 ],
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