Masterarbeit_Simulation/ANR_high_level.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Funktionen und Abhängigkeiten\n",
    "\n",
    "import time\n",
    "import os\n",
    "from ipywidgets import interact\n",
    "from scipy import signal\n",
    "from scipy.signal import savgol_filter\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import soundfile as sf\n",
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "# Soundfile laden\n",
    "def load_wav(filename):\n",
    "    y, fs = sf.read(filename, dtype='float32')\n",
    "    return fs, y.T\n",
    "\n",
    "# Sensitivätskurve Mikrofon laden (normiert auf 1000 Hz)\n",
    "def load_transfer_function(filename):\n",
    "    df = pd.read_csv(filename, skiprows=3, header=None, dtype=float, sep=\";\")\n",
    "    frequencies = df.iloc[:, 0]\n",
    "    gain = df.iloc[:, 1]\n",
    "    return frequencies, gain\n",
    "\n",
    "# Transferfunktion für frequenzabhängige Veränderung von Signal anlegen\n",
    "def apply_transfer_function_freq(signal, fs, frequencies, gain_dB, delay_seconds=0):\n",
    "    # Signal in Frequenzbereich fouriertransformieren, Frequenzbins berechnen, linearen Gain berechnen\n",
    "    N = len(signal)\n",
    "    freq_signal = np.fft.rfft(signal)\n",
    "    freq_bins = np.fft.rfftfreq(N, d=1/fs)\n",
    "    gain_linear = 10 ** (gain_dB / 20.0)    \n",
    "    # Gain Werte interpolieren auf die tatsächlichen Frequenzbins\n",
    "    # Phasenshift für die Verzögerung berechnen\n",
    "    # Signalfrequenzen modifizieren. \n",
    "    # Signal wieder zurück in Zeitbereich\n",
    "    gain_interp = np.interp(freq_bins, frequencies, gain_linear)\n",
    "    phase_shift = np.exp(-1j * 2 * np.pi * freq_bins * delay_seconds)\n",
    "    modified_freq_signal = freq_signal * gain_interp * phase_shift \n",
    "    modified_signal = np.fft.irfft(modified_freq_signal, n=N)\n",
    "    return modified_signal\n",
    "\n",
    "# ANR Algorithmmus\n",
    "def anr_function(input, ref_noise, coefficients, mu, adaption_step = 1):\n",
    "    coefficient_matrix = np.zeros((len(input), coefficients), dtype=np.float32)\n",
    "    output=np.zeros(input.shape[0], dtype=np.float32)\n",
    "    filter = np.zeros(coefficients, dtype=np.float32)\n",
    "    \n",
    "    for j in range(0, len(input) - len(filter)): \n",
    "        accumulator=0\n",
    "        for i in range(coefficients):\n",
    "            noise=ref_noise[j+i]\n",
    "            accumulator+=filter[i] * noise\n",
    "        output[j] = input[j] - accumulator\n",
    "        corrector = mu * output[j]\n",
    "        if (j % adaption_step) == 0:\n",
    "            for k in range(coefficients):\n",
    "                filter[k] += corrector*ref_noise[j+k]  \n",
    "        coefficient_matrix[j, :] = filter[:]\n",
    "    return output, coefficient_matrix\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für Simple Usecases\n",
    "\n",
    "SIMULATION = False\n",
    "AUDIO = False\n",
    "PLOT = False\n",
    "\n",
    "plot = 'sine_1'\n",
    "\n",
    "# Chirp Generator\n",
    "n=2000 #Sampleanzahl\n",
    "fs=20000 #Samplingrate\n",
    "f0=100 #Startfrequenz\n",
    "f1=1000 #Stopfrequenz\n",
    "t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
    "if plot == 'sine_1':\n",
    "    f_disturber=2000 #Störfrequenz\n",
    "else:\n",
    "    f_disturber=500 #Störfrequenz\n",
    "\n",
    "signal_amplitude=0.5\n",
    "disturber_amplitude=0.25\n",
    "\n",
    "# Parameter setzen\n",
    "coefficients = 16\n",
    "step_size = 0.01\n",
    "indices = [0, coefficients // 2, coefficients - 1]\n",
    "\n",
    "t = np.linspace(0, t1, n)\n",
    "\n",
    "# Zielsignal anlegen\n",
    "desired_signal = signal.chirp(t, f0=f0, f1=f1, t1=t1, method='linear')*signal_amplitude\n",
    "\n",
    "# Störsignal anlegen\n",
    "if plot == 'sine_1' or plot == 'sine_2':\n",
    "    noise_signal = np.sin(2*np.pi*f_disturber*t) * disturber_amplitude\n",
    "else:\n",
    "    noise_signal = np.random.normal(0, 1, n) * disturber_amplitude\n",
    "\n",
    "# Korruptes Zielsignal anlegen\n",
    "corrupted_signal = desired_signal + noise_signal\n",
    "\n",
    "# Zeitachse anlegen, ANR Algorithmus ausführen\n",
    "t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/1000\n",
    "output, coefficient_matrix = anr_function(corrupted_signal, noise_signal, coefficients, step_size, adaption_step=1)\n",
    "\n",
    "# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "error_signal = (output - desired_signal)[:-coefficients]\n",
    "t2 = np.linspace(0, len(error_signal), len(error_signal))/1000\n",
    "\n",
    "# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "snr_before = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(noise_signal**2, t))\n",
    "snr_after = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(error_signal**2, t2))\n",
    "delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "if SIMULATION == True:\n",
    "    # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
    "    desired_signal_simulation = desired_signal*(2**(15)-1)\n",
    "    noise_signal_simulation = noise_signal*(2**(15)-1)\n",
    "    corrupted_signal_simulation = corrupted_signal*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/simple_desired_signal.txt', desired_signal_simulation, fmt='%d')\n",
    "    np.savetxt('simulation_data/simple_corrupted_signal.txt', corrupted_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/simple_noise_signal.txt', noise_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "\n",
    "# Plots des Filterprozesses\n",
    "figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
    "ax0.set_ylim(-1, 1)\n",
    "ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
    "ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
    "ax2.plot(t, noise_signal, c='chocolate', label='Reference noise signal')\n",
    "ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
    "\n",
    "ax0.text(0.5, -0.3, '(a) Desired signal',\n",
    "         transform=ax0.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(c) Reference noise signal',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.5, f'(d) Filter output (SNR Gain = {delta_snr} dB)',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.set_xlabel('time(s)', x=0.05)\n",
    "ax0.set_ylabel('Amplitude')\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax2.set_ylabel('Amplitude')\n",
    "ax3.set_ylabel('Amplitude')\n",
    "\n",
    "# Plots der Filterperfomanz\n",
    "figure2, (ax4, ax5) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax4.set_ylim(-1, 1)\n",
    "ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')\n",
    "for i in indices:\n",
    "    ax5.plot(t2, coefficient_matrix[:,i], label=f'Coefficient {i+1}')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(a) Error signal',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(b) Coefficient values (1st, 8th, 16th)',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel('time(s)', x=0.05)\n",
    "ax4.set_ylabel('Amplitude')\n",
    "ax5.set_ylabel('Coeffcient value')\n",
    "\n",
    "#Grids direkt auf Subplots anwenden\n",
    "ax0.grid(True, linestyle='--', alpha=0.4)\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax0.spines['top'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax0.spines['right'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_plot_1_{plot}', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_plot_2_{plot}', dpi=600)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für intermediate Usecases\n",
    "\n",
    "SIMULATION = False\n",
    "AUDIO = False\n",
    "PLOT = False\n",
    "\n",
    "# Chirp Generator\n",
    "n=2000 #Sampleanzahl\n",
    "fs=20000 #Samplingrate\n",
    "f0=100 #Startfrequenz\n",
    "f1=1000 #Stopfrequenz\n",
    "t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
    "f_disturber=2000 #Störfrequenz\n",
    "\n",
    "# .wav File laden, Tonspuren den Signalen zuordnen, Corrputed Target Signal erstellen\n",
    "fs, data_1 = load_wav(f'./audio_data/Nutzsignal/male.wav')\n",
    "fs, data_2 = load_wav(f'./audio_data/Störsignal/breathing.wav')\n",
    "\n",
    "# Signale laden und zuordnen\n",
    "desired_signal = data_1\n",
    "noise_signal = data_2\n",
    "corrupted_signal = desired_signal + noise_signal\n",
    "\n",
    "# Parameter setzen\n",
    "coefficients = 16\n",
    "step_size = 0.01\n",
    "indices = [0, coefficients // 2, coefficients - 1]\n",
    "\n",
    "# Zeitachse anlegen, ANR Algorithmus ausführen\n",
    "t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/10000\n",
    "output, coefficient_matrix = anr_function(corrupted_signal, noise_signal, coefficients, step_size, adaption_step=1)\n",
    "output = output * max(desired_signal) / max(output)\n",
    "\n",
    "if AUDIO == True:\n",
    "    # Audiodateien zum Vergleich abspeichern\n",
    "    sf.write('corrupted_signal.wav', corrupted_signal, fs)\n",
    "    sf.write('filter_output.wav', output, fs)\n",
    "\n",
    "# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "error_signal = (output - desired_signal)[:-coefficients]\n",
    "t2 = np.linspace(0, len(error_signal), len(error_signal))/10000\n",
    "\n",
    "# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "snr_before = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(noise_signal**2, t))\n",
    "snr_after = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(error_signal**2, t2))\n",
    "delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "if SIMULATION == True:\n",
    "    # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
    "    desired_signal_simulation = desired_signal*(2**(15)-1)\n",
    "    noise_signal_simulation = noise_signal*(2**(15)-1)\n",
    "    corrupted_signal_simulation = corrupted_signal*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/intermediate_desired_signal.txt', desired_signal_simulation, fmt='%d')\n",
    "    np.savetxt('simulation_data/intermediate_corrupted_signal.txt', corrupted_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/intermediate_noise_signal.txt', noise_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "\n",
    "# Plots des Filterprozesses\n",
    "figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
    "ax0.set_ylim(-1, 1)\n",
    "ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
    "ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
    "ax2.plot(t, noise_signal, c='chocolate', label='Reference noise signal')\n",
    "ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
    "\n",
    "ax0.text(0.5, -0.3, '(a) Desired signal',\n",
    "         transform=ax0.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(c) Reference noise signal',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.5, f'(d) Filter output (SNR Gain = {delta_snr} dB)',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.set_xlabel('time(s)', x=0.05)\n",
    "ax0.set_ylabel('Amplitude')\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax2.set_ylabel('Amplitude')\n",
    "ax3.set_ylabel('Amplitude')\n",
    "\n",
    "# Plots der Filterperfomanz\n",
    "figure2, (ax4, ax5) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax4.set_ylim(-1, 1)\n",
    "ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')\n",
    "for i in indices:\n",
    "    ax5.plot(t2, coefficient_matrix[:,i], label=f'Coefficient {i+1}')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(a) Error signal',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(b) Coefficient values (1st, 8th, 16th)',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel('time(s)', x=0.05)\n",
    "ax4.set_ylabel('Amplitude')\n",
    "ax5.set_ylabel('Coeffcient value')\n",
    "\n",
    "#Grids direkt auf Subplots anwenden\n",
    "ax0.grid(True, linestyle='--', alpha=0.4)\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax0.spines['top'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax0.spines['right'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_plot_1_wav', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_plot_2_wav', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_21944\\1847182380.py:71: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.\n",
      "  snr_before = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(noise_signal_vpu**2, t))\n",
      "C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_21944\\1847182380.py:72: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.\n",
      "  snr_after = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(error_signal**2, t2))\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ANpAA92AC3EaJXbt2rQoKChglFvAI4r4brZzvq/R++zGptHDn/MFjpGMvkY76Br2jI4X9yfTR89JDP5dWL/DNyx4gXXiTdOJlUkyMehtiH/Ae4h7wJmIf8B7iPnT0APcojnsA3kPcd6EtxdLbj0tvPiwt/2Ln/LRs6civ+wZN3Gt/idPQIou9n4ecKR14mu+Ax8M3ScWrpDu+K710t/T/7pAmHq7ehtgHvIe4B7yJ2Ae8h7gPDQlwAABCYS1NZrzgS3p/9prU2OCbHxsnTT/Fl/SedpIUFx/uLUV3s0pva2lzxPnSy/+QHrlJWjFPuvYI6bBzpMtvo+ofAAAAAHoJEuAAALTFjqZ/+aEv6f3eU9L28p33jTtAOvoi6YjzpPSccG4lwsUOdpxxlXTUBdKDv5D+9w/p/aelT16ULvqldPYPfQdIAAAAAABhQw9wD/YAt7e8rq5OcXFxjBILeARx30mb1ktvPCS99oC0YdnO+VbVa0nvYy7y9fgGglkV+N1XS/Pe890eMUn6wb2+djhhQuwD3kPcA95E7APeQ9yHjgS4BxPg/kb5NMgHvIW470BVha/FyVuPSrOtxUmjb35Sqq+thQ1oOf5QidcQ7bE/q2xQ1H/8UNpW6vu8nH6VdMktUnJaWDaJ2Ae8h7gHvInYB7yHuA8NCXAPJsAtONasWaMhQ4YQJIBHEPdtqN4uffqy9O6Tvsva6p33TThMOv5b0qFfk5JSwrmV6Iu2bPQlwW2wTJNXIF39d2n6yT26GcQ+4D3EPeBNxD7gPcR96OgBDgDwltoa6bNXpfee9FV8V1fuvG/QaN/Ahjag5aBR4dxK9HVZ/aTrH/W1y/nrd6XiVdIvTpEOP1f63l+l7PxwbyEAAAAAeAIJcABA5Kurlea86av0/vj55oNZ9h/mG8jy8POkkZMleqehK+13vPTPL6VHb5ae/bNvMNXZr0uX/UE64du01AEAAACAbkYCHAAQmSrLfb28P3re196ksmznfbmDpMPO9VV72wCFJL3RnayFzuV/kI78uvTny6Rln0u3f0d68xHpmnukoXuHewsBAAAAIGLRA9yDPcANTfIB7/FE3G9YLs18SfrkJWn+e1J93c77svr7BrO0Su+9D6LyFuHRUC89d4f00C+kmu1SdIyvEvyiX0o5A7rlKT0R+wCaIe4BbyL2Ae8h7kNDAtyDCXB7y+vq6hQXF6coqh4BT4jYuLcE91cfSZ/+z5f4XrOw+f3W0/ugM3zTuANIeqP3KFol/f0aXx96k5Asfe1H0jk/lpLTuuxpIjb2AbSJuAe8idgHvIe4Dx0JcA8mwBklFvCeiIr70iJp1iu+pLf1Ug7u5x0TK40/VJp+inTAKdLgMeHcUqBj896X/nWdtGim73ZGnnThjdJJ35Hi4vd49REV+wBCQtwD3kTsA95D3IeOHuAAgN6toUFa8pk0y6q8X5aWzm5+f0autP+J0rSTfQMOpmaGa0uBzpt4mPTXGdKH/5Huv0Fav1T621XSc7dL3/yNr20Pf8wCAAAAwG4jAQ4A6H02rfdVd3/2mvT5G9K20ub3j9lPmnaSL+lt10kQoi+z0xUPPVs68DTplX9Jj97s62f/2/Olp/4gfft30tRjw72VAAAAANAnkQD3KHoDAd7Tq+O+erv05YfS5zuS3qu+bH5/SoY09Thf0nu/E6Ts/HBtKdB9YuOkU78nHXOR9OxfpGduk5Z9Lt1wnLTv0b5EuB3wiaTYB9AtiHvAm4h9wHuI+9DQA9yDPcABoFcMXrl4ljTnLemLt6SFM6S62p3324/4mP19LU1sGjvd198b8JKtJdK/fyu9dPfO+LCWKJf+xjfAKwAAAACgQyTAPZgAt7e8urpaiYmJHCkCPCLscd/YKK2c70t4z31bmveeVFXRfJm8AmnKMdLU432X6Tk9v51Ab1S0Snr4RumtRy2YpegY6aTLpW/cKOUM6N2xD6DHEfeANxH7gPcQ96EjAe7BBDijxALe0+Nxbz8thSt2Vnh/8Y5UVtJ8GUtwTzrS19rBpoGjfJXfAFq3Yp70wE99g8GahGTpwhuls34gxcW3+hB+8wHvIe4BbyL2Ae8h7kPH+eQAgK5hFd3zP5Bm/U/69H++BHiwxBRpwmG+ZPfko6QRkxi8EuiMEROlW16S5r0v3fcTaeEn0n3XS68/KP3ofmnvA8O9hQAAAADQ65AABwDsfsL7q4+lee9Kc9+RlnwmNdQ3H9Bv7AE7K7z3mtZmlSqATph4mHT7x9IbD0v3/lhau0j64SHSeddLF95EnAEAAABAEBLgHhUXFxfuTQDQ1+K+qlL66iNfwtsmG8QyOOFt+g/19fCedpIv6Z2UumfPCaB11i7ouEukA0+T/n6N9OYjvgEz7eyL6x6Rho8PLMpvPuA9xD3gTcQ+4D3EfWjoAe7BHuAAEJKKrb4kt6vwtoT3p7smvPsN8fXxnnSENPEIKX9YuLYW8Lb3n5Hu+K5UvlmKS5Au/Y2vNzhthgAAAAB4HAlwDybA7S2vqKhQamoqo8QCHtFh3FvSbOnn0rLPpaWzfZcblu+6XF6BL+E9+UgS3kBvs7lQ+stlvipwM/FwNf3oAVWk5vKbD3gIf+sD3kTsA95D3IeOFigeDZDNmzcrJSWFAAG8GPdbS3Ymuf0J7+LVrT+w/zBpwqG+pLc/4c33BtA75QzwDZL5v3ulf/xQmveedMUENR5xkZouvVlRWf3CvYUAegB/6wPeROwD3kPch44EOABEssZGac1Caf57yp35mqJWzpFK1ra+7MBR0ugp0qgp0uip0qh9pfScnt5iAHvC/vA9+Tu+Hvy3XaKorz5Sxqv3qOndR6TDz5OOvcR3UIs/kAEAAAB4BAlwAIikZLdVcq+aL62c7+vZ/eWH0rZSWRfgwHCUlvgavNeORPeOZPfIyVJqZni3H0DXGThS+vMHapzxguru/7kS1nwpvXa/b8of7kuEH3OxNGB4uLcUAAAAALoVPcA92AO8sbFRJSUlysvLUzSDYwF9U02VL8nt2pjMkVbOk1Z9KVVV7LpsQpKaxh2oyuFTlDz9eEWPnS4lp4VjqwGE4zd/40blFS9S9JuPSO8/1fx7YuLhvmT4YedKSSnh3FQAXYS/9QFvIvYB7yHuQ0cC3IMJcAB9TGW5tPyLncluu7S2Jo0Nuy4bGycNGScNm+Cr6h5/qK/K2+YDQFWl9NFz0hsPSV+8ZY0DffOT06WjL5ROvkIaMTHcWwkAAAAAXYYEuAcT4PaW2z7avtIkH+hFGhqkopXS6q+k1QukFV9ISz+XNixrffnMfr42Jtare8QkafgEadDoVpPdxD3gTe3G/sa10luPSK/eJxWu2Dl/3AHSSd/x9QxPTO7xbQawZ/jNB7yJ2Ae8h7gPHQlwDybA7RSJNWvWaMiQIZwiAYRDfZ0v2WRJ7jULdia81y6S6mpaf0y/IdLIfYMGqZwiZQ8IeSA74h7wppBi38YP+OJt6eV/SB8/LzXU++anZEjHXORLhtsBNgB9Ar/5gDcR+4D3EPehYxBMAOgOllAqLZTWL905rVssrV0sFS7fmWBqKT7R18JkyN6+hJO/wjsjt6f3AIBX2B/LU47xTaVF0usPSv/7p++MlP/e5Zv2PlA6/Srp0K/RUgkAAABAn0ICHAD2pJK7eLUvoW1tSjYs33F9x2VtdduPTUzZmegeatM+vst+Q6WYmJ7cCwDYKTtfOv966dzrpM/f9CXCZ/xXWjDDN/3rJ9KZ10gnXi6lRO7ZcgAAAAAiBwlwj0pNTQ33JgC9n1Vpl6yTNq6Wilb5LotX+ZLeVhm5cU3rA1H6RcdI+cOkgaN9vbkHj5EKxvqm3EEhty/pKsQ94E27FftWFb7fcb5pc6H0yr3SC3+TStZK/7xWevRmX2uUs38k5Qzojs0GsAf4zQe8idgHvIe4Dw09wD3YAxzAjuptO9V/83pp03pfQtuS25s3+FqX2KXNby/BbRKSpPwR0sBR0sCR0oCRvku7bX27aRUAIFLYWS1vPSo9+2dpzULfvLgEXyL8vJ/4DuwBAAAAQC9DAtyDCXBrkl9aWqrs7Gya5CPy2Ffati07E9ub1vkuLaFt8/yJ7bIS37IdiYv3tSWxqb9Nw3yXdnvQqE4NRBlOxD3gTd0S+zbGwWevSv/+rfTVRzu/K0+4zJcIt4N/AMKG33zAm4h9wHuI+9DRAsWjKioqXIAAfYolrMs27WhDYi1J1kpbinYktf2J7vVSTVVo64uJlXIG+qb84b7ktlUwZtu8AVJegS/BHSE/JMQ94E1dHvv2nTjtJGn/E6Uv3vG1Q5n/vvTi3b6e4YecLZ1xtW/gzD5wgBCIRPzmA95E7APeQ9yHhgQ4gN7BKgqrKqTKrVJlma/3tg0kWbhi56W1KamuDG19adlS3mApZ5AvqR24HLgzyZ2RGzHJbQDocZbc3vco3zTvPenRX0lfvC2996RvGj3Vlwg//DwpPiHcWwsAAADAo0iAA+gZDQ2+Su2iFdIGf0LbktsrfNXcVtndUb9tP0tiW7W2nWaflb8zqZ07eGeS23pzAwB6xsTDpT+8JS2fK/33Tuntx6Sls6XbLpHu/bF08hXSKd/1fT8DAAAAQA+KyB7gf/vb33TbbbepqKhIkyZN0p133qlp06Z1+Div9AC3t9z20fY1ilOTsSfs66N6u69qe1upbyrf7BtE0iq4N67Z2Xd742qprrbjddqgkSkZvsS2f0DJASN81+3Sem9TSdhpxD3gTWGLfTuo+cq/pBf+5mtR5W87ddg5vqrwsdNpjwJ0E37zAW8i9gHvIe49nAB/8skndfHFF+uee+7R9OnTdfvtt+vpp5/W4sWL1a9fv3Yf65UEONAuS2gHDxZpl9Znu7RI2rpRKt/kS3JvL/e1LGmoD33dlvywym1LaueP2JnYzh8mZfaTUrN8ldt8cQNAZLDfiI+el56/Q/ryg53z99pfOv1qX0Kcg5oAAAAAulHEJcAt6b3//vvrrrvuCoyIWlBQoKuuukrXX399u4/1SgLcXpOSkhLl5eUxSmwksVC2082/+qhFNXaRFJ/oq6pOz/H1xrZ+2w11Un2dVLFl5+CRluyu2Nr5546OkVIzfeu3yaq3rRVJ3hBfH24bSNIGmbQWJTEx3bH36ABxD3hTr4r9ZXN2tEd5XKqr8c3L6u9rj3KytUcZEN7tAyJEr4p7AD2G2Ae8h7j3aA/w2tpazZ49WzfccENgnn0AjjnmGM2YMWOX5WtqatwUnAD3f4BsCl7H/2/vLuDkqM8/jn/j7u5uBEmQ4H/cikPx4qXFpUVKKVacluJQKMWKe4trcIuQhAhJiBMjRIj7/l/fGWYzdznZu9zd3u183q/XZC+7s7Mzu/vM7j7zzPOL/998aoGn8rrej+ljE/mPT5TF9X6sFStW5NQ2Rdfn1DYtWaDqs77X+hnfqdoPE6RZ34fJ6vXrVW39OqW8LvUbSR37SLXqSF+/oWpTRqkspOrU3zBwpBMSzdupWvN2SjVppZQHjnSSu35jqV4jVW/UXKk69ZT/SFqh2/rLezBnXqcq8t6z5cuXl2g5lX2bcvF1YpvYprLeJl+/bNkytWjRIvvb1H0r6aKHpFNvkt56SNVeu1/VfAD2ib8q9cxN0q6/VurQ81St3w6Je53YJrapLNc9Hve5sk2ZXM82sU1J3yb/7e/70d+5sE1leT3bxDbl4jaR31PGif+cSoD/9NNPWrdundq0aZPnev//u+++22j+m266Sddee+1G18+YMUONGjUK/m7YsKFatmypBQsWaOnSpel5mjZtGkw+0uI3W8RfNH3f2bNna82aNXnWoV69esGy4y9s+/btVbNmTU2fPj3POnTu3Flr167VrFmz0tf5he7SpYtWrlypuXPnpq+vVauWOnToEKzf/Pnz09f78fy4rmhftGhDVW+DBg2Cy4ULFwZfjnNhm7LyOq1fp87VVmj9hKFaOn6Eqq1ZGVRUV1+zQo0aNNA6VdPyVWuUqhYGY40a1dWwQUOtWb1KK5cuDqumq1VTzWrVVL9Oba1Zulhrli9RjYVzVGvuFNVYuiC4X2GhnG4S8vUbG66sVUcr+u+mNU1aa339xlrfoKkaduqp2tWlBTMmq/rShaq+/GepWnU1aNJM1WvX0aI1Ka1t2kbrmrTRumZt1W7zbbS2dn3Nmj1749dpxYoNr1NKqrWmujrUa6ClS5ZU3tcpF997pdimdu3aBQf8vHzPlwvblIuvE9vENpX1Ns2cOTP4vPf2+MthpdmmnU5Qm4PPU71v3tbKZ/6mut8PkQY/rWqDn9b63tspddh5mt51e6lm7US8TmwT21SW2+QfhlHce11yYZty8XVim9imst4mr2tU4Jcr25SLrxPbxDaV5TaR35O6du2qxLVA8RPvJ/Xzzz/XjjvumL7+0ksv1UcffaSvvvqq2Apwt0vxGyfeAiUXjxD98MMP6tixY54jJVV5m6Lrg8dcvUqaPSnoUV2tfiNVa9E+SATH+0rnWUcPzOge1z/9oGrzZwVTygM4/vCdtPRnaeVSadVyVVu5TKlVK8LluJe1l+//l6eWHZRyhXeHXkq17yU1baVqNWqqWvUaWu/1WPyTqk3/Tlq7WuoxQNV2O0br3eqkKrxOm7CObFPpKsCnTZsW7OPicV+VtykXXye2iW0q621yYYC/sPpLqq+rtNs0cbiq/e8e6cOnVe2XAZNTzdoq5fYov/qd1LxtTr9ObBPbVJbr7imK+xo1auTENmVyPdvENiV9m/y3k1dOQOVXVbepLK9nm9imXNymnM/vZbguiUuAuwVK/fr19cILL+iwww5LX3/yyScHRxP++9//Fnn/pPQA90vuoyg+quI3T5Xjt6xPmZ42Vpo2Rvp5nlS3gbRwrvT9cGn812Fv67hatcO+1G7bkXL/63XSiiXhtHxJ6dfFj9tzoNSpX9iOpGYtye1DXN3tdXBiuiCuaFu/LtwW98SuXjPs0+1WJu6H2qmP1L6nVK9h6dcNyKW4B5CM2F/4o/Tmv6RX7wvHpTB/tu52jHTY+eHgmQByK+4BlAliH0ge4j5zOZUAjwbBHDRokO6+++7g/z4y4OqHc889l0EwKysnoxfMDntcr1srzZshzZ0WVF0HSeQgUbw+HNBx7tQw8b087NdeKCe6GzULk9seDLI4/nHtftctf5matpE695OatAqT0E50e6pTL5zf6+W/23ZnUEcAAMqaP2c/fUl65S5p7Ocbru+3Q5gI3+XI8OA2AAAAACQtAf7ss88GFd8PPPBAkAi/44479NxzzwU9wPP3Bk9qAtwHBdzvxz2BMz1VYJP9/JP0wVNhO5Gli6Qfp4dtSpzoXvxTWAldEq6w7tBL6rJZmLheuSwcmNH/33xXqV33DS1P3BLFLU6cZF+xNOh/HSSt6zUKq7YbNpMaNfd5E+Wy6UBi4x5A1uVE7E8YKr1yt/TRM2HbMmveTjroLOnA34VnTgHIrbgHUGLEPpA8xH3mcmoQTDvmmGOChuhXXXWV5syZowEDBuitt94qNvmdNPFm9+XOldsX7BhWeBeV0G7bLUxCt+oktesRJqdr1Ar7bTuZ7d7WrTuH7Uac/K5dJ7PH93xtuoQTkGAVGvcAKo0qH/u9t5UufUw641bp9Qel1+4PD2o/fpX09PXSbsdKB58l9d0+z3gfQJJV+bgHUCrEPpA8xH1CK8A3RZIqwOMDYpUrV19fvq/07cdhS5KdDg/bk7TqGLYPadct7M3dpGWY6AZQ9eMeQKWRk7HvKvBPXwzbo4z7csP13bcMB8zc6zfhQXMgoXIy7gEUi9gHkoe4zxwZR5TfIFZP3yB9/bo0a1KY9L7tE6lr/2yvGQAAqMrc+3uP48Lpu6/DATM/elaaPEq651zpX5dIux8r7X+6tNlOVIUDAAAACUcFeAIrwP2Sr1y5UnXr1i2fUWL9lrp497Dq29zK5OqXpYF7lf1jAagccQ+gUkpM7C9ZKL3/hPT6A9K0MRuud8u0fU+R9jpRat0pm2sIVJjExD2APIh9IHmI+8yRAE9gArzcvfcf6daTpDr1pIsekrbdX2rcPNtrBQAAcp2/1o79QnrzX9LHz4eDZJt/EAzcO0yG73x4+B0FAAAAQCKQAE9gAtw9gmbMmKFOnTqVbY+gz16RXvyHNPqT8P+n3SQd+6eyWz6Ayhf3ACq1RMf+iqXSJy9K7z4qjfxww/Vuy+YWKU6G99uBFinIOYmOeyDBiH0geYj7zNEDPKHK/LjHe09It5644f/bHSAdcVHZPgaATcLxTiCZEhv79RpK+54cTrMnS+8+HibD506T3ngwnDr2CRPhe58oteyQ7TUGykxi4x5IOGIfSB7iPjMcHsCm++p16bZTw7/9I/KxydINb0i162R7zQAAAKR23aWTrgm/o9z6gbS3W7XVl34YLz18ufSbztKf95c+fFZavTLbawsAAACgDFEBjk3zzmPSnb+X1q2V9vqN9Id/S5x2AQAAKiN/RxmwRzide4/0yQvS24+E7duGvh1ODZuGLVL2OUXqO4gWKQAAAEAVRw/wBPYA90u+Zs0a1apVq/SjxK5cHia+338i/P8uR0p/flqqWatM1xVAJYp7AFUOsZ+hmd9L77lFymPSj9M3XN+5n7T7cdKuR0pdNsvmGgIZI+6BZCL2geQh7jNHAjyBCfCoUf4mNci//mjp4+fDSqrfXC0d/xcqv4Fcj3sAVRKxXwLr10sjB0vvPCp9+qK0asWG2zr1lXb9dZgM774VleGo1Ih7IJmIfSB5iPtyTIBPmTJFn3zyiaZNm6bly5erVatWGjhwoHbccUfVrVtXVVVSEuAOjunTp6tz586lC5Lvv5HO3jpMeN/0rjRwz/JYTQCVKe4BVEnE/iZYtjhMgnsa/q60ZvWG2zr0Clu/eXJ/caASIe6BZCL2geQh7supB/iTTz6pO++8U0OHDlWbNm3Uvn171atXTwsWLNCkSZOC5PcJJ5ygyy67TF26dCnJolGVPHNTeLnbsSS/AQBAbmrQWNrv1HBa9rP05WthMnzoW9LMidLjV4fTZjtJe58o/d/RUuPm2V5rAAAAAKVNgLvCu3bt2jrllFP04osvqlOnTnluX7Vqlb744gs988wz2nbbbXXffffpqKOOynTxqCpGfxoOGGXHXp7ttQEAACh/DZpIe50QTiuWSp+9LL33H2nE+9LYz8PpvvOlQQeGVeHbHyTVrpPttQYAAABQkgT4zTffrP3226/Q2+vUqaPdd989mG644QZNnTq1rNYRlcW6ddI/fusu+9J+p0ndNs/2GgEAAFSseg3Dim9P82dJg58Ok+GTR0qfvxJOTVpKB5whHXSm1LpzttcYAAAASDQGwUxgD/BSN8n/+k3pL7+SGjWXHpskNWxaXqsHoBwwOAaQTMR+BZnyrfT+E9IHT0o/zQyv8/O+46HSIedKA/Zg4ExUGOIeSCZiH0ge4j4zm/wMOX/+wQcf6PXXX9fChQs3dXGoAH7N1q5dG1yWiHte2v8dRfIbSErcA6jSiP0K1G0L6be3SP+ZKl31kjRgT/8iCdulXLaX9Lstwmpxn1EHlCPiHkgmYh9IHuK+nBLgixYt0sknn6wttthCZ5xxRlAxveuuu2rvvffWwQcfrH79+mnUqFElWSSywIExa9askgfIsHfCy232LZf1AlAJ4x5AlUbsZ0GNmtIuh0u3vi89OFo66CypbgNp2hjppuOlM7eSPn4hTI4D5YC4B5KJ2AeSh7gvpwT4xRdfHAx0eeyxx+rbb7/V/vvvr3Xr1gXXffXVV0EC/IorrijJIlFVzJ0mzfhOql4jrGgCAABA0br2l86/T3p6pnTK9eEZdE6EX3+UdPZA6bNXwrFVAAAAAGR/EEx788039dRTT2m33XbTKaecok6dOgXtT7bffvvg9ltuuUWHHHJIea0rsmno2+HlZjvS/gQAAKAkGjSRjr9COuQc6aU7pJdulyaPkq49XOo5UDrpr9L2B9IjHAAAAMh2BfjcuXPVu3fv4O8OHTqobt26QRI80rlzZ82bN6/s1xJlrlpJf2BF/b+32a9c1gdAJYx7ADmB2K9EXERw0jXS41Ok466Q6jWUvv9Guupg6fwdpE9fpkc4ygRxDyQTsQ8kD3GfmWqpEjSK8aiic+bMUevWrYP/N2rUSCNHjlT37t3TCfL27dsHbVGqIvc0b9KkiX7++Wc1btw426tTeaxdI/26hbR8iXTPEKn3ttleIwAAgKrv55+kF/4uvXK3tGp5eF277tJhF0j7nhxWjgMAAACouBYo9tBDD6lhw4bB3x5p9NFHH1XLli2D/y9ZsmTT1gYVwsc8Vq5cGVTwZ3SkaOwXYfK7SUup59YVsYoAsh33AHICsV/J+bvV6TdLR/xBeuVO6bX7pdmTpfsvkB6+XNr9WGn/06R+O7oSJdtriyqCuAeSidgHkoe4L6cK8K5du2b0hE6ZMkVVUVIqwNevX6/p06cHLWtc1V+sR66Qnr5R2vME6U9PVMQqAsh23APICcR+FbNimfTuY9Kr94WDZUaat5V2PFTa+XBpqz2kWrWzuZao5Ih7IJmIfSB5iPtyqgCfOnVqSWZHrhjyS//vben/DQAAUG7qNZAOOVs6+Cxp7OfS6w9Kn78iLZgjvf5AOLktyqADw2T4dvuHfcQBAAAAlF0LFCTMwrnS98PDv7fZN9trAwAAkPt8xmX/ncNpzWpp5GDps5elL/4bJsMHPxVOteqE38+cDN/h4LClCgAAAIDSJcDvuuuuTGfV+eefn/G8yI5atWplNuOwd8NL9/5u1qZc1wlAJYl7ADmF2K/i3O7EZ+F5Ou8+adyXYTL885elWZOkL18NJ5/2uvmuYTLcU+vO2V5zZBFxDyQTsQ8kD3Ffxj3Au3Xrluf/8+bN0/Lly9W0adPg/4sWLVL9+vXVunVrTZ48WVVRUnqAl8itJ0nv/Uc69nLptBuzvTYAAAAwf4WfOjpMhnuaNCLv7S5eiJLhXTYLq8oBAACABCrRIJiRp556Svfdd5/+/e9/q0+fPsF148eP1xlnnKHf//73OuGEE1QVJSUB7pd86dKlatiwYfGDmp7YTZo7VbrpHWmbfSpqFQFkM+4B5AxiP0FmTwlbpDgZPuZTj4q04bZ23aVt9gt7hnsQzfqNsrmmKGfEPZBMxD6QPMR9OSfAe/TooRdeeEEDBw7Mc/2wYcP061//WlOmTFFVlJQEeMajxM77QTqhU3hK7UuL+LEEVGGMDg0kE7GfUAt/DNuiuE3K8HfDPuKRmrWk/ruEyXD3De/cL5trinJA3APJROwDyUPcl/MgmLNnz9batWs3un7dunWaO3duaRaJymjMZ+Fl9wEkvwEAAKqKZq2lA04PpxVLpRGDpWFvS0PelGZPDgfV9PTQZVLHPtJOh0oD9gwH3azXMNtrDwAAAGQ/Ab7XXnsFrU4eeughbb311unq77POOkt777132a4hsuf74eFl30HZXhMAAACUhhPaOx4cTjbze2noW9JXr0sjP5B+GC89d2s4Va8h9dluQ7uU3ttJNWpkewsAAACAik+AP/zwwzr55JO17bbbpkcbdUX4fvvtFyTFUfnVq1ev+Jkmjwovu29V7usDoJLEPYCcQ+wjjw49pQ7nSoeeKy1bHCbDv35DGvVROO7LuC/D6YlrpUbNpW32lbbdX9p2P6l522yvPTJE3APJROwDyUPcl2MP8MjEiRM1bty44O++ffuqd+/eqsqS0gM8Y8e2lxbMlu74XNpsx2yvDQAAAMrTj9Ol4e+F7VKGvSMtXZT39h4DNiTD++0o1a6TrTUFAAAAKiYBnmuSkgD3S+5t9LYWOkrsonnS0a3Dv19ZTA9wIAlxDyDnEPsotXVrpe++DivE3Tt8wtC8t9epFw6mueMh0l6/kRo2zdaaIh/iHkgmYh9IHuI+cxkPEXrzzTdrxYoVGc371Vdf6fXXXy/BaqCiA2TRokXBZaGm/NL+pH0Pkt9AUuIeQM4h9lFqNWpK/XeSTv6rdM8Q6dm50mVPSHueIDVrI61aIQ1/V7r3POm4DtI/fitNGJbttQZxDyQWsQ8kD3FfDgnwsWPHqnPnzjr77LP15ptvat68eenb3P971KhRuu+++7TTTjvpmGOOUaNGJE2rtKj/d7cts70mAAAAyLZmraW9TpD+9IT0zGzpwdHS726Tum4urVouvfVv6dxtpfMGSW8/Ii1fku01BgAAAEo2CObjjz+ukSNH6p577tHxxx8ftAupUaOG6tSpo+XLlwfzDBw4UL/97W91yimnqG7dupkuGpXR5JHhJQNgAgAAIM6n2HbtH05HXiSN+Ux69X7p0xek8UPC6R+nSx16S722kfpuL22+S/i9skaNbK89AAAAEqZUPcDXr18fVHxPmzYtaIvSsmVLDRgwILisypLSA9yv34IFC9S8eXNVr17ISQBnby19/4101UvSLodX9CoCyEbcA8g5xD4q1MIfpXcekd58SJr1/ca3u62eB8/cfNcwId5nkFS3fjbWNKcR90AyEftA8hD3mWMQzAQmwDMa9OiQBtKa1dJjk6R23bO9RgAAAKhKFs4Niyk8eObYz8Mq8eWL885Ts1ZYIe7BNLfYVeq/s9S4RbbWGAAAADmKBHgCE+DFHiGaOVE6tbdUp57036USR5GAKo8jw0AyEfuoNNatk6aOlsZ8Kn37iTT6E2n+rI3n67JZWBnucWi6byl13SLsP46MEfdAMhH7QPIQ9+XQAxy5ZenSpUGAFOiHCeFlh14kv4GkxD2AnEXso1Jw7+8eW4XTIedIrsGZMzVMiI/29Ik0fZw0bWw4xTVrEybCd/21dNDvs7UFVQpxDyQTsQ8kD3GfGRLgKCIB3jvbawIAAIBcHUizXbdw2vvE8Lqffwrbpbh1ypRR0uRR0uxJYTsVT9+8J61bIx16brbXHgAAAFUICXBsbOYvCfCOJMABAABQQZq0lHY8JJwiK5ZJ08ZIHz8nvXCbdP8F4VmK2+6XzTUFAABAFUICPIGqVaumpk2bBpcFogIcSF7cA8hJxD6qvHoNpL6DpD7bSUsWSG8/Il1/tHT7Z1LLDtLShX6nS607STX4aWPEPZBMxD6QPMR9OQ+CuWzZMt188816//339eOPPwZN1+MmT56sqigpg2AW6/hO0k8/SHd8Lm22Y7bXBgAAAJDWrJYu2zvsF56fk99tuoQFHD23lnpvK/XaRmrVMWy3AgAAgMQqVZnEb3/7W3300Uc68cQT1a5dO440VDE+YDFv3jy1atVq41FifZqpk99GCxQgGXEPIGcR+8gptWpLV78kXbTzhjMW69SX1q+T1qySZk0KpyFvbrhP09YbkuG+9NSivXIZcQ8kE7EPJA9xX84J8DfffFOvv/66dt5559LcHZXAihUrCr5h1vfhZaPmUuMWFbpOALIU9wByGrGPnOsT/sC3YeuTBk2l2nX860+aPyscMHPaWGniMGnCUGnqaGnRj9LXb4RTpHlbqVcsKb7ZTlLj5solxD2QTMQ+kDzEfTkmwJs1a6bmzXPrSyJ+MWdKeNm+R7bXBAAAACi4ErxZmw3/d8WTW5142nK3DdevWiFNHiVNHLohKe4BNRfMkb56LZzMZ7P23k7aZt/w/m6hkmMJcQAAgCQrVQL8uuuu01VXXaXHHntM9evXL/u1Qvb8OC28bN0l22sCAAAAlF6delK/7cMpsnK5NHlkmAz3NP5racZ34aWnp64P53M/cSfCPfXaWuoxUGrRLmubAgAAgApOgN92222aNGmS2rRpo65du6pWrVp5bh8+fPgmrBLKm3u2t2jRouDe7T9O3/ClH0Ay4h5AziL2gXzq1g8HeY8P9P7TTGn4u9Kwd8IkuPuIz50WTp+9nLd1SpQU7zkwvPR35koWX8Q9kEzEPpA8xH05J8APO+yw0twNlYQDo1GjRgXf6C/61qpzha4TgCzGPYCcRewDGWjZQdr3lHCypYukSSOk77+Rvh8eTq4Sd+uU/P3EGzWTdjpcOuFKqW1XVQbEPZBMxD6QPMR95qqlUqlUCebPaYsXL1aTJk30888/q3HjxsrlUWJnz56tdu3abTxK7HmDpPFDpGtekXY6NFurCKAi4x5AziL2gTLi1ilT3E/8l4S4Jw+yuXZNeHvNWtL+v5WOuFBq3zPsS54lxD2QTMQ+kDzEfTlXgKPqW7Pmly/r+S2cG142p8chkJi4B5DTiH2gjFqn9NshnCJrVkvjvpCeuiFsofLa/eHk3uMdektd+ofzb7aT1H3LMEleQYh7IJmIfSB5iPsyToA3b95cEyZMUMuWLdWsWbMi+8ssWLAg08WiMvHJAFECvFmbbK8NAAAAUHnVqi1tuVs4jfpIeuKv0phPpVUrwoE2PQ1+KpzXSfF2PaS23aR23aVO/aQeA6RuW4TJdQAAAGQ/AX777ben+8rccccd5bdGyJ7lS6Q1q8K/m7TK9toAAAAAVYOT4Le+L61bK82ZIk3/Tpr0jTT2C+m7L8O+4m6Z4inOpyt33kzafBdp813DqXWnbG0FAABATqIHeAJ7gPslX7lyperWrZu3kn/mROnU3lK9htJ/l2RzFQFUVNwDyGnEPlAJrF8vzZ4kzZ78yzQpTIR7kM1FP248f5suG5LhW+wqderrUa4yfjjiHkgmYh9IHuK+AhPgfqJXr16d57qqmjxOSgK8UGM+ky7aRWrfQ3r0+2yvDQAAAJC7/DNswWxp3FfS6E/CyUnx9evyztekpdR/lw0J8Z4DpRoM5QQAAJCpUn1zWrZsmS677DI999xzmj9//ka3r1uX70sbKt0osTNmzFCnTp3yjhIb9f9u2jpr6wagguMeQE4j9oFKzJVaLdpLuxweTlFLwnFfbkiI+++ff5I+fyWcrG6DcGDN7Q+UdjosrBiPIe6BZCL2geQh7ss5AX7ppZdq8ODBuv/++3XiiSfq3nvv1cyZM/XAAw/o5ptvLs0iUcEKLPyPTsGk/zeQk+h4BSQTsQ9UIfUbSdvsE062ZrU0cViYDP/2k3CQTfcTH/5uON1/odRza2m3o6W9TwwT6sQ9kFjEPpA8xH05JsBfffVVPf7449p999116qmnatddd1XPnj3VpUsXPfnkkzrhhBNKs1hk25KF4WXjFtleEwAAAAC1akub7RhOR18a9hOfNkYa/l5YEe6E+PfDw+mRP0tb7yvt9RtVb9tfUudsrz0AAEDVTYAvWLBA3bt3D/52r2z/33bZZRedddZZZbuGqDjLfw4v6zfJ9poAAAAAyM+nN3fbIpyOvEhaNC9MhL/3uDT6U2noW6o+9C11cnuVnttI2+4rbb2P1G9HqXadbK89AABAVpSqQYyT31OmTAn+7tu3b9ALPKoMb9q0admuIcqcR4Zt3779xiPELvslAd6ABDiQmLgHkNOIfSDHNW0l/eoM6R+fSI9MlI67Qqmum6taKqVqE4dKT98oXbKHdGRz6YpfSf+9R1r4S9tDADmFz3wgeYj7zFVLlaJZzO23364aNWro/PPP13vvvaeDDz446DmzZs0a/eMf/9AFF1ygqmjx4sVq0qSJfv7556CyPdcb5W/UIP/G46QPn5HOvF064sJsrRqAiox7ADmP2AeSZ/28H1R9xAcbeoVHg91b9RrSdvtLe50obXeA1CC3f/cAScJnPpA8xH05JsDzmzZtmoYNGxb0Ad9yyy1VVSUlAe7gmD59ujp37pw3SFwVMuRN6Y8PS/udms1VBFBRcQ8gpxH7QPJsFPf+uTflW2nYO9LHz0njh2yYuUbNsD2KE+Lb7Cf1HBi2WQFQ5fCZDyQPcV/OPcDz8+CXnlDFLVsUXtICBQAAAMgNPi26+5bhdNTF0vTvpPefCJPhMydKoz8Jp0eukJq0krbZV9p2v/CyWZtsrz0AAEDFJsBXrFih999/XwcddFDw/8svv1yrVq1K3+62KNddd53q1q276WuGipfuAU4fdwAAACAnde4rnXp9OM2eIg17WxryljTifennedIHT4aTuSLcleF9t5fadpPadqVYBgAA5HYC/LHHHtPrr7+eToDfc8896t+/v+rVqxf8/7vvvguar1900UXls7YoXwyCCQAAACRHu27SQWeG05rV0rgvpKFvh9P3w6XvvwmnuEbNpE79pK6bS136Sz22knoMpJc4AADIjR7gu+66qy699NJg0Etr1KiRRo4cqe7duwf/f+KJJ3Tvvffqiy++UFWUlB7ghTbJP6ShtHKZ9Oj3Uvse2Vo1AOWEwTGAZCL2geQpk7j3wJnDPIjmO9KM76Q5U8MK8cLarHToLfXeVuq/s7T5rlKXzegnDlQwPvOB5CHuyyEB3q5duyC53bVr1+D/rVq10pAhQ9L/nzBhgrbbbrsggVwVJSUB7pd8zZo1qlWrlqr5y6qtWysdUCv8+/l5UpOWWV1HABUQ9wByHrEPJE+5xv2KZdLsSdK0MdLU0eHkCvF5Mzaet1Fzqc+gsEK8+y9Tx97hwJsAyhyf+UDyEPeZK9G3j0WLFuXp+T1v3ryNjjrEb0flDZBZs2YFo8SmAyRqf2K0QAGSEfcAch6xDyRPucZ9vQYbBtSMW/ijNHGY9N1X0phPpbFfSEsWSEPfCqdIrTob2qY4Id5rG6nHAKlew7JdTyCB+MwHkoe4L6cEeMeOHTV69Gj16dOnwNtHjRoVzIMqKEqA16kv1fylEhwAAAAAitOstTTogHCytWt+6R8+XJo8Upo8SpoySlqx9Jfe4sM33Nc/2Dv2kXpuLfXytE04+CZFOQAAIBsJ8F/96le66qqrdOCBB6pu3bp5bluxYoWuvfba4DZUQQyACQAAAKAsuKCm76BwiqxfL82Z8ktCfKQ0aURYNf7TzLDHuKfBT4XzupepE+ID95IG7i1ttpNUt37WNgcAACQoAf7nP/9Zzz33XFABfu6556p3797B9ePHj9c999yjtWvXBvOg8tvo1Iili8JLEuBAzuKUKCCZiH0geSpl3Dup3b5HOO1yRN7BNl0t7mS4K8N9OXeaNGFoOD17i1S9htRti7CneJ/tpA69pFadpZYdpFq1s7lVQKVSKWMfQLki7sthEEybMmWKzjrrLL377rtBr5lgIdWqaZ999tF9992n7t27q6pKyiCYBfrsFenaw6V+O0h3fpHttQEAAACQVK4KH/GB9M374fTTDwXP5x/9rbtIm+0YVon33zlMlDPQJgAA2JQEeGTBggX6/vvvg7979uyp5s2bq6pLSgLcL/nKlSuDNjbpI0UL5kjjh4SnFvpUQwC5H/cAch6xDyRPzsW9f646IT7+63CaMEz6cZr043RpzaqN569TLxxg0y1U3Evcg266x3jjqv97FUhU7AMoFnFfAQnwXJSUBPj69es1ffr0YJTY6j4VEUDOI+6BZCL2geRJTNy7p/jP86Spo6Uxn4XTuC+k5UsKnr9JK6lT3w1T575hcrx157CSHKjiEhP7ANKI+8xxbhgAAAAAoGrxD/1mbcIpOoPVSfGZE8Oe4u4n7oE2PbjmvBlhstzT6E/yLqdJy7BavNc2Uud+UrseUrvu4XJJjAMAkBNIgAMAAAAAciMp3qlPOO1x7IbrVyyVfpgQJsPj0/Rx0s8/ScPeCaf8rVSatpYatwyrx1u0CyvGu24hddtcat6OBDkAAFUECfCEqlWrVrZXAUAFI+6BZCL2geQh7vOp11Dq5SrvrfNev3qlNOVbaeLwsGJ81vfS7ElhxfiqFdLcaeFUkEbNpa6bh4Nu+tJV5O49XrtOhWwSUBBiH0ge4j4z9ABPYA9wAAAAAEAh1qyWfvpBWvRL25TFP4WJcPcb9zRzQthuJb+ataQeA6Q+g6QeA8O/u/aXatfNxlYAAIBfkABPYALcL/nSpUvVsGFDRokFEoK4B5KJ2AeSh7ivAK4cd/sUJ8NdQT5llDRxWNhOJb/qNcJBN10d7oR4cLlV2GMcKEPEPpA8xH3maIGS0ACZP3++GjRoQIAACUHcA8lE7APJQ9xXAFd09xwYThHXlc2ZKo3/OpwmjwwH4Vw8X5o2JpwGP7Vh/uZtNyTFPQDngD2lxi2ysjnIDcQ+kDzEfeZIgAMAAAAAsCmceGjXLZx2P2ZDUnz+rDARHiXEfTlzorRgTjgNfXvD/XtvJ223v9R/F6llB6lFe6lBEwbbBABgE5EABwAAAACgrDlx7US2p+0P3HD9imXS1G+lSSOlySOkMZ+FrVSi6vH81eZOhDdvt+Ey/rcv23aT6tav8M0DAKCqIAGeUPXq1cv2KgCoYMQ9kEzEPpA8xH0lV6+B1G+HcIr8NFMa9o409C1p8ihpwWxp2c9hv/HZk8OpMO4z7sE2XUHee1upfU+pYTOpUXOpcXOpfmOqyBOC2AeSh7jPDINgJnAQTAAAAABAJbdyeZgIjya3U4ku50fXzZSWLip6OU1bS30GhVPfQdJmO0n1G1XUVgAAkHUkwBOYAPdL7m30ttIkH0gG4h5IJmIfSB7iPmH8c97V4xOGShOGSBOHhQnyJQvCadWKje9Tq4609d7SzkdIOx4iNWmZjTVHGSP2geQh7jNHC5SEBsiiRYuCJD8BAiQDcQ8kE7EPJA9xnzB+jVt1DKedD9v49qjf+He/9Bcf+7k0Z4r01evhVL26tNUe0h7HS7scITVsmo2tQBkg9oHkIe4zRwIcAAAAAIAk9Bt3xfi0sdKnL0mfvyx9/430zfvhdPdZ0qADw2S4B+2sQ19ZAEBuIAEOAAAAAEASuELQA2Z6+s2V4eCaHz4rffCkNG2M9NnL4eQe4W6R4mT4wD2lGqQOAABVFz3AE9gDfP369VqwYIGaN2+u6j7lDUDOI+6BZCL2geQh7lEqTgtM+VYa/JQ0+Gnpx+l5B9Hc7Rhpz+OlvtuHSXRUOsQ+kDzEfeZIgCcwAQ4AAAAAQIHWrw97hX/wlPTxc9Li+Rtua9ddGvQrqVNfqWMfqWNvqWXHsJc4AACVFAnwBCbAOUIEJA9xDyQTsQ8kD3GPMrV2jTT83TAZ/vkr0splG8/TqJm01Z7S1vtIW+8tte+RjTVNPGIfSB7iPnM08kqopUuXBgECIDmIeyCZiH0geYh7lJmatcKKb08rlklfvSZNGCrNnCDNGC/NniQtWSh9+mI4WfueG1qmdNks21uQKMQ+kDzEfWZIgAMAAAAAgKLVayDtfkw4xSvEJw6Thr8XVoqP+0Ka9b309A3h1H2rMBG+14lSi3bZXHsAQIKRAAcAAAAAAKWrEO+3Qzid8BdpxVLpq9fDwTSHvClNHhlOD/9Z2m5/ab/TpO0PkmrVzvaaAwAShB7gCewB7pfc2+htrcYI3kAiEPdAMhH7QPIQ96g0Fi+QPnlBeu9xacxnG65v3ELacjdps52l/jtLPQeSEC8DxD6QPMR95kiAJzABDgAAAABAhXG/8Hceld59TFowO+9ttetK7XpIbbtKbbtJbbpKnfuF7VNatJdI6gAANhEJ8AQmwD1K7Lx589SqVStGiQUSgrgHkonYB5KHuEeltm6tNPaLsCLc09jPpSULCp/f1eJOhHfqK7XqFE6tO4eXLTuELVgQIPaB5CHuM0cP8IRasWJFtlcBQAUj7oFkIvaB5CHuUWnVqCltsWs42fr10uzJ4TR3qjRnijRnsjR1dFg1vni+NOKDcMrPleHN2m5IjAdTx1+S4x2lpq2kRs2lBk2lGjWUBMQ+kDzEfWZIgAMAAAAAgIrnisUOPcMpv9UrpaljwkE0nSCfN0OaN/2Xyx+kNavCdiqexn9d9OM0bBpWkzshnn+Krvelk+duwVK/UbltMgCg4pEABwAAAAAAlYt7g/feJpzycyfXRfPCZPiPvyTFf/ohnOb9crn4J2n5knD+pYvCSZMye2wnxJ0Ib9paatBYqtdIqt9YatYmHLiz93ZS7Tplu70AgHJDAjyBPDJsixYtGCEWSBDiHkgmYh9IHuIeiRC0P2kdTgUlyCNr10hLFoZ9xj25pUr0d3zy9T/PC5Pp8esLU6uO1Hf7MBnesFm4PtWqh5euHndrluZtw8smraRatct9ME9iH0ge4j5zDIKZwEEwAQAAAABAAVw1Pnda2JP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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plots für komplexen Usecases\n",
    "\n",
    "from pandas import Series\n",
    "\n",
    "\n",
    "SIMULATION = True\n",
    "AUDIO = False\n",
    "PLOT = False\n",
    "SERIES = False\n",
    "\n",
    "# Chirp Generator\n",
    "n=2000 #Sampleanzahl\n",
    "fs=20000 #Samplingrate\n",
    "f0=100 #Startfrequenz\n",
    "f1=1000 #Stopfrequenz\n",
    "t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
    "f_disturber=2000 #Störfrequenz\n",
    "\n",
    "# Parameter setzen\n",
    "coefficients = 16\n",
    "step_size = 0.01\n",
    "delay_r11 = 0.000\n",
    "delay_vpu = 0.002\n",
    "indices = [0, coefficients // 2, coefficients - 1]\n",
    "\n",
    "# .wav File laden, Tonspuren den Signalen zuordnen, Corrputed Target Signal erstellen, Reduced Noise Signal erstellen\n",
    "fs, data_1 = load_wav(f'./audio_data/Nutzsignal/male.wav')\n",
    "fs, data_2 = load_wav(f'./audio_data/Störsignal/breathing.wav')\n",
    "\n",
    "# Sensitivätskurve Mikrofon laden (normiert auf 1000 Hz)\n",
    "frequency_r11, gain_r11 = load_transfer_function('./transfer_functions/R11_normalized.csv')\n",
    "frequency_vpu, gain_vpu = load_transfer_function('./transfer_functions/VPU17BA01_normlized.csv')\n",
    "\n",
    "# Signale laden und zuordnen\n",
    "desired_signal = data_1\n",
    "noise_signal = data_2\n",
    "desired_signal_r11 = apply_transfer_function_freq(desired_signal, fs, frequency_r11, gain_r11, delay_r11)\n",
    "noise_signal_r11 = apply_transfer_function_freq(noise_signal, fs, frequency_r11, gain_r11, delay_r11)\n",
    "noise_signal_vpu = apply_transfer_function_freq(noise_signal, fs, frequency_vpu, gain_vpu, delay_vpu)\n",
    "corrupted_signal = desired_signal_r11 + noise_signal_r11\n",
    "\n",
    "# Zeitachse anlegen, ANR Algorithmus ausführen\n",
    "t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/10000\n",
    "\n",
    "if SERIES == True:\n",
    "    for i in range(16, coefficients+2, 2):\n",
    "        output, coefficient_matrix = anr_function(corrupted_signal, noise_signal_vpu, i, step_size, adaption_step=1)\n",
    "\n",
    "        # Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "        coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "        error_signal = (output - desired_signal_r11)[:-coefficients]\n",
    "        t2 = np.linspace(0, len(error_signal), len(error_signal))\n",
    "\n",
    "        # SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "        snr_before = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(noise_signal_vpu**2, t))\n",
    "        snr_after = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(error_signal**2, t2))\n",
    "        delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "        with open('snr_evaluation/male+drinking', 'a', newline='') as f:\n",
    "            writer = csv.writer(f)\n",
    "            writer.writerow([i, delta_snr])\n",
    "else:\n",
    "        output, coefficient_matrix = anr_function(corrupted_signal, noise_signal_vpu, coefficients, step_size, adaption_step=1)\n",
    "\n",
    "# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "error_signal = (output - desired_signal_r11)[:-coefficients]\n",
    "t2 = np.linspace(0, len(error_signal), len(error_signal))/10000\n",
    "\n",
    "# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "snr_before = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(noise_signal_vpu**2, t))\n",
    "snr_after = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(error_signal**2, t2))\n",
    "delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "if AUDIO == True:\n",
    "    # Audiodateien zum Vergleich abspeichern\n",
    "    sf.write('corrupted_signal.wav', corrupted_signal, fs)\n",
    "    sf.write('filter_output.wav', output, fs)\n",
    "\n",
    "if SIMULATION == True:\n",
    "    # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
    "    desired_signal_simulation = desired_signal_r11*(2**(15)-1)\n",
    "    noise_signal_simulation = noise_signal_vpu*(2**(15)-1)\n",
    "    corrupted_signal_simulation = corrupted_signal*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/complex_desired_signal.txt', desired_signal_simulation, fmt='%d')\n",
    "    np.savetxt('simulation_data/complex_corrupted_signal.txt', corrupted_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/complex_noise_signal.txt', noise_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "\n",
    "# Plots des Filterprozesses\n",
    "figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
    "ax0.set_ylim(-1, 1)\n",
    "ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
    "ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
    "ax2.plot(t, noise_signal_vpu, c='chocolate', label='Reference noise signal')\n",
    "ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
    "\n",
    "ax0.text(0.5, -0.3, '(a) Desired signal',\n",
    "         transform=ax0.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(c) Reference noise signal',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.5, f'(d) Filter output (SNR Gain = {delta_snr} dB)',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.set_xlabel('time(s)', x=0.05)\n",
    "ax0.set_ylabel('Amplitude')\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax2.set_ylabel('Amplitude')\n",
    "ax3.set_ylabel('Amplitude')\n",
    "\n",
    "# Plots der Filterperfomanz\n",
    "figure2, (ax4, ax5) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax4.set_ylim(-1, 1)\n",
    "ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')\n",
    "for i in indices:\n",
    "    ax5.plot(t2, coefficient_matrix[:,i], label=f'Coefficient {i+1}')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(a) Error signal',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(b) Coefficient values (1st, 8th, 16th)',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel('time(s)', x=0.05)\n",
    "ax4.set_ylabel('Amplitude')\n",
    "ax5.set_ylabel('Coeffcient value')\n",
    "\n",
    "# Plot Sensitivitätskurve\n",
    "figure3, (ax6, ax7) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax6.set_ylim(min(gain_r11), max(gain_r11))\n",
    "ax7.set_ylim(min(gain_vpu), max(gain_vpu))\n",
    "ax6.plot(frequency_r11, gain_r11, c='indianred', label='Sensitivity Curve (Primary sensor)' )\n",
    "ax7.plot(frequency_vpu, gain_vpu, c='orangered', label='Sensitivity Curve (Secondary sensor)')\n",
    "\n",
    "ax6.text(0.5, -0.3, '(a) Sensitivity Curve (Primary sensor)',\n",
    "         transform=ax6.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "ax7.text(0.5, -0.5, '(b) Sensitivity Curve (Secondary sensor)',\n",
    "         transform=ax7.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax7.set_xlabel('Frequency (Hz)', x=0.1)\n",
    "ax6.set_ylabel('Gain (dB)')\n",
    "ax7.set_ylabel('Gain (dB)')\n",
    "\n",
    "# Plot für Störsignalvergleich\n",
    "figure4, (ax8, ax9, ax10) = plt.subplots(3, 1, figsize=(15, 10), sharex=True, sharey=True)\n",
    "ax8.set_ylim(1.0, -1.0)\n",
    "ax8.plot(t, noise_signal, c='orange', label='Noise signal')\n",
    "ax9.plot(t, noise_signal_r11, c='darkorange', label='Corruption noise signal (Primary sensor)')\n",
    "ax10.plot(t, noise_signal_vpu, c='peru', label='Reference noise signal (Secondary sensor)')\n",
    "\n",
    "ax8.text(0.5, -0.3, '(a) Noise signal',\n",
    "         transform=ax8.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax9.text(0.5, -0.3, '(b) Corruption noise signal (Primary sensor)',\n",
    "         transform=ax9.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax10.text(0.5, -0.5, '(c) Reference noise signal (Secondary sensor)',\n",
    "         transform=ax10.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax10.set_xlabel('time(s)', x=0.05)\n",
    "ax8.set_ylabel('Amplitude')\n",
    "ax9.set_ylabel('Amplitude')\n",
    "ax10.set_ylabel('Amplitude')\n",
    "\n",
    "#Grids direkt auf Subplots anwenden\n",
    "ax0.grid(True, linestyle='--', alpha=0.4)\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "ax6.grid(True, linestyle='--', alpha=0.4)\n",
    "ax7.grid(True, linestyle='--', alpha=0.4)\n",
    "ax8.grid(True, linestyle='--', alpha=0.4)\n",
    "ax9.grid(True, linestyle='--', alpha=0.4)\n",
    "ax10.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax0.spines['top'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax6.spines['top'].set_visible(False)\n",
    "ax7.spines['top'].set_visible(False)\n",
    "ax8.spines['top'].set_visible(False)\n",
    "ax9.spines['top'].set_visible(False)\n",
    "ax10.spines['top'].set_visible(False)\n",
    "ax0.spines['right'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "ax6.spines['right'].set_visible(False)\n",
    "ax7.spines['right'].set_visible(False)\n",
    "ax8.spines['right'].set_visible(False)\n",
    "ax9.spines['right'].set_visible(False)\n",
    "ax10.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "figure3.tight_layout()\n",
    "figure4.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_plot_1_wav_complex', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_plot_2_wav_complex', dpi=600)\n",
    "    figure3.savefig(f'plots/fig_plot_3_wav_complex', dpi=600)\n",
    "    figure4.savefig(f'plots/fig_plot_4_wav_complex', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für SNR Vergleich\n",
    "\n",
    "PLOT = True\n",
    "\n",
    "# Daten aus .csv laden\n",
    "data_male_breathing = np.loadtxt('snr_evaluation/male+breathing', delimiter=\",\")\n",
    "data_male_chewing = np.loadtxt('snr_evaluation/male+chewing', delimiter=\",\")\n",
    "data_male_scratching = np.loadtxt('snr_evaluation/male+scratching', delimiter=\",\")\n",
    "data_male_drinking = np.loadtxt('snr_evaluation/male+drinking', delimiter=\",\")\n",
    "data_male_coughing = np.loadtxt('snr_evaluation/male+coughing', delimiter=\",\")\n",
    "\n",
    "\n",
    "# Daten laden\n",
    "x = data_male_breathing[:, 0]\n",
    "male_breathing = savgol_filter(data_male_breathing[:, 1], 10, 3)\n",
    "male_chewing = savgol_filter(data_male_chewing[:, 1], 10, 3)\n",
    "male_scratching = savgol_filter(data_male_scratching[:, 1], 10, 3)\n",
    "male_drinking = savgol_filter(data_male_drinking[:, 1], 10, 3)\n",
    "male_coughing = savgol_filter(data_male_coughing[:, 1], 10, 3)\n",
    "\n",
    "# Alle Kurven in ein Array stapeln\n",
    "all_curves = np.vstack([\n",
    "    male_breathing,\n",
    "    male_chewing,\n",
    "    male_scratching,\n",
    "    male_drinking,\n",
    "    male_coughing\n",
    "])\n",
    "\n",
    "# Punktweiser Mittelwert\n",
    "mean_curve = np.mean(all_curves, axis=0)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.plot(x, male_breathing, linestyle='-',  linewidth=1.5, alpha=0.7, label='Breathing Noise')\n",
    "plt.plot(x, male_chewing, linestyle='--', linewidth=1.5, alpha=0.7, label='Chewing Noise')\n",
    "plt.plot(x, male_scratching, linestyle='-.', linewidth=1.5, alpha=0.7, label='Scratching Noise')\n",
    "plt.plot(x, male_drinking, linestyle=':',  linewidth=1.5, alpha=0.7, label='Drinking Noise')\n",
    "plt.plot(x, male_coughing, linestyle=(0, (3, 1, 1, 1)), linewidth=1.5, alpha=0.7, label='Coughing Noise')\n",
    "plt.plot(x, mean_curve, linestyle='--', color='red', linewidth=2.5, label='Mean SNR-Gain')\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 25     # Legende\n",
    "})\n",
    "\n",
    "plt.xlabel(\"Filter length\")\n",
    "plt.ylabel(\"SNR-Gain (dB)\")\n",
    "plt.grid(True, linestyle='--', alpha=0.4)\n",
    "#Spines auf ganzen Plot anwenden\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.legend(frameon=False, loc='lower right')\n",
    "plt.tight_layout()\n",
    "if PLOT == True:\n",
    "    plt.savefig(f'plots/fig_snr_comparison', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots der Störsignale\n",
    "\n",
    "PLOT = True\n",
    "\n",
    "fs, data_1 = load_wav(f'./audio_data/Störsignal/breathing.wav')\n",
    "fs, data_2 = load_wav(f'./audio_data/Störsignal/coughing.wav')\n",
    "fs, data_3 = load_wav(f'./audio_data/Störsignal/scratching.wav')\n",
    "fs, data_4 = load_wav(f'./audio_data/Störsignal/drinking.wav')\n",
    "fs, data_5 = load_wav(f'./audio_data/Störsignal/chewing.wav')\n",
    "\n",
    "t = np.linspace(0, len(data_1), len(data_1))/10000\n",
    "\n",
    "figure1, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, figsize=(15, 15), sharex=True, sharey=True)\n",
    "ax1.set_ylim(1.0, -1.0)\n",
    "ax1.plot(t, data_1, c='darkorange', label='Breathing Noise')\n",
    "ax2.plot(t, data_2, c='indianred', label='Coughing Noise')\n",
    "ax3.plot(t, data_3, c='deepskyblue', label='Scratching Noise')\n",
    "ax4.plot(t, data_4, c='forestgreen', label='Drinking Noise')\n",
    "ax5.plot(t, data_5, c='darkorchid', label='Chewing Noise')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(a) Breathing Noise',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(b) Coughing Noise',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.3, '(c) Scratching Noise',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(d) Drinking Noise',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(e) Chewing Noise',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel(\"time (s)\", x=0.05)\n",
    "ax1.set_ylabel(\"Amplitude\")\n",
    "ax2.set_ylabel(\"Amplitude\")\n",
    "ax3.set_ylabel(\"Amplitude\")\n",
    "ax4.set_ylabel(\"Amplitude\")\n",
    "ax5.set_ylabel(\"Amplitude\")\n",
    "#ax5.xaxis.set_label_coords(0.5, -0.4)\n",
    "\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "if PLOT == True:\n",
    "    plt.savefig(f'plots/fig_noise_signals', dpi=600)\n",
    "figure1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "PLOT = True\n",
    "\n",
    "# Filterlänge\n",
    "N = np.arange(1, 128)\n",
    "# Verschiedene Updateschritte\n",
    "U_values = [1, 0.5, 0.25]\n",
    "\n",
    "C_total_1 = N + (6*N + 8)*U_values[0] + 34\n",
    "C_total_2 = N + (6*N + 8)*U_values[1] + 34\n",
    "C_total_3 = N + (6*N + 8)*U_values[2] + 34\n",
    "\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.plot(N, C_total_1, linestyle='-',  linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[0]}')\n",
    "plt.plot(N, C_total_2, linestyle='--', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[1]}')\n",
    "plt.plot(N, C_total_3, linestyle='-.', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[2]}')\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 25     # Legende\n",
    "})\n",
    "\n",
    "plt.xlabel(\"Filter length\")\n",
    "plt.ylabel(\"Cycles/Sample\")\n",
    "plt.grid(True, linestyle='--', alpha=0.4)\n",
    "#Spines auf ganzen Plot anwenden\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.legend(frameon=False, loc='lower right')\n",
    "plt.tight_layout()\n",
    "if PLOT == True:\n",
    "    plt.savefig(f'plots/fig_c_total', dpi=600)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}