{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_7020\\3320183906.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_7020\\3320183906.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": {
      "image/png": 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HqIz6NvOgUt3ZU2iDdvpZnriTK/neIRX3INP5LL/fbdl+ziAXgfTaMHrEJg+IGTU/bTU/NGCfrc0f1V14mrbalzYBntzOLsbyD6nu4sxHruX/lPzWBcUgg2i63IfpO9cjcdDmdBSHiXfLBq2LHvWxPAibALe90rMptGMiAKC0UQIFZc8mnjMNPBYlnTRmqskOAEFPQP2cCNuSIVGegBZjgicsiRdMbYKnEBIUQZfH9tDLNYGcb3wFKVGRad7FuPx+t+Vc5xNG2aCoB8SMqoxFYom8bOO65Kp79+5eElTfTSl0clBPf93xqQseYT7yke3CRRj7gFyXK3HA5lQPe9FWcZj4fFAujuV+5bJ9lnM7AQDgFglwIOE2XtdszfWoTr4AlB97W342dqCqqVOnZn1tLifWrm83j1OqcS0S2cSgnzrULurU2uXxkzS1y5xrcjPoINOZfg/K9khWkjbXWIxz+f1uyzJ+/PhA8wvaY9zvgJhR34kR5vahZdU6UIznMpBnpvWtzg2arhKh6ZK0qd4f1UWEMGhd6Y7NMB+5JHztsvgpMWL3AfoOSjXRGvWx3C87npMUalkaAEB5IwEO/P+TIDXCXNdi1HzVK4jGH4CwaKwHP0kim5hRoiYbv7czJ9IyFPLAlFH2pNbviQOSqYefXuvnQquLC6J2ebJ997Y8hI5TqZKldgCzTEkVP7Hjdz0myxbnw4YNqyvTkUrcyx/Wtmw/g3oc+2EHGc+UbFXPf7/JssQBMaO4gJPYRkqevn73sw9LZ/Lkyd70td0FmU629qK9uJTpIoN9TaHcGVJMdOzyU4bQ7nPj6OTiStTHcr/ssUv7A5UY8qMc2gkAgMJBAhxIuBXWZU9s9QzSCVS6E3MAyIVNuPhJRNmkQKZ9X67loTT/fG9vj2qgLDu9TIknP6/RYHapenZr3558Uq9kjdZJpp6mep+dZtT03Wt5Mn2/tsZuuuNUtljTZ/XTEzDIekz+DOnoc2l6OranS0LGvfxhbsuiedkxTdLR3zRgpRLWmS6+Bx04VHfSBelR7mf7Sh5cL1UvV63zfHr26r3V1dXesmvd+WkH2jv3MsWfvfCR7ruzg8RLpu8ryHoqN/oObO/nVGxd+kyxrv2Etl8Xd95E2Rs7ymO5H5q2nb6OdX63yTDbCQAAZLUaQJ3WrVuvnjBhgpN5VVZWrh4xYoSTeQEoLzq8+92XVVdXr66oqFg9atSoBn/Tc3r07t3bm+aAAQO819fW1macZk1Njfd6/R+GHj16eNPTPjrbvP2wnyfVZw7yGrtsicul/7WeMq3rwYMHN/ibvq90xwQdL/wsS1BaHi17qvnqM2i+2da3Pr8+U/Lr9Hm0vJq2ll2vyxQ7Qdajntc0q6qqvHWZPE3NV3/3c4yNY/nD3pa1nJq//dl+p4nbn5ZH09HfUsVfIr1O89V6CbINa/3Y7yUVLYOWS/PXcui1dr+i5/X+bNPWI/E70HYaFvtdp1p/ot/1vLYLu6xa76m+Hy2j3W6T14emY78DrWO9TvS65Gn52Q/Z/WOmePfzmmJkYz85dvQ59T1m22cmxqAf9tgWxrq03639/vMV5rHcz7LpvYpZ+1rNO92276KdAABANo30T/Y0OVAe1JNItxDPmTMn0lqB6lWmW1/zGRwLANJRjyr1Bg1y27d6jqk3Vtu2bb3ei+p5qBIJ6jmnXnbqSae/2dubMw3qpn2cyk/kWt81cX+cqqec3T+rfEGQXqdaL/qMidOzPZzt50n3GvVwTVffWOtJt5bb3r62JnKmda31qXWrdSpax8m9jfU9qOdn8rJIPus21fJofdtekvr8Wh47uKHfY5qWzU5DvSr1eWws2Njp169f3eCDua5HvU69DW3PXc3D9pS1PYKz9XCOc/nD3pbV01XLbbcFxYvWjz5TYskDfR5tu+m2Ga27VL0x9Rn8xpvWQapYtsulWuXp6HNm2p5tjXG7jrVcdh8VFruM9o4NbX/2u9d89P0n7/v0nnTfs74HfXeJvdi1XdttS/PQZ9B89L99Pqx9VS77s2Jje+Qn3oGh+NNz2WJD69x+3+nu9tC+wG5HqXpt2+9e+6Ns89My2Z7Sqfbrffv2zbtcS67HcnvMzfQZU8W93S7S7RejbCcAABAECXAgiRqmaghHlZxWA1MNUDWUS3VAHgDx78eU3KipqYll/koYKJFVKgkWFJbkBHgpi3tbBoAo0E4AALhGDXAgiXrFqFGWaXCoXNnam7anGQBEQb3I1AMsrrqm6mEWxT4UKDdxbssAEAXaCQCAOJAAB1LQbXy6bdDPiOpBKPmt3uVh3q4LAMl0gU23NcfRs0o9VtWri/0cUNzbMgBEgXYCACAOlEABAKAEqceo6r9GPaZBMl081IW+Ui9NgfiUUwmUOLdlAIgC7QQAQBzoAQ4AQAlSzyqVdNIgU657dXFSCxT3tgwAUaCdAACICz3AAQAo8Z5WGncg6luNVdNTvVQZ4BdRU5xpQOlRo0Z5ieFy4WpbBoAo0E4AAMSJHuAAAJQwJcz69OkT+Xw0D93SzEktokwAt2nTxkt+21Io+n3IkCGmHLjalgEgCrQTAABxogc4AAAlbtKkSd5Jp3rMRkEJyG7dunmD9QEo3m0ZAKJAOwEAEDcS4AAAlAHbazbsupsaoE+PHj16hDpdAG63ZQCIAu0EAEAhIAEOAAAAAAAAAChJ1AAHAAAAAAAAAJQkEuAAAAAAAAAAgJJEAhwAAAAAAAAAUJJIgAMAAAAAAAAAShIJcAAAAAAAAABASSIBDgAAAAAAAAAoSSTAAQAAAAAAAAAliQQ4AAAAAAAAAKAkkQAHAAAAAAAAAJhS9P8A6T00DwEDPtEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6PmqdNflQ4UMlYlEPeGlxyOl3nS5Lxi2RFU+skPHp40u8JvV1rWdqnfuPc+W0O04zrQV1/3HHhjtct9HXv45FrZmzRv52+G9lxq3J8e9f+95crPdQ1A7sq7wLKwrC83QHDRokycnJ5o+tyV1Pk11q8leDojo0Aa6zqwYK/XLRr18/6dWrl8ycObPC5ZWVnZ1tXlz6fIbCxKKFhYWydetWadu2bUgdVdNT/t+95V2TDNOEbLcbu8kZ951hks3rXlwnV79/tTRqfuxDrFaRurvxkxs9JrAqQytP/tPxPzJg2gDpNbqXOdVaK1vqx9cv0XZhzPoxJY6YW4/bonsLqRdbr8RyraTduHijXPXaVaYyrFWvVnLBUxe4kvdW9ejp95xuvsBYLSI69OsgF0+/WCKiIuTpDk+71qdfeE4Zfor8/vHv8ulDn7qS8g/mP2g+tOiHje43dTcf9vULtv5+4pUnSvNuzc0HGHPbggdLxJT1HJ40+CQZtmCYx+dGHyc8MnTisLayvpCWjvtx+8dJg4QG5mdNbOtpw77SGF107aISy/SLtMa/fhm09BzT03wZ1vhTN395s7xw1gtV3KLQdvMXN8sHf/nAfAk5knnsgIW7pJFJcsJlJ5Tpb25xT5QprYDVilJNcvjyhb+2CdX3wGCn76taxV1TdF/lvv+6ev3V8sapxa8lfY0k9k90VbbO6D5D2p7VVi5//nLX+3d8x3ipF1fPTMb5p4f/JKded6orwZg8Kdl8uddkt56B5u6B3AdM65O4jnEyf8ixuQ8G/t/AMu2keo7uKZfMuMRU2unnADXiuxHSuGVjSZmTIp89/JlZpkkDK/moSYP3R70vvf7SyxwA1ZZOC69ZKBc8fUGZxD6Ca1+lLcV0Xg2lSSlto7Pq2eLJKPWg0XVLrjNnaOlZX970GNHDJJu8vZdoUlw/D+hBF02Uqy7DusiP836s9DjHp403CS9Ls67NzIGnPeuOtSu86vWrzD7hgzElD27d9PlN5jPs6xe9Xu5j6AHhi/57UYlCGa1C19dN79t6y8v9X5Z9PxZPxH331rslpk2M63YbPtxgEun6PnnSVSeZ5LzS/cWTrZ80v+vr2FP7Fn2/tj5zOYH3PzgZVzvm75Dk8Z7nzmjVu5XsXOn5LE59DekZSpqE1oNh6o7f75D/dvqv+VnfG60Etu5DXu73svk5PCrcvEb1ALH7vmbDRxvKvMbPmnSW+d6vn4v1bNL3RrxX4vrT7z1d+j3WTyLrBm19bUBhXxViCXBNbmtSV5MR8+fPl6uuuqrMbTp16mRamGgSW2/vSzW3rlvvGxcXF1AJ8KFDh5qxa/V3abq8Q4cOMmXKFBk1apRP6yUBHhpKJ/ZKMxWnm+92TbxT+vb6ZVa/1Fb3sbUadu0La8tUzihNTHuqRNMP0Vox616d+9KfXjI/63itlhIPHH1ADu45aL7sVtQ//JavbikxWd9pd59mqshK63xxZ9nwwQbX7w/mPWgSocsmLStzW32OdPusylpruxu3biz3bLvH7NP0dDT90q9fwN+/7X1z1F0rdXJ25JhKWOsorybd9XbuR331y4yuW6vk9QuUjq3HLRyxrwxTuZ15xFVBofFhxbqeAqgTMnrrda9fLi9/4fJyq68RfKwvEtofddnEZaYNi+X2X26X7/77nUTWjzSJPU2MuVe56mtUDzpqAs19MjM94PXxfR9L+3Pbm8SAP4Tqe2Cg07NI6sbULVEFpgklTTa1P6+964tybTF0wVDpOLCjaVHy1nVvud6jd3y3w3VgT7+4a4Lc0uSkJuaAoZ2TamoSXKvutNLbm7PuP0sO7DogUQ2jzJwIlq7XdjXjP5pTXFU+6cAkU62riUs9o0R1vqiz1IspeYD+q2lfmbNoLvrfRVRu1bJ9lZ4FqQdQtPCgqKDIFDVoCyBT0VwqcV2aJm59aZFiaX1aa/P5tmWPlhV+Fq9Njjv9OLPf0f2LnvVhvW71c6h+HnUX3zneFJbc9v1t8kjEI67lfe7sY86k1ANQ+pqxPme7HxTQxLe+T2pSb/Mnm2Xo/KFy8pCTHdkm3v9gF6uASdv06Xfobdu3yUsdir+b+koT2e5nJis9s8N9Th0tyNI2h9b7qScTsyaasyvcX4O+iG0fKx36dyixL9QDbvpe/dmjn0mnCzpJu7PbVWnd8A37qhBLgGuLj4kTJ8qAAQNMqxJPBg4cKJs2bZING44lpnyhFebDhw8PmAS4jlefD01+e2t1MmHCBJk6dapkZGT4dECABHjwck/I+PKhu/vN3U2SujStiNHJrrzNTq99RvVL36WzLy3xpc+OD/x6mtfuNbuldZ/WrlPHaqvbfrhNmnVpVmK7tTpNP9Brcl7du+NeUwlTWu+/9i7xBVwrVLXi6NTrTzXtGS585sLiJNrdH3usUoVnesaBngJ86ZxLpelJTeW1i16TQU8OMlX9mszQU4YBd1asuB8oq8jYPWPNv9pTMXXpsXlGxu4dKw2bNjStbrTPvD9fu6H0HhgsdDIt6+woPVir7br0zKhpTaZJINEkWfqGivv012bnPHiOfP7osQl7Ncl/3cfXlbiN9d6v1eal22ih5vdVesBbPw/rGTyVafeDqmt7dlszuWZl3PjpjebsTZ0jRT/bu/fo1zNOr118rez9fq9pV3bOA+eUaZuirWm01/JJV1b+YDLvf6jKd2g98+G9ke+Z9kDnPXqeOVPEOtPYqu7uM7mPvN3/bQl2wxYNk7gOceYziM438NL5L8mF/7nQfF+FfdhXhVgCvDLGjBljdkrTp0+v0v012asV4BpcgUCT36tWrTLJbW9SUlKkZ8+epgp8/PjxlV53KCbAtbWOnjkQbDuUNc+vMT18r37vajNRnJVIvez5y0z7EzvEJcZJ0qgk03OsdA9x60vfOQ+dYyqctfe2jicUac/F/b/st6V6qLRz/36u69RtPSXuhbNfkHZ/aicDpw2s9rqDkcbg2zcG/4dSBBYrAa6T8GorKv0CER5R/J6klXJ6mnpsO/vnKgnm98Bgoj1DtcJbzyQoPUEsapeu13Q1n4v0zA79WqaThlptrS6ZfolZpu1ZtG/z4NcHe+x/rElabd+mfZj1YJk7PeitZ6K0OaNNmbkbgll191XahuOZE55xZGyomdfV969/b37WOUz0M671PUNbvOgcK9bPMW1jTOGJvta0ZaK3My80ptYtWie/vPKLaY+k31WA0vRgjE6crHNB6fe2xXcemxNL5wcq74yiUFG6DdqtK24183rpXDvm82xkuJk/TH8/4fITyryvoXx8VvcuZBPga9YUn5rRo0ePaiXRZ8wo2VuwNtL2JpqsT0pKktWrV1f7dqGeAA8UWvGgR5yvePEKadisocwfNt/0ktYPdnt/2CsJnRPMaZV1Gx+bTKamT63UD5x6WqMe+dbTotyrolCzyXVVeiKVUKe9XHXCyieaP+HvoQAeW+vovAivXVB89oFW09zy9S2m/6J1+up9u+8rMTcDgou2ytHTp9+45A3p93g/M6GdewIVwU2TK1s+2yKdLuokH/31I9fy4844zswvon3JtT2aFht88a8vTLWslWTQViw4Rtvq6XwO2jLPtDiLCDNtbRAc9HuO+wS3FdHXScLxCeY++l3FnWt+nqtOMq2a+NwMnQz9rRvekj5/7WPml5h+StUKLEOZe0tS631MC+Hc3bD8BulwXslJuAFfhWwCPJRY7U+GDBlieqKXx3oT9yUsQi0BXluOqOnfSE/fa961uekR/clDn5gP61olrImQ90e/79P69NQj7VUJ6CnyF/y7eELQUKP9ZPV1pX3aS3/wAgLR8LeH2zr5Xm15DwwV+oVQk3MH9hwwB661RcCjkY/6e1gIcJrU04kU//zOn0u0pXM/bT/QVbSvMsmWMJEVT66QpWN9n6Aaoa1Ry0bme1fTLk3lLz/8xXW2hb6evLV6RGDRAyC71uyStme2LfE31UnNF9+1WG785EZTWDazx0y/jjPU3LXpLlNBrn+f+vHH5jXBMXxW944EeDVo4jcQEr7a11v7e+vkljNnlr+D7tixo5ngUyvAtRK8MkIxAe7vnkr6stV+zt8+/a2rCkEnaQLsEtsh1vRj13YpOqmQVhfq6dPWRJrBQF9HOulpw+YNTaV38sRk0+MTCAXaakoPoOrEun3H9i3Tkqo2vwcGy/5HE3DZ27JNtenhtMMmya103oZV01eZyeNWPLHC30NFCLoz9U5ZMHyBnHHvGXLKn08pM3FbICi9r9JWBDqJqk7MqGdPaE9ewAl3bb7LnJ2hZ9rqWVna8zlxQKLEtIkx+3k90yBYDjQF67wZnzz4ifz6zrHCsInZE83+T+disiY2hv/1Hd9Xvn/1e+l8SWcZ9H+DPLYIC0V8VveOBHgVabJXj6gUFFS/H6/TrMktta+39veuTAK8vMkySyMBXrHyKmrMqZZ/HFU+nH7YNZNzvbh68tt7v8mbl79p6/gBO7hPdKoTb2q/No1xbRsSERVhlusHRZ20T08JrB9n3xF6fT2l/ZpmTjO0HsvT60zHsuPbHdLmzDZmuVYKvHDOC+bL+86VO20bDxBsrvngGlPdNitplpl48KZPb5LIepHmfUlfZ1u2bDHvgRERx15/oUQTabp/0ySa0oOCul/Rll56FlZE3QhzUE3nvFCHMw7L1PipxbeNqSu5Wbl+HT9gl1u+ukXa9G1jXgu6j6hJ+hrUCanPmnSWOS0+a1uWPNW2eKJw9adH/ySfPvhpjY4JqKzjTj/OJO10joYrX7lSOl3YyXxm1US5xrZ+FwyWghN/0H3SzlU7zUEIKRLZ/NlmU9Bz+j2nS8bGDHNQQpPcOim07jv2rNvj7yHDRta8Wfrv6feebg4+6ZlP+veuTUUQGpvaluyrqV9Jyx4t5cQris/YXP/qelPd3nFQRzO/z5HMIxLVMMqcfVAvpp4kjUiSI1lHzG31d0/5Kv2s3q5dOxLgpZAAr6JNmzaZZHEgTII5evRomTVrVqUS4DoJpk6GqZXiWjFeGaGWAK/pPtkAAAAAAABAZUw6OEnqNKAq3l3NHqqvAcuXL3f8MXSyyLlz55oJIwOB9v+xU25urrm4J8CVHgxwPyCgR5tKHyDQKky9OLVcH9McUSt1XMeu5e6nQgEAAAAAAAC1yeqZq+W0u06zNUdWWEvze5WtdA+6BLhO9KiVyDUhNjZWAoEm7FVCQoIt65s8ebI8/HDZKuht27ZJ48aNzc+NGjWSJk2amOT7gQMHSjxnetm3b58cPnzYtVzHpvfdtWuX5OXluZY3b95c6tevb9btHvStWrWSyMhI04rEnZ6SnZ+fLzt3HmtvoC8kPf3jyJEjsmfPsdOboqKipHXr1mZ8aWlpruX6ePq4GkfWc2dtk04UCQAAAAAAANRGLS5vYfJoduXCmtTi/F779u1DswVKp06dTA9r/SNoj24nktQaCPoYWgHuHiy11YABAyQ5Odm2FiieKsDbtGkjGRkZJVqg1OYjRNVZri9AfYE3bdrU/F6dsev68w/nm8nHtNfb3h/2SvPuzWXfj/vk80c/N5OT6QSXX/zzixLrAPypcevGkrMjx/x83577TI8y7U1WkFtgZuW2Yt7qx31o3yHZ/8t+aXt22xLrKb2P2Pb1Nnnx7Belw/kd5Lql15W4rXnN5BXKz2//LIn9E01fRJ2w8uQhJ0vDpg3LvM50oqtWSa0kLDLM9Puu36S+pP+WLq9f9LqcMfYM06v3p3k/OfYcAYFIJ+YaumCovHDWC6ZX9fj08Wa5TtSo15k5KgoLXe+B+kFV38d0mXv//WCtNMk9kCt5h/PMvB3WvAbW7XUi6jZntTH7I7N+CZO9P+6VQ2mH5PePfpeTh50sz532nBQVBNXHboSortd2lR639pC3b3xbzv7b2dJzVE+/fS7Xx/z0H59K+oZ0Of+x8yXmuBjz+5ePfWn7dgN2GzJ3iJw4+MTi94YwMf1+Te/iyAgpyC8w89nUaVinRl9PwfDdPTcnV/IO5pnHNHMH/ZYmbc9qK9+/9r1E1Y8yk/xahswfIguGHvsdwfWdtduN3SS+U7x0+XOX4rmjworj0YnY037eOvGttbx0vO//eb+s/N9KOXPSmRLdOtrM/6bfo3U8615aJ+/c/I653dUfXC2dLugkP7zxg7Q/t700atXItQ6rd/jGJRtNH3D3iaqtseg8he75qlDYR4RXsgI86BLgAwcONP25N2zY4OjjaEJ5+PDhAZEAHzp0qCxYsMCnBPj8+fNNNX1lhFoPcH/MqqsffnTSrAZNGphJO14Z8IokjUqSXmN6ycf3fGxmGgecEH1ctNyw7AZJOD5Bjh48aj40WpO2OiF7R7Y0at7ITChX05PlLLx6ofzy9i81+rhATet1Wy9ZNX2V+bn/1P7mzCZ9XetBpXqxZSfSKY2Z5e1lvqgcLTBfzpt3bW6+0GxbsU0++8dn/h4aQlCLHi1MkuicB8+R1y58TfpP6W8mmHz94tfl3H+cK8eddpwECmtf1bJJS5NAPJpzVA7uOyh71u+RXSm7KC6BbfQ7WcqslDLLe47uKZfMuES2f7NdnjvjObnps5tMMYj7AWPUPppgPLT/kCz48wIzcaZOtNugaQNzcGLxXYv9PTx4cOmcS6Xr1V1NUSH4rB5SCfBhw4aZcvvp06c7+jia7NUK8GCbBFMn9tTq9qVLl0r//v0rtX4S4P63e+1umdljps/363ZDN0n/Pd1U3gKWAdMGmKPJB/YckFY9W0ko0op2nTFek1D6+gIC0cXTLzbVI3qAZ9C/B0mbvm3M8sKCQvNFLljeA4NR3qE8iawX6TrguOHDDRLTLkZ+eesX+eTBT/w9PASI65ZcZ97HwyLC5OiBo7JqxirZ+sVW6XxRZ0mekCznPXqenPPAOcUVp3UiJJhUZl+lX4PXPL9G3hvxXo2PD4HjwmculI/++pHX6/9e9HfZ/Olm+ejOj+SyOZdJbIdY19lACD56QFDfk9UDuQ/I8geXy9dTvza/61mqWqwGZ7Tu01queu0q+ebpb2TlMyvlTw//Sc649wyp04iJHt3xWT2EEuALFy40/w4ePNjxxxozZozMmDFDarupU6fKhAkTTEW3VnaXR5P62uJl9erVkpSUVKn1kwD3P30Za/Xqj3N/NL9f/X7xaTOa4NCj2Ad2HzCtKVTmlkyJaRtj2lXol2t3WuG7/G/L5dunv62Rcesp9To2PS0puk20OcoO5zXr2kz2fr/X9Xu9uHpyz7Z7TFsTjZcmJzbx6/hqGz29bEps+QcPgZp22t2nybdPFe+re4zoIZfNvsy8F2jLIU126b9OvJZr43tgqNnw0QbJzc6VhX9eaCY3Ou+R86RO4zrySPgj/h4aHNSqdyvT0ssyMWuibP1yq6RtSJPuN3U3SdzottHy27u/mfjoN7mf9Lilh9f16f4imCtRfdlX6XPx/uj3zSnod6beKZuWbTLtXRBcrO8dlaFVv3oWZO+/9C4RU5s2bJK6mXWlZY+W8tL5L0m7c9tJv3/1c3DUqG10f6HfmbRVXN3Gdc2ynJ05ru/aejDkzSveNGdvo/r0e6qejazPr54ljIrxWT2EEuAoS9ufaBsUrejWyu7yWB+EfQmLUEuA63Ojjf91IoDa9sVB+05lbcky7Sqqs3271+yWWT1nuZbpF2w7kuKJAxIldemxo+J/O/I3V5+sXWt2yayk4sfU08w0eXPq9afK+lfWix20yinQq+YaNm8oB/ccrPB22j6kMN/72Sl3/H6H/LfTf83PWhV6+t2n2zrOYE2CP9vlWVfvc8Af2pzZRrZ9VXzGzkOFD5mDhs27NZcGCQ1qbAy1+T0w1P3yzi8mIdr/8f7y1nVvmf2WtrCYcWrtL9YIVTrnxablm7xef8/2e6Rhs4auNnj6WemFs1+QC566QJJGVK5QJVRVd1+lCfB1L69zZGyoOVENo2TENyOk2SnNzO+ZmzPl6Q5Pu66/9qNr5ffFv5uzYpuf2tx8htbWk6ZXcCm8/8EXOmeInkWqxQo/zisuUsOx3vfufdj1M63Oi/bJ3z+RFU+skBbdW8joNaPNPCo1+Rk3WLCv8o4EeAjQim6t7E5MTJSNGzdW+3ahngAPFdqLWfuL65csPeL6cNjDXm9785c3S9sz28qnD39abt9SPUXw8djHXUfE9Xd3+kVDT/HWyR4s79zyjqx9Ya35+eIZF8sHYz6o0vaM2z9OpjWZJk7S5+kvP/7FHPWvTl92/RCgE6rNHzK/xAf4+w/cL4vvXuw6GHHc6ceZU786DuxY4u+jz6t+6Pr075/K19OKT8kblTLKdYBBr9cJVvVAA6do+sZ6ngc+OVCW3LvE38NBkEwmq18ETh56sqlM0wpPnTxxxf+tkMNpx2ZTV3dtust8cT/93tNl0P8N8tPIEWi05/GMbjNc7y9aHRzIZ7XoGW6asApEw98aLnOvnOv6fXzaeFNFqAetf174s/m8c8Z9Z5j+29bfiy+v/qGvk2//+62ZbFvPmNTPeLpP1rMlrSIC+Id+jrU+j+kBooQTEkxrH2+3LU2rdXXOi8j6kby+4DhN4n7x2BfS7fpuMm/wPPO7e3V4l+FdzKTC7418zxSyBWs7Lj3gq+91VvHbnu/3yKJrFpkz12PbxbpuqxOYaksTXptwAgnwUolcndxSq6TT09PNMk0G9+7dW6666ioJZNbkluX9uXXbBwwYYPqEa7/wygq1BLieUrJr1y5p2bJlSJ1S8tPCn0okZK1qZOsIrWX7t9tN8tbqhWa55etbpM0ZbcyRcE2sn//Y+eb3imhfSu1Rrr1rtT/lx/d+LN/8+xuPt71r813ydPtjVR3uVZO3fHmLrHx2pXx4+4cV9r9eOm5pmUls9E374N6D8sObP8iwhcPk/1r8X5n7Ppj3oKkc0deZp1PRTxp8klw661Jz/VNtnzI9Xsv7sL5j5Q4zU7R+sNcPTXoqpn4o+OyRz6TLsC7Sundr131ev+R12fDBhjIf9rd8vsW0mNEP+o81fEwi6kbIA0ceKPc5gHd64EA/uLY7ux2tUVChYYuGydKxS02Lpwv/e6GrEvfMCWeaKt2cXTmSkZphDiB64n5g69rF10qnQZ1MmyInJ6KtSKi+BwYyfc/R3rT6HnLWhLPMsnWvrJO3b6i9LR56/aWX7Fq9S3Z8u8O17M6Nd0r9+Prm/Uy36ZunvqnxA5GaMNMqtdK0tZyO77/H/1cyN2W6znrTM6z0wHWTE5qYzw76OUYnOtWz23Sf4G3iWd036LZaiQLUrn2VJnG+mvaVLL9/ua3rDTbxneMlfUPxd2qLTgLpLVnd584+8t1/viux7C8//UU+eeAT6XhBR9n25TbpdGEnM0/N5OjJZmJTPUtVi3Ws98tTrztVEk5MMPfxlgCvKt7/UF3WGbp6IPS3938zhVMarxpXEWkRMrOb73N6lTZ2z1h5ovkTZZbrY+l3dPczovvc0Ue++2/xa+6hgofM93itwNbb2fF679CvgxzJOCIjvhtR5bln4Dv2Vd6RAP/DpEmTTK/s8mgf7YkTJwZkktdqg6I9wLUXeHmTZWZkZEhs7LGjcBUJxQR4KPdUOpxxWLK3Z0t062hTsa0fQrWnXmmaeF3z3BrX6aOjVo+SlkktbRnD0glLyyTYu9/SXS5/7vIySaMblt9gEsXW5BilK9nrJ9SXs+8/2xyZP/7i4+WKl64wb/7Pnf6cmSRq6IKhElW/7IzS37/xvTlqbbnu4+tMJbZ7olSPbOsEHXpKuvrrb3+VhM4JrsR+eFS4/DT/J9cpYGO+H2N6m1WlKnvJ2CWmYrS8D/tazaQJembIto9WgmllmPYL1aSG9TewTl3/93H/LnMfPaChr5/LX7zcHAixDH97uHzxry9K9HitTbRCxZpnINDpASTrdFQ9u0IPxlVm0qLSH+5b9Gghse1jTZJKv0Rof32dKEsP9F3+wuVl9nm6T9Dk2AmXnVCpcepBsFXPrjIHDBu3LO4t6W+h/h4YTHRyVN1/6en+UxOmmmRSZeiZR3qGUXX0Hd+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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 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 = False\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",
    "    dsp_desired_signal = desired_signal_r11*(2**(15)-1)\n",
    "    dsp_noise_signal = noise_signal_vpu*(2**(15)-1)\n",
    "    dsp_corrupted_signal = corrupted_signal*(2**(15)-1)\n",
    "    dsp_output = output*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/dsp_desired_signal.txt', dsp_desired_signal, fmt='%d')\n",
    "    np.savetxt('simulation_data/dsp_corrupted_signal.txt', dsp_corrupted_signal, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/dsp_noise_signal.txt', dsp_noise_signal, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('filter_output/python_output.txt', dsp_output, fmt='%d', delimiter=\"\\n\")\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": null,
   "metadata": {},
   "outputs": [],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gefundene Dateien im Ordner 'filter_output': ['dsp_output.txt', 'python_output.txt']\n",
      "\n",
      "Inhalt von dsp_output.txt (200001 Zeilen):\n",
      "-33\n",
      "-37\n",
      "-38\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "\n",
      "Inhalt von python_output.txt (200000 Zeilen):\n",
      "-33\n",
      "-37\n",
      "-38\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "-39\n",
      "Numerischer Vergleich/Plot fehlgeschlagen: operands could not be broadcast together with shapes (200001,) (200000,) \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Vergleich der Einträge in den Textdateien in filter_output\n",
    "import os\n",
    "\n",
    "# Verzeichnis und Dateien auflisten\n",
    "folder = 'filter_output'\n",
    "files = sorted([f for f in os.listdir(folder) if f.endswith('.txt')])\n",
    "print(f\"Gefundene Dateien im Ordner '{folder}': {files}\")\n",
    "\n",
    "# Inhalt jeder Datei einlesen und anzeigen (erste Zeilen)\n",
    "for fname in files:\n",
    "    path = os.path.join(folder, fname)\n",
    "    with open(path, 'r') as f:\n",
    "        data = f.read().strip().splitlines()\n",
    "    print(f\"\\nInhalt von {fname} ({len(data)} Zeilen):\")\n",
    "    for line in data[:10]:  # nur erste 10 Zeilen ausgeben\n",
    "        print(line)\n",
    "\n",
    "# Numerischen Vergleich und Plot\n",
    "try:\n",
    "    arrays = [np.loadtxt(os.path.join(folder, f), dtype=np.int64) for f in files]\n",
    "    if arrays:\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        for arr, fname in zip(arrays, files):\n",
    "            plt.plot(arr, label=fname)\n",
    "        plt.title('Inhalt der Textdateien')\n",
    "        plt.xlabel('Index')\n",
    "        plt.ylabel('Wert')\n",
    "        plt.legend()\n",
    "        plt.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "        if len(arrays) > 1:\n",
    "            # Plot differences zwischen nacheinanderfolgenden Dateien\n",
    "            plt.figure(figsize=(12, 6))\n",
    "            for i in range(len(arrays)-1):\n",
    "                diff = arrays[i] - arrays[i+1]\n",
    "                plt.plot(diff, label=f\"{files[i]} - {files[i+1]}\")\n",
    "            plt.title('Differenzen zwischen benachbarten Dateien')\n",
    "            plt.xlabel('Index')\n",
    "            plt.ylabel('Differenz')\n",
    "            plt.legend()\n",
    "            plt.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "        plt.show()\n",
    "except Exception as e:\n",
    "    print(f\"Numerischer Vergleich/Plot fehlgeschlagen: {e}\")\n"
   ]
  }
 ],
 "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
}