Masterarbeit_Simulation/ANR_high_level.ipynb

{
 "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",
    "from scipy.stats import pearsonr\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):\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",
    "    modified_freq_signal = freq_signal * gain_interp\n",
    "    modified_signal = np.fft.irfft(modified_freq_signal, n=N)\n",
    "    return modified_signal\n",
    "\n",
    "# High-Level ANR Algorithmmus - nicht deterministisch, daher nicht gleiche Ergebnisse wie in C\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",
    "    adaption_step = 10\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) != 4:\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",
    "# Low-Level ANR Algorithmmus (wie in C)\n",
    "def anr_function_c(input, ref_noise, coefficients, mu, adaption_step = 1):\n",
    "    counter = 0\n",
    "    sample_count = len(input)\n",
    "    filter_line = np.zeros(coefficients)\n",
    "    sample_line = np.zeros(coefficients)\n",
    "    output = np.zeros(sample_count)\n",
    "    coeffient_matrix = np.zeros((sample_count, coefficients))\n",
    "    adaption_step = 1\n",
    "    \n",
    "    for n in range(sample_count):\n",
    "        # Reference Noise Signal in Sample Line\n",
    "        sample_line = np.roll(sample_line, 1)\n",
    "        sample_line[0] = ref_noise[n]\n",
    "        # apply_fir_filter: Akkumulator berechnen\n",
    "        accumulator = np.dot(filter_line, sample_line)\n",
    "        # update_output: Output/Error berechnen\n",
    "        error = input[n] - accumulator\n",
    "        output[n] = error\n",
    "        # update_filter_coeffcients: Filterkoeffizienten adaptieren\n",
    "        # Reduced-update Codeblock\n",
    "        #if (n % adaption_step) == 0:     # bei Rate x/adatpion_step:  if (n % adaption_step) < x:\n",
    "        # Error-driven Codeblock\n",
    "        #if (abs(error)*ref_noise[n]) > 0:  # nur adaptieren wenn Fehler über Schwellwert\n",
    "        #    counter += 1\n",
    "        # filter_line += mu * error * sample_line\n",
    "        filter_line += mu * error * sample_line\n",
    "        # Filterkoeffizienten expoertieren\n",
    "        coeffient_matrix[n, :] = filter_line\n",
    "    #print(f\"Anpassungen: {counter}\")\n",
    "    return output, coeffient_matrix\n",
    "    \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",
    "COMPLEX = 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",
    "noise_delay = 0.000\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",
    "# 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",
    "if COMPLEX == True:\n",
    "    desired_signal_r11 = apply_transfer_function_freq(desired_signal, fs, frequency_r11, gain_r11)\n",
    "    noise_signal_r11 = apply_transfer_function_freq(noise_signal, fs, frequency_r11, gain_r11)\n",
    "    noise_signal_vpu = apply_transfer_function_freq(noise_signal, fs, frequency_vpu, gain_vpu)\n",
    "else:\n",
    "    desired_signal_r11 = desired_signal\n",
    "    noise_signal_r11 = noise_signal\n",
    "    noise_signal_vpu = noise_signal\n",
    "\n",
    "# Noise Delay bedeutet, dass das Corruption Noise Signal im Corrupted Signal verzögert ist (zum Reference Noise Signal)\n",
    "if noise_delay != 0:\n",
    "    # Delay von ms in Samples umrechnen, 0-Array erzeugen\n",
    "    delay_samples = int(noise_delay * fs)\n",
    "    noise_signal_r11_delayed = np.zeros_like(noise_signal_r11)\n",
    "    # Schneided die Delay Samples vom ursprünglichen Array ab und schreibt sie nach entsprechend vielen Nullen ins neue Array\n",
    "    noise_signal_r11_delayed[delay_samples:] = noise_signal_r11[:-delay_samples]\n",
    "    # Corrupted Signal mit verzögertem Noise\n",
    "    corrupted_signal = desired_signal_r11 + noise_signal_r11_delayed\n",
    "else:\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))/1000\n",
    "output, coefficient_matrix = anr_function_c(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))/20000\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_r11**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 SIMULATION == True:\n",
    "    # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
    "    dsp_desired_signal_r11 = desired_signal_r11*(2**(15)-1)\n",
    "    dsp_noise_signal_r11 = noise_signal_r11*(2**(15)-1)\n",
    "    dsp_noise_signal_vpu = noise_signal_vpu*(2**(15)-1)\n",
    "    dsp_corrupted_signal = corrupted_signal*(2**(15)-1)\n",
    "    python_output = output*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/simple_dsp_desired_signal_r11.txt', dsp_desired_signal_r11, fmt='%d')\n",
    "    np.savetxt('simulation_data/simple_dsp_noise_signal_r11.txt', dsp_noise_signal_r11, fmt='%d')\n",
    "    np.savetxt('simulation_data/simple_dsp_noise_signal_vpu.txt', dsp_noise_signal_vpu, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/simple_dsp_corrupted_signal.txt', dsp_corrupted_signal, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('filter_output/simple_python_output.txt', python_output, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('filter_output/simple_python_filter_coefficients.txt', coefficient_matrix, fmt='%.4f', delimiter=\",\")\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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_37188\\3939203997.py:86: 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_r11**2, t))\n",
      "C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_37188\\3939203997.py:87: 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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",
      "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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",
      "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 intermediate/komplexen Usecases\n",
    "\n",
    "from pandas import Series\n",
    "\n",
    "COMPLEX= True\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 = 45\n",
    "step_size = 0.01\n",
    "noise_delay = 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",
    "if COMPLEX == True:\n",
    "    desired_signal_r11 = apply_transfer_function_freq(desired_signal, fs, frequency_r11, gain_r11)\n",
    "    noise_signal_r11 = apply_transfer_function_freq(noise_signal, fs, frequency_r11, gain_r11)\n",
    "    noise_signal_vpu = apply_transfer_function_freq(noise_signal, fs, frequency_vpu, gain_vpu)\n",
    "else:\n",
    "    desired_signal_r11 = desired_signal\n",
    "    noise_signal_r11 = noise_signal\n",
    "    noise_signal_vpu = noise_signal\n",
    "\n",
    "# Noise Delay bedeutet, dass das Corruption Noise Signal im Corrupted Signal verzögert ist (zum Reference Noise Signal)\n",
    "if noise_delay != 0:\n",
    "    # Delay von ms in Samples umrechnen, 0-Array erzeugen\n",
    "    delay_samples = int(noise_delay * fs)\n",
    "    noise_signal_r11_delayed = np.zeros_like(noise_signal_r11)\n",
    "    # Schneided die Delay Samples vom ursprünglichen Array ab und schreibt sie nach entsprechend vielen Nullen ins neue Array\n",
    "    noise_signal_r11_delayed[delay_samples:] = noise_signal_r11[:-delay_samples]\n",
    "    # Corrupted Signal mit verzögertem Noise\n",
    "    corrupted_signal = desired_signal_r11 + noise_signal_r11_delayed\n",
    "else:\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))/20000\n",
    "\n",
    "if SERIES == True:\n",
    "    for i in range(16, coefficients+2, 2):\n",
    "        output, coefficient_matrix = anr_function_c(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))/20000\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_r11**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+breathing', 'a', newline='') as f:\n",
    "            writer = csv.writer(f)\n",
    "            writer.writerow([i, delta_snr])\n",
    "else:\n",
    "        output, coefficient_matrix = anr_function_c(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))/20000\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_r11**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_r11 = desired_signal_r11*(2**(15)-1)\n",
    "    dsp_noise_signal_r11 = noise_signal_r11*(2**(15)-1)\n",
    "    dsp_noise_signal_vpu = noise_signal_vpu*(2**(15)-1)\n",
    "    dsp_corrupted_signal = corrupted_signal*(2**(15)-1)\n",
    "    python_output = output*(2**(15)-1)\n",
    "    python_coefficient_matrix = coefficient_matrix*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/complex_dsp_desired_signal_r11.txt', dsp_desired_signal_r11, fmt='%d')\n",
    "    np.savetxt('simulation_data/complex_dsp_noise_signal_r11.txt', dsp_noise_signal_r11, fmt='%d')\n",
    "    np.savetxt('simulation_data/complex_dsp_noise_signal_vpu.txt', dsp_noise_signal_vpu, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/complex_dsp_corrupted_signal.txt', dsp_corrupted_signal, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('filter_output/complex_python_output.txt', python_output, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('filter_output/complex_python_filter_coefficients.txt', python_coefficient_matrix, fmt='%d', delimiter=\",\")\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",
    "    if COMPLEX == 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",
    "    else:\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für SNR Vergleich\n",
    "\n",
    "PLOT = False\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_gain = 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_gain, 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='upper left')\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))/20000\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": [
    "# Filterlänge und Update-Schritte Vergleich\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "PLOT = False\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vergleich Output High/Low-Level\n",
    "\n",
    "PLOT = False\n",
    "COMPLEX = True\n",
    "\n",
    "if COMPLEX == True:\n",
    "    python_output = np.loadtxt('filter_output/complex_python_output.txt', delimiter=\",\")/(2**(15)-1)\n",
    "    dsp_output = np.loadtxt('filter_output/complex_dsp_output.txt', delimiter=\",\")[:-1]/(2**(15)-1)\n",
    "else:\n",
    "    python_output = np.loadtxt('filter_output/simple_python_output.txt', delimiter=\",\")/(2**(15)-1)\n",
    "    dsp_output = np.loadtxt('filter_output/simple_dsp_output.txt', delimiter=\",\")[:-1]/(2**(15)-1)\n",
    "\n",
    "diff = python_output - dsp_output\n",
    "\n",
    "if COMPLEX == True:\n",
    "    t = np.linspace(0, 200000, 200000)/20000\n",
    "else:\n",
    "    t = np.linspace(0, 2000, 2000)/200\n",
    "\n",
    "figure1, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(15, 9), sharex=True, sharey=True)\n",
    "ax1.set_ylim(1.0, -1.0)\n",
    "ax1.plot(t, python_output, linestyle='-', c='deepskyblue',  linewidth=1, alpha=1, label='High Level Simulation')\n",
    "ax2.plot(t, dsp_output, linestyle='-', c='indianred', linewidth=1, alpha=1, label='Low Level Simulation')\n",
    "ax3.plot(t, python_output, linestyle='-', c='deepskyblue',  linewidth=1, alpha=1)\n",
    "ax3.plot(t, dsp_output, linestyle='-', c='indianred', linewidth=1, alpha=0.7)\n",
    "ax3.plot(t, diff, linestyle='-', c='green', linewidth=2, alpha=0.7, label=f'Error Amplitude')\n",
    "\n",
    "figure2, ax4 = plt.subplots(1, 1, figsize=(15, 7))\n",
    "ax4.hist(diff, bins=100, density=True, color='green',edgecolor='black', alpha=0.7)\n",
    "ax4.set_yscale('log')\n",
    "\n",
    "mean = np.mean(diff)\n",
    "std = np.std(diff)\n",
    "ax4.axvline(mean, linestyle='-', linewidth=3, label='Mean')\n",
    "ax4.axvline(mean + std, linestyle='--', linewidth=2, label='+1 Sigma')\n",
    "ax4.axvline(mean - std, linestyle='--', linewidth=2, label='-1 Sigma')\n",
    "\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",
    "ax1.text(0.5, -0.3, '(a) High Level Simulation',\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) Low Level Simulation',\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'(c) Comparision High/Low Level Simulation',\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",
    "ax1.set_ylabel(\"Amplitude\")\n",
    "ax2.set_ylabel(\"Amplitude\")\n",
    "ax3.set_ylabel(\"Amplitude\")\n",
    "ax3.set_ylabel(\"Amplitude\")\n",
    "\n",
    "ax4.set_xlabel(\"Error Amplitude\")\n",
    "ax4.set_ylabel(\"Samples\")\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",
    "\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",
    "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",
    "ax3.legend(frameon=False, loc='upper right')\n",
    "ax4.legend(frameon=False, loc='upper right')\n",
    "\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_high_low_comparison', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_high_low_comparison_hist', dpi=600)\n",
    "figure1.show()\n",
    "figure2.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Single Reduced Update Plot\n",
    "\n",
    "import matplotlib.ticker as mtick\n",
    "from scipy.interpolate import make_interp_spline\n",
    "\n",
    "PLOT = False\n",
    "\n",
    "# Daten aus .csv laden\n",
    "update_rate_breathing = np.loadtxt('update_rate_evaluation/update_rate_breathing.csv', delimiter=\";\", skiprows=1)\n",
    "\n",
    "# Daten laden\n",
    "x = update_rate_breathing[:, 0]\n",
    "update_gain_breathing = update_rate_breathing[:, 2]\n",
    "update_cycles_breathing = update_rate_breathing[:, 4]\n",
    "update_load_breathing = update_rate_breathing[:, 5]\n",
    "update_cycles_breathing_new = update_rate_breathing[:, 7]\n",
    "update_load_breathing_new = update_rate_breathing[:, 8]\n",
    "\n",
    "# Sortieren\n",
    "idx = np.argsort(x)\n",
    "x = x[idx]\n",
    "update_gain_breathing = update_gain_breathing[idx]\n",
    "update_cycles_breathing = update_cycles_breathing[idx]\n",
    "update_load_breathing = update_load_breathing[idx]\n",
    "update_cycles_breathing_new = update_cycles_breathing_new[idx]\n",
    "update_load_breathing_new = update_load_breathing_new[idx]\n",
    "\n",
    "# Smoothing\n",
    "x_smooth = np.linspace(x.min(), x.max(), 300)\n",
    "\n",
    "update_gain_smooth = make_interp_spline(x, update_gain_breathing)(x_smooth)\n",
    "update_cycles_smooth = make_interp_spline(x, update_cycles_breathing)(x_smooth)\n",
    "update_load_smooth = make_interp_spline(x, update_load_breathing)(x_smooth)\n",
    "update_cycles_smooth_new = make_interp_spline(x, update_cycles_breathing_new)(x_smooth)\n",
    "update_load_smooth_new = make_interp_spline(x, update_load_breathing_new)(x_smooth)\n",
    "\n",
    "diff_smooth = np.abs(update_gain_smooth - update_cycles_smooth)\n",
    "idx_max = np.argmax(diff_smooth)\n",
    "x_max = x_smooth[idx_max]\n",
    "y1_max = update_gain_smooth[idx_max]\n",
    "y2_max = update_cycles_smooth[idx_max]\n",
    "\n",
    "# Plot\n",
    "figure1, ax1 = plt.subplots(figsize=(15, 7))\n",
    "ax1.plot(x_smooth, update_gain_smooth, linestyle='--', color='indianred', linewidth=2, alpha=0.9, label='SNR-Gain')\n",
    "ax1.plot(x_smooth, update_cycles_smooth, linestyle='-.', color='skyblue', linewidth=2, alpha=0.9, label='Cycles/Sample')\n",
    "ax1.plot(x_smooth, update_load_smooth, linestyle=':', color='forestgreen', linewidth=2, alpha=0.9, label='DSP Load')\n",
    "ax1.plot([x_max, x_max], [y1_max, y2_max], color='black', linestyle=':', linewidth=2)\n",
    "\n",
    "ax1.scatter(x, update_gain_breathing, color='indianred', s=40)\n",
    "ax1.scatter(x, update_cycles_breathing, color='skyblue', s=40)\n",
    "ax1.scatter(x, update_load_breathing, color='forestgreen', s=40)\n",
    "\n",
    "# Plot\n",
    "figure2, ax2 = plt.subplots(figsize=(15, 7))\n",
    "ax2.plot(x_smooth, update_gain_smooth, linestyle='--', color='indianred', linewidth=2, alpha=0.9, label='SNR-Gain')\n",
    "ax2.plot(x_smooth, update_cycles_smooth, linestyle='-.', color='skyblue', linewidth=2, alpha=0.3)\n",
    "ax2.plot(x_smooth, update_load_smooth, linestyle=':', color='forestgreen', linewidth=2, alpha=0.3)\n",
    "ax2.plot(x_smooth, update_cycles_smooth_new, linestyle='-', color='skyblue', linewidth=2, alpha=0.9, label='Cycles/Sample (New)')\n",
    "ax2.plot(x_smooth, update_load_smooth_new, linestyle='-', color='forestgreen', linewidth=2, alpha=0.9, label='DSP Load (New)')\n",
    "\n",
    "ax2.scatter(x, update_gain_breathing, color='indianred', s=40)\n",
    "ax2.scatter(x, update_cycles_breathing, color='skyblue', s=40, alpha=0.3)\n",
    "ax2.scatter(x, update_load_breathing, color='forestgreen', s=40, alpha=0.3)\n",
    "ax2.scatter(x, update_cycles_breathing_new, color='skyblue', s=40)\n",
    "ax2.scatter(x, update_load_breathing_new, color='forestgreen', s=40)\n",
    "\n",
    "ax1.text(x_max, (y1_max + y2_max)/2+0.1,\n",
    "         f'Max. offset at update rate {x_max:.2f}',\n",
    "         fontsize=20,\n",
    "         ha='left')\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",
    "\n",
    "ax1.set_xlabel(\"Update Rate\")\n",
    "ax1.set_ylabel(\"Relative Performance\")\n",
    "ax2.set_xlabel(\"Update Rate\")\n",
    "ax2.set_ylabel(\"Relative Performance\")\n",
    "ax1.grid(True, linestyle='-.', alpha=0.4)\n",
    "ax2.grid(True, linestyle='-.', alpha=0.4)\n",
    "#Spines auf ganzen Plot anwenden\n",
    "ax1.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax1.invert_xaxis()\n",
    "ax2.invert_xaxis()\n",
    "ax1.legend(frameon=False, loc='upper right')\n",
    "ax2.legend(frameon=False, loc='upper right')\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_snr_update_rate', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_snr_update_rate_new', dpi=600)\n",
    "figure1.show()\n",
    "figure2.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multi Reduced Update Plot\n",
    "\n",
    "import matplotlib.ticker as mtick\n",
    "from scipy.interpolate import make_interp_spline\n",
    "\n",
    "PLOT = False\n",
    "\n",
    "# Daten aus .csv laden\n",
    "update_rate_breathing = np.loadtxt('update_rate_evaluation/update_rate_breathing.csv', delimiter=\";\", skiprows=1)\n",
    "update_rate_chewing = np.loadtxt('update_rate_evaluation/update_rate_chewing.csv', delimiter=\";\", skiprows=1)\n",
    "update_rate_coughing = np.loadtxt('update_rate_evaluation/update_rate_coughing.csv', delimiter=\";\", skiprows=1)\n",
    "update_rate_drinking = np.loadtxt('update_rate_evaluation/update_rate_drinking.csv', delimiter=\";\", skiprows=1)\n",
    "update_rate_scratching = np.loadtxt('update_rate_evaluation/update_rate_scratching.csv', delimiter=\";\", skiprows=1)\n",
    "\n",
    "# Daten laden\n",
    "x = update_rate_breathing[:, 0]\n",
    "update_gain_breathing = update_rate_breathing[:, 2]\n",
    "update_cycles_breathing = update_rate_breathing[:, 4]\n",
    "update_load_breathing = update_rate_breathing[:, 5]\n",
    "update_gain_chewing = update_rate_chewing[:, 2]\n",
    "update_cycles_chewing = update_rate_chewing[:, 4]\n",
    "update_load_chewing = update_rate_chewing[:, 5]\n",
    "update_gain_coughing = update_rate_coughing[:, 2]\n",
    "update_cycles_coughing = update_rate_coughing[:, 4]\n",
    "update_load_coughing = update_rate_coughing[:, 5]\n",
    "update_gain_drinking = update_rate_drinking[:, 2]\n",
    "update_cycles_drinking = update_rate_drinking[:, 4]\n",
    "update_load_drinking = update_rate_drinking[:, 5]\n",
    "update_gain_scratching = update_rate_scratching[:, 2]\n",
    "update_cycles_scratching = update_rate_scratching[:, 4]  \n",
    "update_load_scratching = update_rate_scratching[:, 5] \n",
    "\n",
    "# Sortieren\n",
    "idx = np.argsort(x)\n",
    "x = x[idx]\n",
    "update_gain_breathing = update_gain_breathing[idx]\n",
    "update_cycles_breathing = update_cycles_breathing[idx]\n",
    "update_load_breathing = update_load_breathing[idx]\n",
    "update_gain_chewing = update_gain_chewing[idx]\n",
    "update_cycles_chewing = update_cycles_chewing[idx]\n",
    "update_load_chewing = update_load_chewing[idx]\n",
    "update_gain_coughing = update_gain_coughing[idx]\n",
    "update_cycles_coughing = update_cycles_coughing[idx]\n",
    "update_load_coughing = update_load_coughing[idx]\n",
    "update_gain_drinking = update_gain_drinking[idx]\n",
    "update_cycles_drinking = update_cycles_drinking[idx]\n",
    "update_load_drinking = update_load_drinking[idx]\n",
    "update_gain_scratching = update_gain_scratching[idx]\n",
    "update_cycles_scratching = update_cycles_scratching[idx]\n",
    "update_load_scratching = update_load_scratching[idx]\n",
    "\n",
    "# Smoothing\n",
    "x_smooth = np.linspace(x.min(), x.max(), 300)\n",
    "\n",
    "gain_smooth_breathing = make_interp_spline(x, update_gain_breathing)(x_smooth)\n",
    "cycles_smooth_breathing = make_interp_spline(x, update_cycles_breathing)(x_smooth)\n",
    "load_smooth_breathing = make_interp_spline(x, update_load_breathing)(x_smooth)\n",
    "gain_smooth_chewing = make_interp_spline(x, update_gain_chewing)(x_smooth)\n",
    "cycles_smooth_chewing = make_interp_spline(x, update_cycles_chewing)(x_smooth)\n",
    "load_smooth_chewing = make_interp_spline(x, update_load_chewing)(x_smooth)\n",
    "gain_smooth_coughing = make_interp_spline(x, update_gain_coughing)(x_smooth)\n",
    "cycles_smooth_coughing = make_interp_spline(x, update_cycles_coughing)(x_smooth)\n",
    "load_smooth_coughing = make_interp_spline(x, update_load_coughing)(x_smooth)\n",
    "gain_smooth_drinking = make_interp_spline(x, update_gain_drinking)(x_smooth)\n",
    "cycles_smooth_drinking = make_interp_spline(x, update_cycles_drinking)(x_smooth)\n",
    "load_smooth_drinking = make_interp_spline(x, update_load_drinking)(x_smooth)\n",
    "gain_smooth_scratching = make_interp_spline(x, update_gain_scratching)(x_smooth)\n",
    "cycles_smooth_scratching = make_interp_spline(x, update_cycles_scratching)(x_smooth)\n",
    "load_smooth_scratching = make_interp_spline(x, update_load_scratching)(x_smooth)\n",
    "\n",
    "diff_smooth_breathing = gain_smooth_breathing - cycles_smooth_breathing\n",
    "diff_smooth_chewing = gain_smooth_chewing - cycles_smooth_chewing\n",
    "diff_smooth_coughing = gain_smooth_coughing - cycles_smooth_coughing\n",
    "diff_smooth_drinking = gain_smooth_drinking - cycles_smooth_drinking\n",
    "diff_smooth_scratching = gain_smooth_scratching - cycles_smooth_scratching\n",
    "\n",
    "# Alle Kurven in ein Array stapeln\n",
    "stack_difference = np.vstack([\n",
    "    diff_smooth_breathing,\n",
    "    diff_smooth_chewing,\n",
    "    diff_smooth_coughing,\n",
    "    diff_smooth_drinking,\n",
    "    diff_smooth_scratching\n",
    "])\n",
    "\n",
    "# Alle Kurven in ein Array stapeln\n",
    "stack_load = np.vstack([\n",
    "    load_smooth_breathing,\n",
    "    load_smooth_chewing,\n",
    "    load_smooth_coughing,\n",
    "    load_smooth_drinking,\n",
    "    load_smooth_scratching\n",
    "])\n",
    "\n",
    "# Punktweiser Mittelwert\n",
    "mean_gain = np.mean(stack_difference, axis=0)\n",
    "mean_load = np.mean(stack_load, axis=0)   \n",
    "\n",
    "idx_max_gain = np.argmax(mean_gain)\n",
    "x_max_gain = x_smooth[idx_max_gain]\n",
    "y_max_gain = mean_gain[idx_max_gain]\n",
    "x_max_load = x_smooth[idx_max_gain]\n",
    "y_max_load = mean_load[idx_max_gain]\n",
    "\n",
    "\n",
    "# Plot\n",
    "figure1, ax1 = plt.subplots(figsize=(15, 7))\n",
    "ax1.plot(x_smooth, diff_smooth_breathing, linestyle='--', color='indianred', linewidth=1.5, alpha=0.7, label='Breathing Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_chewing, linestyle='-.', color='skyblue', linewidth=1.5, alpha=0.7, label='Chewing Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_coughing, linestyle=':', color='forestgreen', linewidth=1.5, alpha=0.7, label='Coughing Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_drinking, linestyle='--', color='darkorange', linewidth=1.5, alpha=0.7, label='Drinking Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_scratching, linestyle='-.', color='darkorchid', linewidth=1.5, alpha=0.7, label='Scratching Noise')\n",
    "ax1.plot(x_smooth, mean_gain, linestyle='--', color='red', linewidth=2.5, alpha=1, label='Mean Performance Gain')\n",
    "ax1.plot([x_max_gain, x_max_gain], [y_max_gain, 0], color='black', linestyle=':', linewidth=2)\n",
    "\n",
    "ax1.text(x_max_gain+0.38, y_max_gain+0.03,\n",
    "         f'{y_max_gain*100:.1f} \\% mean performance gain at update rate {x_max_gain:.2f}',\n",
    "         fontsize=20,\n",
    "         ha='left')\n",
    "\n",
    "figure2, ax2 = plt.subplots(figsize=(15, 7))\n",
    "ax2.plot(x_smooth, load_smooth_breathing, linestyle='--', color='indianred', linewidth=1.5, alpha=0.7, label='Breathing Noise')\n",
    "ax2.plot(x_smooth, load_smooth_chewing, linestyle='-.', color='skyblue', linewidth=1.5, alpha=0.7, label='Chewing Noise')\n",
    "ax2.plot(x_smooth, load_smooth_coughing, linestyle=':', color='forestgreen', linewidth=1.5, alpha=0.7, label='Coughing Noise')\n",
    "ax2.plot(x_smooth, load_smooth_drinking, linestyle='--', color='darkorange', linewidth=1.5, alpha=0.7, label='Drinking Noise')\n",
    "ax2.plot(x_smooth, load_smooth_scratching, linestyle='-.', color='darkorchid', linewidth=1.5, alpha=0.7, label='Scratching Noise')\n",
    "ax2.plot(x_smooth, mean_load, linestyle='--', color='blue', linewidth=2.5, alpha=1, label='Mean DSP Load')\n",
    "ax2.plot([x_max_load, x_max_load], [y_max_load, 0], color='black', linestyle=':', linewidth=2)\n",
    "\n",
    "ax2.text(x_max_load+0.32, y_max_load-0.04,\n",
    "         f'{y_max_load*100:.1f} \\% mean DSP load at update rate {x_max_load:.2f}',\n",
    "         fontsize=20,\n",
    "         ha='left')\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",
    "ax1.set_xlabel(\"Update Rate\")\n",
    "ax2.set_xlabel(\"Update Rate\")\n",
    "ax1.set_ylabel(\"Performance Gain\")\n",
    "ax2.set_ylabel(\"DSP Load\")\n",
    "ax1.grid(True, linestyle='-.', alpha=0.4)\n",
    "ax2.grid(True, linestyle='-.', alpha=0.4)\n",
    "ax1.set_ylim(0, 1)\n",
    "ax2.set_ylim(0, 0.7)\n",
    "#Spines auf ganzen Plot anwenden\n",
    "ax1.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax1.legend(frameon=False, loc='upper right')\n",
    "ax2.legend(frameon=False, loc='upper right')\n",
    "ax1.invert_xaxis()\n",
    "ax2.invert_xaxis()\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_gain_update_rate', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_load_update_rate', dpi=600)\n",
    "figure1.show()\n",
    "figure2.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Single Error Threshold Plot\n",
    "\n",
    "import matplotlib.ticker as mtick\n",
    "from scipy.interpolate import make_interp_spline\n",
    "\n",
    "PLOT = False\n",
    "\n",
    "# Daten aus .csv laden\n",
    "data_error_threshold = np.loadtxt('threshold_evaluation/error_threshold_breathing.csv', delimiter=\";\", skiprows=1)\n",
    "\n",
    "# Daten laden\n",
    "x = data_error_threshold[:, 0]\n",
    "error_gain = data_error_threshold[:, 2]\n",
    "error_cycles = data_error_threshold[:, 6]\n",
    "error_load = data_error_threshold[:, 7]\n",
    "error_cycles_new = data_error_threshold[:, 9]\n",
    "error_load_new = data_error_threshold[:, 10]\n",
    "\n",
    "# Sortieren\n",
    "idx = np.argsort(x)\n",
    "x = x[idx]\n",
    "error_gain = error_gain[idx]\n",
    "error_cycles = error_cycles[idx]\n",
    "error_load = error_load[idx]\n",
    "error_cycles_new = error_cycles_new[idx]\n",
    "error_load_new = error_load_new[idx]\n",
    "# Smoothing\n",
    "x_smooth = np.linspace(x.min(), x.max(), 300)\n",
    "\n",
    "gain_smooth = make_interp_spline(x, error_gain)(x_smooth)\n",
    "cycles_smooth = make_interp_spline(x, error_cycles)(x_smooth)\n",
    "load_smooth = make_interp_spline(x, error_load)(x_smooth)\n",
    "cycles_smooth_new = make_interp_spline(x, error_cycles_new)(x_smooth)\n",
    "load_smooth_new = make_interp_spline(x, error_load_new)(x_smooth)\n",
    "\n",
    "diff_smooth = np.abs(gain_smooth - cycles_smooth)\n",
    "idx_max = np.argmax(diff_smooth)\n",
    "x_max = x_smooth[idx_max]\n",
    "y1_max = gain_smooth[idx_max]\n",
    "y2_max = cycles_smooth[idx_max]\n",
    "\n",
    "# Plot\n",
    "figure1, ax1 = plt.subplots(figsize=(15, 7))\n",
    "ax1.plot(x_smooth, gain_smooth, linestyle='--', color='indianred', linewidth=2, alpha=0.9, label='SNR-Gain')\n",
    "ax1.plot(x_smooth, cycles_smooth, linestyle='-.', color='skyblue', linewidth=2, alpha=0.9, label='Cycles/Sample')\n",
    "ax1.plot(x_smooth, load_smooth, linestyle=':', color='forestgreen', linewidth=2, alpha=0.9, label='DSP Load')\n",
    "ax1.plot([x_max, x_max], [y1_max, y2_max], color='black', linestyle=':', linewidth=2)\n",
    "\n",
    "ax1.scatter(x, error_gain, color='indianred', s=40)\n",
    "ax1.scatter(x, error_cycles, color='skyblue', s=40)\n",
    "ax1.scatter(x, error_load, color='forestgreen', s=40)\n",
    "\n",
    "figure2, ax2 = plt.subplots(figsize=(15, 7))\n",
    "ax2.plot(x_smooth, gain_smooth, linestyle='--', color='indianred', linewidth=2, alpha=0.9, label='SNR-Gain')\n",
    "ax2.plot(x_smooth, cycles_smooth, linestyle='-.', color='skyblue', linewidth=2, alpha=0.3)\n",
    "ax2.plot(x_smooth, load_smooth, linestyle=':', color='forestgreen', linewidth=2, alpha=0.3)\n",
    "ax2.plot(x_smooth, cycles_smooth_new, linestyle='-', color='skyblue', linewidth=2, alpha=0.9, label='Cycles/Sample (New)')\n",
    "ax2.plot(x_smooth, load_smooth_new, linestyle='-', color='forestgreen', linewidth=2, alpha=0.9, label='DSP Load (New)')\n",
    "\n",
    "\n",
    "ax2.scatter(x, error_gain, color='indianred', s=40)\n",
    "ax2.scatter(x, error_cycles, color='skyblue', s=40, alpha=0.3)\n",
    "ax2.scatter(x, error_load, color='forestgreen', s=40, alpha=0.3)\n",
    "ax2.scatter(x, error_cycles_new, color='skyblue', s=40,)\n",
    "ax2.scatter(x, error_load_new, color='forestgreen', s=40)\n",
    "\n",
    "ax1.text(x_max, (y1_max + y2_max)/2+0.21,\n",
    "         f'Max. offset at threshold {x_max:.2f}',\n",
    "         fontsize=20,\n",
    "         ha='left')\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",
    "ax1.set_xlabel(\"Error Threshold\")\n",
    "ax1.set_ylabel(\"Relative Performance\")\n",
    "ax2.set_xlabel(\"Error Threshold\")\n",
    "ax2.set_ylabel(\"Relative Performance\")\n",
    "ax1.grid(True, linestyle='-.', alpha=0.4)\n",
    "ax2.grid(True, linestyle='-.', alpha=0.4)\n",
    "#Spines auf ganzen Plot anwenden\n",
    "ax1.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax1.legend(frameon=False, loc='upper right')\n",
    "ax2.legend(frameon=False, loc='upper right')\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_snr_error_threshold', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_snr_error_threshold_new', dpi=600)\n",
    "figure1.show()\n",
    "figure2.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multi Error Threshold Plot\n",
    "\n",
    "import matplotlib.ticker as mtick\n",
    "from scipy.interpolate import make_interp_spline\n",
    "\n",
    "PLOT = False\n",
    "\n",
    "# Daten aus .csv laden\n",
    "error_threshold_breathing = np.loadtxt('threshold_evaluation/error_threshold_breathing.csv', delimiter=\";\", skiprows=1)\n",
    "error_threshold_chewing = np.loadtxt('threshold_evaluation/error_threshold_chewing.csv', delimiter=\";\", skiprows=1)\n",
    "error_threshold_coughing = np.loadtxt('threshold_evaluation/error_threshold_coughing.csv', delimiter=\";\", skiprows=1)\n",
    "error_threshold_drinking = np.loadtxt('threshold_evaluation/error_threshold_drinking.csv', delimiter=\";\", skiprows=1)\n",
    "error_threshold_scratching = np.loadtxt('threshold_evaluation/error_threshold_scratching.csv', delimiter=\";\", skiprows=1)\n",
    "\n",
    "# Daten laden\n",
    "x = error_threshold_breathing[:, 0]\n",
    "error_gain_breathing = error_threshold_breathing[:, 2]\n",
    "error_cycles_breathing = error_threshold_breathing[:, 6]\n",
    "error_load_breathing = error_threshold_breathing[:, 7]\n",
    "error_gain_chewing = error_threshold_chewing[:, 2]\n",
    "error_cycles_chewing = error_threshold_chewing[:, 6]\n",
    "error_load_chewing = error_threshold_chewing[:, 7]\n",
    "error_gain_coughing = error_threshold_coughing[:, 2]\n",
    "error_cycles_coughing = error_threshold_coughing[:, 6]\n",
    "error_load_coughing = error_threshold_coughing[:, 7]\n",
    "error_gain_drinking = error_threshold_drinking[:, 2]\n",
    "error_cycles_drinking = error_threshold_drinking[:, 6]\n",
    "error_load_drinking = error_threshold_drinking[:, 7]\n",
    "error_gain_scratching = error_threshold_scratching[:, 2]\n",
    "error_cycles_scratching = error_threshold_scratching[:, 6]  \n",
    "error_load_scratching = error_threshold_scratching[:, 7] \n",
    "\n",
    "# Sortieren\n",
    "idx = np.argsort(x)\n",
    "x = x[idx]\n",
    "error_gain_breathing = error_gain_breathing[idx]\n",
    "error_cycles_breathing = error_cycles_breathing[idx]\n",
    "error_load_breathing = error_load_breathing[idx]\n",
    "error_gain_chewing = error_gain_chewing[idx]\n",
    "error_cycles_chewing = error_cycles_chewing[idx]\n",
    "error_load_chewing = error_load_chewing[idx]\n",
    "error_gain_coughing = error_gain_coughing[idx]\n",
    "error_cycles_coughing = error_cycles_coughing[idx]\n",
    "error_load_coughing = error_load_coughing[idx]\n",
    "error_gain_drinking = error_gain_drinking[idx]\n",
    "error_cycles_drinking = error_cycles_drinking[idx]\n",
    "error_load_drinking = error_load_drinking[idx]\n",
    "error_gain_scratching = error_gain_scratching[idx]\n",
    "error_cycles_scratching = error_cycles_scratching[idx]\n",
    "error_load_scratching = error_load_scratching[idx]\n",
    "\n",
    "# Smoothing\n",
    "x_smooth = np.linspace(x.min(), x.max(), 300)\n",
    "\n",
    "gain_smooth_breathing = make_interp_spline(x, error_gain_breathing)(x_smooth)\n",
    "cycles_smooth_breathing = make_interp_spline(x, error_cycles_breathing)(x_smooth)\n",
    "load_smooth_breathing = make_interp_spline(x, error_load_breathing)(x_smooth)\n",
    "gain_smooth_chewing = make_interp_spline(x, error_gain_chewing)(x_smooth)\n",
    "cycles_smooth_chewing = make_interp_spline(x, error_cycles_chewing)(x_smooth)\n",
    "load_smooth_chewing = make_interp_spline(x, error_load_chewing)(x_smooth)\n",
    "gain_smooth_coughing = make_interp_spline(x, error_gain_coughing)(x_smooth)\n",
    "cycles_smooth_coughing = make_interp_spline(x, error_cycles_coughing)(x_smooth)\n",
    "load_smooth_coughing = make_interp_spline(x, error_load_coughing)(x_smooth)\n",
    "gain_smooth_drinking = make_interp_spline(x, error_gain_drinking)(x_smooth)\n",
    "cycles_smooth_drinking = make_interp_spline(x, error_cycles_drinking)(x_smooth)\n",
    "load_smooth_drinking = make_interp_spline(x, error_load_drinking)(x_smooth)\n",
    "gain_smooth_scratching = make_interp_spline(x, error_gain_scratching)(x_smooth)\n",
    "cycles_smooth_scratching = make_interp_spline(x, error_cycles_scratching)(x_smooth)\n",
    "load_smooth_scratching = make_interp_spline(x, error_load_scratching)(x_smooth)\n",
    "\n",
    "diff_smooth_breathing = gain_smooth_breathing - cycles_smooth_breathing\n",
    "diff_smooth_chewing = gain_smooth_chewing - cycles_smooth_chewing\n",
    "diff_smooth_coughing = gain_smooth_coughing - cycles_smooth_coughing\n",
    "diff_smooth_drinking = gain_smooth_drinking - cycles_smooth_drinking\n",
    "diff_smooth_scratching = gain_smooth_scratching - cycles_smooth_scratching\n",
    "\n",
    "# Alle Kurven in ein Array stapeln\n",
    "stack_difference = np.vstack([\n",
    "    diff_smooth_breathing,\n",
    "    diff_smooth_chewing,\n",
    "    diff_smooth_coughing,\n",
    "    diff_smooth_drinking,\n",
    "    diff_smooth_scratching\n",
    "])\n",
    "\n",
    "# Alle Kurven in ein Array stapeln\n",
    "stack_load = np.vstack([\n",
    "    load_smooth_breathing,\n",
    "    load_smooth_chewing,\n",
    "    load_smooth_coughing,\n",
    "    load_smooth_drinking,\n",
    "    load_smooth_scratching\n",
    "])\n",
    "\n",
    "# Punktweiser Mittelwert\n",
    "mean_gain = np.mean(stack_difference, axis=0)\n",
    "mean_load = np.mean(stack_load, axis=0)   \n",
    "\n",
    "idx_max_gain = np.argmax(mean_gain)\n",
    "x_max_gain = x_smooth[idx_max_gain]\n",
    "y_max_gain = mean_gain[idx_max_gain]\n",
    "x_max_load = x_smooth[idx_max_gain]\n",
    "y_max_load = mean_load[idx_max_gain]\n",
    "\n",
    "\n",
    "# Plot\n",
    "figure1, ax1 = plt.subplots(figsize=(15, 7))\n",
    "ax1.plot(x_smooth, diff_smooth_breathing, linestyle='--', color='indianred', linewidth=1.5, alpha=0.7, label='Breathing Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_chewing, linestyle='-.', color='skyblue', linewidth=1.5, alpha=0.7, label='Chewing Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_coughing, linestyle=':', color='forestgreen', linewidth=1.5, alpha=0.7, label='Coughing Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_drinking, linestyle='--', color='darkorange', linewidth=1.5, alpha=0.7, label='Drinking Noise')\n",
    "ax1.plot(x_smooth, diff_smooth_scratching, linestyle='-.', color='darkorchid', linewidth=1.5, alpha=0.7, label='Scratching Noise')\n",
    "ax1.plot(x_smooth, mean_gain, linestyle='--', color='red', linewidth=2.5, alpha=1, label='Mean Performance Gain')\n",
    "ax1.plot([x_max_gain, x_max_gain], [y_max_gain, 0], color='black', linestyle=':', linewidth=2)\n",
    "\n",
    "ax1.text(x_max_gain, y_max_gain+0.01,\n",
    "         f'{y_max_gain*100:.1f} \\% mean performance gain at error threshold {x_max_gain:.2f}',\n",
    "         fontsize=20,\n",
    "         ha='left')\n",
    "\n",
    "figure2, ax2 = plt.subplots(figsize=(15, 7))\n",
    "ax2.plot(x_smooth, load_smooth_breathing, linestyle='--', color='indianred', linewidth=1.5, alpha=0.7, label='Breathing Noise')\n",
    "ax2.plot(x_smooth, load_smooth_chewing, linestyle='-.', color='skyblue', linewidth=1.5, alpha=0.7, label='Chewing Noise')\n",
    "ax2.plot(x_smooth, load_smooth_coughing, linestyle=':', color='forestgreen', linewidth=1.5, alpha=0.7, label='Coughing Noise')\n",
    "ax2.plot(x_smooth, load_smooth_drinking, linestyle='--', color='darkorange', linewidth=1.5, alpha=0.7, label='Drinking Noise')\n",
    "ax2.plot(x_smooth, load_smooth_scratching, linestyle='-.', color='darkorchid', linewidth=1.5, alpha=0.7, label='Scratching Noise')\n",
    "ax2.plot(x_smooth, mean_load, linestyle='--', color='blue', linewidth=2.5, alpha=1, label='Mean DSP Load')\n",
    "ax2.plot([x_max_load, x_max_load], [y_max_load, 0], color='black', linestyle=':', linewidth=2)\n",
    "\n",
    "ax2.text(x_max_load, y_max_load+0.01,\n",
    "         f'{y_max_load*100:.1f} \\% mean DSP load at error threshold {x_max_load:.2f}',\n",
    "         fontsize=20,\n",
    "         ha='left')\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",
    "ax1.set_xlabel(\"Error Threshold\")\n",
    "ax2.set_xlabel(\"Error Threshold\")\n",
    "ax1.set_ylabel(\"Performance Gain\")\n",
    "ax2.set_ylabel(\"DSP Load\")\n",
    "ax1.grid(True, linestyle='-.', alpha=0.4)\n",
    "ax2.grid(True, linestyle='-.', alpha=0.4)\n",
    "ax1.set_ylim(0, 1)\n",
    "ax2.set_ylim(0, 0.7)\n",
    "#Spines auf ganzen Plot anwenden\n",
    "ax1.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax1.legend(frameon=False, loc='upper right')\n",
    "ax2.legend(frameon=False, loc='upper right')\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_gain_error_threshold', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_load_error_threshold', dpi=600)\n",
    "figure1.show()\n",
    "figure2.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.9.13)",
   "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
}