{ "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\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", " 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", " if (n % adaption_step) == 0: # bei Rate x/adatpion_step: if (n % adaption_step) < x:\n", " filter_line += mu * error * sample_line\n", " # Filterkoeffizienten expoertieren\n", " coeffient_matrix[n, :] = filter_line\n", " return output, coeffient_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", "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": [], "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_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='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": [ "# 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", "data_reduced_update = np.loadtxt('reduced_update.txt', delimiter=\",\")\n", "\n", "# Daten laden\n", "x = data_reduced_update[:, 0]\n", "reduced_gain = data_reduced_update[:, 2]\n", "reduced_cycles = data_reduced_update[:, 4]\n", "reduced_computing = data_reduced_update[:, 5]\n", "\n", "# Sortieren\n", "idx = np.argsort(x)\n", "x = x[idx]\n", "reduced_gain = reduced_gain[idx]\n", "reduced_cycles = reduced_cycles[idx]\n", "reduced_computing = reduced_computing[idx]\n", "\n", "# Smoothing\n", "x_smooth = np.linspace(x.min(), x.max(), 300)\n", "\n", "gain_smooth = make_interp_spline(x, reduced_gain)(x_smooth)\n", "cycles_smooth = make_interp_spline(x, reduced_cycles)(x_smooth)\n", "comp_smooth = make_interp_spline(x, reduced_computing)(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", "plt.figure(figsize=(15, 7))\n", "plt.plot(x_smooth, gain_smooth, linestyle='--', color='indianred', linewidth=2, alpha=0.9, label='SNR-Gain')\n", "plt.plot(x_smooth, cycles_smooth, linestyle='-.', color='skyblue', linewidth=2, alpha=0.9, label='Cycles/Sample')\n", "plt.plot(x_smooth, comp_smooth, linestyle=':', color='forestgreen', linewidth=2, alpha=0.9, label='DSP Computing')\n", "plt.plot([x_max, x_max], [y1_max, y2_max], color='black', linestyle=':', linewidth=2)\n", "\n", "plt.scatter(x, reduced_gain, color='indianred', s=40)\n", "plt.scatter(x, reduced_cycles, color='skyblue', s=40)\n", "plt.scatter(x, reduced_computing, color='forestgreen', s=40)\n", "\n", "plt.text(x_max, (y1_max + y2_max)/2+0.1,\n", " f'Maximum Offset at {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", "plt.xlabel(\"Update Rate\")\n", "plt.ylabel(\"Relative Performance\")\n", "plt.grid(True, linestyle='-.', alpha=0.4)\n", "#Spines auf ganzen Plot anwenden\n", "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n", "plt.gca().spines['top'].set_visible(False)\n", "plt.gca().spines['right'].set_visible(False)\n", "plt.gca().invert_xaxis()\n", "plt.legend(frameon=False, loc='upper right')\n", "plt.tight_layout()\n", "if PLOT == True:\n", " plt.savefig(f'plots/fig_snr_reduced_update', dpi=600)\n", "plt.show()\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 }