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Masterarbeit_Simulation/ANR_high_level.ipynb
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2026-03-25 15:01:02 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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 = True\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": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_27168\\1942261754.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_27168\\1942261754.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"
]
},
{
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Q+cPxIkWutIhP7H+VIpd3du7+frfJ+t9qo+PbNopM7uPc4yPzPborx9FjDKsZz991uIHe5IAvLH65WuTCEnu3PRCWcf+XbSJ/PtmxzUKWmFjh35WiABDO1iH+woULjRrdGrDWS01N62nc4cOH27lrI4Cut73hhhts3R4AYK851LGvHv3/uI+sZbH+up9I+zYiuxvSf7I3Z6u7j+GnuXM3S70EzdydkhEe3Cbys97peazPvi2y6GzxtRwXGnHZWZJfmifyu+Pj/61bJ/WfeVvkw6EiJ3Wwfx/7GkMTmXYyOW+sIAAO9/zM4kqWtg4PsI/2ubPCwk/e3Ws/AB6Z5a/Hg7kOvQdazkYb7ha6PKn4XIIyMfAGAXAAfmNrdzRx4sQW/1+0aJHMnz/fKF1iNsBMNu2+pKTECHyPHTvWaIIJFBYWer0JQNa49RN7t/v3bpFRx0habdovcpHPS1w4TZcma33LwjYiJ3domX2fqj9sFPnJWpF/nioyNs3vZapOfENkRKlIZYPIP3fYOznPczgz+kGLEzOr6kS6vRLK7r6+p8jOQyJP7Ra5pFikT3uRUzuE/v/pEpHFKVaF09s7GdQI8oTTJ/u9fXytqb7nIvu313Im8zNkEgj+5vQK6u0WV7J8rouzj3vymxJ4N68XuaqnvezvBRHfF9osdN6pIqNTPB7QeuLHvRb6+evHiDxyqvXb3rxO5Ob+1v/+UoeaX2Ybt8+1fXzIAQQWMbT4HDnt08C1li559tlnjcC3BsG19kxTU5Pli5ZAmTdvHsFvGLRzbdeuXelgC6TJH5JYKu81DfBkgg37RS5ZKZLzvMgX3wmVN9AgpHp6t736lqcuFSl52bnAxI6DoeC3+tr7ImcsDQXatWRGJlhdL3LfFnvBbzu1uE07D4ZKrYx5T6Q6IqjzvY+s17k1S5vctTlUfqOuUeQ/lSL3bxG5bo3Is1UiU9d503gz3QGydAfADzaFmou+HrGI8dFdof4HXp3U62dASx7ZeW118iWV4LcO+1c9bPYHf5nh8HeG1XGT5+AAi/x+TocnPGj8p5MLq/c5119kzPvRezUk47ywuum6fzMts9Bb5habSRlOStT/VSdLX6tJ/XUK8rm2ljt7NuCrLwA/IYaWmKOvjAbCly1b5uRdIkvppMiuXbuMfwG4z25GsXm7dGZ5pJoNmy59Xxd5seZopryWN9DMqotWinw+hZrptY3WStRYMWR562XUGmhv+6LIZht1lBMFjf3GbrDwnBWhEjx6+9JXMmPC4P/W0eTQpEHedi+K9HgtFKSZWtE6C9tLOjHzswQNLCP9aLX1yZd4LvBBs7+qQyJLqo5OGMIbTk2+JetJBwPI3/gge1aQDLB4Cv7B3lBZGBVv13XvFvvb8uFekfUxXovhFrO16yyW1vu5S/Xdv/pu7MC4Jjf0e13k/JUiBS8lDpb7jR4L1BxMz7n25YfLiwFwHzG0xByfGhg4cKAMGDDA6btFFqqro+gtkC6ZMk/8WBqX9scKFmrzwlS87ECG5Z+3iayvT/x3a/aFagJH87dtIhviBLl7vRaqHb3WwuNY8QUPGqUmYrckyLqIE/u/b5eM4HTdfCdL8aQzfnD96vjZ8Vt9MFnz+03WJ6GerwqtIgiKzq+IDHtL5E6bKzT+u1vk7KWh7MzIySn9PnyjtmXjP/jLQ9uj7481c/ic5SJ3W1ixpn/7lXdEnklz9qmXn6u9Fh57b2NoJZmWhdGxEW9S9IYkJ+EiJ1xjqbGYMf1ti6v9fuvC6ialJcii+f3Go8kNpgqHjpPSRY/HSl7NlV9syJfle1K7Lz2WtLKP8rPKQ6GJISAIiKF5EPOYOnWqFBUVuXHXAACHT9Y+/7bIDptB3Zw0ZwWOfE88tyfNDT/tBpT/slXkhDdFTopR//Q7Fk4utXHY8W+IDFsVe6m0VW+meJLlVm11J2hWfia48mPrWXWJaMabWcLFCenMTrcy8aHlgbymk1BWfNrh+rdelkH51fqjP0+0mdn52XdE3tobys7U1Sxahur410MZnR1fEjl3hUj7F0VmpZDhivQFprQUi+6vhqwI7UeuX5O42ab+7ZM2So2lW7pX5ejradLmlK9bKEdix392R/9e2ZXE9+rjFlYC6Nh2212bRAYuO7rt0b6XTsywOvNPH54YmllbLENXphYOsnIs6fQ+yik6caGrNrq8EpoYep8gOBB4rgTAqeMNAJnhoW1HD4Tt0DjST9eI7HE5AKjZXJoVmE6xzkv9kjj43r7YJQK0rvH3D5dD2HQ4i/Saj0UuXhlq/JisJdUixS+HsseyXbQT+B+tCWXKZ8Iy6PC6rKnQz5LVAK2VoE86Xzor71P3V1sGi9zKfPcjK4Ent/wyLACu/pnk6gqt6x7N2v2tazNf9bHIU7tCk6twX46NiWbt+TGlInpWeCx3ZlBPk3qHDyhifWdpT5LL3xL5V1hgWr8Gv5ogqaCmwV5/Ba39HK28kr6XyUh1xZ0TdP++sk7kV58krl9e3yiy/WDmlRvT1TLZRvvdlL8RKmVj+vPWzHvvAARg1XtNTY2ccMIJXm8GPKSNVEtKSox/AbgnXgkMK16rDS3Vd9O7daFsLr/o1EZ8I1rzR83wvTViSfDL1aFmkS/ViCysshcEV4UvSVbTTKFur0b/nWbK35MBZSi0zvubDmT9vZrCfTSnIRCkn/lYVlhcHXpbjKX1t7u05D6aO9L4WKZENe21nv+UtaG64277+gfJTbxF9jVI5EvvhiZXM2HyKp3WuVDSIZkjeg28aiAqVhmKoHD6Y3dzxASS6fsfhpoqXxs2Zt+xkO1qZ4xrZnksHyX5udJG0+mu/R2Llk9aGmfVmK5U6PCSyLGviuS+EFo550UT1li0+fr5K47Wfw+nq2W2Odz3xc900jNav4vfbRKZlObPFeAkYmgZGgCvrKyUigq+fbIZgxfIjGZTdrKDkuVVUPH+GMvjm30WzIw0cHnroMFFq47+3NCc2gH+rZ8kf5sgZNToku7wTKFoHrRQC9MPtImnl01JIz8OOiFzu816z6ksy7ayyiKSZgBuT2NQI1EZEDfGlpY+ivVYD28TOeZVkRkbQ3XHP/OW+01gNZBk1Wabn2vN3IyWfXrpqlCZBQ22xeqnEERXuFBu7JMkAmwbD4SCiLEMXxW93rY2T80kicbvW3XOZEzr5HekC1e6M9H5rQ+c64Giq8++8HbrGtI3rHGv9ne48NUh+lbFOyuNzG7Xbdcm2fr9EX7RSTqd2NyY5oap2nxdk1a+/n703x/3WvKf3Ss/Su071itaX/5Qc+wm1AvT3DsAcAoxtMTyxIYlS5ZIWVmZ9OvXL+bv7aqurpa5c+cab1ymmT17tixcuFA6d+58JJA/YcIEGT58uNeblnG0c+3OnTulW7dukpvry3kaAPq953BTvWhe8Ghp5uoYmUqxyo54IfLkWE9IYm23SRsepZK1r82t9PLC2SIXW9xVTw7AnLaVJp6ZtLfSIObKQSJnd0r/Y0cOoWoXJtIq9qc+fqNlBe/2SQ8Akxs1fKNlaep3i64s+XlEc7v/VYk8sFXkhz1Tf9xY780baegfEHmuqO9917CyWxps08uGc0V6t5fAS7YWrk6odWsb/2+2JTE5oRmp8SyuFjl9qcjqc1per81TM0miuaOJKTSiDHfQ5nGLnZuFl1lxwn8qQ5eb+orc0l9k0tpQpm46hJfe0+/ac4qc6T2iF53cnFEmMqmP9e9H3VdqEkPXfPtNvD+MMrFrx/v7Qt/9Vg1YJvJhxHj1yr8TfEYve1uk+VPp2hrAOcTQXAiAX3755bJo0SJjVkEDvNGaXY4aNcooY5KKTAqAa9B+2LBhMnjwYJk/f37U62fNmuXpNmai+voMa6kNZCC7J0Xp5NTBulP89JJp/fauL4vsujB246lIt9jI4I7mklUi7w0RObVj4r91Ors33axm2do9IfWKBjNv6icytMjb1zOdiSpzo5QNiuWZKFlgbT14j/+6TeQ7x0b/XZVLAXktO9KxTcvl/ZHBb5PZZyDVz4SuTPCLaNnFqs/rIo+eJtIlX+SMjiKd8yWQkv2YD14u8sl58f/G6VPxNT48TWh2+O/beRy/8NPqLa3BrRevaMDX6bdDkwOu7ynSPkFpPS1bcnKURpt/OF7kup6Jjz3Cy57EKzemQXarxzHJrohJtvwNAHuIoTkcAF+6dKk0H94baiD8iiuuaPU3mgGtwV8NYuvPyQSz9XaZVv5k/PjxxvOMDHLr8168eLH0799fBg0aJFdeeaVn2wgAmRBczgR+aYIZnpGqy4NP6Zi4XILT7tokMvMkyViDLWY/r7K4DD3Tci3MzLq3B4ucUZgdY+gfSQTAo2nrwZusjQBjBcAf3enOY/Z5TWTduSJFeSK//SR28DsRDahoPEWXm8d77TSrUbMiU6HN5+wyyri0OZr9/e04ZRxGvtc6CPXVrqGszA4+6hGRimhNDFPtJ+L2BOGiDCxbkKj2fJ4Dr1kqq9aSvWm0+tJBMv5j5+9z0HKR94bG/5towW/14zWhi+m1ASLnFrf8m3s3t6z9Ho/b8x1aE70kP/nvZp3sOrlD6Du+fW5oXxKU71oAPg+AT58+XaZMmSIjRoyIGvxWWh5FM8RXr7bXHUcD62PHjpVMoNu6YMECo/RJNBoE18C3lkIZM2ZMRmW2A4AXNuwPNZos9Wlm3SIf1hj9tEfLvmPVUMwUVgNuVkt0ZFoGuOmHq0MlbeoaQwFPt0V+bF5NY5mjVJvq+e2c+88u1Z2vbBApfjmUXXh3gj4M+rnRmqltckS65Ysc1zaUGT1vh8g1q0P3pYG8f50ucnnn6OPEia8SbT5n143rRO47MfTzv3aJPJpEf4zwINSpHULlYI4vEBlWIpKXabNiLtVRDi8l4dZKhVdcKAfktkTB6WR3KfuaWmfpPr7L+VJS8cpcIPnM8md2i3y2S+r3dd7h0kFNlxxdWWU1+K1Wa6a5hVV9dr+ztSZ6MqVF1teHSh3tjTJr3rudyMwTRS4rjf89q3mbuo/qlIZjGwD+l/RhmQZztfSJ1umORQPgqdS9HjJkiFRV+TDCEMWMGTOMoHa852sG87VGOKzRCZQuXbpQwB8IsKlRspW3HhDp+3roZFmbBT3hUnZjqtmYsB4An+xQDVMvXWpxguGdJBuW+YU2JtPyExrwrEjDykkNVIYvrf/Su+JbkQ0eo52Ip0N4M7Z0ShT8Nv9Ga6Zq/eUzl4l0e1WkzQsiX/8gFPw2M7w/947IL9e3vr1+FhIFAjUQkq6mx6k0d9ZglgacPvO2SP6LIrNiNFMOos9q3dwo7+NXXRzfvz9cXksf9+Yon610f1f8LCwb14mmrcmeBT0REezu+JLIN+OsZrA6yZDwcXeGjtnilddAbPrdGKvcjJ3v/vYvHi2flYxTllr/20qb+yRtem2FlvTr/0bsfa42ytXGnse/Ef8zevYykaKXQ/1vXqsROfEN68k4K/eEzkuATEEMLTFX8hI02zmVch/FxcUZUS5Ey7VoBrgG/OMxfx9v0gAt6aDt1KkTgxfwUZMrp03f0Pq6VyMyuL4asdTc78uVs81BCycyt2Vw/e83akMn9VZ5FRx1Mhv+oe2JAx0nWzyBjMet0h3x2Alshpf+0CDtcI9WW+w8ZL1OtZ/95pNQIML0lXdEhiwX+WyCJrMaCElHA+ImB0qxhLvqY5EtWRJA+W+lSO4Loe/M6Z+kp5H1L9b7p5zbn7aGSkolQ4Nzsew4KPJYCtnbTvlnRNmodfWt6z/77VgtE70YY5z8vw/s9ff59Cp7CRtfeDv+JH+3V0TOtfCdHYtOkmiw/1frRW6sEHlwq0j56yLv1iXfeFx9ckCk8CWRf0Ycu9Q3iszcLPL23qN9CrS5bqJG8aY/bBIZuFykx2si160WeWynyAcenzcBiRBD8ygAPmDAAOOSipkzZ4rfLVsWOmpJFAA3y56sWOGj7j4Z0MF28+bNxr8A3HFaEpke6TLTZ9ly4QEXPWDPe8HLrfGfaE0CM014MC7SuVm429ZMXT1pvHhlqF5nJA10ONHMavT76T+ZLHk5+duET+B4WfLnwsNL283A9y3rj2b5ZRoNRKhnK0We3C2yvE7k+erEt4v2+mt2nr4eqTbrKzxcRuOezSL3Orwf6vmayJUf+XNFk1umrmsZBHfbqWk4nqlNMIH2rMOLl+02hY1ctZKqcR8dHV+aEVv2Rmilnl73UnXrwCPsebvO2eMsK9+p0cSaxNH627rCZ9chkTdSmCTUbGxdLaSrgaZtEPneR6FSO2csE1l1+H7tTHbqiqNwP10rcrW9aryG3286+rPuF7T3g37P7E+y+SeQTsTQEvNtZbraWv8XcjMD2toAMxEzSE4Q3LpDhzxa7wsgbVbs8Xd9bV3GH14mAi3VNooUvhjKBnuh2vrSVj8G4yK9bPPkMdNp9queNL5UEzo5dZOeTCaTYZ/OpojRJLuc3OkMcH2tdKxp4Nvrcg9OfPdfHifT0MqKEw0oa3aevh6aeZwKrRG7rLZl0MNJD2wNTR7dvkHk1+tDme/atFFLgW3zWYb4f1Oskx8eBE+HiWkqs6Ulon7wYahOcjR2a23HClivT7L+tsnMcHVyH2Zm9mtGrNIgqF538arWgUfYc/2aUHA1/HtubRpKkll1fQrB5EjPxfhsDlgeCn5//0P/JtRotrlmyU/4SOQf20V+sz40EbtmX6gXxuwtocn9h7bFnpjVUnME0uEWYmjx+bIdQE1NjRFUbmz09zfD7t2hI8RkGltq/XQA8JJmMV3hk5q7g5aL7LpApEt+6hl8bvnuByLfP07kklVeb4k/adkPrTOqbuorckv/0M/6fkYrc+NHf9sm8u1jjy7v1vda60tmIy1jYPrHDpEZ5aHvDA3+ZBqtNT6jTGRSn9CJql0a+NGGhot9MClijrVMp9/9djIHXzxb5KLDh91OTwIMSUOOyqSw3hea/a7M78lreoj8op/IMfmhBnb6Her2KmYtz/LIdpGf9T76WHZLG3jVF+GOjeltOhveePb6nqHjg7MK7d+nlriYd5o4urrvoZNFvkW/koxTcPj7vV97+xMgTk32fufwMZFpcZoSVEa/Z7/0z9DlIs+cGTqncEtjWJb87K0tSzFFKsgVuaBY5OHtIvN3ivzpJJHJFaGs/vOKRF4deLTcnpZYmdwn1EAagHtympv9F3JYt26dlJeX+z51X2uda2PLSZMmGc0w4xk0aJCR/T1r1qy49c0PHDhgXMIz4Xv37m00BS0qKjpyfW5ubqvXR2v96MWt6/Ux9eMS+ZFx8np9TM0ifGSHSF1dnXQqLJScnNDft9xIXb6QI81RelDnhF0fft6gz6nVY+Yc/tvIuw+7vtV9RPn7I/cTY1siz1+ibYueeBh/H+W5mte3uJ8of3/kPlLdlhjXh/+9lW0Jf9xo2xOth3jk4ybaFknw/jm1LYk+S0c+B3Hev/D7ifVZOvKcXNyWezY3S2UDtcEAAJmnvH2zfKazlirJ3v3Y+UXNUrE/R7YdFPnWMc3SPlekrV5yRA4050h9U/ORxnBac1evL2iTIx1zm6VDrsj+ptD1D27P3tfQb77TvdnIftXjswNNzbKomvcG3rm4uFkuLg715snLyXG8xA8S0yD5SQXNRvmvv+8IfR/84NjmhOfofrg+2vn8kestnLtbuj7BObdT17uy7W49J2mWurq9ck15gZxXnOub+F46Ypb6WI5ngC9ZskTS0VhSm0WWlpaK37mRzT1t2jS55ZZbWl2/ceNGo6C9KiwslK5duxqPr0Fik2ai62Xnzp1SX390zZR2gtXbbt26tcWSiO7du0tBQYFx3+Ef1h49ekheXp5s2NAyda9Pnz7S0NAgW7YcXVOkH7q+ffvK/v37Zfv2o+lV+fn50rNnT2P7zEx5pY+nj6tZ/vpem8Kf0ytbcmROZRfN9RE58vRiHQQ6cb2b9+3V9X7aFqeu99O2OHW9n7YFAAD/W7s/x/E63Znm1dqj+/GHDgdGWmL/n2n+2mIygvcJ3nqxJidmY06kx2u1emn5XfCnbUfCsTFu5afr/bQtTl3vp22Jdr3+v5MM3bJLetbU+Sa+V5eGmGW/fv1ivEYpZIBrWRJ9YumgL0r4C+tHI0aMkEWLFhnZ35oFHg8Z4Nau18fU+qNHlmDrthizWy23RfOPQ7NkkasEDm97jOvD/765OSfhzFyrybYof9+cYEa0ycJMqfFjghm+VvcT8ffmb1PeFkmcLW11W+Jtj1iYRU60LfqbRDOrTmxL5G+ibU/oLYw/Q9tqW6JsuyPbYiybjr0t92V54AAAkLnO6tgsp3QQ+efO7A0SXnVcs+xrypE9jc0ytFOzUUf6YHOOHDQOK3KkpqHZyJQ3M8PrG3Xpfq7UNTbLnkaRjrm6PL9Zblzv23ZQWWdU12Y5tYPIse1yjPfmex9l7+cb3ru2R7N8ckBE+wOf3SlH9jc2y/SNfCbT7Rd9Q+eKv94Qeu1/068pbHV8ZNZ1vOsTx0acvD58W1qe/9rZdu+eU/iptPGYxrl7lG1x6vrIbYkRC0t0/Re7NMkZHcOvJgPcVga4BsA1qq/Baf05mdrXVun9V1SEFcjLAE4G6tu1a2dcIukbGvmmxnqT3bze/KC5db0+5tBikcGdmoxZHg3+5+bq38X6QDtxvV9m7Jy83k/b4tT1ftoWp673Zluu6ynyk7Uta/166dDFInm5IjUNIiU+rDN8zwm65FDkw32hBj2IbelAkcFH52rlutUi92wW3/vHKSJjjtHSQaH/NzaL5KXYVC8omj8lcqBJ5KZ1It3bivwsTQ3nnNR4iUhDs0i7F+3fx+1lIhMz6/A0cK7oKvLo6fpTaKB+VuvUOljn2Is671/oEmrAqZ+v6gaRYaWhsm36HaTlMPJjns+FZwHGOr5IdNyRI1f1FFlRJ/LpkqPff042pj1wcWrjzs++d6zIT3qJnN4x9J59/X2Rf+5I/n7GHSfywEmt35vPdxE55lV72/b0GSKfc7CWO9Lnn6dqiSORb36gWdjebMObA0WGFLXOLJ2ehnr7Hw4VOflNe7fVmvx/PCH0s5sNtq0yexx94/1QI+v/niny560iV358tD9J1SGR5XtE9jWJfLFLtL4PoSt+VWb+P9fmd7+bsZRY19vZP/GcUtlGDQZv3LhBenfUGFqub+J70bh9vSMB8LKyMuNJrV7tYBvgKDSreuzYseJ3diYAdOIA1viwPD0QCCd3DDWJ8cPB4SfnhoLfqjhP5K3BImctE9/o007kqh4ibXI0A0bkgRNFxn/s9Vb5z8GLowdq7j7B/wHw584S+VRE1TV9vxefJTLsLck6AwtDATE179TQv+1yQ80wVSYFwAcVitx7YiiwpzWQU5kE0GDkLZ+EgpVe+U53kal97QcH/KK8vciac5OfaAoFv4/6Vncx6llrlvMXuqa+T1t0tsgfNoYmiJ32t5NF+rcPbeugTombW+p3kF7cVpofCro7FYCKpNnnbnvh7PQ2qf5ZL5Hreon0bd/y+r+fYi8AroGwaLq1tbd9ey4UKcwT6ZovsuvoKvKUXVQs8hIlMdKWRPDCAO+O0weHqq564qQOIn88XuRHa5K7nSaq/O7wcYqa0qd1I/b/nCHy+RQnhnT10Qf7Qj//qp9In/Yi3fNFSvJE1u0PNbHUFTYa0DabcT5y+FhKje8h8uWuIse0PfodPJzwEBxEDM3BALgGfIcPHy5uGzJkiFHyw++SCWab9XDcyJoHgEykmQ564BjuzELxFQ0EhAchxvXQRjQiz6c5S9DPNHMxdpai/0UGv02X+r8ViSt+fDijUTOSRnVr/XsdDplwaD20k8gbg1peV5onUtVg7/40iP7rfu4ER624rFTkwVNCP++7KJRB9vDR0owZY0inoxMryQR4NfgfSYPIo45xZrs0WKh+3Fvkrs2hQIZT3h0iclrYUmS/0wCUk544XeQr74prLi4ROSZfZIeDwd5YKs4R6V8Q+zvip71E7tyU3H1qwMxJGvw2983f+8iZ+3x7sMgZhaFJq+FviZzcQeS+E0R+uV6kW75Ir3YiV7znzGNls/AVdGqdft7eSP92xJqg23uRSMeXUr//teeIlEd5XjNPtPcd9NTpoUnQcDf1FSluIzJ13dHr+kWcc1gx91SRc4pE5u4QY1WGfpcvrQ0dS1wWEQo6t9jafZrBbwDpl9Qpq2ZlpyMAXlxcHLdOtl+Ul4emGa2UbDEbZpIBDsAvTopxEpcuHbWwoM8VtPFXZowfXdtTAuv7x0rWycsRGdApNNkT7ST446Eit1jrM5PQlceJrB4qrpgeJaty03n2loKb0pGRGy+IGP699NApIrsvkIwxoDC02uLNQSL9ClquAnpncOizEI8Z/HdLeOagkwFg/chkUvDbSf86/JnVbMcTCtwvR+K2tXGC3+Hfn8no4WIg7NvHilwQEVC1S4Pf5nfgc2eL3H9iaP/wq/6hbPivRpksRXL+fUbr68K/K/2gQ5vQiqhoE5JWPXm6SFlBaOXgjX1C1322c6gc4oQeof+PKBX5msXJzdvKWge/zf3klL4tr4tctRHPI6eEnquWx9PbabkS87t8SFHr4DeAAAbAR44caVzSYebMmeJ3WhJGhXc7jcX8m4EDw86kEJOW2tHOrtHqCAFwxgcuBZ5SPfHTTA49WX5lQOjg028yIG6fNncfL9K+TeL62pnq3hNCtVTDg49BdnxBKFgV9286iNzkQAB8yVkis04K3d+k3uI4LdsS7eQ9WXqi64cAeLTJuFwf9Un4RV+RNwaGghr6va1Z6jr2Nbi9/2KRFYOjr7bQVUCnFzozkfb4afZvG/7Wzj6chZiqswtDE0bZRGt+G30DLhb5Yth3ycfnuPN4WjZNJfreckLP1i2aUg6AP3+2uEYz0l8eKDK6W+olZqzQchCwT7OLnTbZxr51i4WJYi1vp6sAkvXsmSJfOjxWdeXgb8tC3xdPn3m0HKL52X04wbGjnie8P0Rk4uEgeizLBoVWs1VeEEq80ax6K76ahu8UwGnE0BLzy7FzRjKz4RNlgJvBbzNgjsR00Obl5TF4ARd5PbxilVHQTA49WT7f4lLCdDMbhcHaZ+hrKWQKeU2D+5/tkp7gitf0JFSDdXaCxHZ8utS9EgBKa3Y7rcCjo+be7byrr6ylD7RxcqKVEpoJOrToaDkkDdjr2NfgdrTJiGgZplpDORVfSSHQp9tu6t1e5M6wjPBktcsJlYdYOTg0wZOJtPeFHeZnMh2fzfCyaeel4XjByuc42UmyE9Lw+dASDqnQ8g9WaBYv3PkOtrtPn16eXBKCTmQeZ2Gip1OeyDU2viNGJPEZ0bG0/fzo5c20H4+eJ5xiYXWN9lyYf1qo1nYyWfWJkjsAPyKGlljaDuVra2vlsccek6uvvtoopaKXqVOnGtdlKq3nrRndiQLgy5aFOrpNmDAhTVuW+bSD7YYNG4x/AQST1QCVZoT7iR9njj/v0YmnG0HGdNJGRTjKq+NlN96GhmbnVyRYXZLttNdiLB50c7JCm4bWXhgKTN91Quy/00zvaNnpdnypi/2MxFSM6da67Ml3bJTUOK1DaBKp/mKRq3tm/uoeJC/ZDHCn/ekk57/X29isXw3rzL4I8crPJOOOcntJCNcn8b1VlGevJFIytFa2lkl77DSRpktCQX3t7aEl2gC0RgwtsbSc+mmgu7S0VEaPHi2zZ8+WBQsWGJfbbrvNuK5NmzZy4403GkHyTKPPTenziWX+/PnGv5lQ1xwA0sXqrjlabT8veVkGIZbCNiKNl6T/cbUhlhVa+9ePxjt8EnUeAQBL/hNR69SNIfXk7ujXj00hiG3UFE2w3DrdpRfcqlOvS9I1yy/RGHYq+B3evC/8c7HrAmsZianU/p4bpXSKrkqouTD27T7XOVS/9pd9Rd4bEqpf++7QUEZvEBKvtByBWV7Ei74if44SyM0EXgfAT3Uho9wvn2ctqzP/1FAg1U4zQz+7wMEVDNr88We97Y3NZI5vv35McjXmw0siJUNX0WiNeSc/h4l2W5phDiCYXA2AL1myRLp06WIEupubm+NeZsyYIYMHD5ZPPvlEMsmoUaOMLPBp06bFLH+iQf9Zs2YZGeMAgMzIHtZsvkwpgfKjXqHtslrb0CmNFv8uWu1fPzjgcIKEnQwnP7jaxkSA1t6063NdvBtTqZ5Ep3v4rxrszYTcsNLWY1iDvOH12he5MLGlGX7mpelTIl1cKI8T7qe942c4RmbU3to/FJT/z5mh+rU39xc5tWPL+rVBoeVF+rWzvqNe6ODnwU4Gvpae8ZrT4/F/Zyb3904fVp3eMbnn1MmlVSnawFjL6ow6JhRIXTlIAqVHgkm+T5VY3y/f0r/19d8MywIfXupMmS8td/XigMxsKP7e0FBpr6l9Wpf4Or+IDHMgyFw7XFu5cqWMGDFCqqqqpLi4WCZNmiQLFy6UtWvXGin5etGf9bqJEycaQfA1a9bIoEGDMi4Ivnjx4qglTjT4PWzYMOO5k/0NIIjO6WT/tn5fnBXrRNKPZQHN+qda2/DBk/05ifHfJE/k02F/Eh/Cd4eI/CbKiWW4rjEau/rdV2xkZlmpvWmVG0HceJMbVgNli6ME9NId5zwrwfL3dMb8NMg7o1zk9YEiO89vHST3g29EZPhrndrfH66D+/IAkRt6t2xym8j3jwvVodXnrEH5qX3dD8r7ydpzrH/Ja+10p9iZFFt3rnjO6eMDrV+cjFjv1rlF9oLVyTbQ1DHmNP0ejlwFU5Kf/ORAqjT7/HIXvvOsNEJNVApQJ+bqL4q9Xw6f+NUVWBr4daLutY5TK6sPtWmmn2jJKy3tdWtZ6N/vHXs0q/2fKdbMB+BvSVZvsk4DvxrU1uzncePGRf2b/v37Gxf9W80A10zxKVOmGGVR3nzzTckUmtm9fPnyIyVdOncOFWOtrKw0npfZLBPW5ebmSp8+fYx/AQRTe58Pb+0WnwkZ4EsHtj5h/e6H8W+jZRymb0j9sZOZxLi8s8j440Qe2CoZmQF+WsfQ5efrJHDsrsbQEhC3OvA5apPmyY3iPJEdhxLfx6Wl3o7/ZRayHPu6UArgD8c70xTPCw+fIvLIjqP/1+BG+HumpQZuLxfZeVCkW1vrdWj1ko0y6TjczVI5XpVASfbVb7Yw6afbuON8kX1NIr1ec3bfYDYldbKsWLTvYVXiWhQj9kStG+VgLimxVh5KV550feXodVqiqKx969JR0YRvtmbS/7a/yLQNrbPs7Ui0TzyjY/qaa9v155NDq338Uu4HsIsYWmKuvDK333678a82h4wV/I5GM6W1YaRennvuOck0uv1a71vLnehFfyb4bY9OnjQ0NBj/AnDPQIdPVpINnln10VBJu9/HCAL18FEgRJvDRTae0hONHyVoZHTlcc48frL3c/+J7mSIpSMDPDwTPNIx+SIvWsji8quLbFZoM7OmUuVGUDlePfZ7UshGS+f58QAL388/6eX843bP4Azn8ACGNtWM9dmyGvzOdnoc/us+DZ489s/7tvx/5H7t6YheAn6eOE/X902s06ZZJ4n0bx+qbXzoEpHS/Pi9BdxYqdfBRtQhXgb64E4ix7k8jsOzm904I/0kiVULuvJEV6FsPDfUEFInG6wEv9W440JNv81SKNECvVY+D25MqPoFwW8EATE0jwLgc+fOlTlz5ki/fv2Svq3W0545c6ZxQfbSQbtlyxYGL+CyVEsPjAgteLElmXIRJ7rQ2CmRWNlF4bUUvRbr7ftDjADfF7qEmovlOFRqQE+ik/28aQZmogC9n2uAaxZ4pO0X2A8ie03fR7tBm/AT5pPyD/rqYDRemYpo5QAiacmMaNJ5jmxlYkCbUGrtfy3tkUwwJZ5M/Syb1pwjcqeWPWEpe8r0OPybuZsS/p2+3okk+zXz6/4i1ReK/PF4kc3ntR4Pn43oJeCmnhaPV5w+PnBqclBLPlScm3xtYyd7tXS1MbF2bc/4r80Slxtsh7/++lo49f3/wtmhYHYfGyt4erVPPlirK1h0LD0Utl9rl+P+Ofbei2Jn8ANwHjE0jwLgWvv6iiuusH17rZet9cEBAO5K9eRGM7TaBjRrItaxQ6Y0O3v0tNbXPXWGyPeOcybTJZWPTqwAfbo5WbM2U91mIXAVL/iq9ZF3ntckd3fb6asa4JeWpPa+f627t1likbWs49Ha/1rWQ4Mpq1NcLaPL1d3KBEyX8gKRn/T2/7L7TPLXk+LPFurr7UZ5EC1XdH2v1k0C012n1+q41+9EJyV7uNHZ4dUbTmaA6zFDvyT2uZ/vHGq0mCg5ojRNpVD0tXDi67/iHJGLPZhkjDx2ffqMo0dxWkrFDXwHA/AbV07jy8rKfHEfAID4Zp9krRFSLO1yRfZcJGkxLMOzEt0QrwbmFd1CGUa/irIYy4mTOCczw7zyaQc+U5pVn6m0hmiqwU7NLNOgywltD8mni+19KE7u4Hy2pjZJi+dDm4HivmkKDoc3a0zG8R1Sy0KdeaL92yK4nMhsTnWiK/zbZWyMCaJt50ugJDvhdqqDzYnVt2287z+MkWV+fpHIs2c6n6H9aprKqqWaAb77gtAxWf8C8YVPlYg802Oz/KJPs5GRnmpQHwAygW/z2Lp0yeAzSjgih2JcgOsGdgoti0yFNtRJh/lRMprd5Pf47qBCkfYWJi9+2DNU0/fqsJNSJzKmnMwM88ohB97kTN5TOdmwTPfZ805t9kXJDSuBNi0JEK+2bCzfcqjuuZtNgrXOr11OT0YgOMfj3VLMLg4vv2CHlRXd3V2qCX25R2UckvkaKHchi/dkGwH1QZ1i9wA5oYO13gbKaumMdDUmPjvBdn8/xr7h9+UiDZc4n53vhFPaNcjN/ZqNsiqp0KB+UcTxaG2K5xYA7CGGFp8rYQut471q1aqU7iNR3Zra2tqU7h/+pp1r+/btSwdbIA3snjzEKzHgBq03HS2bOVvFC+BF1kLecr7IfWFBsU42A+AFuUezpickWUvUjw7ZjOLXBODE7n9nOr/P7qpLQmyyUpfbqoMW4/BXRfkM/+eM+BmFGlwfG6cxm1NOSSGTUyfGzrJ4+0MXt2wq6McgDfxxPP5Sipm2uirJijvK/TcpHaspttuSOTyz8/p0yUvfNps9Q6xmG19nsVeINqF2g9lgc/25Iq8NCE0G/F9EY9Zwfzo59Ny+2EVkepnIvotCGd8/7u1OmS+/nWtrs/preoQmClYOsn+cCcA+YmiJufLKTJ06VaZNm5bS7SdMmBDz9zU1NVJaSkeFINMJkPr6egr4A2nyGRvNLH9wnKTdj3ul77H8/u0zpU9qkxwDbWT/rj0ntIR5w7kiwwKwG7a7FLko7MTu+ILUsm8fOSW0CiNW9phbnAw4O7HPdvKA9Ktdrf2dZiRqgOL+sJr0Z3ZMXHfW7eSaRQ40drOyjQ+fEqoL+9DJIrNPTG9TQWQGL47Hxx2XfMkvt3kVzEsmQcHOO3SnC4H9HAdey6GdEn8Pm7TclTb3dut7uG97kXOLQz+fVxz9+/mNgaF/tbb3v84QmdzH+Xrwfh/bx7YTuedEkZWDRc6Os4oKgHuIoXkUAC8uLpYxY8YYgexkzZkzx3jDBgyInWZQWVnJmxpw+v5u376d9xnwoGGiZkXeaaEx3rEuLTX2y0mo3799zkixfIWdGN5x7UIBs6A0j7wkhVUMz58dKitzS8SqhMuTmEw6tYPI17uHGr1p9pgGY/V+tfmX29nf4UF8P+yzczxYHWG6qqfIvFNF/nSSWFoK7nYynxOTS1ZKqGgNWPXNY0XGB2BFB5wXPrbTlcSaE6cuvpYiSRTsPN3hOthecjsDPN79P3G6zQnINKyQiXTB4QC1k2LVU4/2/TzUwQnldOFcGwgexnViroQS1q9fL4MGDZKFCxfK5ZdfbmRzl5TEP8usrq6WWbNmGT9PnjxZlixZEvNvZ86cSQY4ADjcqf3NgSKP7BC5uZ/Ie3sT3yaFagcp+fspItevFtndIBnpJ71EHt0psuGA/fuIVrohWclmsa4YJIGSl5N68DxaAN3q3a45J3pDRfN+X6sRuX+LyEPbxXGXuRxgt8PJrOoGG8f9o2M01YvGzUCgBuGdup/Tlsb/m1QboCK7RK52cUtznAnw/1pYHTHrRJELVkogJJMBPsHGqrx4d/9liytpIumEbqTLSls3hOzySuz7SPYrPLL+tNu+fozIP3ak9zEBAD4NgGsNcC1TYlq0aFFSt7fy94kC6gCA5AwpCl2sNPtRXpU0/Eb30OVgk0i7F917HKcnz2/qK/KLvqEMal12nPO89due0iGUrakB0Z/1ErndQoZ+Il2TqJs55ySRAQFb0up1coS+/vpZiEWXWutFM8zL3nDucZ85Q1ylS8HPWZH87XJ8UNvda68MEDmvyLnsRY0JNcb4/YGw2t+A1WCsZl9//yN/fzdrr4qgSOapTEyiLJqVEiRO0t4K4bTXwCotlbEsdqJAsuU3dIXAHRslbYkYZgD8SZuZ8gCAgATAO3fubGR0uxGsDr9fBFt+Pp2YAC8zwn/bX+T/1lkLWM0oE5lcIWnVNlekc55IpUuZ4KnGR3/aS+TOTSIjSkUeO02kMIU97pXHhRophTeyTNXME0X6vZ747+afKjIqiexYr2h92H2N1hsgNnuYyaxNFKNlycWqU65BECdiuovPErm01N19tt2l4E4GwO1kgCfDjck/zVo93+Fl/Nf0FLlrc+vrf9k39P0JJHs8Hq2ZnzZPdVKq33U6ga8lgPY7NBE2qbf4fmXM146x12gxVlb/EouNKmP5dT+RX6w/+v9ok71nFYaaRlY1iHzl3Za/09JgydLEAM0Evynscd18X7RZ68b9oaaXmYpzbSB4GNceBMDLysokJydHli1bZtQDd9rs2bNt1RdH5tDOtT17Wmz/DcAVnRIsKQ0/15rUJ/0BcPXaQJGT3nTnvs9Msca2lrG4rdzeSWkkN+J52tjp4MUibeNk0b88wJ3amk4ZUChya3+Rz4SdgFrNrPcySXhCj+QzL5sc+BD0aJueffZ/zxT5zNtHf9+7ncjGA84t9U/E7WaO2jT47zuczY78nAvbrAGhaAHwn3gY0ENmiRzbo7uJfOdDd8dbswPByX0XibxeK/LRPpHvRWSsd8lLroTaDAdWXPl15YjZ3DHcc2eJdEyxpMj/9RXp1S4UjNZ9dCzaNDJS9xRiN7/oJ/KjXqFJ404vi6t+luHfo5xrA8HDuE7MlfwPzfgePny4K8FvNXbsWDLBA04L9+/Zs4cC/oCHvp+gnuTJSTaac8OJHdwrT5Fq4Fq/vZwIfpvBajfkH86ij+bTJe4Gv79is75ouBWDWwa//SDHhffTqdjwyR3Ts88ObwSqDT0/3yV9z/HEApFBLpfr+X82shNj+cYx7gS/VbQs77uPt776AIgc2wVtRDad5/Zjpn4fGgTXElLfPa7ld4xO6u68wNp9FLYRWXeOf2ul60SjBvp15YiTPRQ+5cAqId0efe03nBdqspsMu7XHTdrgWVfchTd3R2ucawPBw7j2KAA+YsQIow64WzSwPmzYMNfuH97TQbt7924GL+AhzQD6UozAjNZaLAnwCqtE55IjLZygOfn19VUHgsWx7LpA5J4TWj7nL3dpXbPTaQ+fIln52bm8VKQsyQDJdQ4kc+hy7XTusx85JbQ0/JFTY0+yhHMqfvOXk8V1Gtw5zaHJN7dXIgwsdL6BLrJHtLGtzVO1ieG1PUWWunC6l2r2cSSt0awlORovCU3qWg0W676xn41gdluHaySVtY8+kaUTjTohkaq/hn1ntvOouctFYZPt2iPFqeMmu8cZ958ggce5NhA8jGuPAuDjx483Lm569tlnXb1/AEDsoFQqS1Sddr5DTePCJTps0KBeIsc4VG7iuLbOZndF0vvWWsFNnxJpPnx54gxnTqzTGeQwrRwknspxIbttepmk7Po0r4jUGq7/OiOUjdfHQsa7EyVQNGPT6TrasTg1Jt0+RwmfyNJSBPEarwJWaRPDu08QGVzkbKD39YHO73u+1FXk5/1ajtl/W5jgbWdzrDi9u452f05OZH37WJEzDq8OWneueEL7pNx7QmhixaljA32/dbWOZskn6yoqCABAIHEYDABwJMijwaegSBSUstJAzqlAnEcJWRnrDIu127XJqxe+k+RycLNUTaqcuA+7vn+stbJDqSpO43fQX05y5n7cztE5tp1I/UUirw0QmdzH5QcDEpgTZ9wM6SRyjgsT2tGcFJHZPShiv3FbCpOOWnrFLT/sEZqkdnoia+VgkdoLRY5rJ57o2lbkhz1DEytO0wmVJ053/n4BAJnHtwHwyy+/3OtNgMcKCtJURA9A3Pq0VoM2Hw91e2syh5USKTjq2LbJv2YLYtT31LrrVkpuTO2T/kmjreeJdLAZpLXynGJ5w72qdJb22Rp8PytB/fE7y0UuLhaZa2F1hR9o5qs2kU31tbUymZaq9m1Cze6cbDSK7OHk8fgP4vQW8XLBdqeI79eJKewfflcucnM/cczQsEkBNwLE5n4z8jUIEl15pQH+aD0jshnn2kDwMK4zMABeU1MjixYt8noz4HEH2+7duxv/AvDOqG6hJcnRApaROnmUAT4uQbNOO1I9EXfyRN6JzFi/y7FR93Nkt9i/u9VC9p5bZWVy4jRG02xcu5YOciaA4tU+O1EGumYevjBAZEyMSTcr0h1A0+eU6mvrRHkbIAjH4+kcv26WFdOyT790MACupUFMiSYSEZsG+N8a3PK6Xu1EbuprfZVCkHCuDQQP4zoxX74yGvwuKSnxejPgIS3cX11dTQF/wGN6khhtSfI3ure+zqvswu/aKCmh3h4sku/SNjv5zZUpGbGpvl5W6kR7OSliVaz6pY/GyFi3KtnGmX7bZ//5JJEebUVmnyiBM7zU3u20WZ42FAT8Kp3H401pPOSP3PWPjjOh6rXSfJFVg0OB2XgTv0jszMKWDS41Uz+yL0e8VQpBwrk2EDyMa48D4EuWLJGxY8fKCSecIF26dLF0adOmjYwZM8bNzUIGYPAC/g726HLZSG4Fk93K5NJa0b09DD49f7a1vzs5izK+tBFWu5zUg/7RPp/pDIBfXhr9xDtVz5yRfNPXu5PMrHdrn63jbdN5IuMdbN4W6R+neve5jdZUb0yCYBVHOPC7dB6Pj01h9UeqivNENp8n0iVP5FofNkA8qzAUmHUzcz1baIPLhktEai4Uuagk9N5nI861geBhXCeW52YNb83k5sUHgMz39Bkin3sn9HNOnHIAFxaLvFyT+P5O7SBpocuF39ob+/c5HpZAucTCQqensqxx01e7iey9OBTAfnCbyDOVkpE0o+x/VamvUoj0mS6hkhtdXrF+m2t8FMyxGryZ0kdk+obk7//iEu+W1n++S+vvm9IER9kcIQMtS6555atdQ/0ZdlxArfxsoMcYWqpGlReI3No/dOx6W7nXWwYAyLgA+AMPPCALFy6UgQMHGhngZWVlSZU0mTlzppE9DgDwh892OfpznzhZ01/sYi0A/vSZkhZf7CrytWNEpq5ref0NvUP/upVN5dTc7xeysJmmmb2tJTOOey3631zp8yXK4R+rwjYiDzhY9kOboD17psjuQ6Hste98ILK4OvrfrjknMzMGp9oMgHvt1YEiHV86+v+mKL0Tth1sWRcegMg1PdL7XdUloreG2ZyY4Hd2mhpRBxwAEEyuBMBnzZolkyZNkunTp9u6ff/+/Y2yKchuhYUOrBcH4Jj/nSny122pZ8isH9okfds7W4FLlyzfs7n19Tf2EXl4e+vrBxz+ennwZJELV/q7CWa20oaRp3cUeTdKBr+Vz+BlpSLPhmVhh/t4qKTN7gtE8hwuODei89GfnzlT5PlqkcveDv1/7Tkih5pDwdZ0Lu12cp/tVUPdVGkQ7aOhIie9Gb2mceTE2L+ybIUHMlM6jsd/l4ZSTeH0u3HCcSKztopnhnTy7rEBxbk2EDyMaw9qgGvdGbvBb6W1wCmdkt20c23Xrl3pYAv4yGWdRf5+aigDNRYryVM5LozrWCeSBW1ab9PJHUJZ4eqC4ui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",
"text/plain": [
"<Figure size 1500x1200 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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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": {
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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 = True\n",
"AUDIO = False\n",
"PLOT = False\n",
"SERIES = False\n",
"\n",
"# Chirp Generator\n",
"n=2000 #Sampleanzahl\n",
"fs=20000 #Samplingrate\n",
"f0=100 #Startfrequenz\n",
"f1=1000 #Stopfrequenz\n",
"t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
"f_disturber=2000 #Störfrequenz\n",
"\n",
"# Parameter setzen\n",
"coefficients = 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 = True\n",
"\n",
"# Daten aus .csv laden\n",
"data_male_breathing = np.loadtxt('snr_evaluation/male+breathing', delimiter=\",\")\n",
"data_male_chewing = np.loadtxt('snr_evaluation/male+chewing', delimiter=\",\")\n",
"data_male_scratching = np.loadtxt('snr_evaluation/male+scratching', delimiter=\",\")\n",
"data_male_drinking = np.loadtxt('snr_evaluation/male+drinking', delimiter=\",\")\n",
"data_male_coughing = np.loadtxt('snr_evaluation/male+coughing', delimiter=\",\")\n",
"\n",
"\n",
"# Daten laden\n",
"x = data_male_breathing[:, 0]\n",
"male_breathing = savgol_filter(data_male_breathing[:, 1], 10, 3)\n",
"male_chewing = savgol_filter(data_male_chewing[:, 1], 10, 3)\n",
"male_scratching = savgol_filter(data_male_scratching[:, 1], 10, 3)\n",
"male_drinking = savgol_filter(data_male_drinking[:, 1], 10, 3)\n",
"male_coughing = savgol_filter(data_male_coughing[:, 1], 10, 3)\n",
"\n",
"# Alle Kurven in ein Array stapeln\n",
"all_curves = np.vstack([\n",
" male_breathing,\n",
" male_chewing,\n",
" male_scratching,\n",
" male_drinking,\n",
" male_coughing\n",
"])\n",
"\n",
"# Punktweiser Mittelwert\n",
"mean_curve = np.mean(all_curves, axis=0)\n",
"\n",
"# Plot\n",
"plt.figure(figsize=(15, 7))\n",
"plt.plot(x, male_breathing, linestyle='-', linewidth=1.5, alpha=0.7, label='Breathing Noise')\n",
"plt.plot(x, male_chewing, linestyle='--', linewidth=1.5, alpha=0.7, label='Chewing Noise')\n",
"plt.plot(x, male_scratching, linestyle='-.', linewidth=1.5, alpha=0.7, label='Scratching Noise')\n",
"plt.plot(x, male_drinking, linestyle=':', linewidth=1.5, alpha=0.7, label='Drinking Noise')\n",
"plt.plot(x, male_coughing, linestyle=(0, (3, 1, 1, 1)), linewidth=1.5, alpha=0.7, label='Coughing Noise')\n",
"plt.plot(x, mean_curve, linestyle='--', color='red', linewidth=2.5, label='Mean SNR-Gain')\n",
"\n",
"plt.rcParams.update({\n",
" \"text.usetex\": True,\n",
" \"font.family\": \"serif\",\n",
" 'font.size': 16, # Standardtext\n",
" 'axes.labelsize': 30, # Achsenbeschriftungen\n",
" 'xtick.labelsize': 25, # Tick-Beschriftungen\n",
" 'ytick.labelsize': 25,\n",
" 'legend.fontsize': 25 # Legende\n",
"})\n",
"\n",
"plt.xlabel(\"Filter length\")\n",
"plt.ylabel(\"SNR-Gain (dB)\")\n",
"plt.grid(True, linestyle='--', alpha=0.4)\n",
"#Spines auf ganzen Plot anwenden\n",
"plt.gca().spines['top'].set_visible(False)\n",
"plt.gca().spines['right'].set_visible(False)\n",
"plt.legend(frameon=False, loc='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 = True\n",
"\n",
"# Filterlänge\n",
"N = np.arange(1, 128)\n",
"# Verschiedene Updateschritte\n",
"U_values = [1, 0.5, 0.25]\n",
"\n",
"C_total_1 = N + (6*N + 8)*U_values[0] + 34\n",
"C_total_2 = N + (6*N + 8)*U_values[1] + 34\n",
"C_total_3 = N + (6*N + 8)*U_values[2] + 34\n",
"\n",
"plt.figure(figsize=(15, 7))\n",
"plt.plot(N, C_total_1, linestyle='-', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[0]}')\n",
"plt.plot(N, C_total_2, linestyle='--', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[1]}')\n",
"plt.plot(N, C_total_3, linestyle='-.', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[2]}')\n",
"\n",
"plt.rcParams.update({\n",
" \"text.usetex\": True,\n",
" \"font.family\": \"serif\",\n",
" 'font.size': 16, # Standardtext\n",
" 'axes.labelsize': 30, # Achsenbeschriftungen\n",
" 'xtick.labelsize': 25, # Tick-Beschriftungen\n",
" 'ytick.labelsize': 25,\n",
" 'legend.fontsize': 25 # Legende\n",
"})\n",
"\n",
"plt.xlabel(\"Filter length\")\n",
"plt.ylabel(\"Cycles/Sample\")\n",
"plt.grid(True, linestyle='--', alpha=0.4)\n",
"#Spines auf ganzen Plot anwenden\n",
"plt.gca().spines['top'].set_visible(False)\n",
"plt.gca().spines['right'].set_visible(False)\n",
"plt.legend(frameon=False, loc='lower right')\n",
"plt.tight_layout()\n",
"if PLOT == True:\n",
" plt.savefig(f'plots/fig_c_total', dpi=600)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_27168\\3014463257.py:101: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
" figure1.show()\n",
"C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_27168\\3014463257.py:102: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
" figure2.show()\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1500x900 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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TSICLzE7NysDNATnw+gGpr6t3dZtIS0ROvn2SJDh4rwRYQiyVcAL8a1/7milj4tDZ3Ddu3DBJXLfa2nKvSl3u9MMDx5EjR3ztCwBgqeHh4bxKfensqXg8XtQ+AQAA2JKYTkg8FJe6F+ukrTf3+/NYNCbR96Lm9REJcABA2SfAdZFFLduhcs34TkeT5QAAlHLye+8reyU6GXV9m5nYjAwODUp4NlzUvgEAANjU0N4gTd1NrtrOyEzR+wMAQEkkwDXprQXar127VtDtdaa4lv9AcaxcuZIC+oBHGkPd3d3EUoXSmU2a/K5/qV4aI42ubjN2bUzi78aZBZ5iXuZlcH7QXAMA4CfOSYAdvFcC7CCWSjwBruVLduzYUfDttda1M4O81Gi/tmzZIpcuXcqrTMuJEyfk7NmzZna80tremuT38ncqVGNjI8EDeKQxpLGEyqbJb7czomIjsaL3J6geLTzyuwsAABickwDveK8E2EEslXgCfM+ePZ5uHw6H5Rvf+IaUUtL74sWLJoF97NixxQS2mwS43nb79u2ydetWs7hnuu3Jtb6Xw+3bt2Xjxo2sIAt4XI15cHBQVq9eTSxVYE1v6nnbE5KQbKjeINcT1/3uCgCgwiWfk5gFDhSO90qAHcRSiSfAbSSv33nnHSkF7e3tZta2ztTWGdvnzp2Ty5cvu779a6+9Zm6fmuTW5Pn58+dl3bp1Zkb5vn37ZLnooqQAvCOWKremd7nU856bnTPJfL+T/ppwANxau/Z/kfmFaglVJfzuCgJyrEzcjMpdvzuDwOCcBNjBeyXADmKphBPg+bh586ZJLOu0ft2pmnA+fPiwbNq0SUrB2NhYwbfVZLnWM9eZ4+loElwT3zr+l19+Oa+SKgAAuzO6743dk6avNbmq6V0O9bxnp2Zl4OaAHHj9gNTX1VdM0h/B17hixO8uIGDHSqyKYwYAAKCS+Z4A1xnQP/3pT5ds++53v2tKjHzta1+TIDt69GjOeuhaLkbLqmiN8IMHDy5r/wCgXBU6o/tLLV9yVdO7HOp5J6YTEg/Fpe7FOmnrbauIpD8AAAAAoPL4ngBPR2dEf+tb3wp0AlxrfOsM8M2bN2dtt379enN96tSpZUuA9/T0sAgm4JHG0KpVq4ilEqUzvzX5Xf9SfcXM6C5UQ3uDr0l/rbF6M3GTWqsAAN9xTlre8mqO1tZW6erqKlqfsPx4rwTYQSyVcQJckxbODPAg00UzkxPcmThlT/KpK+5VTU0NwQN4pDFELJU+TX5XyozuIItL5X3wgMJNTDwjC/M1UhWKS2vrbb+7gwAcK9Pz+nr7F353BwHBOWn5yqs5Ii0ROfn2SZLgZYT3SoAdxFJAEuC3bt0yZUA0GawzorPRhHdym9RFI4PGSWjrApi5aJL8xo0b5ja5ZozboCvI6qfsrCALeFuN+fbt2/LMM88QS4DHxcaerX5WriWu+d0VBMQnH78k8blmqamdktaNf+p3dxCAYyUkuh7Ej/3uDgJ2TmIWePHLq6lYNCbR96JmIhwJ8PLBeyXADmIpAAnw999/X7Zu3Zr3iqU6I1oXwXz11VclyKLRx3Vn81nYMuiz3gEAAAAAlc1teTXHjMwUtT8AABQtAf7aa6+ZxPeuXbtk27Zti6VAdGa31vhOTfxeunTJzILWBSE3bdokQZdrxnsyJ0mu4892f/ncp5qZmTEXh36q7nyCpBeHfoqU/LPSr1fopVjb9TH1+Ej9cMTmdsbEmIo5JqXb0t1/UMdUTvtJ78/5mljVp/8cC5/+S94eqgplbO/MAMvUXmeLJXPap25f7GtVaMnv0rXX/jl/n+T26fqe3F7/Dm7aO49ZHape0j5T3xfbVxfe3ulLanunX3qtbZ3b6M+ZxupsT26ffH/pxmp6kaZ9tv2Xq33qmJz2yWPKduzlam/j2HM71lztMx1LS/afi/bJ950rDpa0Tzruku/Ny7GX3Md07bPtv+T26fbTkjElHUfJ7TPuDxftl4wpaZ85cu2PfI89p32uv7uzvaD2ofTtMz1nO7OgssdZ0t8kx3O8l+fs5MdI9xycbkw2nuNzHXtenrMztU899gp9zs7UPnlMtp+zM7ZPfg6Ux31L91yQ73O88zfK59iz/Zyduj3f5+xM7TMde/nGzeL2T19rOq8HK/k1bDmNKV3eIehjKsf9xJhKe0zZ8g5BHVPI8n5yOzO+KAnw8+fPm2Tt9evXZd26dUt+p0nenTt3mhIcqQlzpfW/tc3Xv/51CTIbs7n1w4ALFy4sJsaPHDliftayKocOHcpZX1zbf//7339iu+4bLYOiB0tzc7N0dnaa/k5NTS1JyutleHhYYrHPauNGIhFpaWmRO3fuyNzc3OL27u5uaWxsNPebfLBqsX6tV6Rf2UimX9/QxeaGhoYWt2l/1qxZI9PT03Lv3r3F7bW1tdLb22v658ysV/p4+rjj4+NLPhxgTIxpOcakX9HU/jmxVA5jKqf9pGPYsHaDjMiIdFR1SGeoc7H9+MK43J2/K92hbglXhc22ns4eebD68bh7Q73SVPXZrCVtq7dZW71W6qRusf1A+PECTxuqNyx5Q6eLZ2n9UP0KdbKRqhFpWtEkz7c9L83VzYtv/PRr1iuqVsjq0OrFtrMyK8MyLD0re5a0f7jwUD6e/zjtmLT959d/fkn7kfkRiS5E045J/zZbN22V32r7rcX2g/OD8mjhUdox6ZvZ3/3q78rTbU8vtte+10iNrKv+7FzvjKmltmVJex2T3o/+zZ8KPbXYPrbw+PjRMT3V+ZS0f7Xd3CYRSjyxn1LHtKpz1WL7qaqpJ/ZT6ph0vznt9e+Vbj8lj8lpvyq8Su7InbT7KXlMTvtwc1gmZCLnsee0r2mokYQkinLsafsPqj+Q+lD9ku2Zjr32tnZ5X96XjrqOJe0zHXv1zfVyRa7I0yuelmeqn0m7n5LHFG94XFv32dZnpbP6s/vJdOyNV4+bhFPycf0bqRF9dtGkS7oxuT32nDGtXLFySft0zxHJY1oXXicdX+1YbJ9uPyWP6bnIc4vHnbbP9BzhHHvPdz6/2H5F9YqMzxHOmLo61snd+0vuStqq2mRVdau1Y89pP1sz6+rYc9qPVY2Z26d7jkgek7av21Qn0zKdcT8lH3vaPrb+8XNHtueIa58eK7WhWvN8qrI9R+Q69pL3U+qY7st9aQ+3L2mf7djT56C1q9cuaZ/t2NP2L3zhhSXtsx176sWvvChr2tYsts927NVX1y+Jg2znJ2dMvZ29i8dqVagq4/nJGVPyc/Z01XTG85MzpuT2eixlOj85Y0p+jtfn7EznJ2dMTvvO1k6JStSMaVVolawMrTTPL5MLk0vG5LRfsWKFxCSW9dhzxqS3ubfynutjT9v/ZsVvzDk32/nJ0R3pNs/ZLTUtS9pnOvaaw83mOXtlw0r5XPXn0u6n5DFVrXj8Gndd8zrpqX4cQ9mOvVjt47h8LvyctCY9B2U99kL1sum5Tea1m74erPTXsOU0prGxscUkVbmMqRz3E2Mq7TH19PSYia3JeYegj6nX8n7S2/mWAD9x4oSZ6Z2a/FbaQU3oZprl/YMf/EC+9a1vmVrYa9eulaBydpgeUIU6ePCgpz5oKZl/9a/+1ZIZ4KtXr5bnn3/+idIsmlRPrlfuBFZqHTZnuwZhuu16/6nb9aIBkUw/odEDP3W7amhoSLtdD/ympie/ShcOh5/4QIUxMaZij0nv57d+67cWH78cxlRO+0lP4NdvXZfwprCMLozKWGJssZ0zI+re/D2TsFAjIyNya/CWvCAvyCfzn6SdoXUrcWtxu7YfGx+T1bJarieuL+mH0z61pvX8wrw8fPRQfvngl9LZ9llyQOkb4OT2Th/v3L+zpL2zPdOYfnPjN9L4oPGJ9pnGdPHKRZn9e7OL7Z3t6cak/f/Ln/+lvPDlF5a01zfZ6ep3T85Nyq9+/qvF9k5f9A3zZGJySd+dmYDRkah88PMPzG0ibZEn9lPqmJLbd7R1PLGfUsek+y21/RP7KWlMS9q3d2TcT86YnPbPb3peIl2RnMfekvYSKcqxp+0TiYTMzM+k3U+pYxp5MGKuR2dH0441dUzDU8Pm+uNHH8tM4rNvnWU69kanH08QuDZxTcYin91PpmNP71P7nxwHcdm2+Bhejz11/9F9+eXPf/lE+0zH3s3xm0vap9tPyWP6MPrh4nGU3D7TsffLkV8uaZ9uPyWPaXhU31T8/pL7erDwQB4m7lk79pz2X9z0RamX+pzHXnL7TM8RyWMy7a98IF/8u1/MuJ+Sjz1tr893z8vzWZ8jnMUM5+bn5N79O9IlXVmfI3Ide8n7Kd2YNDbTPWdnGpOed9oetD3RPtOYPvj1B1L1oOqJ5+xMY/rrv/1rmdg68UT7dMeejvfSzy8tOe5S91PqmG6O3HziOTvb81665/hsx1665/hsx17qc3yuY2+x/aYXpCPSsbifnPrfqcdeatxkO/aSn7Pv5HHsaXt9raDn3Gznp8X20cfP2ZPxyYz7KfnYGx5//Jx9f/q+LCQ+S2pkOvaGHz1uf3PqpkwlPks8ZDr29PlXfTj+oXS2d6bdT6lj0vPTlQ+vmPenya/vKvU1bLmMSfv9pS996YmZmUEeUznuJ8ZU+mPS9s8++2zavENQx1TM/bTsCXD9pG/79u1pf6ezls+dO5e1zMkbb7wh3/72t+VHP/qRBF3yJxrLrb6+3lxSOaUBkgMo01cGirk9tQ+2tzMmxpRpu42+6yeh+sZYn+hTfxfUMWXbHrQxOV+fSv0acLLk7fpGs9D2mRbJyrh9waSTXbU3X3dL0z5TH81XTfNon5hPpG2fqe96zBejfa3Umjf12ta5jdPfbPsjuX2usTpJ/NT22fZfoe3d9D25/27bF3Lsue272/apfUzuu5v2yfftNg6cfjm/W7B8rGr/0rXPtj/yaZ98HC35W2cZa77t0/Ux2/4o9FjN2ZdPtxfUfj57+9Q4cL4Km73vST/neI4v5nO2b8/xRXrOXtzu4Tm70Of4Yjxnp8aZzrJ2Pjyx+Zzt5tiz/Zyduj3f5+yCn+PziAOz/dOvsCe/Hsz2ek8/KHDKe7qhyZTUJEmpvoYtl9flzn41ZYOSfh/kMZXjfmJMpT+mbHmHoI5pOfbTsiXAsy38qAlwLXPyR3/0R2W9IGQ+i186kj/NKCb9eoM+VqYTFYDc9ESkX/HRTzOJJaBw+rVv/Tp3ull6AAD4dU7KlKyFv/Tr7ntf2SvRSfcTzSItETn59sm8ZgrCG94rAXYQS/YUJQGereyHlkXR2uADAwOmHkwm+S74WGrySWY7Yy0kaQ4AAAAAQCXQmd+a/K5/qV4aI40528eiMYm+FzW3IwEOAJWrxo+yH7t37zaXv/3bvy3bGeAbNmww184Cltk4Yy3WDPD+/n5zSf4qKgAAAAAAQaTJ76buJ+vHpjMjn61RAQCoTO6LpeRh//798tZbb5n/nz9/Xn7/939f3n333cXf6yKXFy9elD/8wz9Me/t/+2//7bKVAykWLfXidia700YX/iyGvr4+uXr1qly4cMH8zNcmADuIJcAOvmaOfFSH5iQUmjXXgJtjpUpm/e4KAoRzEmAH75UAO4ilEp4BrgtgvvzyyyYJrMlunQWtCe8/+IM/ML//8pe/LN/4xjfk9OnTcvnyZZMw19voTGjdduLECTl+/LgE2Y4dO1zNAHeS307CfDlo7aB8CsUDeJLGULYyTgDcJxqo/418fP63T/ndBQTsWBn5cETu+N0ZBALnJMAO3isBdhBLJZ4AV++8845Jfmu9b7Vly5Ylv9dEt5YJ+eijj+TgwYNP1AnXBHqQaT1vndGtCf5s9IMBpR8CLJdYLCYtLS18igR4XIxienpaGhoaiCXAoxVVK+TRwiO/uwEAAOckwALeKwF2EEv2FHUa8BtvvCHz8/MmCf7Tn/70id/rdp0JrjvUuYTDYTl37py0trZK0B0+fNhcnzlzJmMb/SBA7du3b9n6df/+ffO3BlA4jaF79+4RS4BHIQnJ6tBqcw0AgJ84JwF28F4JsINYCsAM8NQZ3dkSwOPj44szobUUSrnYtWuXmQV+5MgR8/905U+cci86Y7xYWAQTQDkYHh6WiYkJV20HBgYkHo8XvU8AAAAobXOzc+a1oVs6Ga+rq6uofQIAlGECPBed9R2ExLfW83ZKmuis7tTSLenoIqA6Ni1xklzXXJPful3vo9izv3URTL1o4kj/1gAQxOT33lf2SnQy6qr9TGxGBocGJTzLcx5Qbu4M/R1JJOqlunpGelb9jd/dQQCOldmErrnzC7+7A8AHs1OzMnBzQA68fkDq6+pd3SbSEpGTb58kCQ4AZaQkEuClbOfOnYuz050FK9WhQ4fMxZm5fenSpbQLWerv9XfHjh2T3bt3S0dHh9muC34ePXp0cbHM5VRbW7vsjwmUI2Jp+egHeJr8rn+pXhojjTnbj10bk/i7cWaBB8CCLMiszJprwI0HDz4n8blmqamdIgEOV8dKSNx9ewjgnFR+EtMJiYfiUvdinbT15v7WdSwak+h7UfPakwS4N7xXAuwglsogAX7r1i1Zu3atlLKzZ89auR83s8WXy6pVq8xKsgAKpzHU29vrdzcqjia/m7qbcraLjcSWpT/wTpMMNxM3/e4GAACck8pYQ3uDq9eQamp2ipIpHvFeCbCDWPIxAZ6r/mq2xSuvXLli6mFrGREtJ+LYsmWLKRHyzW9+M9/uoACTk5PS0tLCCrKAB7oIxdTUlDQ3NxNLgEfhqrCML4z73Q0AADgnVThKptjBeyXADmLJpwT4t771LbNoY6Y/upYA0TrXX/va15743eHDh00ZEOWsXuqUD9ESI1omRG+ri2KuWbOmkLHAJS2/0tPTQ/AAHujzWDQalaamJmIJ8CAkIXkq9JRMJib97goAoMIln5PmZd7v7sAHlEyxg/dKgB3Ekj151cF444035KOPPpJXX33V7AS9fOMb35B33nnHJFWvXbuWNvn97W9/2yS/tb0uwqi1r8fGxsxt9KL//9GPfmRur7PBc80yR376+/tl48aNsm3bNr+7AgAAAABAIEqm5Lq4WZsGAOC/vAtB6yzvrVu3mmutj63Jb02Ca2I7nfPnz5uZ3WrDhg1mpvd3vvOdJe31//v27TOJcJ39/fLLL3sZE1L09fXJ1atX5cKFC353BQAAAAAAAABKtwb4j3/8YzObW2eCu3Ho0KHF/2t5k3Xr1mVtrwlzTa6/++678gd/8Af5dg8uNDbyKTVgA7EE2Flw7OHCQ3MNAICfOCcB9vBeCbCDWPJhBvj4+LiZqa2JbDdu3rxpFrzUOjW7du2STZs25byN1gX/7ne/a8qtoDhWrlxpVpIFUDiNoe7ubmIJ8EiTDB/Pf0yyAQDgO85JgB28VwLsIJbsyesvqOVOdu/e7SqRrc6dO7f4/z179rh+HH0MXRgTxfHgwYPFhUgBFEZjiFgCvKuSKolURcw1AAB+4pwE2MF7JcAOYsmnBPiJEyfyqs+dPFN8x44drm/X0dFhdjCKQ2fyEzyAN5yIADs0ydAZ6iTZANdaW25La/i6uQbcHCsNVe5KNwKckwA7eK8E2EEs+VQD/MaNG2YBTLd0FreWP9Ga3q2trXk9jpZCAQAAAJL1rn7P7y4gYMfKyIcjfncFAAAAQZkBrp86uE1ka/1vZxZ3PrO/nQS4Js1hR39/v2zcuFG2bdvmd1cAAAAAAAAAoDQT4OFwWK5cuZJ3/e+dO3dKMWeaI7u+vj65evWqXLhwwfzc3Nzsd5eAskAsAd7pQmPjC+MsOAYA8B3nJMAe3isBdhBLPpRA0aS0ljVxswhmofW/1Q9+8IMlt4ddkUiEFWQBjzSGOjs7/e5GoA0PD8vExISrtgMDAxKPx4veJyw/TTLcnb/rdzcAAOCcBFjCeyXADmLJpwT4rl275NChQ/Lqq69mbXf+/HkzA1zrf2vyO5/632+++aasW7dOvva1r+XTNeQhGo2aT5BIggOFm5+fl9HRUbNoL7FUWPJ77yt7JToZddV+JjYjg0ODEp4NF71vWF660Fh3qFvuzd/zuysIiI9+83WZi6+Q2ppH8rnP/7nf3UEAjpWquH7Y+i/87g4Cdk5iFjhQON4rAXYQSz4lwPft2yfHjx+XP/zDP5Q/+7M/y1j7e/fu3Ys/a8LcrfHxcTl69KicOXMmn24hT1NTU353ASibWNITEfKnM781+V3/Ur00Rhpzth+7Nibxd+PMAi/TZEO4Kiz35b7fXUFAaEIzPsdXQeH+WAnJvN9dQQDPSSTAAW94rwTYQSz5kABXWprkc5/7nFy+fNkkw3/v935vMZnxzjvvmIS3JrJ19rcmzN3O5Nbb62zxY8eOuSqxAgAIPk1+N3U35WwXG4ktS38AAAAAAECFJ8DXr19v6oDrLG+ntndbW5s8ePDA/H9h4fEn5fv375cf/ehHrhLfJ06cWJwprqVTtmzZImvWrMm3awAAH+pzO7TcVVdXV9H6BAAAAAAAUPQEuNq8ebNcv37dJK1//OMfy40bNxYT4bpQpm7fvn171hrhWupEb+fc1vHGG2+YmeU6e9xNAh35C4fDZoY+gMJpDOlzXjnGUr71uR2RloicfPskSXDkRb9iPjI/wlfNAQC+45wE2FHO75WA5UQs+ZwAd2gSWy/50uS4JsrhD4IHsHciKkf51udWsWhMou9FzW1JgCMfmmSILuT3YQsAAMXAOQmwo5zfKwHLiVgqkQS411nIWB79/f3mkkgkzM/379+X5uZmVpAFPK7GrDOlNdlbrrHktj63Y0ZmitoflO+CY72hXvlk/hO/uwIAqHDJ5yRmgS+fudk5GRgYcNVW27EoeumrhPdKwHIglsogAY7l09fXZy46M1M/eIjFWEwOsCFIsZRPTW/eWGC5kw1NVU3mGgCAUjknkQBfHrNTszJwc0AOvH5A6uvqc7afic3I4NCghGfDgUzgV9K6OUF6rwSUMmLJDhLgAFDm8q3pXYpvLAAAAFB+EtMJiYfiUvdinbT15v6a/9i1MYm/Gy+ZyRr5JvAV6+YAwPIjAQ4AZS7fmt6l9sYCAAAA5a2hvcFV6b3YSCzQCXzWzQEAf5AAr0AdHR0sggl4pDEUiUQCFUtua3qX2hsLlLd5mZe783fNNeBGT8/fyPx8jYRCfEgHd8fKw6Exuet3ZxAInJNQ7AR+paybE8T3SkApIpbsIQFegVpaWggewCONIY0lAN6NL4z73QUESFv7R353AQE7VubvjvjdFQQI5yTAO94rAXYQS/awhGgFGhoaMivJAiicxtAnn3xCLAEe6UJj66rXsQgmAMB3nJMAO3ivBNhBLNlDArwCzc3N+d0FoCwQS4B3mmSokzqSDQAA33FOAuzhvRJgB7FkByVQAAAAEBgz02FZkJBUybzUN1CqALmPlfgCs6YAAAAqGQlwAAAABMaNG/9Q4nPNUlM7JV/Y+Kd+dwcBOFZCMiEi/83v7gCAMTc7JwMDA3ndprW1Vbq6uorWJwAodyTAK0B/f7+5JBIJ8/PKlStZBBPwSGOou7ubWAI8mpd5GZwfNNcAAPiJcxKKbXZqVgZuDsiB1w9IfV2969tFWiJy8u2TgUmC814JsINYsocEeAXo6+szl4mJCQmHw9LY2EjwAB5pDGksAfDu0cIjv7sAAIDBOQnFlJhOSDwUl7oX66Stt83VbWLRmETfi5r380FKgPNeCfCOWLKHBHgFun37tmzcuFFCIdZABQqlqzAPDg7K6tWrfYml4eFh8yLYDf2KZTweL3qfgEKEJCQbqjfI9cR1v7sCAKhwyeckZoGjmBraG6Spu8l1+xmZkSDx+70SUC6IJXtIgFeghYUFv7sAlAW/YkmT33tf2SvRyair9jOxGRkcGpTwbLjofQMKTTgAAFAKOCcBdpB3AOwgluwgAQ4AAaMzvzX5Xf9SvTRGcn8dauzamMTfjTMLHAAAAAAAVBwS4AAQUJr8dvPVydhIbFn6AwAAAAAAUGr4flcF6unpYRFMwCONoVWrVhFLgEdaY/Vm4ia1VgEAvuOcBNjBeyXADmLJHhLgFaimpobgATzSGCKWADviQnkeAEBp4JwEeMd7JcAOYskeEuAVSFeQ1ZVkARROY+j27dvEEmBhsbFnq59l0TEAgO84JwF28F4JsINYsoca4AAAAAiMzz375yILVSJVC353BQE5VkZ/E5W7fncGAAAAviEBDgAlYHh4WCYmJly1HRgYkHicr+cCqEy1tY/87gICdqxUV0353RUAAAD4iAQ4AJRA8nvvK3slOhl11X4mNiODQ4MSng0XvW8AAAAAAABBRgK8Aq1evVpCIeraAV5oDD3zzDNWYklnfmvyu/6lemmMNOZsP3ZtTOLvxpkFjrIwL/NyLXHNXAMA4CfOSUDpvVcCKhmxZA8J8ArQ399vLolEwvysSbOFhQVWkQU80BjSWKqtrbUWS5r8bupuytkuNhKz8nhAqaiRGpmVWb+7gYAYjX5BEvO1Uh2ak47Ir/3uDgJwrMTmx0TkF353BwHBOQkozfdKQCUiluzhI4QK0NfXJ1evXpULFy6Yn+/cuWOCCEDhNIaGhoaIJcCjkIRkXfU6cw24ce/eFrk79HfNNeDmWJmc/x2/u4KA4JwE2MF7JcAOYskezuwAAAAAAAAAgLJECRQAAAAAAIASNTc7JwMDA67bt7a2SldXV1H7BABBQgK8AlE3CLCDWALsYLExAECp4JyEUjM7NSsDNwfkwOsHpL6u3tVtIi0ROfn2SV+T4LxXAuwgluwgAV6BWEEW8E5jaM2aNX53AyiLRMO1xDW/uwEAAOcklKTEdELiobjUvVgnbb1tOdvHojGJvheViYkJ3xLgvFcC7CCW7CEBXoFisZi0tLTwKRLggS5CMT09LQ0NDcQS4NGKqhXyaOGR390AAIBzEkpWQ3uDNHU3uWo7IzPiJ94rAXYQS/aQAK9A9+/fN58EEzyAtxPRvXv3zDcqiCWgcCEJyerQambcAQBK6pxEKRQEmd81w3mvBNhBLNlDAhwAAAAAAKAMBLVmOAAUEwlwAMCyzzTRdvF4vOh9AgAAACpJEGuGA0CxkQCvQLW1tX53ASgLxFLhM01mYjMyODQo4dnwsvQPpWtBFmRWZs014EZ9/bhUh2alpjbmd1cQkGNlYXbC764gIDgnoZz4XTOc90qAHcSSHSTAK9CqVavMSrIACqcx1Nvb63c3AjvTZOzamMTfjTMLHCbJcDNx0+9uIEDWb/gLv7uAgB0rIx+OyMd+dwaBwDkJsIP3SoAdxJI9JMAr0OTkpLS0tFBAH/C4GMXU1JQ0NzenjaXh4WHzNcJKKwfidqZJbISZm/hMuCos4wvjfncDAADOSahIthfNzPVeCYA7xJI9JMAr0OjoqPT09BA8gMcTUTQalaampidiSZPfe1/ZK9HJqKv7ohwIKllIQvJU6CmZTEz63RUAQIVLPifNy7zf3QECu2hmtvdKANwjluwhAQ4AlunMb01+179UL42RxpztKQcCAAAAwA8smgmgEpAAB4Ai0eQ35UAAwK7bA9slEW+Q6pppeWbNeb+7gwAcK/GElrP4hd/dAYCS5veimQBQTCTAK1BjY+4ZqQByI5YAOwuOPVx4aK4BNx4+7JH4XLPU1E753RUE5FgJCSXG4A7nJMAe3isBdhBLdpAAr0ArV640K8kCKJzGUHd3t9/dAAJPkwwfz3/sdzcAAOCcBFjCeyXADmLJHhLgFejBgwfS0tJCAX3A42IU4+PjEg6HiSXAgyqpko6qDhldGPW7KwCACpd8TmIWOGDvvdLw8LCpGe5Wa2sr9cUB8g5WkQCvAP39/eaSSCTMzxo8Tz/9NMEDeDwR6YdJ+uKMWAK8JRs6Q50ylhjzuysAgAqXfE4iAQ7Yea80MjIie1/ZK9HJqOvbR1oicvLtkyTBUfHIO9hDArwC9PX1mYt+4qqfGgEAAAAAABSb5iE0+V3/Ur00RnLXMo5FYxJ9L2puRwIcgC0kwAEAAAAAAFA0mvxu6m5y1XZGZoreHwCVhZUQK1Bzc7PfXQDKArEEeKdfMR9fGOer5gAA33FOAuzhvRJgB7FkBzPAK1AkEjEryQIonMZQZ2en390AAk+TDHfn7/rdDQAAOCcBlvBeCbCDWLKHLGgFikajMj8/73c3gEDTGNIFXYglwPuCY0+FnjLXAAD4iXMSYAfvlQA7iCV7SIBXoKmpKb+7AJQFYgnwTpMM4aowyQa41tHxa4l0/sJcA26OlRWh9/3uCgKCcxJgD++VADuIJTsogQIAAIDA6H7qkt9dQMCOlZEHI353BQAAAD5iBjgAAAAAAAAAoCyRAK9A4XBYqqr4Wh/ghcZQW1sbsQRYWHBsZH7EXAMA4CfOSYAdvFcC7CCW7KEESgUieID8DQ8Py8TERNpFZVMNDAxIPB5fpp4BwaZJhujCk3EEAMBy45wE2E3aAfCGWLKHBHgFun//vjQ3N0soxBcAALfJ772v7JXoZHTJiWjD2g1y/dZ1WVhYOktoJjYjg0ODEp4N+9BbIFh0obHeUK98Mv+J311BQPz66v9R4nPNUlM7JV/Y+Kd+dwcBOFZCoh9gf9Pv7iBg5yRmgQOFm5+fN++hurq6/O4KUDaxRA7PGxLgFSgWi/ndBWBZZmdn0tramteLMb1vTX7Xv1QvjZFGsy0kIXmq5SkZ3TQq8zK/pP3YtTGJvxtnFjjgMtnQVNVkrgEAKJVzEglwwBvyDoAdxJIdJMABlN3s7FwiLRE5+fbJvGckaPK7qbtpMQFeV10nTSuankiAx0Y4QQEAAAAAAJQCEuAAAi3d7OxsYtGYRN+LmtvxlTwAAAAAAIDyRgK8AnV0dLAIJspO8uzsXGZkxvPj6azvu/N3n5j9DSA/xBIAoFRwTgLs0HxDJBIpOO8wNzsnAwMDed0m3zKXQCXEEj5DArzEnThxQh48eGBWfXWu9+3b5+k+W1paCB7AgvGFcb+7AJQFYgkAUCo4JwHeab5B8w6FmJ2alYGbA3Lg9QNSX1df9DKXQLnGEpYiAV7Cjh07Zq4PHjy4ZNuhQ4fk6NGjBd/v0NCQNDc3s4Is4IEujrS2eq3cStxikSTAUiwBAOAnXt8BdszPz8udO3ekp6cn79smphMSD8Wl7sU6aettc3UbylyiEmKJHJ43JMBL1I0bN+TIkSMyNja2ZLsmw9vb22X//v2yfv36gu57bm7OUi+Byn6DVCd15po3SICdWAIAwE+8vgPslCjRpJ1+g/3Ro0cyODgo8Xg878doaG9wXeLSVplLoBSRw7ODBHiJOn78uGzdujXt73bs2GFmgGsbAAAAAACA5eCmRInOVN303Ca58uEViT2MyeDQoIRnw8veVwBwkAAvUefOncuYANeZ32fOnFn2PgEAAAAAgMrlpkRJSEKyomWFtH2uTRLXEhJ/N17QLHAAsIUEeJ70azxbtmyRS5cumQUp81nM8uzZs9LR0WF+Hh0dNWVMdDZ3OpcvX5Y9e/ak/Z2uAKslUgq1cuVKFsEEPJqXeRmcHzTXAApHLCFfq5/5mSzMh6QqxDEDd8fKxMCY3PW7MwgEzkmAvRIl96ruSeOKRmkYaSiJsiypWltbqReOkqe5u+7ubnJ4FpAAd5n0vnjxoklgOwtTagLbTQJcb7t9+3Yzm/v06dNptxdaykTvI58kvKOxsZHgASx4tPDI7y4AZYFYQj6am4f87gICdqzMhEb87goChHMSELxYclOWJVWkJSIn3z5JEhwlTXN3msODdyTAc9AFJ3XWts7U1hnbWppEZ2e79dprr5nbpya5NXF9/vx5WbdunZlRvm/fviWJ7WwKSXonu337tmzcuJEVZAEP9Gt9G6o3yPXEdWYJAZZiCQAAP/H6Dgjm6zs3ZVmSxaIxib4XlYmJCRLgKGm6oKwuJLt69WpyeB6RAM9hbGys4NtqslxrdevM8UyJbE18a2L95Zdf9pzYdmthgRXNAVsv7AB4RywBAEoF5yQguLGUqyxLshmZKXp/ABvI4dnB2b2Ijh49apLamep8K6fOt9YId+RKhDszxJcrYQ4AAFAqpqZWyeTE0+YacHOszMyv8bsrAAAA8BEJ8CLRJLXOAF+/fn3Wds7vT5069cTvotFo0foHAAAQRIO3vya3bv4fzDXg5lh5MP+P/O4KAAAAfEQCvEh00UyVKwHuzOJOrSuus8Yz1QK/fv26bN68ueC+9fT0sAgm4JHWhbyZuEl9SMAjYgkAUCo4JwF2EEuAHZq7W7VqFTk8C0iAF4mT0NYFMHNxkuTJSfCdO3fKjRs30rbX7dnKquRSU1ND8AAWxCXudxeAskAsAQBKBeckwA5iCfBOc3fk8OwgAV4kTvmSfOp0j46OLv5/165dpoRKOrpdF84slK4gqyvJAvC2qMuz1c+yUBLgEbEEACgVnJMAO4glwA7N3d2+fZscngU1Nu4ET8pUviQdJ0mePONbZ4UfPHhQDh06ZBbTdOjPuj1XaRU1MzNjLo6JiQlzrYGTHDyhUOiJYNJPl/RSrO36mLqSbepqtja3M6bKGJNzn/riKvkFlvN1u0wvutLFQba+67Xzb0E+a5N8/wuf/quSKqmurpZQ1Wftne3674k+VoWWtM/Udxvt9fGd/mfqe7r2qX1PHZNzm8X9lWmsnz5mavuMfXfR3ulLanvdZ+n6km5/mLF++ql6pr5nap+r76mc/ZatvTMefYzk9pmOpcXx63Hqor3zmNWhz46jbH1fbF9dePt0+8n5uzrX+R57ye3dHHvp2mfbf7nap47JaZ88Jjdxk6m9jWPP7Vhztc90LC3Zfy7aJ993Ps/Zycdd8r15Ofbyfc7O1D7dfir2c/ySMSXtM0eu/ZHvsWfzOTtj+1D25/jUOHD3HJ/0N8nxHO/lOTv5MdI9B6cbk43n+FzHnpfn7EztU4+9Qp+zM7VPHpPt5+yM7ZOfA+Vx39I9F+T7HO/8jfI59mw/Z6duz/c5O1P7TMdevnFT6LHn5Tk7aK/LCzn2SuV1uXPcFPt1eaHP2YW8J+S9O2Na7jEp3ZbP/VTafgp9+vySCwnwIkmezV0oTXyfOHHCJL03bNhgkup6vW/fPle3P3LkiHz/+99/Yrvej84C14OlublZOjs7TX+npqaWJOX1Mjw8LLFYbHF7JBKRlpYWuXPnjszNzS1u7+7ulsbGRnO/yQer1irSr2voJ1bJnnnmGYnH4zI0NLS4TfuzZs0amZ6elnv37i1ur62tld7eXtO/5IVB9fH0ccfHx5d84MCYKmtM2j/1282/LQ3VDYvbteacfu1OZx4kuyJXpKG+wfTFGW+uMc3Ozsqm5zbJipYVUlddJw8XHsrQ/JA0VzWb+3deWI0vjMvd+bvydPPT0vHVDnm67Wlprm6WkfkRiS5EpTfUK01VTYv3r231Ns+2PSvtX21fbD84PyiPFh7JhuoNS16sOWN6vvP5Je2vJa5JjdTIuup1i221T7p9RdUK6e3sXWyv/df7CVeF5anQU4vtdUwfz38sHVUdsqpz1WL7RChh+tkd6ja3caSOqaezx9xmoe7xcbW2eq3USd1i+9QxOe2nQlOLM0SSpY5J27d+pVUeyAMzptWh1YttZ2X2iTFp+4UvPH5BrWPqDHUutnf2U/KYtP2D1Y+Pz0z7KXlM2n4gPGD+n2k/pY5ppGpEmlY0yfNtz5v9lrqfUsc0LMPSs7JnSfvk/ZQ6Jm3/+fWfX9I+27E3IiOyddNW+a2231psn+3Y0zcUv/vV31087tLtp+QxtdS2LGmfbj+p2MLjONcxPdX5VF7HXvKxOlU19cR+Sh2Tc9xpe/17pdtPyWNy2q8Kr5I7cifnsee0DzeHZUImch57TvuahhpJSKIox562/6D6A6kP1S/ZnunYa29rl/flfemo61jSPtOxV99cb55Xn17xtDxT/Uza/ZQ8pnjD4+fsZ1uflc7qz+4n07E3Xj1u3mQnH9e/kRrRs4C+8U03JrfHnjOmlStWLmmf7jkieUzrwuuWPMen20/JY3ou8tyS5+xMzxHOsZf8HL+iekXG5whnTF0d6+Tu/SV3JW1VbbKqutXasee0n62ZdXXsOe3HqsbM7TOdn5wxafu6TXUyLdNZz0/OsaftY+sfP3dke4649umxUhuqNc+nbs5P2Y695P2UOqb7cl/aw+1L2mc79vQ5aO3qtUvaZzv2tP0LX3hhSftsx5568Ssvypq2NYvtsx179dX1S+Ig2/nJGVPy64uqUFXG85MzpuTn7Omq6ayvjXRMye31WMr22kjHlPwcr8/Z2V4b6Zic9p2tnRKVqBnTqtAqWRlaaZ5fJhcml4zJab9ixQqJSSznayPnWL238p7rY0/b/2bFb8w5N9drI9Ud6TbP2S01LUvaZzr2msPN5jl7ZcNK+Vz159Lup+QxVa14nGBc17xOeqofx1C2Yy9W+zgunws/J61Jz0E2j72Oho4l7bO9NiqH1+WFHHul8rpc+6mx1N3ZXdTX5YUce4W8J+S9O2PyY0y6hp9ObHVyeOUwpl7L+0lv5wYJ8CJxdpgeUF64TXanc/jwYflX/+pfLZkBvnr1anOQ6HXypyRaqzy5XrkTWF1dXUvu09muQZhuu95v6na9aEAk08fWAz91u2poaEi7XQ/8pqbPTn6OcDgsra2fneQYU2WNyfnmxH+f+u9LHtdJSusLtmS6fXpm2sRBav8zjamurk6ufHhF2j7XZhKYziyFqYUpuZ64vvhYzmyHj6c+ll/+/JfywpdfkM62zsXtn8x/knYGw7UH1+SDn3+w2N7Zrved2nf1y5FfPtFeX2ymjlXpi9uPRz5ebB9pe/ycpC8cJxOTi+2cPo4ujEp0JPpE+3vz98yb+9T2zphGRkbMbb646YvSKZ1yK3Er7VidMSW3d94UpI41eUym/d9+IF/c8kUzpuT2Tl+Sx2Ta//oDef73njdjGkuMPdE+eUza/tbgLXlBXsi4n5LHpO3HxsdktazOuJ+eGNPCvDx89FB++eCXZr+l7qd0Y7pz/86S9sn7Kd2YfnPjN9L4oPGJ9pnGdPHKRZn9e7OL7bMde9r/v/z5Xy4ed+n2U7LJuUn51c9/9UQcpB572i99E65jGh4ZzuvYSz5WO9oeP49kO/ac4y65fbZjb0n79o6cx57T/vlNz0ukK5Lz2FvSXiJFOfa0fSKRkJn5mYzPEUvaPxgx16Ozo2nHmjqm4alhc/3xo49lJvHZt84yHXuj048nCFybuCZjkc/uJ9Oxp/ep/U+Og7hsW3wML8ees/3+o/tpn7MzHXs3x28uaZ9uPyWP6cPoh2mf4zMde6nP8en2U/KYhkf1TcXvL7mvBwsP5GHinrVjL/k5u17qcx57ye2znZ+WtL/ygXzx734x6/nJOfa0vT7fPS/PZ32OcOrPzs3Pyb37d6RLunKen7Ide8n7Kd2YNDbTPWdnGpOed9oetD3RPtOY9LxW9aDqiefsTGP667/9a5nYOvFE+3THno730s8vLTnuUvdT6phujtx84jk72/NeutcX2Y69dM/x2Y691Of4XMfeYvtNL0hHpMOM6WHioenzR4mPzIdDyWNKjZtcr42U3uZOHseettfXCnrOzfXayLSPPn7OnoxP5nxtpIbHHz9n35++LwuJz5IamY694UeP29+cuilTic8SD5mOPX3+VR+Ofyid7Z1p95PXY0/PI+me4zMde0F/XV7IsVcqr8u1nxpL90fuF/V1eaHHXr7vCXnvzpj8GlN9ff0TObygj6m5SPspGxLgRZb8icZy0yDRSyr9FEY/tUmW6SsDxdzuBE+xtjOmyhiTc58mQZdmlfFMK4+brxGnua9MfTdfj0t6DCfxku7+9Xf6plnfvCT/PvkrgUv6uDCftn2mvntpn/z4mfqern2mvjvbndvkHOunj5naPmPfPbR3viaVq+9Oe+fT7Xzb5+rLE9tT9lu29ubrbmnaZ/z76nGaR/vE/JPHUba+pzvubLR3YinfYy+5fa6xpt5/rr57aZ9v3LhtX8ix57bvbtun9jG5727aJ993Ps/ZycfRguVjNd/n7FJ8jk/Xx2z7o9BjNWdfvDzHz2dvnxoH7p7jk37O8RxfzOds357ji/Scvbjdw3N2oc/xxXjOTm6jH5r8JvGbtLHt9TnbzbFn+zk7dXu+z9kFP8fnEQe2jtUgPWfn87rcxnN2rmOvGM/xus2JpWK/Lvdy7OX7npD37ozJjzFpDi+f+wnCmKqKvJ/StnXdEnnJZ/FLR/KnGcWkX01IV1cIQH70q4AAvCOWAAClgnMSYAexBHinuTtyeHaQAC+SfJLZTrmUQpLmhdD6PgQP4I3Wx9M6eJkWVAHgDrEEACgVnJMAO4glwA7N3WltbXJ43vGRXJHoYpXJ9YndLJhZrBng/f395pL8NSUAAIAg+sLGP/W7CwjYsTLy4Yjc9bszAAAA8A0J8CJZv369uU5evTQTp83mzZuL0pe+vj5z0UUwtaA8UOnmZudkYGDAdXttq187AgAAAAAAQLCQAC+SHTt2uJoB7iS/nYT5ckhXUB6oFLNTszJwc0AOvH5A6uueXCQ2nZnYjAwODUp4NuxqMRUA+SGWAAClgnMSYAexBNhBDs8OEuBFovW8dUb35cuXs7a7ePGiud6/f/8y9UzkmWeeyWulVKCcJKYTEg/Fpe7FOmnrdVd3f+zamMTfjS+ZBa4v6K4lrhWxp0BlIJYAAKWCcxJgB7EE2KG5uzVr1vjdjbJAFrSIDh8+bK7PnDmTsc3p06fN9b59+5atX7FYjAL6qHgN7Q3S1N3k6qJt01lRtWLZ+w2UI2IJ+bh3d4sMffJVcw24OVYm53/H764gQDgnAXYQS4B3mrsjh2cHM8CLaNeuXWYW+JEjR8z/05U/OXHihBw/ftzMGC+W1EUw79+/L11dXXyNokwNDw+beu/5aG1tNccE3NMVzVeHVpuZDXy9D7ATS4Abo6NfkPhcs9TUTkn3U5f87g4CcKyEJL/XRahcvL4D7OD1HWCHJr7v3btnKjmQw/OGBHgetJ63U9JEZ3UfPHgw523Onz8v27dvNyVONNGdnPzW7XofxZ79zSKYlZX83vvKXolORvO6XaQlIiffPkkSHAAAAAAAAGWFBHgOO3fuXKzT7SxYqQ4dOmQuzsztS5cupV3IUn+vvzt27Jjs3r1bOjo6zPbR0VE5evTo4mKZgA36IYcmv+tfqpfGSKOr28SiMYm+FzW3JQEOAAAAAACAckICPIezZ89auR83s8WXS21trd9dQJFp8ltrV7s1IzNF7U85WpAFmZVZcw2gcMQSAKBUcE4C7CCWAHvI4dlBArwCrVq1yqwkC6Bw+mLuZuKm390AAo9YAgCUCs5JgB3EEmCH5u56e3v97kZZIAtagSYnJ1lBFrAgXEVNfcAGYgkAUCo4JwF2EEuAd5q7I4dnBzPAK0B/f7+5JBKJxfrjPT09rCALeFzZ/KnQUzKZmJR5mfe7O0BZxBIAAH7i9R1gRzm+vhseHjbrZrnV2trKGlvwTBPf0WhUmpqayOF5RAK8AvT19ZmLPlmHw3wKCwAAAAAA4Db5vfeVvRKdjLq+TaQlIiffPkkSHCgRJMABAAAAAACANHQyoSa/61+ql8ZIY872sWhMou9Fze1IgAOlgQR4BWpszP2EDSD3wi4PFx6ysjngEbGEfDU13ZFEvEGqa6b97goCcqzEH4773RUEBOckwI5yjSVNfjd1N7lqOyMzRe8PKgM5PDtIgFeglStXmpVkARROX8x9PP+x390AAo9YQr6eWXPe7y4gYMfKyIcjctvvziAQOCcBdhBLgB2au+vu7va7G2WBLGgFevDgASvIAh5VSZVEqiLmGkDhiCUAQKngnATYQSwBdmjujhyeHcwAr0Dj4+Py9NNPs4IsSnbF7NnZWamrq3PVdmBgQOLxuCw3fTHXGeqUscRY2X21D/ArlgAA8BOv7wA7eH0H2E2At7a2ksPziAR4Bejv7zeXRCLhd1dQgfJdMXtudk6GBoekd02v1NTkfoqaic3I4NCghGfDFnoLAAAAAACAckICvAL09fWZi87ADYd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"text/plain": [
"<Figure size 1500x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Vergleich Output High/Low-Level\n",
"\n",
"PLOT = True\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 = True\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"
]
}
],
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"display_name": ".venv",
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"name": "python3"
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