Masterarbeit_Simulation/DSP_output.ipynb

In [5]:

# Plot DSP Output

import numpy as np
import matplotlib.pyplot as plt

PLOT = False

dsp_desired_signal_r11 = np.genfromtxt("./simulation_data/complex_dsp_desired_signal_r11.txt", dtype=int)/(2**(15)-1)
dsp_noise_signal_r11 = np.genfromtxt("./simulation_data/complex_dsp_noise_signal_r11.txt", dtype=int)/(2**(15)-1)
dsp_noise_signal_vpu = np.genfromtxt("./simulation_data/complex_dsp_noise_signal_vpu.txt", dtype=int)/(2**(15)-1)
dsp_corrupted_signal = np.genfromtxt("./simulation_data/complex_dsp_corrupted_signal.txt", dtype=int)/(2**(15)-1)
t = np.linspace(0, len(dsp_corrupted_signal), len(dsp_corrupted_signal))/20000

output = np.genfromtxt("./filter_output/complex_dsp_output.txt", dtype=int)[:-1]/(2**(15)-1) # letzte Zeile löschen

error_signal = (output - dsp_desired_signal_r11)
t2 = np.linspace(0, len(error_signal), len(error_signal))/20000

# SNR davor/danach in dB berechnen, SNR Ratio berechnen, 
snr_before = 10 * np.log10(np.trapz(dsp_desired_signal_r11**2, t) / np.trapz(dsp_noise_signal_r11**2, t))
snr_after = 10 * np.log10(np.trapz(dsp_desired_signal_r11**2, t) / np.trapz(error_signal**2, t2))
delta_snr = round(snr_after - snr_before, 2)

# Plots des Filterprozesses
figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)
ax0.set_ylim(-1, 1)
ax0.plot(t, dsp_desired_signal_r11, c='deepskyblue', label='Desired signal')
ax1.plot(t, dsp_corrupted_signal, c='royalblue', label='Corrupted signal')
ax2.plot(t, dsp_noise_signal_r11, c='chocolate', label='Reference noise signal')
ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')

ax0.text(0.5, -0.3, '(a) Desired signal',
         transform=ax0.transAxes,
         fontsize=25,
         fontweight='normal',
         ha='center',
         va='bottom')

ax1.text(0.5, -0.3, '(b) Corrupted signal',
         transform=ax1.transAxes,
         fontsize=25,
         fontweight='normal',
         ha='center',
         va='bottom')

ax2.text(0.5, -0.3, '(c) Reference noise signal',
         transform=ax2.transAxes,
         fontsize=25,
         fontweight='normal',
         ha='center',
         va='bottom')

ax3.text(0.5, -0.5, f'(d) DSP Filter output (SNR Gain = {delta_snr} dB)',
         transform=ax3.transAxes,
         fontsize=25,
         fontweight='normal',
         ha='center',
         va='bottom')

ax3.set_xlabel('time(s)', x=0.05)
ax0.set_ylabel('Amplitude')
ax1.set_ylabel('Amplitude')
ax2.set_ylabel('Amplitude')
ax3.set_ylabel('Amplitude')

# Plots der Filterperfomanz
figure2, (ax4) = plt.subplots(1, 1, figsize=(15, 4), sharex=True)
ax4.set_ylim(-1, 1)
ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')

ax4.text(0.5, -0.30, 'Error signal',
         transform=ax4.transAxes,
         fontsize=25,
         fontweight='normal',
         ha='center',
         va='bottom')

ax4.set_xlabel('time(s)', x=0.05)
ax4.set_ylabel('Amplitude')

#Grids direkt auf Subplots anwenden
ax0.grid(True, linestyle='--', alpha=0.4)
ax1.grid(True, linestyle='--', alpha=0.4)
ax2.grid(True, linestyle='--', alpha=0.4)
ax3.grid(True, linestyle='--', alpha=0.4)
ax4.grid(True, linestyle='--', alpha=0.4)

#Spines direkt auf Subplots anwenden
ax0.spines['top'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax4.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax3.spines['right'].set_visible(False)
ax4.spines['right'].set_visible(False)

# Schriftgrößen für LaTeX-Dokument
plt.rcParams.update({
    "text.usetex": True,
    "font.family": "serif",
    'font.size': 16,          # Standardtext
    'axes.labelsize': 30,     # Achsenbeschriftungen
    'xtick.labelsize': 25,    # Tick-Beschriftungen
    'ytick.labelsize': 25,
    'legend.fontsize': 15     # Legende
})

figure1.tight_layout()
figure2.tight_layout()

if PLOT == True:
    figure1.savefig(f'plots/fig_plot_1_dsp_complex', dpi=600)
    figure2.savefig(f'plots/fig_plot_2_dsp_complex', dpi=600)

plt.show()