excitation = generation.log_sweep(fstart, fstop, duration, fs) N = len(excitation) # Noise in measurement chain noise_level_db = -30 noise = measurement_chain.additive_noise(noise_level_db) # FIR-Filter-System dirac_system = measurement_chain.convolution([1.0]) # Combinate system elements system = measurement_chain.chained(dirac_system, noise) # Lists beta = 7 fade_in_list = np.arange(0, 1001, 1) fade_out = 0 # Spectrum of dirac for reference dirac = np.zeros(pad * fs) dirac[0] = 1 dirac_f = np.fft.rfft(dirac) def get_results(fade_in): excitation_windowed = excitation * windows.window_kaiser(
awgn = -30 noise_system = measurement_chain.additive_noise(awgn) # FIR-Filter-System # FIR-Filter-System f_low = 5000 f_high = 6000 order = 2 bandstop_system = measurement_chain.bandstop(f_low, f_high, fs, order) # Combinate system elements system = measurement_chain.chained(bandstop_system, noise_system) # Lists beta = 7 fade_in = 0 fade_out_list = np.arange(0, 1001, 1) t_noise = 0.004 # Spectrum of bandstop for reference bandstop_f = calculation.butter_bandstop(f_low, f_high, fs, N * 2 + 1, order) def get_results(fade_out): excitation_windowed = excitation * windows.window_kaiser(N, fade_in,