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,
