# Not yet verified with both MATLAB and Python testbenches
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
[docs]
def ntf_analyzer(ntf, flow, fhigh, is_plot=None):
"""
Analyze the performance of NTF (Noise Transfer Function)
Args:
ntf: The noise transfer function (in z domain) - scipy.signal.TransferFunction or tuple (num, den)
flow: Low bound frequency of signal band (relative to Fs)
fhigh: High bound frequency of signal band (relative to Fs)
is_plot: Optional plotting flag (1 to plot, None or 0 to skip)
Returns:
noiSup: Integrated noise suppression of NTF in signal band in dB (compared to NTF=1)
"""
w = np.linspace(0, 0.5, 2**16)
# Convert NTF to transfer function if needed
if isinstance(ntf, tuple):
# ntf is (numerator, denominator) tuple
num, den = ntf
tf = signal.TransferFunction(num, den, dt=1) # dt=1 for discrete time
else:
tf = ntf
# Calculate frequency response for discrete-time system
w_rad = w * 2 * np.pi
_, mag = signal.dfreqresp(tf, w_rad)
mag = np.abs(mag)
# Calculate noise suppression in signal band
band_mask = (w > flow) & (w < fhigh)
np_val = np.sum(mag[band_mask]**2) / len(w)
noiSup = -10 * np.log10(np_val)
# Plot if requested
if is_plot == 1:
plt.semilogx(w, 20 * np.log10(mag))
if flow > 0:
plt.semilogx([flow, flow], 20 * np.log10([np.min(mag), np.max(mag)]), 'k--')
plt.semilogx([fhigh, fhigh], 20 * np.log10([np.min(mag), np.max(mag)]), 'k--')
plt.xlabel('Normalized Frequency')
plt.ylabel('Magnitude (dB)')
plt.title('NTF Frequency Response')
plt.grid(True)
plt.show()
return noiSup