analyze_spectrum#

Overview#

analyze_spectrum performs FFT-based spectrum analysis for ADC characterization, computing key metrics including ENOB, SNR, SNDR, SFDR, THD, and harmonic content. This is the primary tool for frequency-domain ADC performance evaluation.

Syntax#

from adctoolbox import analyze_spectrum

# Simplest usage
result = analyze_spectrum(signal, fs=100e6)

# With harmonic analysis
result = analyze_spectrum(signal, fs=100e6, harmonic=5)

# With custom windowing
result = analyze_spectrum(signal, fs=100e6, window='blackman', nfft=8192)

# With averaging
result = analyze_spectrum(signal, fs=100e6, num_avg=4, mode='coherent')

# With interactive plotting
result = analyze_spectrum(signal, fs=100e6, show_plot=True)

Parameters#

signal (array_like) — Input ADC signal (1D array)
fs (float) — Sampling frequency in Hz
harmonic (int, default=9) — Number of harmonics to analyze (2-20)
window (str, default='blackman') — Window function: 'rect', 'hann', 'hamming', 'blackman', 'flattop'
nfft (int, optional) — FFT length. If None, uses len(signal)
num_avg (int, default=1) — Number of segments for averaging
mode (str, default='coherent') — Averaging mode: 'coherent' or 'power'
show_plot (bool, default=False) — Display interactive spectrum plot
ax (matplotlib axis, optional) — Axis for plotting

Returns#

Dictionary containing:

Metrics:

enob — Effective Number of Bits
snr_db — Signal-to-Noise Ratio (dB)
sndr_db — Signal-to-Noise-and-Distortion Ratio (dB)
sfdr_db — Spurious-Free Dynamic Range (dB)
thd_db — Total Harmonic Distortion (dB)
sinad_db — Signal-to-Noise-and-Distortion (alternative name for SNDR)
nsd_dbfs_hz — Noise Spectral Density (dBFS/Hz)

Spectrum Data:

freq — Frequency bins (Hz)
magnitude_db — Spectrum magnitude (dBFS)
fundamental_bin — FFT bin of fundamental frequency
fundamental_freq — Fundamental frequency (Hz)
harmonic_bins — FFT bins of harmonics (list)
harmonic_powers — Power of each harmonic (dB, list)

Algorithm#

1. Signal Preprocessing#

# Apply window
windowed_signal = signal * window_function

# Zero-pad if nfft > len(signal)
if nfft > len(signal):
    windowed_signal = np.pad(windowed_signal, (0, nfft - len(signal)))

2. FFT Computation#

spectrum = np.fft.fft(windowed_signal, n=nfft)
magnitude = np.abs(spectrum[:nfft//2])
magnitude_db = 20 * np.log10(magnitude / nfft * 2)  # Convert to dBFS

3. Fundamental Detection#

# Find peak in first Nyquist zone (excluding DC)
fundamental_bin = np.argmax(magnitude[1:nfft//2]) + 1
fundamental_freq = fundamental_bin * fs / nfft

4. Harmonic Identification#

harmonic_bins = [fundamental_bin * k for k in range(2, harmonic + 1)]
# Fold harmonics above Nyquist back into spectrum
harmonic_bins_folded = [fold_bin_to_nyquist(h, nfft) for h in harmonic_bins]

5. Metric Calculation#

# Signal power (fundamental)
P_signal = magnitude[fundamental_bin]**2

# Harmonic power
P_harmonics = sum([magnitude[h]**2 for h in harmonic_bins_folded])

# Noise power (excluding signal, harmonics, DC)
P_noise = sum(magnitude**2) - P_signal - P_harmonics - magnitude[0]**2

# Metrics
SNR = 10 * log10(P_signal / P_noise)
THD = 10 * log10(P_harmonics / P_signal)
SNDR = 10 * log10(P_signal / (P_noise + P_harmonics))
ENOB = (SNDR - 1.76) / 6.02
SFDR = fundamental_mag_db - max_spur_mag_db

Examples#

Example 1: Basic Spectrum Analysis#

import numpy as np
from adctoolbox import analyze_spectrum

# Generate test signal
N = 2**13
fs = 100e6
fin = 123/N * fs  # Coherent frequency
t = np.arange(N) / fs
signal = 0.5 * np.sin(2*np.pi*fin*t) + np.random.randn(N) * 10e-6

# Analyze
result = analyze_spectrum(signal, fs=fs)

print(f"ENOB: {result['enob']:.2f} bits")
print(f"SNDR: {result['sndr_db']:.2f} dB")
print(f"SFDR: {result['sfdr_db']:.2f} dB")
print(f"THD: {result['thd_db']:.2f} dB")

Example 2: With Interactive Plotting#

result = analyze_spectrum(signal, fs=800e6, show_plot=True)
# Opens interactive plot showing spectrum, harmonics, and metrics

Example 3: Windowing Comparison#

windows = ['rect', 'hann', 'blackman', 'flattop']

for win in windows:
    result = analyze_spectrum(signal, fs=fs, window=win)
    print(f"{win:10s}: SFDR={result['sfdr_db']:6.2f} dB, "
          f"SNDR={result['sndr_db']:6.2f} dB")

Example 4: Coherent Averaging#

# Average 4 segments coherently
result = analyze_spectrum(signal, fs=fs, num_avg=4, mode='coherent')
# Reduces noise floor by ~6 dB (factor of 4)

Window Function Selection#

Window	ENBW*	Peak Side Lobe	Use Case
rect	1.0	-13 dB	Coherent signals only
hann	1.5	-32 dB	General purpose
hamming	1.36	-43 dB	Better side lobe rejection
blackman	1.73	-58 dB	High dynamic range (default)
flattop	3.77	-93 dB	Accurate amplitude measurement

*ENBW = Equivalent Noise Bandwidth (bins)

Interpretation#

ENOB (Effective Number of Bits)#

ENOB ≈ N-bit ADC: Ideal performance
ENOB < N - 2: Poor performance, investigate noise/distortion
ENOB > N: Oversampling or dithering improving effective resolution

SFDR (Spurious-Free Dynamic Range)#

SFDR > 80 dB: Excellent linearity
60 dB < SFDR < 80 dB: Good for most applications
SFDR < 60 dB: Significant spurs, check for:
- Power supply coupling
- Clock feedthrough
- Intermodulation products

THD (Total Harmonic Distortion)#

THD < -80 dB: Very linear
-60 dB < THD < -80 dB: Acceptable for most uses
THD > -60 dB: Nonlinearity issues

Common Issues#

Non-Coherent Sampling#

# BAD: Arbitrary frequency causes spectral leakage
signal = np.sin(2*np.pi*10.1234e6*t)  # Non-coherent

# GOOD: Use coherent frequency
from adctoolbox import find_coherent_frequency
fin_coh, bin_num = find_coherent_frequency(fs, 10e6, N)
signal = np.sin(2*np.pi*fin_coh*t)

Insufficient FFT Length#

# BAD: Low resolution
result = analyze_spectrum(signal[:512], fs=fs)  # Only 512 points

# GOOD: More frequency resolution
result = analyze_spectrum(signal, fs=fs, nfft=8192)

References#

IEEE Std 1241-2010, "IEEE Standard for Terminology and Test Methods for ADCs"
F. J. Harris, "On the Use of Windows for Harmonic Analysis with the DFT," Proc. IEEE, 1978
Application Note AN-9675, "Coherent Sampling Calculator," Analog Devices