PyNAFF 1.2.0

Numerical Analysis of Fundamental Frequencies for one or more signals

PyNAFF

Authors:

• Foteini Asvesta (fasvesta .at. cern .dot. ch)
• Nikos Karastathis (nkarast .at. cern .dot. ch)
• Panagiotis Zisopoulos (pzisopou .at. cern .dot. ch)

A Python implementation of J. Laskar's Numerical Analysis of Fundamental Frequencies (NAFF) method.

Installation

python -m pip install PyNAFF

Single BPM

import numpy as np
import PyNAFF as pnf

t = np.arange(3001)
signal = np.sin(2.0 * np.pi * 0.12345 * t)
result = pnf.naff(signal, turns=500, nterms=1, window=1)

# Each row is:
# [order, frequency, amplitude, real amplitude, imaginary amplitude]
frequency = result[0, 1]

turns is the number of integration intervals, so the input must contain at least turns + 1 observations. For real sinusoids, the reported amplitude is the magnitude of one complex Fourier coefficient, equal to half the sinusoid's peak amplitude.

Multiple BPMs

Place observations on axis 0 and BPMs on axis 1:

signals = np.column_stack([
    np.sin(2.0 * np.pi * 0.12345 * t),
    2.0 * np.sin(2.0 * np.pi * 0.27123 * t),
])
results = pnf.naff(signals, turns=500, nterms=1)

# results.shape == (2 BPMs, 1 term, 5 values)
frequencies = results[:, 0, 1]
amplitudes = results[:, 0, 2]

For multi-BPM input, unused term rows are filled with NaN when extraction for a BPM stops before nterms.

The tol option controls duplicate residual handling as a fraction of one FFT bin. If NAFF stops early because a residual peak is very close to a previously extracted frequency, increasing tol can let it remove that residual and continue to weaker frequencies. The default is 1e-4; large values can also turn spectral leakage into spurious frequencies, so compare results across several values. nterms is an upper bound, not a guaranteed result count.

For real input, prefer getFullSpectrum=False. A full spectrum contains both positive and negative conjugate frequencies, and each one occupies a result row.