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 module that implements the Numerical Analysis of Fundamental Frequencies method of J. Laskar. The code works either as a script (as the original code of Laskar) or loaded as a module in Python/Julia code or jupyter-like notebooks (i.e. SWAN).
Installation:
The module is ported in PyPi so the user can simply run:
pip install --user PyNAFFor from Git:
pip install --user git+https://github.com/nkarast/PyNAFF.gitExample of Usage
import PyNAFF as pnf
import numpy as np
t = np.linspace(1, 3000, num=3000, endpoint=True)
Q = 0.12345
signal = np.sin(2.0*np.pi*Q*t)
# Signature: pnf.naff(data, turns=300, nterms=1, skipTurns=0, getFullSpectrum=False, window=1)
# Docstring:
# The driving function for the NAFF algorithm.
# Inputs :
# * data : NumPy array with TbT data
# * turns : number of points to consider from the input data
# * nterms : maximum number of harmonics to search for in the data sample
# * skipTurns : number of observations (data points) to skip from the start of the input iterable
# * getFullSpectrum : [True | False]
# If True, a normal FFT is used (both negative and positive freq.)
# If False, an rFFT is used (only positive frequencies)
# * window : the order of window to be applied on the input data (default =1)
# Returns : Array with frequencies and amplitudes in the format:
# [order of harmonic, frequency, Amplitude, Re{Amplitude}, Im{Amplitude}]
pnf.naff(signal, turns=500, nterms=1, skipTurns=0, getFullSpectrum=False, window=1)
# outputs an array of arrays for each frequency. Each sub-array includes:
# [order of harmonic, frequency, Amplitude, Re{Amplitude}, Im{Amplitude]
# My frequency is simply
pnf.naff(signal, 500, 1, 0 , False)[0][1]
