# 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](http://www.sciencedirect.com/science/article/pii/001910359090084M).
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](https://pypi.org/project/PyNAFF/) so the user can simply run:
```bash
pip install --user PyNAFF
```
or from Git:
```bash
pip install --user git+https://github.com/nkarast/PyNAFF.git
```
## Example of Usage
```python
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]
```
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"description": "# PyNAFF\n\nAuthors:\n\n* Foteini Asvesta (fasvesta .at. cern .dot. ch)\n* Nikos Karastathis (nkarast .at. cern .dot. ch)\n* Panagiotis Zisopoulos (pzisopou .at. cern .dot. ch)\n\nA Python module that implements the [Numerical Analysis of Fundamental Frequencies method of J. Laskar](http://www.sciencedirect.com/science/article/pii/001910359090084M).\nThe 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).\n\n\n## Installation:\n\nThe module is ported in [PyPi](https://pypi.org/project/PyNAFF/) so the user can simply run:\n\n```bash\npip install --user PyNAFF\n```\n\nor from Git:\n```bash\npip install --user git+https://github.com/nkarast/PyNAFF.git\n```\n\n\n## Example of Usage\n```python\nimport PyNAFF as pnf\nimport numpy as np\n\nt = np.linspace(1, 3000, num=3000, endpoint=True)\nQ = 0.12345\nsignal = np.sin(2.0*np.pi*Q*t)\n\n\n# Signature: pnf.naff(data, turns=300, nterms=1, skipTurns=0, getFullSpectrum=False, window=1)\n# Docstring:\n# The driving function for the NAFF algorithm.\n# Inputs :\n# * data : NumPy array with TbT data\n# * turns : number of points to consider from the input data\n# * nterms : maximum number of harmonics to search for in the data sample\n# * skipTurns : number of observations (data points) to skip from the start of the input iterable\n# * getFullSpectrum : [True | False]\n# If True, a normal FFT is used (both negative and positive freq.)\n# If False, an rFFT is used (only positive frequencies)\n# * window : the order of window to be applied on the input data (default =1)\n# Returns : Array with frequencies and amplitudes in the format:\n# [order of harmonic, frequency, Amplitude, Re{Amplitude}, Im{Amplitude}]\n\npnf.naff(signal, turns=500, nterms=1, skipTurns=0, getFullSpectrum=False, window=1)\n\n# outputs an array of arrays for each frequency. Each sub-array includes:\n# [order of harmonic, frequency, Amplitude, Re{Amplitude}, Im{Amplitude]\n\n\n# My frequency is simply \npnf.naff(signal, 500, 1, 0 , False)[0][1]\n\n```\n",
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