Skip to main content

PyNFFTls

This Python module provides the Fast Lomb-Scargle periodogram developed by B. Leroy (2012, Astron. Astrophys. 545, A50)

It is based on the Non-equispaced Fast Fourier Transform (NFFTn http://www-user.tu-chemnitz.de/~potts/nfft/) as well as the FFTW3 library (http://www.fftw.org/). Both librairies must be installed.

Calling sequence:

(f,p) = period(t,y,ofac,hifac)

For more details, see the associated documentation For a complete example, see nfftls_test.py

This Python module also provides the following methods: - the Non-equidistant Fast Fourier Transform (NFFT) of a time series: (f,A) = nfft(t,y,p,d). For more details, see the associated documentation - the Discrete Fourier Transform (DFT) of a time series: A = dft(t,y,f). For more details, see the associated documentation

For a complete example, see nfftls_test2.py

Change history: 1.6 (29/11/2020): module made compatible with NFFT version 3.5.3 1.5 (2/02/2020): module interface is now based on Cython, module now compatible with python 3 1.4 (11/04/2019): interface of nfft() changed, this function can now compute an over-sampled fourier transform 1.3 (10/04/2019): correct a bug that lead to over-estimate the frequency by a relative factor of 1e-5 1.2 (4/06/2013): 1.1 (18/01/2013): 1.0 (3/01/2013): initial version

  1. Samadi, LESIA (http://lesia.obspm.fr), Observatoire de Paris, 22 Dec. 2012

Metadata

Release files for pynfftls 1.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pynfftls 1.6
File Size Uploaded
pynfftls-1.6.tar.gz 28.9 kB Details

Release files / pynfftls-1.6.tar.gz

Download URL pynfftls-1.6.tar.gz
Size 28.9 kB
Tags Source
SHA-256 checksum
How to use checksums
b22f1044359ef5b1ba2b1b16197e800148cb9112f5d492b85e5e4a6f2b841c13
BLAKE2b-256 checksum
How to use checksums
07fd519e5b416d84a9aafae82b2891046287f76d43b255db46ffc9bb035eb5d8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.10.6

Release history Release notifications | RSS feed

This release

1.6 This release

1 release file

1.5

1 release file

1.3

1 release file

1.2

1 release file

1.1

1 release file

1.0

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page