./wip/py-anfft, FFT package for Python, based on FFTW

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Branch: CURRENT, Version: 0.2, Package name: py310-anfft-0.2, Maintainer: jihbed.research

ANFFT is an FFT package for Python, based on FFTW. It provides a
multi-threaded, self-tuning FFT interface via high-level functions
similar to the "fft" and "fftn" routines found in NumPy and SciPy.

ANFFT is intended to be used in situations where large numbers of expensive
FFTs are required, for which the built-in NumPy or SciPy FFTs are not
acceptable. By default, ANFFT provides immediate results by using
FFTW's "estimate" mode, which does not require tuning. However, each
high-level function provides a keyword named "measure" which will invoke the
full FFTW planning machinery. Plans for a given shape and type of array
are cached for the length of a Python session, and accummulated FFTW "wisdom"
is stored across Python sessions in a dotfile. You don't need to know anything
about FFTW internals to use ANFFT.


Required to run:
[math/py-numpy] [lang/python37]

Required to build:
[pkgtools/cwrappers]

Master sites:

RMD160: 9450475a5b019a45181174c568136390c2bcec36
Filesize: 20.354 KB

Version history: (Expand)

CVS history: (Expand)


   2014-01-15 00:11:15 by Kamel Derouiche | Files touched by this commit (2) 
Log message:
Update component
   2012-10-06 19:13:24 by Aleksej Saushev | Files touched by this commit (44) 
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't or might probably not have staged installation.
   2011-10-28 19:53:44 by Kamel Derouiche | Files touched by this commit (4) | Imported package
Log message:
Import py27-anfft-0.1 as wip/py-anfft.

ANFFT is an FFT package for Python, based on FFTW.  It provides a
multi-threaded, self-tuning FFT interface via high-level functions
similar to the "fft" and "fftn" routines found in NumPy and \ 
SciPy.

ANFFT is intended to be used in situations where large numbers of expensive
FFTs are required, for which the built-in NumPy or SciPy FFTs are not
acceptable. By default, ANFFT provides immediate results by using
FFTW's "estimate" mode, which does not require tuning.  However, each
high-level function provides a keyword named "measure" which will \ 
invoke the
full FFTW planning machinery.  Plans for a given shape and type of array
are cached for the length of a Python session, and accummulated FFTW \ 
"wisdom"
is stored across Python sessions in a dotfile.  You don't need to know anything
about FFTW internals to use ANFFT.