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We are happy to announce the release of pyFFTW v0.11.0. This release is the result of more than two years of work by 9 contributors.
pyFFTW is a pythonic wrapper around FFTW 3, the speedy FFT library. The ultimate aim is to present a unified interface for all the possible transforms that FFTW can perform.
Both the complex DFT and the real DFT are supported, as well as on arbitrary axes of abitrary shaped and strided arrays. Operating FFTW in multithreaded mode is supported.
pyFFTW implements the numpy and scipy fft interfaces in order for users to take advantage of the speed of FFTW with minimal code modifications. A dask fft interface is provided as a drop-in replacement for the equivalent module in dask.
New features
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Dask interface
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A complete drop-in replacement of Dask Array's FFT module is provided.
It includes all numpy-like FFT functions, which use FFTW under the hood.
Other functions from Dask Array's FFT module are imported as a convenience.
Fast transform planning utility
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A routine ``next_fast_len`` has been added to allow the user to easily determine sizes for which the FFT computation is computationally efficient.
Expanded support for norm keyword argument in the numpy interfaces
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Support for the ``norm`` keyword argument has been expanded to all numpy FFT interfaces. It was previously present only for the complex-valued routines.
Support for norm keyword argument in FFTW builders
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A ``norm`` keyword argument has been added to the FFTW builders allowing the normalization of pre-planned FFTs to be chosen in the same manner as for the numpy interfaces.
Dynamic library detection at build and run time
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setup.py has been overhauled to dynamically detect the variants of FFTW that are present. Previously single, double and long double libraries were all required at build time. Now, compilation will attempt to detect which
variants are present and compile based on that.
It also now possible to specify an additional search path for the fftw libraries by setting the environment variable ``PYFFTW_LIB_DIR``.
One caveat is that dynamic library detection is not used by default on the Windows platform, as the default assumption is that on Windows, a full set of precompiled libraries have been downloaded from fftw.org. Alternatively, if the environment variable ``PYFFTW_WIN_CONDAFORGE`` is defined, dynamic detection of
libraries named fftw3.dll, fftw3f.dll and fftw3l.dll is attempted. This is for compatibility with the CMake-based FFTW build used by conda-forge.
The builders and interfaces all detect at runtime which variants are available and select the most suitable precision for the input's dtype. For example, if single-precsion FFTW libraries are not available, double-precision FFTs will be
used to transform single-precision inputs. The PyFFTW test suite also detects at runtime which variants have been compiled and only runs tests against the available precisions.
OpenMP threading support
------------------------
Building with OpenMP-based threading is also now supported. If both OpenMP and posix threaded libraries are found, the OpenMP variant will be preferred. If the user desires to instead link to posix threads by default, the
environment variable ``PYFFTW_USE_PTHREADS`` should be defined.
Custom Configuration of Planners and Interfaces
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The new module pyfftw.config can be used to assign values for the default number of threads (via ``pyfftw.config.NUM_THREADS``) and default FFTW planner effort (via ``pyfftw.config.PLANNER_EFFORT``). It is also now possible to define the environment variables ``PYFFTW_NUM_THREADS`` and ``PYFFTW_PLANNER_EFFORT`` to determine the default values at import time.
Bugs Fixed
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A platform-dependent bug that results in potentially overwriting a previously computed output upon repeated calls to the numpy interfaces was fixed (199).
Fix to potential platform-dependent integer overflow in empty_aligned (192).
rfftfreq is now present in the numpy fft interfaces for numpy >= 1.8 (207)
Other changes
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float16 inputs are now transformed using single rather than double precision.
The default planning for the numpy and scipy interfaces has changed from FFTW_MEASURE to FFTW_ESTIMATE. This results in faster planning. In cases where the same transform is to be repeated many times, it is likely advantageous to manually specify FFTW_MEASURE instead (or use the FFTW builders
to pre-plan the FFT).
FutureWarnings related to NumPy multiindexing in NumPy 1.15 are avoided by using more modern indexing conventions.
version number handling is now automatically handled by versioneer
All documentation is now built and hosted at Read the Docs (http://pyfftw.readthedocs.io).
Authors
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* Frederik Beaujean +
* Dennis Brakhane +
* Henry Gomersall
* John Kirkham +
* Antony Lee +
* Gregory R. Lee
* Iblis Lin +
* Matthew D. Scholefield +
* Hsiou-Yuan (Herbert) Liu +
A total of 9 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.