Metadata-Version: 1.1
Name: pyfftlog
Version: 0.2.0
Summary: Logarithmic Fast Fourier Transform
Home-page: https://github.com/prisae/pyfftlog
Author: Dieter Werthmüller
Author-email: dieter@werthmuller.org
License: CC0-1.0
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        `pyfftlog` - A python version of FFTLog
        =======================================
        
        This is a python version of the logarithmic FFT code *FFTLog* as presented in
        Appendix B of `Hamilton (2000)
        <http://dx.doi.org/10.1046/j.1365-8711.2000.03071.x>`_ and published at
        `casa.colorado.edu/~ajsh/FFTLog <http://casa.colorado.edu/~ajsh/FFTLog>`_.
        
        A simple `f2py`-wrapper (`fftlog`) can be found on `github.com/prisae/fftlog
        <https://github.com/prisae/fftlog>`_.  Tests have shown that `fftlog` is a bit
        faster than `pyfftlog`, but `pyfftlog` is easier to implement, as you only need
        `NumPy` and `SciPy`, without the need to compile anything.
        
        I hope that `FFTLog` will make it into `SciPy` in the future, which will make
        this project redundant. (If you have the bandwidth and are willing to chip in
        have a look at `SciPy PR #7310 <https://github.com/scipy/scipy/pull/7310>`_.)
        
        Be aware that `pyfftlog` has not been tested extensively. It works fine for the
        test from the original code, and my use case, which is `pyfftlog.fftl` with
        `mu=0.5` (sine-transform), `q=0` (unbiased), `k=1`, `kropt=1`, and `tdir=1`
        (forward). Please let me know if you encounter any issues.
        
        - **Documentation**: https://pyfftlog.readthedocs.io
        - **Source Code**: https://github.com/prisae/pyfftlog
        
        
        Description of FFTLog from the FFTLog-Website
        ---------------------------------------------
        
        FFTLog is a set of fortran subroutines that compute the fast Fourier or Hankel
        (= Fourier-Bessel) transform of a periodic sequence of logarithmically spaced
        points.
        
        FFTLog can be regarded as a natural analogue to the standard Fast Fourier
        Transform (FFT), in the sense that, just as the normal FFT gives the exact (to
        machine precision) Fourier transform of a linearly spaced periodic sequence, so
        also FFTLog gives the exact Fourier or Hankel transform, of arbitrary order m,
        of a logarithmically spaced periodic sequence.
        
        FFTLog shares with the normal FFT the problems of ringing (response to sudden
        steps) and aliasing (periodic folding of frequencies), but under appropriate
        circumstances FFTLog may approximate the results of a continuous Fourier or
        Hankel transform.
        
        The FFTLog algorithm was originally proposed by `Talman (1978)
        <http://dx.doi.org/10.1016/0021-9991(78)90107-9>`_.
        
        *For the full documentation, see* `casa.colorado.edu/~ajsh/FFTLog
        <http://casa.colorado.edu/~ajsh/FFTLog>`_.
        
        
        Installation
        ------------
        
        You can install pyfftlog either via **conda**:
        
        .. code-block:: console
        
           conda install -c conda-forge pyfftlog
        
        or via **pip**:
        
        .. code-block:: console
        
           pip install pyfftlog
        
        
        License, Citation, and Credits
        ------------------------------
        
        Released to the public domain under the `CC0 1.0 License
        <http://creativecommons.org/publicdomain/zero/1.0>`_.
        
        All releases have a Zenodo-DOI, which can be found on `10.5281/zenodo.3830364
        <https://doi.org/10.5281/zenodo.3830364>`_.
        
        Be kind and give credits by citing `Hamilton (2000)
        <http://dx.doi.org/10.1046/j.1365-8711.2000.03071.x>`_. See the
        `references-section
        <https://pyfftlog.readthedocs.io/en/stable/references.html>`_ in the manual for
        full references.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
