# out_of_core_fft <a href=”https://travis-ci.org/moble/out_of_core_fft”><img align=”right” hspace=”3” alt=”Status of automatic build and test suite” src=”https://travis-ci.org/moble/out_of_core_fft.svg?branch=master”></a> <a href=”https://github.com/moble/out_of_core_fft/blob/master/LICENSE”><img align=”right” hspace=”3” alt=”Code distributed under the open-source MIT license” src=”http://moble.github.io/spherical_functions/images/MITLicenseBadge.svg”></a>
Fourier transforms are highly nonlocal, which can cause problems when dealing with very large data sets. In particular, standard algorithms cannot work with data sets too large to fit into memory. On the other hand, the classic Cooley-Tukey FFT algorithm shows that discrete Fourier transforms can be split up into smaller sub-problems. This module provides functions for FFTs that can work with the data directly on disk, extracting small subsets that fit into memory, working on each individually, and then combining back onto disk to get the final result. This implementation is based on the algorithm presented by Thomas H. Cormen in “Algorithms for parallel processing” (1999). A nontrivial part of the implementation involves transposing the data on disk, for which I created a relatively simple, but fairly fast, function included here simply as transpose.
These functions assume that the data to be manipulated are stored in HDF5 files. The FFT and inverse FFT are called with something like
`python import out_of_core_fft out_of_core_fft.fft('input.h5', 'x', 'output.h5', 'X') out_of_core_fft.ifft('input2.h5', 'X', 'output2.h5', 'x') `
Here, x and X are names for the datasets within the HDF5 files. Note that nothing is returned, because the result is stored on disk, as requested.
See the docstrings for more details.
The work of creating this code was supported in part by the Sherman Fairchild Foundation and by NSF Grants No. PHY-1306125 and AST-1333129.
TODO: Figure out how to actually get changelog content.
Changelog content for this version goes here.