https://github.com/moble/out_of_core_fft

Perform FFT on data set that does not fit into memory
https://github.com/moble/out_of_core_fft

Last synced: 30 days ago
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Perform FFT on data set that does not fit into memory

• Host: GitHub
• URL: https://github.com/moble/out_of_core_fft
• Owner: moble
• License: mit
• Created: 2016-01-26T17:46:21.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2019-05-24T20:15:01.000Z (over 5 years ago)
• Last Synced: 2024-10-04T15:05:52.064Z (about 1 month ago)
• Language: Python
• Homepage:
• Size: 24.4 KB
• Stars: 7
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# out_of_core_fft  

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`.

## Usage

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.

## Acknowledgments

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.