Metadata-Version: 2.2
Name: sfts
Version: 0.1.1
Summary: Scalable data-analysis framework for long-duration gravitational-wave signals
Author-email: Rodrigo Tenorio <rodrigo.tenorio.marquez@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Rodrigo Tenorio
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Source, https://github.com/Rodrigo-Tenorio/sfts
Project-URL: Issue tracker, https://github.com/Rodrigo-Tenorio/sfts/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Natural Language :: English
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: jax
Requires-Dist: jaxopt
Provides-Extra: cuda
Requires-Dist: jax[cuda12]; extra == "cuda"

# `sfts`

[![arXiv](https://img.shields.io/badge/arXiv-2502.11823-b31b1b.svg)](https://arxiv.org/abs/2502.11823)

Short Fourier Transforms for Fresnel-weighted Template Summation.

Implementation of gravitational-wave data-analysis tools described in [Tenorio & Gerosa (2025)][sfts]
to operate using Short Fourier Transforms (SFTs).

See [this simple example](./examples/bns_inspiral.py) for a quick-start on
using [`iphenot`][iphenot] (`/ˈaɪv ˈnɒt/`) and SFTs.

The package is composed of two main modules:

1. [iphenot.py][iphenot]: [jaxified](https://github.com/jax-ml/jax) re-implementation of the
inspiral part of the  [`IMRPhenomT` waveform approximant][LALPhenomT].
1. [kernels.py](./src/sfts/kernels.py): Fresnel and Dirichlet kernels to compute scalar products using SFTs.

# How to install

`sfts` can be pulled in from PyPI:
```
$ pip install sfts
```

To pull in `jax`'s GPU capabilities, use:

```
$ pip install sfts[cuda]
```

Alternatively, this repository itself is pip-installable.

# Cite

If the tools provided by `sfts` were useful to you, we would appreciate a citation of
[the accompanying paper][sfts]:
```
@article{Tenorio:2025yca,
    author = "Tenorio, Rodrigo and Gerosa, Davide",
    title = "{SFTs: a scalable data-analysis framework for long-duration gravitational-wave signals}",
    eprint = "2502.11823",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    month = "2",
    year = "2025"
}
```
Whenever applicable, please consider also citing the `IMRPhenomT` papers [listed here][LALPhenomT].

[sfts]: https://arxiv.org/abs/2502.11823
[LALPhenomT]: https://git.ligo.org/lscsoft/lalsuite/-/blob/master/lalsimulation/lib/LALSimIMRPhenomTPHM.c
[iphenot]: ./src/sfts/iphenot.py
