fmralign

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Functional alignment and template estimation library for functional Magnetic Resonance Imaging data

fmralign 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# fmralign

[![Code style: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
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![python version](https://img.shields.io/badge/python-3.10_|_3.11_|_3.12_|_3.13_|_3.14-blue?style=for-the-badge)
![license](https://img.shields.io/github/license/fmralign/fmralign?style=for-the-badge)

[Functional alignment for fMRI](https://fmralign.github.io/fmralign-docs)
(functional Magnetic Resonance Imaging) data.

This light-weight Python library provides access to a range of functional
alignment methods, including Procrustes and Optimal Transport.
It is compatible with and inspired by [Nilearn](http://nilearn.github.io).
Alternative implementations of these ideas can be found in the
[pymvpa](http://www.pymvpa.org), [brainiak](http://brainiak.org)
or [neuroboros](http://neuroboros.github.io) packages.

## Getting Started

### Installation

You can access the latest stable version of fmralign directly with the PyPi
package installer:

```bash
pip install fmralign
```

Optimal Transport methods can be installed with the optional dependency `[ot]`,
which installs both supported backends (`geomloss` and `POT`):

```bash
pip install fmralign[ot]
```

If you only need one backend, you can install it individually:

```bash
pip install fmralign[ot_geomloss]  # geomloss backend only
pip install fmralign[ot_pot]       # POT backend only
```

For development or bleeding-edge features, fmralign can also be installed
directly from source:

```bash
git clone https://github.com/fmralign/fmralign
cd fmralign
pip install -e .
```

### Documentation

You can find an introduction to functional alignment, a user guide and some examples
on how to use the package on the documentation [website](https://fmralign.github.io/fmralign-docs).

## License

This project is licensed under the Simplified BSD License.

## Acknowledgments

This project has received funding from the European Union’s Horizon
2020 Research and Innovation Programme under Grant Agreement No. 785907
(HBP SGA2).
This project was supported by [Digiteo](http://www.digiteo.fr).

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