# Frequently Asked Questions

## Which Python versions are supported?

amica supports **Python 3.10 and newer**.

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## Does amica require JAX?

No.

amica runs out of the box using the NumPy backend. Installing the `jax` extra enables hardware acceleration on supported systems.

```bash
pip install "amica[jax]"
```

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## Does amica require a GPU?

No.

The NumPy backend runs on any machine. If JAX with GPU support is installed, amica will automatically use the GPU.

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## Can I use amica with MNE-Python?

Yes.

amica provides a high-level `fit_ica` function that returns a standard `mne.preprocessing.ICA` object, allowing you to use the full MNE visualization and artifact-rejection workflow.

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## What input format does amica expect?

The core API expects a NumPy array with shape

```text
(n_channels, n_samples)
```

When using the MNE interface, simply pass an `mne.io.Raw` object.

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## Can amica fit multiple ICA models?

Yes.

AMICA supports fitting multiple ICA models (`num_models > 1`) to capture non-stationary data. See the examples and API documentation for details.

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## How does amica compare with the original MATLAB AMICA?

amica is designed to reproduce the original AMICA algorithm while providing a native Python implementation, optional JAX acceleration, and seamless integration with the scientific Python ecosystem.

Validation experiments comparing amica with the original MATLAB implementation are available in the documentation.

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## Where can I find examples?

See the **Examples** section of the documentation for:

- MNE-Python integration
- Pure NumPy/JAX workflows
- Validation examples
- HPC/Slurm execution

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## I found a bug. Where should I report it?

Please open an issue on GitHub:

**https://github.com/snesmaeili/amica/issues**

When possible, include:

- your operating system
- Python version
- amica version
- backend (NumPy or JAX)
- a minimal reproducible example

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## Can I contribute?

Absolutely!

Please read the [Contributing Guide](contributing.md) before opening a pull request.