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# jamica
**Native Python AMICA for scientific EEG workflows.**
jamica provides an open Python implementation of **Adaptive Mixture Independent Component Analysis (AMICA)** with a modern scientific Python API, optional JAX acceleration, and MNE-Python integration.
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:::{grid-item-card} Quick Start
:link: examples
:link-type: doc
Install jamica, fit AMICA on MNE data, and run the core examples.
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:::{grid-item-card} Background
:link: explanation
:link-type: doc
Understand AMICA, mixture ICA, optimization, and the design of jamica.
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:::{grid-item-card} API Reference
:link: api
:link-type: doc
Browse the public Python API, classes, functions, and configuration objects.
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:::{grid-item-card} FAQ
:link: faq
:link-type: doc
Find troubleshooting notes for installation, JAX, MNE integration, and validation.
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## Why jamica?
- Native Python implementation of AMICA
- Optional JAX CPU/GPU acceleration
- Drop-in MNE-Python integration
- Multi-model AMICA support
- Numerical validation against the Fortran AMICA 1.7 reference implementation
- Designed for reproducible EEG and neuroimaging workflows
## Minimal Example
```python
from jamica import Amica, AmicaConfig
config = AmicaConfig(max_iter=2000, num_mix_comps=3)
model = Amica(config, random_state=42)
result = model.fit(data)
sources = model.transform(data)
```
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examples
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## Project Links
- [GitHub repository](https://github.com/snesmaeili/jamica)
- [PyPI package](https://pypi.org/project/jamica/)
- [Issue tracker](https://github.com/snesmaeili/jamica/issues)