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jamica - Adaptive Mixture Independent Component Analysis, powered by JAX jamica - Adaptive Mixture Independent Component Analysis, powered by JAX

``` # 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. ::::{grid} 1 1 2 2 :gutter: 3 :::{grid-item-card} Quick Start :link: examples :link-type: doc Install jamica, fit AMICA on MNE data, and run the core examples. ::: :::{grid-item-card} Background :link: explanation :link-type: doc Understand AMICA, mixture ICA, optimization, and the design of jamica. ::: :::{grid-item-card} API Reference :link: api :link-type: doc Browse the public Python API, classes, functions, and configuration objects. ::: :::{grid-item-card} FAQ :link: faq :link-type: doc Find troubleshooting notes for installation, JAX, MNE integration, and validation. ::: :::: ## 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) ``` ```{toctree} --- maxdepth: 2 caption: User Guide hidden: true --- examples explanation faq ``` ```{toctree} --- maxdepth: 2 caption: Reference hidden: true --- api auto_examples/index contributing ``` ## Project Links - [GitHub repository](https://github.com/snesmaeili/jamica) - [PyPI package](https://pypi.org/project/jamica/) - [Issue tracker](https://github.com/snesmaeili/jamica/issues)