Python Package ================== pyAVS is the Python package for loading and preprocessing the AVS dataset -- MEG, eye tracking, and structural MRI, ported from the analysis pipelines behind the AVS dataset paper into a documented, reusable library. .. toctree:: :maxdepth: 2 :hidden: composer_guide ../tutorials/index ../examples/index ../api/index Three Steps: Configure, Compose, Analyze --------------------------------------------- 1. **Configure** -- point pyAVS at your local copy of the dataset once, with :func:`pyavs.set_data_path` or the ``pyavs configure`` CLI command (see :doc:`../installation`). 2. **Compose** -- use :class:`~pyavs.preprocessing.composer.AVSComposer` (see :doc:`composer_guide`) to load MEG data, run ICA/filtering, and align eye-tracking events into epoched MEG data with rich metadata. 3. **Analyze** -- source-reconstruct, extract ROI/population codes, or hand epochs off to your own analysis code. See :doc:`../tutorials/index` and :doc:`../examples/index` for worked examples across preprocessing, source reconstruction, object detection, and configuration/reproducibility. .. grid:: 2 :gutter: 3 .. grid-item-card:: AVSComposer Guide :link: composer_guide :link-type: doc The recommended entry point for MEG + eye-tracking data fusion, with a full worked example and advanced configuration options. .. grid-item-card:: Tutorials :link: ../tutorials/index :link-type: doc Narrative, end-to-end walkthroughs: MEG + eye tracking, and source reconstruction + population codes. .. grid-item-card:: Examples :link: ../examples/index :link-type: doc Focused, runnable examples for source reconstruction, cross-session filters, configuration, object detection, and reproducibility. .. grid-item-card:: API Reference :link: ../api/index :link-type: doc Full API documentation, organized by submodule (preprocessing, source, scenes, dataloader, and more).