API Reference#

The public API of coco-pipe. Most workflows use a small set of high-level entry points listed under Selected APIs below; the Full Module Index documents every public and internal symbol, generated directly from the source.

For conceptual background and worked examples, see the User Guide.

Selected APIs#

The most commonly used classes and functions, grouped by module.

Decoding#

`coco_pipe.decoding.Experiment`	Main executor for decoding experiments.
`coco_pipe.decoding.ExperimentConfig`	Master configuration for a Decoding Experiment.
`coco_pipe.decoding.ExperimentResult`	Unified Container for Experiment Results.
`coco_pipe.decoding.get_capabilities`	Return machine-readable capability metadata for a given estimator.
`coco_pipe.decoding.list_capabilities`	Return capability metadata for all registered estimators.
`coco_pipe.decoding.register_estimator`	Decorator to register a custom estimator class under a specific name.
`coco_pipe.decoding.run_statistical_assessment`	Orchestrate the statistical assessment of experiment results.

Dimensionality Reduction#

`coco_pipe.dim_reduction.DimReduction`	Manage one dimensionality reduction workflow.
`coco_pipe.dim_reduction.BaseReducer`	Abstract base class for all dimensionality reduction implementations.
`coco_pipe.dim_reduction.METHODS`	Built-in immutable sequence.
`coco_pipe.dim_reduction.evaluation.MethodSelector`	Compare and rank already-scored dimensionality reduction methods.
`coco_pipe.dim_reduction.interpret_features`	Run one or more feature interpretation analyses.

Reports#

`coco_pipe.report.Report`	The main report container.
`coco_pipe.report.Section`	A logical section of the report.
`coco_pipe.report.from_experiment_result`	Build a decoding report from an `~coco_pipe.decoding.result.ExperimentResult`.
`coco_pipe.report.from_reductions`	Create a comparative report from multiple dimensionality reduction results.
`coco_pipe.report.from_container`	Create a standard report from a DataContainer.
`coco_pipe.report.merge_reports`	Merge multiple reports into a single comparison report.

Visualization#

`coco_pipe.viz.plot_embedding`	Plot an explicit 2D or 3D embedding.
`coco_pipe.viz.plot_decoding_scores`	Plot aggregate scalar decoding scores by model and metric.
`coco_pipe.viz.plot_confusion_matrix`	Plot an aggregated confusion matrix from decoding diagnostics.
`coco_pipe.viz.plot_roc_curve`	Plot receiver-operating-characteristic curves.
`coco_pipe.viz.plot_topomap`	Plot a topographic map for sensor values using MNE.
`coco_pipe.viz.set_coco_theme`	Set rcParams globally.
`coco_pipe.viz.save_figure`	Save a Matplotlib figure with coco_pipe defaults.