pca

Principal component analysis.

Use method="pca" to find orthogonal components that explain maximal variance in the data.

When to choose this method

Choose PCA when you want a strong unsupervised baseline with minimal assumptions.

• Best for exploratory analysis when you do not want to commit to task labels.

• Useful for denoising and quick visualization of dominant population patterns.

• Good first pass before trying more specialized methods such as dPCA or GPFA.

PCA is not task-aware: the largest-variance directions are not always the most behaviorally relevant directions.

ds = da.ephys.reduce(
    method="pca",
    n_components=5,
)

Key outputs

• projections: component activity over samples/time.

• weights: per-unit loading for each component.

• explained_variance_ratio: variance explained by each component.