deepof.post_hoc.select_time_bin

deepof.post_hoc.select_time_bin(embedding: deepof_table_dict | None = None, soft_counts: deepof_table_dict | None = None, breaks: deepof_table_dict | None = None, supervised_annotations: deepof_table_dict | None = None, bin_size: int = 0, bin_index: int = 0, precomputed: ndarray | None = None)

Select a time bin and filters all relevant objects (embeddings, soft_counts, breaks, and supervised annotations).

Parameters:

• embedding (TableDict) – A dictionary of embeddings, where the keys are the names of the experimental conditions, and the values are the embeddings for each condition.

• soft_counts (TableDict) – A dictionary of soft counts, where the keys are the names of the experimental conditions, and the values are the soft counts for each condition.

• breaks (TableDict) – A dictionary of breaks, where the keys are the names of the experimental conditions, and the values are the breaks for each condition.

• supervised_annotations (TableDict) – table dict with supervised annotations per animal experiment across time.

• bin_size (int) – The size of the time bin to select.

• bin_index (int) – The index of the time bin to select.

• precomputed (np.ndarray) – Boolean array. If provided, ignores every othe parameter and just indexes each experiment using the provided mask.

Returns:

A tuple of the filtered embeddings, soft counts, and breaks.