deepof.visuals.plot_stationary_entropy

deepof.visuals.plot_stationary_entropy(coordinates: deepof_coordinates, embeddings: deepof_table_dict, soft_counts: deepof_table_dict, breaks: deepof_table_dict | None = None, add_stats: str = 'Mann-Whitney', bin_size: int | None = None, bin_index: int = 0, exp_condition: str | None = None, verbose: bool = False, ax: Any | None = None, save: bool = False)

Compute and plots transition stationary distribution entropy per condition.

Parameters:

• coordinates (coordinates) – deepOF project where the data is stored.

• embeddings (table_dict) – table dict with neural embeddings per animal experiment across time.

• soft_counts (table_dict) – table dict with soft cluster assignments per animal experiment across time.

• breaks (table_dict) – table dict with changepoint detection breaks per experiment.

• exp_condition (str) – Name of the experimental condition to use when plotting. If None (default) the first one available is used.

• add_stats (str) – test to use. Mann-Whitney (non-parametric) by default. See statsannotations documentation for details.

• bin_size (int) – bin size for time filtering.

• bin_index (int) – index of the bin of size bin_size to select along the time dimension.

• verbose (bool) – if True, prints test results and p-value cutoffs. False by default.

• ax (plt.AxesSubplot) – axes where to plot the current figure. If not provided, new figure will be created.

• save (bool) – Saves a time-stamped vectorized version of the figure if True.