deepof.visuals.plot_transitions

deepof.visuals.plot_transitions(coordinates: deepof_coordinates, embeddings: deepof_table_dict, soft_counts: deepof_table_dict, breaks: deepof_table_dict | None = None, bin_size: int | None = None, bin_index: int = 0, exp_condition: str | None = None, visualization='networks', silence_diagonal=False, cluster: bool = True, axes: list | None = None, save: bool = False, **kwargs)

Compute and plots transition matrices for all data or per condition. Plots can be heatmaps or networks.

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.

• 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. new figure will be created.

• visualization (str) – visualization mode. Can be either ‘networks’, or ‘heatmaps’.

• silence_diagonal (bool) – If True, diagonals are set to zero.

• cluster (bool) – If True (default) rows and columns on heatmaps are hierarchically clustered.

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

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