deepof.visuals

General plotting functions for the deepof package.

Functions

animate_skeleton(coordinates, experiment_id)	Render a FuncAnimation object with embeddings and/or motion trajectories over time.
annotate_video(coordinates, tag_dict, vid_index)	Render a version of the input video with all supervised taggings in place.
export_annotated_video(coordinates[, ...])	Export annotated videos from both supervised and unsupervised pipelines.
heatmap(dframe, bodyparts[, xlim, ylim, ...])	Return a heatmap of the movement of a specific bodypart in the arena.
output_cluster_video(cap, out, frame_mask, ...)	Output a video with the frames corresponding to the cluster.
output_unsupervised_annotated_video(...[, ...])	Given a video, and soft_counts per frame, outputs a video with the frames annotated with the cluster they belong to.
output_videos_per_cluster(video_paths, ...)	Given a list of videos, and a list of soft counts per video, outputs a video for each cluster.
plot_arena(coordinates, center, color, ax, i)	Plot the arena in the given canvas.
plot_cluster_detection_performance(...[, ...])	Plot either a confusion matrix or a bar chart with balanced accuracy for cluster detection cross validated models.
plot_distance_between_conditions(...[, ...])	Plot the distance between conditions across a growing time window.
plot_embeddings(coordinates[, embeddings, ...])	Return a scatter plot of the passed projection.
plot_enrichment(coordinates[, embeddings, ...])	Violin plots per cluster per condition.
plot_gantt(coordinates, experiment_id[, ...])	Return a scatter plot of the passed projection.
plot_heatmaps(coordinates, bodyparts[, ...])	Plot heatmaps of the specified body parts (bodyparts) of the specified animal (i).
plot_normative_log_likelihood(embeddings, ...)	Plot a bar chart with normative log likelihoods per experimental condition, and compute statistics.
plot_shap_swarm_per_cluster(coordinates, ...)	Plot a swarm plot of the SHAP values for a given cluster.
plot_stationary_entropy(coordinates, ...[, ...])	Compute and plots transition stationary distribution entropy per condition.
plot_transitions(coordinates, embeddings, ...)	Compute and plots transition matrices for all data or per condition.
tag_annotated_frames(frame, font, ...)	Annotate a given frame with on-screen information about the recognised patterns.