behavenet.plotting¶

Plotting and video documentation.

behavenet.plotting Package¶

Utility functions shared across multiple plotting modules.

Functions¶

`concat`(ims[, axis])	Concatenate two channels along x or y direction (useful for data with multiple views).
`get_crop`(im, y_0, y_ext, x_0, x_ext)	Get crop of image, filling in borders with zeros.
`load_latents`(hparams, version[, dtype])	Load all latents as a single array.
`load_metrics_csv_as_df`(hparams, lab, expt, …)	Load metrics csv file and return as a pandas dataframe for easy plotting.
`save_movie`(save_file, ani[, frame_rate])	Save out matplotlib ArtistAnimation

behavenet.plotting.ae_utils Module¶

Plotting and video making functions for autoencoders.

Functions¶

`make_ae_reconstruction_movie_wrapper`(…[, …])	Produce movie with original video, reconstructed video, and residual.
`make_reconstruction_movie`(ims[, titles, …])	Produce movie with original video and reconstructed videos.

behavenet.plotting.cond_ae_utils Module¶

Functions¶

`get_input_range`(input_type, hparams[, …])	Helper function to compute input range for a variety of data types.
`compute_range`(values_list[, min_p, max_p])	Compute min and max of a list of numbers using percentiles.
`get_labels_2d_for_trial`(hparams, sess_ids[, …])	Return scaled labels (in pixel space) for a given trial.
`get_model_input`(data_generator, hparams, model)	Return images, latents, and labels for a given trial.
`interpolate_2d`(interp_type, model, ims_0, …)	Return reconstructed images created by interpolating through latent/label space.
`interpolate_1d`(interp_type, model, ims_0, …)	Return reconstructed images created by interpolating through latent/label space.
`interpolate_point_path`(interp_type, model, …)	Return reconstructed images created by interpolating through multiple points.
`plot_2d_frame_array`(ims_list[, markers, …])	Plot list of list of interpolated images output by `interpolate_2d()` in a 2d grid.
`plot_1d_frame_array`(ims_list[, markers, …])	Plot list of list of interpolated images output by `interpolate_1d()` in a 2d grid.
`make_interpolated`(ims, save_file[, markers, …])	Make a latent space interpolation movie.
`make_interpolated_multipanel`(ims, save_file)	Make a multi-panel latent space interpolation movie.
`fit_classifier`(model, data_generator[, …])	Fit classifier model from latent space to session id.
`plot_psvae_training_curves`(lab, expt, …[, …])	Create training plots for each term in the ps-vae objective function.
`plot_hyperparameter_search_results`(lab, …)	Create a variety of diagnostic plots to assess the ps-vae hyperparameters.
`plot_label_reconstructions`(lab, expt, …[, …])	Plot labels and their reconstructions from an ps-vae.
`plot_latent_traversals`(lab, expt, animal, …)	Plot video frames representing the traversal of individual dimensions of the latent space.
`make_latent_traversal_movie`(lab, expt, …)	Create a multi-panel movie with each panel showing traversals of an individual latent dim.
`plot_mspsvae_training_curves`(hparams, alpha, …)	Create training plots for each term in the ps-vae objective function.
`plot_mspsvae_hyperparameter_search_results`(…)	Create a variety of diagnostic plots to assess the msps-vae hyperparameters.
`make_session_swap_movie`(sess_ids, hparams, …)	Create a multipanel movie, each panel showing reconstruction with different session context.

behavenet.plotting.arhmm_utils Module¶

Plotting and video making functions for ARHMMs.

Functions¶

`get_discrete_chunks`(states[, include_edges])	Find occurences of each discrete state.
`get_state_durations`(latents, hmm[, …])	Calculate frame count for each state.
`get_latent_arrays_by_dtype`(data_generator[, …])	Collect data from data generator and put into dictionary with dtypes for keys.
`get_model_latents_states`(hparams, version[, …])	Return arhmm defined in `hparams` with associated latents and states.
`make_syllable_movies_wrapper`(hparams, save_file)	Present video clips of each individual syllable in separate panels.
`make_syllable_movies`(ims_orig, state_list, …)	Present video clips of each individual syllable in separate panels
`real_vs_sampled_wrapper`(output_type, …[, …])	Produce movie with (AE) reconstructed video and sampled video.
`make_real_vs_sampled_movies`(ims_recon, …)	Produce movie with (AE) reconstructed video and sampled video.
`plot_real_vs_sampled`(latents, latents_samp, …)	Plot real and sampled latents overlaying real and (potentially sampled) states.
`plot_states_overlaid_with_latents`(latents, …)	Plot states for a single trial overlaid with latents.
`plot_state_transition_matrix`(model[, deridge])	Plot Markov transition matrix for arhmm.
`plot_dynamics_matrices`(model[, deridge])	Plot autoregressive dynamics matrices for arhmm.
`plot_obs_biases`(model)	Plot observation bias vectors for arhmm.
`plot_obs_covariance_matrices`(model)	Plot observation covariance matrices for arhmm.

behavenet.plotting.decoder_utils Module¶

Plotting functions for decoders.

Functions¶

`get_r2s_by_trial`(hparams, model_types)	For a given session, load R^2 metrics from all decoders defined by hparams.
`get_best_models`(metrics_df)	Find best decoder over l2 regularization and learning rate.
`get_r2s_across_trials`(hparams, best_models_df)	Calculate R^2 across all test trials (rather than on a trial-by-trial basis)
`make_neural_reconstruction_movie_wrapper`(…)	Produce movie with original video, ae reconstructed video, and neural reconstructed video.
`make_neural_reconstruction_movie`(ims_orig, …)	Produce movie with original video, ae reconstructed video, and neural reconstructed video.
`plot_neural_reconstruction_traces_wrapper`(hparams)	Plot ae latents and their neural reconstructions.
`plot_neural_reconstruction_traces`(traces_ae, …)	Plot ae latents and their neural reconstructions.