pyrovelocity.plot

pyrovelocity.plot.align_trajectory_diff(adatas, velocity_embeds, density=0.3, smooth=0.5, input_grid=None, input_scale=None, min_mass=1.0, embed='umap', autoscale=False, length_cutoff=10)

pyrovelocity.plot.compute_mean_vector_field(posterior_samples, adata, basis='umap', n_jobs=1, spliced='spliced_pyro', raw=False)

pyrovelocity.plot.compute_volcano_data(posterior_samples, adata, time_correlation_with='s', selected_genes=None, negative=False)

Return type:

None

pyrovelocity.plot.denoised_umap(posterior_samples, adata, cell_state='state_info')

pyrovelocity.plot.get_clone_trajectory(adata, average_start_point=True, global_traj=True, times=[2, 4, 6], clone_num=None)

pyrovelocity.plot.get_posterior_sample_angle_uncertainty(posterior_angles)

pyrovelocity.plot.mae_per_gene(pred_counts, true_counts)

Computes mean average error between counts and predicted probabilities.

Return type:

ndarray

pyrovelocity.plot.plot_arrow_examples(adata, v_maps, embeds_radian, embed_mean, ax=None, fig=None, cbar=True, basis='umap', n_sample=30, scale=0.0021, alpha=0.02, index=19, index2=0, scale2=0.04, num_certain=3, num_total=4, p_mass_min=1.0, density=0.3, arrow_size=4, customize_uncertain=None)

pyrovelocity.plot.plot_dynamic_pyro(adata, gene, losses, summary, velocity, fix_param_list, alpha, beta, gamma, scale, t_, t)

pyrovelocity.plot.plot_evaluate_dynamic_orig(adata, gene='Cpe', velocity=None, ax=None)

pyrovelocity.plot.plot_gene_ranking(posterior_samples, adata, ax=None, time_correlation_with='s', selected_genes=None, assemble=False, data='correlation', negative=False, adjust_text_bool=False, show_marginal_histograms=False)

Return type:

None

pyrovelocity.plot.plot_mean_vector_field(posterior_samples, adata, ax, basis='umap', n_jobs=1, scale=0.2, density=0.4, spliced='spliced_pyro', raw=False)

pyrovelocity.plot.plot_multigenes_dynamical(summary, alpha, beta, gamma, t_, t, adata, gene='Cpe', scale=None, ax=None, raw=False)

pyrovelocity.plot.plot_posterior_time(posterior_samples, adata, ax=None, fig=None, basis='umap', addition=True, position='left', s=3)

pyrovelocity.plot.plot_state_uncertainty(posterior_samples, adata, kde=True, data='denoised', top_percentile=0.9, ax=None, basis='umap')

pyrovelocity.plot.plot_vector_field_uncertain(adata, embed_mean, embeds_radian_or_magnitude, fig=None, cbar=True, basis='umap', scale=0.002, cbar_pos=[0.22, 0.28, 0.5, 0.05], p_mass_min=3.5, only_grid=False, ax=None, autoscale=False, density=0.3, arrow_size=5, uncertain_measure='angle', cmap='winter', cmax=0.305)

pyrovelocity.plot.project_grid_points(emb, velocity_emb, uncertain=None, p_mass_min=1.0, density=0.3, autoscale=False)

pyrovelocity.plot.rainbowplot(volcano_data, adata, posterior_samples, fig=None, genes=None, data=['st', 'ut'], cell_state='clusters', basis='umap', num_genes=5, add_line=True, negative=False, scvelo_colors=False)

Return type:

None

pyrovelocity.plot.set_colorbar(smp, ax, orientation='vertical', labelsize=None, fig=None, position='right', rainbow=False)

pyrovelocity.plot.us_rainbowplot(genes, adata, posterior_samples, data=['st', 'ut'], cell_state='clusters')

Return type:

Figure

pyrovelocity.plot.vector_field_uncertainty(adata, posterior_samples, basis='tsne', n_jobs=1, denoised=False)

Run cosine similarity-based vector field across posterior samples

Return type:

Tuple[ndarray, ndarray, ndarray]