vame.evaluate_model(config) # Step 4: # Segment motifs/pose vame.pose_segmentation(config) #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # The following are optional choices to create motif videos, communities/hierarchies of behavior, # community videos # OPTIONIAL: Create motif videos to get insights about the fine grained poses vame.motif_videos(config, videoType='.mp4') # OPTIONAL: Create behavioural hierarchies via community detection vame.community(config, umap_vis=False, cut_tree=2) # OPTIONAL: Create community videos to get insights about behavior on a hierarchical scale vame.community_videos(config) # OPTIONAL: Down projection of latent vectors and visualization via UMAP vame.visualization(config, label=None) #options: label: None, "motif", "community" # OPTIONAL: Use the generative model (reconstruction decoder) to sample from # the learned data distribution, reconstruct random real samples or visualize # the cluster center for validation vame.generative_model( config, mode="centers") #options: mode: "sampling", "reconstruction", "centers
vame.evaluate_model(config) # Step 4: # Segment motifs/pose vame.pose_segmentation(config) #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # The following are optional choices to create motif videos, communities/hierarchies of behavior, # community videos # OPTIONIAL: Create motif videos to get insights about the fine grained poses vame.motif_videos(config, videoType='.mp4') # OPTIONAL: Create behavioural hierarchies via community detection vame.community(config, show_umap=False, cut_tree=2) # OPTIONAL: Create community videos to get insights about behavior on a hierarchical scale vame.community_videos(config) # OPTIONAL: Down projection of latent vectors and visualization via UMAP vame.visualization(config, label=None) #options: label: None, "motif", "community" # OPTIONAL: Use the generative model (reconstruction decoder) to sample from # the learned data distribution, reconstruct random real samples or visualize # the cluster center for validation vame.generative_model( config, mode="centers" ) #options: mode: "sampling", "reconstruction", "centers", "motifs"
model_name=modelName, cluster_method='kmeans', n_cluster=15) #Plot transition matrices files = os.listdir(os.path.join(projectPath, 'results/')) n_cluster = 10 plot_transitions(config, files, n_cluster, modelName, cluster_method='kmeans') ###NEW COMMUNITY PLOTTING vame.pose_segmentation(config) import matplotlib matplotlib.use('Qt5Agg') vame.community(config, show_umap=False) ###Run groupwise comparisons ctrl_mice = [ 'C1-RT', 'C3-RB', 'C5-NP', 'C5-RT', 'C9_LT', 'C12_NP', 'C13_RT', 'C14_LT', 'C14_LB', 'C15_RT', 'C16_RB' ] cko_mice = [ 'C2-RB', 'C3-LT', 'C4-NP', 'C4-RT', 'C10_NP', 'C12_RT', 'C13_NP', 'C14_RT', 'C15_NP', 'C16_NP' ] #phases=['Saline', 'Phase1', 'Phase2', 'Phase3'] phases = ['11-06', '10-24'] group1 = ctrl_mice group2 = cko_mice