Defeat Recording Data

Main Behavior Pickle File: Overall dictionary from (mouse,defeat_day)-->data data.keys() # body_part_tracking, extracted_features, fiber_photometry data['body_part_tracking] is a dictionary from body part to x,y coordinates over time (T frame, 120 Hz) data['extracted_features'] is a 12xT matrix of feature traces (120 Hz) data['fiber_photometry'] is a dictionary from 'fpts' or 'fpnac' to raw GCaMP recordings from the tail striatum or nucleus accumbens respectively (1017 Hz) Model Pickle Files: - 2 sets of random forest classifiers (trained RF model, cutoff for probability of behavior ocurring) dictionary of behavior name: (trained sklearn.ensemble.RandomForestClassifier, classification probability cutoff) - t-SNE model: dictionary: 'training': features in training set 'projection': sklearn.neural_network.MLPClassifier maps raw features to t-SNE space 'labeled_map': where clusters are in t-SNE space