API Overview

Modules

• interpolation: Interpolate over low confidence data
• io: Loading and saving tracking and behavior annotation files
• utils: Small helper utilities
• video: Basic video tracking and behavior class that houses data

Classes

• io.BufferedIOBase: Base class for buffered IO objects.
• io.IOBase: The abstract base class for all I/O classes, acting on streams of
• io.RawIOBase: Base class for raw binary I/O.
• io.TextIOBase: Base class for text I/O.
• io.UnsupportedOperation
• video.EthologyFeaturesAccessor
• video.EthologyIOAccessor
• video.EthologyMLAccessor
• video.EthologyMetadataAccessor
• video.EthologyPoseAccessor

Functions

• interpolation.interpolate_lowconf_points: Interpolate raw tracking points if their probabilities are available.
• io.create_behavior_labels: Create behavior labels from BORIS exported csv files.
• io.get_sample_data: Load a sample dataset of 5 mice social interaction videos. Each video is approx. 5 minutes in duration
• io.get_sample_data_paths_dlcboris: Get path to sample data files provided with package.
• io.get_sample_nwb_paths: Get path to a sample NWB file with tracking data for testing and dev purposes.
• io.get_sample_sleap_paths: Get path to a sample SLEAP h5 file with tracking data for testing and dev purposes.
• io.load_data: Load an object from a pickle file
• io.load_sklearn_model: Load sklearn model from file
• io.read_DLC_tracks: Read in tracks from DLC.
• io.read_NWB_tracks: Read in tracks from NWB PoseEstimiationSeries format (something saved using the DLC2NWB package).
• io.read_boris_annotation: Read behavior annotation from BORIS exported csv file.
• io.read_sleap_tracks: Read in tracks from SLEAP.
• io.save_DLC_tracks_h5: Save DLC tracks in h5 format.
• io.save_sklearn_model: Save sklearn model to file
• io.uniquifier: Return a sequence (e.g. list) with unique elements only, but maintaining original list order
• utils.checkFFMPEG: Check for ffmpeg dependencies
• utils.check_keras
• video.add_randomforest_predictions: Perform cross validation of a RandomForestClassifier to predict behavior based on
• video.create_dataset: Creates DataFrame that houses pose-tracking data and behavior annotations, along with relevant metadata, features and behavior annotation labels.
• video.create_metadata: Prepare a metadata dictionary for defining a ExperimentDataFrame.
• video.get_sample_openfield_data: Load a sample dataset of 1 mouse in openfield setup. The video is the sample that comes with DLC.
• video.load_experiment: Load DataFrame from file.

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