deepof.utils module

deepof.utils	Functions and general utilities for the deepof package.
deepof.utils.align_trajectories(data[, mode])	Remove rotational variance on the trajectories.
deepof.utils.angle(bpart_array)	Return a numpy.ndarray with the angles between the provided instances.
deepof.utils.arena_parameter_extraction(...)	Return x,y position of the center, the lengths of the major and minor axes, and the angle of the recognised arena.
deepof.utils.automatically_recognize_arena(...)	Return numpy.ndarray with information about the arena recognised from the first frames of the video.
deepof.utils.bp2polar(tab)	Return the DataFrame in polar coordinates.
deepof.utils.bpart_distance(dataframe[, ...])	Return a pandas.DataFrame with the scaled distances between all pairs of body parts.
deepof.utils.closest_side(polygon, ...)	Find the closest side in other polygons to a reference side in the first polygon.
deepof.utils.cluster_transition_matrix(...)	Compute the transition matrix between clusters and the autocorrelation in the sequence.
deepof.utils.compute_animal_presence_mask(quality)	Compute a mask of the animal presence in the video.
deepof.utils.compute_areas(coords[, animal_id])	Compute relevant areas (head, torso, back, full) for the provided coordinates.
deepof.utils.compute_dist(pair_array[, ...])	Return a pandas.DataFrame with the scaled distances between a pair of body parts.
deepof.utils.connect_mouse([animal_ids, ...])	Create a nx.Graph object with the connectivity of the bodyparts in the DLC topview model for a single mouse.
deepof.utils.edges_to_weighted_adj(adj, edges)	Convert an edge feature matrix to a weighted adjacency matrix.
deepof.utils.enumerate_all_bridges(G)	Enumerate all 3-node connected sequences in the given graph.
deepof.utils.extract_polygonal_arena_coordinates(...)	Read a random frame from the selected video, and opens an interactive GUI to let the user delineate the arena manually.
deepof.utils.filter_columns(columns, selected_id)	Given a set of TableDict columns, returns those that correspond to a given animal, specified in selected_id.
deepof.utils.filter_short_bouts(...[, ...])	Filter out cluster assignment bouts shorter than min_bout_duration.
deepof.utils.fit_ellipse_to_polygon(polygon)	Fit an ellipse to the provided polygon.
deepof.utils.full_outlier_mask(experiment, ...)	Iterate over all body parts of experiment, and outputs a dataframe where all x, y positions are replaced by a boolean mask, where True indicates an outlier.
deepof.utils.get_arenas(coordinates, tables, ...)	Extract arena parameters from a project or coordinates object.
deepof.utils.gmm_compute(x, n_components, ...)	Fit a Gaussian Mixture Model to the provided data and returns evaluation metrics.
deepof.utils.gmm_model_selection(x, ...[, ...])	Run GMM clustering model selection on the specified X dataframe.
deepof.utils.interpolate_outliers(...[, ...])	Mark all outliers in experiment and replaces them using a uni-variate linear interpolation approach.
deepof.utils.iterative_imputation(project, ...)	Perform iterative imputation on occluded body parts.
deepof.utils.kleinberg(offsets[, s, gamma, ...])	Apply Kleinberg's algorithm (described in 'Bursty and Hierarchical Structure in Streams').
deepof.utils.load_segmentation_model(path)
deepof.utils.load_table(tab, table_path, ...)	Loads a table into a structured pandas data frame.
deepof.utils.mask_outliers(time_series, ...)	Return a mask over the bivariate trajectory of a body part, identifying as True all detected outliers.
deepof.utils.moving_average(time_series[, lag])	Fast implementation of a moving average function.
deepof.utils.rename_track_bps(loaded_tab, ...)	Renames all body parts in the provided dataframe.
deepof.utils.retrieve_corners_from_image(...)	Open a window and waits for the user to click on all corners of the polygonal arena.
deepof.utils.rolling_speed(dframe[, window, ...])	Return the average speed over n frames in pixels per frame.
deepof.utils.rolling_window(a, window_size, ...)	Return a 3D numpy.array with a sliding-window extra dimension.
deepof.utils.rotate(p, angles[, origin])	Return a 2D numpy.ndarray with the initial values rotated by angles radians.
deepof.utils.rupture_per_experiment(...[, ...])	Apply the rupture method independently to each experiment, and concatenate into a single dataset at the end.
deepof.utils.scale_animal(feature_array, scale)	Scales features in the provided array.
deepof.utils.scale_table(coordinates, ...[, ...])	Scales features in a table controlling for both individual body size and interanimal variability.
deepof.utils.set_missing_animals(...[, ...])	Set the coordinates of the missing animals to NaN.
deepof.utils.simplify_polygon(polygon[, ...])	Simplify a polygon using the Ramer-Douglas-Peucker algorithm.
deepof.utils.smooth_boolean_array(a[, scale])	Return a boolean array in which isolated appearances of a feature are smoothed.
deepof.utils.smooth_mult_trajectory(series)	Return a smoothed a trajectory using a Savitzky-Golay 1D filter.
deepof.utils.split_with_breakpoints(a, ...)	Split a numpy.ndarray at the given breakpoints.
deepof.utils.str2bool(v)	Return the passed string as a boolean.
deepof.utils.tab2polar(cartesian_df)	Return a pandas.DataFrame in which all the coordinates are polar.