deepof.utils

Functions and general utilities for the deepof package.

Functions

align_trajectories(data[, mode])	Remove rotational variance on the trajectories.
angle(bpart_array)	Return a numpy.ndarray with the angles between the provided instances.
arena_parameter_extraction(frame, arena_type)	Return x,y position of the center, the lengths of the major and minor axes, and the angle of the recognised arena.
automatically_recognize_arena(coordinates, ...)	Return numpy.ndarray with information about the arena recognised from the first frames of the video.
bp2polar(tab)	Return the DataFrame in polar coordinates.
bpart_distance(dataframe[, arena_abs, arena_rel])	Return a pandas.DataFrame with the scaled distances between all pairs of body parts.
closest_side(polygon, reference_side)	Find the closest side in other polygons to a reference side in the first polygon.
cluster_transition_matrix(cluster_sequence, ...)	Compute the transition matrix between clusters and the autocorrelation in the sequence.
compute_animal_presence_mask(quality[, ...])	Compute a mask of the animal presence in the video.
compute_areas(coords[, animal_id])	Compute relevant areas (head, torso, back, full) for the provided coordinates.
compute_dist(pair_array[, arena_abs, arena_rel])	Return a pandas.DataFrame with the scaled distances between a pair of body parts.
connect_mouse([animal_ids, ...])	Create a nx.Graph object with the connectivity of the bodyparts in the DLC topview model for a single mouse.
edges_to_weighted_adj(adj, edges)	Convert an edge feature matrix to a weighted adjacency matrix.
enumerate_all_bridges(G)	Enumerate all 3-node connected sequences in the given graph.
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.
filter_columns(columns, selected_id)	Given a set of TableDict columns, returns those that correspond to a given animal, specified in selected_id.
filter_short_bouts(cluster_assignments, ...)	Filter out cluster assignment bouts shorter than min_bout_duration.
fit_ellipse_to_polygon(polygon)	Fit an ellipse to the provided polygon.
full_outlier_mask(experiment, likelihood, ...)	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.
get_arenas(coordinates, tables, arena, ...)	Extract arena parameters from a project or coordinates object.
gmm_compute(x, n_components, cv_type)	Fit a Gaussian Mixture Model to the provided data and returns evaluation metrics.
gmm_model_selection(x, n_components_range, ...)	Run GMM clustering model selection on the specified X dataframe.
interpolate_outliers(experiment, likelihood, ...)	Mark all outliers in experiment and replaces them using a uni-variate linear interpolation approach.
iterative_imputation(project, tab_dict, lik_dict)	Perform iterative imputation on occluded body parts.
kleinberg(offsets[, s, gamma, n, T, k])	Apply Kleinberg's algorithm (described in 'Bursty and Hierarchical Structure in Streams').
load_segmentation_model(path)
load_table(tab, table_path, table_format[, ...])	Loads a table into a structured pandas data frame.
mask_outliers(time_series, likelihood, ...)	Return a mask over the bivariate trajectory of a body part, identifying as True all detected outliers.
moving_average(time_series[, lag])	Fast implementation of a moving average function.
rename_track_bps(loaded_tab, ...)	Renames all body parts in the provided dataframe.
retrieve_corners_from_image(frame, ...)	Open a window and waits for the user to click on all corners of the polygonal arena.
rolling_speed(dframe[, window, rounds, ...])	Return the average speed over n frames in pixels per frame.
rolling_window(a, window_size, window_step)	Return a 3D numpy.array with a sliding-window extra dimension.
rotate(p, angles[, origin])	Return a 2D numpy.ndarray with the initial values rotated by angles radians.
rupture_per_experiment(table_dict, ...[, ...])	Apply the rupture method independently to each experiment, and concatenate into a single dataset at the end.
scale_animal(feature_array, scale)	Scales features in the provided array.
scale_table(coordinates, feature_array, scale)	Scales features in a table controlling for both individual body size and interanimal variability.
set_missing_animals(coordinates, tab_dict, ...)	Set the coordinates of the missing animals to NaN.
simplify_polygon(polygon[, relative_tolerance])	Simplify a polygon using the Ramer-Douglas-Peucker algorithm.
smooth_boolean_array(a[, scale])	Return a boolean array in which isolated appearances of a feature are smoothed.
smooth_mult_trajectory(series[, alpha, w_length])	Return a smoothed a trajectory using a Savitzky-Golay 1D filter.
split_with_breakpoints(a, breakpoints)	Split a numpy.ndarray at the given breakpoints.
str2bool(v)	Return the passed string as a boolean.
tab2polar(cartesian_df)	Return a pandas.DataFrame in which all the coordinates are polar.