Eyetracking¶

gap_fill(gaze_xyz, gaze_status, valid_status, gaze_timestamps, max_gap_time=0.075, verbose=True)¶

Fills gaps in eye-tracking data with interpolation and handles ignored gaps.

Parameters:¶

gaze_xyz¶

A NumPy array containing 3D gaze coordinates (x, y, z).

gaze_status¶

An array indicating the status of gaze data (e.g., tracked or lost).

valid_status¶

An array indicating the validity status of gaze data.

gaze_timestamps¶

An array of timestamps corresponding to gaze data.

max_gap_time : float¶

The maximum allowable gap duration for interpolation in seconds (default: 0.075 seconds).

verbose : bool¶

Whether to print verbose information about gap filling (default: True).

Returns:¶

A NumPy array with gaps in the eye-tracking data filled by interpolation.

Return type:¶

numpy.ndarray

This function takes eye-tracking data, including gaze coordinates, gaze status, validity status, and timestamps, and performs gap filling. Gaps in the data are identified and categorized as interpolated gaps or ignored gaps based on the specified max_gap_time. Interpolated gaps are filled with interpolated values, while ignored gaps are marked as NaN.

The function returns the gap-filled eye-tracking data as a NumPy array.

Example usage:

import numpy as np
from your_module import gap_fill

# Define your eye-tracking data
gaze_xyz = np.array(...)
gaze_status = np.array(...)
valid_status = np.array(...)
gaze_timestamps = np.array(...)

# Fill gaps in the data
filled_data = gap_fill(gaze_xyz, gaze_status, valid_status, gaze_timestamps)