replay_trajectory_classification.likelihoods.diffusion.estimate_diffusion_position_density#

estimate_diffusion_position_density(positions: ndarray, edges: ndarray, is_track_interior: ndarray | None = None, is_track_boundary: ndarray | None = None, position_std: float = 3.0, bin_distances: ndarray | None = None, block_size: int | None = 100) → ndarray[source]#

Kernel density estimate over all position bins using diffusion.

Parameters:

  • place_bin_centers (np.ndarray, shape (n_position_bins, n_position_dims))

  • positions (np.ndarray, shape (n_time, n_position_dims))

  • position_std (float)

Returns:

position_density

Return type:

np.ndarray, shape (n_position_bins,)