replay_trajectory_classification.likelihoods.multiunit_likelihood_integer.fit_multiunit_likelihood_integer#

fit_multiunit_likelihood_integer(position: ndarray, multiunits: ndarray, place_bin_centers: ndarray, mark_std: ndarray, position_std: ndarray | float, is_track_boundary: ndarray | None = None, is_track_interior: ndarray | None = None, edges: list[ndarray] | None = None, block_size: int = 100, use_diffusion: bool = False, **kwargs) → dict[source]#

Fits the clusterless place field model.

Parameters:

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

  • multiunits (np.ndarray, shape (n_time, n_marks, n_electrodes))

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

  • mark_std (float) – Amount of smoothing for the mark features. Standard deviation of kernel.

  • position_std (float or array_like, shape (n_position_dims,)) – Amount of smoothing for position. Standard deviation of kernel.

  • is_track_boundary (None or np.ndarray, shape (n_bins,))

  • is_track_interior (None or np.ndarray, shape (n_bins,))

  • edges (None or list of np.ndarray)

  • block_size (int) – Size of data to process in chunks

  • use_diffusion (bool) – Use diffusion to respect the track geometry.

Returns:

encoding_model

Return type:

dict