replay_trajectory_classification.likelihoods.multiunit_likelihood_integer.estimate_log_joint_mark_intensity#

estimate_log_joint_mark_intensity(decoding_marks: ndarray, encoding_marks: ndarray, mark_std: ndarray | float, occupancy: ndarray, mean_rate: float, place_bin_centers: ndarray | None = None, encoding_positions: ndarray | None = None, position_std: ndarray | None = None, max_mark_diff: int = 6000, set_diag_zero: bool = False, position_distance: ndarray | None = None) → ndarray[source]#

Finds the joint intensity of the marks and positions in log space.

Parameters:

  • decoding_marks (np.ndarray, shape (n_decoding_spikes, n_features))

  • encoding_marks (np.ndarray, shape (n_encoding_spikes, n_features))

  • mark_std (float or np.ndarray, shape (n_features,))

  • occupancy (np.ndarray, shape (n_position_bins,))

  • mean_rate (float)

  • place_bin_centers (None or np.ndarray, shape (n_position_bins, n_position_dims)) – If None, position distance must be not None

  • encoding_positions (None or np.ndarray, shape (n_decoding_spikes, n_position_dims)) – If None, position distance must be not None

  • position_std (None or float or array_like, shape (n_position_dims,)) – If None, position distance must be not None

  • max_mark_diff (int) – Maximum distance between integer marks.

  • set_diag_zero (bool)

  • position_distance (np.ndarray, shape (n_encoding_spikes, n_position_bins)) – Precalculated distance between position and position bins.

Returns:

log_joint_mark_intensity

Return type:

np.ndarray, shape (n_decoding_spikes, n_position_bins)