replay_trajectory_classification.clusterless_simulation.make_continuous_replay#
- make_continuous_replay(sampling_frequency: int = 1000, track_height: float = 175, running_speed: float = 15, place_field_means: ndarray = array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190]), replay_speedup: int = 120.0, n_tetrodes: int = 5, n_features: int = 4, mark_spacing: float = 5) tuple[ndarray, ndarray][source]#
Creates a simulated continuous replay.
- Parameters:
sampling_frequency (int, optional) – Samples per second
track_height (float, optional) – Height of the simulated track
running_speed (float, optional) – Simualted speed of the animal
place_field_means (np.ndarray, optional) – Location of the center of the Gaussian place fields.
replay_speedup (int, optional) – Number of times faster the replay event is faster than the running speed
n_tetrodes (int, optional) – Number of simulated tetrodes
n_features (int, optional) – Number of simulated features
mark_spacing (float, optional) – Spacing between Gaussian mark features
- Returns:
replay_time (np.ndarray, shape (n_time,)) – Time in seconds.
test_multiunits (np.ndarray, shape (n_time, n_features, n_tetrodes)) – Binned clusterless spike times and features. NaN indicates no spike. Non-Nan indicates spike.