elephant.statistics.optimal_kernel_bandwidth¶

elephant.statistics.
optimal_kernel_bandwidth
(spiketimes, times=None, bandwidth=None, bootstrap=False)[source]¶ Calculates optimal fixed kernel bandwidth [st3], given as the standard deviation sigma.
Original matlab code (sskernel.m) http://2000.jukuin.keio.ac.jp/shimazaki/res/kernel.html has been ported to Python by Subhasis Ray, NCBS.
Parameters:  spiketimesnp.ndarray
Sequence of spike times (sorted to be ascending).
 timesnp.ndarray or None, optional
Time points at which the kernel bandwidth is to be estimated. If None, spiketimes is used. Default: None
 bandwidthnp.ndarray or None, optional
Vector of kernel bandwidths (standard deviation sigma). If specified, optimal bandwidth is selected from this. If None, bandwidth is obtained through a goldensection search on a logexp scale. Default: None
 bootstrapbool, optional
If True, calculates the 95% confidence interval using Bootstrap. Default: False
Returns:  dict
 ‘y’np.ndarray
Estimated density.
 ‘t’np.ndarray
Points at which estimation was computed.
 ‘optw’float
Optimal kernel bandwidth given as standard deviation sigma
 ‘w’np.ndarray
Kernel bandwidths examined (standard deviation sigma).
 ‘C’np.ndarray
Cost functions of bandwidth.
 ‘confb95’tuple of np.ndarray
Bootstrap 95% confidence interval: (lower level, upper level). If bootstrap is False, confb95 is None.
 ‘yb’np.ndarray
Bootstrap samples. If bootstrap is False, yb is None.
If no optimal kernel could be found, all entries of the dictionary are set to None.