randomize_auto_cool_perm -D# [-W#] [general options] [cooling options] [permutation options] file
The specified number of shortest lags of the autocorrelation function without periodic continuation is matched with the data. The cost is given by the maximum deviation in any lag, weighted by 1/lag.
-D number of lags for autocorrelation
-W type of average: 0=max(c), 1=|c|/lag, 2=(c/lag)**2, 3=max(c)/lag (default 0)
randomize_autop_cool_perm -D# [-W#] [general options] [cooling options] [permutation options] file
The specified number of shortest lags of the periodically continued autocorrelation function is matched with the data. The cost is given by the maximum deviation in any lag, weighted by 1/lag.
-D number of lags for autocorrelation
-W type of average: 0=max(c), 1=|c|/lag, 2=(c/lag)**2, 3=max(c)/lag (default 0)
randomize_generic_cool_perm [general options] [cooling options] [permutation options] file
The generic module is provided as the easiest starting point of individual extensions. Virtually anything can be specified as part of the cost function by editing the source and recompiling (recommended: rename the module). In the distribution, as an example the maximal deviation in either one of
< x x >, < x x >, n-1 n n-2 n 2 2 2 < x x >, < x x >, < x x >, < x x x >, n-1 n n-1 n n-2 n n-2 n-1 n 2 2 3 3 < x x >, < x x >, < x x > n-1 n n-1 n n-1 nis minimised. After thorough annealing, this can be used to make randomly shuffled Hénon attractors - whatever that is good for.
randomize_spikespec_cool_event [-F# -## -i] [general options] [cooling options] [permutation options] file
For an explanation of the inter-event spectrum see spikespec. S(f) is computed for # frequencies between 0 and -F (no binning). By default, a sequence of event times is expected. If the flag -i is set, the data is taken to be inter-event intervals.
-F maximal frequency (2*l / total time)
-# number of frequencies (F* total time /2)
-i expect intervals rather than times