Description of the program: boxcount


This program estimates the Renyi entopy of Qth order using a partition of the embedding space instead of using the Grassberger-Procaccia scheme. I should mention that the memory requirement does not increase exponentially like 1/epsilonM but only like M*(length of series). So it can also be used for small epsilon and large M.
No finite sample corrections are implemented so far.

Usage:

boxcount [Options]

Everything not being a valid option will be interpreted as a potential datafile name. Given no datafile at all, means read stdin. Also - means stdin

Possible options are:

Option Description Default
-l# number of data points to be used whole file
-x# number of lines to be ignored 0
-c# column to be read 1
-d# delay for the delay vectors 1
-M# maximal embedding dimension 10
-Q# Order of the entropy 2.0
-E# maximal length scale 1 (data are rescaled to [0:1])
-e# minimal length scale 1e-3 (data are rescaled to [0:1])
-## number of epsilon values 20
-o[#] output file name 'datafile'.box (or if data were read from stdin: stdin.box)
-V# verbosity level
  0: only panic messages
  1: add input/output messages
1
-h show these options none


Description of the Output:

The output file contains four columns for each embedding dimension from 1 to M and for each epsilon value:
  1. epsilon (in rescaled units)
  2. epsilon (in natural units)
  3. Qth order entropy (HQ(dimension,epsilon))
  4. Qth order differential entropy ( HQ(dimension,epsilon)-HQ(dimension-1,epsilon))
The slope of the third line gives an estimate of DQ.
View the C-source.
See also d2, c2naive, and c1
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