TOOLBOX

the toolbox contains MATLAB routines for computing recurrence plots and related problems as M-files

parameters in [ ] are optional

download: CRP.m, CRQA.m, tt.m;
sorry, software is only available for members of the nonlinear dynamics group

also visit mathtools.net to survey other useful toolboxes
still under construction,

routine	description
recurrence plot, cross recurrence plot	CRP(X [,Y [,M,T,E,A]]) creates a cross recurrence plot/ recurrence plot results could be stored into the workspace allows to change the parameters interactively by using a GUI the source-data X and test-data Y can be one- or a two-coloumn vectors (then, in the first column have to be the time); if the test-data Y is not specified, a simple recurrence plot is created parameters: dimension M, delay T and the size of neighbourhood E; the flag A allows to switch between various methods of finding neighbours or CRPs, resp.: A=1 -> Euclidean norm A=2 -> Euclidean norm between normalized vectors A=3 -> fixed amount of neighbours A=4 -> distance coded matrix (global CRP) parameters not needed to specify current limitation: for higher speed in output the whole matrix of the recurrence plot is in the work space - this limits the application of long data series
complexity measures based on (cross) recurrence plots	CRQA(X [,Y [,W,M,T,E,L,A]]) recurrence quantification analysis (WEBBER) vs. time (away from mean diagonal) of cross-recurrence with the first vector X and the second Y Lmin is the minimal length for a line if W=[] then the whole plot will calculated B is the maximum forwarded (backwarded) time J=time scale Y(1)=RR of diagonalline Y(2)=DET Y(3)=L Y(4)=ENTR
length of vertical lines in recurrence plots	TT mean trapping time and its distribution. A=TT(X) computes the mean of the length of the vertical line structures in a recurrence plot, so called trapping time TT. [A B]=TT(X) computes the TT and the distribution of the length of the vertical line structures, stored in B.
estimation main diagonal path of a RP	[X, Y]=TRACK(RP) estimates the path of the main diagonal line in a RP RP is a N x N-Matrix
heaviside function	Y=H(X) heaviside function y=H(x) with y=1 for x>=0 and y=0 for x<0 X can be an one-column or one-row vector
transformation to pretended distibutions	Y=TRAFO(X [,A]) transformation of data X to a pretended distribution Y, where A=0 normal distribution (defeault), A=1 uniform distribution, A=2 exponential distribution X is an one-column or two-column vector (then, in the first column have to be the time)
mutual information	S=MI(X1, X2, T, [,A]) mutual information between the data in the vectors X1 and X2 (A=0 -> without plot) X1 and X2 are one-column vectors
auto mutual information	M=autoMI(X, T) computes the auto mutual information of the vector X X is an one-column or two-column vector (then, in the first column have to be the time) vectors
estimation entropy	E=ENTROPY(X) estimates the entropy E of a vector X
phase space diameter	[Y Z]=PSS(X,M,T) computes the maximal (Y) and the averaged (Z) phase space diameter of data X, with the embedding parameters dimension M and lag T
walsh transformation	Y=WALSH(X) computes the walsh transformation s=walsh(x) where Y and X are one-column vectors
walsh power spectrum	Y=WALSHPOWER(X) computes the walsh power spectrum s2=(walsh(x))2 where Y and X are one-column vectors
etc.