taucrp.m 9.03 KiB
function X = taucrp(varargin)
%TAUCRP Creates a close returns plot.
% R=TAUCRP(X [,Y] [,param1,param2,...]) creates a cross
% recurrence plot/ recurrence plot R for a limited range of
% past and future states, also known as close returns plot.
%
% R=TAUCRP(X [,Y],M,T,E,W) uses the dimension M, delay T, the size
% of neighbourhood E and the range W of past and future time
% steps.
%
% If X and Y are multi-column vectors then they will be
% considered as phase space vectors (TAUCRP can be used
% for real phase space vectors without embedding).
%
% Parameters: dimension M, delay T, the size of neighbourhood
% E and the range W are the first four numbers after the data
% series; further parameters can be used to switch between
% various methods of finding the neighbours of the phasespace
% trajectory and to suppress the normalization of the data.
%
% Methods of finding the neighbours/ of plot.
% maxnorm - Maximum norm.
% euclidean - Euclidean norm.
% minnorm - Minimum norm.
% maxnorm - Maximum norm, fixed recurrence rate.
% fan - Fixed amount of nearest neighbours.
% distance - Distance coded matrix (global CRP, Euclidean norm).
%
% Normalization of the data series.
% normalize - Normalization of the data.
% nonormalize - No normalization of the data.
%
% Parameters not needed to be specified.
%
%
% Examples: a = sin((1:1000) * 2 * pi/67);
% w = 160;
% X = taucrp(a,2,17,.2,w,'nonorm','euclidean');
% imagesc(1:size(X,2),-w:w,X), colormap([1 1 1; 0 0 0])
%
% See also CRP, CRP2, CRP_BIG, JRP, CRQA.
%
% References:
% Marwan, N., Romano, M. C., Thiel, M., Kurths, J.:
% Recurrence Plots for the Analysis of Complex Systems, Physics
% Reports, 438(5-6), 2007.
% Copyright (c) 2008 by AMRON
% Norbert Marwan, Potsdam University, Germany
% http://www.agnld.uni-potsdam.de
%
% $Date$
% $Revision$
%
% $Log$
warning off
global errcode props nonorm
errcode=0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% programme properties
init_properties
m_init = 1;
tau_init = 1;
eps_init = 0.1;
w_init = 100;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% check and read the input
error(nargchk(1,8,nargin));
if nargout>1, error('Too many output arguments'), end
check_meth={'ma','eu','mi','rr','fa','di'}; % maxnorm, euclidean, nrmnorm, fan, distance
check_norm={'non','nor'}; % nonormalize, normalize
check_gui={'gui','nog','sil'}; % gui, nogui, silent
if isnumeric(varargin{1}) % read commandline input
varargin{9}=[];
% transform any int to double
intclasses = {'uint8';'uint16';'uint32';'uint64';'int8';'int16';'int32';'int64'};
flagClass = [];
for i = 1:length(intclasses)
i_int=find(cellfun('isclass',varargin,intclasses{i}));
if ~isempty(i_int)
for j = 1:length(i_int)
varargin{i_int(j)} = double(varargin{i_int(j)});
end
flagClass = [flagClass; i_int(:)];
end
end
if ~isempty(flagClass)
disp(['Warning: Input arguments at position [',num2str(flagClass'),'] contain integer values']);
disp(['(now converted to double).'])
end
i_double=find(cellfun('isclass',varargin,'double'));
i_char=find(cellfun('isclass',varargin,'char'));
% check the text input parameters for method, gui and normalization
temp_meth=0;
temp_norm=0;
temp_gui=0;
if ~isempty(i_char)
for i=1:length(i_char),
varargin{i_char(i)}(4)='0';
temp_meth=temp_meth+strcmpi(varargin{i_char(i)}(1:2),check_meth');
temp_norm=temp_norm+strcmpi(varargin{i_char(i)}(1:3),check_norm');
temp_gui=temp_gui+strcmpi(varargin{i_char(i)}(1:3),check_gui');
end
method=min(find(temp_meth));
nonorm=min(find(temp_norm))-1;
nogui=min(find(temp_gui))-1;
if isempty(method), method=1; end
if isempty(nonorm), nonorm=1; end
if isempty(nogui), nogui=0; end
if method>length(check_meth), method=length(check_meth); end
if nonorm>1, nonorm=1; end
if nogui>2, nogui=2; end
else
method=1; nonorm=1; nogui=0;
end
if nogui==0 & nargout>0, nogui=1; end
% get the parameters for creating RP
if max(size(varargin{1}))<=3
error('To less values in data X.')
end
x=double(varargin{1});
if isempty(varargin{2}) | ~isnumeric(varargin{2}), y=x; else
y=double(varargin{2}); end
if (isnumeric(varargin{2}) & max(size(varargin{2}))==1) | ~isnumeric(varargin{2})
y=x;
if ~isempty(varargin{i_double(2)}), m0=varargin{i_double(2)}(1); else m0=m_init; end
if ~isempty(varargin{i_double(3)}), t=varargin{i_double(3)}(1); else t=tau_init; end
if ~isempty(varargin{i_double(4)}), e=varargin{i_double(4)}(1); else e=eps_init; end
if ~isempty(varargin{i_double(5)}), w=varargin{i_double(5)}(1); else w=w_init; end
else if ~isempty(varargin{i_double(3)}), m0=varargin{i_double(3)}(1); else m0=m_init; end
if ~isempty(varargin{i_double(4)}), t=varargin{i_double(4)}(1); else t=tau_init; end
if ~isempty(varargin{i_double(5)}), e=varargin{i_double(5)}(1); else e=eps_init; end
if ~isempty(varargin{i_double(6)}), w=varargin{i_double(6)}(1); else w=w_init; end
end
t=round(t); m0=round(m0); mflag=method;
if e<0, e=1; disp('Warning: The threshold size E cannot be negative and is now set to 1.'), end
if t<1, t=1; disp('Warning: The delay T cannot be smaller than one and is now set to 1.'), end
if m0 < 1, m0 = 1; end
if t < 1, t = 1; end
if size(x,1)==1, x=x'; end, if size(y,1)==1, y=y'; end
m=max([size(x,2) size(y,2)]);
if w > size(x,1); w = size(x,1); end
if method==8 & (m*m0) > 1,
m0=1;
error(['The neighbourhood criterion ''Oder matrix''',10,'is not implemented - use crp or crp_big instead.'])
end
if method==9 & (m*m0) == 1,
m0=2;
disp(['Warning: For order patterns recurrence plots the dimension must',10,...
'be larger than one. ',...
'Embedding dimension is set to ',num2str(m0),'.'])
end
action='init';
if ~isempty(find(isnan(x)))
disp('NaN detected (in first variable) - will be cleared.')
for k=1:size(x,2), x(find(isnan(x(:,k))),:)=[]; end
end
if ~isempty(find(isnan(y)))
disp('NaN detected (in second variable) - will be cleared.')
for k=1:size(y,2), y(find(isnan(y(:,k))),:)=[]; end
end
if size(x,1) < t*(m-1)+1 | size(y,1) < t*(m-1)+1
error(['Too less data',10,...
'Either too much NaN or the number of columns in the vectors do not match.'])
end
Nx=size(x,1); Ny=size(y,1);
NX=Nx-t*(m0-1);NY=Ny-t*(m0-1);
x0=zeros(Nx,m);y0=zeros(Ny,m);
x0(1:size(x,1),1:size(x,2))=x;
y0(1:size(y,1),1:size(y,2))=y;
if nonorm==1,
x=(x0-repmat(mean(x0),Nx,1))./repmat(std(x0),Nx,1);
y=(y0-repmat(mean(y0),Ny,1))./repmat(std(y0),Ny,1);
end
if ~isempty(find(isnan(x))), for k=1:size(x,2), x(find(isnan(x(:,k))),:)=[]; end, end
if ~isempty(find(isnan(y))), for k=1:size(y,2), y(find(isnan(y(:,k))),:)=[]; end, end
if size(x,1) < t*(m0-1)+1 | size(y,1) < t*(m0-1)+1
error(['Too less data',10,...
'Either too much NaN or the number of columns in the vectors do not match.'])
end
else
error('No valid input given!')
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if m0>1
x2=x(1:end-t*(m0-1),:);
y2=y(1:end-t*(m0-1),:);
for i=1:m0-1,
x2(:,m*i+1:m*(i+1))=x(1+t*i:end-t*(m0-i-1),:);
y2(:,m*i+1:m*(i+1))=y(1+t*i:end-t*(m0-i-1),:);
end
x=x2; y=y2; Nx=size(x,1); Ny=size(y,1); m=m0*m; clear x2 y2
end
x1=repmat(x,1,Ny);
for mi=1:m, x2(:,mi)=reshape(rot90(x1(:,0+mi:m:Ny*m+mi-m)),Nx*Ny,1); end
y1=repmat(y,Nx,1); x1=x2; clear x2
% create embedding vectors
NX = Nx - t*(m0-1); NY = Ny - t*(m0-1);
[idx add] = meshgrid(linspace(0,(m0-1)*t,m0), 1:Nx-(m0-1)*t);
idx = idx + add;
x1 = []; y1 = [];
for i = 1:m
x1 = [x1 reshape(x(idx,i),size(idx,1),size(idx,2))];
y1 = [y1 reshape(y(idx,i),size(idx,1),size(idx,2))];
end
% indices for which the recurrences should be computed
[i1 i2] = meshgrid(1:NX,-w:w);
i2 = i2 + i1;
i_remove = i2 > length(y1) | i2 < 1;
i2(i_remove) = 1;
% compute distance matrix
D = (x1(i1,:) - y1(i2,:));
switch method
case 1
D2 = max(abs(D),[],2);
X = double(D2 < e);
case 2
D2 = sqrt(sum(D.^2, 2));
X = double(D2 < e);
case 3
D2 = sum(abs(D), 2);
X = double(D2 < e);
case 4
D2 = max(abs(D),[],2);
SS = sort(D2(:));
idx = ceil(e * length(SS));
e_ = SS(idx);
X = double(D2 < e_);
case 5
D2 = sqrt(sum(D.^2, 2));
D3 = reshape(D2,size(i1,1),size(i1,2));
[SS, JJ] = sort(D3,1); JJ = JJ';
mine = round((2*w+1)*e);
X1(NX*(2*w+1)) = 0;
X1(JJ(:,1:mine)+repmat([0:(2*w+1):NX*(2*w+1)-1]',1,mine)) = 1;
X = reshape(X1,(2*w+1),NX);
case 6
D2 = sqrt(sum(D.^2, 2));
X = double(D2);
end
clear X1 SS JJ s px D D_ D2 D3
X = reshape(X,size(i1,1),size(i1,2)); X(i_remove) = 0;
%imagesc(X)