mfbox_pre_remmean_run.m

toolbox for spm 5 for data, model free analysis

function [X,mask,grid,timeline,params,private]=mfbox_pre_remmean_run(X,mask,grid,timeline,params,runflag,private)
% remove means
%
% Usage:
%  [X,mask,grid,timeline,params,private]=mfbox_pre_remmean_run(X,mask,grid,timeline,params,runflag,private)
%
%  X        - (NxT) data
%             or struct for mfbox_databackend('getdata',X,i),[],1);
%  mask     - data mask (XxYxZ) with sum(mask(:))==N
%  grid     - 3d positions (3xN) of the data values
%  timeline - timeline (1xT)
%  params   - struct with
%             priority - evaluation priority
%             meanm    - temporal, spatial mean removal
%                        spatial, temporal mean removal
%                        spatial bg rescale, temporal mean removal
%                        spatial mean removal
%                        temporal mean remova
%  runflag  - -1 get default parameter
%              0 interactive ask parameters
%              1 interactive ask parameters and run
%              2 run
%  private  - private data to enable plot updates while selecting (see also mfbox_databackend)
%
% Copyright by Peter Gruber and Fabian J. Theis
% Signal Processing & Information Theory group
% Institute of Biophysics, University of Regensburg, Germany
% Homepage: http://research.fabian.theis.name
%           http://www-aglang.uni-regensburg.de
%
% This file is free software, subject to the 
% GNU GENERAL PUBLIC LICENSE, see gpl.txt

error(nargchk(1,7,nargin));
error(nargchk(1,6,nargout));

if (isstruct(X)), s = [X.dim,X.timesteps];
else, s = size(X);
end
dim = s(1:(end-1));
if (length(dim)<2), dim = [1,dim]; end
n = s(end);

if (nargin<2), mask = true(dim); end
dim = size(mask);
if (length(dim)==length(s)), n = 1; end

if (nargin<3) | isempty(grid) , grid = mfbox_mkgrid(dim)'; end
if (nargin<4), timeline = 0:(n-1); end
if (nargin<5), params = []; end
if (nargin<6), runflag = 1; end
if (nargin<7), private = []; end

meanms = { 'temporal, spatial mean removal' ...
    'spatial bg rescale, temporal mean removal' ...
    'spatial mean removal' ...
    'temporal mean removal'};
params = mfbox_checkparam(params,'pre','remmean', ...
    struct('meanm',meanms{1},'priority',60));

if (abs(runflag-0.5)<1)
    if (exist('OCTAVE_HOME')~=5)% matlab
        [params,private] = mfbox_pre_remmeang(X,mask,grid,timeline,params,runflag,private);
    elseif (exist('OCTAVE_HOME')==5) %octave
        [params,runflag] = mfbox_getparam(params,runflag,'mfbox_pre_remmeang.py');
    end
end

if (runflag>0 && isstruct(params))
    if (isstruct(X))
        oX = X;
        X = zeros(sum(mask(:)),n,'single');
        for i=1:n
            v = single(reshape(mfbox_databackend('getdata',oX,i),[],1));
            X(:,i) = v(mask);
        end
        grid = grid(:,mask);
    else
        sgr = size(grid);
        sm = size(mask);
        g = mat2cell(min(repmat(sm',1,sgr(2)), ...
            max(ones(sgr),round(grid))), ...
            ones(1,sgr(1)),sgr(2));
        nmask = mask(sub2ind(sm,g{:})); 
        X = reshape(X,[],n);
        if (any(nmask==0))
            X = X(nmask,:);
            grid = grid(:,nmask);
        end
    end

    switch params.meanm
        case 'temporal, spatial mean removal'
            Xm = mean(X,2);
            for i=1:size(X,1), X(i,:) = X(i,:)-Xm(i); end
            Xm = mean(X,1);
            for i=1:size(X,2), X(:,i) = X(:,i)-Xm(i); end
        case 'spatial, temporal mean removal'
            Xm = mean(X,1);
            for i=1:size(X,2), X(:,i) = X(:,i)-Xm(i); end
            Xm = mean(X,2);
            for i=1:size(X,1), X(i,:) = X(i,:)-Xm(i); end
        case 'spatial bg rescale, temporal mean removal'
            v = mean(X,2);
            v(v.^2>eps) = 1./v(v.^2>eps);
            for i=1:size(X,1)
                X(i,:) = X(i,:)*v(i);
            end
            X = X.*repmat(v,1,n);
            Xm = mean(X,1);
            for i=1:size(X,2), X(:,i) = X(:,i)-Xm(i); end
            Xm = mean(X,2);
            for i=1:size(X,1), X(i,:) = X(i,:)-Xm(i); end
        case 'spatial mean removal'
            Xm = mean(X,1);
            for i=1:size(X,2), X(:,i) = X(:,i)-Xm(i); end
        case 'temporal mean removal'
            Xm = mean(X,2);
            for i=1:size(X,1), X(i,:) = X(i,:)-Xm(i); end
    end
end