mfbox_pre_denoise_run.m

toolbox for spm 5 for data, model free analysis

function [X,mask,grid,timeline,params,private]=mfbox_pre_denoise_run(X,mask,grid,timeline,params,runflag,private)
% denoise data
%
% Usage:
%  [X,mask,grid,timeline,params,private]=mfbox_pre_denoise_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
%             delaydim - delaydim
%             clusters - clusters
%             steps    - steps
%  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), 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

params = mfbox_checkparam(params,'pre','denoise', ...
    struct('priority',50,'delaydim',3,'clusters',10,'steps',2));

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

if (runflag>0 && isstruct(params))
    if (runflag<3)
        prgs = mfbox_progress([],'title','Denoise','string','Denoising Data...', ...
            'progress',[1,n+1]); drawnow;
    end

    if (isstruct(X))
        rmask = false(size(mask));
        ndim = zeros(1,ndims(mask));
        for i=1:length(ndim)
            t = mask;
            for j=1:length(ndim), if (j~=i), t = max(t,[],j); end; end
            ref{i} = t(:)>0;
            ndim(i) = sum(ref{i});
        end
        subsasgn(rmask,struct('type','()','subs',{ref}),true);
        oX = X;
        X = zeros(sum(mask(:)),n,'single');
        grid=grid(:,mask);
        for i=1:n
            v = reshape(mfbox_databackend('getdata',oX,i),[],1);
            v = reshape(single(mfbox_denoise(reshape(v(rmask),ndim), ...
                params.delaydim,params.clusters, ...
                params.steps,'kmeans','pca','avg','var','mdl',32,1)),[],1);
            X(:,i) = v(mask(rmask));
            if (runflag<3)
                mfbox_progress(prgs,'string','Denoising Data...','progress', ...
                    [i+1,n+1]);
            end
        end
    else
        X = reshape(X,[],n);
        for i=1:n
            X(:,i) = reshape(single(mfbox_denoise({double(X(:,i)),grid}, ...
                params.delaydim,params.clusters,params.steps, ...
                'kmeans','pca','avg','var','mdl',32,1)),[],1);
            if (runflag<3)
                mfbox_progress(prgs,'string','Denoising Data...','progress',[i+1,n+1]);
            end
        end
    end
    if (runflag<3), mfbox_progress(prgs,'close',[]); end
end