function out = spm_run_norm(job)
% SPM job execution function
% takes a harvested job data structure and call SPM functions to perform
% computations on the data.
% Input:
% job    - harvested job data structure (see matlabbatch help)
% Output:
% out    - computation results, usually a struct variable.
%__________________________________________________________________________
% Copyright (C) 2005-2013 Wellcome Trust Centre for Neuroimaging

% $Id: spm_run_norm.m 6578 2015-10-15 15:22:12Z volkmar $


for i=1:numel(job.subj)

    jobi      = job;
    jobi.subj = job.subj(i);

    %-Normalise: Estimate
    %----------------------------------------------------------------------
    if isfield(jobi,'eoptions')
        normalise(jobi);
    end

    %-Normalise: Write
    %----------------------------------------------------------------------
    if isfield(jobi,'woptions')
        write_norm(jobi);
    end
end

%-Dependencies
%--------------------------------------------------------------------------
for i=1:numel(job.subj)
    if ~isfield(job.subj(i),'def'),
        out(i).def = {spm_file(char(job.subj(i).vol), 'prefix','y_', 'ext','.nii')};
    end
    
    if isfield(job,'woptions'),
        out(i).files = spm_file(job.subj(i).resample, 'prefix','w');
    end
end
%==========================================================================

%==========================================================================
function normalise(job)
% Estimate deformations via Segmentation
preproc8.channel.vols     = '<UNDEFINED>';
preproc8.channel.biasreg  = job.eoptions.biasreg;
preproc8.channel.biasfwhm = job.eoptions.biasfwhm;
preproc8.channel.write    = [0 0];

tpm = job.eoptions.tpm{:};
Nii = nifti(tpm);
for i=1:size(Nii.dat,4),
    preproc8.tissue(i) = struct('tpm',   {{[tpm ',' num2str(i)]}},...
                                'ngaus', Inf,...
                                'native',[0 0],...
                                'warped',[0 0]);
end
preproc8.warp.mrf    = 0;
preproc8.warp.reg    = job.eoptions.reg;
preproc8.warp.affreg = job.eoptions.affreg;
preproc8.warp.fwhm   = job.eoptions.fwhm;
preproc8.warp.samp   = job.eoptions.samp;
preproc8.warp.write  = [0 1];
preproc8.savemat     = 0;

for i=1:numel(job.subj),
    preproc8.channel.vols = job.subj(i).vol;
    spm_preproc_run(preproc8);
end
%==========================================================================

%==========================================================================
function write_norm(job)
% Write the spatially normalised data

defs.comp{1}.def         = '<UNDEFINED>';
defs.comp{2}.idbbvox.vox = job.woptions.vox;
defs.comp{2}.idbbvox.bb  = job.woptions.bb;
defs.out{1}.pull.fnames  = '';
defs.out{1}.pull.savedir.savesrc = 1;
defs.out{1}.pull.interp  = job.woptions.interp;
defs.out{1}.pull.mask    = 1;
defs.out{1}.pull.fwhm    = [0 0 0];
defs.out{1}.pull.prefix  = job.woptions.prefix;

for i=1:numel(job.subj)
    defs.out{1}.pull.fnames = job.subj(i).resample;
    if ~isfield(job.subj(i),'def')
        defs.comp{1}.def = {spm_file(char(job.subj(i).vol), 'prefix','y_', 'ext','.nii')};
    else
        defs.comp{1}.def = job.subj(i).def;
    end

    Nii = nifti(defs.comp{1}.def);
    vx  = sqrt(sum(Nii.mat(1:3,1:3).^2));
    o   = Nii.mat\[0 0 0 1]';
    o   = o(1:3)';
    dm  = size(Nii.dat);
    bb  = [-vx.*(o-1) ; vx.*(dm(1:3)-o)];

    defs.comp{2}.idbbvox.vox = job.woptions.vox;
    defs.comp{2}.idbbvox.bb  = job.woptions.bb;
    defs.comp{2}.idbbvox.vox(~isfinite(defs.comp{2}.idbbvox.vox)) = vx(~isfinite(defs.comp{2}.idbbvox.vox));
    defs.comp{2}.idbbvox.bb(~isfinite(defs.comp{2}.idbbvox.bb)) = bb(~isfinite(defs.comp{2}.idbbvox.bb));
    spm_deformations(defs);
end
