function analyze(runid,group,subj,tries)
% Analyze investors using SPM2
%
% input:
% runid - unique run identifier
% group - group identifier
% subj - subject
% tries - # of times to retry on failure
%
% Misha Koshelev
% February 24th, 2010
% Montague Laboratory

    if ~exist('tries','var')
        tries=3;
    end
    if regexp(runid,'3.*')
        analyze3(runid,group,subj,tries);
        return;
    elseif strcmp(runid,'4')
        analyze4(runid,group,subj,tries);
        return;
    elseif regexp(runid,'6.*')
        analyze6(runid,group,subj,tries);
        return;
    elseif strcmp(runid,'7')
        analyze7(runid,group,subj,tries);
        return;
    elseif strcmp(runid,'8')
        analyze8(runid,group,subj,tries);
        return;
    end

    warning off;
    group
    subj

    % save paths
    paths=path;pwds=pwd;
    addpath(genpath(pwd));

    succeed=false;
    try
        % load data
        target=['/mnt/nfs/proj/trust_agnostic',filesep,group,filesep,subj];
        cd([target,filesep,'other']);
        load('FileInfo');
        load('motion');
        load('source');
        source=[source,filesep,'GLMs',filesep,subj];
        cd('..');
        if exist(runid,'dir')
            system(['rm -rf ',runid]);
        end
        mkdir(runid);

        % image files
        cd([source,filesep,'run1']);
        f=spm_get('Files',pwd,'sw*.img');
        cd([target,filesep,runid]);

        spm_defaults;
        global defaults;

        % HRF
        SPM.xBF.name='hrf'; %'hrf (with time derivative)';
        SPM.xBF.order=1;
        SPM.xBF.length=32.1250; % length in seconds
        SPM.xBF.T=16; % number of time bins per scan
        SPM.xBF.T0=1; % middle slice/timebin
        SPM.xBF.UNITS='secs'; % OPTIONS: 'scans'|'secs' for onsets
        SPM.xBF.Volterra=1; % OPTIONS: 1|2=order of convolution

        % Trials
        sess=1;i=1;
        events=fieldnames(FileInfo.EventStruct{1});
        for j=1:length(events)
            event=FileInfo.EventStruct{1}.(events{j});

            if strcmp(events{j},'ScreenInvAmount') % 4359    SHOWING MESSAGE <Make Initial Investment> AND PIC <0>
                % rounds 1&2 only - no regressor
                SPM.Sess(sess).U(i).name={[events{j},'1-2']};
                SPM.Sess(sess).U(i).ons=event.values(1:2);
                SPM.Sess(sess).U(i).dur=event.dur(1:2);
                SPM.Sess(sess).U(i).P(1).name='none';
                i=i+1;

                % rounds 3-10 - regressed with i&r's at past 2 time points
                SPM.Sess(sess).U(i).name={[events{j},'3-10']};
                SPM.Sess(sess).U(i).ons=event.values(3:10);
                SPM.Sess(sess).U(i).dur=event.dur(3:10);
                SPM.Sess(sess).U(i).P.name='r(t-1)';
                SPM.Sess(sess).U(i).P.h=1;
                SPM.Sess(sess).U(i).P.P=FileInfo.TruRatio(2:end-1)';
                SPM.Sess(sess).U(i).P(end+1).name='i(t-1)';
                SPM.Sess(sess).U(i).P(end).h=1;
                SPM.Sess(sess).U(i).P(end).P=FileInfo.InvRatio(2:end-1)';
                SPM.Sess(sess).U(i).P(end+1).name='r(t-2)';
                SPM.Sess(sess).U(i).P(end).h=1;
                SPM.Sess(sess).U(i).P(end).P=FileInfo.TruRatio(1:end-2)';
                SPM.Sess(sess).U(i).P(end+1).name='i(t-2)';
                SPM.Sess(sess).U(i).P(end).h=1;
                SPM.Sess(sess).U(i).P(end).P=FileInfo.InvRatio(1:end-2)';
                i=i+1;
            else
                SPM.Sess(sess).U(i).name={events{j}};
                SPM.Sess(sess).U(i).ons=event.values;
                SPM.Sess(sess).U(i).dur=event.dur;
                SPM.Sess(sess).U(i).P(1).name='none';
                i=i+1;
            end
        end

        if length(motion) == 0, % no movement parameters
            SPM.Sess(sess).C.C=[];
            SPM.Sess(sess).C.name={};
        elseif size(motion, 2) > 20;
            SPM.Sess(sess).C.name={'first scan', 'last scan'};
            SPM.Sess(sess).C.C=motion;
        else % assume movement parameters
            SPM.Sess(sess).C.C=motion;
            SPM.Sess(sess).C.name={'X','Y','Z','x','y','z'};
        end

        % global normalization: OPTIONS:'Scaling'|'None'
        SPM.xGX.iGXcalc='None';

        % low frequency confound: high-pass cutoff (secs) [Inf=no filtering]
        SPM.xX.K(sess).HParam=128;

        % intrinsic autocorrelations: OPTIONS: 'none'|'AR(1) + w'
        SPM.xVi.form='AR(1) + w'; %'AR(1) + w';
        SPM.nscan(sess)=size(f,1);
        SPM.xY.P=f;
        SPM.xY.RT=2; % Time between scans

        SPM=spm_fmri_spm_ui_m(SPM,'');
        SPM=spm_spm_m(SPM);
        succeed=true;
    catch
        e=lasterror;
        e.message
        e.identifier
        for i=1:length(e.stack)
            s=e.stack(i);
            s.file
            s.name
            s.line
        end
    end

    path(paths);
    cd(pwds);

    warning on;

    if ~succeed&&tries>1
        system('sleep 10');
        analyze(runid,group,subj,tries-1);
    end