THE AUDITORY MODELING TOOLBOX

Applies to version: 0.9.9

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EXP_BAUMGARTNER2014 - Results from Baumgartner et al. (2014)

Program code:

function varargout = exp_baumgartner2014(varargin)
%EXP_BAUMGARTNER2014 Results from Baumgartner et al. (2014)
%   Usage: data = exp_baumgartner2014(flag) 
%
%   EXP_BAUMGARTNER2014(flag) reproduces figures of the study from 
%   Baumgartner et al. (2014).
%
%
%   The following flags can be specified
%
%     'fig2'    Reproduce Fig.2:
%               Binaural weighting function best fitting results from 
%               Morimoto (2001) labeled as [1] and Macpherson and Sabin (2007) 
%               labeled as [2] in a least squared error sense.
%
%     'fig3'    Reproduce Fig.3:
%               Prediction examples. Actual responses and response predictions 
%               for three exemplary listeners when listening to median-plane 
%               targets in the baseline condition. 
%               Actual response angles are shown as open circles. 
%               Probabilistic response predictions are encoded by brightness 
%               according to the color bar to the right. Actual (A:) and 
%               predicted (P:) quadrant error rates (QE) and local polar 
%               RMS errors (PE) are listed above each panel.
%               
%     'fig4'    Reproduce Fig.4:
%               Model parametrization. Partial and joint prediction residues 
%               as functions of the degree of selectivity and the motoric 
%               response scatter. Residuum functions are normalized to the 
%               minimum residuum obtained for the optimal parameter value.
%
%     'fig5'    Reproduce Fig.5:
%               Effect of band limitation and spectral warping. Actual 
%               responses and response predictions for listener NH12 when 
%               listening to broadband (BB), low-pass filtered (LP), or 
%               spectrally warped (W) DTFs of the median plane. Data were 
%               pooled within pm15^circ of lateral angle.
%               All other conventions are as in Fig.3.
%
%     'fig6'    Reproduce Fig.6:
%               Effect of band limitation and spectral warping. 
%               Listeners were tested with broadband (BB), low-pass 
%               filtered (LP), and spectrally warped (W) DTFs. 
%               Actual: experimental results from Majdak et al. (2013). 
%               Part.: Model predictions for the actual eight participants 
%               based on the actually tested target positions. Pool: Model 
%               predictions for our listener pool based on all possible 
%               target positions. Symbols and whiskers show median values 
%               and inter-quartile ranges, respectively. Symbols were 
%               horizontally shifted to avoid overlaps. Dotted horizontal 
%               lines represent chance rate. Correlation coefficients, r, 
%               and prediction residues, e, specify the correspondence 
%               between actual and predicted listener-specific performances.
%
%     'fig7'    Reproduce Fig.7:
%               Effect of spectral resolution in terms of varying the number 
%               of spectral channels of a channel vocoder. Actual responses 
%               and response predictions for exemplary listener NH12. 
%               Results for 24, 9, and 3 channels are shown. All other 
%               conventions are as in Fig.3.
%
%     'fig8'    Reproduce Fig.8:
%               Effect of spectral resolution in terms of varying the number 
%               of spectral channels of a channel vocoder. Actual experimental 
%               results are from Goupell et al. (2010). Stimulation with broadband 
%               click trains (CL) represents an unlimited number of channels. 
%               All other conventions are as in Fig.6.
%
%     'fig9'    Reproduce Fig.9:
%               Effect of non-individualized HRTFs in terms of untrained 
%               localization with others' instead of own ears. Statistics 
%               summaries with open symbols represent actual experimental 
%               results replotted from Fig.,13 of Middlebrooks (1999), 
%               statistics with filled symbols represent predicted results.
%               Horizontal lines represent 25th, 50th, and 75th percentiles, 
%               the whiskers represent 5th and 95th percentiles, and crosses 
%               represent minima and maxima. Circles and squares denote mean values.
%
%     'fig10'   Reproduce Fig.10:
%               Effect of spectral ripples. Actual experimental results (circles) 
%               are from Macpherson and Middlebrooks (2003). Predicted results (filled circles) 
%               were modeled for our listener pool (squares). Either the ripple 
%               depth of 40,dB (top) or the ripple density of one ripple/octave 
%               (bottom) was kept constant. Ordinates show the listener-specific 
%               difference in error rate between a test and the baseline condition. 
%               Baseline performances are shown in the bottom right panel.
%               Symbols and whiskers show median values and inter-quartile ranges, 
%               respectively. Symbols were horizontally shifted to avoid overlaps. 
%               Diamonds with dashed lines show predictions (P) of the model 
%               without positive spectral gradient extraction (PSGE).
%
%     'fig11'   Reproduce Fig.11:
%               Effect of high-frequency attenuation in speech localization. 
%               Actual experimental results are from Best et al. (2005). 
%               Absolute polar angle errors (top) and QE (bottom) were averaged 
%               across listeners. Circles and squares show actual and predicted 
%               results, respectively. Diamonds with dashed lines show predictions 
%               of the model without positive spectral gradient extraction.
%
%     'fig12'   Reproduce Fig.12:
%               Listener-specific likelihood statistics used to evaluate 
%               target-specific predictions for baseline condition. Bars 
%               show actual likelihoods, dots show mean expected likelihoods, 
%               and whiskers show tolerance intervals with 99% confidence 
%               level of expected likelihoods.
%
%     'fig13'   Reproduce Fig.13:
%               Exemplary baseline predictions. Same as Fig.3 but for listeners 
%               where actual likelihoods were outside the tolerance intervals.    
%
%     'fig14'   Reproduce Fig.14:
%               Baseline performance as a function of the magnitude of the 
%               lateral response angle. Symbols and whiskers show median 
%               values and inter-quartile ranges, respectively. Open symbols 
%               represent actual and closed symbols predicted results. Symbols 
%               were horizontally shifted to avoid overlaps. Triangles with 
%               dashed lines show predictions (P) of the model without the 
%               sensomotoric mapping (SMM) stage.  
%
%     'tab1'    Reproduce Tab.1:
%               Listener-specific sensitivity calibrated on the basis of N 
%               baseline targets in proximity of the median plane (+-30deg). 
%               Listeners are labeled as NHl. Actual and predicted quadrant 
%               errors (QE) and local polar RMS errors (PE) are shown pairwise 
%               (Actual | Predicted).
%
%     'tab2'    Reproduce Tab.2:
%               The effects of model configurations on the prediction residues. 
%               PSGE: model with or without positive spectral gradient extraction. 
%               MBA: model with or without manual bandwidth adjustment to the 
%               stimulus bandwidth. Prediction residues between actual and 
%               predicted PE and QE are listed for acute performance with 
%               the broadband (BB), low-passed (LP) and warping (W) conditions 
%               of the experiments from Majdak et al. (2013).
%
%     'tab3'    Reproduce Tab.3:
%               Performance predictions for binaural, ipsilateral, and 
%               contralateral listening conditions. The binaural weighting 
%               coefficient was varied in order to represent the three 
%               conditions: binaural: Phi = 13^circ; 
%               ipsilateral: Phi rightarrow +0^circ; 
%               contralateral: Phi rightarrow -0^circ. 
%               Prediction residues and correlation coefficients between 
%               actual and predicted results are shown together with predicted 
%               average performances. 
%
%     'fig5_baumgartner2015aro'    Reproduce Fig.5 of Baumgartner et al. (2015):
%                                  Effect of background noise on reliability of contralateral  
%                                  cues for various lateral eccentricities. Top row:
%                                  Across-listener averages of performance measures for
%                                  contralateral ear. Bottom row: Contralateral re ipsilateral
%                                  averages of performance measures.
%
%     'fig2_baumgartner2015jaes'   Reproduce Fig.2 of Baumgartner and Majdak (2015):
%                                  Example showing the spectral discrepancies obtained by VBAP. 
%                                  The targeted spectrum is the HRTF for 20 deg polar angle. 
%                                  The spectrum obtained by VBAP is the superposition of two 
%                                  HRTFs from directions 40 deg polar angle apart of each 
%                                  other with the tar- geted source direction centered in between.
%
%     'fig4_baumgartner2015jaes'   Reproduce Fig.4 of Baumgartner and Majdak (2015):
%                                  Response predictions to sounds created by VBAP with two 
%                                  loudspeakers in the median plane positioned at polar 
%                                  angles of -15 and 30 deg, respectively. Predictions for 
%                                  two exemplary listeners and pooled across all listeners. 
%                                  Each column of a panel shows the predicted PMV of 
%                                  polar-angle responses to a certain sound. Note the 
%                                  inter-individual differences and the generally small 
%                                  probabilities at response angles not occupied by the loudspeakers.
%
%     'fig5_baumgartner2015jaes'   Reproduce Fig.5 of Baumgartner and Majdak (2015):
%                                  Listener-specific increases in polar error as a function of 
%                                  the panning angle. Increase in polar error defined as 
%                                  the difference between the polar error obtained by the 
%                                  VBAP source and the polar error obtained by the real 
%                                  source at the corresponding panning angle. Same loudspeaker 
%                                  arrangement as for Fig. 4. Note the large inter-individual 
%                                  differences and the increase in polar error being largest 
%                                  at panning angles centered between the loudspeakers, i.e., 
%                                  at panning ratios around R = 0 dB.
%
%     'fig6_baumgartner2015jaes'   Reproduce Fig.6 of Baumgartner and Majdak (2015):
%                                  Panning angles for the loudspeaker arrangement of Fig. 4 
%                                  judged best for reference sources at polar angles of 
%                                  0 or 15 deg in the median plane. Comparison between 
%                                  experimental results from [2] and simulated results 
%                                  based on various response strategies: PM, CM, and both 
%                                  mixed. Dotted horizontal line: polar angle of the reference 
%                                  source. Hor- izontal line within box: median; 
%                                  box: inter-quartile range (IQR); 
%                                  whisker: within quartile +-1.5 IQR; 
%                                  star: outlier. 
%                                  Note that the simulations predicted a bias similar to 
%                                  the results from Pulkki (2001) for the reference source at 0 deg.
%
%     'tab1_baumgartner2015jaes'   Reproduce Tab.1 of Baumgartner and Majdak (2015):
%                                  Means and standard deviations of responded panning angles for the 
%                                  two reference sources (Ref.) together with corresponding GOFs 
%                                  evaluated for the actual results from Pulkki (2001) and 
%                                  predicted results based on various response strategies. 
%                                  Note the relatively large GOFs for the simulations based on 
%                                  mixed response strategies indicating a good correspondence 
%                                  between actual and predicted results.
%
%     'fig7_baumgartner2015jaes'   Reproduce Fig.7 of Baumgartner and Majdak (2015):
%                                  Increase in polar error (defined as in Fig. 5) as a function 
%                                  of loudspeaker span in the median plane with panning ratio 
%                                  R = 0 dB. Black line with gray area indicates mean 
%                                  +-1 standard deviation across listeners. Note that the 
%                                  increase in polar error monotonically increases with 
%                                  loudspeaker span.
%
%     'fig8_baumgartner2015jaes'   Reproduce Fig.8 of Baumgartner and Majdak (2015):
%                                  Effect of loudspeaker span in the median plane on coefficient 
%                                  of determination, r^2, for virtual source directions 
%                                  created by VBAP. Separate analysis for frontal, rear, 
%                                  and overall (frontal and rear) targets. Data pooled 
%                                  across listeners. Note the correspondence with the 
%                                  results obtained by Bremen et al. (2010).
%
%     'tab3_baumgartner2015jaes'   Reproduce Tab.3 of Baumgartner and Majdak (2015):
%                                  Predicted across-listener average of increase in polar 
%                                  errors as referred to a reference system containing 
%                                  loudspeakers at all considered directions. Distinction 
%                                  between mean and maximum degradation across directions. 
%                                  N: Number of loudspeakers. Ele.: Elevation of second layer. 
%                                  Notice that this elevation has a larger effect on mean 
%                                  and maximum degradation than N.
%
%     'fig9_baumgartner2015jaes'   Reproduce Fig.9 of Baumgartner and Majdak (2015):
%                                  Predicted polar error as a function of the lateral and 
%                                  polar angle of a virtual source created by VBAP in 
%                                  various multichannel systems. Open circles indicate 
%                                  loudspeaker directions. Reference shows polar error 
%                                  predicted for a real source placed at the virtual 
%                                  source directions investigated for systems A, ..., F.
%
%   Further, cache flags (see amt_cache) and plot flags can be specified:
%
%     'plot'    Plot the output of the experiment. This is the default.
%
%     'noplot'  Don't plot, only return data.
%
%   Requirements: 
%   -------------
%
%   1) SOFA API v0.4.3 or higher from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
% 
%   2) Data in hrtf/baumgartner2014
%
%   3) Statistics Toolbox for Matlab (for some of the figures)
%
%   Examples:
%   ---------
%
%   To display Fig.2 use :
%
%     exp_baumgartner2014('fig2');
%
%   To display Fig.3 use :
%
%     exp_baumgartner2014('fig3');
%
%   To display Fig.4 use :
%
%     exp_baumgartner2014('fig4');
%
%   To display Fig.5 use :
%
%     exp_baumgartner2014('fig5');
%
%   To display Fig.6 use :
%
%     exp_baumgartner2014('fig6');
%
%   To display Fig.7 use :
%
%     exp_baumgartner2014('fig7');
%
%   To display Fig.8 use :
%
%     exp_baumgartner2014('fig8');
%
%   To display Fig.9 use :
%
%     exp_baumgartner2014('fig9');
%
%   To display Fig.10 use :
%
%     exp_baumgartner2014('fig10');
%
%   To display Fig.11 use :
%
%     exp_baumgartner2014('fig11');
%
%   To display Fig.12 use :
%
%     exp_baumgartner2014('fig12');
%
%   To display Fig.13 use :
%
%     exp_baumgartner2014('fig13');
%
%   To display Fig.14 use :
%
%     exp_baumgartner2014('fig14');
%
%   To display Fig.5 of Baumgartner et al. (2015) use :
%
%     exp_baumgartner2014('fig5_baumgartner2015aro');
%
%   To display Fig.2 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('fig2_baumgartner2015jaes');
%
%   To display Fig.4 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('fig4_baumgartner2015jaes');
%
%   To display Fig.5 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('fig5_baumgartner2015jaes');
%
%   To display Fig.6 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('fig6_baumgartner2015jaes');
%
%   To display Fig.7 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('fig7_baumgartner2015jaes');
%
%   To display Fig.8 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('fig8_baumgartner2015jaes');
%
%   To display Fig.9 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('fig9_baumgartner2015jaes');
%
%   To display Tab.1 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('tab1_baumgartner2015jaes');
%
%   To display Tab.3 of Baumgartner and Majdak (2015) use :
%
%     exp_baumgartner2014('tab3_baumgartner2015jaes');
%
%   See also: baumgartner2014 data_baumgartner2014
%
%   References:
%     R. Baumgartner, P. Majdak, and B. Laback. The reliability of
%     contralateral spectral cues for sound localization in sagittal planes.
%     In Midwinter Meeting of the Association for Research in Otolaryngology,
%     Baltimore, MD, Feb 2015.
%     
%     R. Baumgartner, P. Majdak, and B. Laback. Modeling sound-source
%     localization in sagittal planes for human listeners. The Journal of the
%     Acoustical Society of America, 136(2):791-802, 2014.
%     
%     R. Baumgartner and P. Majdak. Modeling Localization of Amplitude-Panned
%     Virtual Sources in Sagittal Planes. J. Audio Eng. Soc.,
%     63(7/8):562-569, Aug. 2015. [1]http ]
%     
%     References
%     
%     1. http://www.aes.org/e-lib/browse.cfm?elib=17842
%     
%     bremen2010pinna goupell2010numchan macpherson2007 macpherson2003ripples  
%     majdak2013spatstrat middlebrooks1999nonindividualized morimoto2001
%     pulkki2001localization 
%
%   Url: http://amtoolbox.sourceforge.net/amt-0.9.9/doc/experiments/exp_baumgartner2014.php

% Copyright (C) 2009-2015 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 0.9.9
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program.  If not, see <http://www.gnu.org/licenses/>.


% AUTHOR: Robert Baumgartner


%% ------ Check input options --------------------------------------------

definput.import={'amt_cache'};
definput.keyvals.FontSize = 12;
definput.keyvals.MarkerSize = 6;
definput.flags.type = {'missingflag','fig2','fig3','fig4','fig5','fig6',...
                       'fig7','fig8','fig9','fig10','fig11','fig12','fig13',...
                       'fig14','tab1','tab2','tab3',...
                       'fig5_baumgartner2015aro',...
                       'fig2_baumgartner2015jaes','fig4_baumgartner2015jaes',...
                       'fig5_baumgartner2015jaes','fig6_baumgartner2015jaes',...
                       'fig7_baumgartner2015jaes','fig8_baumgartner2015jaes',...
                       'fig9_baumgartner2015jaes','tab1_baumgartner2015jaes',...
                       'tab3_baumgartner2015jaes',...
                       };
definput.flags.plot = {'plot','noplot'};


[flags,kv]  = ltfatarghelper({'FontSize','MarkerSize'},definput,varargin);


if flags.do_missingflag
  flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
             sprintf('%s or %s',definput.flags.type{end-1},definput.flags.type{end})];
  error('%s: You must specify one of the following flags: %s.',upper(mfilename),flagnames);
end;


%% General Plot Settings
TickLength = [0.02,0.04];

%% ------ FIG 2 -----------------------------------------------------------
if flags.do_fig2
  
  % Original Data
  Morimoto_left = [1,0.5,0];
  Morimoto_right = 1-Morimoto_left;
  Morimoto_ang = [60,0,-60];

  Macpherson_left = [3.75,0.5,-4]/10+0.5;
  Macpherson_right = [-3.75,-0.5,4.75]/10+0.5;
  Macpherson_ang = [30,0,-30];

  data =  [Morimoto_left, -Macpherson_right, Macpherson_left];
  lat =   [Morimoto_ang ,  Macpherson_ang  , Macpherson_ang];

  % Fit Slope
  bwslope = 1:0.1:90;
  resid = zeros(size(bwslope));
  for ii = 1:length(bwslope)
    binw_left = 1./(1+exp(-lat/bwslope(ii))); 
    resid(ii) = rms(binw_left-data);
  end
  [~,idmin] = min(resid);
  contralateralGain = bwslope(idmin);
  amt_disp(['Phi: ' num2str(contralateralGain,'%2.0f') ' deg'])
  
  % Calculate specific weights to plot
  lat = -90:5:90;
  binw_left = 1./(1+exp(-lat/contralateralGain)); % weight of left ear signal with 0 <= binw <= 1
  binw_right = 1-binw_left;
  
  if flags.do_plot
    figure;
    plot(lat,binw_left)
    hold on
    plot(lat,binw_right,'r--')
    plot(Morimoto_ang,Morimoto_left,'vk','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w');
    plot(Macpherson_ang,Macpherson_left,'ok','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')

    plot(Morimoto_ang,Morimoto_left,'vb','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
    plot(Morimoto_ang,Morimoto_right,'vr','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
    plot(Macpherson_ang,Macpherson_left,'ob','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
    plot(Macpherson_ang,Macpherson_right,'or','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')

    l = legend('\itL','\itR','[1]','[2]');
    set(l,'Location','East','FontSize',kv.FontSize-1)
    set(gca,'XLim',[lat(1) lat(end)],'YLim',[-0.05 1.05],'XTick',-60:30:60,'FontSize',kv.FontSize)
    xlabel('\phi_k (deg)','FontSize',kv.FontSize)
    ylabel('w_{\zeta}(\phi_k)','FontSize',kv.FontSize)
  end
  
  % Output
  clear data
  data.contralateralGain = contralateralGain;
  
end

%% ------ FIG 3&13 --------------------------------------------------------
if flags.do_fig3 || flags.do_fig13
  
  latseg = [-20,0,20]; ii = 2; % centers of lateral segments
%   dlat =  10;         % lateral range (+-) of each segment

  s = data_baumgartner2014('baseline',flags.cachemode);
  
  if flags.do_fig3
    idselect = ismember({s.id},{'NH15','NH22','NH62'});
  else
    idselect = ismember({s.id},{'NH12','NH39','NH18'});
  end
  s = s(idselect);

  %% LocaMo
  qe = zeros(length(s),length(latseg));
  pe = zeros(length(s),length(latseg));
  for ll = 1:length(s)

      s(ll).sphrtfs{ii} = 0;     % init
      s(ll).p{ii} = 0;        % init

      [s(ll).sphrtfs{ii},polang] = extractsp( latseg(ii),s(ll).Obj );
      [s(ll).p{ii},respangs] = baumgartner2014(...
          s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
          'S',s(ll).S,'lat',latseg(ii),'polsamp',polang); 

      [ qe(ll,ii),pe(ll,ii) ] = baumgartner2014_pmv2ppp( ...
          s(ll).p{ii} , polang , respangs , s(ll).target{ii});

      if flags.do_plot
        if ll ==1; figure; end
        subplot(1,3,ll)
        Nmax = min(150,s(ll).Ntargets{ii});
        idplot = round(1:s(ll).Ntargets{ii}/Nmax:s(ll).Ntargets{ii});
        plot_baumgartner2014(s(ll).p{ii},polang,respangs,...
                  s(ll).target{ii}(idplot),s(ll).response{ii}(idplot),...
                  'MarkerSize',kv.MarkerSize,'cmax',0.05,'nocolorbar');
        title({['A: PE = ' num2str(s(ll).pe_exp_lat(ii),2) '\circ, QE = ' num2str(s(ll).qe_exp_lat(ii),2) '%'];['P: PE = ' num2str(pe(ll,ii),2) '\circ, QE = ' num2str(qe(ll,ii),2) '%']},...
          'FontSize',kv.FontSize-1)
        text(90,240,s(ll).id,'FontSize',kv.FontSize,...
          'Color','w','HorizontalAlignment','center')
        xlabel('Target Angle (deg)','FontSize',kv.FontSize)
        ylabel('Response Angle (deg)','FontSize',kv.FontSize)
        set(gca,'FontSize',kv.FontSize-1)
        set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
        set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})
      end

  end
  
  s = rmfield(s,{'Obj','itemlist','sphrtfs'}); % reduce file size 
  
  varargout{1} = s;
  
end

%% ------ FIG 4 -----------------------------------------------------------
if flags.do_fig4
  
  [perr,perr_exp,qerr,qerr_exp,gamma,mrs,Ntargets] = amt_cache('get','parametrization',flags.cachemode);
  
  if isempty(perr)
    amt_disp('Note that this procedure may last several hours!','progress')
    
    gamma = [1,3,3,3,4,4,4,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,8,8,8,9,9,9,...
      10,10,10,12,12,12,16,16,16,30,30,30];%,100,100,100];
    mrs = [0,17,18,19,19,20,21,19,20,21, 0,10,100,16,17,18,19,20,21,23,30, 5,...
      19,20,21,19,20,21,19,20,21,19,20,21,19,20,21,19,20,21,19,20,21];%,19,20,21];

    latseg = -60:20:60; % centers of lateral segments
    dlat =  10;  % lateral range (+-) of each segment
        
    for g = 1:length(gamma)
      
      cname = ['result_baseline_g' num2str(gamma(g),'%u') '_mrs' num2str(mrs(g),'%u')];
      [s,qe, pe] = amt_cache('get',cname);
      if isempty(s)

        s = data_baumgartner2014('baseline','gamma',gamma(g),'mrsmsp',mrs(g),flags.cachemode);

        qe_exp = zeros(length(s),length(latseg));
        pe_exp = zeros(length(s),length(latseg));
        for ll = 1:length(s)

          s(ll).target = [];
          s(ll).response = [];
          s(ll).Nt = [];
          for ii = 1:length(latseg)

            latresp = s(ll).itemlist(:,7);
            idlat = latresp <= latseg(ii)+dlat & latresp > latseg(ii)-dlat;
            s(ll).mm2 = s(ll).itemlist(idlat,:);

            s(ll).mm2(:,7) = 0; % set lateral angle to 0deg such that localizationerror works also outside +-30deg

            pe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'rmsPmedianlocal'));
            qe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'querrMiddlebrooks'));

            s(ll).target{ii} = real(s(ll).mm2(:,6)); % polar angle of target
            s(ll).response{ii} = real(s(ll).mm2(:,8)); % polar angle of response
            s(ll).Nt{ii} = length(s(ll).target{ii});

          end
        end


        %% LocaMo
        qe = zeros(length(s),length(latseg));
        pe = zeros(length(s),length(latseg));
        for ll = 1:length(s)

          for ii = 1:length(latseg)

            s(ll).sphrtfs{ii} = 0;     % init
            s(ll).p{ii} = 0;        % init

            [s(ll).sphrtfs{ii},polang] = extractsp( latseg(ii),s(ll).Obj );
            [s(ll).p{ii},respangs] = baumgartner2014(...
                s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
                'S',s(ll).S,'lat',latseg(ii),'polsamp',polang,...
                'gamma',gamma(g),'mrsmsp',mrs(g)); 

            if s(ll).Nt{ii} > 0
              [ qe(ll,ii),pe(ll,ii) ] = baumgartner2014_pmv2ppp( ...
                  s(ll).p{ii} , polang , respangs , s(ll).target{ii});
            else
              qe(ll,ii) = NaN; 
              pe(ll,ii) = NaN;
            end

          end

        end
        s = rmfield(s,{'Obj','itemlist','mm2','sphrtfs'}); % reduce file size
        amt_cache('set',cname,s,qe, pe, qe_exp, pe_exp)
      end
    end

    %% Combine results to single mat file
    perr = zeros(length(s),length(latseg),length(gamma));
    qerr = perr;
    for g = 1:length(gamma)
      fn = ['result_baseline_g' num2str(gamma(g),'%u') '_mrs' num2str(mrs(g),'%u')];
      [s,qerr(:,:,g), perr(:,:,g),qerr_exp,perr_exp] = amt_cache('get',fn,flags.cachemode);
    end
    
    % Number of targets for each listener and lateral segment
    Ntargets = zeros(length(s),7);
    for jj = 1:length(s)
      Ntargets(jj,:) = [s(jj).Nt{:}];
    end
    
    amt_cache('set','parametrization',perr,perr_exp,qerr,qerr_exp,gamma,mrs,Ntargets)
   
  end
  
  [qerr0,perr0] = baumgartner2014_pmv2ppp(ones(72,44)); % chance performances

  % extract all different gammas
  g = gamma;
  gamma = unique(gamma);

  Nset = size(perr,3);
  idnum = Ntargets ~= 0;
  relNt = Ntargets/sum(Ntargets(:));

  % Compute all residues
  resid.perr = zeros(length(gamma),1);
  resid.qerr = resid.perr;
  for ii = 1:Nset

    dperr = perr_exp - perr(:,:,ii);
    dqerr = qerr_exp - qerr(:,:,ii);
    resid.perr(ii) = sqrt( relNt(idnum)' * (dperr(idnum)).^2 );
    resid.qerr(ii) = sqrt( relNt(idnum)' * (dqerr(idnum)).^2 );

  end
  resid.total = resid.perr/perr0 + resid.qerr/qerr0;

  % Select optimal residues for various gamma
  id_g = zeros(length(gamma),1);
  etotal_g = zeros(length(gamma),1);
  for ii = 1:length(gamma)
    idgamma = find(g == gamma(ii));
    [etotal_g(ii),id] = min(resid.total(idgamma));
    id_g(ii) = idgamma(id);
  end
  eperr_g = resid.perr(id_g);
  eqerr_g = resid.qerr(id_g);

  [tmp,idopt] = min(etotal_g);
  etotal_g = etotal_g / etotal_g(idopt);
  eperr_g = eperr_g / eperr_g(idopt);
  eqerr_g = eqerr_g / eqerr_g(idopt);
  amt_disp(['Optimal Gamma: ' num2str(gamma(idopt),'%u') ' dB^-1'])

  % Select residues for optimal gamma and various mrs
  idgammaopt = find(g == gamma(idopt));
  mrs_gopt = mrs(idgammaopt);
  [mrssort,idsort] = sort(mrs_gopt);
  idmrs = idgammaopt(idsort);
  [tmp,idopt_mrs] = min(resid.total(idmrs));
  idnorm = idmrs(idopt_mrs);
  etotal_gopt = resid.total(idmrs) / resid.total(idnorm);
  eperr_gopt = resid.perr(idmrs) / resid.perr(idnorm);
  eqerr_gopt = resid.qerr(idmrs) / resid.qerr(idnorm);
  amt_disp(['Optimal MRS: ' num2str(mrssort(idopt_mrs),'%u') ' deg'])
  
  if flags.do_plot
    
    %% Plot residues for various gamma

    % Interpolate data
    gamma_int = logspace(0,2.1,1000);
    inttype = 'cubic';
    dperr_int = interp1(log10(gamma),eperr_g,log10(gamma_int),inttype);
    dqerr_int = interp1(log10(gamma),eqerr_g,log10(gamma_int),inttype);
    dtot_int = interp1(log10(gamma),etotal_g,log10(gamma_int),inttype);

    % Plot
    figure;
    subplot(1,2,1)
    semilogx(gamma_int,dperr_int,'k: ')
    hold on
    semilogx(gamma_int,dqerr_int,'k--')
    semilogx(gamma_int,dtot_int,'k-')
    semilogx(gamma(idopt),0.95,'vk','MarkerFaceColor','k','MarkerSize',kv.MarkerSize+1)

    leg = legend('PE','QE','PE&QE','\{\epsilon,\Gamma\}_{opt}');
    set(leg,'Location','northeast','FontSize',kv.FontSize-1)

    ylabel('e(\Gamma) / e(\Gamma_{opt})','FontSize',kv.FontSize)
    xlabel('\Gamma (dB^{-1})','FontSize',kv.FontSize)

    set(gca,'XLim',[gamma(1)-0.1 gamma(end)+20],'YLim',[0.91 1.7],'XMinorTick','on',...
      'FontSize',kv.FontSize-1)
    set(gca,'XTick',[1:10,20:10:100],'XTickLabel',{1,2,3,'',5,'','','','',10,20,30,'',50,'','','','',100})
    set(gca,'TickLength',TickLength)

    %% Plot residues for optimal gamma and various mrs

    % Interpolation
    mrs_int = 0:0.1:45;
    inttype = 'cubic';
    dperr_int = interp1(mrssort,eperr_gopt,mrs_int,inttype);
    dqerr_int = interp1(mrssort,eqerr_gopt,mrs_int,inttype);
    dtot_int = interp1(mrssort,etotal_gopt,mrs_int,inttype);

    % Plot
    subplot(1,2,2)
    plot(mrs_int,dperr_int,'k:')
    hold on
    plot(mrs_int,dqerr_int,'k--')
    plot(mrs_int,dtot_int,'k-')
    plot(mrssort(idopt_mrs),0.95,'vk','MarkerFaceColor','k','MarkerSize',kv.MarkerSize+1)

    ylabel('e(\epsilon) / e(\epsilon_{opt})','FontSize',kv.FontSize)
    xlabel('\epsilon (deg)','FontSize',kv.FontSize)

    set(gca,'XLim',[mrssort(1) 32],'YLim',[0.91 1.7],'XMinorTick','on',...
      'FontSize',kv.FontSize-1)
    set(gca,'TickLength',TickLength)
    
  end
end

%% ------ FIG 12 & TAB 1 -------------------------------------------------
if flags.do_fig12 || flags.do_tab1
  
  [s,qe,pe,qe_exp,pe_exp,latseg] = amt_cache('get','baseline',flags.cachemode);
  if isempty(s)
    
    latseg = -60:20:60; % centers of lateral segments
    dlat =  10;  % lateral range (+-) of each segment

    s = data_baumgartner2014('baseline',flags.cachemode);

    qe_exp = zeros(length(s),length(latseg));
    pe_exp = zeros(length(s),length(latseg));
    for ll = 1:length(s)

      s(ll).target = [];
      s(ll).response = [];
      s(ll).Nt = [];
      for ii = 1:length(latseg)
        
        latresp = s(ll).itemlist(:,7);
        idlat = latresp <= latseg(ii)+dlat & latresp > latseg(ii)-dlat;
        s(ll).mm2 = s(ll).itemlist(idlat,:);

        s(ll).mm2(:,7) = 0; % set lateral angle to 0deg such that localizationerror works outside +-30deg

        pe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'rmsPmedianlocal'));
        qe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'querrMiddlebrooks'));

        s(ll).target{ii} = real(s(ll).mm2(:,6)); % polar angle of target
        s(ll).response{ii} = real(s(ll).mm2(:,8)); % polar angle of response
        s(ll).Nt{ii} = length(s(ll).target{ii});

      end
    end


    %% LocaMo
    qe = zeros(length(s),length(latseg));
    pe = zeros(length(s),length(latseg));
    for ll = 1:length(s)

      for ii = 1:length(latseg)

        s(ll).sphrtfs{ii} = 0;     % init
        s(ll).p{ii} = 0;        % init

        [s(ll).sphrtfs{ii},polang{ii}] = extractsp( latseg(ii),s(ll).Obj );
        [s(ll).p{ii},respangs{ii}] = baumgartner2014(...
            s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
            'S',s(ll).S,'lat',latseg(ii),'polsamp',polang{ii}); 

        if s(ll).Nt{ii} > 0
          [ qe(ll,ii),pe(ll,ii) ] = baumgartner2014_pmv2ppp( ...
              s(ll).p{ii} , polang{ii} , respangs{ii} , s(ll).target{ii});
        else
          qe(ll,ii) = NaN; 
          pe(ll,ii) = NaN;
        end

      end
      
      [s(ll).la,s(ll).le,s(ll).ci,s(ll).lr] = baumgartner2014_likelistat(s(ll).p,polang,respangs,s(ll).target,s(ll).response);
      
      
    end
%     sum( ci(:,1)-la' <=0 & ci(:,2)-la' >=0 )

    s = rmfield(s,{'Obj','itemlist','mm2','sphrtfs'}); % reduce file size 
    amt_cache('set','baseline',s,qe,pe,qe_exp,pe_exp,latseg)
  end
  
  varargout{1} = struct('s',s, 'qe',qe, 'pe',pe, 'qe_exp',qe_exp, 'pe_exp',pe_exp,...
      'latseg',latseg);
  
  flags.do_pm30deglat = true; % consider lateral range of +-30 deg

  
  Ns = length(s);
  relfreq = zeros(Ns,length(latseg));
  Ntall = nan(1,Ns);
  for jj = 1:Ns
    Ntlat = [s(jj).Nt{:}];
    Ntall(jj) = sum(Ntlat);
    relfreq(jj,:) = Ntlat/Ntall(jj);
  end

  if flags.do_pm30deglat
    idlat = find(latseg <= 30 & latseg >= -30);
  else % consider only median plane (+-10 deg)
    idlat = latseg == 0;
  end
  relfreqPerSubject = relfreq(:,idlat)./repmat(sum(relfreq(:,idlat),2),1,3);
  pe = sum(relfreqPerSubject .* pe(:,idlat) , 2);
  qe = sum(relfreqPerSubject .* qe(:,idlat) , 2);

  
  if flags.do_fig12

    % IDs for xlabel
    NHs = nan(Ns,4);
    for ll = 1:Ns
      NHs(ll,:) = s(ll).id;
    end

    if flags.do_plot

      [tmp,idsort] = sort([s.la]);
      la = [s.la];
      le = [s.le];
      ci = [s.ci]';
      lr = [s.lr]';
      
      fig=figure;

      plot_baumgartner2014_likelistat(la(idsort),le(idsort),ci(idsort,:),lr(idsort,:))
      ylabel({'Likelihood'},'FontSize',kv.FontSize)
      xlabel('Listener (NH)','FontSize',kv.FontSize)
      set(gca,'XLim',[0 Ns+1],'XTickLabel',NHs(idsort,3:4),...
          'YMinorTick','on','FontSize',kv.FontSize)
      
      % Bottom Line
      hold on
      ylim = [min(lr(:,1)) max(lr(:,4))];
      plot([0,Ns+1],[ylim(1),ylim(1)]+0.0015*diff(ylim),'k')
     
      set(fig,'PaperPosition',[1,1,9,3.5])

    end
    
  end
  
  if flags.do_tab1

    Labels = {'ID','N','S','actual QE','predicted QE','actual PE','predicted PE'};    

    mtx = zeros(length(Labels),length(s));
    for ll = 1:length(s)
      mtx(1,ll) = str2double(s(ll).id(3:end));
      mtx(2,ll) = sum([s(ll).Ntargets{:}]);
      mtx(3,ll) = s(ll).S;
      mtx(4,ll) = s(ll).qe_exp;
      mtx(5,ll) = qe(ll);
      mtx(6,ll) = s(ll).pe_exp;
      mtx(7,ll) = pe(ll);
    end
    
    [tmp,idsort] = sort(mtx(1,:)); % sort acc. to ID
    mtx = mtx(:,idsort);
    
    varargout{1} = mtx;
    varargout{2} = Labels;
    
  end

end

%% ------ FIG 14 ----------------------------------------------------------
if flags.do_fig14
  
  [s,qe,pe,qe_exp,pe_exp,latseg] = amt_cache('get','baseline',flags.cachemode);
  if isempty(s)
    exp_baumgartner2014('fig5',flags.cachemode);
    [s,qe,pe,qe_exp,pe_exp,latseg] = amt_cache('get','baseline',flags.cachemode);
  end
  
  [paradata.perr,~,paradata.qerr,~,paradata.g,paradata.mrs] = ...
    amt_cache('get','parametrization',flags.cachemode);

  idmrs0 = paradata.g == 6 & paradata.mrs == 0;
  mrs0.pe = paradata.perr(:,:,idmrs0);
  mrs0.qe = paradata.qerr(:,:,idmrs0);

  %% # of targets
  Ns = length(s);
  Nlat = length(latseg);
  Ntlat = zeros(Ns,Nlat);
  relfreq = zeros(Ns,Nlat);
  Ntall = zeros(Ns,1);
  for jj = 1:Ns
    Ntlat(jj,:) = [s(jj).Nt{:}];
    Ntall(jj) = sum(Ntlat(jj,:));
    relfreq(jj,:) = Ntlat(jj,:)/Ntall(jj);
  end
  relfreq = relfreq.*repmat(Ntall,1,Nlat)/sum(Ntall);

  %% Pooling to lateralization
  idlat0 = round(Nlat/2);
  idleft = idlat0-1:-1:1;
  idright = idlat0+1:Nlat;
  latseg = latseg(idlat0:end);
  relfreqLR = Ntlat(:,idleft) ./ (Ntlat(:,idleft) + Ntlat(:,idright) + eps);

  pe = [pe(:,idlat0) , relfreqLR.*pe(:,idleft) + (1-relfreqLR).*pe(:,idright)];
  pe_exp = [pe_exp(:,idlat0) , relfreqLR.*pe_exp(:,idleft) + (1-relfreqLR).*pe_exp(:,idright)];
  qe = [qe(:,idlat0) , relfreqLR.*qe(:,idleft) + (1-relfreqLR).*qe(:,idright)];
  qe_exp = [qe_exp(:,idlat0) , relfreqLR.*qe_exp(:,idleft) + (1-relfreqLR).*qe_exp(:,idright)];
  relfreq = [relfreq(:,idlat0) , relfreq(:,1:idlat0-1) + relfreq(:,Nlat:-1:idlat0+1)];

  mrs0.pe = [mrs0.pe(:,idlat0) , relfreqLR.*mrs0.pe(:,idleft) + (1-relfreqLR).*mrs0.pe(:,idright)];
  mrs0.qe = [mrs0.qe(:,idlat0) , relfreqLR.*mrs0.qe(:,idleft) + (1-relfreqLR).*mrs0.qe(:,idright)];

  %% Evaluation Metrics
  idnum = not(isnan(pe_exp) | isnan(pe));
  dpe = sqrt( relfreq(idnum)' * (pe_exp(idnum) - pe(idnum)).^2 );
  dqe = sqrt( relfreq(idnum)' * (qe_exp(idnum) - qe(idnum)).^2 );
  r_pe = corrcoef(pe_exp(idnum),pe(idnum));
  r_qe = corrcoef(qe_exp(idnum),qe(idnum));

  mrs0.dpe = sqrt( relfreq(idnum)' * (pe_exp(idnum) - mrs0.pe(idnum)).^2 );
  mrs0.dqe = sqrt( relfreq(idnum)' * (qe_exp(idnum) - mrs0.qe(idnum)).^2 );
  [mrs0.r_pe,mrs0.p_pe] = corrcoef(pe_exp(idnum),mrs0.pe(idnum));
  [mrs0.r_qe,mrs0.p_qe] = corrcoef(qe_exp(idnum),mrs0.qe(idnum));


  %% Quartiles
  quart_pe = zeros(3,length(latseg),2); % 1st dim: 25/50/75 quantiles; 2nd dim: lat; 3rd dim: model/experiment/mrs0
  quart_qe = zeros(3,length(latseg),2);
  qlow = 0.25;
  qhigh = 0.75;
  for ii = 1:length(latseg)

    id = not(isnan(pe(:,ii)));
    quart_pe(:,ii,1) = quantile(pe(id,ii),[qlow .50 qhigh]);
    quart_pe(:,ii,3) = quantile(mrs0.pe(id,ii),[qlow .50 qhigh]);
    id = not(isnan(pe_exp(:,ii)));
    quart_pe(:,ii,2) = quantile(pe_exp(id,ii),[qlow .50 qhigh]);

    id = not(isnan(qe(:,ii)));
    quart_qe(:,ii,1) = quantile(qe(id,ii),[qlow .50 qhigh]);
    quart_qe(:,ii,3) = quantile(mrs0.qe(id,ii),[qlow .50 qhigh]);
    id = not(isnan(qe_exp(:,ii)));
    quart_qe(:,ii,2) = quantile(qe_exp(id,ii),[qlow .50 qhigh]);

  end


  if flags.do_plot
     
    dx = 3;
    
    %% PE

    fig = figure;
    subplot(1,2,1)
    errorbar(latseg-dx,quart_pe(2,:,1),...
      quart_pe(2,:,1)-quart_pe(1,:,1),...
      quart_pe(3,:,1)-quart_pe(2,:,1),...
      'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
    hold on
    errorbar(latseg+dx,quart_pe(2,:,3),...
      quart_pe(2,:,3)-quart_pe(1,:,3),...
      quart_pe(3,:,3)-quart_pe(2,:,3),...
      'vk--','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
    errorbar(latseg,quart_pe(2,:,2),...
      quart_pe(2,:,2)-quart_pe(1,:,2),...
      quart_pe(3,:,2)-quart_pe(2,:,2),...
      'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w');
    
    titstr = {['w/ SMM:  e_{PE} = ' num2str(dpe,'%0.1f') '\circ , r_{PE} = ' num2str(r_pe(2),'%0.2f')];...
      ['w/o SMM: e_{PE} = ' num2str(mrs0.dpe,'%0.1f') '\circ , r_{PE} = ' num2str(mrs0.r_pe(2),'%0.2f')]};
    amt_disp(titstr,'progress');
    title(titstr,'FontSize',kv.FontSize)
    set(gca,'XLim',[min(latseg)-2*dx,max(latseg)+2*dx],'YLim',[21.1,45.9],...
      'YMinorTick','on','FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))
    ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
    xlabel('Magnitude of Lateral Angle (deg)','FontSize',kv.FontSize)


    %% QE

    subplot(1,2,2)
    errorbar(latseg-dx,quart_qe(2,:,1),...
      quart_qe(2,:,1)-quart_qe(1,:,1),...
      quart_qe(3,:,1)-quart_qe(2,:,1),...
      'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
    hold on
    errorbar(latseg+dx,quart_qe(2,:,3),...
      quart_qe(2,:,3)-quart_qe(1,:,3),...
      quart_qe(3,:,3)-quart_qe(2,:,3),...
      'vk--','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
    errorbar(latseg,quart_qe(2,:,2),...
      quart_qe(2,:,2)-quart_qe(1,:,2),...
      quart_qe(3,:,2)-quart_qe(2,:,2),...
      'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w');
    titstr = {['w/ SMM:  e_{QE} = ' num2str(dqe,'%0.1f') '% , r_{QE} = ' num2str(r_qe(2),'%0.2f')];...
      ['w/o SMM: e_{QE} = ' num2str(mrs0.dqe,'%0.1f') '% , r_{QE} = ' num2str(mrs0.r_qe(2),'%0.2f')]};
    amt_disp(titstr,'progress');
    title(titstr,'FontSize',kv.FontSize)
    set(gca,'XLim',[min(latseg)-2*dx,max(latseg)+2*dx],'YLim',[2.1,26.9],...
      'XTick',latseg,'YMinorTick','on','FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))
    ylabel('Quadrant Error (%)','FontSize',kv.FontSize)
    xlabel('Magnitude of Lateral Angle (deg)','FontSize',kv.FontSize)

    l = legend('P with SMM','P w/o SMM','Actual');
    set(l,'FontSize',kv.FontSize-1,'Location','northwest')

    set(fig,'PaperPosition',[1,1,10,3.5])
    
  end
end

%% ------ FIG 5 -----------------------------------------------------------
if flags.do_fig5
  
  latdivision = 0;  % lateral angle
  dlat = 15;

  % Experimental Settings
  Conditions = {'BB','LP','W'};


  %% Computations
  s = data_baumgartner2014('pool',flags.cachemode);  
  s = s(ismember({s.id},'NH12'));
  amt_disp(['Listener: ' s.id])
  chance = [];
  for C = 1:length(Conditions)

    Cond = Conditions{C};

    %% Data

    % Experimental data
    data = data_majdak2013(Cond);
    for ll = 1:length(s)
      if sum(ismember({data.id},s(ll).id)) % participant ?
        s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx;
        for ii = 1:length(latdivision)
          latresp = s(ll).itemlist(:,7);
          idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
          mm2 = s(ll).itemlist(idlat,:);
          chance = [chance;mm2];
          s(ll).target{ii} = mm2(:,6); % polar angle of target
          s(ll).response{ii} = mm2(:,8); % polar angle of response
        end
      end
    end


    for ll = 1:length(s)
        for ii = 1:length(latdivision)
            s(ll).spdtfs{ii} = 0;     % init
            s(ll).polang{ii} = 0;   % init
            [s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
              latdivision(ii),s(ll).Obj);

            if C == 1       % Learn 
                s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
            elseif C == 2   % Dummy
                temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
                s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
            elseif C == 3   % Warped
                s(ll).spdtfs_c{ii} = warp_hrtf(s(ll).spdtfs{ii},s(ll).fs);
            end

        end
    end


    %% Run Model

    for ll = 1:length(s)
      qe = zeros(1,length(latdivision));
      pe = zeros(1,length(latdivision));
      qe_t = zeros(1,length(latdivision));
      pe_t = zeros(1,length(latdivision));
      for ii = 1:length(latdivision)

        [s(ll).p{ii},rang] = baumgartner2014(...
              s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
              'S',s(ll).S,'lat',latdivision(ii),...
              'polsamp',s(ll).polang{ii});
        respangs{ii} = rang;

        [ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(s(ll).p{ii} , s(ll).polang{ii} , rang);

        if sum(ismember({data.id},s(ll).id)) % if participant then actual targets
          [ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
              s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );

        end

      end

      % Model results of pool
      s(ll).qe_pool(C,1) = mean(qe); 
      s(ll).pe_pool(C,1) = mean(pe);

      if sum(ismember({data.id},s(ll).id)) % participant ?
        % Actual experimental results
        s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
        s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
        s(ll).Nt(C,1) = size(s(ll).itemlist,1);
        % Model results of participants (actual target angles)
        if length(latdivision) == 3
          s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
              qe_t(2)*length(s(ll).target{2}) + ...
              qe_t(3)*length(s(ll).target{3}))/...
              (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
          s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
              pe_t(2)*length(s(ll).target{2}) + ...
              pe_t(3)*length(s(ll).target{3}))/...
              (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
        else 
          s(ll).qe_part(C,1) = mean(qe_t);
          s(ll).pe_part(C,1) = mean(pe_t);
        end

        if flags.do_plot

          if C == 1; figure; end
          subplot(1,3,C)
          ii = find(latdivision==0);
          responses = [];
          targets = [];
          for jj = ii
            responses = [responses;s(ll).response{jj}];
            targets = [targets;s(ll).target{jj}];
          end
          plot_baumgartner2014(s(ll).p{ii},s(ll).polang{ii},rang,...
                targets,responses,'MarkerSize',kv.MarkerSize,'cmax',0.05,'nocolorbar')
          text(90,240,Cond,...
            'FontSize',kv.FontSize,'Color','w','HorizontalAlignment','center')
          Nt = length(targets);
          tmp.m = [zeros(Nt,5) targets(:) zeros(Nt,1) responses(:)];
          tmp.qe = localizationerror(tmp.m,'querrMiddlebrooks');
          tmp.pe = localizationerror(tmp.m,'rmsPmedianlocal');
          title({['A: PE = ' num2str(tmp.pe,2) '\circ, QE = ' num2str(tmp.qe,2) '%'];...
            ['P: PE = ' num2str(pe_t(ii),2) '\circ, QE = ' num2str(qe_t(ii),2) '%']},'FontSize',kv.FontSize-1)
          xlabel('Target Angle (deg)','FontSize',kv.FontSize)
          ylabel('Response Angle (deg)','FontSize',kv.FontSize)
          set(gca,'FontSize',kv.FontSize-1)
          set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
          set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})

        end
        
%         [la(C,ll),le(C,ll),ci(C,ll,:)] = baumgartner2014_likelistat(s(ll).p,s(ll).polang,respangs,s(ll).target,s(ll).response);
        
      end

    end

  end
  
  varargout{1} = s;

end


%% ------ FIG 6 -----------------------------------------------------------
if flags.do_fig6
  
  [s,cc] = amt_cache('get','spatstrat',flags.cachemode);
  if isempty(s)
    
    latdivision = [-20,0,20];            % lateral angle
    dlat = 10;

    % Experimental Settings
    Conditions = {'BB','LP','W'};

    %% Computations
    s = data_baumgartner2014('pool',flags.cachemode);
%     chance = [];
    for C = 1:length(Conditions)

      Cond = Conditions{C};

      %% Data

      % Experimental data
      data = data_majdak2013(Cond);
      for ll = 1:length(s)
        if sum(ismember({data.id},s(ll).id)) % if actual participant
          s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx; 
          for ii = 1:length(latdivision)
            latresp = s(ll).itemlist(:,7);
            idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
            mm2 = s(ll).itemlist(idlat,:);
%             chance = [chance;mm2];
            s(ll).target{ii} = mm2(:,6); % polar angle of target
            s(ll).response{ii} = mm2(:,8); % polar angle of response
          end
        end
      end


      for ll = 1:length(s)
          for ii = 1:length(latdivision)
              s(ll).spdtfs{ii} = 0;     % init
              s(ll).polang{ii} = 0;   % init
              [s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
                latdivision(ii),s(ll).Obj);

              if C == 1       % Learn 
                  s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
              elseif C == 2   % Dummy
                temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
                s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
              elseif C == 3   % Warped
                  s(ll).spdtfs_c{ii} = warp_hrtf(s(ll).spdtfs{ii},s(ll).fs);
              end

          end
      end


      %% Run Model

      for ll = 1:length(s)
        qe = zeros(1,length(latdivision));
        pe = zeros(1,length(latdivision));
        qe_t = zeros(1,length(latdivision));
        pe_t = zeros(1,length(latdivision));
        for ii = 1:length(latdivision)

          [s(ll).p{ii},rang] = baumgartner2014(...
                s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
                'S',s(ll).S,'lat',latdivision(ii),...
                'polsamp',s(ll).polang{ii});
          respangs{ii} = rang;

          [ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(s(ll).p{ii} , s(ll).polang{ii} , rang);

          if sum(ismember({data.id},s(ll).id)) % if actual participant actual targets
            [ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
                s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );

          end

        end

        % Model results of pool
        wlat = cos(deg2rad(latdivision)); % weighting compensating lateral compression
        wlat = wlat/sum(wlat);
        s(ll).qe_pool(C,1) = wlat * qe(:); 
        s(ll).pe_pool(C,1) = wlat * pe(:);

        if sum(ismember({data.id},s(ll).id)) % if actual participant 
          % Actual experimental results
          s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
          s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
          s(ll).Nt(C,1) = size(s(ll).itemlist,1);
          % Model results of participants (actual target angles)
          if length(latdivision) == 3
            s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
                qe_t(2)*length(s(ll).target{2}) + ...
                qe_t(3)*length(s(ll).target{3}))/...
                (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
            s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
                pe_t(2)*length(s(ll).target{2}) + ...
                pe_t(3)*length(s(ll).target{3}))/...
                (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
          else 
            s(ll).qe_part(C,1) = mean(qe_t);
            s(ll).pe_part(C,1) = mean(pe_t);
          end

          [la(C,ll),le(C,ll),ci(C,ll,:)] = baumgartner2014_likelistat(s(ll).p,s(ll).polang,respangs,s(ll).target,s(ll).response);
        end

      end
    end
    s = rmfield(s,{'Obj','spdtfs_c','spdtfs'});% reduce file size

    %% Compute Chance Performance
%     chance = repmat(chance,10,1);
%     id_chance = randi(size(chance,1),size(chance,1),1);
%     chance(:,8) = chance(id_chance,6);
%     pe_chance = localizationerror(chance,'rmsPmedianlocal');
%     qe_chance = localizationerror(chance,'querrMiddlebrooks');

    [r,p] =  corrcoef([s.qe_exp],[s.qe_part]);
    cc.qe.r = r(2);
    cc.qe.p = p(2);
    amt_disp(['QE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')]);

    [r,p] =  corrcoef([s.pe_exp],[s.pe_part]);
    cc.pe.r = r(2);
    cc.pe.p = p(2);
    amt_disp(['PE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')]);

    amt_cache('set','spatstrat',s,cc)
  end
  varargout{1} = s;
  varargout{2} = cc;
  
  %% Measures

  % Quartiles
  quart_pe_part = quantile([s.pe_part]',[.25 .50 .75]);
  quart_qe_part = quantile([s.qe_part]',[.25 .50 .75]);

  quart_pe_pool = quantile([s.pe_pool]',[.25 .50 .75]);
  quart_qe_pool = quantile([s.qe_pool]',[.25 .50 .75]);

  quart_pe_exp = quantile([s.pe_exp]',[.25 .50 .75]);
  quart_qe_exp = quantile([s.qe_exp]',[.25 .50 .75]);

  % RMS Differences
  % individual:
  Ntargets = [s.Nt]'; % # of targets
  relfreq = Ntargets/sum(Ntargets(:));
  sd_pe = ([s.pe_part]'-[s.pe_exp]').^2; % squared differences
  dpe = sqrt(relfreq(:)' * sd_pe(:));    % weighted RMS diff.
  sd_qe = ([s.qe_part]'-[s.qe_exp]').^2;
  dqe = sqrt(relfreq(:)' * sd_qe(:));

  % Chance performance
%   qe0 = qe_chance;
%   pe0 = pe_chance;
  [qe0,pe0] = baumgartner2014_pmv2ppp('chance');

  if flags.do_plot
    
    dx = 0.15;
    
    figure 

    subplot(121)
    errorbar((1:3)+dx,quart_pe_part(2,:),...
        quart_pe_part(2,:) - quart_pe_part(1,:),...
        quart_pe_part(3,:) - quart_pe_part(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    hold on
    errorbar((1:3)-dx,quart_pe_pool(2,:),...
        quart_pe_pool(2,:) - quart_pe_pool(1,:),...
        quart_pe_pool(3,:) - quart_pe_pool(2,:),...
        'ks-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    errorbar((1:3),quart_pe_exp(2,:),...
        quart_pe_exp(2,:) - quart_pe_exp(1,:),...
        quart_pe_exp(3,:) - quart_pe_exp(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','w');

    plot([0,4],[pe0,pe0],'k:')

    title(['e_{PE} = ' num2str(dpe,'%0.1f') '\circ , r_{PE} = ' num2str(cc.pe.r,'%0.2f')],...
      'FontSize',kv.FontSize)
    ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
    set(gca,...
        'XLim',[0.5 3.5],...
        'XTick',1:3,...
        'YLim',[27 54.9],...
        'XTickLabel',{'BB';'LP';'W'},...
        'YMinorTick','on','FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))

    subplot(122)
    errorbar((1:3)+dx,quart_qe_part(2,:),...
        quart_qe_part(2,:) - quart_qe_part(1,:),...
        quart_qe_part(3,:) - quart_qe_part(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    hold on
    errorbar((1:3)-dx,quart_qe_pool(2,:),...
        quart_qe_pool(2,:) - quart_qe_pool(1,:),...
        quart_qe_pool(3,:) - quart_qe_pool(2,:),...
        'ks-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    errorbar((1:3),quart_qe_exp(2,:),...
        quart_qe_exp(2,:) - quart_qe_exp(1,:),...
        quart_qe_exp(3,:) - quart_qe_exp(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','w');

    l = legend('Part.','Pool','Actual');
    set(l,'Location','northwest','FontSize',kv.FontSize-1)
    
    plot([0,4],[qe0 qe0],'k:')

    title(['e_{QE} = ' num2str(dqe,'%0.1f') '% , r_{QE} = ' num2str(cc.qe.r,'%0.2f')],...
      'FontSize',kv.FontSize)
    ylabel('Quadrant Error (%)','FontSize',kv.FontSize)
    set(gca,...
        'XLim',[0.5 3.5],...
        'XTick',1:3,...
        'YLim',[0.1 54],...
        'XTickLabel',{'BB';'LP';'W'},...
        'YAxisLocation','left',...
        'YMinorTick','on','FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))

    set(gcf,'PaperPosition',[1,1,10,3.5])
    
  end
end

%% ------ FIG 7 -----------------------------------------------------------
if flags.do_fig7
  
  % Model Settings
  latdivision = 0;            % lateral angle
  dlat = 10;

  % Experimental Settings
  Conditions = {'N24','N9','N3'};

  % Vocoder Settings 
  flow = 300;     % lowest corner frequency
  fhigh = 16000;  % highest corner frequency
  N = [24,9,3];

  %% Computations
  s = data_baumgartner2014('pool',flags.cachemode);
  s = s(ismember({s.id},'NH12')); 
  amt_disp(['Listener: ' s.id])
  chance = [];
  for C = 1:length(Conditions)

    Cond = Conditions{C};

    %% Data

    % Experimental data
    data = data_goupell2010(Cond);
    for ll = 1:length(s)
      if sum(ismember({data.id},s(ll).id)) % if actual participant
        s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx; 
        for ii = 1:length(latdivision)
          latresp = s(ll).itemlist(:,7);
          idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
          mm2 = s(ll).itemlist(idlat,:);
          chance = [chance;mm2];
          s(ll).target{ii} = mm2(:,6); % polar angle of target
          s(ll).response{ii} = mm2(:,8); % polar angle of response
        end
      end
    end

    % SP-DTFs
    for ll = 1:length(s)
        for ii = 1:length(latdivision)
            s(ll).spdtfs{ii} = 0;   % init
            s(ll).polang{ii} = 0;   % init
            [s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(latdivision(ii),s(ll).Obj);
        end
    end


    %% Genereate conditional HRIRs

    stimPar.SamplingRate = s(ll).fs;
    imp = [1;zeros(2^12-1,1)]; % smooth results for 2^12
    for ll = 1:length(s)
      for ii = 1:length(latdivision)

        if N(C)==Inf
            s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};

        else
          n = N(C);

          [syncrnfreq, GETtrain] = GETVocoder('',imp,n,flow,fhigh,0,100,stimPar);
          corners = [syncrnfreq(1);syncrnfreq(:,2)];

          ref = s(ll).spdtfs{ii};

          cond = zeros(length(imp),size(ref,2),2);

          for ch = 1:size(ref,3)
              for ang = 1:size(ref,2)
                  cond(:,ang,ch) = channelize('', 0.5*ref(:,ang,ch), ref(:,1), imp, n, corners, [], ...
                                  GETtrain, stimPar, 1, 0.01*s(ll).fs, 0.01*s(ll).fs);
              end

          end

          s(ll).spdtfs_c{ii} = cond;

        end
      end
    end


    %% Run Model

    for ll = 1:length(s)
      clear qe pe qe_t pe_t
      for ii = 1:length(latdivision)

        [p,rang] = baumgartner2014(...
              s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
              'S',s(ll).S,'lat',latdivision(ii),...
              'polsamp',s(ll).polang{ii});

        [ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(p , s(ll).polang{ii} , rang);

        if sum(ismember({data.id},s(ll).id)) % if participant then actual targets
          [ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
              p , s(ll).polang{ii} , rang , s(ll).target{ii} );
        end

      end

      % Model results of pool
      s(ll).qe_pool(C,1) = mean(qe); 
      s(ll).pe_pool(C,1) = mean(pe);

      if sum(ismember({data.id},s(ll).id)) % if actual participant
        % Actual experimental results
        s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
        s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
        s(ll).Nt(C,1) = size(s(ll).itemlist,1);
        % Model results of participants (actual target angles)
        if length(latdivision) == 3
          s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
              qe_t(2)*length(s(ll).target{2}) + ...
              qe_t(3)*length(s(ll).target{3}))/...
              (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
          s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
              pe_t(2)*length(s(ll).target{2}) + ...
              pe_t(3)*length(s(ll).target{3}))/...
              (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
        else 
          s(ll).qe_part(C,1) = mean(qe_t);
          s(ll).pe_part(C,1) = mean(pe_t);
        end

        if flags.do_plot && latdivision(ii) == 0

          if C==1; figure; end
          subplot(1,3,C)
          plot_baumgartner2014(p,s(ll).polang{ii},rang,...
                s(ll).target{ii},s(ll).response{ii},...
                    'MarkerSize',kv.MarkerSize,'cmax',0.05,'nocolorbar');
          title({['A: PE = ' num2str(s(ll).pe_exp(C,1),2) '\circ, QE = ' num2str(s(ll).qe_exp(C,1),2) '%'];...
            ['P: PE = ' num2str(s(ll).pe_part(C,1),2) '\circ, QE = ' num2str(s(ll).qe_part(C,1),2) '%']},'FontSize',kv.FontSize-1)
          text(90,240,Cond,...
            'FontSize',kv.FontSize,'Color','w','HorizontalAlignment','center')
          xlabel('Target Angle (deg)','FontSize',kv.FontSize)
          ylabel('Response Angle (deg)','FontSize',kv.FontSize)
          set(gca,'FontSize',kv.FontSize-1)
          set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
          set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})

        end

      end

    end
  end
  
  varargout{1} = s;
  
end

%% ------ FIG 8 ----------------------------------------------------------
if flags.do_fig8
  
  [s,cc,N] = amt_cache('get','numchan',flags.cachemode);
  if isempty(s)
    
    % Model Settings
    latdivision = 0; % lateral angle
    dlat = 10;

    % Experimental Settings
    Conditions = {'CL','N24','N18','N12','N9','N6','N3'};

    % Vocoder Settings 
    N = fliplr([3,6,9,12,18,24,30]);	% # of vocoder channels
    flow = 300;     % lowest corner frequency
    fhigh = 16000;  % highest corner frequency


    %% Computations
    s = data_baumgartner2014('pool',flags.cachemode);
%     chance = [];
    for C = 1:length(Conditions)

      Cond = Conditions{C};

      %% Data

      % Experimental data
      data = data_goupell2010(Cond);
      for ll = 1:length(s)
        if sum(ismember({data.id},s(ll).id)) % if actual participant
          s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx; 
          for ii = 1:length(latdivision)
            latresp = s(ll).itemlist(:,7);
            idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
            mm2 = s(ll).itemlist(idlat,:); 
%             chance = [chance;mm2];
            s(ll).target{ii} = mm2(:,6); % polar angle of target
            s(ll).response{ii} = mm2(:,8); % polar angle of response
          end
        end
      end

      % SP-DTFs
      for ll = 1:length(s)
        for ii = 1:length(latdivision)
          s(ll).spdtfs{ii} = 0;   % init
          s(ll).polang{ii} = 0;   % init
          [s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(latdivision(ii),s(ll).Obj);
        end
      end


      %% Genereate conditional HRIRs

      stimPar.SamplingRate = s(ll).fs;
      imp = [1;zeros(2^12-1,1)]; % smooth results for 2^12
      for ll = 1:length(s)
        for ii = 1:length(latdivision)

          if C==1
            s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};

          else
            n = N(C);

            [syncrnfreq, GETtrain] = GETVocoder('',imp,n,flow,fhigh,0,100,stimPar);
            corners = [syncrnfreq(1);syncrnfreq(:,2)];

            ref = s(ll).spdtfs{ii};

            cond = zeros(length(imp),size(ref,2),2);

            for ch = 1:size(ref,3)
              for ang = 1:size(ref,2)
                  cond(:,ang,ch) = channelize('', 0.5*ref(:,ang,ch), ref(:,1), imp, n, corners, [], ...
                                  GETtrain, stimPar, 1, 0.01*s(ll).fs, 0.01*s(ll).fs);
              end
            end
            s(ll).spdtfs_c{ii} = cond;

          end
        end
      end


      %% Run Model

      for ll = 1:length(s)
        clear qe pe qe_t pe_t
        for ii = 1:length(latdivision)

          [p,rang] = baumgartner2014(...
                s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
                'S',s(ll).S,'lat',latdivision(ii),...
                'polsamp',s(ll).polang{ii});
              
          s(ll).p{ii} = p;    
          respangs{ii} = rang;

          [ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(p , s(ll).polang{ii} , rang);

          if sum(ismember({data.id},s(ll).id)) % if actual participant actual targets
            [ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
                p , s(ll).polang{ii} , rang , s(ll).target{ii} );
          end

        end

        % Model results of pool
        s(ll).qe_pool(C,1) = mean(qe); 
        s(ll).pe_pool(C,1) = mean(pe);

        if sum(ismember({data.id},s(ll).id)) % if actual participant 
          % Actual experimental results
          s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
          s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
          s(ll).Nt(C,1) = size(s(ll).itemlist,1);
          % Model results of participants (actual target angles)
          if length(latdivision) == 3
            s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
                qe_t(2)*length(s(ll).target{2}) + ...
                qe_t(3)*length(s(ll).target{3}))/...
                (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
            s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
                pe_t(2)*length(s(ll).target{2}) + ...
                pe_t(3)*length(s(ll).target{3}))/...
                (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
          else 
            s(ll).qe_part(C,1) = mean(qe_t);
            s(ll).pe_part(C,1) = mean(pe_t);
          end

          [la(C,ll),le(C,ll),ci(C,ll,:)] = baumgartner2014_likelistat(s(ll).p,s(ll).polang,respangs,s(ll).target,s(ll).response);
          
        end

      end
      amt_disp(['Condition ' Cond ' completed.'],'progress')
    end
    
%     Crange = max(chance(:,8))-min(chance(:,8));
%     chance(:,8) = Crange*rand(size(chance,1),1)-min(chance(:,8));
%     pe_chance = localizationerror(chance,'rmsPmedianlocal');
%     qe_chance = localizationerror(chance,'querrMiddlebrooks');

    [r,p] =  corrcoef([s.qe_exp],[s.qe_part]);
    cc.qe.r = r(2);
    cc.qe.p = p(2);
    
    amt_disp(['QE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')]);

    [r,p] =  corrcoef([s.pe_exp],[s.pe_part]);
    cc.pe.r = r(2);
    cc.pe.p = p(2);
    amt_disp(['PE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')]);
    
    s = rmfield(s,{'spdtfs','spdtfs_c','Obj','itemlist'});
    
    amt_cache('set','numchan',s,cc,N)
  end
  varargout{1} = s;
  varargout{2} = cc;
  varargout{3} = N;
  
  %% Measures

  % Quartiles
  quart_pe_part = fliplr(quantile([s.pe_part]',[.25 .50 .75]));
  quart_qe_part = fliplr(quantile([s.qe_part]',[.25 .50 .75]));

  quart_pe_pool = fliplr(quantile([s.pe_pool]',[.25 .50 .75]));
  quart_qe_pool = fliplr(quantile([s.qe_pool]',[.25 .50 .75]));

  quart_pe_exp = fliplr(quantile([s.pe_exp]',[.25 .50 .75]));
  quart_qe_exp = fliplr(quantile([s.qe_exp]',[.25 .50 .75]));

  % RMS Differences
  % individual:
  Ntargets = [s.Nt]'; % # of targets
  relfreq = Ntargets/sum(Ntargets(:));
  sd_pe = ([s.pe_part]'-[s.pe_exp]').^2; % squared differences
  dpe = sqrt(relfreq(:)' * sd_pe(:));    % weighted RMS diff.
  sd_qe = ([s.qe_part]'-[s.qe_exp]').^2;
  dqe = sqrt(relfreq(:)' * sd_qe(:));

  % Chance performance
%   qe0 = qe_chance;
%   pe0 = pe_chance;
  [qe0,pe0] = baumgartner2014_pmv2ppp('chance');

  
  if flags.do_plot
    
    dx = 0.7;
    figure

    %% PE
    subplot(121)
    errorbar(fliplr(N)+dx,quart_pe_part(2,:),...
        quart_pe_part(2,:) - quart_pe_part(1,:),...
        quart_pe_part(3,:) - quart_pe_part(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    hold on
    errorbar(fliplr(N)-dx,quart_pe_pool(2,:),...
        quart_pe_pool(2,:) - quart_pe_pool(1,:),...
        quart_pe_pool(3,:) - quart_pe_pool(2,:),...
        'ks-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    errorbar(fliplr(N),quart_pe_exp(2,:),...
        quart_pe_exp(2,:) - quart_pe_exp(1,:),...
        quart_pe_exp(3,:) - quart_pe_exp(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','w');
    plot([0,2*max(N)],[pe0,pe0],'k:')
    xlabel('Num. of Channels','FontSize',kv.FontSize)
    ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)

    title(['e_{PE} = ' num2str(dpe,'%0.1f') '\circ , r_{PE} = ' num2str(cc.pe.r,'%0.2f')],...
      'FontSize',kv.FontSize)
    set(gca,'XLim',[1 32],'XTick',[3 6 9 12 18 24 30],...
        'XTickLabel',{3;6;9;12;18;24;'CL'},...
        'YLim',[27 54.9],...
        'YMinorTick','on','FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))

    %% QE
    subplot(122)
    errorbar(fliplr(N)+dx,quart_qe_part(2,:),...
        quart_qe_part(2,:) - quart_qe_part(1,:),...
        quart_qe_part(3,:) - quart_qe_part(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    hold on
    errorbar(fliplr(N)-dx,quart_qe_pool(2,:),...
        quart_qe_pool(2,:) - quart_qe_pool(1,:),...
        quart_qe_pool(3,:) - quart_qe_pool(2,:),...
        'ks-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','k');
    errorbar(fliplr(N),quart_qe_exp(2,:),...
        quart_qe_exp(2,:) - quart_qe_exp(1,:),...
        quart_qe_exp(3,:) - quart_qe_exp(2,:),...
        'ko-','MarkerSize',kv.MarkerSize,...
        'MarkerFaceColor','w');

    l = legend('Part.','Pool','Actual');
    set(l,'Location','northeast','FontSize',kv.FontSize-1)
      
    plot([0,2*max(N)],[qe0,qe0],'k:')
      
    title(['e_{QE} = ' num2str(dqe,'%0.1f') '% , r_{QE} = ' num2str(cc.qe.r,'%0.2f')],...
      'FontSize',kv.FontSize)
    xlabel('Num. of Channels','FontSize',kv.FontSize)
    ylabel('Quadrant Error (%)','FontSize',kv.FontSize)
    set(gca,'XLim',[1 32],'XTick',[3 6 9 12 18 24 30],...
        'XTickLabel',{3;6;9;12;18;24;'CL'},...
        'YLim',[0.1 54],...
        'YMinorTick','on',...
        'YAxisLocation','left','FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))
      

    set(gcf,'PaperPosition',[1,1,10,3.5])

  end
end

%% ------ FIG 9 ----------------------------------------------------------
if flags.do_fig9
  
  [qe_pool,pe_pool,pb_pool] = amt_cache('get','nonindividual',flags.cachemode);
  if isempty(qe_pool)
    
    % Settings
    latdivision = [-20,0,20];  % lateral center angles of SPs
    flow = 4e3;

    s = data_baumgartner2014('pool',flags.cachemode);
    ns = length(s);

    % DTFs of the SPs
    for ll = 1:ns
      for ii = 1:length(latdivision)

        s(ll).latang{ii} = latdivision(ii);
        s(ll).polangs{ii} = [];
        s(ll).spdtfs{ii} = [];
        [s(ll).spdtfs{ii},s(ll).polangs{ii}] = extractsp(...
            s(ll).latang{ii},s(ll).Obj);

      end
    end

    amt_disp('Please wait a moment!','progress');
    qe = zeros(ns,ns,length(latdivision)); % init QEs
    pe = qe;           % init PEs
    pb = qe;           % init Polar Biases
    for ll = 1:ns    % listener
        for jj = 1:ns    % ears
            for ii = 1:length(latdivision) % SPs

              s(ll).p{jj,ii} = [];
              s(ll).respangs{ii} = [];
              [s(ll).p{jj,ii},s(ll).respangs{ii}] = baumgartner2014(...
                  s(jj).spdtfs{ii},s(ll).spdtfs{ii},s(ll).fs,...
                  'S',s(ll).S,'lat',s(ll).latang{ii},...
                  'polsamp',s(ll).polangs{ii},'flow',flow);

              [ qe(ll,jj,ii),pe(ll,jj,ii),pb(ll,jj,ii) ] = baumgartner2014_pmv2ppp( ...
                  s(ll).p{jj,ii} , s(jj).polangs{ii} , s(ll).respangs{ii});

            end
        end
        amt_disp([' Subject ' num2str(ll,'%2u') ' of ' num2str(ns,'%2u')],'progress');
    end

    lat_weight = cos(pi*latdivision/180);     %lateral weight compensating compression of polar dimension
    lat_weight = lat_weight/sum(lat_weight);  % normalize
    lat_weight = repmat(reshape(lat_weight,[1,1,length(latdivision)]),[ns,ns,1]);
    qe_pool = sum(qe.*lat_weight,3);
    pe_pool = sum(pe.*lat_weight,3);
    pb_pool = sum(pb.*lat_weight,3);

    amt_cache('set','nonindividual',qe_pool,pe_pool,pb_pool);
  end
  varargout{1} = {qe_pool,pe_pool,pb_pool};
  
  data = data_middlebrooks1999;
  
  %% Model outcomes
  ns = size(pe_pool,1);
  own = eye(ns) == 1;
  other = not(own);
  pb_pool = abs(pb_pool);
  qe_own.quantiles = quantile(qe_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
  pe_own.quantiles = quantile(pe_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
  pb_own.quantiles = quantile(pb_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
  qe_own.mean = mean(qe_pool(own));
  pe_own.mean = mean(pe_pool(own));
  pb_own.mean = mean(pb_pool(own));
  
  qe_other.quantiles = quantile(qe_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
  pe_other.quantiles = quantile(pe_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
  pb_other.quantiles = quantile(pb_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
  qe_other.mean = mean(qe_pool(other));
  pe_other.mean = mean(pe_pool(other));
  pb_other.mean = mean(pb_pool(other));
  
  if flags.do_plot
    dx = -0.2;
    Marker = 'ks';
    data.Marker = 'ko';
    MFC = 'k'; % Marker Face Color
    data.MFC = 'w';
    
    figure;
    subplot(131)
    middlebroxplot(1-dx,qe_own.quantiles,kv.MarkerSize)
    plot(1-dx,qe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
    middlebroxplot(1+dx,data.qe_own.quantiles,kv.MarkerSize)
    plot(1+dx,data.qe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
    middlebroxplot(2-dx,qe_other.quantiles,kv.MarkerSize)
    plot(2-dx,qe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
    middlebroxplot(2+dx,data.qe_other.quantiles,kv.MarkerSize)
    plot(2+dx,data.qe_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
    ylabel('Quadrant Errors (%)','FontSize',kv.FontSize)
    set(gca,'YLim',[-2 43],'XLim',[0.5 2.5],...
      'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))

    subplot(132)
    plot(1-dx,pe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
    hold on
    plot(1+dx,data.pe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)

    middlebroxplot(1-dx,pe_own.quantiles,kv.MarkerSize)
    plot(1-dx,pe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
    middlebroxplot(1+dx,data.pe_own.quantiles,kv.MarkerSize)
    plot(1+dx,data.pe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
    middlebroxplot(2-dx,pe_other.quantiles,kv.MarkerSize)
    plot(2-dx,pe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
    middlebroxplot(2+dx,data.pe_other.quantiles,kv.MarkerSize)
    plot(2+dx,data.pe_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
    ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
    set(gca,'YLim',[-2 62],'XLim',[0.5 2.5],...
      'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))

    subplot(133)
    middlebroxplot(1-dx,pb_own.quantiles,kv.MarkerSize)
    plot(1-dx,pb_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
    middlebroxplot(1+dx,data.pb_own.quantiles,kv.MarkerSize)
    plot(1+dx,data.pb_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
    middlebroxplot(2-dx,pb_other.quantiles,kv.MarkerSize)
    plot(2-dx,pb_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
    middlebroxplot(2+dx,data.pb_other.quantiles,kv.MarkerSize)
    plot(2+dx,data.pb_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)

    ylabel('Magnitude of Elevation Bias (deg)','FontSize',kv.FontSize)
    set(gca,'YLim',[-2 55],'XLim',[0.5 2.5],...
      'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
        'TickLength',2*get(gca,'TickLength'))
  end
end

%% ------ FIG 10 ----------------------------------------------------------
if flags.do_fig10
  
  [pe_exp1,pe_exp2,pe_flat,noDCN] = amt_cache('get','ripples',flags.cachemode);
  if isempty(pe_exp1)
    
    do_exp1 = true;
    do_exp2 = true;
    plotpmv = false;

    density = [0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 6, 8]; % ripples/oct
    depth =   10:10:40;        % ripple depth (peak-to-trough) in dB

    %% Stimulus: 
    % 250-ms bursts, 20-ms raised-cosine fade in/out, flat from 0.6-16kHz

    fs = 48e3;    % sampling rate
    flow = 1e3;   % lower corner frequency of ripple modification in Hz
    fhigh = 16e3; % upper corner frequency of ripple modification in Hz
    Nf = 2^10;    % # Frequency bins

    f = 0:fs/2/Nf:fs/2;	% frequency bins
    id600 = find(f<=600,1,'last'); % index of 600 Hz (lower corner frequency of stimulus energy)
    idlow = find(f<=flow,1,'last'); % index of flow (ripples)
    idhigh = find(f>=fhigh,1,'first');  % index of fhigh (ripples)
    N600low = idlow - id600 +1;   % # bins without ripple modification
    Nlowhigh = idhigh - idlow +1; % # bins with ripple modification     % 
    O = log2(f(idlow:idhigh)/1e3);   % freq. trafo. to achieve equal ripple density in log. freq. scale

    % Raised-cosine "(i.e., cos^2)" ramp 1/8 octave wide
    fup = f(idlow)*2^(1/8);       % upper corner frequency of ramp upwards 
    idup = find(f<=fup,1,'last');
    Nup = idup-idlow+1;
    rampup = cos(-pi/2:pi/2/(Nup-1):0).^2;
    fdown = f(idhigh)*2^(-1/8);  % lower corner frequency of ramp downwards
    iddown = find(f>=fdown,1,'first');
    Ndown = idhigh-iddown+1;
    rampdown = cos(0:pi/2/(Ndown-1):pi/2).^2;
    ramp = [rampup ones(1,Nlowhigh-Nup-Ndown) rampdown];
    ramp = [-inf*ones(1,id600-1) zeros(1,N600low) ramp -inf*ones(1,Nf - idhigh)];

    % Ripples of Experiment I
    Sexp1 = zeros(Nf+1,length(density),2);  % 3rd dim: 1:0-phase 2:pi-phase
    Sexp1(idlow:idhigh,:,1) = (40/2* sin(2*pi*density'*O+ 0))';  % depth: 40dB, 0-phase
    Sexp1(idlow:idhigh,:,2) = (40/2* sin(2*pi*density'*O+pi))';  % depth: 40dB, pi-phase
    Sexp1 = repmat(ramp',[1,length(density),2]) .* Sexp1;
    Sexp1 = [Sexp1;Sexp1(Nf:-1:2,:,:)];
    Sexp1(isnan(Sexp1)) = -100;
%     sexp1 = ifftreal(10.^(Sexp1/20),2*Nf);
    sexp1 = real(ifft(10.^(Sexp1/20),2*Nf));
    sexp1 = circshift(sexp1,Nf);  % IR corresponding to ripple modification
    % Ripples of Experiment II
    Sexp2 = zeros(Nf+1,length(depth),2);  % 3rd dim: 1:0-phase 2:pi-phase
    Sexp2(idlow:idhigh,:,1) = (depth(:)/2*sin(2*pi*1*O+ 0))';  % density: 1 ripple/oct, 0-phase
    Sexp2(idlow:idhigh,:,2) = (depth(:)/2*sin(2*pi*1*O+pi))';  % density: 1 ripple/oct, pi-phase
    Sexp2 = repmat(ramp',[1,length(depth),2]) .* Sexp2;
    Sexp2 = [Sexp2;Sexp2(Nf-1:-1:2,:,:)];
    Sexp2(isnan(Sexp2)) = -100;
%     sexp2 = ifftreal(10.^(Sexp2/20),2*Nf);
    sexp2 = real(ifft(10.^(Sexp2/20),2*Nf));
    sexp2 = circshift(sexp2,Nf);  % IR corresponding to ripple modification


    %% Modeling
    for psge = 0:1

      if psge == 1
        s = data_baumgartner2014('pool',flags.cachemode);
      else % recalib
        s = data_baumgartner2014('pool','do',psge,flags.cachemode);
      end

    latseg = 0;   % centers of lateral segments
    runs = 5;     % # runs of virtual experiments

    pe_exp1 = zeros(length(latseg),length(s),length(density),2);
    pe_exp2 = zeros(length(latseg),length(s),length(depth),2);
    pe_flat = zeros(length(latseg),length(s));
    for ss = 1:length(s)
      for ll = 1:length(latseg)

        [spdtfs,polang] = extractsp(latseg(ll),s(ss).Obj);

        % target elevation range of +-60 deg
        idt = find( polang<=60 | polang>=120 );
        targets = spdtfs(:,idt,:);
        tang = polang(idt);

        [pflat,rang] = baumgartner2014(targets,spdtfs,...
            'S',s(ss).S,'polsamp',polang,...
            'lat',latseg(ll),'stim',[1;0],'do',psge); % Impulse
        mflat = baumgartner2014_virtualexp(pflat,tang,rang,'runs',runs);
        [f,r] = localizationerror(mflat,'sirpMacpherson2000');
        pe_flat(ll,ss) = localizationerror(mflat,f,r,'perMacpherson2003');

        if plotpmv, 
          figure; 
          plot_baumgartner2014(pflat,tang,rang,mflat(:,6),mflat(:,8));title(num2str(pe_flat(ll,ss),2));pause(0.5); 
        end 

        if do_exp1  % Exp. I
        for ii = 1:2*length(density)

          [p,rang] = baumgartner2014(targets,spdtfs,...
            'S',s(ss).S,'polsamp',polang,...
            'lat',latseg(ll),'stim',sexp1(:,ii),'do',psge);
          m = baumgartner2014_virtualexp(p,tang,rang,'runs',runs);
          pe_exp1(ll,ss,ii) = localizationerror(m,f,r,'perMacpherson2003');% - pe_flat(ll,ss);

          if plotpmv; figure; plot_baumgartner2014(p,tang,rang,m(:,6),m(:,8));title([num2str(density(mod(ii-1,10)+1)) 'ripples/oct; PE:' num2str(pe_exp1(ll,ss,ii),2) '%']);pause(0.5); end

        end
        end

        if do_exp2 % Exp. II
        for ii = 1:2*length(depth)

          [p,rang] = baumgartner2014(targets,spdtfs,...
            'S',s(ss).S,'polsamp',polang,...
            'lat',latseg(ll),'stim',sexp2(:,ii),'do',psge);
          m = baumgartner2014_virtualexp(p,tang,rang,'runs',runs);
          pe_exp2(ll,ss,ii) = localizationerror(m,f,r,'perMacpherson2003');% - pe_flat(ll,ss);

          if plotpmv; plot_baumgartner2014(p,tang,rang,m(:,6),m(:,8));title([num2str(depth(mod(ii-1,4)+1)) 'dB; PE:' num2str(pe_exp2(ll,ss,ii),2) '%']);pause(0.5); end

        end
        end

      end
      amt_disp([num2str(ss,'%2u') ' of ' num2str(length(s),'%2u') ' subjects completed'],'progress');

    end
    amt_disp(' ','progress')

    if length(latseg) > 1
      pe_exp1 = squeeze(mean(pe_exp1));
      pe_exp2 = squeeze(mean(pe_exp2));
      pe_flat = squeeze(mean(pe_flat));
    else 
      pe_exp1 = squeeze(pe_exp1);
      pe_exp2 = squeeze(pe_exp2);
      pe_flat = squeeze(pe_flat);
    end

    %% Save
      if psge==0
        noDCN.pe_exp1 = pe_exp1;
        noDCN.pe_exp2 = pe_exp2;
        noDCN.pe_flat = pe_flat;
        delete(which('baumgartner2014calibration.mat'))
      end
    end

    amt_cache('set','ripples',pe_exp1,pe_exp2,pe_flat,noDCN)
  end
  varargout{1} = {pe_exp1,pe_exp2,pe_flat,noDCN};
  
  dcn_flag = true;
  
  % Original data:
  data = data_macpherson2003;


  %% Phase condition handling
  pe_exp1 = mean(pe_exp1,3);
  data.pe_exp1 = mean(data.pe_exp1,3);
  pe_exp2 = mean(pe_exp2,3);
  data.pe_exp2 = mean(data.pe_exp2,3);
  if dcn_flag
      noDCN.pe_exp1 = mean(noDCN.pe_exp1,3);
      noDCN.pe_exp2 = mean(noDCN.pe_exp2,3);
  end
  idphase = 1;
  
  
  %% Increase
  pe_exp1 = pe_exp1 - repmat(pe_flat(:),1,size(pe_exp1,2));
  pe_exp2 = pe_exp2 - repmat(pe_flat(:),1,size(pe_exp2,2));
  if dcn_flag
      noDCN.pe_exp1 = noDCN.pe_exp1 - repmat(noDCN.pe_flat(:),1,size(noDCN.pe_exp1,2));
      noDCN.pe_exp2 = noDCN.pe_exp2 - repmat(noDCN.pe_flat(:),1,size(noDCN.pe_exp2,2));
  end


  %% Statistics
  quart_pe_flat = quantile(pe_flat,[.25 .50 .75]);
  quart_pe_data_flat = quantile(data.pe_flat,[.25 .50 .75]);

  quart_pe_exp1 = quantile(pe_exp1,[.25 .50 .75]);
  quart_pe_data_exp1 = quantile(data.pe_exp1,[.25 .50 .75]);

  quart_pe_exp2 = quantile(pe_exp2,[.25 .50 .75]);
  quart_pe_data_exp2 = quantile(data.pe_exp2,[.25 .50 .75]);

  if dcn_flag
      noDCN.quart_pe_flat = quantile(noDCN.pe_flat,[.25 .50 .75]);
      noDCN.quart_pe_exp1 = quantile(noDCN.pe_exp1,[.25 .50 .75]);
      noDCN.quart_pe_exp2 = quantile(noDCN.pe_exp2,[.25 .50 .75]);
  end

  
  if flags.do_plot
    
    dx = 1.05;
    FontSize = kv.FontSize;
    MarkerSize = kv.MarkerSize;
    
    % Exp1
    figure;
    
    subplot(2,8,1:8)
    errorbar(data.density/dx,quart_pe_exp1(2,:,idphase),...
        quart_pe_exp1(2,:,idphase) - quart_pe_exp1(1,:,idphase),...
        quart_pe_exp1(3,:,idphase) - quart_pe_exp1(2,:,idphase),...
        'ks-','MarkerSize',MarkerSize,...
        'MarkerFaceColor','k');
    hold on
    if dcn_flag
        errorbar(data.density*dx,noDCN.quart_pe_exp1(2,:,idphase),...
        noDCN.quart_pe_exp1(2,:,idphase) - noDCN.quart_pe_exp1(1,:,idphase),...
        noDCN.quart_pe_exp1(3,:,idphase) - noDCN.quart_pe_exp1(2,:,idphase),...
        'kd--','MarkerSize',MarkerSize-1,...
        'MarkerFaceColor','k');
    end
    errorbar(data.density,quart_pe_data_exp1(2,:,idphase),...
        quart_pe_data_exp1(2,:,idphase) - quart_pe_data_exp1(1,:,idphase),...
        quart_pe_data_exp1(3,:,idphase) - quart_pe_data_exp1(2,:,idphase),...
        'ko-','MarkerSize',MarkerSize,...
        'MarkerFaceColor','w');
    set(gca,'XScale','log','YMinorTick','on')
    set(gca,'XLim',[0.25/1.2 8*1.2],'XTick',data.density,'YLim',[-16 59],'FontSize',FontSize)
    xlabel('Ripple Density (ripples/octave)','FontSize',FontSize)
    ylabel({'Increase in';'Polar Error Rate (%)'},'FontSize',FontSize)

    if dcn_flag
        leg = legend('P with PSGE','P w/o PSGE','Actual');
    else
        leg = legend('Predicted','Actual');
    end
    set(leg,'FontSize',FontSize-1,'Location','southwest')
    legpos = get(leg,'Position');
    legpos(1) = legpos(1)+0.05;
    set(leg,'Position',legpos)

    %% Exp2

    subplot(2,8,9:13)
    errorbar(data.depth-1,quart_pe_exp2(2,:,idphase),...
        quart_pe_exp2(2,:,idphase) - quart_pe_exp2(1,:,idphase),...
        quart_pe_exp2(3,:,idphase) - quart_pe_exp2(2,:,idphase),...
        'ks-','MarkerSize',MarkerSize,...
        'MarkerFaceColor','k');
    hold on
    if dcn_flag
        errorbar(data.depth+1,noDCN.quart_pe_exp2(2,:,idphase),...
        noDCN.quart_pe_exp2(2,:,idphase) - noDCN.quart_pe_exp2(1,:,idphase),...
        noDCN.quart_pe_exp2(3,:,idphase) - noDCN.quart_pe_exp2(2,:,idphase),...
        'kd--','MarkerSize',MarkerSize-1,...
        'MarkerFaceColor','k');
    end
    errorbar(data.depth,quart_pe_data_exp2(2,:,idphase),...
        quart_pe_data_exp2(2,:,idphase) - quart_pe_data_exp2(1,:,idphase),...
        quart_pe_data_exp2(3,:,idphase) - quart_pe_data_exp2(2,:,idphase),...
        'ko-','MarkerSize',MarkerSize,...
        'MarkerFaceColor','w');
    set(gca,'XLim',[data.depth(1)-5 data.depth(end)+5],'XTick',data.depth,...
      'YLim',[-16 59],'YMinorTick','on','FontSize',FontSize)
    xlabel('Ripple Depth (dB)','FontSize',FontSize)
    ylabel({'Increase in';'Polar Error Rate (%)'},'FontSize',FontSize)
    ytick = get(gca,'YTick');
    ticklength = get(gca,'TickLength');

    %% Baseline
    subplot(2,8,14:15)
    errorbar(-0.5,quart_pe_flat(2),...
        quart_pe_flat(2) - quart_pe_flat(1),...
        quart_pe_flat(3) - quart_pe_flat(2),...
        'ks-','MarkerSize',MarkerSize,...
        'MarkerFaceColor','k');
    hold on
    if dcn_flag
        errorbar(0.5,noDCN.quart_pe_flat(2),...
        noDCN.quart_pe_flat(2) - noDCN.quart_pe_flat(1),...
        noDCN.quart_pe_flat(3) - noDCN.quart_pe_flat(2),...
        'kd-','MarkerSize',MarkerSize-1,...
        'MarkerFaceColor','k');
    end
    errorbar(0,quart_pe_data_flat(2),...
        quart_pe_data_flat(2) - quart_pe_data_flat(1),...
        quart_pe_data_flat(3) - quart_pe_data_flat(2),...
        'ko-','MarkerSize',MarkerSize,...
        'MarkerFaceColor','w');
    set(gca,'XLim',[-3 3],'XTick',0,'XTickLabel',{'Baseline'},...
      'YLim',[-15 59],'YTick',ytick,'TickLength',3*ticklength,...
      'FontSize',FontSize,'YAxisLocation','right')
    xlabel(' ','FontSize',FontSize)
    ylabel({'Polar Error Rate (%)'},'FontSize',FontSize)

    %% Overall correlation between actual and predicted median values
    if dcn_flag
      m_pe_pred = [quart_pe_exp1(2,:,idphase) quart_pe_exp2(2,:,idphase)];
      m_pe_pred_noDCN = [noDCN.quart_pe_exp1(2,:,idphase) noDCN.quart_pe_exp2(2,:,idphase)];
      m_pe_actual = [quart_pe_data_exp1(2,:,idphase) quart_pe_data_exp2(2,:,idphase)];
      r = corrcoef(m_pe_pred,m_pe_actual);
      rDCN = r(2);
      r = corrcoef(m_pe_pred_noDCN,m_pe_actual);
      rnoDCN = r(2);
      
%       r = corrcoef(m_pe_pred,m_pe_pred_noDCN);
%       rInter = r(2);
%       [t,p] = corrdifftest(rDCN,rnoDCN,rInter,14,'hotelling')
%       z = corrdifftest(rDCN,rnoDCN,rInter,14,'steiger')

      amt_disp('Correlation between actual and predicted median values (15 conditions):')
      amt_disp(['w/  PSGE: r = ' num2str(rDCN,'%0.2f')])
      amt_disp(['w/o PSGE: r = ' num2str(rnoDCN,'%0.2f')])
    end
    
  end
end

%% ------ FIG 11 ----------------------------------------------------------
if flags.do_fig11
  
  [ape_all,qe_all,ape_BBnoise,qe_BBnoise] = amt_cache('get','highfreqatten_do1',flags.cachemode);
  noDCN = amt_cache('get','highfreqatten_do0',flags.cachemode);
  if isempty(ape_all) || isempty(noDCN.ape_all)
    
%     fnHarvard = fullfile(amt_basepath,'signals','HarvardWords');
%     if not(exist(fnHarvard,'dir'))
%       amt_disp('The Harvard word list is missing.') 
%       amt_disp('Please, contact Virginia Best (ginbest@bu.edu) or Craig Jin (craig.jin@sydney.edu.au) for providing their speech recordings.')
%       amt_disp(['Then, move the folder labeled HarvardWords to: ' fullfile(amt_basepath,'auxdata','baumgartner2014') '.'])
%       return
%     end
    fnHarvard = fullfile(amt_basepath,'auxdata','baumgartner2014','HarvardWords');
      
    amt_disp('Note that this computation may take several hours!','progress')
    
    %% Settings
    latseg = 0;%[-20,0,20];   % centers of lateral segments
    NsampModel = 260; % # of modeled speech samples (takes 30min/sample); max: 260
    startSamp = 1; 

    plotpmv = false;
    plotspec = false;


    %% Load Data

    % Speech Samples from Harvard Word list
    speechsample = amt_cache('get','best2005speechSamples');
    if isempty(speechsample)
      fs_orig = 80e3; % Hz
      fs = 48e3;   % Hz
      p_resamp = fs/fs_orig;
      kk = 1;
      if NsampModel <= 51
        Nsamp = NsampModel;
        Nlists = 1;
      else
        Nsamp = 260;
        Nlists = 5;
      end
      lsamp = 120000*p_resamp;
      speechsample = cell(Nsamp,1);
      for ii = 1:Nlists
        tmp.list = ['list' num2str(ii,'%1.0u')];
        tmp.path = fullfile(fnHarvard,tmp.list);
        tmp.dir = dir(fullfile(tmp.path,'*.mat'));
        for jj = 1:length(tmp.dir)
          if jj > Nsamp; break; end
%           load(fullfile(tmp.path,tmp.dir(jj).name))
          sig = amt_load('baumgartner2014',fullfile('HarvardWords',tmp.list,tmp.dir(jj).name));
          signal = resample(sig.word,p_resamp*10,10);
          gcurve = exp(-0.5 * (0:0.001:10).^2) ./ (sqrt(2*pi));
          env = filter(gcurve,1,signal.^2);
          idon = max(find(env > 5e7,1,'first')-1e3,1);
          idoff = min(find(env > 5e7,1,'last')+1e3,lsamp);
          lwin = idoff-idon+1;
          speechsample{kk} = signal(idon:idoff) .* tukeywin(lwin,0.01)';
          kk = kk + 1;
        end
      end
      amt_cache('set','best2005speechSamples',speechsample)
    end

    % FIR Low-pass filters at 8kHz
    % Brick-wall (aka sinc-filter): fir1(200,1/3) -> -60 dB
    x=amt_load('baumgartner2014','highfreqatten_filters.mat');
    lp{1} = [1 zeros(1,100)];
    lp{2} = x.fir20db;
    lp{3} = x.fir40db;
    lp{4} = x.fir60db;

    %% Model Data
    for psge = 0:1

      s = data_baumgartner2014('pool','do',psge,flags.cachemode);
     
      cname = ['result_best2005noise_do' num2str(psge,'%u')];
      [ape_BBnoise,qe_BBnoise] = amt_cache('get',cname,flags.cachemode);
      if isempty(ape_BBnoise)
        ape_BBnoise = zeros(1,length(s),length(latseg));
        qe_BBnoise = ape_BBnoise;
        for ss = 1:length(s)
          for ll = 1:length(latseg)
            [spdtfs,polang] = extractsp(latseg(ll),s(ss).Obj);
            [p,rang] = baumgartner2014(spdtfs,spdtfs,'do',psge,...
                  'S',s(ss).S,'polsamp',polang,'lat',latseg(ll),'notprint');
            ape_BBnoise(1,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang,'absPE');
            qe_BBnoise(1,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang);

            if plotpmv; figure; plot_baumgartner2014(p,polang,rang); title(num2str(ape_BBnoise(1,ss,ll),2)); end

          end
        end
        % Pool Lateral Segments
        if length(latseg) > 1
          ape_BBnoise = mean(ape_BBnoise,3);
          qe_BBnoise = mean(qe_BBnoise,3);
        end
        amt_cache('set',cname,ape_BBnoise,qe_BBnoise);
      end
    end
    
    ape_all = zeros(length(lp),length(s),NsampModel-startSamp+1,2);
    qe_all = ape_all;
    for kk = startSamp:NsampModel 
      for psge = 0:1
        cname = ['result_best2005speech_samp' num2str(kk) '_do' num2str(psge,'%u')];
        [ape_lat,qe_lat] = amt_cache('get',cname,flags.cachemode);
        if isempty(ape_lat)

          s = data_baumgartner2014('pool','do',psge,flags.cachemode);

          ape_lat = zeros(length(lp),length(s),length(latseg));
          qe_lat = ape_lat;
          for ss = 1:length(s)
            for ll = 1:length(latseg)
              for ilp = 1:length(lp)

                stim = filter(lp{ilp},1,speechsample{kk});

                if plotspec; figure; audspecgram(stim(:),fs,'dynrange',150); end

                [spdtfs,polang] = extractsp(latseg(ll),s(ss).Obj);
                [p,rang] = baumgartner2014(spdtfs,spdtfs,'do',psge,...
                  'S',s(ss).S,'polsamp',polang,...
                  'lat',latseg(ll),'stim',stim,'notprint');
                ape_lat(ilp,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang,'absPE');
                qe_lat(ilp,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang);

                if plotpmv; figure; plot_baumgartner2014(p,polang,rang); title(num2str(ape_lat(ilp,ss,ll),2)); end

              end
            end
          end
          % Pool Lateral Segments
          if length(latseg) > 1
            ape_lat = mean(ape_lat,3);
            qe_lat = mean(qe_lat,3);
          end
          amt_cache('set',cname,ape_lat,qe_lat)
          amt_disp([num2str(kk,'%1.0u') ' of ' num2str(NsampModel,'%2.0u') ' samples completed'],'progress')
        end
        ape_all(:,:,kk,psge+1) = ape_lat;
        qe_all(:,:,kk,psge+1) = qe_lat;

      end
    end

    noDCN.ape_all = ape_all(:,:,:,1);
    noDCN.qe_all = qe_all(:,:,:,1);
    [noDCN.ape_BBnoise,noDCN.qe_BBnoise] = amt_cache('get','result_best2005noise_do1');
    amt_cache('set','highfreqatten_do0',noDCN)
    
    ape_all = ape_all(:,:,:,2);
    qe_all = qe_all(:,:,:,2);
    [ape_BBnoise,qe_BBnoise] = amt_cache('get','result_best2005noise_do1');
    amt_cache('set','highfreqatten_do1',ape_all,qe_all,ape_BBnoise,qe_BBnoise)
    
  end
  
  varargout{1} = {ape_all,qe_all,ape_BBnoise,qe_BBnoise};
  varargout{2} = noDCN;
  
  data = data_best2005;
  
  % Pool Samples
  ape_pooled = mean(ape_all,3);
  qe_pooled = mean(qe_all,3);
  noDCN.ape_pooled = mean(noDCN.ape_all,3);
  noDCN.qe_pooled = mean(noDCN.qe_all,3);

  % Confidence Intervals or standard errors
  df_speech = size(ape_all,2)-1;%*size(ape_all,3)-1;
  tquant_speech = 1;%icdf('t',.975,df_speech);
  seape_speech = std(ape_pooled,0,2)*tquant_speech/(df_speech+1);
  df_noise = size(ape_BBnoise,2)-1;
  tquant_noise = 1;%icdf('t',.975,df_noise);
  seape_noise = std(ape_BBnoise,0,2)*tquant_noise/(df_noise+1);
  seape = [seape_noise;seape_speech];
  % DCN
  df_speech = size(noDCN.ape_all,2)-1;%*size(ape_all,3)-1;
  tquant_speech = 1;%icdf('t',.975,df_speech);
  seape_speech = std(noDCN.ape_pooled,0,2)*tquant_speech/(df_speech+1);
  df_noise = size(noDCN.ape_BBnoise,2)-1;
  tquant_noise = 1;%icdf('t',.975,df_noise);
  seape_noise = std(noDCN.ape_BBnoise,0,2)*tquant_noise/(df_noise+1);
  noDCN.seape = [seape_noise;seape_speech];

  % Means
  ape = mean([ape_BBnoise ; ape_pooled],2);
  qe = mean([qe_BBnoise ; qe_pooled],2);
  noDCN.ape = mean([noDCN.ape_BBnoise ; noDCN.ape_pooled],2);
  noDCN.qe = mean([noDCN.qe_BBnoise ; noDCN.qe_pooled],2);


  if flags.do_plot
    
    dx = 0;
    MarkerSize = kv.MarkerSize;
    FontSize = kv.FontSize;
    
    xticks = 0:size(ape_all,1);
    ape0 = baumgartner2014_pmv2ppp('absPE','chance');
    
    figure;
    subplot(211)
    h(1) = errorbar(xticks-dx,ape,seape,'ks');
    set(h(1),'MarkerFaceColor','k','MarkerSize',MarkerSize,'LineStyle','-')
    hold on
    h(3) = errorbar(xticks+dx,noDCN.ape,noDCN.seape,'kd');
    set(h(3),'MarkerFaceColor','k','MarkerSize',MarkerSize-1,'LineStyle','--')
    h(2) = errorbar(xticks,data.ape,data.seape,'ko');
    set(h(2),'MarkerFaceColor','w','MarkerSize',MarkerSize,'LineStyle','-')
    plot([-0.5 4.5],[ape0 ape0],'k:') % chance performance
%     ylabel('| \theta - \vartheta | (deg)','FontSize',FontSize)
    ylabel('Polar Error (deg)','FontSize',FontSize)
    set(gca,'XTick',xticks,'XTickLabel',[],'FontSize',FontSize)
    set(gca,'XLim',[-0.5 4.5],'YLim',[12 95],'YMinorTick','on')

    pos = get(gca,'Position');
    pos(2) = pos(2)-0.11;
    set(gca,'Position',pos)

%     leg = legend('Predicted with edge extraction','Predicted without edge extraction','Actual');
%     set(leg,'FontSize',FontSize-2,'Location','northoutside')
%     pos = get(leg,'Position');
%     pos(2) = pos(2)+0.14;
%     set(leg,'Position',pos)

    qe0 = baumgartner2014_pmv2ppp('QE','chance');
    
    subplot(212)
    h(1) = plot(xticks-dx,qe,'ks');
    set(h(1),'MarkerFaceColor','k','MarkerSize',MarkerSize,'LineStyle','-')
    hold on
    h(3) = plot(xticks+dx,noDCN.qe,'kd');
    set(h(3),'MarkerFaceColor','k','MarkerSize',MarkerSize-1,'LineStyle','--')
    h(2) = plot(xticks([1 2 5]),data.qe([1 2 5]),'ko'); 
    % In Baumgartner et al. (2014), we accidentially missed the actual data 
    % for the -20dB and -40dB conditions. Uncomment the following line to   
    % also show these data points.
%     h(2) = plot(xticks,data.qe,'ko');
    set(h(2),'MarkerFaceColor','w','MarkerSize',MarkerSize,'LineStyle','-')
    plot([-0.5 4.5],[qe0 qe0],'k:') % chance performance
    ylabel('Quadrant Err. (%)','FontSize',FontSize)
    set(gca,'XTick',xticks,'XTickLabel',data.meta,'FontSize',FontSize,...
      'XLim',[-0.5 4.5],'YLim',[-3 54],'YMinorTick','on')

  end
end


%% ------ TAB 2 ---------------------------------------------------------- 
if flags.do_tab2
  
  [qe_exp,pe_exp,qe_part,pe_part] = amt_cache('get','spatstrat_do0',flags.cachemode);
  if isempty(qe_exp)
    
    latdivision = [-20,0,20];            % lateral angle
    dlat = 10;

    % Experimental Settings
    Conditions = {'BB','LP','W'};

    %% Computations
    for ido = 0:1

      if ido == 1
        s = data_baumgartner2014('pool',flags.cachemode);
      else % recalib
        s = data_baumgartner2014('pool','do',0,flags.cachemode);
      end
    
      for C = 1:length(Conditions)

        Cond = Conditions{C};

        %% Data

        % Experimental data
        data = data_majdak2013(Cond);
        
        % Consider only actual participants
        idpart = [];
        for ii = 1:length(data)
          idpart = [idpart,find(ismember({s.id},data(ii).id))];
        end
        s = s(idpart);
        
        for ll = 1:length(s)
          s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx; 
          for ii = 1:length(latdivision)
            latresp = s(ll).itemlist(:,7);
            idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
            mm2 = s(ll).itemlist(idlat,:);
            s(ll).target{ii} = mm2(:,6); % polar angle of target
            s(ll).response{ii} = mm2(:,8); % polar angle of response
          end
        end


        for ll = 1:length(s)
          for ii = 1:length(latdivision)
            s(ll).spdtfs{ii} = 0;     % init
            s(ll).polang{ii} = 0;   % init
            [s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
              latdivision(ii),s(ll).Obj);

            if C == 1       % Learn 
                s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
            elseif C == 2   % Dummy
              temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
              s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
            elseif C == 3   % Warped
                s(ll).spdtfs_c{ii} = warp_hrtf(s(ll).spdtfs{ii},s(ll).fs);
            end

          end
        end


      %% Run Model

        for ll = 1:length(s)
            
          fh = [8500,18000]; % Hz
          for ff=1:length(fh)
            qe = zeros(1,length(latdivision));
            pe = zeros(1,length(latdivision));
            qe_t = zeros(1,length(latdivision));
            pe_t = zeros(1,length(latdivision));
            for ii = 1:length(latdivision)

              if C == 1
                fhigh = 18000;
              else
                fhigh = fh(ff);
              end
              
              [s(ll).p{ii},rang] = baumgartner2014(...
                    s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
                    'S',s(ll).S,'lat',latdivision(ii),...
                    'polsamp',s(ll).polang{ii},'do',ido,'fhigh',fhigh);
              respangs{ii} = rang;

              [ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
                  s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );

            end

            % Model results of participants
            if length(latdivision) == 3
              qe_part(ll,C,2*ido+ff) = (qe_t(1)*length(s(ll).target{1}) + ...
                  qe_t(2)*length(s(ll).target{2}) + ...
                  qe_t(3)*length(s(ll).target{3}))/...
                  (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
              pe_part(ll,C,2*ido+ff) = (pe_t(1)*length(s(ll).target{1}) + ...
                  pe_t(2)*length(s(ll).target{2}) + ...
                  pe_t(3)*length(s(ll).target{3}))/...
                  (length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
            else 
              s(ll).qe_part(C,2*ido+ff) = mean(qe_t);
              s(ll).pe_part(C,2*ido+ff) = mean(pe_t);
            end
          end

          % Actual experimental results
          qe_exp(ll,C) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
          pe_exp(ll,C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
%           s(ll).Nt(C,1) = size(s(ll).itemlist,1);
            
        end
      end
    end
    s = rmfield(s,{'Obj','spdtfs_c','spdtfs'});% reduce file size
    amt_cache('set','spatstrat_do0',qe_exp,pe_exp,qe_part,pe_part)
  end
  
  result = struct('qe_exp',qe_exp,'pe_exp',pe_exp,'qe_part',qe_part,'pe_part',pe_part);
  
  meta = {'DCN no,  BWA yes';...
          'DCN no,  BWA no ';...
          'DCN yes, BWA yes';...
          'DCN yes, BWA no ';...
          };
  
  [qe0,pe0] = baumgartner2014_pmv2ppp('chance');

  qe_exp = permute(result.qe_exp,[2,1]);
  pe_exp = permute(result.pe_exp,[2,1]);
  qe_all = permute(result.qe_part,[2,1,3]);
  pe_all = permute(result.pe_part,[2,1,3]);

  % Statistics
  for cond = 1:3

    if cond == 1
      amt_disp('BB:')
    elseif cond == 2
      amt_disp('LP:')
    else
      amt_disp('W:')
    end

    Ns = size(pe_exp,2);

    group{1} = repmat(meta{1},Ns,1);
    for im = 2:length(meta)
      group{1} = [group{1} ; repmat(meta{im},Ns,1)];
    end
    group{2} = repmat(1:Ns,1,length(meta));

      data = sqrt(((qe_all(cond,:,:) - repmat(qe_exp(cond,:),[1,1,length(meta)]))/qe0).^2) + ...
             sqrt(((pe_all(cond,:,:) - repmat(pe_exp(cond,:),[1,1,length(meta)]))/pe0).^2);

      [p,t,stat] = friedman(squeeze(data),1);
%       [p,t,stat] = anovan(data(:),group,'display','off');

      amt_disp(['  Chi-sq = ' num2str(t{2,5},'%5.2f') ', p = ' num2str(p(1),'%3.3f')])
      
      if p(1) < 0.05
        figure
        [c,m,h,nms] = multcompare(stat,'display','on');
        set(gca,'YTickLabel',flipud(meta))
      end

  end


  e_qe = zeros(length(meta),3);  % model, BB/LP/W
  e_pe = e_qe;
  r_pe = e_qe;
  r_qe = e_qe;
  for m = 1:length(meta)
    for c = 1:3

      e_qe(m,c) = rms(qe_all(c,:,m) - qe_exp(c,:));
      e_pe(m,c) = rms(pe_all(c,:,m) - pe_exp(c,:));
      tmp.r = corrcoef(qe_all(c,:,m) , qe_exp(c,:));
      r_qe(m,c) = tmp.r(2);
      tmp.r = corrcoef(pe_all(c,:,m) , pe_exp(c,:));
      r_pe(m,c) = tmp.r(2);

    end
  end

  %% Write Table
  mtx = [e_pe e_qe];
  mtx(:,1:2:end) = e_pe;
  mtx(:,2:2:end) = e_qe;
  mtx = round(mtx*10)/10;
  columnLabels = {'Spect. Proc.',...
    '$e_\mathrm{PE}$','$e_\mathrm{QE}$',...
    '$e_\mathrm{PE}$','$e_\mathrm{QE}$',...
    '$e_\mathrm{PE}$','$e_\mathrm{QE}$'};
  rowLabels = meta;
  
  varargout{1} = mtx;
  varargout{2} = rowLabels; 
  varargout{3} = columnLabels; 
  
end
 

%% ------ TAB 3 ----------------------------------------------------------    
if flags.do_tab3
  
  [s,qe,pe,qe_exp,pe_exp,latseg,bwcoef] = amt_cache('get','binWeighting',flags.cachemode);
  if isempty(s)
    
    bwcoef = [13 eps -eps Inf];
    latseg = -60:20:60; % centers of lateral segments
    dlat =  10;  % lateral range (+-) of each segment

    s = data_baumgartner2014('baseline',flags.cachemode);

    qe_exp = zeros(length(s),length(latseg));
    pe_exp = zeros(length(s),length(latseg));
    for ll = 1:length(s)

      s(ll).target = [];
      s(ll).response = [];
      s(ll).Nt = [];
      for ii = 1:length(latseg)
        
        latresp = s(ll).itemlist(:,7);
        idlat = latresp <= latseg(ii)+dlat & latresp > latseg(ii)-dlat;
        s(ll).mm2 = s(ll).itemlist(idlat,:);

        s(ll).mm2(:,7) = 0; % set lateral angle to 0deg such that localizationerror works outside +-30deg

        pe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'rmsPmedianlocal'));
        qe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'querrMiddlebrooks'));

        s(ll).target{ii} = real(s(ll).mm2(:,6)); % polar angle of target
        s(ll).response{ii} = real(s(ll).mm2(:,8)); % polar angle of response
        s(ll).Nt{ii} = length(s(ll).target{ii});

      end
    end


    %% LocaMo
    
    qe = zeros(length(s),length(latseg),length(bwcoef));
    pe = zeros(length(s),length(latseg),length(bwcoef));
    for b = 1:length(bwcoef)
      for ll = 1:length(s)

        for ii = 1:length(latseg)

          s(ll).sphrtfs{ii} = 0;     % init
          s(ll).p{ii} = 0;        % init

          [s(ll).sphrtfs{ii},polang{ii}] = extractsp( latseg(ii),s(ll).Obj );
          [s(ll).p{ii},respangs{ii}] = baumgartner2014(...
              s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
              'S',s(ll).S,'lat',latseg(ii),'polsamp',polang{ii},...
              'bwcoef',bwcoef(b)); 

          if s(ll).Nt{ii} > 0
            [ qe(ll,ii,b),pe(ll,ii,b) ] = baumgartner2014_pmv2ppp( ...
                s(ll).p{ii} , polang{ii} , respangs{ii} , s(ll).target{ii});
          else
            qe(ll,ii,b) = NaN; 
            pe(ll,ii,b) = NaN;
          end

        end

      end
    
    amt_disp([num2str(b,'%1.0f') ' of ' num2str(length(bwcoef),'%1.0f') ' completed'],'progress')
    end
    
    s = rmfield(s,{'Obj','itemlist','mm2','sphrtfs'}); % reduce file size 
    amt_cache('set','binWeighting',s,qe,pe,qe_exp,pe_exp,latseg,bwcoef)
    
  end
  
%   load(fn);
  varargout{1} = {s,qe,pe,qe_exp,pe_exp,latseg,bwcoef};
  
  %% # of targets
  Ns = length(s);
  Nlat = length(latseg);
  Ntlat = zeros(Ns,Nlat);
  relfreq = zeros(Ns,Nlat);
  Ntall = zeros(Ns,1);
  for jj = 1:Ns
    Ntlat(jj,:) = [s(jj).Nt{:}];
    Ntall(jj) = sum(Ntlat(jj,:));
    relfreq(jj,:) = Ntlat(jj,:)/Ntall(jj);
  end
  relfreq = relfreq.*repmat(Ntall,1,Nlat)/sum(Ntall);

  %% Pooling to lateralization
  idlat0 = round(Nlat/2);
  idleft = idlat0-1:-1:1;
  idright = idlat0+1:Nlat;
  latseg = latseg(idlat0:end);
  relfreqLR = Ntlat(:,idleft) ./ (Ntlat(:,idleft) + Ntlat(:,idright) + eps);

  relfreq = [relfreq(:,idlat0) , relfreq(:,1:idlat0-1) + relfreq(:,Nlat:-1:idlat0+1)];
  pe_exp = [pe_exp(:,idlat0) , relfreqLR.*pe_exp(:,idleft) + (1-relfreqLR).*pe_exp(:,idright)];
  qe_exp = [qe_exp(:,idlat0) , relfreqLR.*qe_exp(:,idleft) + (1-relfreqLR).*qe_exp(:,idright)];

  %% Evaluation Metrics  
  for b=1:length(bwcoef)
    
    % pooling lat.
    pe_b = [pe(:,idlat0,b) , relfreqLR.*pe(:,idleft,b) + (1-relfreqLR).*pe(:,idright,b)];
    qe_b = [qe(:,idlat0,b) , relfreqLR.*qe(:,idleft,b) + (1-relfreqLR).*qe(:,idright,b)];
    
    idnum = not(isnan(pe_exp) | isnan(pe_b));
    dpe(b) = sqrt( relfreq(idnum)' * (pe_exp(idnum) - pe_b(idnum)).^2 );
    dqe(b) = sqrt( relfreq(idnum)' * (qe_exp(idnum) - qe_b(idnum)).^2 );
    Mpe(b) = relfreq(idnum)' * pe_b(idnum);
    Mqe(b) = relfreq(idnum)' * qe_b(idnum);
    r = corrcoef(pe_exp(idnum),pe_b(idnum));
    r_pe(b) = r(2);
    r = corrcoef(qe_exp(idnum),qe_b(idnum));
    r_qe(b) = r(2);
    
  end

  %% Table

  % Write Table
  mtx = [round([dpe' dqe']*10)/10 , round([r_pe' r_qe']*100)/100 , round([Mpe' Mqe']*10)/10];
  columnLabels = {'','$e_\mathrm{PE}$','$e_\mathrm{QE}$','$r_\mathrm{PE}$',...
    '$r_\mathrm{QE}$','$\overline{\mathrm{PE}}$','$\overline{\mathrm{QE}}$'};
  rowLabels = {'$\Phi = 13^\circ$','$\Phi \rightarrow +0^\circ$','$\Phi \rightarrow -0^\circ$','$\Phi \rightarrow \infty^\circ$'};

  varargout{1} = mtx;
  varargout{2} = rowLabels;
  varargout{3} = columnLabels;
  
end
  

%% ------ FIG 5 of baumgartner2015aro -------------------------------------
if flags.do_fig5_baumgartner2015aro
  
  [perr,qerr,snrFront,bwcoef,lat] = amt_cache('get','fig5_baumgartner2015aro',flags.cachemode);
  if isempty(perr)
    
    snrFront = -20:2:40; % in dB

    latecc = [10,30,50];% lateral eccentricities

    mrs = 17; % no sensorimotor mapping

    s = data_baumgartner2014('pool',flags.cachemode);

    bwcoef = [13,+eps,-eps]; % configuration of binaural weighting stage (binaural, ipsilateral, contralateral

    lat = [-fliplr(latecc),latecc];

    maskerNoise = noise(0.05*s(1).Obj.Data.SamplingRate,1,'white');
    targetNoise = noise(0.05*s(1).Obj.Data.SamplingRate-255,1,'white');

    perr = nan(length(snrFront),length(bwcoef),length(lat),length(s));
    qerr = nan(length(snrFront),length(bwcoef),length(lat),length(s));
    for isub=1:length(s)
      idfrontal = find(s(isub).Obj.SourcePosition(:,1)==0 & s(isub).Obj.SourcePosition(:,2)==0);
      frontalDtfs = shiftdim(s(isub).Obj.Data.IR(idfrontal,:,:),2);
      frontalTarget = convolve(targetNoise,frontalDtfs);
      lvl = mean(dbspl(frontalTarget)); % level of frontal target stimulus in dB
      for ilat = 1:length(lat)
        [spdtfs,tang] = extractsp(lat(ilat),s(isub).Obj);
        targets = convolve(targetNoise,spdtfs);
        targets = reshape(targets,[length(targets),size(targets,2)/2,2]);
        for isnr = 1:length(snrFront)
          targetsPlusMasker = targets + ...
            repmat(setdbspl(maskerNoise,lvl-snrFront(isnr)),[1,size(targets,2),2]);
          for ibwc = 1:length(bwcoef)
            [p,rang] = baumgartner2014(targetsPlusMasker,s(isub).Obj,...
              'S',s(isub).S,'mrsmsp',mrs,...
              'lat',lat(ilat),'bwcoef',bwcoef(ibwc));
            [ qerr(isnr,ibwc,ilat,isub) , perr(isnr,ibwc,ilat,isub) ] = ...
              baumgartner2014_pmv2ppp(p,tang,rang);
          end
        end
      end
      amt_disp([num2str(isub) ' of ' num2str(length(s)) ' completed'])
    end

    amt_cache('set','fig5_baumgartner2015aro',perr,qerr,snrFront,bwcoef,lat)
    
  end
  
  r = struct('perr',perr,'qerr',qerr,'snrFront',snrFront,'bwcoef',bwcoef,'lat',lat);
  varargout{1} = r;
  
  if flags.do_plot
    
    % pool left/right
    perr = (r.perr(:,:,length(r.lat)/2:-1:1,:) + r.perr(:,:,1+length(r.lat)/2:length(r.lat),:))/2;
    qerr = (r.qerr(:,:,length(r.lat)/2:-1:1,:) + r.qerr(:,:,1+length(r.lat)/2:length(r.lat),:))/2;
    latecc = r.lat(1+length(r.lat)/2:length(r.lat)); % lateral eccentricity

    perr_ipsipro = squeeze(perr(:,3,:,:) - perr(:,2,:,:)); % contra minus ipsi
    qerr_ipsipro = squeeze(qerr(:,3,:,:) - qerr(:,2,:,:)); 

    snr_int = r.snrFront(1):r.snrFront(end);

    figure
    
    % display 0-error line
    for ii = 1:2
      subplot(2,2,2+ii)
      plot(snr_int,zeros(length(snr_int),1),'k:')
      hold on
    end
    
    color = [ 0.2081    0.1663    0.8292;...
              0.8292    0.1663    0.2081;...
              0.8081    0.6081    0.2081];
    for ii=1:length(latecc)
  
      subplot(2,2,1)
      abspecontra_int = interp1(r.snrFront,mean(perr(:,ii,3,:),4),snr_int,'spline');
      h(ii) = plot(snr_int,abspecontra_int,'Color',color(ii,:));  hold on
      xlabel('SNR (dB)','FontSize',kv.FontSize)
      ylabel('PE_{contra} (deg)','FontSize',kv.FontSize)
      axis([-20,40,31,54])
      set(gca,'FontSize',kv.FontSize)

      subplot(2,2,2)
      absqecontra_int = interp1(r.snrFront,mean(qerr(:,ii,3,:),4),snr_int,'spline');
      h(ii) = plot(snr_int,absqecontra_int,'Color',color(ii,:));  hold on
      xlabel('SNR (dB)','FontSize',kv.FontSize)
      ylabel('QE_{contra} (deg)','FontSize',kv.FontSize)
      axis([-20,40,6,49])
      set(gca,'FontSize',kv.FontSize)

      subplot(2,2,3)
      pe_int = interp1(r.snrFront,mean(perr_ipsipro(:,ii,:),3),snr_int,'spline');
      h(ii) = plot(snr_int,pe_int,'Color',color(ii,:));
      xlabel('SNR (dB)','FontSize',kv.FontSize)
      ylabel('PE_{contra} - PE_{ipsi} (deg)','FontSize',kv.FontSize)
      axis([-20,40,-4,29])
      set(gca,'FontSize',kv.FontSize)

      subplot(2,2,4)
      qe_int = interp1(r.snrFront,mean(qerr_ipsipro(:,ii,:),3),snr_int,'spline');
      plot(snr_int,qe_int,'Color',color(ii,:))
      xlabel('SNR (dB)','FontSize',kv.FontSize)
      ylabel('QE_{contra} - QE_{ipsi} (deg)','FontSize',kv.FontSize)
      axis([-20,40,-4,29])
      set(gca,'FontSize',kv.FontSize)
    end

    subplot(2,2,3)
    legendentries = [repmat('\phi = \pm',length(latecc),1) num2str(latecc(:)) repmat('\circ',length(latecc),1)];
    leg = legend(h,legendentries,'Location','north');
    set(leg,'FontSize',kv.FontSize)
    
  end
  
end

%% ------------------------------------------------------------------------
%  ---- baumgartner2015jaes -----------------------------------------------
%  ------------------------------------------------------------------------
%% ------ FIG 2 of baumgartner2015jaes ------------------------------------
if flags.do_fig2_baumgartner2015jaes
  pol1 = 0;
  pol2 = 40;

  s = data_baumgartner2014('pool');

  fs = s(1).Obj.Data.SamplingRate;

  [dtfs,pol] = extractsp(0,s(1).Obj);

  polphant = pol1 + (pol2-pol1)/2;

  dtf1 = 10*dtfs(:,pol==pol1,1);
  dtf2 = 10*dtfs(:,pol==pol2,1);
  dtfreal = 10*dtfs(:,pol==polphant,1);
  dtfphant = (dtf1+dtf2)/2;

  %%
  if flags.do_plot
    
    figure

    set(gca,'LineWidth',1)
    plotfft(fft(dtfphant),fs,'posfreq')
    hold on
    plotfft(fft(dtfreal),fs,'posfreq')
    plotfft(fft(dtf1),fs,'posfreq')
    plotfft(fft(dtf2),fs,'posfreq');
    
    % Set line styles
    Color =  {[0.4660    0.6740    0.1880];...
              [0.3010    0.7450    0.9330];...
              [0.8500    0.3250    0.0980];...
              [     0    0.4470    0.7410]};
    LineWidth = [.5,.5,1,1];
    LineStyle = {':',':','--','-'};
    ch = get(gca,'Children');
    for ii = 1:length(ch)
      set(ch(ii),'Color',Color{ii},'LineStyle',LineStyle{ii},'LineWidth',LineWidth(ii));
    end
    
    leg = legend([num2str(polphant) '\circ VBAP'],...
      [num2str(polphant) '\circ source'],...
      ['  ' num2str(pol1) '\circ source'],...
      [num2str(pol2) '\circ source']);
    set(leg,'Location','southeast','FontSize',kv.FontSize)

    set(gca,'XLim',[500,17500],'YLim',[-39.9,9.9],'FontSize',kv.FontSize)

    xticks = get(gca,'XTick');
    set(gca,'XTickLabel',xticks/1000)
    xlabel({'Frequency (kHz)';' '},'FontSize',kv.FontSize)
    
  end
  
end

%% ------ FIG 4 of baumgartner2015jaes ------------------------------------
if flags.do_fig4_baumgartner2015jaes
  
  [peI,s,pol0,DL,respang] = amt_cache('get','panningangle',flags.cachemode);
  
  if isempty(peI)
  
    MRS = 0;

    pol1 = -15; % polar angle of lower Lsp.
    pol2 = 30; % polar angle of higher Lsp.

    lat = 0; % must be 0 otherwise VBAP wrong!

    s = data_baumgartner2014('pool');

    dPol = pol2-pol1;
    s(1).spdtfs = [];
    [s(1).spdtfs,polang] = extractsp(lat,s(1).Obj); 
    idtest = find(polang >= pol1 & polang <= pol2);
    qeI = zeros(length(s),length(idtest));
    peI = qeI;

    for ii = 1:length(idtest)

      id1 = idtest(1); % ID lower pos.
      id2 = idtest(end); % ID higher pos.
      id0 = idtest(ii);  % ID pos. of phantom source
      pol0(ii) = polang(id0);

      % VBAP
      [p(1,1),p(1,2),p(1,3)] = sph2cart(lat,deg2rad(pol0(ii)),1);
      [L(1,1),L(1,2),L(1,3)] = sph2cart(lat,deg2rad(pol1),1);
      [L(2,1),L(2,2),L(2,3)] = sph2cart(lat,deg2rad(pol2),1);
      g = p/L;
      g = g/norm(g);

      DL(ii) = db(g(1)) - db(g(2));

      for ll = 1:length(s)

          s(ll).spdtfs = extractsp(lat,s(ll).Obj);

          % superposition
          s(ll).dtfs2{ii} = g(1)*s(ll).spdtfs(:,id1,:) + g(2)*s(ll).spdtfs(:,id2,:);

          [s(ll).p1(:,ii),respang] = baumgartner2014(...
            s(ll).spdtfs(:,id0,:),s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
            'mrsmsp',MRS,'polsamp',polang);
          s(ll).p2(:,ii) = baumgartner2014(...
            s(ll).dtfs2{ii},s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
            'mrsmsp',MRS,'polsamp',polang);

          [s(ll).qe1(ii),s(ll).pe1(ii)] = baumgartner2014_pmv2ppp(...
            s(ll).p1(:,ii),polang(id0),respang);
          [s(ll).qe2(ii),s(ll).pe2(ii)] = baumgartner2014_pmv2ppp(...
            s(ll).p2(:,ii),polang(id0),respang);

          % Increse of error
          qeI(ll,ii) = s(ll).qe2(ii) - s(ll).qe1(ii);
          peI(ll,ii) = s(ll).pe2(ii) - s(ll).pe1(ii);
      end
      amt_disp([num2str(ii) ' of ' num2str(length(idtest)) ' completed'],'progress');
    end

    amt_cache('set','panningangle',peI,s,pol0,DL,respang);
    
  end
  
  id1 = 'NH71'; % positive example
  id2 = 'NH62'; % negative example
  
  if flags.do_plot
    
    cmp= [0.2081    0.1663    0.5292;
          0.2052    0.2467    0.6931;
          0.0843    0.3472    0.8573;
          0.0157    0.4257    0.8789;
          0.0658    0.4776    0.8532;
          0.0777    0.5300    0.8279;
          0.0356    0.5946    0.8203;
          0.0230    0.6443    0.7883;
          0.0485    0.6793    0.7341;
          0.1401    0.7085    0.6680;
          0.2653    0.7327    0.5916;
          0.4176    0.7471    0.5142;
          0.5624    0.7487    0.4529;
          0.6872    0.7433    0.4029;
          0.7996    0.7344    0.3576;
          0.9057    0.7261    0.3105;
          0.9944    0.7464    0.2390;
          0.9847    0.8141    0.1734;
          0.9596    0.8869    0.1190;
          0.9763    0.9831    0.0538]; % parula colormap (defined for compatibility with older Matlab versions)
    
    figure

    p_pool = nan(size(s(1).p2,1),size(s(1).p2,2),length(s));
    for ll = 1:length(s)
      p_pool(:,:,ll) = s(ll).p2;
      if strcmp(s(ll).id,id1)
        subplot(1,4,1)
        plot_baumgartner2014(s(ll).p2,pol0,respang,'cmax',0.08)
        colormap(cmp)
        title(s(ll).id,'FontSize',kv.FontSize)
        xlabel(''); 
        ylabel('Response angle (deg)','FontSize',kv.FontSize);
        colorbar off
      elseif strcmp(s(ll).id,id2)
        subplot(1,4,2)
        plot_baumgartner2014(s(ll).p2,pol0,respang,'cmax',0.08)
        colormap(cmp)
        xlabel('Panning angle (deg)','FontSize',kv.FontSize);      
        ylabel(''); set(gca,'YTickLabel',[]);
        title(s(ll).id,'FontSize',kv.FontSize)
        colorbar off
      end
    end
    p_pool = mean(p_pool,3);
    subplot(1,4,3)
    plot_baumgartner2014(p_pool,pol0,respang,'cmax',0.08)
    colormap(cmp)
    title('Pool','FontSize',kv.FontSize)
    xlabel(''); 
    ylabel(''); set(gca,'YTickLabel',[]);
    colorbar off

    subplot(1,4,4)
    ymax = 8;
    y = -0.15:0.1:ymax+0.15;
    Ny = length(y);
    pcolor(1:2,y,repmat(y(:),1,2))
    shading flat
    axis tight
    colormap(cmp)
    title({' ';' '})
    set(gca,'XTick',[],'YTick',0:1:ymax,... 
      'YDir','normal','YAxisLocation','right','FontSize',kv.FontSize)
    ylabel({'Probability density (% per 5\circ)'},'FontSize',kv.FontSize)
    set(gca,'Position',get(gca,'Position').*[1.05,1,0.3,1])
  
  end
  
end

%% ------ FIG 5 of baumgartner2015jaes ------------------------------------
if flags.do_fig5_baumgartner2015jaes
  
  [peI,s,pol0,DL,respang] = amt_cache('get','panningangle',flags.cachemode);
  
  if isempty(peI)
    exp_baumgartner2014('fig4_baumgartner2015jaes','noplot',flags.cachemode);
    [peI,s,pol0,DL,respang] = amt_cache('get','panningangle',flags.cachemode);
  end
  
  figure

  plot(peI')
  ylabel('Increase in polar error (deg)')

  % Panning angle axis
  set(gca,'XTick',1:10,'XTickLabel',round(pol0),...
      'YMinorTick','on','XLim',[1,10],'YLim',[-9,59])
  xlabel('Panning angle (deg)')
  
end

%% ------ FIG 6 of baumgartner2015jaes ------------------------------------
if flags.do_fig6_baumgartner2015jaes
  
  results = amt_cache('get','replicatePulkki2001',flags.cachemode);
   
  if isempty(results)
  
    amt_disp('Results may slightly vary from simulation to simulation because noise stimulus is not fixed.','progress')
    
    MRS = 0;

    pol1 = -15; % polar angle of lower Lsp.
    pol2 = 30; % polar angle of higher Lsp.

    polphant = [0,15]; % polar angles of phantom sources
    Pmax = nan(length(polphant),23); % max Probabilities
    panang_Pmax = nan(length(polphant),23); % panning angle selected by max P
    panang_Cen = nan(length(polphant),23); % panning angle selected by centroid
    for pp = 1:length(polphant)

    lat = 0; % must be 0 otherwise VBAP wrong!

    s = data_baumgartner2014('pool');

    s(1).spdtfs = [];
    [s(1).spdtfs,polang] = extractsp(lat,s(1).Obj); 

    % restrict response range
    idrang = find(polang >= pol1 & polang <= pol2); % to range between loudspeakers
    % idrang = find(polang <= 90); % to the front

    idtest = find(polang >= pol1 & polang <= pol2);
    id1 = find(polang >= pol1,1); % ID lower pos.
    id2 = find(polang <= pol2,1,'last'); % ID higher pos.
    tang = polang(id1:id2);

    for ll = 1:length(s)
      s(ll).spdtfs = extractsp(lat,s(ll).Obj);

      for ii = 1:length(idtest)

        id0 = idtest(ii);  % ID pos. of phantom source

        % VBAP
        [p(1,1),p(1,2),p(1,3)] = sph2cart(lat,deg2rad(polang(id0)),1);
        [L(1,1),L(1,2),L(1,3)] = sph2cart(lat,deg2rad(pol1),1);
        [L(2,1),L(2,2),L(2,3)] = sph2cart(lat,deg2rad(pol2),1);
        g = p/L;
        g = g/norm(g);

        DL(ii) = db(g(1)) - db(g(2));

        % superposition
        s(ll).dtfs2{ii} = g(1)*s(ll).spdtfs(:,id1,:) + g(2)*s(ll).spdtfs(:,id2,:);    

        [s(ll).p(:,ii),rang] = baumgartner2014(...
              s(ll).dtfs2{ii},s(ll).spdtfs(:,idrang,:),s(ll).fs,'S',s(ll).S,...
              'mrsmsp',MRS,'polsamp',polang(idrang),'rangsamp',5,...
              'stim',noise(10000,1,'pink')); % phantom source

      end

      id_rang = rang == polphant(pp);

      % interpolation between target angles
      tang_int = tang(1):1:tang(end);
      p_int = interp2(tang(:)',rang(:),s(ll).p,tang_int(:)',rang(:),'spline');
      p_int = p_int./repmat(sum(p_int,1),size(p_int,1),1); % normalize to PMVs

      % Variant 1: max P at source direction
      [Pmax(pp,ll),id_best_pan] = max(p_int(id_rang,:));
      panang_Pmax(pp,ll) = tang_int(id_best_pan);

      % Variant 2: centroid closest to source direction
      M = rang*p_int;
      [tmp,id_best_pan] = min(abs(M-polphant(pp)));
      panang_Cen(pp,ll) = tang_int(id_best_pan);

    end
      fprintf([num2str(pp) ' of ' num2str(length(polphant)) ' completed \n']);
    end

    results = struct('panang_Pmax',panang_Pmax,'Pmax',Pmax,...
      'panang_Cen',panang_Cen,'polphant',polphant,'DL',DL,'rang',rang);
    
    amt_cache('set','replicatePulkki2001',results);
    
  end
  
  [panang_varStrat,nCM_varStrat,p_varStrat,muhat,sigmahat] = amt_cache('get','replicatePulkki2001_varStrat',flags.cachemode);
  if isempty(panang_varStrat)
    
    pulkki01 = data_pulkki2001;
    [muhat(1),sigmahat(1)] = normfit(pulkki01(1,:));
    [muhat(2),sigmahat(2)] = normfit(pulkki01(2,:));
    
    Nsub = size(results.panang_Cen,2);
    panang_all = [];
    p = [];
    nCM = [];
    ii = 1;
    for inCM = 1:Nsub+1
      c = nchoosek(1:Nsub,inCM-1); % listeners with CM strategy
      lenC = size(c,1);
      nCM = [nCM;repmat(inCM,lenC,1)];
      panang_all = cat(3,panang_all , nan(2,Nsub,lenC));
      p = [p ; nan(lenC,2)];
      for ic = 1:lenC
        idCM = false(1,Nsub);
        idCM(c(ic,:)) = true;
        panang_all(:,:,ii) = [results.panang_Cen(:,idCM) results.panang_Pmax(:,not(idCM))];
        [tmp.h1,p(ii,1)] = kstest((panang_all(1,:,ii)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
        [tmp.h2,p(ii,2)] = kstest((panang_all(2,:,ii)-muhat(2))/sigmahat(2));
        ii = ii+1;
      end
      disp([num2str(inCM) ' of ' num2str(Nsub+1) ' done'])
    end
    [tmp.min,idmax] = max(sum(p,2)); % best fit
    panang_varStrat = panang_all(:,:,idmax);
    nCM_varStrat = nCM(idmax);
    p_varStrat = p(idmax,:);
    
    amt_cache('set','replicatePulkki2001_varStrat',panang_varStrat,nCM_varStrat,p_varStrat,muhat,sigmahat)
    
  end
  
  if flags.do_plot
    
    pulkki01 = data_pulkki2001;
    Nsub = size(results.panang_Cen,2);
    
    figure
    for ii = 1:2

      subplot(1,2,ii)

      X = nan(size(pulkki01,2)*size(pulkki01,3),4);
      X(:,1) = pulkki01(ii,:);
      X(1:Nsub,2) = results.panang_Pmax(ii,:)';
      X(1:Nsub,3) = results.panang_Cen(ii,:)';

      X(1:Nsub,4) = panang_varStrat(ii,:)';

      plot([0,5],(ii-1)*15*[1,1],'k:') 
      hold on

      boxplot(X,'symbol','k*','outliersize',3)

      set(gca,'YLim',[-17,32], 'XTickLabel',{'[2]','PM','CM','Mixed'});
	  if ~verLessThan('matlab','8.4'), set(gca,'XTickLabelRotation',45); end
      if ii==1; 
        ylabel('Panning angle (deg)')
        text(0.7,27.5,'0\circ')
      else
        set(gca,'YTickLabel',[]); 
        text(0.7,27.5,'15\circ')
      end
    end
    
  end
  
end

%% ------ FIG 7 of baumgartner2015jaes ------------------------------------
if flags.do_fig7_baumgartner2015jaes
  
  [peI,dPol] = amt_cache('get','loudspeakerspan',flags.cachemode);
  
  if isempty(peI)
    
    MRS = 0;

    flags.do_fig20 = false;
    flags.do_fig19 = false;

    s = data_baumgartner2014('pool');

    if flags.do_fig19
      dPol = [0 30,60];
      s = s(2); % NH12
    else
      dPol = 10:10:90; 
    end
    lat = 0;

    s(1).spdtfs = [];
    [s(1).spdtfs,polang] = extractsp(lat,s(1).Obj); 
    peI = zeros(length(s),length(dPol));
    peA = zeros(length(s),length(dPol)+1);
    ii = 0;
    while ii < length(dPol)
      ii = ii + 1;

      % find comparable angles
      id0 = [];
      id1 = [];
      id2 = [];
      for jj = 1: length(polang)
          t0 = find( round(polang) == round(polang(jj)+dPol(ii)/2) );
          t2 = find( round(polang) == round(polang(jj)+dPol(ii)) );
          if ~isempty(t0) && ~isempty(t2)
              id0 = [id0 t0];
              id1 = [id1 jj];
              id2 = [id2 t2];
          end
      end
      pol2{ii} = (polang(id1)+polang(id2)) /2;

      amt_disp([' Span: ' num2str(dPol(ii)) 'deg'],'progress');
      for ll = 1:length(s)

          s(ll).spdtfs = extractsp(lat,s(ll).Obj);

          % superposition
          s(ll).dtfs2{ii} = s(ll).spdtfs(:,id1,:) + s(ll).spdtfs(:,id2,:);

          [s(ll).p1{ii},respang] = baumgartner2014(...
            s(ll).spdtfs(:,id0,:),s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
            'mrsmsp',MRS,'polsamp',polang);
          s(ll).p2{ii} = baumgartner2014(...
            s(ll).dtfs2{ii},s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
            'mrsmsp',MRS,'polsamp',polang);

          % RMS Error
          [s(ll).qe1{ii},s(ll).pe1{ii}] = baumgartner2014_pmv2ppp(...
            s(ll).p1{ii},polang(id0),respang);
          [s(ll).qe2{ii},s(ll).pe2{ii}] = baumgartner2014_pmv2ppp(...
            s(ll).p2{ii},pol2{ii},respang);

          % Increse of error
          peI(ll,ii) = s(ll).pe2{ii} - s(ll).pe1{ii};

      end

    end

    amt_cache('set','loudspeakerspan',peI,dPol)
    
  end
    
  peIm = mean(peI,1);
  peIstd = std(peI,1,1);
    
  if flags.do_plot
    
    figure
    p = patch([dPol,fliplr(dPol)],[peIm+peIstd,fliplr(peIm-peIstd)],.7*[1,1,1]);
    set(p,'EdgeColor',.7*[1,1,1]);
    hold on
    h = plot(dPol,peIm,'k');
    set(h,'LineWidth',1)
    set(gca,'XTick',dPol,'XTickLabel',dPol,...
          'YMinorTick','on','Box','on','Layer','top')
    axis([9.8,90.2,-2,27])
    ylabel({'Increase in polar error (deg)'})
    xlabel({'Loudspeaker span (deg)';''})
    
  end
  
end

%% ------ FIG 8 of baumgartner2015jaes ------------------------------------
if flags.do_fig8_baumgartner2015jaes
  
  [r2,dPol] = amt_cache('get','loudspeakerspan_r2',flags.cachemode);
  
  if isempty(r2)
    MRS = 0;

    s = data_baumgartner2014('pool');

    dPol = 0:10:105; 

    lat = 0;
    runs = 100;

    
    DL = -13:5:7; % panning ratios in dB
    
    s(1).spdtfs = [];
    [s(1).spdtfs,polang] = extractsp(lat,s(1).Obj); 
    r2 = zeros(length(s),length(dPol));
    ii = 0;
    while ii < length(dPol) % various spans
      ii = ii + 1;

      disp([' Span: ' num2str(dPol(ii)) 'deg']);

      r2total = nan(length(s),length(DL));
      r2front = nan(length(s),length(DL));
      r2rear = nan(length(s),length(DL));
      for idl = 1:length(DL)

      % find comparable angles
      id1 = []; % id of speaker with smaller polar angle
      id2 = []; % id of speaker with larger polar angle
      for jj = 1: length(polang)
%           t0 = find( round(polang) == round(polang(jj)+ipf*dPol(ii)/polFrac) );
          t2 = find( round(polang) == round(polang(jj)+dPol(ii)) );
          if ~isempty(t2)
%               id0 = [id0 t0];
              id1 = [id1 jj];
              id2 = [id2 t2];
          end
      end

      g = inv([1,1;1,-10^(DL(idl)/20)]) * [1;0]; % derived from db(g1/g2)=DL and g1+g2=1 (chosen arbitrarily since energy preservation is not relevant here)
      
      pol0 = nan(length(id1),1); % panning angles
      for jj = 1:length(id1)
        [L(1,1),L(1,2),L(1,3)] = sph2cart(0,deg2rad(polang(id1(jj))),1);
        [L(2,1),L(2,2),L(2,3)] = sph2cart(0,deg2rad(polang(id2(jj))),1);
        t = g'*L;
        [azi,ele,tmp.r] = cart2sph(t(1),t(2),t(3));
        [tmp.lat,pol0(jj)] = sph2hor(rad2deg(azi),rad2deg(ele));
      end

      for ll = 1:length(s) % various listeners

          s(ll).spdtfs = extractsp(lat,s(ll).Obj);

          % superposition
          s(ll).dtfs2{ii} = g(1)*s(ll).spdtfs(:,id1,:) + g(2)*s(ll).spdtfs(:,id2,:);

          [s(ll).p2{ii},rang] = baumgartner2014(...
            s(ll).dtfs2{ii},s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
            'mrsmsp',MRS,'polsamp',polang,'rangsamp',1); % phantom

          % total polar range 
          m2 = baumgartner2014_virtualexp(s(ll).p2{ii},pol0,rang,'runs',runs);

          % R2 via correlation coefficient
          r = corrcoef(m2(:,8),m2(:,6));
          r2total(ll,idl) = r(2);

          % restricted to frontal polar range 
          idfront = rang <= 90;
          respangfront = rang(idfront);
          idpol0front = pol0 <= 90;
          pol0front = pol0(idpol0front);
          p = s(ll).p2{ii}(idfront,idpol0front);

          m2front = baumgartner2014_virtualexp(p,pol0front,respangfront,'runs',runs);

          r = corrcoef(m2front(:,8),m2front(:,6));
          r2front(ll,idl) = r(2);

          % restricted to rear polar range 
          idrear = rang >= 90;
          respangrear = rang(idrear);
          idpol0rear = pol0 >= 90;
          pol0rear = pol0(idpol0rear);
          p = s(ll).p2{ii}(idrear,idpol0rear);

          m2rear = baumgartner2014_virtualexp(p,pol0rear,respangrear,'runs',runs);

          r = corrcoef(m2rear(:,8),m2rear(:,6));
          r2rear(ll,idl) = r(2);

      end
      end
      r2.total(:,ii) =  nanmean(r2total,2);
      r2.front(:,ii) =  nanmean(r2front,2); 
      r2.rear(:,ii) =  nanmean(r2rear,2);
    end
 
    amt_cache('set','loudspeakerspan_r2',r2,dPol)

  end
    
  % data extracted from Fig.6 (data:AVG) of Bremen et al. (2010, J Neurosci,
  % 30:194-204) via http://arohatgi.info/WebPlotDigitizer
  bremen2010.pol = 15:15:105;
  bremen2010.r2 = [.85 , .81 , .63 , .38 , .19 , .11 , .21];
  
  if flags.do_plot

    figure
    h(1) = plot(bremen2010.pol,bremen2010.r2,'bo-');
    hold on
    h(2) = plot(dPol,mean(r2.front),'bo-');

    h(3) = plot(dPol,mean(r2.rear),'rs-');
    h(4) = plot(dPol,mean(r2.total),'kd-');

    set(h(1),'MarkerSize',kv.MarkerSize,'MarkerFaceColor','b')
    set(h(2:4),'MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
    set(gca,'XLim',[-5,110],'YLim',[-.05,1.05])

    leg = legend('Frontal from [18]','Frontal','Rear','Overall','Location','best');
    set(leg,'FontSize',kv.FontSize)

    xlabel({'Loudspeaker span (deg)';''})
    ylabel('\it{r}^{ 2}')
  end
  
end

%% ------ FIG 9 of baumgartner2015jaes ------------------------------------
if flags.do_fig9_baumgartner2015jaes
  
  SysName{1}  = 'NHK 22.2 (without bottom layer)';
  LSPsetup{1} = [ 0,0 ; 30,0 ; 60,0 ;  90,0 ; 135,0 ; ...
                180,0 ;-30,0 ;-60,0 ; -90,0 ;-135,0 ; ...
                  0,45; 45,45; 90,45; 135,45; 180,45; ...
               -135,45;-90,45;-45,45;   0,90];

  SysName{2}  = 'Samsung 11.2';
  LSPsetup{2} = [ 0,0 ; 60,0 ;  90,0 ; 135,0 ; ...
                       -60,0 ; -90,0 ;-135,0 ; ...
                 45,45;135,45; -45,45;-135,45];

  SysName{3}  = 'Samsung 10.2';
  LSPsetup{3} = [ 0,0 ; 60,0 ;  90,0 ; 135,0 ; ...
                       -60,0 ; -90,0 ;-135,0 ; ...
                 45,45;180,45; -45,45];

  SysName{4}  = 'USC 10.2';
  LSPsetup{4} = [ 0,0 ; 30,0 ; 60,0 ;  115,0 ; ...
                180,0 ;-30,0 ;-60,0 ; -115,0 ; ...
                 45,45;-45,45];

  SysName{5}  = 'Auro-3D 10.1';
  LSPsetup{5} = [ 30,0 ; 30,30 ; 135,30 ; 135,0;...
            0,0 ;-30,0 ;-30,30 ;-135,30 ;-135,0; 0,90];        

  SysName{6}  = 'Auro-3D 9.1';
  LSPsetup{6} = [ 30,0 ; 30,30 ; 135,30 ; 135,0;...
            0,0 ;-30,0 ;-30,30 ;-135,30 ;-135,0]; 
  
  latall = -45:5:45;
  polall = 0:10:180;
          
  pe = amt_cache('get','locaVBAP',flags.cachemode);
  
  if isempty(pe)
    
    MRS = 0;

    s = data_baumgartner2014('pool');
    
    pe = zeros(length(latall),length(polall),length(s),length(LSPsetup),2); % predicted local polar RMS errors
    for ll = 1:length(LSPsetup)
      for aa = 1:length(latall)
        lat = latall(aa);

        for pp = 1:length(polall)
          pol = polall(pp);

          % Select LSP-Triangle and compute VBAP gains
          [source_pos(1),source_pos(2)] = hor2sph(lat,pol);
          Nlsp = length(LSPsetup{ll});
          [g,IDsp] = vbap(LSPsetup{ll},source_pos);


          for jj = 1:length(s)

            % Compute binaural impulse response of loudspeaker triple
            dtfs = permute(double(s(jj).Obj.Data.IR),[3 1 2]);
            lsp_hrirs = zeros(length(IDsp),size(dtfs,1),2);
            for ii = 1:length(IDsp)
    %           [lat_sp,pol_sp] = sph2horpolar(LSPsetup{ll}(IDsp(ii),1),LSPsetup{ll}(IDsp(ii),2));
              idx = findNearestPos_locaVBAP(...
                LSPsetup{ll}(IDsp(ii),1:2),s(jj).Obj.SourcePosition(:,1:2));
              lsp_hrirs(ii,:,:) = squeeze(dtfs(:,idx,:));
            end
            target(:,1,1) = g*lsp_hrirs(:,:,1);
            target(:,1,2) = g*lsp_hrirs(:,:,2);

            % SP-template
            [spdtfs,polang] = extractsp(lat,s(jj).Obj);

            % Run loca model
            [p,rang] = baumgartner2014(...
                    target,spdtfs,s(jj).fs,'S',s(jj).S,...
                    'lat',lat,'polsamp',polang,'mrsmsp',MRS);

            m = baumgartner2014_virtualexp(p,pol,rang,'runs',1000);
            pe(aa,pp,jj,ll,1) = localizationerror(m,'precPnoquerr');
            [~,pe(aa,pp,jj,ll,2)] = baumgartner2014_pmv2ppp(p,pol,rang);

          end
        end
      end
      amt_disp([num2str(ll) ' of ' num2str(length(LSPsetup)) ' done'],'progress')
    end
    amt_cache('set','locaVBAP',pe)
  end
 
  MRS = 0;
  pe_ref = amt_cache('get','locaVBAP_ref',flags.cachemode);
  if isempty(pe_ref)
    
    s = data_baumgartner2014('pool');

    latall = -45:5:45;
    polall = 0:10:180;
    pe_ref = zeros(length(latall),length(polall),length(s),1,2); % predicted local polar RMS errors

    %% Computations

    for jj = 1:length(s)

      dtfs = permute(double(s(jj).Obj.Data.IR),[3 1 2]);

      for aa = 1:length(latall)
        lat = latall(aa);
        for pp = 1:length(polall)
          pol = polall(pp);
          [lat_sp,pol_sp,idx] = findNearestPos_locaVBAP_ref(lat,pol,s(jj).Obj.SourcePosition(:,1:2));
          target = dtfs(:,idx,:);

          % SP-template
          [spdtfs,polang] = extractsp(lat,s(jj).Obj);

          % Run loca model
          [p,rang] = baumgartner2014(...
                  target,spdtfs,s(jj).fs,'S',s(jj).S,...
                  'mrsmsp',MRS,'lat',lat,'polsamp',polang);

          m = baumgartner2014_virtualexp(p,pol,rang,'runs',1000);
          pe_ref(aa,pp,jj,1,1) = localizationerror(m,'precPnoquerr');
          [~,pe_ref(aa,pp,jj,1,2)] = baumgartner2014_pmv2ppp(p,pol,rang);

        end
      end
    end
    
    amt_cache('set','locaVBAP_ref',pe_ref)
  end

  N = length(LSPsetup);
  eRMS = pe_ref(:,:,:,:,2);
  eRMS(:,:,:,2:N+1) = pe(:,:,:,:,2);

  if flags.do_plot

    labels = {'Reference';'\it A';'\it B';'\it C';'\it D';'\it E';'\it F'};
    labels = labels(1:N+1,:);
    
    figure 
    for ll = 1:N+1

      pemean = squeeze(mean(eRMS(:,:,:,ll),3));
      subplot(1,N+2,ll)
      imagesc(latall,polall,pemean')
      set(gca,'YTick',0:30:180,'XTick',-30:30:30)
      axis equal tight
      colormap hot
      if MRS == 0
        ymin = 15.5;
        ymax = 49.5;
      else
        ymin = 15.5;
        ymax = 49.5;
      end
      caxis([ymin ymax])
      if ll==1; 
        ylabel('Polar angle (deg)','FontName','Helvetica'); 
      else 
        set(gca,'YTickLabel',[])
      end

      if ll==4; xlabel('Lateral angle (deg)','FontName','Helvetica'); end

      title(labels{ll},'FontName','Helvetica')

      % Loudspeaker positions
      if ll > 1
        [lat_lsp,pol_lsp] = sph2hor(LSPsetup{ll-1}(:,1),LSPsetup{ll-1}(:,2));
        idlat = abs(lat_lsp) <= max(abs(latall))+1;
        hold on
        h2 = plot(lat_lsp(idlat),pol_lsp(idlat),'wo');
        set(h2,'MarkerSize',2*3.5);
        h1 = plot(lat_lsp(idlat),pol_lsp(idlat),'ko');
        set(h1,'MarkerSize',2*3);
      end
    end

    subplot(1,N+2,N+2)
    y = ymin:1:ymax;
    Ny = length(y);
    pcolor(1:2,y,repmat(y(:),1,2))
    shading flat
    axis tight
    colormap hot
    title({' ';' '})
    set(gca,'XTick',[],'YTick',20:5:45,... %,'TickLength',[0.12,0.12]
      'YDir','normal','YAxisLocation','right','FontSize',kv.FontSize)
    ylabel({'Polar error (deg)'},'FontSize',kv.FontSize)
    set(gca,'Position',get(gca,'Position').*[1.03,1.8,0.3,0.8])
  end
  
end

%% ------ Tab 1 of baumgartner2015jaes ------------------------------------
if flags.do_tab1_baumgartner2015jaes
  
  results = amt_cache('get','replicatePulkki2001',flags.cachemode);
  [panang_varStrat,nCM_varStrat,p_varStrat,muhat,sigmahat] = amt_cache('get','replicatePulkki2001_varStrat',flags.cachemode);
  if isempty(panang_varStrat)
    exp_baumgartner2014('fig6_baumgartner2015jaes','noplot',flags.cachemode)
  end
  
  pulkki01 = data_pulkki2001;
  
  [h1_pul,p1_pul] = kstest((pulkki01(1,:)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
  [h2_pul,p2_pul] = kstest((pulkki01(2,:)-muhat(2))/sigmahat(2));
  [h1_pm,p1_pm] = kstest((results.panang_Pmax(1,:)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
  [h2_pm,p2_pm] = kstest((results.panang_Pmax(2,:)-muhat(2))/sigmahat(2));
  [h1_cm,p1_cm] = kstest((results.panang_Cen(1,:)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
  [h2_cm,p2_cm] = kstest((results.panang_Cen(2,:)-muhat(2))/sigmahat(2));

  amt_disp('p-values of K.S.-test:')
  amt_disp('Real source at 0 deg:')
  amt_disp(['Results Pulkki (2001): p = ' num2str(p1_pul,'%3.2f')])
  amt_disp(['Probability Maximiz.:  p = ' num2str(p1_pm,'%3.2f')])
  amt_disp(['Centroid Match:        p = ' num2str(p1_cm,'%3.2f')])
  amt_disp(['Individual strategy:   p = ' num2str(p_varStrat(1),'%3.2f') ' (#CM = ' num2str(nCM_varStrat) ')'])
  amt_disp('Real source at 15 deg:')
  amt_disp(['Results Pulkki (2001): p = ' num2str(p2_pul,'%3.2f')])
  amt_disp(['Probability Maximi.:   p = ' num2str(p2_pm,'%3.2f')])
  amt_disp(['Centroid Match:        p = ' num2str(p2_cm,'%3.2f')])
  amt_disp(['Individual strategy:   p = ' num2str(p_varStrat(2),'%3.2f') ' (#CM = ' num2str(nCM_varStrat) ')'])
  
end

%% ------ Tab 3 of baumgartner2015jaes ------------------------------------
if flags.do_tab3_baumgartner2015jaes
  
  pe = amt_cache('get','locaVBAP',flags.cachemode);
  pe_ref = amt_cache('get','locaVBAP_ref',flags.cachemode);
  if isempty(pe)
    exp_baumgartner2014('fig9_baumgartner2015jaes','noplot',flags.cachemode);
  end
  
  N = size(pe,4); % # loudspeakers
  eRMS = pe_ref(:,:,:,:,2);
  eRMS(:,:,:,2:N+1) = pe(:,:,:,:,2);
  
  labels = {'Reference';'\it A';'\it B';'\it C';'\it D';'\it E';'\it F'};
  labels = labels(1:N+1,:);

  amt_disp('RMS error difference from reference averaged across directions')
  amt_disp('System  min  mean  max')
  for ll = 2:N+1
    IeRMS = eRMS(:,:,:,ll) - eRMS(:,:,:,1);
    IeRMS = mean(IeRMS,3); % average across listeners
    amt_disp([labels{ll} ' ' num2str(min(IeRMS(:)),'%2.1f') ' ' num2str(mean(IeRMS(:)),'%2.1f') ' ' num2str(max(IeRMS(:)),'%2.1f')])
  end
  
end

end



%% ------------------------------------------------------------------------
%  ---- INTERNAL FUNCTIONS ------------------------------------------------
%  ------------------------------------------------------------------------
function hM_warped = warp_hrtf(hM,fs)
% warps HRTFs acc. to Walder (2010)
% Usage: hM_warped = warp_hrtf(hM,fs)

N = fs;
fu = 2800;
fowarped = 8500;
fo = 16000;

fscala = [0:fs/N:fs-fs/N]';
hM_warped = zeros(512,size(hM,2),size(hM,3));
fuindex = max(find(fscala <= fu)); % 2800
fowindex = min(find(fscala >= fowarped));
foindex = min(find(fscala >= fo));

for canal = 1:size(hM,3)
   for el = 1:size(hM,2)
        yi = ones(fs/2+1,1)*(10^-(70/20));
        flin1 = [fscala(1:fuindex-1)];
        flin2 = [linspace(fscala(fuindex),fscala(foindex),fowindex-fuindex+1)]';
        fscalawarped = [flin1 ; flin2];

        % interpolate
        x = fscala(1:foindex);
        H = fft(hM(:,el,canal),N);
        Y = H(1:foindex);
        xi = fscalawarped;
        yi(1:length(xi),1) = interp1(x, Y, xi,'linear');

        yges=([yi; conj(flipud(yi(2:end-1)))]);
        hges = ifft([yges(1:end)],length(yges));
        hges=fftshift(ifft(yges));
        hwin=hges(fs/2-256:fs/2+768);
        hwinfade = FW_fade(hwin,512,24,96,192);
        hM_warped(1:end,el,canal)=hwinfade;
            
    end
end
end

function [syncrnfreq, GETtrain] = GETVocoder(filename,in,channum,lower,upper,alpha,GaussRate,stimpar)
warning('off')
% channel/timing parameters
srate=stimpar.SamplingRate;
nsamples=length(in); % length of sound record
duration=nsamples/srate; % duration of the signal (in s)
t=0:1/srate:duration;
t=t(1:nsamples);
extendedrange = 0;
if alpha == -1 % Log12ER case
    extendedrange = 1;
    alpha = 0.28;
elseif alpha == 0 % use predefined alphas
  switch channum
    case 3
      alpha=1.42;
    case 6
      alpha=0.67;
    case 9
      alpha=0.45;
    case 12
      alpha=0.33;
    case 18
      alpha=0.22;
    case 24
      alpha=0.17;
    otherwise 
      error(['Alpha not predefined for channels number of ' num2str(channu)]);
  end
end

% Synthesis: These are the crossover frequencies that the output signal is mapped to
crossoverfreqs = logspace( log10(lower), log10(upper), channum + 1);
if extendedrange == 1
    crossoverfreqs = [300,396,524,692,915,1209,1597,2110,2788,4200,6400,10000,16000];
end
syncrnfreq(:,1)=crossoverfreqs(1:end-1);
syncrnfreq(:,2)=crossoverfreqs(2:end);
for i=1:channum
    cf(i) = sqrt( syncrnfreq(i,1)*syncrnfreq(i,2) );
end

% Pulse train parameters
Gamma = alpha*cf;  
if extendedrange == 1
    Gamma(9) = 1412;
    Gamma(10) = 2200;
    Gamma(11) = 3600;
    Gamma(12) = 6000;
end
N = duration*GaussRate; % number of pulses
fN = floor(N);
Genv=zeros(fN,nsamples);
GETtrain=zeros(channum,nsamples);

% Make pulse trains
for i = 1:channum
    Teff = 1000/Gamma(i);
    if Teff > 3.75
    % if modulation depth is not 100%, make pulse train then modulate
        for n = 1:N
            % delay pulses by half a period so first Gaussian pulse doesn't
            % start at a max
            T = (n-0.5)/N*duration;
            Genv(n,:) = sqrt(Gamma(i)) * exp(-pi*(Gamma(i)*(t-T)).^2);
        end
        Genv_train(i,:) = sum(Genv);
        %modulate carrier
        GETtrain(i,:) = Genv_train(i,:) .* sin(2*pi*cf(i)*t);
        %normalize energy
        Energy(i) = norm(GETtrain(i,:))/sqrt(length(t));
%         Energy(i) = rms(GETtrain(i,:)); % !!!!!!!!!!!!
        GETtrain(i,:) = GETtrain(i,:)/Energy(i);
    else
    % if modulation depth is 100%, make modulated pulses and replicate
        T=(0.5)/N*duration;
        Genv=zeros(fN,nsamples);
        Genv(1,:) = sqrt(Gamma(i)) * exp(-pi*(Gamma(i)*(t-T)).^2) .* sin(2*pi*cf(i)*t - T + pi/4); %!!! (t-T)
        Genv=repmat(Genv(1,:),[fN 1]);
        for n=1:N
            T = round((n)/N*nsamples);
            Genv(n,:)=circshift(Genv(n,:),[1 T-1]);
        end
        GETtrain(i,:) = sum(Genv);
        %normalize energy
        Energy(i) = norm(GETtrain(i,:))/sqrt(length(t));
%         Energy(i) = rms(GETtrain(i,:)); % !!!!!!!!!!!!
        GETtrain(i,:) = GETtrain(i,:)/Energy(i);
    end
end

end

function out=channelize(fwavout, h, h0, in, channum, corners, syncrnfreq, ...
                        GETtrain, stimpar, amp, fadein, fadeout)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% *** v1.2.0                                                           %
% GET Vocoder scaled by energy, not envelope                           %
%                                                                      %
% *** v1.1.0                                                           %
% Added Gaussian Envelope Tone (GET) Vocoder                           %
% GET pulse train is generated and passed to this function             %
% M. Goupell, May 2008                                                 %
%                                                                      %
% *** v1.0.0                                                           %
% Modified from ElecRang/matlab/makewav.m  v1.3.1                      %
% To be used with Loca by M. Goupell, Nov 2007                         % 
% Now program receives a sound rather than reading a speech file       %
% Rewrote according to the specifications (PM, Jan. 2008)              %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% h = hrtf
% h0 = hrtf for reference position
% in = reference noise
% noise = number of channels of noise vectors

srate=stimpar.SamplingRate;
N=length(in);               % length of sound record
d=0.5*srate;                % frequency scalar

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Read corner frequencies for channels                %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Synthesis: These are the crossover frequencies that the output signal is mapped to
% calculated now in GETVocoder, passed to this function

% Analysis: These are the crossover frequencies that the input signal is subdivided by
if length(corners)<=channum
  error('You need at least one more corner frequency than number of channels');
end
anacrnfreq(:,1)=corners(1:end-1);
anacrnfreq(:,2)=corners(2:end);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Analysis: Filter Signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
inX=fftfilt(h,in);

order=4;     %order of butterworth filter
% out=zeros(1,N);
% filtX=zeros(channum,N);

for i=1:channum
   [b, a]=butter(order, anacrnfreq(i,:)/d);
   out=filter(b, a, inX);  % bandpass-filtering
%    E(i)=norm(out,1)/sqrt(N);
   E(i) = rms(out);
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Synthesis                                                      %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% each channel
for i=1:channum
    outX(i,:) = GETtrain(i,:) * E(i);    
end
% sum me up scotty
out=sum(outX,1);

% for i = 1:channum
%     subplot(channum/3,3,i)
%     plot(outX(i,(1:480)))
% end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Save file                                                      %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

out=out*(10^(amp/20))/sqrt(sum(out.^2))*sqrt(sum(in.^2))*sqrt(sum(h.^2))/sqrt(sum(h0.^2));
% amt_disp(20*log10(sqrt(sum(out.^2))));
ii=max(max(abs(out)));
if ii>=1
  error(['Maximum amplitude value is ' num2str(20*log10(ii)) 'dB. Set the HRTF scaling factor lower to avoid clipping']);
end
out=FW_fade(out,0,fadein,fadeout);
% wavwrite(out,srate,stimpar.Resolution,fwavout);
end

function out = FW_fade(inp, len, fadein, fadeout, offset)
% FW_FADE crop/extend and fade in/out a vector.
%
% OUT = FW_FADE(INP, LEN, FADEIN, FADEOUT, OFFSET) crops or extends with zeros the signal INP
% up to length LEN. Additionally, the result is faded in/out using HANN window with 
% the length FADEIN/FADEOUT, respectively. If given, an offset can be added to show
% where the real signal begins.
%
% When used to crop signal, INP is cropped first, then faded out. 
% When used to extend signal, INP is faded out first, then extended too provide fading.
% 
% INP:     vector with signal (1xN or Nx1)
% LEN:     length of signal OUT (without OFFSET)
% FADEIN:  number of samples to fade in, beginning from OFFSET
% FADEOUT: number of samples to fade out, ending at the end of OUT (without OFFSET)
% OFFSET:  number of offset samples before FADEIN, optional
% OUT:     cropped/extended and faded vector
% 
% Setting a parameter to 0 disables corresponding functionality.

% ExpSuite - software framework for applications to perform experiments (related but not limited to psychoacoustics).
% Copyright (C) 2003-2010 Acoustics Research Institute - Austrian Academy of Sciences; Piotr Majdak and Michael Mihocic
% Licensed under the EUPL, Version 1.1 or ? as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "Licence")
% You may not use this work except in compliance with the Licence. 
% You may obtain a copy of the Licence at: http://ec.europa.eu/idabc/eupl
% Unless required by applicable law or agreed to in writing, software distributed under the Licence is distributed on an "AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
% See the Licence for the specific language governing  permissions and limitations under the Licence. 

% 7.11.2003
% 22.08.2005: improvement: INP may be 1xN or Nx1 now.
% Piotr Majdak (piotr@majdak.com)

ss=length(inp);    % get length of inp
	% offset
if ~exist('offset','var')
	offset = 0;
end
offset=round(offset);
len=round(len);
fadein=round(fadein);
fadeout=round(fadeout);
if offset>ss
	error('OFFSET is greater than signal length');
end
	% create new input signal discarding offset
inp2=inp(1+offset:end);
ss=length(inp2);

  % fade in
if fadein ~= 0 
  if fadein > ss
    error('FADEIN is greater than signal length');
  end
  han=hanning(fadein*2);
  if size(inp2,1)==1
    han=han';
  end
  inp2(1:fadein) = inp2(1:fadein).*han(1:fadein);
end
  % fade out window
  
if len == 0    
  len = ss;
end
if len <= ss
    % crop and fade out
  out=inp2(1:len);
    % fade out
  if fadeout ~= 0
    if fadeout > len
      error('FADEOUT is greater than cropped signal length');
    end
    han = hanning(2*fadeout);
    if size(out,1)==1
      han=han';
    end
    out(len-fadeout+1:end)=out(len-fadeout+1:end).*han(fadeout+1:end);
  end
else
    % fade out and extend
  if fadeout ~= 0
    if fadeout > ss
      error('FADEOUT is greater than signal length');
    end
    han = hanning(2*fadeout);
    inp2(ss-fadeout+1:end)=inp2(ss-fadeout+1:end).*han(fadeout+1:end);
  end
  out = [zeros(offset,1); inp2; zeros(len-ss,1)];
end
end

function middlebroxplot(x,quantiles,MarkerSize)

lilen = 0.14; % length of horizontal lines

% Symbols
plot(x,quantiles(1),'kx','MarkerSize',MarkerSize) % min
hold on
plot(x,quantiles(7),'kx','MarkerSize',MarkerSize) % max

% Horizontal lines
line(x+0.5*[-lilen,lilen],repmat(quantiles(2),2),'Color','k') % lower whisker
line(x+[-lilen,lilen],repmat(quantiles(3),2),'Color','k') % 25% Quartile
line(x+[-lilen,lilen],repmat(quantiles(4),2),'Color','k') % Median
line(x+[-lilen,lilen],repmat(quantiles(5),2),'Color','k') % 75% Quartile
line(x+0.5*[-lilen,lilen],repmat(quantiles(6),2),'Color','k') % upper whisker

% Vertical lines
line([x,x],quantiles(2:3),'Color','k') % connector lower whisker
line([x,x],quantiles(5:6),'Color','k') % connector upper whisker
line([x,x]-lilen,quantiles([3,5]),'Color','k') % left box edge
line([x,x]+lilen,quantiles([3,5]),'Color','k') % left box edge

end

function [idx,posN] = findNearestPos_locaVBAP(posdesired,posexist)
% FINDNEARESTPOS_LOCAVABAP finds nearest position. Data given in spherical
% coordinates
%
% Usage:    [idx,posN] = findNearestPos(posdesired,posexist)

ds = deg2rad(posdesired);
es = deg2rad(posexist);

[d(:,1),d(:,2),d(:,3)] = sph2cart(ds(:,1),ds(:,2),ones(size(ds,1),1));
[e(:,1),e(:,2),e(:,3)] = sph2cart(es(:,1),es(:,2),ones(size(es,1),1));

D = e-repmat(d,length(es),1);
[Dmin,idx] = min(sum(D.^2,2));

posN = posexist(idx,:);

end

function [latN,polN,idx] = findNearestPos_locaVBAP_ref(lat,pol,aziele)
% FINDNEARESTPOS_LOCAVBAP_REF finds nearest position according to lat. and pol. angle
%
% Usage:    [latN,polN,idx] = findNearestPos(lat,pol,aziele)

[positions(:,1),positions(:,2)] = sph2hor(aziele(:,1),aziele(:,2));
d_lat = abs(lat-positions(:,1));
d_pol = abs(pol-positions(:,2));
d = d_lat+d_pol;
[d_min,idx] = min(d);
latN = positions(idx,1);
polN = positions(idx,2);

end

function [g,IDspeaker] = vbap(lsp_coord,source_pos)
%VBAP Returns array of gains for VBAP triplet of speakers.         
%   Usage: [g,IDspeaker] = vbap(speaker_coord,source_pos)
%
%   Input Parameters:
%     lsp_coord  : spherical (azi,ele) coordinates of loudspeakers
%     source_pos : spherical (azi,ele) coordinates of phantom source
%
%   Output Parameters:
%     g          : VBAP gains of loudspeaker triplet
%     IDspeaker  : indices of selected loudspeaker triplet

Nlsp = size(lsp_coord,1);

% Convert to spherical coordinates winth angles in radians
speaker_sph = deg2rad(lsp_coord);
source_sph = deg2rad(source_pos);

% Convert to cartesian coordinates
[speaker_cart(:,1),speaker_cart(:,2),speaker_cart(:,3)] = sph2cart(...
  speaker_sph(:,1),speaker_sph(:,2),ones(Nlsp,1));
[source_cart(1),source_cart(2),source_cart(3)] = sph2cart(...
  source_sph(1),source_sph(2),1);

% Add imaginary speaker below
if min(lsp_coord(:,2)) >= 0
  speaker_cart = [speaker_cart;0,0,-10];
end

% Select lsp. triplet
dt = DelaunayTri(speaker_cart);
ch = convexHull(dt);
% figure; trisurf(ch, dt.X(:,1),dt.X(:,2),dt.X(:,3), 'FaceColor', 'cyan')
d = zeros(length(ch),1);
for ii = 1:length(ch)
  d(ii) = sum(dist(source_cart,speaker_cart(ch(ii,:),:)));
end
[~,IDch] = min(d);
IDspeaker = ch(IDch,:);

% Compute lsp. gains
g = source_cart / speaker_cart(IDspeaker,:);
g = g / norm(g);


end

function d = dist(x,Y)

d = sqrt(sum((repmat(x,size(Y,1),1)-Y).^2,2));

end