function data = exp_baumgartner2017(varargin)
%EXP_BAUMGARTNER2017 - Experiments of Baumgartner et al. (2017)
%   Usage: data = exp_baumgartner2017(flag) 
%
%   EXP_BAUMGARTNER2017(flag) reproduces figures of the study from 
%   Baumgartner et al. (2017).
%
%   The following flags can be specified
%
%     boyd2012 models experiments from Boyd et al. (2012; Fig.1, top).
%         Average externalization ratings of 1 talker for NH participants 
%         against mix point as a function of microphone position (ITE/BTE) 
%         and frequency response (BB/LP). The reference condition (ref) is 
%         the same as ITE/BB. Error bars show SEM. 
%
%     hartmann1996 models experiments from Hartmann & Wittenberg (1996; Fig.7-8)
%         1st panel: Synthesis of zero-ILD signals. Only the harmonics 
%         from 1 to nprime had zero interaural level difference; 
%         harmonics above nprime retained the amplitudes of the baseline  
%         synthesis. Externalization scores as a function of the boundary  
%         harmonic number nprime. Fundamental frequency of 125 Hz.
%         2nd panel: Synthesis of signals to test the ISLD hypothesis. 
%         Harmonics at and below the boundary retained only the interaural 
%         spectral level differences of the baseline synthesis. Higher 
%         harmonics retained left and right baseline harmonic levels. 
%         Externalization scores as a function of the boundary
%         frequency.
%
%     hassager2016 models experiments from Hassager et al. (2016; Fig.6). 
%         The mean of the seven listeners perceived sound source 
%         location (black) as a function of the bandwidth factor 
%         and the corresponding model predictions (colored). 
%         The model predictions have been shifted slightly to the right 
%         for a better visual interpretation. The error bars are one 
%         standard error of the mean.
%
%   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/baumgartner2017
%
%   3) Statistics Toolbox for Matlab (for some of the figures)
%
%   Examples:
%   ---------
%
%   To display results for Fig.1 from Boyd et al. (2012) use :
%
%     exp_baumgartner2017('boyd2012');
%
%   To display results for Fig.7-8 from Hartmann & Wittenberg (1996) use :
%
%     exp_baumgartner2017('hartmann1996');
%
%   To display results for Fig.6 from Hassager et al. (2016) use :
%
%     exp_baumgartner2017('hassager2016');
%
%   References:
%     R. Baumgartner, P. Majdak, H. Colburn, and B. Shinn-Cunningham.
%     Modeling sound externalization based on listener-specific spectral
%     cues. In Acoustics â17 Boston: The 3rd Joint Meeting of the Acoustical
%     Society of America and the European Acoustics Association, Boston, MA,
%     Jun 2017.
%     
%     A. W. Boyd, W. M. Whitmer, J. J. Soraghan, and M. A. Akeroyd. Auditory
%     externalization in hearing-impaired listeners: The effect of pinna cues
%     and number of talkers. J. Acoust. Soc. Am., 131(3):EL268-EL274, 2012.
%     [1]arXiv | [2]www: ]
%     
%     W. M. Hartmann and A. Wittenberg. On the externalization of sound
%     images. J. Acoust. Soc. Am., 99(6):3678-88, June 1996.
%     
%     H. G. Hassager, F. Gran, and T. Dau. The role of spectral detail in the
%     binaural transfer function on perceived externalization in a
%     reverberant environment. J. Acoust. Soc. Am., 139(5):2992-3000, 2016.
%     [3]arXiv | [4]www: ]
%     
%     References
%     
%     1. http://arxiv.org/abs/%20http://dx.doi.org/10.1121/1.3687015
%     2. http://dx.doi.org/10.1121/1.3687015
%     3. http://arxiv.org/abs/http://dx.doi.org/10.1121/1.4950847
%     4. http://dx.doi.org/10.1121/1.4950847
%     
%
%   Url: http://amtoolbox.sourceforge.net/amt-0.9.8/doc/experiments/exp_baumgartner2017.php

% Copyright (C) 2009-2015 Piotr Majdak and Peter L. Søndergaard.
% This file is part of AMToolbox version 0.9.8
%
% 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, Acoustics Research Institute, Vienna, Austria

definput.import={'amt_cache'};
definput.flags.type = {'missingflag','boyd2012','hartmann1996','hassager2016'};
definput.flags.quickCheck = {'','quickCheck'};
definput.keyvals.Sintra = 2;
definput.keyvals.Sinter = 2;

[flags,kv]  = ltfatarghelper({},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

%% Hassager et al. (2016)
if flags.do_hassager2016
  azi = [0,50];
  flp = 6000; % lowpass filtered noise stimuli
  
  Pext_A = data_hassager2016;
  B = Pext_A.B;
  
  fncache = ['hassager2016_Sintra',num2str(kv.Sintra*100,'%i'),'_Sinter',num2str(kv.Sinter*100,'%i')];
  Pext = amt_cache('get',fncache,flags.cachemode);
  if isempty(Pext)
    
    data = data_baumgartner2017;
    if flags.do_quickCheck
      data = data(1:5);
    end

    Pext = nan(length(B),length(data),length(azi));
    Pext = {Pext,Pext};
    for isubj = 1:length(data)
      Obj = data(isubj).Obj;
      for iazi = 1:length(azi)
        idazi = Obj.SourcePosition(:,1) == azi(iazi) & Obj.SourcePosition(:,2) == 0;
        template = squeeze(shiftdim(Obj.Data.IR(idazi,:,:),2));
        for iB = 1:length(B)
          amt_disp(num2str(iB),'volatile');
          if isnan(B(iB))
            target = template;
          else
            Obj_tar = sig_hassager2016(Obj,B(iB));
            target = squeeze(shiftdim(Obj_tar.Data.IR(idazi,:,:),2));
          end
          Pext{1}(iB,isubj,iazi) = baumgartner2017(target,template,'S',kv.Sinter,'flow',100,'fhigh',flp,'interaural'); % Obj instead of single template
          Pext{2}(iB,isubj,iazi) = baumgartner2017(target,template,'S',kv.Sintra,'flow',100,'fhigh',flp,'lat',azi(iazi));
        end
      end
      amt_disp([num2str(isubj),' of ',num2str(length(data)),' subjects completed.'],'progress')
    end
     
    if not(flags.do_quickCheck)
      amt_cache('set',fncache,Pext);
    end
  end
  
  %% Plot
  BplotTicks = logspace(log10(0.25),log10(64),9);
  BplotTicks = round(BplotTicks*100)/100;
  BplotStr = ['Ref.';num2str(BplotTicks(:))];
  BplotTicks = [BplotTicks(1)/2,BplotTicks];
  B(1) = B(2)/2;
  Ns = size(Pext{1},2);
  
  figure 
  symb = {'-bs','-rd'};
  for iazi = 1:length(azi)
    subplot(1,2,iazi)
    h(3) = plot(B,Pext_A.rating(:,iazi),'-ko');
    hold on
    for m = 1:2
      h(m) = errorbar(B,mean(Pext{m}(:,:,iazi),2),std(Pext{m}(:,:,iazi),0,2)/sqrt(Ns),symb{m});
    end
    set(h,'MarkerFaceColor','w')
    set(gca,'XTick',BplotTicks,'XTickLabel',BplotStr,'XScale','log')
    axis([BplotTicks(1)/1.5,BplotTicks(end)*1.5,0.8,5.2])
    xlabel('Bandwidth Factor [ERB]')
    ylabel('Mean Externalization Rating')
    title([num2str(azi(iazi)),'\circ'])
  end
  leg = legend({'Actual','Interaural','Monaural'},'Location','southwest');
  set(leg,'Box','off')
  
end

%% Hartmann & Wittenberg (1996)
if flags.do_hartmann1996
  
  exp = {'ILD','ISLD'};
  nprime = [0,1,8,14,19,22,25,38];
  
  fncache = ['hartmann1996_Sintra',num2str(kv.Sintra*100,'%i'),'_Sinter',num2str(kv.Sinter*100,'%i')];
  Pext = amt_cache('get',fncache,flags.cachemode);
  if isempty(Pext)
    azi = -37;
    data = data_baumgartner2017;
    if flags.do_quickCheck
      data = data(1:5);
    end
    Pext{1} = nan(length(data),length(nprime),length(exp));
    Pext{2} = nan(length(data),length(nprime),length(exp));
    for isub = 1:length(data)
      Obj = data(isub).Obj;
      template = sig_hartmann1996(0,'Obj',Obj,'dur',0.1);
      for ee = 1:length(exp)
        for nn = 1:length(nprime)
          target = sig_hartmann1996(nprime(nn),'Obj',Obj,'dur',0.1,exp{ee});
          Pext{1}(isub,nn,ee) = baumgartner2017(target,template,'c1',3,'c2',0,'S',kv.Sinter,'flow',100,'fhigh',6000,'interaural');
          Pext{2}(isub,nn,ee) = baumgartner2017(target,template,'c1',3,'c2',0,'S',kv.Sintra,'flow',100,'fhigh',6000,'lat',azi);
        end
      end
      amt_disp([num2str(isub),' of ',num2str(length(data)),' subjects completed.'],'progress')
    end
    if not(flags.do_quickCheck)
      amt_cache('set',fncache,Pext);
    end
  end
  
  Ns = size(Pext{1},1);
  
  figure
  for ee = 1:length(exp)
    act = data_hartmann1996(exp{ee});
    subplot(1,2,ee)
    h(1) = plot(act.avg.nprime,act.avg.Escore,'-ko');
    hold on
    h(2) = errorbar(nprime,mean(Pext{1}(:,:,ee)),std(Pext{1}(:,:,ee))/sqrt(Ns),'-bs');
    h(3) = errorbar(nprime,mean(Pext{2}(:,:,ee)),std(Pext{2}(:,:,ee))/sqrt(Ns),'-rd');
    set(h,'MarkerFaceColor','w')
    if ee == 1
      xlabel('n\prime (Highest harmonic with ILD = 0)')
    else
      xlabel('n\prime (Highest harmonic with altered amplitudes)')
    end
    ylabel('Externalization score')
    title(exp{ee})
    axis([0,39,-0.1,3.1])
    if ee == 2
      leg = legend('Actual','Interaural cues','Monaural cues','Location','southwest');
      set(leg,'Box','off')
    end
  end
end

%% Boyd et al. (2012)
if flags.do_boyd2012
  flp = [nan,6500]; % Low-pass cut-off frequency
  ele = 0; 
  azi = -30;
  data = data_boyd2012;
  
  fncache = ['boyd2012_Sintra',num2str(kv.Sintra*100,'%i'),'_Sinter',num2str(kv.Sinter*100,'%i')];
  E = amt_cache('get',fncache,flags.cachemode);
  if isempty(E)
    
    Eboyd = cat(3,[data.ITE.BB(:),data.BTE.BB(:)],[data.ITE.LP(:),data.BTE.LP(:)]);
    mix = data.mix/100;

    %%
    sig = noise(50e3,1,'pink');
    BTE = data_baumgartner2017('BTE');
    subjects = data_baumgartner2017;
    if flags.do_quickCheck
      subjects = subjects(1:5);
    end
    fs = subjects(1).Obj.Data.SamplingRate;

    Ebaum = nan([size(Eboyd),length(subjects)]);
    Ehass = Ebaum;
    for isub = 1:length(subjects)
      ITE = subjects(isub).Obj;

      %% Unmixed stimuli
      stim{1} = SOFAspat(sig,ITE,azi,ele);
      stim{2} = SOFAspat(sig,BTE.Obj,azi,ele);
      template = stim{1};

      %% Model simulations
      target = cell(length(mix),2,2);
      fhigh = [16e3,flp(2)];
      for c = 1:2
        for lp = 1:2
          for m = 1:length(mix)
            target{m,c,lp} = sig_boyd2012(stim{c},sig,mix(m),flp(lp),fs);
% % Description in paper is ambiguous about whether broadband ILDs were
% % adjusted to the template:
%           temSPL = dbspl(template); 
%             for ch = 1:2
%               target{m,c,lp}(:,ch) = setdbspl(target{m,c,lp}(:,ch),temSPL(ch));
%             end
            Ebaum(m,c,lp,isub) =  baumgartner2017( target{m,c,lp},template,...
              'S',kv.Sintra,'flow',100,'c1',100,'c2',0,'fhigh',fhigh(1),'lat',azi);
            Ehass(m,c,lp,isub) =  baumgartner2017( target{m,c,lp},template,...
              'S',kv.Sinter,'flow',100,'c1',100,'c2',0 ,'fhigh',fhigh(1),'interaural');
          end
        end
      end
      amt_disp([num2str(isub),' of ',num2str(length(subjects)),' subjects completed.'],'progress')

    end

    seEbaum = std(Ebaum,0,4);
    Ebaum = mean(Ebaum,4);
    seEhass = std(Ehass,0,4);
    Ehass = mean(Ehass,4);
    seEboyd = 0*seEhass;

    E.m = {Eboyd,Ehass,Ebaum};
    E.se = {seEboyd,seEhass,seEbaum};
    
    if not(flags.do_quickCheck)
      amt_cache('set',fncache,E);
    end
  end

  %% Plot
  figure
  dataLbl = {'Actual','Interaural','Monaural'};
  symb = {'ko','bs','rd'};
  condLbl = {'ITE / BB','BTE / BB','ITE / LP','BTE / LP'};
  hax = tight_subplot(1,4,0,[.15,.1],[.1,.05]);
  for cc = 1:length(condLbl)
    axes(hax(cc))
    for ee = 1:length(E.m)
      h = errorbar(data.mix,E.m{ee}(:,cc),E.se{ee}(:,cc),['-',symb{ee}]);
      set(h,'MarkerFaceColor','w')
      hold on
    end
    set(gca,'XDir','reverse')
    xlabel('Mix (%)')
    if cc == 1
      ylabel('Externalization score')
    else
      set(gca,'YTickLabel',[])
    end
    title(condLbl{cc})
    axis([-20,120,-5,105])
    if cc == 4
      leg = legend(dataLbl);
      set(leg,'Box','off','Location','north')
    end
  end
%   set(leg,'Location','eastoutside','Position',get(leg,'Position')+[.1,.2,0,0])
end







%%%%%%%%%% INTERNAL FUNCTIONS FOR VISUALIZATION %%%%%%%%%%
function ha = tight_subplot(Nh, Nw, gap, marg_h, marg_w)

% tight_subplot creates "subplot" axes with adjustable gaps and margins
%
% ha = tight_subplot(Nh, Nw, gap, marg_h, marg_w)
%
%   in:  Nh      number of axes in hight (vertical direction)
%        Nw      number of axes in width (horizontaldirection)
%        gap     gaps between the axes in normalized units (0...1)
%                   or [gap_h gap_w] for different gaps in height and width 
%        marg_h  margins in height in normalized units (0...1)
%                   or [lower upper] for different lower and upper margins 
%        marg_w  margins in width in normalized units (0...1)
%                   or [left right] for different left and right margins 
%
%  out:  ha     array of handles of the axes objects
%                   starting from upper left corner, going row-wise as in
%                   going row-wise as in
%
%  Example: ha = tight_subplot(3,2,[.01 .03],[.1 .1],[.1 .1])
%           for ii = 1:6; axes(ha(ii)); plot(randn(10,ii)); end
%           set(ha(1:4),'XTickLabel',''); set(ha,'YTickLabel','')

% Pekka Kumpulainen 20.6.2010   @tut.fi
% Tampere University of Technology / Automation Science and Engineering


if nargin<3; gap = .02; end
if nargin<4 || isempty(marg_h); marg_h = .05; end
if nargin<5; marg_w = .05; end

if numel(gap)==1; 
    gap = [gap gap];
end
if numel(marg_w)==1; 
    marg_w = [marg_w marg_w];
end
if numel(marg_h)==1; 
    marg_h = [marg_h marg_h];
end

axh = (1-sum(marg_h)-(Nh-1)*gap(1))/Nh; 
axw = (1-sum(marg_w)-(Nw-1)*gap(2))/Nw;

py = 1-marg_h(2)-axh; 

ha = zeros(Nh*Nw,1);
ii = 0;
for ih = 1:Nh
    px = marg_w(1);
    
    for ix = 1:Nw
        ii = ii+1;
        ha(ii) = axes('Units','normalized', ...
            'Position',[px py axw axh], ...
            'XTickLabel','', ...
            'YTickLabel','');
        px = px+axw+gap(2);
    end
    py = py-axh-gap(1);
end
end

end