%DEMO_BAUMGARTNER2014 Demo for sagittal-plane localization model from Baumgartner et al. (2014)
%
%   DEMO_BAUMGARTNER2014(flag) demonstrates how to compute and visualize
%   the baseline prediction (localizing broadband sounds with own ears)
%   for a listener of the listener pool and the median plane using the
%   sagittal-plane localization model from Baumgartner et al. (2014).
%
%   Figure 1: Baseline prediction
%
%      This demo computes the baseline prediction (localizing broadband
%      sounds with own ears) for an exemplary listener (NH58).
%
%      Predicted polar response angle probability of subject NH58 as a
%      function of the polar target angle with probabilities encoded by
%      brigthness.
%
%   See also: baumgartner2014 exp_baumgartner2014 baumgartner2014_virtualexp
%   localizationerror
%
%   Url: http://amtoolbox.org/amt-1.6.0/doc/demos/demo_baumgartner2014.php


%   #Author: Robert Baumgartner (2014)

% This file is licensed unter the GNU General Public License (GPL) either
% version 3 of the license, or any later version as published by the Free Software
% Foundation. Details of the GPLv3 can be found in the AMT directory "licences" and
% at <https://www.gnu.org/licenses/gpl-3.0.html>.
% You can redistribute this file and/or modify it under the terms of the GPLv3.
% This file is distributed without any warranty; without even the implied warranty
% of merchantability or fitness for a particular purpose.

%% Check dependencies
if ~exist('regress') && isoctave
  disp('Warning: !!!!!!!!!!')
  disp('Missing dependencies')
  disp('Please install statistics package either')
  disp('pkg install -Forge statistics')
  disp('or (if Ubuntu)')
  disp('sudo apt install octave-statistics')
  disp('and load them')
  disp('pkg load statistics')
  disp('!!!!!!!!!!')
  return
end

%% Settings

subID = 'NH58';   % subject ID of exemplary listener
lat = 0;          % lateral target angle in degrees
runs = 3;         % # of virtual experimental runs


%% Get listener's data

s = data_baumgartner2014('pool');   % load data of listener pool
ids = find(ismember({s.id},subID));  % index of exemplary listener


%% Run model with individual sensitivity S

[p,rang,tang] = baumgartner2014(s(ids).Obj,s(ids).Obj,'S',s(ids).S,'lat',lat);


%% Run virtual experiment

m = baumgartner2014_virtualexp(p,tang,rang,'runs',2);


%% Calcualte performance measures

amt_disp('Performance Predictions:','documentation');
amt_disp('------------------------','documentation');

% via expectancy values:
[qe,pe] = baumgartner2014_pmv2ppp(p,tang,rang,'print');

% and/or via responses drawn from virtual experiments
[f,r] = localizationerror(m,'sirpMacpherson2000');
perMacpherson2003 = localizationerror(m,f,r,'perMacpherson2003');
amt_disp(['Local polar error rate (%)        ' num2str(perMacpherson2003,'%4.1f')],'documentation');


%% Plot results

figure;
plot_baumgartner2014(p,tang,rang,m(:,6),m(:,8));
title(['Baseline prediction for ' s(ids).id]);