%demo_reijniers2014 Demo for the spherical localization model REIJNIERS2014
%
%   DEMO_REIJNIERS2014 demonstrates how to compute and visualize 
%   the baseline prediction (localizing broadband sounds with own ears) 
%   on the full sphere using the localization model from Reijniers et al. (2014).
%
%   Figure 1: Baseline prediction
% 
%      Averaged polar and lateral accuracy for an exemple listener (NH12)
%      localizing broadband sounds with own ears.
%   
%
%   See also: reijniers2014 exp_reijniers2014 reijniers2014_featureextraction
%
%   Url: http://amtoolbox.org/amt-1.6.0/doc/demos/demo_reijniers2014.php


%   #Author: Roberto Barumerli (2019): Original implementation for the AMT. 

% 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. 

%% Settings

subID = 'NH12';   % subject ID of exemplary listener
az = 15;         % azimuth target angle in degrees
el = 30;         % elevation target angle in degrees
num_exp = 100;      % # of virtual experimental runs
display_level = 'no_debug'; % set to 'debug' to see more information, set to 'no_debug' to have less mess on your display

assert(numel(az)==numel(el))

amt_disp('Experiment conditions:','documentation');
for i=1:length(az)
    amt_disp(sprintf('  Azimuth: %0.1f deg; Elevation: %0.1f deg', az(i), el(i)),'documentation');
end
amt_disp(sprintf('  Repetitions: %i', num_exp),'documentation');
amt_disp('------------------------','documentation');


%% Get listener's data
SOFA_obj = amt_load('baumgartner2013', 'ARI_NH12_hrtf_M_dtf 256.sofa');


%% Preprocessing source information for both directions
[template, target] = reijniers2014_featureextraction(SOFA_obj, ... 
                    'targ_az', az, 'targ_el', el, display_level);

%% Run virtual experiments
[doa, params] = reijniers2014(template, target, 'num_exp', num_exp, display_level);

%% Calcualte performance measures 
amt_disp('Performance Predictions:','documentation');

lat_acc = reijniers2014_metrics(doa, 'accL');
pol_acc = reijniers2014_metrics(doa, 'accP');

amt_disp(sprintf('  Lateral accuracy: %0.2fdeg', lat_acc),'documentation');
amt_disp(sprintf('  Polar accuracy: %0.2fdeg', pol_acc),'documentation');

%% Plot results
plot_reijniers2014(params.template_coords, ...
                   squeeze(params.post_prob(1, 1, :)), ...
                   'target', doa.real(1,:));
title('Posterior probability density of the first experiment');