THE AUDITORY MODELING TOOLBOX
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EXP_ZIEGELWANGER2014 - Figures from Ziegelwanger and Majdak (2014)
Program code:
function exp_ziegelwanger2014(varargin)
%EXP_ZIEGELWANGER2014 Figures from Ziegelwanger and Majdak (2014)
% Usage: data = exp_ziegelwanger2014(flag)
%
% EXP_ZIEGELWANGER2014(flags) reproduces figures of the paper from
% Ziegelwanger and Majdak (2014).
%
% The following flags can be specified:
%
% 'redo' Recalculate results.
% 'cached' Use cached results. Default.
%
% 'fig3' Reproduce Fig. 3:
%
% TOAs resulting from TOA estimators in the horizontal plane
% applied on calculated HRTFs of the objects Sphere, SAT,
% STP, as well on measured HRTFs of an exemplary listener
% (NH89, ARI).
%
% 'fig5' Reproduce Fig. 5:
%
% Estimated on-axis model parameters from HRTFs calculated
% for the centered object Sphere. Condition: combination of
% actual parameters used in HRTF simulation. Circle and
% cross: Parameters estimated for the left and right ears,
% respectively. Gray lines: Actual parameters.
%
% 'fig6' Reproduce Fig. 6:
%
% Estimated on-axis model parameters from left-ear (top row)
% and right-ear (bottom row) HRTFs of human listeners. Lines:
% normal distribution fitted to the data. phi_e: Positive and
% negative values corresponding to the left and right ears,
% respectively.
%
% 'fig7' Reproduce Fig. 7:
%
% Estimated on-axis model IRDs from acoustically measured
% HRTFs of listeners. Line: normal distribution fitted to the
% data.
%
% 'fig8' Reproduce Fig. 8:
%
% Relative TOAs (top row) and on-axis model fit residuals
% (bottom row) from HRTFs of STP (left column) and of an
% exemplary listener (NH89, ARI; right column). Black points:
% data classified as outliers by the ESD test with an upper
% bound of outlier rate of 1% (see ziegelwanger2014). The
% reference for the relative TOAs is the smallest TOA in each
% HRTF set. Horizontal lines: pm1 sampling interval.
%
% 'fig9' Reproduce Fig. 9:
%
% Relative TOAs of an exemplary listener (NH89, ARI) in the
% interaural horizontal plane for the left (black) and right
% (gray) ears as results from the MCM estimator (symbols) and
% the on-axis model (lines). The reference for the relative
% TOAs is the smallest TOA in HRTF sets of both ears.
%
% 'fig10' Reproduce Fig. 10:
%
% Estimated on-axis model parameters from HRTFs calculated
% for the non-centered object Sphere. Other details as in
% Fig. 5.
%
% 'fig12' Reproduce Fig. 12:
%
% Estimated on-axis model parameters from HRTFs calculated
% for the non-centered object Sphere. Other details as in
% Fig. 5. phi_e: Positive and negative values correspond to
% the left and right ears, respectively.
%
% 'tab1' Reproduce Tab. 1:
%
% ANRs and parameter errors (average pm1 standard deviation)
% resulting from fitting the on-axis model to TOAs estimated
% from HRTFs of the Sphere. Parameter errors: Differences
% between the estimated and actual parameters.
%
% 'tab2' Reproduce Tab. 2:
%
% Parameters and ANRs resulting from fitting the on-axis
% model to TOAs estimated from HRTFs of the objects Sphere,
% SAT, STP. Actual parameters: r=87,5 mm, phi_e=90deg, and
% theta_e=0deg.
%
% 'tab3' Reproduce Tab. 3:
%
% Parameters (average pm1 standard deviation) and ANRs
% (median) resulting from fitting the on-axis model to TOAs
% estimated from acoustically measured HRTFs of human
% listeners. L: Left ear. R: Right ear. All: Results for all
% listeners. NH89: Results for a single listener (NH89, ARI).
%
% 'tab5' Reproduce Tab. 5:
%
% Parameters and ANRs (average pm1 standard deviation)
% resulting from fitting the off-axis model to TOAs estimated
% from HRTFs of SAT, STP, and all listeners (All). Full: Fits
% to full TOA sets. O-A: Fits to the outlier-adjusted TOA
% sets. L: Left ear. R: Right ear.
%
% 'tab6' Reproduce Tab. 6:
%
% ANRs and parameter errors (average pm1 standard deviation)
% resulting from fitting the off-axis model to TOAs estimated
% from HRTFs of the object Sphere. Centered: Conditions in
% which r and vec(e) varied from M=0. Non-centered:
% Conditions in which M varied. Other defails as in Tab. 5.
%
% Requirements:
% -------------
%
% 1) SOFA API from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
%
% 2) Optimization Toolbox for Matlab
%
% 3) Optional: Data in hrtf/ziegelwanger2014. Will be downloaded on demand.
%
% Examples:
% ---------
%
% To display Fig. 3, use :
%
% exp_ziegelwanger2014('fig3');
%
% To display Fig. 5, use :
%
% exp_ziegelwanger2014('fig5');
%
% To display Fig. 6, use :
%
% exp_ziegelwanger2014('fig6');
%
% To display Fig. 7, use :
%
% exp_ziegelwanger2014('fig7');
%
% To display Fig. 8, use :
%
% exp_ziegelwanger2014('fig8');
%
% To display Fig. 9, use :
%
% exp_ziegelwanger2014('fig9');
%
% To display Fig. 10, use :
%
% exp_ziegelwanger2014('fig10');
%
% To display Fig. 12, use :
%
% exp_ziegelwanger2014('fig12');
%
% To display Tab. 1, use :
%
% exp_ziegelwanger2014('tab1');
%
% To display Tab. 2, use :
%
% exp_ziegelwanger2014('tab2');
%
% To display Tab. 3, use :
%
% exp_ziegelwanger2014('tab3');
%
% To display Tab. 5, use :
%
% exp_ziegelwanger2014('tab5');
%
% To display Tab. 6, use :
%
% exp_ziegelwanger2014('tab6');
%
% See also: ziegelwanger2014, ziegelwanger2014_onaxis,
% ziegelwanger2014_offaxis, data_ziegelwanger2014
%
% References:
% H. Ziegelwanger and P. Majdak. Modeling the direction-continuous
% time-of-arrival in head-related transfer functions. J. Acoust. Soc.
% Am., 135:1278--1293, 2014.
%
%
% Url: http://amtoolbox.sourceforge.net/amt-0.10.0/doc/experiments/exp_ziegelwanger2014.php
% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 0.10.0
%
% 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: Harald Ziegelwanger, Acoustics Research Institute, Vienna,
% Austria
%% ------ Check input options --------------------------------------------
definput.flags.type = {'missingflag',...
'fig3','fig5','fig6','fig7','fig8','fig9',...
'fig10','fig12','tab1','tab2','tab3','tab5',...
'tab6'};
definput.import={'amt_cache'}; % get the flags of amt_cache
% Parse input options
[flags,~] = 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;
%% Figure 3
if flags.do_fig3
%load data
Obj{1}=data_ziegelwanger2014('Sphere',flags.cachemode);
Obj{2}=data_ziegelwanger2014('SAT',flags.cachemode);
Obj{3}=data_ziegelwanger2014('STP',flags.cachemode);
Obj{4}=data_ziegelwanger2014('NH89',flags.cachemode);
%plot figure
figure('Position',[ 520 221 732 577]);
methodLabel=['MAX';'CTD';'AGD';'MCM'];
sty=[': ';'-.';'- ';'--'];
clr=[0 0 0;...
0 0 0;...
0 0 0;...
0 0 0];
lw=[3 1 1 1];
for method=1:4
subplot(2,2,method)
for hrtf=1:4
if hrtf==4
Obj{4}.Data.toaEst{method}=Obj{4}.Data.toaEst{method}+110;
end
h=plot_ziegelwanger2014(Obj{hrtf},Obj{hrtf}.Data.toaEst{method},4,'k',0,1,1,sty(hrtf,:),lw(hrtf));
set(h,'color',clr(method,:));
hold on
end
ylabel('TOA (ms)','Fontname','Arial','Fontsize',14);
xlabel('');
grid off
ylim([2.9,4.3])
xlim([-10,370])
set(gca,'Fontname','Arial','Fontsize',10)
title('')
switch(method)
case 1
xlabel('')
case 2
ylabel('')
xlabel('')
case 3
l=legend('Sphere','SAT','STP','NH89','Location','NorthWest');
set(l,'Fontsize',9,'Fontname','Arial')
case 4
ylabel('')
end
switch(method)
case 1
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.08 0.05 -0.08]);
set(gca,'xticklabel',[]);
case 2
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.05 0.08 0.05 -0.08]);
set(gca,'yticklabel',[]);
set(gca,'xticklabel',[]);
case 3
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.285 0.05 -0.08]);
set(gca,'xticklabel',{'0' '90' '180' '270' '360 '});
case 4
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.05 0.285 0.05 -0.08]);
set(gca,'yticklabel',[]);
set(gca,'xticklabel',{'0' '90' '180' '270' '360 '});
end
text(300,4.1,methodLabel(method,:),'Fontname','Arial','Fontsize',14)
end
end
%% Figure 5
if flags.do_fig5
%load data
data=data_ziegelwanger2014('SPHERE_ROT',flags.cachemode);
for ii=1:length(data.results)
p_onaxis{1}(:,:,ii)=data.results(ii).MAX{1}.p_onaxis;
p_onaxis{2}(:,:,ii)=data.results(ii).CTD{1}.p_onaxis;
p_onaxis{3}(:,:,ii)=data.results(ii).AGD{1}.p_onaxis;
p_onaxis{4}(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
end
%plot figure
figure
tmp=get(gcf,'Position');
set(gcf,'Position',tmp.*[1 1 1 2]);
sym='ox';%plot symbols
ms=8;%markersize
lw1=2;%linewidth1
lw2=2;%linewidth2
fs=18;%fontsize
ls='k-';%linestyle
lc=[0.5 0.5 0.5];
h=[];
% radii
h(end+1)=subplot(411);
var=[squeeze(p_onaxis{4}(1,1,:))*1000 squeeze(p_onaxis{4}(1,2,:))*1000 data.radius(:)];
for ch=1:size(p_onaxis{4},2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
hold on
end
plot(1:14,var(1:14,3),ls,'Linewidth',lw2,'color',lc)
plot(15:28,var(15:28,3),ls,'Linewidth',lw2,'color',lc)
plot(29:42,var(29:42,3),ls,'Linewidth',lw2,'color',lc)
for ch=1:size(p_onaxis{4},2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
hold on
end
clear var;
ylabel('r (mm)','Fontname','Arial','Fontsize',fs)
ylim([72,105])
xlim([-1,44])
set(gca,'xtick',1:42)
set(gca,'ytick',[80 90 100])
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0 0 0]);
%phi
h(end+1)=subplot(412);
var=[squeeze(p_onaxis{4}(2,1,:))/pi*180 squeeze(p_onaxis{4}(2,2,:))/pi*180 data.phi+ones(length(data.phi),1)*90 data.phi-ones(length(data.phi),1)*90];
for ch=1:size(p_onaxis{4},2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
hold on
end
for ch=1:size(p_onaxis{4},2)
plot(1:9,var(1:9,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(10:14,var(10:14,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(15:23,var(15:23,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(24:28,var(24:28,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(29:37,var(29:37,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(38:42,var(38:42,2+ch),ls,'Linewidth',lw2,'color',lc)
end
for ch=1:size(p_onaxis{4},2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
hold on
end
clear var;
set(gca,'YTick',[-90 90])
ylabel(' _e (deg)','Fontname','Arial','Fontsize',fs)
xlim([-1,44])
set(gca,'xtick',1:42)
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.055 0 0]);
%theta
h(end+1)=subplot(413);
var=[squeeze(p_onaxis{4}(3,1,:))/pi*180 squeeze(p_onaxis{4}(3,2,:))/pi*180 data.theta -data.theta];
for ch=1:size(p_onaxis{4},2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
hold on
end
for ch=1:size(p_onaxis{4},2)
plot(1:9,var(1:9,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(10:14,var(10:14,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(15:23,var(15:23,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(24:28,var(24:28,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(29:37,var(29:37,2+ch),ls,'Linewidth',lw2,'color',lc)
plot(38:42,var(38:42,2+ch),ls,'Linewidth',lw2,'color',lc)
end
for ch=1:size(p_onaxis{4},2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
hold on
end
clear var;
ylabel('\theta_e (deg)','Fontname','Arial','Fontsize',fs)
xlabel('Condition','Fontname','Arial','Fontsize',fs)
ylim([-15,15])
xlim([-1,44])
set(gca,'xtick',1:42)
set(gca,'xticklabel',[])
set(gca,'ytick',[-10 0 10])
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.11 0 0]);
for ii=1:length(h)
set(h(ii),'fontsize',14)
set(h(ii),'Linewidth',2)
tmp=findobj(h(ii),'Type','patch');
set(tmp,'EdgeColor','k');
end
set(gcf,'color',[1 1 1])
end
%% Figure 6
if flags.do_fig6
%load data
hrtf={'ARI','CIPIC','LISTEN'};
for kk=1:length(hrtf)
data=data_ziegelwanger2014(hrtf{kk},flags.cachemode);
if kk==3
data.results=data.results([1:27 29:end]);
end
for ii=1:length(data.results)
temp1(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
temp2(:,1:size(data.results(ii).MCM{1}.p_offaxis,2),ii)=data.results(ii).MCM{1}.p_offaxis;
temp3(ii)=mean([data.results(ii).MCM{1}.performance.on_axis{1}.resnormS ...
data.results(ii).MCM{1}.performance.on_axis{2}.resnormS]);
temp4(ii)=mean([data.results(ii).MCM{1}.performance.off_axis{1}.resnormS ...
data.results(ii).MCM{1}.performance.off_axis{2}.resnormS]);
temp5(ii)=mean([data.results(ii).MCM{1}.performance.on_axis{1}.resnormP ...
data.results(ii).MCM{1}.performance.on_axis{2}.resnormP]);
temp6(ii)=mean([data.results(ii).MCM{1}.performance.off_axis{1}.resnormP ...
data.results(ii).MCM{1}.performance.off_axis{2}.resnormP]);
temp8(ii)=data.results(ii).MCM{1}.performance.on_axis{1}.resnormS;
temp9(ii)=data.results(ii).MCM{1}.performance.on_axis{2}.resnormS;
temp10(ii)=data.results(ii).MCM{1}.performance.off_axis{1}.resnormS;
temp11(ii)=data.results(ii).MCM{1}.performance.off_axis{2}.resnormS;
end
p_onaxis{kk}=temp1;
p_offaxis{kk}=temp2;
resnormS_onaxis{kk}=temp3;
resnormS_offaxis{kk}=temp4;
resnormP_onaxis{kk}=temp5;
resnormP_offaxis{kk}=temp6;
resnormS_onaxis_left{kk}=temp8;
resnormS_onaxis_right{kk}=temp9;
resnormS_offaxis_left{kk}=temp10;
resnormS_offaxis_right{kk}=temp11;
clear temp1 temp2 temp3 temp4 temp5 temp6 temp7 temp8 temp9 temp10 temp11
end
%plot figure
figure('PaperUnits','centimeters','PaperType','A4','Paperposition',[0, 0, 21, 29.7],'Units','centimeters','Position',[0 0 21 29.7],'Resize','off')
fs=14;%fontsize
lw=1.1;%linewidth
h=[];
%radii
temp=1;
for kk=1:length(hrtf)
var(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(mean(p_onaxis{kk}(1,:,:)*1000,2));
varl(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(p_onaxis{kk}(1,1,:)*1000);
varr(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(p_onaxis{kk}(1,2,:)*1000);
temp=size(var,1)+1;
end
h(1)=subplot(631);
colormap('gray');
binranges = 45:5:135;
bincounts=histc(varl,binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0.6 0.6 0.6],'edgecolor',[0 0 0]);
hold on
box on
[mu,sigma]=normfit(varl);
y=normpdf(binranges,mu,sigma);
plot(binranges,y*100*5,'k','linewidth',lw)
plot([-500 500],[0 0],'k');
xlim([45,135])
ylim([-1,25])
ylabel('Left ear ','Fontname','Arial','Fontsize',fs)
set(gca,'xtick',[60 80 100 120])
set(gca,'xticklabel',[]);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0 0.04 0]);
h(2)=subplot(634);
binranges = 45:5:135;
bincounts=histc(varr,binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0.6 0.6 0.6],'edgecolor',[0 0 0]);
hold on
box on
[mu,sigma]=normfit(varr);
y=normpdf(binranges,mu,sigma);
plot(binranges,y*100*5,'k','linewidth',lw)
plot([-500 500],[0 0],'k');
xlim([45,135])
ylim([-1,25])
set(gca,'xtick',[60 80 100 120])
xlabel('r (mm)','Fontname','Arial','Fontsize',fs)
ylabel('Right ear ','Fontname','Arial','Fontsize',fs)
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.03 0.04 0]);
clear var varl varr;
% phi_e
temp=1;
for kk=1:length(hrtf)
varl(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(p_onaxis{kk}(2,1,:))*180/pi;
varr(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
mod(squeeze(p_onaxis{kk}(2,2,:))*180/pi,360);
temp=size(varl,1)+1;
end
h(3)=subplot(632);
binranges = 55:2:125;
bincounts=histc(varl,binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0.6 0.6 0.6],'edgecolor',[0 0 0]);
hold on
box on
[mu,sigma]=normfit(varl);
y=normpdf(binranges,mu,sigma);
plot(binranges,y*100*2,'k','linewidth',lw)
plot([-500 500],[0 0],'k');
xlim([55,125])
ylim([-1,25])
set(gca,'xticklabel',[]);
set(gca,'yticklabel',[]);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.02 0 0.04 0]);
clear y
varr=abs(varr-360);
h(4)=subplot(635);
binranges = 55:2:125;
bincounts=histc(varr,binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0.6 0.6 0.6],'edgecolor',[0 0 0]);
hold on
box on
[mu,sigma]=normfit(varr);
y=normpdf(binranges,mu,sigma);
plot(binranges,y*100*2,'k','linewidth',lw)
plot([-500 500],[0 0],'k');
xlim([55,125])
ylim([-1,25])
xlabel(' _e (deg)','Fontname','Arial','Fontsize',fs)
set(gca,'xtick',[60 80 100 120]);
set(gca,'xticklabel',{'\pm60','\pm80','\pm100','\pm120'});
set(gca,'yticklabel',[]);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.02 0.03 0.04 0]);
clear varl varr a
% theta_e
temp=1;
for kk=1:length(hrtf)
varl(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(p_onaxis{kk}(3,1,:))*180/pi;
varr(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(p_onaxis{kk}(3,2,:))*180/pi;
temp=size(varl,1)+1;
end
h(5)=subplot(633);
binranges = -25:2:15;
bincounts=histc(varl,binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0.6 0.6 0.6],'edgecolor',[0 0 0]);
hold on
box on
[mu,sigma]=normfit(varl);
y=normpdf(binranges,mu,sigma);
plot(binranges,y*100*2,'k','linewidth',lw)
plot([-500 500],[0 0],'k');
xlim([-25,15])
ylim([-1,25])
set(gca,'xticklabel',[]);
set(gca,'yticklabel',[]);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.04 0 0.04 0]);
h(6)=subplot(636);
binranges = -25:2:15;
bincounts=histc(varr,binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0.6 0.6 0.6],'edgecolor',[0 0 0]);
hold on
box on
[mu,sigma]=normfit(varr);
y=normpdf(binranges,mu,sigma);
plot(binranges,y*100*2,'k','linewidth',lw)
plot([-500 500],[0 0],'k');
xlim([-25,15])
ylim([-1,25])
xlabel('\theta_e (deg)','Fontname','Arial','Fontsize',fs)
set(gca,'yticklabel',[]);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.04 0.03 0.04 0]);
clear varl varr
for ii=1:length(h)
set(h(ii),'linewidth',lw)
set(h(ii),'TickLength',[0.015 0.015])
set(h(ii),'Fontname','Arial','Fontsize',12)
tmp=findobj(h(ii),'Type','patch');
set(tmp,'EdgeColor','k');
end
set(gcf,'color',[1 1 1])
end
%% Figure 7
if flags.do_fig7
%load data
hrtf={'ARI','CIPIC','LISTEN'};
for kk=1:length(hrtf)
data=data_ziegelwanger2014(hrtf{kk},flags.cachemode);
if kk==3
data.results=data.results([1:27 29:end]);
end
for ii=1:length(data.results)
temp1(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
temp2(:,:,ii)=data.results(ii).MCM{1}.p_offaxis;
temp3(:,:,ii)=data.results(ii).MCM{2}.p_offaxis;
end
p_onaxis{kk}=temp1;
p_offaxis{kk,1}=temp2;
p_offaxis{kk,2}=temp3;
clear data temp1 temp2 temp3
end
data=data_ziegelwanger2014('SPHERE_ROT',flags.cachemode);
for ii=1:length(data.results)
temp(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
end
p_onaxis{4}=temp;
clear data temp
Obj=data_ziegelwanger2014('Sphere',flags.cachemode);
[~,temp]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,1);
p_onaxis{4}(:,:,end+1)=temp.p_onaxis;
clear data temp Obj
Obj=data_ziegelwanger2014('SAT',flags.cachemode);
[~,temp]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,1);
p_onaxis{4}(:,:,end+1)=temp.p_onaxis;
clear data temp Obj
Obj=data_ziegelwanger2014('STP',flags.cachemode);
[~,temp]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,1);
p_onaxis{4}(:,:,end+1)=temp.p_onaxis;
clear data temp Obj
data=data_ziegelwanger2014('SPHERE_DIS',flags.cachemode);
for ii=1:length(data.results)
temp1(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
temp2(:,:,ii)=data.results(ii).MCM{1}.p_offaxis;
end
p_onaxis{5}=temp1;
p_offaxis{5}=temp2;
clear data temp1 temp2
%plot figure
figure('PaperUnits','centimeters','PaperType','A4','Paperposition',[0, 0, 21, 29.7],'Units','centimeters','Position',[0 0 21 29.7],'Resize','off')
fs=14;%fontsize
temp=1;
for kk=1:3
varl(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(p_onaxis{kk}(1,1,:)*1000);
varr(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
squeeze(p_onaxis{kk}(1,2,:)*1000);
temp=size(varl,1)+1;
end
h(1)=subplot(621);
binranges = -105:5:105;
bincounts=histc(varl(:,1)-varr(:,1),binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0.6 0.6 0.6]);
hold on
[mu,sigma]=normfit(varl(:,1)-varr(:,1));
y=normpdf(binranges,mu,sigma);
plot(binranges,y*100*5,'k','linewidth',1.1)
ylabel('Rel. Freq.','Fontname','Arial','Fontsize',fs)
xlabel('IRD (mm)','Fontname','Arial','Fontsize',fs)
xlim([-80,85])
ylim([-1,16])
set(gca,'xtick',[-60 -30 0 30 60])
clear varl varr y
clear varl varr temp
temp=1;
for kk=1:3
varl(temp:temp+size(p_offaxis{kk,2},3)-1,:)= ...
squeeze(p_offaxis{kk,2}(1,1,:)*1000);
varr(temp:temp+size(p_offaxis{kk,2},3)-1,:)= ...
squeeze(p_offaxis{kk,2}(1,2,:)*1000);
temp=size(varl,1)+1;
end
h(2)=subplot(623);
binranges = -105:1:105;
bincounts=histc(varl(:,1)-varr(:,1),binranges);
bar(binranges,bincounts/sum(bincounts)*100,'Facecolor',[0 0 0]);
hold on
[mu,sigma]=normfit(varl(:,1)-varr(:,1));
y=normpdf(binranges,mu,sigma);
% plot(binranges,y*100*0.5,'k','linewidth',1.1)
xlabel('IRD (mm)')
ylabel('Rel. Freq.','Fontname','Arial','Fontsize',fs)
xlabel('IRD (mm)','Fontname','Arial','Fontsize',fs)
xlim([-80,85])
ylim([-3,48])
set(gca,'xtick',[-60 -30 0 30 60])
set(gca,'ytick',[0 20 40])
clear varl varr y
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.03 0 0]);
for ii=1:length(h)
set(h(ii),'Linewidth',1.1)
set(h(ii),'TickLength',[0.015 0.015])
tmp=findobj(h(ii),'Type','patch');
set(tmp,'EdgeColor','k');
end
set(gcf,'color',[1 1 1])
end
%% Figure 8
if flags.do_fig8
siglevel=0.05;
outlierrate=0.01; % upper bound for the outlier rate
Obj=data_ziegelwanger2014('STP',flags.cachemode);
idx=Obj.Data.toaEst{4}(:,1)>-100000;
stpEst=Obj.Data.toaEst{4}(idx,1);
[~,results]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,2);
stpMod=results.toa(idx,1);
stpRes=stpEst-stpMod;
Obj=data_ziegelwanger2014('NH89',flags.cachemode);
fs=Obj.Data.SamplingRate*1e-6;
idx=Obj.Data.toaEst{4}(:,1)>-100000;
sp=Obj.SourcePosition(idx,:);
[lat,~]=geo2horpolar(sp(:,1),sp(:,2));
nhEst=Obj.Data.toaEst{4}(idx,1);
[~,results]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,2);
nhMod=results.toa(idx,1);
nhRes=nhEst-nhMod;
res=stpRes;
est=stpEst;
[~,idx]=deleteoutliers(res,siglevel*outlierrate*length(stpEst));
figure('Position',[620 229 560*2 497],'resize','off');
h(1)=subplot(2,2,1);
box on; hold on;
plot(lat,est/fs-min(est/fs),'.','MarkerEdgeColor',[0.5 0.5 0.5],'MarkerSize',8);
plot(lat(idx),est(idx)/fs-min(est/fs),'.k','MarkerSize',24);
axis([-99 99 -50 995]);
set(gca,'XTickLabel',[],'FontName','Arial');
ylabel('Relative TOA (\mus)','FontName','Arial','FontSize',18);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0 0.06 0]);
h(2)=subplot(2,2,3);
box on; hold on;
plot(lat,res/fs,'.','MarkerEdgeColor',[0.5 0.5 0.5],'MarkerSize',8)
plot(lat(idx),res(idx)/fs,'.k','MarkerSize',24);
line([-99 99],1./[-fs -fs],'LineStyle','--','Color',[0 0 0]);
line([-99 99],1./[fs fs],'LineStyle','--','Color',[0 0 0]);
axis([-99 99 -80 195])
xlabel('\Phi_h (deg)','FontName','Arial','FontSize',18);
ylabel('Residual error (\mus)','FontName','Arial','FontSize',18);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.11 0.06 0]);
res=nhRes;
est=nhEst;
[~,idx]=deleteoutliers(res,siglevel*outlierrate*length(nhEst));
h(3)=subplot(2,2,2);
box on; hold on;
plot(lat,est/fs-min(est/fs),'.','MarkerEdgeColor',[0.5 0.5 0.5],'MarkerSize',8);
plot(lat(idx),est(idx)/fs-min(est/fs),'.k','MarkerSize',24);
axis([-99 99 -50 995]);
set(gca,'XTickLabel',[],'FontName','Arial');
set(gca,'yticklabel',[]);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.03 0 0.06 0]);
h(4)=subplot(2,2,4);
box on; hold on;
plot(lat,res/fs,'.','MarkerEdgeColor',[0.5 0.5 0.5],'MarkerSize',8)
plot(lat(idx),res(idx)/fs,'.k','MarkerSize',24);
line([-99 99],1./[-fs -fs],'LineStyle','--','Color',[0 0 0]);
line([-99 99],1./[fs fs],'LineStyle','--','Color',[0 0 0]);
axis([-99 99 -80 195])
xlabel('\Phi_h (deg)','FontName','Arial','FontSize',18);
set(gca,'yticklabel',[]);
tmp=get(gca,'Position');
set(gca,'Position',tmp+[-0.03 0.11 0.06 0]);
for ii=1:length(h)
set(h(ii),'linewidth',1.1)
set(h(ii),'Fontname','Arial','Fontsize',14)
end
set(gcf,'color',[1 1 1])
end
%% Figure 9
if flags.do_fig9
%load data
Obj=data_ziegelwanger2014('NH89',flags.cachemode);
toa1=Obj.Data.toaEst{4};
[~,tmp]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,1);
toa2=tmp.toa;
%plot figure
h=subplot(1,1,1);
plot_ziegelwanger2014(Obj,toa2-min(min(toa1))-1,4,'k',0,1,1,'-',3);
h2=plot_ziegelwanger2014(Obj,toa1-min(min(toa1))-1,4,'k',0,1,1,'o',1);
set(h2,'MarkerFaceColor',[0 0 0]);
h3=plot_ziegelwanger2014(Obj,toa2-min(min(toa1))-1,4,'k',0,2,1,'-',3);
set(h3,'Color',[0.5 0.5 0.5]);
h4=plot_ziegelwanger2014(Obj,toa1-min(min(toa1))-1,4,'k',0,2,1,'o',1);
set(h4,'MarkerFaceColor',[.5 .5 .5],'MarkerEdgeColor',[.5 .5 .5]);
xlim([-9 369])
ylim([-0.04 0.98])
grid off
xlabel(' ');
ylabel('Relative TOA (ms)','Fontname','Arial','Fontsize',22)
title('')
set(gca,'xticklabel',{'0' '90' '180' '270' '360 '});
set(gca,'ytick',[0 0.2 0.4 0.6 0.8])
set(gca,'Fontname','Arial','fontsize',18)
set(h,'linewidth',2)
end
%% Figure 10
if flags.do_fig10
%load data
data=data_ziegelwanger2014('SPHERE_DIS',flags.cachemode);
for ii=1:length(data.results)
p_onaxis(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
end
p1=p_onaxis(:,:,[1:3 length(data.xM)/3+1:length(data.xM)/3+3 length(data.xM)/3*2+1:length(data.xM)/3*2+3]);
r1=data.radius([1:3 length(data.xM)/3+1:length(data.xM)/3+3 length(data.xM)/3*2+1:length(data.xM)/3*2+3]);
yM1=data.yM([1:3 length(data.xM)/3+1:length(data.xM)/3+3 length(data.xM)/3*2+1:length(data.xM)/3*2+3]);
%plot figure
figure
tmp=get(gcf,'Position');
set(gcf,'Position',tmp.*[1 1 1 2]);
sym='ox';%plot symbols
ms=8;%markersize
lw1=2;%linewidth
lw2=2;%linewidth
fs=18;%fontsize
ls='k-';%linestyle
lc=[0.5 0.5 0.5];
h=[];
%radii
h(end+1)=subplot(611);
var=[squeeze(p1(1,1,:))*1000 squeeze(p1(1,2,:))*1000 r1];
for ch=1:size(p1,2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
hold on
end
plot(1:3,r1(1:3),ls,'Linewidth',lw2,'color',lc)
hold on
plot(4:6,r1(4:6),ls,'Linewidth',lw2,'color',lc)
plot(7:9,r1(7:9),ls,'Linewidth',lw2,'color',lc)
set(gca,'xtick',1:9)
set(gca,'xticklabel',[])
clear var;
ylabel('r (mm)','Fontname','Arial','Fontsize',fs)
ylim([51,129])
xlim([0.5,9.5])
%yM
h(end+1)=subplot(612);
plot(1:3,-yM1(1:3)*1000,ls,'Linewidth',lw2,'color',lc)
hold on
plot(4:6,-yM1(4:6)*1000,ls,'Linewidth',lw2,'color',lc)
plot(7:9,-yM1(7:9)*1000,ls,'Linewidth',lw2,'color',lc)
set(gca,'xtick',1:9)
set(gca,'xticklabel',[])
clear var;
xlabel('Condition','Fontname','Arial','Fontsize',fs)
ylabel('y_M (mm) ','Fontname','Arial','Fontsize',fs)
ylim([-5 25])
xlim([0.5,9.5])
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.02 0 0]);
for ii=1:length(h)
set(h(ii),'fontsize',14)
set(h(ii),'Linewidth',2)
tmp=findobj(h(ii),'Type','patch');
set(tmp,'EdgeColor','k');
end
set(gcf,'color',[1 1 1])
end
%% Figure 12
if flags.do_fig12
%load data
data=data_ziegelwanger2014('SPHERE_DIS',flags.cachemode);
for ii=1:length(data.results)
p_offaxis(:,:,ii)=data.results(ii).MCM{1}.p_offaxis;
end
center=[data.xM(1:length(data.xM)/3) data.yM(1:length(data.yM)/3) data.zM(1:length(data.zM)/3)];
[~,idx3]=sort(squeeze(center(:,3)));
[~,idx2]=sort(squeeze(center(:,1)));
[~,idx1]=sort(squeeze(center(:,2)));
idx=idx3(idx2(idx1));
idx=[idx; idx+length(data.xM)/3; idx+length(data.xM)/3*2];
data.radius=data.radius(idx);
p_offaxis=p_offaxis(:,:,idx);
data.xM=data.xM(idx);
data.yM=data.yM(idx);
data.zM=data.zM(idx);
%plot figure
figure
tmp=get(gcf,'Position');
set(gcf,'Position',tmp.*[1 1 1.5 2]);
sym='ox';%plot symbols
ms=8;%markersize
lw1=2;%linewidth
lw2=2;%linewidth
fs=18;%fontsize
ls='k-';%linestyle
lc=[0.5 0.5 0.5];
h=[];
%radii
h(end+1)=subplot(611);
var=[squeeze(p_offaxis(1,1,:))*1000 squeeze(p_offaxis(1,2,:))*1000 data.radius];
plot(var(:,3),ls,'Linewidth',lw2,'color',lc)
hold on
for ch=1:size(p_offaxis,2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
end
ylabel('r (mm)','Fontname','Arial','Fontsize',fs)
xlim([-1 size(var,1)+2])
ylim([72 108])
set(gca,'xtick',1:size(var,1))
set(gca,'xticklabel',[])
clear var;
%xM
h(end+1)=subplot(612);
var=[squeeze(p_offaxis(2,1,:))*1000 squeeze(p_offaxis(2,2,:))*1000 -data.xM*1000];
plot(var(:,3),ls,'Linewidth',lw2,'color',lc)
hold on
for ch=1:size(p_offaxis,2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
end
ylabel('x_M (mm) ','Fontname','Arial','Fontsize',fs)
xlim([-1 size(var,1)+2])
ylim([-28 8])
set(gca,'xtick',1:size(var,1))
set(gca,'xticklabel',[])
clear var;
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.02 0 0]);
%yM
h(end+1)=subplot(613);
var=[squeeze(p_offaxis(3,1,:))*1000 squeeze(p_offaxis(3,2,:))*1000 -data.yM*1000];
plot(var(:,3),ls,'Linewidth',lw2,'color',lc)
hold on
for ch=1:size(p_offaxis,2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
end
ylabel('y_M (mm) ','Fontname','Arial','Fontsize',fs)
xlim([-1 size(var,1)+2])
ylim([-8 28])
set(gca,'xtick',1:size(var,1))
set(gca,'xticklabel',[])
clear var;
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.04 0 0]);
%zM
h(end+1)=subplot(614);
var=[squeeze(p_offaxis(4,1,:))*1000 squeeze(p_offaxis(4,2,:))*1000 -data.zM*1000];
plot([1 size(var,1)],var([1 3],3),ls,'Linewidth',lw2,'color',lc)
hold on
plot([1 size(var,1)],var([2 4],3),ls,'Linewidth',lw2,'color',lc)
for ch=1:size(p_offaxis,2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
end
% set(gca,'YTick',[-10 0])
ylabel('z_M (mm)','Fontname','Arial','Fontsize',fs)
xlim([-1 size(var,1)+2])
ylim([-14 4])
set(gca,'xtick',1:size(var,1))
set(gca,'xticklabel',[])
clear var;
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.06 0 0]);
%phi
h(end+1)=subplot(615);
var=[squeeze(p_offaxis(6,1,:))/pi*180 squeeze(p_offaxis(6,2,:))/pi*180 ones(length(data.zM),1)*90 -ones(length(data.zM),1)*90];
for ch=1:size(p_offaxis,2)
plot(abs(var(:,2+ch)),ls,'Linewidth',lw2,'color',lc)
hold on
end
for ch=1:size(p_offaxis,2)
plot(abs(var(:,ch)),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
end
set(gca,'YTick',[-90 90])
ylabel(' _e (deg)','Fontname','Arial','Fontsize',fs)
xlim([-1 size(var,1)+2])
ylim([82,98])
set(gca,'xtick',1:size(var,1))
set(gca,'xticklabel',[])
set(gca,'ytick',[85 90 95])
set(gca,'yticklabel',{'\pm85','\pm90','\pm95'})
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.08 0 0]);
clear var;
%theta
h(end+1)=subplot(616);
var=[squeeze(p_offaxis(7,1,:))/pi*180 squeeze(p_offaxis(7,2,:))/pi*180 zeros(length(data.zM),1) zeros(length(data.zM),1)];
for ch=1:size(p_offaxis,2)
plot(var(:,2+ch),ls,'Linewidth',lw2,'color',lc)
hold on
end
for ch=1:size(p_offaxis,2)
plot(var(:,ch),['k' sym(ch)],'Markersize',ms,'Linewidth',lw1);
end
ylabel('\theta_e (deg)','Fontname','Arial','Fontsize',fs)
xlabel('Condition','Fontname','Arial','Fontsize',fs)
ylim([-7,7])
xlim([-1 size(var,1)+2])
set(gca,'xtick',1:size(var,1))
set(gca,'xticklabel',[])
set(gca,'ytick',[-5 0 5])
tmp=get(gca,'Position');
set(gca,'Position',tmp+[0 0.10 0 0]);
clear var;
for ii=1:length(h)
set(h(ii),'fontsize',14)
set(h(ii),'Linewidth',2)
tmp=findobj(h(ii),'Type','patch');
set(tmp,'EdgeColor','k');
end
set(gcf,'color',[1 1 1])
end
%% Table 1
if flags.do_tab1
%load data
data=data_ziegelwanger2014('SPHERE_ROT',flags.cachemode);
for ii=1:length(data.results)
p_onaxis{1}(:,:,ii)=data.results(ii).MAX{1}.p_onaxis;
p_onaxis{2}(:,:,ii)=data.results(ii).CTD{1}.p_onaxis;
p_onaxis{3}(:,:,ii)=data.results(ii).AGD{1}.p_onaxis;
p_onaxis{4}(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
end
resnormS=zeros(length(data.results),4);
for ii=1:length(data.results)
resnormS(ii,1)=mean([data.results(ii).MAX{1}.performance.on_axis{1}.resnormS ...
data.results(ii).MAX{1}.performance.on_axis{2}.resnormS]);
resnormS(ii,2)=mean([data.results(ii).CTD{1}.performance.on_axis{1}.resnormS ...
data.results(ii).CTD{1}.performance.on_axis{2}.resnormS]);
resnormS(ii,3)=mean([data.results(ii).AGD{1}.performance.on_axis{1}.resnormS ...
data.results(ii).AGD{1}.performance.on_axis{2}.resnormS]);
resnormS(ii,4)=mean([data.results(ii).MCM{1}.performance.on_axis{1}.resnormS ...
data.results(ii).MCM{1}.performance.on_axis{2}.resnormS]);
end
out=zeros(4,8);
out(1,7)=mean(resnormS(:,1))*1e6;
out(2,7)=mean(resnormS(:,2))*1e6;
out(3,7)=mean(resnormS(:,3))*1e6;
out(4,7)=mean(resnormS(:,4))*1e6;
out(1,8)=std(resnormS(:,1))*1e6;
out(2,8)=std(resnormS(:,2))*1e6;
out(3,8)=std(resnormS(:,3))*1e6;
out(4,8)=std(resnormS(:,4))*1e6;
idx=1:size(p_onaxis{1},3);
% radii
varl=[[squeeze(p_onaxis{1}(1,1,idx)) ...
squeeze(p_onaxis{2}(1,1,idx))...
squeeze(p_onaxis{3}(1,1,idx)) ...
squeeze(p_onaxis{4}(1,1,idx))]*1000 ...
data.radius(idx)];
varr=[[squeeze(p_onaxis{1}(1,2,idx)) ...
squeeze(p_onaxis{2}(1,2,idx))...
squeeze(p_onaxis{3}(1,2,idx)) ...
squeeze(p_onaxis{4}(1,2,idx))]*1000 ...
data.radius(idx)];
err=abs([varl(:,1:4); varr(:,1:4)]-[varl(:,5); varr(:,5)]*[1 1 1 1]);
out(1,1)=mean(err(:,1));
out(2,1)=mean(err(:,2));
out(3,1)=mean(err(:,3));
out(4,1)=mean(err(:,4));
out(1,2)=std(err(:,1));
out(2,2)=std(err(:,2));
out(3,2)=std(err(:,3));
out(4,2)=std(err(:,4));
%phi
varl=[[squeeze(p_onaxis{1}(2,1,idx)) ...
squeeze(p_onaxis{2}(2,1,idx))...
squeeze(p_onaxis{3}(2,1,idx)) ...
squeeze(p_onaxis{4}(2,1,idx))]/pi*180 ...
data.phi(idx)+ones(length(data.phi(idx)),1)*90];
varr=[mod([squeeze(p_onaxis{1}(2,2,idx)) ...
squeeze(p_onaxis{2}(2,2,idx))...
squeeze(p_onaxis{3}(2,2,idx)) ...
squeeze(p_onaxis{4}(2,2,idx))]/pi*180,360) ...
mod(data.phi(idx)-ones(length(data.phi(idx)),1)*90,360)];
err=abs([varl(:,1:4); varr(:,1:4)]-[varl(:,5); varr(:,5)]*[1 1 1 1]);
out(1,3)=mean(err(:,1));
out(2,3)=mean(err(:,2));
out(3,3)=mean(err(:,3));
out(4,3)=mean(err(:,4));
out(1,4)=std(err(:,1));
out(2,4)=std(err(:,2));
out(3,4)=std(err(:,3));
out(4,4)=std(err(:,4));
%theta
varl=[[squeeze(p_onaxis{1}(3,1,idx)) ...
squeeze(p_onaxis{2}(3,1,idx))...
squeeze(p_onaxis{3}(3,1,idx)) ...
squeeze(p_onaxis{4}(3,1,idx))]/pi*180 ...
data.theta(idx)];
varr=[[squeeze(p_onaxis{1}(3,2,idx)) ...
squeeze(p_onaxis{2}(3,2,idx))...
squeeze(p_onaxis{3}(3,2,idx)) ...
squeeze(p_onaxis{4}(3,2,idx))]/pi*180 ...
-data.theta(idx)];
err=abs([varl(:,1:4); varr(:,1:4)]-[varl(:,5); varr(:,5)]*[1 1 1 1]);
out(1,5)=mean(err(:,1));
out(2,5)=mean(err(:,2));
out(3,5)=mean(err(:,3));
out(4,5)=mean(err(:,4));
out(1,6)=std(err(:,1));
out(2,6)=std(err(:,2));
out(3,6)=std(err(:,3));
out(4,6)=std(err(:,4));
%display data
rows=['MAX ||';...
'CTD ||';...
'AGD ||';...
'MCM ||'];...
fprintf('\nTab. I.:\n')
fprintf('----------------------------------------------------------------------\n')
fprintf('EST || r error (mm) | phi_e error (deg) | theta_e errror | ANR (\mus) \n')
fprintf('----------------------------------------------------------------------\n')
fprintf('----------------------------------------------------------------------\n')
for ii=1:4
fprintf(rows(ii,:))
fprintf(' %4.1f \pm %3.1f | %4.1f \pm %3.1f | %4.1f \pm %3.1f | %4.1f \pm %3.1f\n',out(ii,:)');
end
fprintf('----------------------------------------------------------------------\n\n')
end
%% Table 2
if flags.do_tab2
%load data
out=zeros(3,16);
Obj1=data_ziegelwanger2014('Sphere',flags.cachemode);
for method=1:4
[Obj1,results]=ziegelwanger2014(Obj1,Obj1.Data.toaEst{method},0,1,[[0.08; pi/18*8; 0; 0] [0.08; -pi/18*10; 0; 0]]);
out(1,method+0)=results.p_onaxis(1,1)*1000;
out(1,method+4)=results.p_onaxis(2,1)*180/pi;
out(1,method+8)=results.p_onaxis(3,1)*180/pi-1;
out(1,method+12)=results.performance.on_axis{1}.resnormS*1e06;
end
Obj2=data_ziegelwanger2014('SAT',flags.cachemode);
for method=1:4
[Obj2,results]=ziegelwanger2014(Obj2,Obj2.Data.toaEst{method},0,1,[[0.08; pi/18*8; 0; 0] [0.08; -pi/18*10; 0; 0]]);
out(2,method+0)=results.p_onaxis(1,1)*1000;
out(2,method+4)=results.p_onaxis(2,1)*180/pi;
out(2,method+8)=results.p_onaxis(3,1)*180/pi-1;
out(2,method+12)=results.performance.on_axis{1}.resnormS*1e06;
end
Obj3=data_ziegelwanger2014('STP',flags.cachemode);
for method=1:4
[Obj3,results]=ziegelwanger2014(Obj3,Obj3.Data.toaEst{method},0,1,[[0.08; pi/18*8; 0; 0] [0.08; -pi/18*10; 0; 0]]);
out(3,method+0)=results.p_onaxis(1,1)*1000;
out(3,method+4)=results.p_onaxis(2,1)*180/pi;
out(3,method+8)=results.p_onaxis(3,1)*180/pi-1;
out(3,method+12)=results.performance.on_axis{1}.resnormS*1e06;
end
%display data
rows=['Sphere ||';...
'SAT ||';...
'STP ||'];
fprintf('\nTab. II.:\n')
fprintf('----------------------------------------------------------------------------------------------------------\n')
fprintf(' || r error (mm) | phi_e error (deg) | theta_e errror | ANR (\mus) \n')
fprintf(' || MAX | CTD | AGD | MCM | MAX | CTD | AGD | MCM | MAX | CTD | AGD | MCM | MAX | CTD | AGD | MCM \n')
fprintf('----------------------------------------------------------------------------------------------------------\n')
fprintf('----------------------------------------------------------------------------------------------------------\n')
for ii=1:3
fprintf(rows(ii,:))
fprintf(' %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f| %4.1f\n',out(ii,:)');
end
fprintf('----------------------------------------------------------------------------------------------------------\n\n')
end
%% Table 3
if flags.do_tab3
%load data
out=zeros(4,8);
hrtf={'ARI','CIPIC','LISTEN'};
for kk=1:length(hrtf)
data=data_ziegelwanger2014(hrtf{kk},flags.cachemode);
if kk==3
data.results=data.results([1:27 29:end]);
end
for ii=1:length(data.results)
temp1(:,:,ii)=data.results(ii).MCM{1}.p_onaxis;
temp3(ii)=mean([data.results(ii).MCM{1}.performance.on_axis{1}.resnormS ...
data.results(ii).MCM{1}.performance.on_axis{2}.resnormS]);
temp5(ii)=mean([data.results(ii).MCM{1}.performance.on_axis{1}.resnormP ...
data.results(ii).MCM{1}.performance.on_axis{2}.resnormP]);
temp8(ii)=data.results(ii).MCM{1}.performance.on_axis{1}.resnormS;
temp9(ii)=data.results(ii).MCM{1}.performance.on_axis{2}.resnormS;
end
p_onaxis{kk}=temp1;
resnormS_onaxis{kk}=temp3;
resnormP_onaxis{kk}=temp5;
resnormS_onaxis_left{kk}=temp8;
resnormS_onaxis_right{kk}=temp9;
clear temp1 temp3 temp5 temp8 temp9
end
% radii
out(1,1)=mean([mean(squeeze(p_onaxis{1}(1,1,:)*1000)) ...
mean(squeeze(p_onaxis{2}(1,1,:)*1000)) ...
mean(squeeze(p_onaxis{3}(1,1,:)*1000))]);
out(1,2)=std([reshape(p_onaxis{1}(1,1,:)*1000,1,numel(p_onaxis{1}(1,1,:))) ...
reshape(p_onaxis{2}(1,1,:)*1000,1,numel(p_onaxis{2}(1,1,:))) ...
reshape(p_onaxis{3}(1,1,:)*1000,1,numel(p_onaxis{3}(1,1,:)))]);
out(2,1)=mean([mean(squeeze(p_onaxis{1}(1,2,:)*1000)) ...
mean(squeeze(p_onaxis{2}(1,2,:)*1000)) ...
mean(squeeze(p_onaxis{3}(1,2,:)*1000))]);
out(2,2)=std([reshape(p_onaxis{1}(1,2,:)*1000,1,numel(p_onaxis{1}(1,2,:))) ...
reshape(p_onaxis{2}(1,2,:)*1000,1,numel(p_onaxis{2}(1,2,:))) ...
reshape(p_onaxis{3}(1,2,:)*1000,1,numel(p_onaxis{3}(1,2,:)))]);
% phi_e
out(1,3)=mean([mean(squeeze(p_onaxis{1}(2,1,:)*180/pi)) ...
mean(squeeze(p_onaxis{2}(2,1,:)*180/pi)) ...
mean(squeeze(p_onaxis{3}(2,1,:)*180/pi))]);
out(1,4)=std([reshape(p_onaxis{1}(2,1,:)*180/pi,1,numel(p_onaxis{1}(2,1,:))) ...
reshape(p_onaxis{2}(2,1,:)*180/pi,1,numel(p_onaxis{2}(2,1,:))) ...
reshape(p_onaxis{3}(2,1,:)*180/pi,1,numel(p_onaxis{3}(2,1,:)))]);
out(2,3)=mean([mean(squeeze(p_onaxis{1}(2,2,:)*180/pi)) ...
mean(squeeze(p_onaxis{2}(2,2,:)*180/pi)) ...
mean(squeeze(p_onaxis{3}(2,2,:)*180/pi))]);
out(2,4)=std([reshape(p_onaxis{1}(2,2,:)*180/pi,1,numel(p_onaxis{1}(2,2,:))) ...
reshape(p_onaxis{2}(2,2,:)*180/pi,1,numel(p_onaxis{2}(2,2,:))) ...
reshape(p_onaxis{3}(2,2,:)*180/pi,1,numel(p_onaxis{3}(2,2,:)))]);
% theta_e
out(1,5)=mean([mean(squeeze(p_onaxis{1}(3,1,:)*180/pi)) ...
mean(squeeze(p_onaxis{2}(3,1,:)*180/pi)) ...
mean(squeeze(p_onaxis{3}(3,1,:)*180/pi))]);
out(1,6)=std([reshape(p_onaxis{1}(3,1,:)*180/pi,1,numel(p_onaxis{1}(3,1,:))) ...
reshape(p_onaxis{2}(3,1,:)*180/pi,1,numel(p_onaxis{2}(3,1,:))) ...
reshape(p_onaxis{3}(3,1,:)*180/pi,1,numel(p_onaxis{3}(3,1,:)))]);
out(2,5)=mean([mean(squeeze(p_onaxis{1}(3,2,:)*180/pi)) ...
mean(squeeze(p_onaxis{2}(3,2,:)*180/pi)) ...
mean(squeeze(p_onaxis{3}(3,2,:)*180/pi))]);
out(2,6)=std([reshape(p_onaxis{1}(3,2,:)*180/pi,1,numel(p_onaxis{1}(3,2,:))) ...
reshape(p_onaxis{2}(3,2,:)*180/pi,1,numel(p_onaxis{2}(3,2,:))) ...
reshape(p_onaxis{3}(3,2,:)*180/pi,1,numel(p_onaxis{3}(3,2,:)))]);
out(1,7)=mean([resnormS_onaxis_left{1} resnormS_onaxis_left{2} resnormS_onaxis_left{3}])*1e6;
out(1,8)=std([resnormS_onaxis_left{1} resnormS_onaxis_left{2} resnormS_onaxis_left{3}])*1e6;
out(2,7)=mean([resnormS_onaxis_right{1} resnormS_onaxis_right{2} resnormS_onaxis_right{3}])*1e6;
out(2,8)=std([resnormS_onaxis_right{1} resnormS_onaxis_right{2} resnormS_onaxis_right{3}])*1e6;
Obj=data_ziegelwanger2014('NH89',flags.cachemode);
[~,results]=ziegelwanger2014(Obj,4,0,1);
out(3:4,[1 3 5])=results.p_onaxis(1:3,:)'.*([1;1]*[1000 180/pi 180/pi]);
out(3,7)=results.performance.on_axis{1}.resnormS*1e6;
out(4,7)=results.performance.on_axis{2}.resnormS*1e6;
%display data
rows=['All | L ||';...
' | R ||';...
'NH89 | L ||';...
' | R ||'];...
fprintf('\nTab. III.:\n')
fprintf('-----------------------------------------------------------------------\n')
fprintf(' | Ear || r (mm) | phi_e (deg) | theta_e (deg) | ANR (\mus) \n')
fprintf('-----------------------------------------------------------------------\n')
fprintf('-----------------------------------------------------------------------\n')
for ii=1:4
fprintf(rows(ii,:))
fprintf(' %5.1f \pm %4.1f | %5.1f \pm %4.1f | %5.1f \pm %3.1f | %4.1f \pm %4.1f\n',out(ii,:)');
end
fprintf('-----------------------------------------------------------------------\n\n')
end
%% Table 5
if flags.do_tab5
%load data
out=zeros(12,14);
Obj=data_ziegelwanger2014('SAT',flags.cachemode);
[~,results]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,1);
out(1:2,[1 7 9 11 3 5])=results.p_offaxis([1:4 6:7],:)'.*([1;1]*[1000 1000 1000 1000 180/pi 180/pi]);
out(1,13)=results.performance.off_axis{1}.resnormS*1e6;
out(2,13)=results.performance.off_axis{2}.resnormS*1e6;
[~,results]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},[0.05 0.01],1);
out(3:4,[1 7 9 11 3 5])=results.p_offaxis([1:4 6:7],:)'.*([1;1]*[1000 1000 1000 1000 180/pi 180/pi]);
out(3,13)=results.performance.off_axis{1}.resnormS*1e6;
out(4,13)=results.performance.off_axis{2}.resnormS*1e6;
Obj=data_ziegelwanger2014('STP',flags.cachemode);
[~,results]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},0,1);
out(5:6,[1 7 9 11 3 5])=results.p_offaxis([1:4 6:7],:)'.*([1;1]*[1000 1000 1000 1000 180/pi 180/pi]);
out(5,13)=results.performance.off_axis{1}.resnormS*1e6;
out(6,13)=results.performance.off_axis{2}.resnormS*1e6;
[~,results]=ziegelwanger2014(Obj,Obj.Data.toaEst{4},[0.05 0.01],1);
out(7:8,[1 7 9 11 3 5])=results.p_offaxis([1:4 6:7],:)'.*([1;1]*[1000 1000 1000 1000 180/pi 180/pi]);
out(7,13)=results.performance.off_axis{1}.resnormS*1e6;
out(8,13)=results.performance.off_axis{2}.resnormS*1e6;
out(1:8,5)=out(1:8,5)-1;
hrtf={'ARI','CIPIC','LISTEN'};
for kk=1:length(hrtf)
data=data_ziegelwanger2014(hrtf{kk},flags.cachemode);
if kk==3
data.results=data.results([1:27 29:end]);
end
for jj=1:2
for ii=1:length(data.results)
temp1(:,:,ii)=data.results(ii).MCM{jj}.p_offaxis;
temp3(ii)=mean([data.results(ii).MCM{jj}.performance.off_axis{1}.resnormS ...
data.results(ii).MCM{jj}.performance.off_axis{2}.resnormS]);
temp5(ii)=mean([data.results(ii).MCM{jj}.performance.off_axis{1}.resnormP ...
data.results(ii).MCM{jj}.performance.off_axis{2}.resnormP]);
temp8(ii)=data.results(ii).MCM{jj}.performance.off_axis{1}.resnormS;
temp9(ii)=data.results(ii).MCM{jj}.performance.off_axis{2}.resnormS;
end
p_offaxis{kk,jj}=temp1;
resnormS_offaxis{kk,jj}=temp3;
resnormP_offaxis{kk,jj}=temp5;
resnormS_offaxis_left{kk,jj}=temp8;
resnormS_offaxis_right{kk,jj}=temp9;
clear temp1 temp3 temp5 temp8 temp9
end
end
% radii
for jj=1:2
out(9+(jj-1)*2,1)=mean([mean(squeeze(p_offaxis{1,jj}(1,1,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(1,1,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(1,1,:)*1000))]);
out(9+(jj-1)*2,2)=std([reshape(p_offaxis{1,jj}(1,1,:)*1000,1,numel(p_offaxis{1,jj}(1,1,:))) ...
reshape(p_offaxis{2,jj}(1,1,:)*1000,1,numel(p_offaxis{2,jj}(1,1,:))) ...
reshape(p_offaxis{3,jj}(1,1,:)*1000,1,numel(p_offaxis{3,jj}(1,1,:)))]);
out(10+(jj-1)*2,1)=mean([mean(squeeze(p_offaxis{1,jj}(1,2,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(1,2,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(1,2,:)*1000))]);
out(10+(jj-1)*2,2)=std([reshape(p_offaxis{1,jj}(1,2,:)*1000,1,numel(p_offaxis{1,jj}(1,2,:))) ...
reshape(p_offaxis{2,jj}(1,2,:)*1000,1,numel(p_offaxis{2,jj}(1,2,:))) ...
reshape(p_offaxis{3,jj}(1,2,:)*1000,1,numel(p_offaxis{3,jj}(1,2,:)))]);
end
% phi_e
for jj=1:2
out(9+(jj-1)*2,3)=mean([mean(squeeze(p_offaxis{1,jj}(6,1,:)*180/pi)) ...
mean(squeeze(p_offaxis{2,jj}(6,1,:)*180/pi)) ...
mean(squeeze(p_offaxis{3,jj}(6,1,:)*180/pi))]);
out(9+(jj-1)*2,4)=std([reshape(p_offaxis{1,jj}(6,1,:)*180/pi,1,numel(p_offaxis{1,jj}(6,1,:))) ...
reshape(p_offaxis{2,jj}(6,1,:)*180/pi,1,numel(p_offaxis{2,jj}(6,1,:))) ...
reshape(p_offaxis{3,jj}(6,1,:)*180/pi,1,numel(p_offaxis{3,jj}(6,1,:)))]);
out(10+(jj-1)*2,3)=mean([mean(squeeze(p_offaxis{1,jj}(6,2,:)*180/pi)) ...
mean(squeeze(p_offaxis{2,jj}(6,2,:)*180/pi)) ...
mean(squeeze(p_offaxis{3,jj}(6,2,:)*180/pi))]);
out(10+(jj-1)*2,4)=std([reshape(p_offaxis{1,jj}(6,2,:)*180/pi,1,numel(p_offaxis{1,jj}(6,2,:))) ...
reshape(p_offaxis{2,jj}(6,2,:)*180/pi,1,numel(p_offaxis{2,jj}(6,2,:))) ...
reshape(p_offaxis{3,jj}(6,2,:)*180/pi,1,numel(p_offaxis{3,jj}(6,2,:)))]);
end
% theta_e
for jj=1:2
out(9+(jj-1)*2,5)=mean([mean(squeeze(p_offaxis{1,jj}(7,1,:)*180/pi)) ...
mean(squeeze(p_offaxis{2,jj}(7,1,:)*180/pi)) ...
mean(squeeze(p_offaxis{3,jj}(7,1,:)*180/pi))]);
out(9+(jj-1)*2,6)=std([reshape(p_offaxis{1,jj}(7,1,:)*180/pi,1,numel(p_offaxis{1,jj}(7,1,:))) ...
reshape(p_offaxis{2,jj}(7,1,:)*180/pi,1,numel(p_offaxis{2,jj}(7,1,:))) ...
reshape(p_offaxis{3,jj}(7,1,:)*180/pi,1,numel(p_offaxis{3,jj}(7,1,:)))]);
out(10+(jj-1)*2,5)=mean([mean(squeeze(p_offaxis{1,jj}(7,2,:)*180/pi)) ...
mean(squeeze(p_offaxis{2,jj}(7,2,:)*180/pi)) ...
mean(squeeze(p_offaxis{3,jj}(7,2,:)*180/pi))]);
out(10+(jj-1)*2,6)=std([reshape(p_offaxis{1,jj}(7,2,:)*180/pi,1,numel(p_offaxis{1,jj}(7,2,:))) ...
reshape(p_offaxis{2,jj}(7,2,:)*180/pi,1,numel(p_offaxis{2,jj}(7,2,:))) ...
reshape(p_offaxis{3,jj}(7,2,:)*180/pi,1,numel(p_offaxis{3,jj}(7,2,:)))]);
end
% xM
for jj=1:2
out(9+(jj-1)*2,7)=mean([mean(squeeze(p_offaxis{1,jj}(2,1,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(2,1,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(2,1,:)*1000))]);
out(9+(jj-1)*2,8)=std([reshape(p_offaxis{1,jj}(2,1,:)*1000,1,numel(p_offaxis{1,jj}(2,1,:))) ...
reshape(p_offaxis{2,jj}(2,1,:)*1000,1,numel(p_offaxis{2,jj}(2,1,:))) ...
reshape(p_offaxis{3,jj}(2,1,:)*1000,1,numel(p_offaxis{3,jj}(2,1,:)))]);
out(10+(jj-1)*2,7)=mean([mean(squeeze(p_offaxis{1,jj}(2,2,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(2,2,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(2,2,:)*1000))]);
out(10+(jj-1)*2,8)=std([reshape(p_offaxis{1,jj}(2,2,:)*1000,1,numel(p_offaxis{1,jj}(2,2,:))) ...
reshape(p_offaxis{2,jj}(2,2,:)*1000,1,numel(p_offaxis{2,jj}(2,2,:))) ...
reshape(p_offaxis{3,jj}(2,2,:)*1000,1,numel(p_offaxis{3,jj}(2,2,:)))]);
end
% yM
for jj=1:2
out(9+(jj-1)*2,9)=mean([mean(squeeze(p_offaxis{1,jj}(3,1,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(3,1,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(3,1,:)*1000))]);
out(9+(jj-1)*2,10)=std([reshape(p_offaxis{1,jj}(3,1,:)*1000,1,numel(p_offaxis{1,jj}(3,1,:))) ...
reshape(p_offaxis{2,jj}(3,1,:)*1000,1,numel(p_offaxis{2,jj}(3,1,:))) ...
reshape(p_offaxis{3,jj}(3,1,:)*1000,1,numel(p_offaxis{3,jj}(3,1,:)))]);
out(10+(jj-1)*2,9)=mean([mean(squeeze(p_offaxis{1,jj}(3,2,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(3,2,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(3,2,:)*1000))]);
out(10+(jj-1)*2,10)=std([reshape(p_offaxis{1,jj}(3,2,:)*1000,1,numel(p_offaxis{1,jj}(3,2,:))) ...
reshape(p_offaxis{2,jj}(3,2,:)*1000,1,numel(p_offaxis{2,jj}(3,2,:))) ...
reshape(p_offaxis{3,jj}(3,2,:)*1000,1,numel(p_offaxis{3,jj}(3,2,:)))]);
end
% zM
for jj=1:2
out(9+(jj-1)*2,11)=mean([mean(squeeze(p_offaxis{1,jj}(4,1,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(4,1,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(4,1,:)*1000))]);
out(9+(jj-1)*2,12)=std([reshape(p_offaxis{1,jj}(4,1,:)*1000,1,numel(p_offaxis{1,jj}(4,1,:))) ...
reshape(p_offaxis{2,jj}(4,1,:)*1000,1,numel(p_offaxis{2,jj}(4,1,:))) ...
reshape(p_offaxis{3,jj}(4,1,:)*1000,1,numel(p_offaxis{3,jj}(4,1,:)))]);
out(10+(jj-1)*2,11)=mean([mean(squeeze(p_offaxis{1,jj}(4,2,:)*1000)) ...
mean(squeeze(p_offaxis{2,jj}(4,2,:)*1000)) ...
mean(squeeze(p_offaxis{3,jj}(4,2,:)*1000))]);
out(10+(jj-1)*2,12)=std([reshape(p_offaxis{1,jj}(4,2,:)*1000,1,numel(p_offaxis{1,jj}(4,2,:))) ...
reshape(p_offaxis{2,jj}(4,2,:)*1000,1,numel(p_offaxis{2,jj}(4,2,:))) ...
reshape(p_offaxis{3,jj}(4,2,:)*1000,1,numel(p_offaxis{3,jj}(4,2,:)))]);
end
for jj=1:2
out(9+(jj-1)*2,13)=mean([resnormS_offaxis_left{1,jj} resnormS_offaxis_left{2,jj} resnormS_offaxis_left{3,jj}])*1e6;
out(9+(jj-1)*2,14)=std([resnormS_offaxis_left{1,jj} resnormS_offaxis_left{2,jj} resnormS_offaxis_left{3,jj}])*1e6;
out(10+(jj-1)*2,13)=mean([resnormS_offaxis_right{1,jj} resnormS_offaxis_right{2,jj} resnormS_offaxis_right{3,jj}])*1e6;
out(10+(jj-1)*2,14)=std([resnormS_offaxis_right{1,jj} resnormS_offaxis_right{2,jj} resnormS_offaxis_right{3,jj}])*1e6;
end
%display data
rows=['SAT | Full | L ||';...
' | Full | R ||';...
' | O-A | L ||';...
' | O-A | R ||';...
'STP | Full | L ||';...
' | Full | R ||';...
' | O-A | L ||';...
' | O-A | R ||';...
'ALL | Full | L ||';...
' | Full | R ||';...
' | O-A | L ||';...
' | O-A | R ||'];
fprintf('\nTab. V.:\n')
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n')
fprintf(' | TOAset | Ear || r (mm) | phi_e (deg) | theta_e (deg)| x_M (mm) | y_M (mm) | z_M (mm) | ANR (\mus) \n')
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n')
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n')
for ii=1:12
fprintf(rows(ii,:))
fprintf(' %4.1f \pm %3.1f | %5.1f \pm %3.1f | %5.1f \pm %4.1f | %4.1f \pm %4.1f | %4.1f \pm %4.1f | %4.1f \pm %4.1f | %4.1f \pm %4.1f\n',out(ii,:)');
end
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n\n')
end
%% Table 6
if flags.do_tab6
%load data
out=zeros(8,14);
hrtf={'SPHERE_ROT','SPHERE_DIS'};
for kk=1:length(hrtf)
data=data_ziegelwanger2014(hrtf{kk},flags.cachemode);
for jj=1:2
for ii=1:length(data.results)
temp1(:,:,ii)=data.results(ii).MCM{jj}.p_offaxis;
temp3(ii)=mean([data.results(ii).MCM{jj}.performance.off_axis{1}.resnormS ...
data.results(ii).MCM{jj}.performance.off_axis{2}.resnormS]);
temp8(ii)=data.results(ii).MCM{jj}.performance.off_axis{1}.resnormS;
temp9(ii)=data.results(ii).MCM{jj}.performance.off_axis{2}.resnormS;
end
p_offaxis{kk,jj}=temp1;
resnormS_offaxis{kk,jj}=temp3;
resnormS_offaxis_left{kk,jj}=temp8;
resnormS_offaxis_right{kk,jj}=temp9;
if kk==1
radius{kk,jj}=[data.radius data.radius];
phi{kk,jj}=[90+data.phi 270+data.phi];
theta{kk,jj}=[data.theta -data.theta];
xM{kk,jj}=zeros(length(data.radius),2);
yM{kk,jj}=zeros(length(data.radius),2);
zM{kk,jj}=zeros(length(data.radius),2);
else
radius{kk,jj}=[data.radius data.radius];
phi{kk,jj}=ones(length(data.radius),1)*[90 270];
theta{kk,jj}=zeros(length(data.radius),2);
xM{kk,jj}=-[data.xM data.xM];
yM{kk,jj}=-[data.yM data.yM];
zM{kk,jj}=-[data.zM data.zM];
end
end
clear data temp1 temp3 temp8 temp9
end
for kk=1:2
for jj=1:2
for ch=1:2
err=squeeze(p_offaxis{kk,jj}(1,ch,:))*1000-radius{kk,jj}(:,ch);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),1)=mean(err);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),2)=std(err);
err=mod(squeeze(p_offaxis{kk,jj}(6,ch,:))*180/pi,360)-phi{kk,jj}(:,ch);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),3)=mean(err);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),4)=std(err);
err=squeeze(p_offaxis{kk,jj}(7,ch,:))*180/pi-theta{kk,jj}(:,ch);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),5)=mean(err);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),6)=std(err);
err=(squeeze(p_offaxis{kk,jj}(2,ch,:))-xM{kk,jj}(:,ch))*1000;
out(1+(kk-1)*4+(jj-1)*2+(ch-1),7)=mean(err);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),8)=std(err);
err=(squeeze(p_offaxis{kk,jj}(3,ch,:))-yM{kk,jj}(:,ch))*1000;
out(1+(kk-1)*4+(jj-1)*2+(ch-1),9)=mean(err);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),10)=std(err);
err=(squeeze(p_offaxis{kk,jj}(4,ch,:))-zM{kk,jj}(:,ch))*1000;
out(1+(kk-1)*4+(jj-1)*2+(ch-1),11)=mean(err);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),12)=std(err);
out(1+(kk-1)*4+(jj-1)*2+(ch-1),13)=mean(resnormS_offaxis_left{kk,jj})*1e6;
out(1+(kk-1)*4+(jj-1)*2+(ch-1),14)=std(resnormS_offaxis_left{kk,jj})*1e6;
out(1+(kk-1)*4+(jj-1)*2+(ch-1),13)=mean(resnormS_offaxis_right{kk,jj})*1e6;
out(1+(kk-1)*4+(jj-1)*2+(ch-1),14)=std(resnormS_offaxis_right{kk,jj})*1e6;
end
end
end
%display data
rows=['Centered | Full | L ||';...
' | Full | R ||';...
' | O-A | L ||';...
' | O-A | R ||';...
'Non-centered | Full | L ||';...
' | Full | R ||';...
' | O-A | L ||';...
' | O-A | R ||'];
fprintf('\nTab. V.:\n')
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n')
fprintf('Condition | TOAset | Ear || r error (mm) | phi_e error (deg) | theta_e errror | x_M error (mm) | y_M error (mm) | z_M error (mm) | ANR (\mus) \n')
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n')
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n')
for ii=1:8
fprintf(rows(ii,:))
fprintf(' %4.1f \pm %3.1f | %4.1f \pm %3.1f | %4.1f \pm %3.1f | %4.1f \pm %3.1f | %4.1f \pm %3.1f | %4.1f \pm %3.1f | %4.1f \pm %3.1f\n',out(ii,:)');
end
fprintf('-------------------------------------------------------------------------------------------------------------------------------------------------\n\n')
end
end
function idx=ARI_FindPosition(data,azimuth,elevation)
psi=sin(elevation/180*pi).*sin(data.APV(:,2)/180*pi) + ...
cos(elevation/180*pi).*cos(data.APV(:,2)/180*pi).*...
cos(azimuth/180*pi-data.APV(:,1)/180*pi);
[~,idx]=min(acos(psi));
end
function [lat,pol]=geo2horpolar(azi,ele)
warning('off');
azi=mod(azi+360,360);
ele=mod(ele+360,360);
razi = deg2rad(azi);
rele = deg2rad(ele);
rlat=asin(sin(razi).*cos(rele));
rpol=asin(sin(rele)./cos(rlat));
idx=find(cos(rlat)==0);
rpol(idx)=0;
pol = rad2deg(rpol);
lat = rad2deg(rlat);
idx = find(razi>pi/2 & razi < 3*pi/2 & (rele < pi/2 | rele > 3*pi/2));
pol(idx)=180-pol(idx);
idx = find(~(razi>pi/2 & razi < 3*pi/2) & rele > pi/2 & rele < 3*pi/2);
pol(idx)=180-pol(idx);
end
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