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Binmethod_v2.m
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% Montecarlo - 3D beam, kogia detectability model.
% Kait Frasier
% 7/23/2013
% JAH 9/2017 to make grid search for best fit to percent RL plot
%{
% Modeling individual click detectability = "group counting method"
% This means that every point generated by the model just represents a
% group of animals. The parameters of each are drawn from a series of
% probability distributions, which ultimately combine to dictate a recieved
% level (RL), at which point a threshold is used to decide whether or not
% the group is "heard".
%}
tic
clearvars
% Parameters
n = 500; % the number of model runs that will feature in the probability distribution
N = 100000; % simulate this many points per model run
maxRange = 1000; % in meters
thresh = 132;% click detection threshold (amplitude in dB pp)
RLbins = 120.5:1:190.5;
fignum = 150; % if fignum = 0 make no figures
spVec = {'kogia'}; itSp = 1; species = spVec{itSp};
% Parameters that can have multiple values have %***
% assume that some parameters have been determined
% using the click data = diveDepth, descAngle, direct, botAngle
% *** in loop
diveDepth = [430,100]; %***
% diveDepth = [250:10:600]'; %***
% div2 = 100*ones(1,length(diveDepth));
% diveDepth = [diveDepth,div2'];
sdiveDepth = size(diveDepth);
diveDepths = [25,25];
SLm = [210,10]; SLs = [2,3];
descAnglem = [63,5];
sdescAngle = size(descAnglem);
descAngles = [5,5];
clickStartm = [50,50]; clickStarts = [10,10];
directm = [22,2];
sdirect = size(directm);
minBeamAmpm = [33,3];
minBeamAmps = [2,3];
descentPm = [.10 , .05];
% Parameters Unique to the Bin Method
rotHoriz = [180,20];%***
% rotHoriz = [90:10:270]';
% rot2 = 20*ones(1,19);
% rotHoriz= [rotHoriz,rot2'];
srotHoriz = size(rotHoriz);
rotHorizs = [10,10];
rotVertForage = [65,5]; %*** %***
% rotVertForage = [30:5:90]';
% rot2 = 5*ones(1,length(rotVertForage));
% rotVertForage= [rotVertForage,rot2'];
srotVertForage = size(rotVertForage);
rotVertForages = [5,5];
rotVertDive = [30,20]; %***
% rotVertDive = [0:5:55]';
% rot2 = 20*ones(1,length(rotVertDive));
% rotVertDive= [rotVertDive,rot2'];
srotVertDive = size(rotVertDive);
rotVertDives = [5,10];
%
TLprofile_directory = 'E:\Data\ESME\';
saveDir = 'E:\Data\John Reports\KogiaBin\';
% Check if output directory exits
if ~exist(saveDir,'dir')
mkdir(saveDir)
end
% file with grid search results
fnParms = fullfile(saveDir,sprintf('BinModel_Parms_%s.txt',...
species));
fid = fopen(fnParms,'a');
fprintf(fid,'\r\n Num Iterations = %10d',N);
% Parameters that do not change in the loop
fprintf(fid,'\r\n %4d %4d %4d %4d %4d %4d %4d %4d',...
diveDepth,diveDepths,SLm,SLs);
fprintf(fid,'\r\n %4d %4d %4d %4d %4d %4d %4d %4d',...
descAnglem,descAngles,clickStartm,clickStarts);
fprintf(fid,'%4d %4d %4d %4d %4d %4d %4d %4d %4d %4d',...
directm,minBeamAmpm,minBeamAmps);
fprintf(fid,' %7.3f %7.3f',...
descentPm);
best = [1000]; %arbitrary large number
%
siteVec = {'DT','GC','MC'};
nsim = sdiveDepth(1) * srotHoriz(1) * srotVertForage(1) * srotVertDive(1);
dif = zeros(nsim,length(siteVec)); dif3 = zeros(nsim,1);
pD = zeros(nsim,length(siteVec)); pDs = zeros(nsim,length(siteVec));
for itSite = 1:length(siteVec)
site = siteVec{itSite};
% TL model
polarFile = fullfile(TLprofile_directory,...
sprintf('%skogia_Jan\\118kHz\\%szoom1_118_3DTL_2.mat',site,site));
disp(polarFile);
outdir = fileparts(polarFile);
load(polarFile)
numAngle = length(thisAngle);
rr_int = round(rr(2)-rr(1)); % figure out what the range step size is
nrr_new = rr_int*nrr;
rr_new = 0:rr_int:nrr_new; % What are the real values of the range vector? (in m)
pDetTotal = nan(n,1);
binVec = 0:100:maxRange;
binnedPercDet = nan(n,length(binVec)-1);
binnedCountsDet = nan(n,length(binVec)-1);
% Load measured Percent PP by site
if (strcmp(siteVec{1,itSite}, 'MC'));
load('E:\JAH\Kogia\Detections\MC_Kogia\MC_Kogia_binlog.mat');
elseif (strcmp(siteVec{1,itSite}, 'GC'));
load('E:\JAH\Kogia\Detections\GC_Kogia\GC_Kogia_binlog.mat');
elseif (strcmp(siteVec{1,itSite}, 'DT'));
load('E:\JAH\Kogia\Detections\DT_Kogia\DT_Kogia_binlog.mat');
end
% center of bins and nper number of percent for measurements
botbin = 116; topbin = 140; %used for both model and measurements
icen = find(center > botbin & center < topbin);
lnbper = log10(nbper(icen)); %used for goodness of fit
inot = find(~isinf(lnbper));
isim = 0; %counter for num of simulations per site
%
for itParms1 = 1: sdiveDepth(1)
diveDepthm = diveDepth(itParms1,:);
for itParms2 = 1: srotHoriz(1)
rotHorizm = rotHoriz(itParms2,:);
for itParms3 = 1: srotVertForage(1)
rotVertForagem = rotVertForage(itParms3,:);
for itParms4 = 1: srotVertDive(1)
rotVertDivem = rotVertDive(itParms4,:);
% variables to pick from a distribution for CV estimation
diveDepth_mean = diveDepthm(1) + diveDepthm(2)*rand(n,1); % mean dive altitude is somewhere between 175 and 225m
diveDepth_std = diveDepths(1) + diveDepths(2)*rand(n,1);% dive depth std. dev. is 10 to 20m
SL_mean = SLm(1) + SLm(2)*rand(n,1); % mean source level is between 210 and 220 dB pp - gervais
SL_std = SLs(1) + SLs(2)*rand(n,1); % add 2 to 5 db std dev to source level.
descAngle_mean = descAnglem(1) + descAnglem(2)*rand(n,1); % mean descent angle in deg, from tyack 2006 for blainvilles;
descAngle_std = descAngles(1) + descAngles(2)*rand(n,1); % std deviation of descent angle in deg, from tyack 2006 for blainvilles;
clickStart_mean = clickStartm(1) + clickStartm(2)*rand(n,1); % depth in meters at which clicking starts, from tyack 2006 for blainvilles;
clickStart_std = clickStarts(1) + clickStarts(2)*rand(n,1); % depth in meters at which clicking starts, from tyack 2006 for Cuvier's - blainvilles was unbelievably large;
%directivity = directm(1) + directm(2)*rand(n,1); % directivity is between 25 and 27 dB PP (Zimmer et al 2005: Echolocation clicks of free-ranging Cuvier's beaked whales)
descentPerc = descentPm(1) + descentPm(2)*rand(n,1);
%
minBeamAmp_mean = minBeamAmpm(1) + minBeamAmpm(2)*rand(n,1); % minimum off-axis dBs down from peak
minBeamAmp_std = minBeamAmps(1) + minBeamAmps(2)*rand(n,1);
rotHorizDeg = rotHorizm(1) + rotHorizm(2)*rand(n,1);
rotHorizDeg_std = rotHorizs(1) + rotHorizs(2)*rand(n,1);
rotVertDegForage = rotVertForagem(1) + rotVertForagem(2)*rand(n,1);
rotVertDegForageStd = rotVertForages(1) + rotVertForages(2)*rand(n,1);
rotVertDegDive = rotVertDivem(1) + rotVertDivem(2)*rand(n,1);
rotVertDegDiveStd = rotVertDives(1) + rotVertDives(2)*rand(n,1);
for itr_n = 1:n % number of simulations loop
if rem(itr_n,100) == 0
fprintf('TL computation %d of %d\n', itr_n, n)
end
%%%%% Location Computation %%%%%
% rand location
randVec = ceil(rand(2,N)'.*repmat([2*maxRange, 2*maxRange], [N, 1]))...
- repmat([maxRange, maxRange], [N, 1]);
[theta, rho] = cart2pol(randVec(:,1),randVec(:,2)); % convert to polar coord.
clear randVec % trying to save on memory
% trim out the locations that are beyond the max range (corners of the
% 2*maxRange X 2*maxRange square, since now we are using a pi*maxRange^2
% circle)
jjj = 1;
rho2 = [];
theta2 = [];
for iii = 1:length(rho)
if rho(iii) < maxRange
rho2(jjj,1) = rho(iii);
theta2(jjj,1) = theta(iii);
jjj = jjj+1;
end
end
thetaDeg = make360(theta2*180/pi);
clear theta
% go from angle to ref indices
[angleRef,radRef] = angle_ref_comp(thetaDeg,rho2,thisAngle);
%%%%% Depth Computation %%%%%
% Compute bottom depth at each randomly selected point
count0 = 1;
tempDepth = zeros(size(angleRef));
keepPoint = ones(size(angleRef));
diveDepthRef = diveDepth_mean(itr_n) + diveDepth_std(itr_n)...
*randn(size(angleRef));
% Find points that are above surface or below bottom and correct them
flyingWhaleIdx = find(diveDepthRef>=sd | diveDepthRef<1);
while ~isempty(flyingWhaleIdx)
diveDepthRef(flyingWhaleIdx) = diveDepth_mean(itr_n)...
+ diveDepth_std(itr_n)*randn(size(flyingWhaleIdx)); % add variation to dive depth,
flyingWhaleIdx = find(diveDepthRef>=sd | diveDepthRef<1);
end
% Assign last n% to a descent phase
% Choose a depth between start of clicking and destination depth
% determine off-axis angle
descentIdx = (floor((1-descentPerc(itr_n,1))*length(rho2))+1:length(rho2))';
dFactor = rand(size(descentIdx));
clickStartVec = clickStart_mean(itr_n,1) + clickStart_std(itr_n,1).*randn(size(descentIdx));
% Find points that are above surface or below bottom and correct them
flyingWhaleIdx2 = find(clickStartVec>=sd | clickStartVec<1);
while ~isempty(flyingWhaleIdx2)
clickStartVec(flyingWhaleIdx2) = clickStart_mean(itr_n,1)...
+ clickStart_std(itr_n,1).*randn(size(flyingWhaleIdx2)); % add variation to dive depth,
flyingWhaleIdx2 = find(clickStartVec>=sd | clickStartVec<1);
end
descentDelta = dFactor.* (diveDepthRef(descentIdx,:) - clickStartVec);
diveDepthRef(descentIdx,1) = clickStartVec + descentDelta;
%%%%% Beam Angle Computation %%%%%
% Assign random beam orientation in horizontal (all orientations equally likely)
randAngleVec = rand(size(rho2)).*359;
theta2deg = theta2.*180./pi;
partAngle = 180 + make180(thetaDeg);
totalOffAxisHoriz = make180(randAngleVec - partAngle');
onAxisHorz = abs(totalOffAxisHoriz) <= rotHorizDeg(itr_n,1) + ...
rotHorizDeg_std(itr_n,1).*randn(1,size(totalOffAxisHoriz,2));
% Compute vertical component of shift between animal and sensor (sd =
% sensor depth)
dZ = abs(sd - diveDepthRef);
zAngle_180 = ceil(abs(atand(dZ./rho2)));
zAngle_180(descentIdx,1) = ceil(abs(atand(dZ(descentIdx,:)./radRef(descentIdx,:))) - ...
descAngle_mean(itr_n,1) + (descAngle_std(itr_n,1).*randn(size(descentIdx,1),1)));
% If they're foraging, allow one amount of vertical rotation
% if diving, different amount
onAxisVert = abs(zAngle_180) <= rotVertDegForage(itr_n,1) + ...
rotVertDegForageStd(itr_n,1)*randn(size(zAngle_180,1),1);
onAxisVert(descentIdx,1) = abs(zAngle_180(descentIdx,1) ) <= ...
(rotVertDegDive(itr_n,1) + rotVertDegDiveStd(itr_n,1)*randn(size(descentIdx,1),1));
% Compute variation to add to source level
SL_adj = SL_mean(itr_n,1) + (SL_std(itr_n,1)*randn(N,1));
% directVec = directivity_mean(itr_n,1)+(directivity_std(itr_n,1)*randn(N,1));
minAmp = minBeamAmp_mean(itr_n,1)+(minBeamAmp_std(itr_n,1)*randn(N,1));
%%%%% Transmission Loss Loop %%%%%
% initialize some variables
RL = nan(size(thetaDeg));
isheard = zeros(size(thetaDeg));
distTL = nan(size(thetaDeg));
%%%%% Transmission Loss Loop %%%%%
for itr2 = 1:length(thetaDeg)
% Compute location of this animal in the transmission loss matrix:
% Find which row you want to look at:
thisRd = rd_all{angleRef(itr2)};
[~,thisDepthIdx] = min(abs(thisRd - round(diveDepthRef(itr2))));
% Record the distance related portion of this transmission loss
thisSortedTL = real(sortedTLVec{angleRef(itr2)});
distTL(itr2) = thisSortedTL(thisDepthIdx,ceil(radRef(itr2)./rr_int));
% Add up all the sources of TL
if (onAxisVert(itr2)+onAxisHorz(itr2))==2
RL(itr2,1) = SL_adj(itr2) - distTL(itr2);
else
RL(itr2,1) = SL_adj(itr2) - distTL(itr2) - minAmp(itr2);
end
% Is the total TL less than the maximum allowed?
if thresh <= RL(itr2,1)
isheard(itr2,1) = 1; % detected it
end
end
pDetTotal(itr_n,1) = sum(isheard)./length(isheard)';
detVsLoc = [thetaDeg, rho2, isheard];
totalSim = rho2';
detSim = rho2(isheard==1)';
RL_keep = RL(isheard==1);
RLforHist(itr_n,:) = hist(RL_keep,RLbins);
% Compute detections in range bins, so you can make a histogram if desired
% Makes more sense for click-based model
% preallocate
binTot = zeros(length(binVec)-1,1);
binDet = zeros(length(binVec)-1,1);
for itr2 = 1:length(binVec)-1
binTot(itr2) = length(find(totalSim>binVec(itr2) & totalSim<binVec(itr2 +1)));
binDet(itr2) = length(find(detSim>binVec(itr2) & detSim<binVec(itr2 +1)));
end
thisPercent = binDet./binTot;
% save the bin counts to the overall set, so you can get means and variances per bin.
binnedPercDet(itr_n,:) = thisPercent';
binnedCountsDet(itr_n,:) = binDet';
% binnedCountsTot(itr_n,:) = binTot';
end
RLnorm = RLforHist./(repmat(nansum(RLforHist,2),1,size(RLforHist,2)));
if (strcmp(species,'kogia'))
%JAH *** remove 16 for other than Kogia!
RLshift = RLbins-16; %16 is due to 320 versus 200
else
RLshift = RLbins+1; % not clear why +1 - Kait?
end
mRL = nanmean(RLnorm)*100; %percentage
iRL = find(RLshift > botbin & RLshift < topbin);
lRL = log10(mRL(iRL));
isim = isim + 1;
dif(isim,itSite) = sum((lnbper(inot) - lRL(inot)).^2);
pD(isim,itSite) = nanmean(pDetTotal)*100;
pDs(isim,itSite) = nanstd(pDetTotal)*100;
fprintf(fid,'\r\n %4s ',site);
fprintf(fid,'%4d %4d %4d %4d %4d %4d %4d %4d',...
rotHorizm,rotHorizs,rotVertForagem,rotVertForages);
fprintf(fid,'%4d %4d %4d %4d dif = %8.4f',...
rotVertDivem,rotVertDives,dif(isim,itSite));
fprintf(fid,' P(det) %1.3f std %1.3f', ...
pD(isim,itSite), pDs(isim,itSite));
if (itSite == length(siteVec))
dif3(isim) = sum(dif(isim,:),2);
fprintf(fid,' Sum = %8.4f',dif3(isim));
if best > dif3(isim)
best = dif3(isim);
fprintf(fid,' Best = %8.4f',best);
end
end
%
if fignum > 0
save(fullfile(saveDir,sprintf('%s_BinModelFinal_%dItr_%s.mat',...
site,itr_n,species)),'-mat');
% Histogram of detectability as a function of range
spots = binVec(1:end-1)+(50);
areas = ((binVec(2:end).^2)*pi)-((binVec(1:end-1).^2)*pi);
means = nanmean(binnedPercDet)*100;
means_keep = (means>0);
spotsprune = spots(means_keep);
means = means(means_keep);
areas = areas(means_keep);
errsTop = nanstd(binnedPercDet(:,means_keep)*100);
errsBot = errsTop;
toobig = (errsTop + means)>100;
toosmall = (means - errsBot)<0;
errsTop(toobig) = 100-means(toobig);
errsBot(toosmall) = -(0-means(toosmall));
% plot Prob Detection
figure(fignum); fignum = fignum +1; %clf
hb1 = bar(spotsprune,means,1);
set(hb1,'EdgeColor','k','FaceColor','w')
hold on
ha = errorbar(spotsprune,means,errsBot,errsTop,'k');
Xdata = get(ha,'Xdata');
temp = 4:3:length(Xdata);
temp(3:3:end) = [];
% xleft and xright contain the indices of the left and right endpoints of the horizontal lines
xleft = temp; xright = temp+1;
Xdata(xleft) = Xdata(xleft) + 20;
Xdata(xright) = Xdata(xright) - 20;
set(ha,'Xdata',Xdata)
plot(spotsprune,means,'-k','LineWidth',3)
set(gca,'XTick',binVec,'FontSize',12)
set(gca,'XTickLabel',binVec)
xlabel(gca,'Horizontal Range (m)','FontSize',14)
ylabel(gca, 'Probability of Detection (%)','FontSize',14)
title({sprintf('Max Horiz. Range = %dm; mean P(det) = %1.2f%%; std = %1.2f%%', ...
maxRange, nanmean(pDetTotal)*100, nanstd(pDetTotal)*100)},'FontSize',14)
print(gcf,'-dpng','-r600',fullfile(saveDir,[site,'_',species,'_grpMod_pDet.png']))
saveas(gca,fullfile(saveDir,[site,'_',species,'_grpMod_pDet.fig']))
% plot percent det versus range
figure(fignum); fignum = fignum +1; clf;
binCountDetMean = nanmean(binnedCountsDet./...
repmat(sum(binnedCountsDet,2),1,size(binnedCountsDet,2)));
binCountDetStd = nanstd(binnedCountsDet./...
repmat(sum(binnedCountsDet,2),1,size(binnedCountsDet,2)));
binId = find(binCountDetMean-binCountDetStd<0);
binCountStdBot = binCountDetStd;
binCountStdBot(binId) = binCountDetMean(binId) ;
hb2 = bar(spots/1000,binCountDetMean,1);
hold on
errorbar(spots/1000,binCountDetMean,binCountStdBot,binCountDetStd,'.k');
set(hb2,'FaceColor',[1,1,1])
set(gca,'XTick',binVec,'FontSize',12)
set(gca,'XTickLabel',binVec)
xlabel(gca,'Horizontal Range (km)','FontSize',14)
ylabel(gca, '% of detections','FontSize',14)
title('Kogia Group: Modeled Detection Ranges','FontSize',14)
saveas(gca,fullfile(saveDir,[site,'_',species,'_grpMod_nDets.fig']))
saveas(gca,fullfile(saveDir,[site,'_',species,'_grpMod_nDets.png']))
%plot Percent det versus RL dBpp
figure(fignum); fignum = fignum +1; clf;
hold on;
hb3 = bar(RLbins,nanmean(RLnorm)*100,1);
set(hb3,'EdgeColor','k','FaceColor','w')
xlim([thresh,160])
ylim([0,30])
xlabel(gca,'RL (dB_p_p re 1\muPa)','FontSize',16)
ylabel(gca, 'Percent of detections','FontSize',16)
set(gca,'FontSize',12)
title(siteVec{1,itSite});
print(gcf,'-dpng','-r300',fullfile(saveDir,[site,'_',species,'_grpkMod_RLdist.png']))
saveas(gca,fullfile(saveDir,[site,'_',species,'_grpMod_RLdist.fig']))
% Percent det versus RL as Log plot
%note -16 is to correct for 100kHz data versus fullband
figure(fignum); fignum = fignum +1; clf;
hb4 = bar(RLbins-16,mRL,1,...
'barwidth', 1, 'basevalue', 1);
set(hb4,'EdgeColor','k','FaceColor','w')
xlim([thresh-1-16,172-16])
ylim([.001,50])
set(gca,'YScale','log')
xlabel(gca,'RL (dB_p_p re 1\muPa)','FontSize',16)
ylabel(gca, 'Percent of detections','FontSize',16)
set(gca,'FontSize',12)
hold on;
plot(center,nbper,'b--o'); % measured data
title([siteVec{1,itSite},' Error= ',num2str(dif(itSite))]);
print(gcf,'-dpng','-r300',fullfile(saveDir,[site,'_',species,'_grpMod_RLlog.png']));
saveas(gca,fullfile(saveDir,[site,'_',species,'_grpMod_RLlog.fig']));
%
save(fullfile(saveDir,sprintf('BinModel_%dItr_%s.mat',...
itr_n,species)),'-mat');
end
end
end
end
end
end
% Make summary Plot
fignum = fignum +1;
if (sdiveDepth > 1)
figure(fignum); fignum = fignum +1; clf;
subplot(2,1,1);
plot(diveDepth(:,1),dif3,'b--o');
xlabel('diveDepth m'); ylabel('Error');
subplot(2,1,2)
for it = 1: length(siteVec)
plot(diveDepth(:,1),pD(:,it));
hold on
end
ylabel('P(det)')
saveas(gca,fullfile(saveDir,[species,'_diveDepthBin.fig']));
save(fullfile(saveDir,sprintf('diveDepthBinModOut_%dItrs_%s.mat',...
itr_n,species)),'-mat');
end
if (srotHoriz > 1)
figure(fignum); fignum = fignum +1; clf;
subplot(2,1,1)
plot(rotHoriz(:,1),dif3,'b--o')
xlabel('rotHoriz deg'); ylabel('Error');
subplot(2,1,2)
for it = 1: length(siteVec)
plot(rotHoriz(:,1),pD(:,it));
hold on
end
ylabel('P(det)')
saveas(gca,fullfile(saveDir,[species,'_rotHoriz.fig']));
save(fullfile(saveDir,sprintf('rotHorizBinModOut_%dItrs_%s.mat',...
itr_n,species)),'-mat');
end
if (srotVertForage > 1)
figure(fignum); fignum = fignum +1; clf;
subplot(2,1,1)
plot(rotVertForage(:,1),dif3,'b--o')
xlabel('rotVertForage deg'); ylabel('Error');
ylabel('P(det)')
subplot(2,1,2)
for it = 1: length(siteVec)
plot(rotVertForage(:,1),pD(:,it));
hold on
end
saveas(gca,fullfile(saveDir,[species,'_rotVertF.fig']));
save(fullfile(saveDir,sprintf('rotVertFBinModOut_%dItrs_%s.mat',...
itr_n,species)),'-mat');
end
if (srotVertDive > 1)
figure(fignum); fignum = fignum +1; clf;
subplot(2,1,1)
plot(rotVertDive(:,1),dif3,'b--o')
xlabel('rotVertDive deg'); ylabel('Error');
%ylim ([min(dif3),max(dif3)])
subplot(2,1,2)
for it = 1: length(siteVec)
plot(rotVertDive(:,1),pD(:,it));
hold on
end
ylabel('P(det)')
saveas(gca,fullfile(saveDir,[species,'_rotVertD.fig']));
save(fullfile(saveDir,sprintf('rotVertDBinModOut_%dItrs_%s.mat',...
itr_n,species)),'-mat');
end
fclose(fid);
toc