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S141106_LearningCurve_Control.m
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% Pooled-trial analysis
%dname = 'C:\Users\James Jun\Desktop\New folder\';
% dname = '/Users/junj10/Desktop/None group learning/';
%dname = '/Users/junj10/Desktop/2013b_RandomNarrow_complete/';
mode = 'random';
switch lower(mode)
case 'random'
% dname = '/Users/junj10/Desktop/2013b_Random_complete/';
dname = 'D:\Dropbox\Ms16_cue learning\vistrack\';
outfname = 'D141026_RandGroup';
case 'none'
dname = '/Users/junj10/Desktop/None group learning/';
outfname = 'D141026_NoneGroup';
case 'stable'
error('not implemented');
end
vsFnames = dir([dname '*_Track.mat']);
nFiles = numel(vsFnames);
vcFnames = {};
for i=1:nFiles
vcFnames{i} = vsFnames(i).name;
end
% determine rotation and center
iData = 1;
S = load(fullfile(dname, vcFnames{iData}));
hFig = figure;
imshow(imadjust(S.img0));
title('click (-50,0), (+50,0)cm');
set(gcf, 'Position', get(0, 'ScreenSize'));
[vrX, vrY] = ginput(2);
xy0 = [mean(vrX), mean(vrY)];
angXaxis = rad2deg(cart2pol(diff(vrX), diff(vrY))); %in rad
pixpercm = sqrt(diff(vrX)^2 + diff(vrY)^2) / 100;
hold on;
plot(vrX, vrY, 'r.');
plot(xy0(1), xy0(2), 'r.');
plot(S.xy0(1), S.xy0(2), 'go');
vcDisp = sprintf('iData: %d, ang: %0.3f deg, pixpercm: %0.3f, x0: %0.1f, y0: %0.1f', ...
iData, angXaxis, pixpercm, xy0(1), xy0(2));
disp(vcDisp);
title(vcDisp);
uiwait(msgbox('press okay to continue'));
close(hFig); drawnow;
% Collect traces
% Rotate image?
warning off;
vsFishID = {'A', 'B', 'C', 'D'};
for iAnimal = 1:numel(vsFishID)
fishID = vsFishID{iAnimal};
vsTrial = [];
csDataId = [];
for iFile=1:nFiles
fname = vcFnames{iFile};
if fname(6) == '_' %exclude probe trials
if strcmp(fname(4), fishID)
vsTrial{end+1} = importTrial(fullfile(dname, fname), ...
'angXaxis', angXaxis, 'pixpercm', pixpercm);
csDataId{end+1} = fname;
end
end
end
eval(sprintf('csDataId_%c = csDataId;', vsFishID{iAnimal}));
eval(sprintf('vsTrial_%s = vsTrial;', fishID));
end
% S = makeStruct(TEOD, EODR, EODA, XH, YH, ...
% VEL, ACC, ANG, AVEL, xy0, ...
% viESAC, vrESAC, vtESAC, vxESAC, vyESAC, img0, dataID, pathLen_cm);
eval(sprintf('save %s vsTrial_*;', outfname));
fprintf('saved %s\n', outfname);
%%
mrColor = [0 0 1; 0 1 0; 1 0 1; 0 1 1; 1 0 0];
nSessions = 8;
nScores = 6; %store med, med_L, med_H, sd, sd_L, sd_H
vsFishID = {'A', 'B', 'C', 'D'};
csGroupFname = {'D140324_LandmarkGroup', 'D141026_NoneGroup', 'D141026_RandGroup'};
csGroup = {'StLm', 'NoLm', 'RnLm', 'RnLmCue', 'RnLm4'};
iGroupRand = 3;
csDataId = [];
nTrialsPerSession = 4;
nAnimals = numel(vsFishID);
nGroups = numel(csGroupFname);
trPathStat = zeros(nSessions, nGroups, nScores);
trDurStat = zeros(nSessions, nGroups, nScores);
trPath = zeros(nAnimals*nTrialsPerSession, nSessions, nGroups);
trDur = zeros(nAnimals*nTrialsPerSession, nSessions, nGroups);
cvPath = cell(nSessions, nGroups);
cvDur = cell(nSessions, nGroups);
for iGroup=1:numel(csGroupFname)
vcPooledFname = csGroupFname{iGroup};
load(vcPooledFname);
for iAnimal = 1:numel(vsFishID)
csDataId = [];
animalID = vsFishID{iAnimal};
eval(sprintf('vsTrial = vsTrial_%s;', animalID));
vrPathLen = zeros(size(vsTrial));
vrDuration = zeros(size(vsTrial));
for iTrial = 1:numel(vsTrial)
S = vsTrial{iTrial};
try
vrPathLen(iTrial) = S.pathLen_cm;
vrDuration(iTrial) = diff(S.TEOD([1 end]));
csDataId{end+1} = S.dataID;
catch
disp(S)
vrPathLen(iTrial) = nan;
vrDuration(iTrial) = nan;
end
end
if iAnimal==1
img0 = vsTrial{end}.img0; %save the background
end
vrPathLen(isnan(vrPathLen)) = [];
vrDuration(isnan(vrDuration)) = [];
eval(sprintf('vrPathLen_%s = vrPathLen;', animalID));
eval(sprintf('vrDuration_%s = vrDuration;', animalID));
eval(sprintf('csDataId_%s_%s = csDataId;', csGroup{iGroup}, animalID));
end
for iSession=1:nSessions
idxRng = (1:4) + (iSession-1)*4;
vrPath = [vrPathLen_A(idxRng), vrPathLen_B(idxRng), vrPathLen_C(idxRng), vrPathLen_D(idxRng)];
trPath(:, iSession, iGroup) = vrPath;
trPathStat(iSession, iGroup, 1) = mean(vrPath);
trPathStat(iSession, iGroup, 2) = median(vrPath);
trPathStat(iSession, iGroup, 3) = std(vrPath);
trPathStat(iSession, iGroup, 4) = iqr(vrPath);
trPathStat(iSession, iGroup, 5) = quantile(vrPath, .25);
trPathStat(iSession, iGroup, 6) = quantile(vrPath, .75);
cvPath{iSession, iGroup} = vrPath(:);
%trPathStat(iSession, iGroup, 5:6) = ...
% bootci(1000, {@(x)mean(x), vrPath});
vrDur = [vrDuration_A(idxRng), vrDuration_B(idxRng), vrDuration_C(idxRng), vrDuration_D(idxRng)];
trDur(:, iSession, iGroup) = vrDur;
trDurStat(iSession, iGroup, 1) = mean(vrDur);
trDurStat(iSession, iGroup, 2) = median(vrDur);
trDurStat(iSession, iGroup, 3) = std(vrDur);
trDurStat(iSession, iGroup, 4) = iqr(vrDur);
trDurStat(iSession, iGroup, 5) = quantile(vrDur, .25);
trDurStat(iSession, iGroup, 6) = quantile(vrDur, .75);
cvDur{iSession, iGroup} = vrDur(:);
%trDurStat(iSession, iGroup, 5:6) = ...
% bootci(1000, {@(x)mean(x), vrDur});
if iGroup == iGroupRand
for iGroup1=4:5
switch iGroup1
case 4
vrPath1 = vrPath(1:end/2);
vrDur1 = vrDur(1:end/2);
case 5
vrPath1 = vrPath(end/2+1:end);
vrDur1 = vrDur(end/2+1:end);
end
trPathStat(iSession, iGroup1, 1) = mean(vrPath1);
trPathStat(iSession, iGroup1, 2) = quantile(vrPath1, .5);
trPathStat(iSession, iGroup1, 3) = std(vrPath1);
trPathStat(iSession, iGroup1, 4) = iqr(vrPath1);
trPathStat(iSession, iGroup1, 5) = quantile(vrPath1, .25);
trPathStat(iSession, iGroup1, 6) = quantile(vrPath1, .75);
cvPath{iSession, iGroup1} = vrPath1(:);
%trPathStat(iSession, iGroup1, 5:6) = ...
% bootci(1000, {@(x)mean(x), vrPath1});
trDurStat(iSession, iGroup1, 1) = mean(vrDur1);
trDurStat(iSession, iGroup1, 2) = quantile(vrDur1, .5);
trDurStat(iSession, iGroup1, 3) = std(vrDur1);
trDurStat(iSession, iGroup1, 4) = iqr(vrDur1);
trDurStat(iSession, iGroup1, 5) = quantile(vrDur1, .25);
trDurStat(iSession, iGroup1, 6) = quantile(vrDur1, .75);
cvDur{iSession, iGroup1} = vrDur1(:);
%trDurStat(iSession, iGroup1, 5:6) = ...
% bootci(1000, {@(x)mean(x), vrDur1});
end
end
end
end
%%
figure;
nRows = 2;
csPrefix = {'Mean', 'Median', 'SD', 'IQR'};
viPrefixPlot = [2 4];
viGroupPlot = [1 2 5];
nCols = numel(viPrefixPlot);
csGroup = {'LM', 'None', 'Rand', 'RandCue', 'Rand4'};
for iRow=1:nRows
switch iRow
case 1
trData = trPathStat;
vcYlabel = 'Path Length (cm)';
ytick = 0:1000:5000;
case 2
trData = trDurStat;
vcYlabel = 'Duration (s)';
ytick = 0:100:500;
end
ylim = ytick([1 end]);
for iCol=1:nCols
iPrefix = viPrefixPlot(iCol);
subplot(nRows, nCols, iCol + (iRow-1)*nCols); hold on;
%for iGroup=1:size(trData,2)
for iGroup = viGroupPlot
errorbar(1:nSessions, trData(:,iGroup,iPrefix), 'color', mrColor(iGroup,:));
end
legend(csGroup{viGroupPlot});
ylabel([csPrefix{iPrefix}, ' ', vcYlabel]);
xlabel('Session #');
set(gca, 'XLim', [.5 (nSessions+.5)], 'XTick', 1:nSessions);
set(gca, 'YLim', ylim, 'YTick', ytick);
end
end
%% learning curve
figure;
csGroup = {'LM', 'None', 'Rand', 'RandCue', 'Rand4'};
viGroup = [1 2 5];
mrPath = trPathStat(:,viGroup,2);
mrPathL = trPathStat(:,viGroup,5);
mrPathH = trPathStat(:,viGroup,6);
mrDur = trDurStat(:,viGroup,2);
mrDurL = trDurStat(:,viGroup,5);
mrDurH = trDurStat(:,viGroup,6);
mrColor = [1 0 0; 0 1 0; 0 0 1];
for iRow=1:2
switch iRow
case 1
mrY = mrPath / 100;
mrL = (mrY - mrPathL/100);
mrH = (mrPathH/100 - mrY);
vcYlabel = 'Path Length (m)';
ytick = 0:10:60;
case 2
mrY = mrDur;
mrL = mrY - mrDurL;
mrH = mrDurH - mrY;
vcYlabel = 'Duration (s)';
ytick = 0:100:600;
end
ylim = ytick([1 end]);
subplot(nRows, 1, iRow);
hold on;
%bar(mrY, 1);
for iGroup = 1:size(mrY,2)
vrX = (1:nSessions) + iGroup/(size(mrY,2)+1) - .5;
vrY = mrY(:,iGroup);
h = bar(vrX, vrY, .2);
set(h, 'FaceColor', mrColor(iGroup,:));
set(h, 'EdgeColor', 'none');
errorbar(vrX, vrY, mrL(:,iGroup), mrH(:,iGroup), 'color', mrColor(iGroup,:), 'LineStyle', 'none');
end
legend(csGroup{viGroup});
ylabel(vcYlabel);
xlabel('Session #');
set(gca, 'XLim', [.5 (nSessions+.5)], 'XTick', 1:nSessions);
set(gca, 'YLim', ylim, 'YTick', ytick);
end
%% plot images
load('D141026_RandGroup');
trImg = [];
figure('Position', get(0, 'ScreenSize'));
subplot(2,2,1);
for iTrial=1:numel(vsTrial)
for iAnimal=1:nAnimals
subplot(2,2,iAnimal);
eval(sprintf('S = vsTrial_%c{iTrial};', vsFishID{iAnimal}));
imshow(imadjust(S.img0));
title(S.dataID);
end
pause(2);
end
%% compare across conditions
mrColor = [0 0 1; 0 1 0; 1 0 0];
cvTime = {1:2, 6:8};
csData = {'mean', 'std', 'mean3/4', 'std3/4', 'median', 'quantile1/4', 'quantile3/4'};
csType = {'Dur', 'Path'};
csTime = {'Early', 'Late'};
csGroup = {'StLm', 'NoLm', 'RnLm'};
trStat_Dur = zeros(numel(csData),numel(cvTime),5);
trStat_Path = zeros(numel(csData),numel(cvTime),5);
for iGroup=1:nGroups
if iGroup == 3
viSession = (size(trDur,1)/2 + 1):size(trDur,1);
else
viSession = 1:size(trDur,1);
end
for iTime = 1:2
for iType = 1:2
eval(sprintf('vr = tr%s(viSession,cvTime{iTime},iGroup);', csType{iType}));
vr=sort(vr(:));
n = round(numel(vr)/4);
n1 = round(n/2); n2 = n - n1;
vr1 = vr(n1+1:end-n2);
vrStat = [mean(vr), std(vr), mean(vr1), std(vr1), median(vr), quantile(vr, 1/4), quantile(vr, 3/4)];
eval(sprintf('trStat_%s(:,iTime,iGroup) = vrStat;', csType{iType}));
end% iType
end %iTme
end %iGroup
%
csYlabel = {'Duration (s)', 'Path length (cm)'};
figure;
for iType=1:2
subplot(2,1,iType); hold on;
eval(sprintf('mrY = trStat_%s(1,:,:);\n', csType{iType}));
eval(sprintf('mrE = trStat_%s(2,:,:);\n', csType{iType}));
mrY = reshape(mrY(:), size(mrY,2), size(mrY,3));
mrE = reshape(mrE(:), size(mrE,2), size(mrE,3));
vcYlabel = csYlabel{iType};
h = bar(mrY);
for iGroup=1:nGroups
vrXplot = (1:2) + .22 * (iGroup-2);
set(h(iGroup), 'FaceColor', mrColor(iGroup,:), 'EdgeColor', 'none');
errorbar(vrXplot, mrY(:,iGroup), zeros(size(mrE,1),1), ...
mrE(:,iGroup), '.', 'Color', mrColor(iGroup,:));
end
legend(csGroup);
set(gca, 'XTick', 1:2, 'XTickLabel', csTime);
ylabel(vcYlabel);
end
title('mean & SD');
% pval
cvData = [];
for iType=1:2
eval(sprintf('trData = tr%s;', csType{iType}));
for iTime=1:2
for iGroup=1:3
if iGroup == 3
viSession = (size(trDur,1)/2 + 1):size(trDur,1);
else
viSession = 1:size(trDur,1);
end
vr = trData(viSession,cvTime{iTime},iGroup);
cvData{iGroup} = vr(:);
[h, p] = lillietest(vr(:));
% fprintf('%s:%s:%s, h: %f, p: %f\n', csType{iType}, csTime{iTime}, csGroup{iGroup}, h, p);
% if h==0
% figure; hist(vr(:),10)
% end
end
[h,pa]=ttest2(cvData{1}, cvData{2});
[h,pb]=ttest2(cvData{1}, cvData{3});
[h,pc]=ttest2(cvData{2}, cvData{3});
fprintf('%s-%s\n', csType{iType}, csTime{iTime});
fprintf('StLm-NoLm: %f\nStLm-RnLm: %f\nNoLm-RnLm: %f\n\n', pa, pb, pc);
end
end
%% display trials selected
animalId = 'E';
nSessions = 8;
eval(sprintf('reshape(csDataId_RnLm_%c(1:4*%d), 4, %d)', animalId, nSessions, nSessions))
%% compare trials
%% Fig. 8B
viGroup = [1 2];
%Group: {'LM', 'None', 'Rand', 'RandCue', 'Rand4'};
cvTime = {1:2, 6:8};
quantLim = [1/8, 7/8];
mrColor = [1 0 0; 0 1 0; 0 0 1];
cvPathE = cell(size(viGroup));
cvPathL = cell(size(viGroup));
mrPathMu = zeros(3,2);
mrPathSd = zeros(3,2);
figure; hold on;
vcMode = 'Path'; %Path, Dur
for iMode = 1:2
subplot(1,2,iMode);
hold on;
for iGroup=1:numel(viGroup)
switch iMode
case 1
cvData = cvPath;
vcYlabel = 'Distance (m)';
factor1 = 100;
case 2
cvData = cvDur;
vcYlabel = 'Duration (s)';
factor1 = 1;
end
cvPathE{iGroup} = quantFilt(cell2vec(cvData(cvTime{1}, viGroup(iGroup))), quantLim)/factor1;
cvPathL{iGroup} = quantFilt(cell2vec(cvData(cvTime{2}, viGroup(iGroup))), quantLim)/factor1;
mrPathMu(iGroup, 1) = nanmean(cvPathE{iGroup});
mrPathMu(iGroup, 2) = nanmean(cvPathL{iGroup});
mrPathSd(iGroup, 1) = bootSemMean(cvPathE{iGroup});
mrPathSd(iGroup, 2) = bootSemMean(cvPathL{iGroup});
vrX = (1:2) + iGroup/(numel(viGroup)+1) - .5;
h = bar(vrX, mrPathMu(iGroup,:), .2);
set(h, 'EdgeColor', 'None');
set(h, 'FaceColor', mrColor(iGroup,:));
errorbar(vrX, mrPathMu(iGroup,:), zeros(size(mrPathSd(iGroup,:))), mrPathSd(iGroup,:), 'k', 'LineStyle', 'none');
[h,pa]=ttest2(cvPathE{iGroup}, cvPathL{iGroup});
fprintf('Group %d: E vs L, p=%f\n', iGroup, pa);
end
kwtest_jjj([cvPathE cvPathL]);
[h,p_E12]=ttest2(cvPathE{1}, cvPathE{2});
% [h,p_E13]=ttest2(cvPathE{1}, cvPathE{3});
% [h,p_E23]=ttest2(cvPathE{3}, cvPathE{2});
[h,p_L12]=ttest2(cvPathL{1}, cvPathL{2});
% [h,p_L13]=ttest2(cvPathL{1}, cvPathL{3});
% [h,p_L23]=ttest2(cvPathL{3}, cvPathL{2});
fprintf('Early: 1-2:%f, 1-3:%f, 2-3:%f\n', p_E12, p_E13, p_E23);
fprintf('Late: 1-2:%f, 1-3:%f, 2-3:%f\n', p_L12, p_L13, p_L23);
ylabel(vcYlabel);
set(gca, 'XTick', 1:2);
set(gca, 'XTickLabel', {'Early', 'Late'});
end
% [Path]
% Group 1: E vs L, p=0.000007
% Group 2: E vs L, p=0.000033
% Group 3: E vs L, p=0.000001
% Early: 1-2:0.469637, 1-3:0.000007, 2-3:0.000039
% Late: 1-2:0.011141, 1-3:0.000142, 2-3:0.129105
% [Duration]
% Group 1: E vs L, p=0.000010
% Group 2: E vs L, p=0.000002
% Group 3: E vs L, p=0.000000
% Early: 1-2:0.705353, 1-3:0.000003, 2-3:0.000003
% Late: 1-2:0.070945, 1-3:0.000568, 2-3:0.067626