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Copy pathshowGainKernelsByCue_kernelPrefDir_pop.m
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showGainKernelsByCue_kernelPrefDir_pop.m
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[saveServer, rootFolder] = getReady();
load(fullfile(saveServer,'param20240625.mat'),'param');
animal = 'hugo';%'ollie';%'andy';% 'andy' '
tgtModality = 'all';%'eyeSpeed';
dataDir = '/mnt/syncitium/Daisuke/cuesaccade_data OBS/figPSTH_pop20231026hugo/';
%% load gain info <> avg tgt resp [HACK]
load(fullfile(dataDir,'fitPSTH_pop20231026hugo.mat'),...
'gainInfo_pop','id_pop','kernel_pop','tlags');
%preferred direction & amplitude of each kernel modality
tgtRange = [0.05 0.15; 0.03 0.25; -0.1 0.1];
prefDirOption = 0;
[kernelPrefDir, kernelAmp] = getKernelPrefDirAmp(kernel_pop, tlags, tgtRange, param.cardinalDir, prefDirOption);
%% load kernel info from tgt units
gainKernelName = fullfile(dataDir,'gainkernel_splt_all.mat');
if exist(gainKernelName, 'file')
load(gainKernelName);
else
id_all = cell(1);
kernel_splt_all = cell(1,10);
prefDir_all = [];
latency_bhv_all = cell(1);
latency_neuro_all = cell(1);
latency_r_all = [];
iii=1;
for yyy = 1:3
switch yyy
case 1
year = '2021';
case 2
year = '2022';
case 3
year = '2023';
end
[loadNames, months, dates, channels] = getMonthDateCh(animal, year, rootFolder);
saveName_splt_pop = [];
for idata = 1:numel(channels)
datech = [months{idata} filesep dates{idata} filesep num2str(channels{idata})];
disp([year ': ' num2str(idata) '/' num2str(numel(channels)) ', ' datech ]);
saveFolder = fullfile(saveServer, year,animal);%17/6/23
saveSuffix = [animal replace(datech,filesep,'_') '_linear_rReg'];
saveName_splt = fullfile(saveFolder, [saveSuffix '_splitPredictor.mat']);
saveName = fullfile(saveFolder, [saveSuffix '.mat']);
if exist(saveName_splt,'file') && exist(saveName, 'file')
try
thisKernelInfo = load(saveName_splt, 'kernelInfo');
kernel_splt_all(iii,1:10) = thisKernelInfo.kernelInfo.kernel;
%prefDir_kernel(iii)
thisid = [animal '/' year '/' datech];
%% load avg resp to tgt
[~, thisIDidx] = intersect(id_pop, thisid);
if ~isempty(thisIDidx)
avgResp_all(:,:,:,iii) = squeeze(gainInfo_pop(thisIDidx).avgTonsetByCue(:,1,:,:));
prefDir_all(iii) = gainInfo_pop(thisIDidx).prefDir;
end
%% load latency
load(saveName,'latency_bhv','latency_neuro','latency_r','mdiffCueTgtOnset','stddiffCueTgtOnset');
latency_bhv_all{iii} = latency_bhv;
latency_neuro_all{iii} = latency_neuro;
latency_r_all(iii) = latency_r;
mdiffCueTgtOnset_all(iii) = mdiffCueTgtOnset;
stddiffCueTgtOnset_all(iii) = stddiffCueTgtOnset;
id_all{iii} = thisid;
iii = iii+1;
catch err
disp(err);
continue;
end
end
end
end
winSamps = gainInfo_pop(1).winSamps;
save(gainKernelName, 'kernel_splt_all','avgResp_all','prefDir_all','id_all','winSamps',...
'latency_bhv_all','latency_neuro_all','latency_r_all','mdiffCueTgtOnset_all','stddiffCueTgtOnset_all');
end
%% exclude NG units
load(fullfile(dataDir,'pickUnitsByClass.mat'),"funcClass",'nUnits');
%% tgt units
highAmp = true;
if highAmp
suffix = '_highAmp';
param.ampTh = .5;
else
suffix = '';
end
for itgtModality = 1:3
switch itgtModality
case 1
tgtModality = 'vision';
case 2
tgtModality = 'eyeSpeed';
case 3
tgtModality = 'eyePosition';
end
for iprefDir =1%:8
prefDir_q = quantizeDir(kernelPrefDir(:,itgtModality), param.cardinalDir);
if ~highAmp
tgtID = id_pop(prefDir_q== param.cardinalDir(iprefDir));
else
tgtID = id_pop(prefDir_q== param.cardinalDir(iprefDir) & ...
kernelAmp(:,itgtModality) > param.ampTh);
end
tgtID = intersect(funcClass.id_all, tgtID);%exclude NG units
[~,tgtIDidx] = intersect(id_all, tgtID);
%kernel_pop: {units kernelType}
%kernel_splt_selected = kernel_splt_all(tgtIDidx,:)';
%tlags = thisKernelInfo.kernelInfo.tlags;
avgResp_selected = avgResp_all(:,:,:,tgtIDidx);
prefDir_selected = prefDir_all(tgtIDidx);
mdiffOnset_selected = mdiffCueTgtOnset_all(tgtIDidx);
stddiffOnset_selected = stddiffCueTgtOnset_all(tgtIDidx);
%% show individual resp
mResp = squeeze(mean(avgResp_selected,4));
seResp = squeeze(ste(avgResp_selected,4));
mdiffOnset = mean(mdiffOnset_selected);
stddiffOnset_selected = mean(stddiffOnset_selected);
dir0 = 1;
dir180 = 5;
figure('position',[0 0 1800 1200]);
ax3(1)=subplot(321);
boundedline(winSamps, squeeze(mResp(dir180,:,1)), squeeze(seResp(dir180,:,1)),'k','transparency',.5);
boundedline(winSamps, squeeze(mResp(dir0,:,1)), squeeze(seResp(dir0,:,1)),'transparency',.5);
title(['wo cue, n=' num2str(numel(tgtIDidx))]); grid on;
ax3(2)=subplot(322);
boundedline(winSamps, squeeze(mResp(dir180,:,2)), squeeze(seResp(dir180,:,2)),'k','transparency',.5);
boundedline(winSamps, squeeze(mResp(dir0,:,2)), squeeze(seResp(dir0,:,2)),'transparency',.5);
vline(-mdiffOnset);
title('w cue'); %legend('180deg','0deg','location','northwest');
linkaxes(ax3); grid on;
%% show avg response to tgt stim before centering
for icue = 1:2
ax1(icue)=subplot(3,2,icue+2);
imagesc(winSamps, param.cardinalDir, squeeze(mean(avgResp_selected(:,:,icue,:),4)));
hline;vline;
if icue==1
vline(-mdiffOnset);
ylabel('target direction');
title('wo cue');
elseif icue==2
title('w cue');
end
end
linkcaxes(ax1);
mcolorbar;
%% show avg response to tgt stim after centering
avgResp_selected_centered = [];
for iunit = 1:size(avgResp_selected,4)
[avgResp_selected_centered(:,:,:,iunit), dirAxis] = dealRespByCue(avgResp_selected(:,:,:,iunit), 1, 0, ...
prefDir_selected(iunit), param.cardinalDir);
end
for icue = 1:2
ax2(icue)=subplot(3,2,icue+4);
imagesc(winSamps, dirAxis, squeeze(nanmean(avgResp_selected_centered(:,:,icue,:),4)));
hline;vline;
if icue==1
vline(-mdiffOnset);
ylabel('centered direction');
end
end
linkcaxes(ax2);
mcolorbar;
screen2png(['avgResp_' animal '_' tgtModality 'kernel_prefDir' num2str(param.cardinalDir(iprefDir)) ...
suffix]);
close all;
% %% latency
% latency_neuro_selected = latency_neuro_all(tgtIDidx);
% latency_neuro_selected = cat(1, latency_neuro_selected{:});
% latency_bhv_selected = latency_bhv_all(tgtIDidx);
% latency_bhv_selected = cat(1, latency_bhv_selected{:});
% latency_r_selected = latency_r_all(tgtIDidx);
%
% figure('visible','on')
% subplot(121);
% hist3([latency_bhv_selected latency_neuro_selected],'CDataMode','auto','FaceColor','interp',...
% 'linestyle','none','edges',{0:.02:.5 0:.02:.5});
% xlabel('behavioural latency'); ylabel('neural latency');
% view(2);
% axis square tight;
% set(gca,'tickdir','out');
%
% subplot(122);
% histogram(latency_r_selected,'BinEdges',-0.7:0.1:0.7); axis square tight;
% vline(0); set(gca,'tickdir','out');
% xlabel('latency correlation'); ylabel('# units');
% screen2png(['latency_selected_' tgtModality num2str(param.cardinalDir(iprefDir)) ...
% suffix '.png']);
% close all;
end
end