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gridSorter/getSpikeTrigWF.m

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function obj=getSpikeTrigWF(obj)
%Detect spikes in raw data and save waveforms
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% OLD FUNCTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
error('This method is an old version, please use MEAAnalysis.getSpikeTrigWF instead');
load(obj.sortingFileNames.fittingFile,'t','ic');
saveFileName='tmpPostProcessing';
neuronNames=ic(1:2,:);
%determine quantization
obj.detectionInt2uV=obj.detectionMaxSpikeAmp/2^(obj.detectionNQuantizationBits-1);
obj=obj.getHighpassFilter;
%start spike detection
fprintf('\nRunning waveform extraction on %s...',obj.dataRecordingObj.dataFileNames{1});
%determine the chunck size
if obj.detectionMaxChunkSize>obj.dataRecordingObj.recordingDuration_ms
startTimes=0;
endTimes=obj.dataRecordingObj.recordingDuration_ms;
else
startTimes=(0:obj.detectionMaxChunkSize:obj.dataRecordingObj.recordingDuration_ms);
endTimes=[startTimes(2:end) obj.dataRecordingObj.recordingDuration_ms];
end
nChunks=numel(startTimes);
obj.nCh=numel(obj.chPar.s2r);
nNeurons=size(ic,2);
nSpkTotal=ic(4,:)-ic(3,:)+1;
windowSamplesRaw=obj.postTotalRawWindow*obj.dataRecordingObj.samplingFrequency/1000;
windowSamplesFiltered=obj.postTotalFilteredWindow*obj.dataRecordingObj.samplingFrequency/1000;
HPstartIdx=((obj.postTotalFilteredWindow-obj.postPreFilteredWindow)*obj.dataRecordingObj.samplingFrequency/1000+1);
HPIdx=HPstartIdx:(HPstartIdx+windowSamplesFiltered-1);
keepAllInMemory=1;
if ispc
userview = memory;
doubleBytes=8;
if userview.MemAvailableAllArrays<(nNeurons*obj.nCh*windowSamplesRaw*doubleBytes*4) & keepAllInMemory
disp('Arrays too big for memory, moving to saving on disk (for using memory change chunk size');
keepAllInMemory=0;
end
end
par.binTRaw=1; %ms
par.binTHP=0.1; %ms
par.binV=0.5;
par.maxV=75;
nBinsRaw=ceil(obj.postTotalRawWindow/par.binTRaw);
nBinsHP=ceil(obj.postTotalFilteredWindow/par.binTHP);
nBinsV=2*par.maxV/par.binV;
if keepAllInMemory
avgRawWF=zeros(nNeurons,obj.nCh,windowSamplesRaw);
stdRawWF=zeros(nNeurons,obj.nCh,windowSamplesRaw);
avgHPWF=zeros(nNeurons,obj.nCh,windowSamplesFiltered);
stdHPWF=zeros(nNeurons,obj.nCh,windowSamplesFiltered);
histRawWF=zeros(nNeurons,obj.nCh,nBinsRaw,nBinsV,'uint8');
histHPWF=zeros(nNeurons,obj.nCh,nBinsHP,nBinsV,'uint8');
else
matFileObj = matfile(obj.sortingFileNames.STWaveformFile,'Writable',true);
if obj.postExtractRawLongWaveformsFromSpikeTimes
matFileObj.avgRawWF=zeros(nNeurons,obj.nCh,windowSamplesRaw);
matFileObj.stdRawWF=zeros(nNeurons,obj.nCh,windowSamplesRaw);
matFileObj.histRawWF=zeros(nNeurons,obj.nCh,nBinsRaw,nBinsV,'uint8');
else
matFileObj.avgRawWF=[];matFileObj.stdRawWF=[];matFileObj.histRawWF=[];
end
if obj.postExtractFilteredWaveformsFromSpikeTimes
matFileObj.avgHPWF=zeros(nNeurons,obj.nCh,windowSamplesFiltered);
matFileObj.stdHPWF=zeros(nNeurons,obj.nCh,windowSamplesFiltered);
matFileObj.histHPWF=zeros(nNeurons,obj.nCh,nBinsHP,nBinsV,'uint8');
else
matFileObj.avgHPWF=[];matFileObj.stdHPWF=[];matFileObj.histHPWF=[];
end
end
%load files if matlab crashed during analysis
if exist([obj.sortingDir filesep saveFileName '_tmp.mat'],'file')
load([obj.sortingDir filesep saveFileName '_tmp.mat']);
startChunk=max(1,lastGoodChunck+1);
else
startChunk=1;
end
tBinRaw=shiftdim(ceil(((1:windowSamplesRaw)/obj.dataRecordingObj.samplingFrequency*1000)/par.binTRaw),-1);
tBinHP=shiftdim(ceil(((1:windowSamplesFiltered)/obj.dataRecordingObj.samplingFrequency*1000)/par.binTHP),-1);
VBins=(-par.maxV+par.binV/2):par.binV:par.maxV;
pPreBaselineSamples=((obj.dataRecordingObj.samplingFrequency(1)/1000)*(obj.postPreRawWindow-obj.detectionPreSpikeWindow-1)):((obj.dataRecordingObj.samplingFrequency(1)/1000)*(obj.postPreRawWindow-obj.detectionPreSpikeWindow));
tBinsRaw=par.binTRaw/2:par.binTRaw:obj.postTotalRawWindow;
tBinsHP=par.binTHP/2:par.binTHP:obj.postTotalFilteredWindow;
try
tic;
%initiate arrays
fprintf('\nExtracting spikes from chunks (total %d): ',nChunks);
for i=startChunk:nChunks
fprintf('%d',i);
%get data
MAll=obj.dataRecordingObj.getData(obj.chPar.s2r(1:obj.nCh),startTimes(i)-obj.postPreRawWindow,endTimes(i)-startTimes(i)+obj.postTotalRawWindow);
MFAll=squeeze(obj.filterObj.getFilteredData(MAll))'; %filter class needs unsqueezed input
MAll=squeeze(MAll)';
nSamples=size(MAll,1);
%tAll=((1:nSamples)/obj.dataRecordingObj.samplingFrequency*1000)-obj.postPreRawWindow+startTimes(i);
for j=1:nNeurons
tTmp=t(ic(3,j):ic(4,j));
tTmp=tTmp(find(tTmp>=startTimes(i) & tTmp< (endTimes(i)-obj.postTotalRawWindow+obj.postPreRawWindow) ));
nSpkTmp=numel(tTmp);
if nSpkTmp>0
startIdx=round((tTmp-startTimes(i))*obj.dataRecordingObj.samplingFrequency/1000);
idx=bsxfun(@plus,startIdx,(0:windowSamplesRaw-1)');
WF=MAll(idx,:);
WF=permute(reshape(WF,[size(idx,1) size(idx,2) obj.nCh]),[2 3 1]);
%WF=bsxfun(@minus,WF,mean(WF(:,:,pPreBaselineSamples),3));
avgRawWF(j,:,:)=avgRawWF(j,:,:)+sum(WF,1);
stdRawWF(j,:,:)=stdRawWF(j,:,:)+sum(WF.^2,1);
idx=idx(HPIdx,:);
WFH=MFAll(idx,:);
WFH=permute(reshape(WFH,[size(idx,1) size(idx,2) obj.nCh]),[2 3 1]);
avgHPWF(j,:,:)=avgHPWF(j,:,:)+sum(WFH,1);
stdHPWF(j,:,:)=stdHPWF(j,:,:)+sum(WFH.^2,1);
for k=1:obj.nCh
hVTmp=round((WF(:,k,:)+par.maxV)/(par.maxV*2/nBinsV)); %the reshape is important since otherwise when nSpkTmp==1 squeeze changes matrix from row to column
p=find(hVTmp>0 & hVTmp<nBinsV);
if numel(p)>1 %if there is only one point to include in the statistics we can reject this trial.
htTmp=repmat(tBinRaw,[numel(tTmp) 1]);
if nSpkTmp>1
histRawWF(j,k,:,:)=histRawWF(j,k,:,:)+shiftdim(uint8(accumarray([htTmp(p) hVTmp(p)],1,[nBinsRaw nBinsV])),-2);
else
histRawWF(j,k,:,:)=histRawWF(j,k,:,:)+shiftdim(uint8(accumarray(squeeze([htTmp(p) hVTmp(p)])',1,[nBinsRaw nBinsV])),-2);
end
hVTmp=round((WFH(:,k,:)+par.maxV)/(par.maxV*2/nBinsV));
p=find(hVTmp>0 & hVTmp<nBinsV);
htTmp=repmat(tBinHP,[numel(tTmp) 1]);
if nSpkTmp>1
histHPWF(j,k,:,:)=histHPWF(j,k,:,:)+shiftdim(uint8(accumarray([htTmp(p) hVTmp(p)],1,[nBinsHP nBinsV])),-2);
else
histHPWF(j,k,:,:)=histHPWF(j,k,:,:)+shiftdim(uint8(accumarray(squeeze([htTmp(p) hVTmp(p)])',1,[nBinsHP nBinsV])),-2);
end
end
end
end
end
tChunk(i)=toc;tic;
fprintf('(%d) ',round(tChunk(i)));
end
for i=1:nNeurons
tmpN=(ic(4,i)-ic(3,i)+1);
stdRawWF(i,:,:)=sqrt((stdRawWF(i,:,:)-avgRawWF(i,:,:))/(tmpN-1));
stdHPWF(i,:,:)=sqrt((stdHPWF(i,:,:)-avgHPWF(i,:,:))/(tmpN-1));
avgRawWF(i,:,:)=avgRawWF(i,:,:)/tmpN;
avgHPWF(i,:,:)=avgHPWF(i,:,:)/tmpN;
end
[mElecs,nElecs]=size(obj.chPar.En);
if obj.postPlotRawLongWaveforms
figurePosition=[100 50 min(1000,100*nElecs) min(900,100*mElecs)];
for i=1:nNeurons
f=figure('position',figurePosition);
neuronString=['Neu ' num2str(ic(1,i)) '-' num2str(ic(2,i))];
infoStr={['nSpk=' num2str(nSpkTotal(i))],neuronString};
[h,hParent]=spikeDensityPlotPhysicalSpace(squeeze(histRawWF(i,:,:,:)),obj.dataRecordingObj.samplingFrequency(1),obj.chPar.s2r,obj.chPar.En,...
'hParent',f,'avgSpikeWaveforms',permute(avgRawWF(i,:,:),[3 1 2]),'logColorScale',0,'spikeShapesIsAHist',1,'xBin',tBinsRaw,'yBin',unique(VBins));
annotation('textbox',[0.01 0.89 0.1 0.1],'FitHeightToText','on','String',infoStr);
printFile=[obj.sortingDir filesep 'neuron' neuronString '-spikeShapeRaw'];
set(f,'PaperPositionMode','auto');
print(printFile,'-djpeg','-r300');
close(f);
end
end
if obj.postPlotFilteredWaveforms
figurePosition=[100 50 min(1000,100*nElecs) min(900,100*mElecs)];
for i=1:nNeurons
f=figure('position',figurePosition);
neuronString=['Neu ' num2str(ic(1,i)) '-' num2str(ic(2,i))];
infoStr={['nSpk=' num2str(nSpkTotal(i))],neuronString};
[h,hParent]=spikeDensityPlotPhysicalSpace(squeeze(histHPWF(i,:,:,:)),obj.dataRecordingObj.samplingFrequency(1),obj.chPar.s2r,obj.chPar.En,...
'hParent',f,'avgSpikeWaveforms',permute(avgHPWF(i,:,:),[3 1 2]),'logColorScale',0,'spikeShapesIsAHist',1,'xBin',tBinsHP,'yBin',unique(VBins));
annotation('textbox',[0.01 0.89 0.1 0.1],'FitHeightToText','on','String',infoStr);
printFile=[obj.sortingDir filesep 'neuron' neuronString '-spikeShapeHP'];
set(f,'PaperPositionMode','auto');
print(printFile,'-djpeg','-r300');
close(f);
end
end
catch errorMsg
if i>1
lastGoodChunck=i-1;
save([obj.sortingDir filesep saveFileName '_tmp.mat'],'avgRawWF','avgHPWF','stdRawWF','stdHPWF','histRawWF','histHPWF','par','lastGoodChunck','-v7.3');
end
rethrow(errorMsg);
end
fprintf('\nTriggered waveform analysis took (%f) hours\nSaving data...\n',sum(tChunk)/60/60);
save(obj.sortingFileNames.STWaveformFile,'avgRawWF','avgHPWF','stdRawWF','stdHPWF','neuronNames','histRawWF','histHPWF','nSpkTotal','par','-v7.3');
fprintf('Deleting temporary files...');
delete([obj.sortingDir filesep saveFileName '_tmp.mat']);
obj=obj.findSortingFiles; %update sorted files