Import_Pipeline %this call all of the importing code, do only once, for everything

%% pick out laser stimuli only for experiments with laser stimulation, do only once

%analysis file to save relevant parameters

%find laser triggers

analysisFile = [path '\analysis\' matfilename 'analysis']; %just the path and name as string

load([path '\analysis\' matfilename '_denoise'],'laserSignal'); %fetch the laser signal from datas

%find laser triggers

stObj = StepWaveform(laserSignal,fs,'On/Off','Laser');

lt = stObj.Triggers; %light on/off

ltOn = find(lt(:,1));ltOff=find(lt(:,2));

fprintf(1,'Got the condition triggers\n')

laserPowers = [0, 0.15, 2, 6, 10, 14, 20, 23];

%laserPowers = [23];

% The laserTrials contains the number of pulses per laser intensity

% (Clustered into one figure)

laserTrials = 3;

close all

figure

plot(laserSignal)

hold on

plot(ltOn,laserSignal(ltOn),'ro')

save(analysisFile,'ltOn','ltOff','laserPowers','laserTrials', '-append')

%% plot some examples

ephyData=dataCell;

%online filtering if you want to look at particular freq. band

% Alpha: 7 - 13 Hz

% Beta: 13 - 30 Hz

% Gamma: 30 - 70 Hz

% Delta: 1 - 4 Hz

% Theta: 4 - 7 Hz

%for cch = 1:size(ephyData,1)

% fprintf(1,'Dealing with channel %d\n', cch)

% [alpha, beta, gamma, delta, theta] = brainwaves(ephyData,ephyData{cch},fs)

% ephyData{cch}= alpha;

%end

%UNCOMMENT THIS IF YOU WANT TO LOOK AT SPIKES

%ephyData=dataCell_sp;

%% PARAMETERS INITIALIZATION -- Modify this section for different experiments if necessary

% The timeLapse variable contains the time before the pulse in milliseconds

% (1 seconds = 1000 milliseconds & 1 millisecond = 0.001 second) and the

% time after the pulse in milliseconds. (30 ms before and 30 ms after)

%can we automate this to figure out desired units?

m = 1e-3; %millisecond scaling factor

k = 1e3; %kilo scaling

timeLapse = [500,1500]*m;

% The laserPowers variable contains the series of laser intensities used to

% stimulate.

[~,cSt] =...

getStacks(false(1,cols), ltOn, 'on', timeLapse, fs ,fs ,[],ephyData);

% Saving plots? if this is 'false' the figures will NOT be saved. If it is

% 'true', the figures will be saved.

saveFlag = false;

Nch = rows;

% Plotting the continuous stack

close all

timeOffset=timeLapse(1);

f = gobjects(8);

for laserPower = 1:numel(laserPowers)

f(laserPower) =...

figure('Name',sprintf('Power %.2f mW',laserPowers(laserPower)),...

'Color',[1,1,1],'Renderer','painters','RendererMode','manual');

for laserTrial = 1:laserTrials

plotEEGchannels(...

reshape(cSt(:,:,laserTrial + laserTrials*(laserPower - 1)),...

Nch,sum(timeLapse)*fs+1),1:Nch,sum(timeLapse)+(1/fs),fs,0.5,timeOffset,f(laserPower));

ax = f(laserPower).Children;

ax.NextPlot = 'add';

end

mSignals=reshape(mean(cSt(:,:,laserPower*3 - 2:laserPower*3),3),...

Nch,sum(timeLapse)*fs+1);

[~,meanP] = plotEEGchannels(mSignals,1:32,sum(timeLapse)+(1/fs),fs,0.5,timeOffset,f(laserPower));

set(meanP,'Color',[0,0,0])

if saveFlag

% figName = fullfile(path, sprintf('LaserStimulation_%.2fmW',laserPowers(laserPower))) %#ok<*UNRCH>

figName = ['LaserStimulation_' num2str(laserPowers(laserPower))]

savefig(f(laserPower),[figName,'.fig'])

print(f(laserPower),[figName,'.eps'],'-depsc','-bestfit')

end

% mean triggered signal across channels, overlaid

% this eventually should be put elsewhere--maybe some flag in plot

% EEGchannels.

figure

dt = 1/fs;

Ns = size(mSignals,2);

timeS = ((0:Ns-1) * dt) - timeOffset;

plot(timeS/m,mSignals);

axis tight

grid on

xlabel ms!!!!

end

%%

%if triggers are available, use those

%if not, use randomly selected points