worflow_VSD_object.m

folder = '\\storage.laur.corp.brain.mpg.de\Data_2\CoattiAlex\Experiments_2015\W66_20150212\RedShirtImaging\vsdtest_D';
filename = 'vsdtest_D002.tsm';
recording_file = strcat(folder,filesep,filename);

obj = redShirt(recording_file);

%% extract raw traces from subset of pixels

% ( 1 ) select pixels of ROIs (downsample image, reduce resolution) % % % % % % % % % % % % % % % % % % % % %
pix_linear_ind = reshape(1:256*256,256,256);
p_r = 8; p_c = 8; step_r = 1:p_r:256; step_c = 1:p_c:256;
roi_id = zeros(p_r * p_c * numel(step_r)*numel(step_c) ,1);
    % pxl_id = reshape(1:length(index{1})*length(index{2}),length(index{1}),length(index{2}));   %reshape(1:256*256,256,256);
pxl_id = pix_linear_ind;
counter = 0;
for c = step_c
    for r = step_r
        counter = counter + 1;
        tempid = pxl_id(r: r + p_r-1, c: c + p_c-1);
        roi_id((1:p_r*p_c) + p_r*p_c*(counter-1) ) = tempid(:);
    end
end
% select the ROIs of the final downsampled image that you want to extract
% traces from
pix_downsample = reshape(1:length(step_r)*length(step_c),length(step_r),length(step_c));
roi_selected  = [133,153,394,503,681,824];

% ( 2 ) INPUTs for getData method % % % % % % % % % % % % % % % % % % % % % %

channels = [];  % choose single pixels of the full 256*256 matrix of linear indices (pxl_linear_ind)
% channels can either be a n*1 vector or a n*m matrix in the case of
% equally sized rois (to be implemented: allow for non-equally sized rois
% by ordering selected pixels in channels as a linear vector and in
% roi_size vector put the total number of pixels in each successive roi
% e.g. roi1 = 10 pxl, roi2 = 15 pxl, roi3 = 5 pxl etc roi_size = [10, 15, 3 ....]
for i = 1:length(roi_selected);
    if i == 1
        channels = roi_id( (roi_selected(i)-1) * p_r*p_c +[1:p_r*p_c] );
    else
        channels = [channels; roi_id( (roi_selected(i)-1) * p_r*p_c +[1:p_r*p_c] )];
    end
end
events_time = 0; % frame number from which to start extracting data
startTime_ms = events_time .* (1/obj.samplingFrequency *1000); %starting frame * duration in ms of one frame
window_frames = 10000;
window_ms = repmat(window_frames * 1/obj.samplingFrequency *1000,1,length(startTime_ms)); % total number of frames in ms
mode = 'fullframe'; % mode 1 = 'fullframe' it extracts traces from all pixels of one frame 
              % mode 2 = 'single_pixels' it extracts traces for each pixels
              % entered in the channels input and treats them separately 
              % mode 3 = 'roi' (an easy outline for ROIs could be
              % rectangular and implement ROIs with any type of shape
              % later)
name = [];
[raw_data, time_intervals] = obj.getData(channels,startTime_ms,window_ms,mode,name);
    nTrials = length(startTime_ms);
    raw_frames = reshape(raw_data,256,256,nTrials,max(window_frames));

[raw_pixels, time_intervals_pixels] = obj.getData(channels,startTime_ms,window_ms,mode,name);
    % pixel order is subjected to two transformations by the algorithm for
    % data extraction
%     ind1 = NaN(1,256*256/2);
%     ind2 = NaN(1,256*256/2);
%     for c = 1:256
%         ind1( [1:128]+128*(c-1) ) = [1:128] + 256*(c-1);
%         ind2( [1:128]+128*(c-1) ) = fliplr([129:256] + 256*(c-1));
%     end
%     deinterleaving_matrix = cat(1,reshape(ind1,128,256),reshape(ind2,128,256));
%     [~,ord] = sort(deinterleaving_matrix(channels));
%     raw_temp = raw_pixels(ord,:,:);
%     raw_pixels_in_frame = zeros( size(raw_temp) );
    
    roi_mask = reshape(zeros(256*256,1),256,256);
    roi_mask(channels) = 1;
    
% test to verify that the data read-in algorithm performs accurately
% (besides being more efficient)
    figure, 
    subplot(121), imagesc(squeeze(squeeze(raw_frames(:,:,1,1))) .* roi_mask), axis square
    subplot(122), 
    test_frame = reshape(zeros(256*256,1),256,256);
    test_frame(channels) =  raw_pixels(1:length(channels),1,1);
    imagesc(test_frame), axis square