worflow_VSD_object_12April_temp.m

%% 1) Generalised workflow

% specify folder name where data of interested are saved
recording_dir = '\\storage.laur.corp.brain.mpg.de\Data_2\CoattiAlex\Experiments_2015\turtle_6March\Ale+6March_sampleT';
cd(recording_dir); content_folder = dir;
% specify data type (it depends on imaging system used)
data_type = 'RedShirt'; % 'Andor'

if strcmp(data_type, 'RedShirt' )
    disp('show tsm files in current folder');
    dir '*.tsm'
    y = dir;     
    %find position of tsm file in current folder
    idx = strfind({y(:).name},'.tsm'); 
    out = find(cellfun(@(v) any(isfinite(v(:))), idx));   
    filename = cell(length(out),1);
    if length(out) == 1
        filename = y(out).name;
    else
        disp('there are more repeats in the same folder');
        for j = 1:length(out)
            filename{j} = y(out(j)).name;
        end
    end
elseif strcmp(data_type, 'Andor' )
        % here ANDOR data    
end
    
%% Initialize an object for VSD data
clear obj
for i = 1:length(filename)
    recording_file = strcat(recording_dir,filesep,filename{i});
    if strcmp(data_type, 'RedShirt' )
       obj(i) = redShirt(recording_file);
    elseif strcmp(data_type, 'Andor' )
        % here ANDOR data
    end
end

%% 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 = pix_linear_ind; % pxl_id = reshape(1:length(index{1})*length(index{2}),length(index{1}),length(index{2}));   %reshape(1:256*256,256,256);
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  = 1:length(step_r)*length(step_c); %[133,153,394,503,681,824];

% ( 2 ) INPUTs for getData method % % % % % % % % % % % % % % % % % % % % % %
% 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 ....]
channels = [];  
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 = obj.recordingDuration_ms * obj.samplingFrequency/1000  ;  % for full recording, obj.recordingDuration_ms * obj.samplingFrequency/1000 
window_ms = repmat(window_frames * 1/obj.samplingFrequency *1000,1,length(startTime_ms)); % total number of frames in ms

% 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)
mode = 'fullframe'; 
name = [];
% flags_reading_data = [];
flags_reading_data = {'saveDataToFileMode',1,'DarkFrameSubtraction',1,'DarkFrame_Last',0,'recompose_channels',0} % default values {1,1,0,0}
[raw_data, time_intervals] = obj.getData(channels,startTime_ms,window_ms,mode,name,flags_reading_data);
    nTrials = length(startTime_ms);
    raw_frames = reshape(raw_data,256,256,nTrials,max(window_frames));
clear raw_data time_intervals
    
% - pool together multiple repeats - note :if it's multiple trials you should use
% the functionality of the object
    max_window_frames = 10000; %max([obj(:).recordingDuration_ms] .* [obj(i).samplingFrequency/1000]);
    data_multiple_trials = zeros(256,256,max_window_frames,length(filename),'uint16');
    flags_reading_data = {'saveDataToFileMode',0,'DarkFrameSubtraction',1,'DarkFrame_Last',0,'recompose_channels',0}
for i = 1:length(filename)
    channels = [];  
    events_time = 0; % frame number from which to start extracting data
    startTime_ms = events_time .* (1/obj(i).samplingFrequency *1000); %starting frame * duration in ms of one frame
    window_frames = 10000;  % for full recording, obj.recordingDuration_ms * obj.samplingFrequency/1000 
    window_ms = repmat(window_frames * 1/obj(i).samplingFrequency *1000,1,length(startTime_ms)); % total number of frames in ms
    [raw_data] = obj(i).getData(channels,startTime_ms,window_ms,mode,name,flags_reading_data);
    data_multiple_trials(:,:,1:window_frames,i) = raw_data;
    clear raw_data
end


[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