master_file_online.m

%% SET ALL DEFAULT OPTIONS HERE

ops0.toolbox_path = 'C:\Users\User\Dropbox\Bruker1\Suite2P-master_online';
addpath(genpath(ops0.toolbox_path))

make_db_online
db0 = db;

ops0.photostim              = 1;
ops0.useGPU                 = 1; % if you can use an Nvidia GPU in matlab this accelerates registration approx 3 times. You only need the Nvidia drivers installed (not CUDA).
ops0.fig                    = 0; % turn off figure generation with 0
ops0.diameter               = 10; % (12) most important parameter. Set here, or individually per experiment in make_db file

% ---- root paths for files and temporary storage (ideally an SSD drive. my SSD is C:/)
%ops0.RootStorage            = '//zserver4/Data/2P'; % Suite2P assumes a folder structure, check out README file
ops0.temp_tiff              = [ops0.RootStorage 'temp.tif']; % copies each remote tiff locally first, into this file
ops0.RegFileRoot            = ops0.RootStorage;  % location for binary file
ops0.DeleteBin              = 1; % set to 1 for batch processing on a limited hard drive
ops0.ResultsSavePath        = [ops0.RootStorage db(1).date filesep]; % a folder structure is created inside
ops0.RegFileTiffLocation    = []; %'D:/DATA/'; % leave empty to NOT save registered tiffs (slow)
% if you want to save red channel tiffs, also set ops0.REDbinary = 1

% ---- registration options ------------------------------------- %
ops0.doRegistration         = 0; % skip (0) if data is already registered
ops0.showTargetRegistration = 1; % shows the image targets for all planes to be registered
ops0.PhaseCorrelation       = 1; % set to 0 for non-whitened cross-correlation
ops0.SubPixel               = Inf; % 2 is alignment by 0.5 pixel, Inf is the exact number from phase correlation
ops0.NimgFirstRegistration  = 500; % number of images to include in the first registration pass 
ops0.nimgbegend             = 0; % frames to average at beginning and end of blocks
ops0.dobidi                 = 1; % infer and apply bidirectional phase offset

% ---- cell detection options ------------------------------------------%
ops0.ShowCellMap            = 1; % during optimization, show a figure of the clusters
ops0.sig                    = 0.5; % (0.5) spatial smoothing length in pixels; encourages localized clusters (MAYBE USE 1?)
ops0.nSVDforROI             = 300; % how many SVD components for cell clustering
ops0.NavgFramesSVD          = 7000; % how many (binned) timepoints to do the SVD based on
ops0.signalExtraction       = 'surround'; % how to extract ROI and neuropil signals: 
%  'raw' (no cell overlaps), 'regression' (allows cell overlaps), 
%  'surround' (no cell overlaps, surround neuropil model)
ops0.maxIterRoiDetection    = 6;

% ----- neuropil options (if 'surround' option) ------------------- %
% all are in measurements of pixels
ops0.innerNeuropil  = 1; % (1) padding around cell to exclude from neuropil
ops0.outerNeuropil  = Inf; % radius of neuropil surround
% if infinity, then neuropil surround radius is a function of cell size
if isinf(ops0.outerNeuropil)
    ops0.minNeuropilPixels = 400; % minimum number of pixels in neuropil surround
    ops0.ratioNeuropil     = 5; % ratio btw neuropil radius and cell radius
    % radius of surround neuropil = ops0.ratioNeuropil * (radius of cell)
end

% ----- spike deconvolution and neuropil subtraction options ----- %
ops0.imageRate              = 30/db(1).nplanes;   % imaging rate (cumulative over planes!). Approximate, for initialization of deconvolution kernel.
ops0.sensorTau              = 2; % decay half-life (or timescale). Approximate, for initialization of deconvolution kernel.
ops0.maxNeurop              = 1; % for the neuropil contamination to be less than this (sometimes good, i.e. for interneurons)

% ----- if you have a RED channel ---------------------- ------------%
ops0.AlignToRedChannel      = 0; % compute registration offsets using red channel
ops0.REDbinary              = 0; % make a binary file of registered red frames
% if db.expred, then compute mean red image for green experiments with red
% channel available while doing registration
ops0.redMeanImg             = 0; 
% for red cell detection (identify_redcells_sourcery.m)
% redratio = red pixels inside / red pixels outside
% redcell = redratio > mean(redratio) + redthres*std(redratio)
% notred = redratio < mean(redratio) + redmax*std(redratio)
ops0.redthres               = 1.5; % the higher the thres the less red cells
ops0.redmax                 = 1; % the higher the max the more NON-red cells

% RUN THE PIPELINE HERE
c = clock;
for iexp = 1
    db = db0(iexp);
    run_pipeline_online(db, ops0);
    
    %add_deconvolution(ops0, db);
end
t_run = clock - c;
t_run = t_run(end-1:end);