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main_kilosort3.m
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main_kilosort3.m
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%% you need to change most of the paths in this block
% addpath(genpath('D:\GitHub\KiloSort')) % path to kilosort folder
addpath('D:\GitHub\npy-matlab') % for converting to Phy
rootZ = 'G:\Spikes\Sample'; % the raw data binary file is in this folder
rootH = 'H:\'; % path to temporary binary file (same size as data, should be on fast SSD)
pathToYourConfigFile = 'D:\GitHub\KiloSort2\configFiles'; % take from Github folder and put it somewhere else (together with the master_file)
chanMapFile = 'neuropixPhase3A_kilosortChanMap.mat';
ops.trange = [0 Inf]; % time range to sort
ops.NchanTOT = 385; % total number of channels in your recording
run(fullfile(pathToYourConfigFile, 'configFile384.m'))
ops.fproc = fullfile(rootH, 'temp_wh.dat'); % proc file on a fast SSD
ops.chanMap = fullfile(pathToYourConfigFile, chanMapFile);
%% this block runs all the steps of the algorithm
fprintf('Looking for data inside %s \n', rootZ)
% main parameter changes from Kilosort2 to v2.5
ops.sig = 20; % spatial smoothness constant for registration
ops.fshigh = 300; % high-pass more aggresively
ops.nblocks = 5; % blocks for registration. 0 turns it off, 1 does rigid registration. Replaces "datashift" option.
% main parameter changes from Kilosort2.5 to v3.0
ops.Th = [9 9];
% is there a channel map file in this folder?
fs = dir(fullfile(rootZ, 'chan*.mat'));
if ~isempty(fs)
ops.chanMap = fullfile(rootZ, fs(1).name);
end
% find the binary file
fs = [dir(fullfile(rootZ, '*.bin')) dir(fullfile(rootZ, '*.dat'))];
ops.fbinary = fullfile(rootZ, fs(1).name);
%%
rez = preprocessDataSub(ops);
rez = datashift2(rez, 1);
[rez, st3, tF] = extract_spikes(rez);
rez = template_learning(rez, tF, st3);
[rez, st3, tF] = trackAndSort(rez);
rez = final_clustering(rez, tF, st3);
rez = find_merges(rez, 1);
rootZ = fullfile(rootZ, 'kilosort3');
mkdir(rootZ)
rezToPhy2(rez, rootZ);
%%