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PLXStandardConfig.m
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function [ops] = PLXStandardConfig(PLXnameAlone,PATH,dataPATH)
clear ops
ops.PLXnameAlone = PLXnameAlone;
ops.PATH = PATH;
ops.dataPATH = dataPATH;
ops.outputFolderName = strcat('Outputs-', ops.PLXnameAlone,date);
ops.outputFolder = mkdir(ops.PATH, ops.outputFolderName);
ops.outputPath = strcat(ops.PATH, ops.outputFolderName, '\\');
ops.GPU = 1; % whether to run this code on an Nvidia GPU (much faster, mexGPUall first)
ops.parfor = 1; % whether to use parfor to accelerate some parts of the algorithm
ops.verbose = 1; % whether to print command line progress
ops.showfigures = 1; % whether to plot figures during optimization
ops.datatype = 'PLEXON'; % binary ('dat', 'bin') or 'openEphys'
ops.fbinary = strcat(ops.outputPath,'PLXconversion.dat'); % will be created for 'openEphys'
ops.fproc = strcat(ops.PATH,'PLXtemp_wh.dat'); % residual from RAM of preprocessed data
ops.root = ops.PATH; % 'openEphys' only: where raw files are
ops.PLXname = strcat(ops.dataPATH, ops.PLXnameAlone);
[n, adchans] = plx_ad_chanmap(ops.PLXname,ops);
ops.fs = 40000; % sampling rate (omit if already in chanMap file)
ops.NchanTOT = n; % total number of channels (omit if already in chanMap file)
ops.Nchan = n; % number of active channels (omit if already in chanMap file)
ops.Nfilt = 32; % number of clusters to use (2-4 times more than Nchan, should be a multiple of 32)
ops.nNeighPC = n; % visualization only (Phy): number of channnels to mask the PCs, leave empty to skip (12)
ops.nNeigh = n*2; % visualization only (Phy): number of neighboring templates to retain projections of (16)
% ops.nNeighPC = 12; % visualization only (Phy): number of channnels to mask the PCs, leave empty to skip (12)
% ops.nNeigh = 16;
% options for channel whitening
ops.whitening = 'full'; % type of whitening (default 'full', for 'noSpikes' set options for spike detection below)
ops.nSkipCov = 1; % compute whitening matrix from every N-th batch (1)
ops.whiteningRange = n; % how many channels to whiten together (Inf for whole probe whitening, should be fine if Nchan<=32)
% define the channel map as a filename (string) or simply an array
ops.chanMap = strcat(ops.PATH,'PLXchanMap.mat'); % make this file using createChannelMapFile.m
ops.criterionNoiseChannels = 0.2; % fraction of "noise" templates allowed to span all channel groups (see createChannelMapFile for more info).
% ops.chanMap = 1:ops.Nchan; % treated as linear probe if a chanMap file
% other options for controlling the model and optimization
ops.Nrank = 3; % matrix rank of spike template model (3)
ops.nfullpasses = 6; % number of complete passes through data during optimization (6)
ops.maxFR = 100000; % maximum number of spikes to extract per batch (20000)
ops.fshigh = 300; % frequency for high pass filtering
% ops.fslow = 2000; % frequency for low pass filtering (optional)
ops.ntbuff = 64; % samples of symmetrical buffer for whitening and spike detection
ops.scaleproc = 200; % int16 scaling of whitened data
ops.NT = 32*1024 + ops.ntbuff;% this is the batch size (try decreasing if out of memory)
% for GPU should be multiple of 32 + ntbuff
% the following options can improve/deteriorate results.
% when multiple values are provided for an option, the first two are beginning and ending anneal values,
% the third is the value used in the final pass.
ops.Th = [3 6 6]; % threshold for detecting spikes on template-filtered data ([6 12 12])
ops.lam = [5 20 20]; % large means amplitudes are forced around the mean ([10 30 30])
ops.nannealpasses = 4; % should be less than nfullpasses (4)
ops.momentum = 1./[20 400]; % start with high momentum and anneal (1./[20 1000])
ops.shuffle_clusters = 1; % allow merges and splits during optimization (1)
ops.mergeT = .1; % upper threshold for merging (.1)
ops.splitT = .1; % lower threshold for splitting (.1)
% options for initializing spikes from data
ops.initialize = 'no'; %'fromData' or 'no'
ops.spkTh = -6; % spike threshold in standard deviations (4)
ops.loc_range = [3 1]; % ranges to detect peaks; plus/minus in time and channel ([3 1])
ops.long_range = [30 6]; % ranges to detect isolated peaks ([30 6])
ops.maskMaxChannels = 5; % how many channels to mask up/down ([5])
ops.crit = .65; % upper criterion for discarding spike repeates (0.65)
ops.nFiltMax = 10000; % maximum "unique" spikes to consider (10000)
% load predefined principal components (visualization only (Phy): used for features)
dd = load('PCspikes2.mat'); % you might want to recompute this from your own data
ops.wPCA = dd.Wi(:,1:7); % PCs
% options for posthoc merges (under construction)
ops.fracse = 0.1; % binning step along discriminant axis for posthoc merges (in units of sd)
ops.epu = Inf;
ops.ForceMaxRAMforDat = 20e9; % maximum RAM the algorithm will try to use; on Windows it will autodetect.