Updated docs

helpers/spikoclust_autosort.m

@@ -68,14 +68,12 @@
 
 if gap_check
 	gap_stats=evalclusters(SPIKE_DATA,'kmeans','gap','klist',clust_check);
-	gap_stats
 	clust_check=[gap_stats.OptimalK-1:gap_stats.OptimalK+1];
-	clust_check
 end
 
 [idx CLUSTER_DATA MODEL]=spikoclust_gmmsort(SPIKE_DATA,...
 	'smem',smem,'garbage',garbage,'clust_check',clust_check,...
-	'pcs',pcs,'workers',workers,'modelselection',modelselection,'outlierpoints',outlierpoints);
+	'workers',workers,'modelselection',modelselection,'outlierpoints',outlierpoints);
 
 MODEL.features=PCS;
 features=size(CLUSTER_DATA,2); % what's the dimensionality of the data used for sorting?

helpers/spikoclust_cluster_quality.m

@@ -4,6 +4,7 @@
 %
 %
 
+
 nmodels=size(MODEL.mu,1);
 
 clusters=unique(LABELS(LABELS>0)); % how many clusters?

helpers/spikoclust_denoise_signal.m

@@ -5,6 +5,10 @@
 %
 %
 
+ndims_ephys=ndims(EPHYS_DATA);
+[samples,ntrials,nchannels]=size(EPHYS_DATA);
+
+if nargin<2 | isempty(CHIN), CHIN=1:nchannels; end
 if nargin<3 | isempty(CHOUT), CHOUT=CHIN; end
 
 nparams=length(varargin);
@@ -41,9 +45,6 @@
 end
 
 car_electrodes=setdiff(1:length(CHIN),exclude_channels); % which electrodes are good for CAR?
-ndims_ephys=ndims(EPHYS_DATA);
-
-[samples,ntrials,nchannels]=size(EPHYS_DATA);
 
 % map each channel appropriately
 
@@ -99,6 +100,3 @@
 		end
 
 end
-
-
-

helpers/spikoclust_gmmsort.m

@@ -23,8 +23,6 @@
 			clust_check=varargin{i+1};
 		case 'clustreplicates'
 			clustreplicates=varargin{i+1};
-		case 'pcs'
-			pcs=varargin{i+1};
 		case 'garbage'
 			garbage=varargin{i+1};
 		case 'workers'

helpers/spikoclust_guisort.m

@@ -16,11 +16,11 @@
 
 % all features excluding IFR and SPIKES.times
 
-features_all={'max','min','ne','^2','neo','wid','pgrad','ngrad','PC1','PC2','PC3','PC4'}; 
-features={'PCA','pose','nege','posgrad','neggrad','min','max','width','ISI'}; 
+features_all={'max','min','ne','^2','neo','wid','pgrad','ngrad','PC1','PC2','PC3','PC4'};
+features={'PCA','pose','nege','posgrad','neggrad','min','max','width','ISI'};
 
 % possible features include, min, max, PCA, width, energy and wavelet coefficients
- 
+
 channel_labels=[];
 colors={'b','r','g','c','m','y','r','g','b'};
 outliercolor='k';
@@ -78,7 +78,7 @@
 if nchannels==1
 
 	geom_features=spikoclust_shape_features(SPIKES.windows);
-	
+
 	if any(strcmp('max',lower(features)))
 		spike_data=[spike_data geom_features(:,1)];
 		property_names{end+1}='max';
@@ -424,10 +424,9 @@ function change_cluster(varargin)
 LABELS=zeros(size(idx));
 
 for i=1:nclust
-	LABELS(idx==clusters(loc(i)))=i;	
+	LABELS(idx==clusters(loc(i)))=i;
 end
 
-
 clustermodel.R(:,1:nclust)=clustermodel.R(:,loc);
 clustermodel.mixing(1:nclust)=clustermodel.mixing(loc);
 clustermodel.sigma=clustermodel.sigma(:,:,loc);
@@ -451,6 +450,11 @@ function change_cluster(varargin)
 	legend_labels{end+1}='Outliers';
 end
 
+
+if all(LABELS==0)
+  LABELS=ones(size(LABELS));
+end
+
 % compute any other stats we want, ISI, etc...
 [WINDOWS TIMES TRIALS SPIKEDATA ISI STATS]=...
 	spikoclust_cluster_quality(SPIKES.storewindows,SPIKES.times,CLUSTER_DATA,LABELS,SPIKES.trial,MODEL);

helpers/spikoclust_robpca.m

@@ -1,14 +1,19 @@
-function [PROJ,PCS,LAMBDA,MODEL]=cov_check(DATA,REPLICATES)
+function [PROJ,PCS,LAMBDA,MODEL]=spikoclust_robpca(DATA,K,REPLICATES)
 %
 %
 %
 %
 %
 
-if nargin<2
+
+if nargin<3 | isempty(REPLICATES)
 	REPLICATES=10;
 end
 
+if nargin<2 | isempty(K)
+	K=6;
+end
+
 likelihood=zeros(1,REPLICATES);
 for i=1:REPLICATES
 	tmp_newmodel{i}=spikoclust_gmem(DATA,[],1,'garbage',1,'merge',0,'debug',0,'display_mode',0);
@@ -19,7 +24,7 @@
 
 [~,loc]=max(likelihood);
 MODEL=tmp_newmodel{loc(1)};
-[PCS,LAMBDA]=eigs(MODEL.sigma(:,:,1));
+[PCS,LAMBDA]=eigs(MODEL.sigma(:,:,1),K);
 LAMBDA=diag(LAMBDA);
 [~,idx]=sort(LAMBDA,'descend');
 PCS=PCS(:,idx);

helpers/spikoclust_spike_detect.m

@@ -12,12 +12,18 @@
 %	threshold for detecting spikes (can pass a vector where each element corresponds to threshold for trial)
 %
 
+
+
 SPIKES=[];
 
 if nargin<1
 	error('Need the input data to continue!');
 end
 
+if isvector(DATA)
+    DATA=DATA(:);
+end
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 % input argument collection

helpers/spikoclust_spike_remove.m

@@ -5,6 +5,9 @@
 %
 %
 
+if isvector(DATA)
+	DATA=DATA(:);
+end
 
 nspikes=length(SPIKES.times);
 edges=round(SPIKES.frame*SPIKES.fs);
@@ -51,4 +54,3 @@
 	SPIKELESS(counter:counter+nsamples)=DATA(coords(i,1):coords(i,2),coords(i,3));
 	counter=counter+nsamples+1;
 end
-

spikoclust_sort.m

@@ -301,7 +301,7 @@
 	spikes=spikoclust_noisewhiten(spikes,spikeless,'maxnoisetraces',maxnoisetraces);
 end
 
-% upsample and align, then downsample and whiten!!!
+% upsample and align, then downsample for sorting!!!
 
 spikes=spikoclust_upsample_align(spikes,'interpolate_fs',interpolate_fs,'align_feature',align_feature);
 [nsamples,ntrials,nchannels]=size(spikes.windows);