Merge pull request #38 from Julie-Fabre/bleeding_edge

Bleeding edge

qualityMetrics/bc_getQualityUnitType.m

@@ -12,7 +12,10 @@
 %   unitType==0 defines all noise units
 %   unitType==1 defines all good units
 %   unitType==2 defines all MUA units
-%   unitType==3 defines all non-somatic units
+%   unitType==3 defines all non-somatic units. If
+%       param.splitGoodAndMua_NonSomatic is true, it specifically defines good
+%       non-somatic units
+%   unitType==4 defines all MUA non-somatic units if param.splitGoodAndMua_NonSomatic is true
 % unitType_string - nUnits x 1 string array indicating the type of each unit (good, mua, noise, non-somatic).
 
 

qualityMetrics/bc_plotGlobalQualityMetric.m

@@ -32,11 +32,15 @@ function bc_plotGlobalQualityMetric(qMetric, param, unitType, uniqueTemplates, t
     %% plot summary of waveforms classified as noise/mua/good
     % 1. single/multi/noise/axonal waveforms
     figure('Color', 'w');
-
-    unitTypeString = {'Noise', 'Single', 'Multi', 'Non-somatic'};
+
+    if param.splitGoodAndMua_NonSomatic == 0
+        unitTypeString = {'Noise', 'Good', 'MUA', 'Non-somatic'};
+    else
+        unitTypeString = {'Noise', 'Somatic Good', 'Somatic MUA', 'Non-somatic Good', 'Non-somatic MUA'};
+    end
     uniqueTemplates_idx = 1:size(uniqueTemplates, 1);
-    for iUnitType = 0:3
-        subplot(2, 2, iUnitType+1)
+    for iUnitType = 0:length(unitTypeString)-1
+        subplot(2, ceil(length(unitTypeString)/2), iUnitType+1)
         title([unitTypeString{iUnitType+1}, ' unit template waveforms']);
         hold on;
         singleU = uniqueTemplates_idx(find(unitType == iUnitType));

qualityMetrics/bc_qualityParamValues.m

@@ -99,7 +99,7 @@
     % in a units waveform to give the minimum prominence to detect peaks using
     % matlab's findpeaks function.
 
-% recording parametrs
+% recording parameters
 param.ephys_sample_rate = 30000; % samples per second
 param.nChannels = 385; %number of recorded channels (including any sync channels)
     % recorded in the raw data. This is usually 384 or 385 for neuropixels
@@ -120,16 +120,10 @@
 
 
 %% classifying units into good/mua/noise parameters 
-param.minAmplitude = 20; % in uV
-param.maxRPVviolations = 0.1; % fraction
-param.maxPercSpikesMissing = 20; % in percentage
-param.minNumSpikes = 300; % number of spikes
+% whether to classify non-somatic units 
+param.splitGoodAndMua_NonSomatic = 0;
 
-param.maxDrift = 100;
-param.minPresenceRatio = 0.7;
-param.minSNR = 0.1;
-
-%waveform 
+% waveform 
 param.maxNPeaks = 2; % maximum number of peaks
 param.maxNTroughs = 1; % maximum number of troughs
 param.somatic = 1; % keep only somatic units, and reject non-somatic ones
@@ -139,8 +133,18 @@
 param.maxWvBaselineFraction = 0.3; % maximum absolute value in waveform baseline
     % should not exceed this fraction of the waveform's abolute peak value
 
-%distance metrics
+% distance metrics
 param.isoDmin = 20; % minimum isolation distance value
 param.lratioMax = 0.1; % maximum l-ratio value
 param.ssMin = NaN; % minimum silhouette score 
+
+% other classification params
+param.minAmplitude = 20; % in uV
+param.maxRPVviolations = 0.1; % fraction
+param.maxPercSpikesMissing = 20; % in percentage
+param.minNumSpikes = 300; % number of spikes
+param.maxDrift = 100;
+param.minPresenceRatio = 0.7;
+param.minSNR = 0.1;
+
 end