Merge pull request #37 from cbimbo/main

Add split good vs. mua for non-somatic units

qualityMetrics/bc_checkParameterFields.m

@@ -28,6 +28,8 @@
 defaultValues.saveAsTSV = 0;
 defaultValues.unitType_for_phy = 0;
 
+% separate good from mua in non-somatic
+defaultValues.splitGoodAndMua_NonSomatic = 0;
 
 %% Check for missing fields and add them with default value
 [param_complete, missingFields] = bc_addMissingFieldsWithDefault(param, defaultValues);

qualityMetrics/bc_getQualityUnitType.m

@@ -17,10 +17,10 @@
 
 
 %% Sanitize and check inputs 
-% sanitize parameter input
+% Sanitize parameter input
 param = bc_checkParameterFields(param);
 
-% check whether to save this classification for automated loading by phy
+% Check whether to save this classification for automated loading by phy
 if (nargin < 3 || isempty(savePath)) && param.unitType_for_phy == 1
     savePath = pwd;
     warning('no save path specified. using current working directory')
@@ -43,9 +43,6 @@
     qMetric.spatialDecaySlope > param.minSpatialDecaySlope | qMetric.waveformDuration_peakTrough < param.minWvDuration | ...
     qMetric.waveformDuration_peakTrough > param.maxWvDuration | qMetric.waveformBaselineFlatness > param.maxWvBaselineFraction) = 0; % NOISE
 
-% Classify non-somatic units
-unitType(qMetric.isSomatic ~= param.somatic & isnan(unitType)) = 3; % NON-SOMATIC
-
 % Classify mua units
 unitType((qMetric.percentageSpikesMissing_gaussian > param.maxPercSpikesMissing | qMetric.nSpikes < param.minNumSpikes & ...
     qMetric.fractionRPVs_estimatedTauR > param.maxRPVviolations | ...
@@ -68,12 +65,25 @@
 % Classify good units
 unitType(isnan(unitType)) = 1; % SINGLE SEXY UNIT
 
+% Classify non-somatic units
+if param.splitGoodAndMua_NonSomatic
+    unitType(qMetric.isSomatic ~= param.somatic & unitType == 1) = 3; % GOOD NON-SOMATIC
+    unitType(qMetric.isSomatic ~= param.somatic & unitType == 2) = 4; % MUA NON-SOMATIC
+else
+    unitType(qMetric.isSomatic ~= param.somatic & ismember(unitType,[1 2])) = 3; % NON-SOMATIC
+end
+
 % Get unit type string
 unitType_string = cell(size(unitType, 1), 1);
 unitType_string(unitType == 0) = {'NOISE'};
-unitType_string(unitType == 3) = {'NON-SOMA'};
 unitType_string(unitType == 1) = {'GOOD'};
 unitType_string(unitType == 2) = {'MUA'};
+if param.splitGoodAndMua_NonSomatic
+    unitType_string(unitType == 3) = {'NON-SOMA GOOD'};
+    unitType_string(unitType == 4) = {'NON-SOMA MUA'};
+else
+    unitType_string(unitType == 3) = {'NON-SOMA'};
+end
 
 %% Save classification for phy
 % save unitType for phy if param.unitType_for_phy is equal to 1

qualityMetrics/bc_qualityParamValuesForUnitMatch.m

@@ -77,14 +77,22 @@
 % recorded in the raw data. This is usually 384 or 385 for neuropixels
 % recordings
 paramBC.nSyncChannels = 1;
-if  ~isempty(ephysMetaDir)
+if exist('ephysMetaDir','var') && ~isempty(ephysMetaDir)
     paramBC.ephysMetaFile = [ephysMetaDir.folder, filesep, ephysMetaDir.name];
     paramBC.gain_to_uV = NaN;
 else
     paramBC.ephysMetaFile = 'NaN';
-    paramBC.gain_to_uV = gain_to_uV;
+    if exist('gain_to_uV','var')
+        paramBC.gain_to_uV = gain_to_uV;
+    else
+        paramBC.gain_to_uV = NaN;
+    end
+end
+if exist('rawFile','var')
+    paramBC.rawFile = rawFile;
+else
+    paramBC.rawFile = 'NaN';
 end
-paramBC.rawFile = rawFile;
 
 % distance metric parameters
 paramBC.computeDistanceMetrics = 0; % whether to compute distance metrics - this can be time consuming 
@@ -115,6 +123,10 @@
 paramBC.isoDmin = 20; 
 paramBC.lratioMax = 0.1;
 paramBC.ssMin = NaN; 
+
+% split good and mua non-somatic
+paramBC.splitGoodAndMua_NonSomatic = 1;
+
 end
 % %bc_qualityParamValues
 % BCparam = struct;