Update source formatting for v2.9.5 release

Illumina · Jun 25, 2018 · 5daae2b · 5daae2b
1 parent 9ce4e66
commit 5daae2b
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Showing 8 changed files with 27 additions and 27 deletions.
diff --git a/docs/userGuide/errorAnalysis.md b/docs/userGuide/errorAnalysis.md
@@ -12,15 +12,15 @@ Error Pattern Analyzer User Guide
 * [Outputs](#outputs)
   * [Counts files](#counts-files)
   * [Error model output](#error-model-output)
-* [Error counting workflow configuration and execution](#error-counting-workflow-configuration-and-execution)
+* [Allele counting workflow configuration and execution](#allele-counting-workflow-configuration-and-execution)
   * [Configuration](#configuration)
     * [Configuration: Excluding regions](#configuration-excluding-regions)
     * [Configuration: Annotating known variants](#configuration-annotating-known-variants)
     * [Configuration: Report observed indels](#configuration-report-observed-indels)
   * [Execution](#execution)
   * [Advanced execution options](#advanced-execution-options)
     * [`--quiet`](#--quiet)
-* [Viewing error counting workflow ouput](#viewing-error-counting-workflow-ouput)
+* [Viewing allele counting workflow ouput](#viewing-allele-counting-workflow-ouput)
   * [Summary output](#summary-output)
   * [Excluding basecalls/indels](#excluding-basecallsindels)
   * [Extended output (for model development)](#extended-output-for-model-development)

diff --git a/src/c++/lib/applications/EstimateVariantErrorRates/IndelModelProduction.cpp b/src/c++/lib/applications/EstimateVariantErrorRates/IndelModelProduction.cpp
@@ -557,7 +557,7 @@ exportModelUsingInputJson(
 
     static const unsigned expectedJsonModelCount(1);
     const unsigned jsonModelCount(indelErrorModelsJson.getIndelErrorModels().size());
-    if(jsonModelCount != expectedJsonModelCount)
+    if (jsonModelCount != expectedJsonModelCount)
     {
         std::ostringstream oss;
         oss << "Expecting indel error model file to describe exactly " << expectedJsonModelCount

diff --git a/src/c++/lib/applications/GetSequenceAlleleCounts/SequenceAlleleCountsPosProcessor.cpp b/src/c++/lib/applications/GetSequenceAlleleCounts/SequenceAlleleCountsPosProcessor.cpp
@@ -163,7 +163,7 @@ resetRegion(
             if (_maxNormalSampleDepthForCandidateVariants > 0.)
             {
                 _maxNormalSampleDepthForCandidateVariants = std::min(_maxNormalSampleDepthForCandidateVariants,
-                                                              static_cast<double>(_opt.max_candidate_indel_depth));
+                                                                     static_cast<double>(_opt.max_candidate_indel_depth));
             }
             else
             {
@@ -311,7 +311,7 @@ mergeIndelObservations(
     const IndelCounts::Context& context,
     const IndelCounts::SingleSampleCandidateVariantContextObservationPattern& indelObservation,
     std::map<IndelCounts::Context,
-        IndelCounts::SingleSampleCandidateVariantContextObservationPattern>& mergedIndelObservations)
+    IndelCounts::SingleSampleCandidateVariantContextObservationPattern>& mergedIndelObservations)
 {
     using namespace IndelCounts;
 
@@ -673,7 +673,7 @@ process_pos_error_counts(
                 if (referenceSTRContext.STRRepeatCount == std::min(maxSTRRepeatCount, indelReportInfo.refRepeatCount))
                 {
                     context = IndelCounts::Context(
-                        referenceSTRContext.patternSize, referenceSTRContext.STRRepeatCount);
+                                  referenceSTRContext.patternSize, referenceSTRContext.STRRepeatCount);
                 }
             }
 

diff --git a/src/c++/lib/blt_util/test/logSumUtilTest.cpp b/src/c++/lib/blt_util/test/logSumUtilTest.cpp
@@ -149,7 +149,7 @@ BOOST_AUTO_TEST_CASE( benchmarkLogSums )
         TimeTracker tt;
         tt.resume();
         double sum(0);
-        for (unsigned i(0); i<repeatCount;++i)
+        for (unsigned i(0); i<repeatCount; ++i)
         {
             for (double value(startValue); value > minValue; value *= valueFactor)
             {
@@ -164,7 +164,7 @@ BOOST_AUTO_TEST_CASE( benchmarkLogSums )
         TimeTracker tt;
         tt.resume();
         double sum(0);
-        for (unsigned i(0); i<repeatCount;++i)
+        for (unsigned i(0); i<repeatCount; ++i)
         {
             for (double value(startValue); value > minValue; value *= valueFactor)
             {
@@ -180,7 +180,7 @@ BOOST_AUTO_TEST_CASE( benchmarkLogSums )
         TimeTracker tt;
         tt.resume();
         float sum(0);
-        for (unsigned i(0); i<repeatCount;++i)
+        for (unsigned i(0); i<repeatCount; ++i)
         {
             for (float value(startValue); value > minValue; value *= valueFactor)
             {
@@ -195,7 +195,7 @@ BOOST_AUTO_TEST_CASE( benchmarkLogSums )
         TimeTracker tt;
         tt.resume();
         float sum(0);
-        for (unsigned i(0); i<repeatCount;++i)
+        for (unsigned i(0); i<repeatCount; ++i)
         {
             for (float value(startValue); value > minValue; value *= valueFactor)
             {

diff --git a/src/c++/lib/errorAnalysis/BasecallCounts.hh b/src/c++/lib/errorAnalysis/BasecallCounts.hh
@@ -56,7 +56,7 @@ struct Context
         Archive& ar,
         const unsigned /* version */)
     {
-        ar & repeatCount;
+        ar& repeatCount;
     }
 
     unsigned repeatCount = 1;
@@ -146,10 +146,10 @@ struct SingleSampleContextObservationPattern
         Archive& ar,
         const unsigned /* version */)
     {
-        ar & strand0.refAlleleCount;
-        ar & strand0.altAlleleCount;
-        ar & strand1.refAlleleCount;
-        ar & strand1.altAlleleCount;
+        ar& strand0.refAlleleCount;
+        ar& strand0.altAlleleCount;
+        ar& strand1.refAlleleCount;
+        ar& strand1.altAlleleCount;
     }
 
 
@@ -344,12 +344,12 @@ struct ContextData
     {
         // adding error.data instead of error here to reduce the total
         // serialization template depth:
-        ar & counts.data;
-        ar & counts.refAlleleBasecallErrorPhredProbs;
-        ar & excludedRegionSkipped;
-        ar & depthSkipped;
-        ar & emptySkipped;
-        ar & noiseSkipped;
+        ar& counts.data;
+        ar& counts.refAlleleBasecallErrorPhredProbs;
+        ar& excludedRegionSkipped;
+        ar& depthSkipped;
+        ar& emptySkipped;
+        ar& noiseSkipped;
     }
 
     SingleSampleContextData counts;
@@ -423,7 +423,7 @@ public:
         Archive& ar,
         const unsigned /* version */)
     {
-        ar & _data;
+        ar& _data;
     }
 
 private:

diff --git a/src/c++/lib/errorAnalysis/IndelCounts.hh b/src/c++/lib/errorAnalysis/IndelCounts.hh
@@ -158,8 +158,8 @@ struct Context
         Archive& ar,
         const unsigned /* version */)
     {
-        ar & repeatPatternSize;
-        ar & repeatCount;
+        ar& repeatPatternSize;
+        ar& repeatCount;
     }
 
     unsigned getRepeatPatternSize() const

diff --git a/src/python/scoringModelTraining/germline/bin/evs_learn.py b/src/python/scoringModelTraining/germline/bin/evs_learn.py
@@ -112,7 +112,7 @@ def getDataSet(inputs, args) :
         print "Reading '%s'" % (inputFile)
         df = pandas.read_csv(inputFile, na_values=".")
         df.fillna("0", inplace=True)
-        # Remove false negatives before any subsampling:                                                                                                                                      
+        # Remove false negatives before any subsampling:
         df = df[df["tag"] != "FN"]
 
         if args.sample_input:
@@ -129,7 +129,7 @@ def getDataSet(inputs, args) :
             else:
                 print "TP: %d FP: %d" % (tps.shape[0], fps.shape[0])
             if tps.shape[0] < fps.shape[0]:
-                rows_selected = random.sample(fps.index, tps.shape[0])  
+                rows_selected = random.sample(fps.index, tps.shape[0])
                 fps = pandas.DataFrame(fps.ix[rows_selected])
             elif fps.shape[0] < tps.shape[0]:
                 rows_selected = random.sample(tps.index, fps.shape[0])

diff --git a/src/python/scoringModelTraining/somatic/bin/evs_learn.py b/src/python/scoringModelTraining/somatic/bin/evs_learn.py
@@ -128,7 +128,7 @@ def getDataSet(inputs, sample_input, balance_per_sample) :
                 rows_selected = random.sample(fps.index, tps.shape[0])
                 fps = pandas.DataFrame(fps.ix[rows_selected])
             elif fps.shape[0] < tps.shape[0]:
-                rows_selected = random.sample(tps.index, fps.shape[0])  
+                rows_selected = random.sample(tps.index, fps.shape[0])
                 tps = pandas.DataFrame(tps.ix[rows_selected])
             print "Downsampled to TP: %d FP: %d" % (tps.shape[0], fps.shape[0])
             df = pandas.concat([tps, fps])