STYLE: Use reange-baed loops from C++11

C++11 Range based for loops can be used in Used as a more readable equivalent to the traditional for loop operating over a range of values, such as all elements in a container, in the forward direction.. Range based loopes are more explicit for only computing the end location once for containers. SRCDIR=/Users/johnsonhj/src/BT-11/ANTs #My local SRC BLDDIR=/Users/johnsonhj/src/BT-11/ANTs-build #My local BLD cd /Users/johnsonhj/src/BT-11/ANTs-build run-clang-tidy.py -extra-arg=-D__clang__ -checks=-*,modernize-loop-convert -header-filter=.* -fix
ANTsX · Nov 8, 2018 · 0003573 · 0003573
1 parent 827aff4
commit 0003573
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Showing 14 changed files with 37 additions and 39 deletions.
diff --git a/Examples/ANTSJacobian.cxx b/Examples/ANTSJacobian.cxx
@@ -181,9 +181,9 @@ ComputeJacobian(TDisplacementField* field, char* fnm, char* maskfn, bool uselog
   if( projvec.length() > 2 )
     {
     antsjacobiansplit(projvec, 'x', v);
-    for( std::vector<std::string>::size_type i = 0; i < v.size(); ++i )
+    for(const auto & i : v)
       {
-      std::cout << v[i] << '\n';
+      std::cout << i << '\n';
       }
     }
 

diff --git a/Examples/ConvertImage.cxx b/Examples/ConvertImage.cxx
@@ -169,9 +169,9 @@ int ConvertImage( int argc, char *argv[] )
     rescaleFileTypes.emplace_back(".bmp" );
 
     bool isRescaleType = false;
-    for( unsigned int i = 0; i < rescaleFileTypes.size(); i++ )
+    for(auto & rescaleFileType : rescaleFileTypes)
       {
-      if( strstr( argv[3], rescaleFileTypes[i].c_str() ) != nullptr )
+      if( strstr( argv[3], rescaleFileType.c_str() ) != nullptr )
         {
         isRescaleType = true;
         break;

diff --git a/Examples/CreateDTICohort.cxx b/Examples/CreateDTICohort.cxx
@@ -604,9 +604,9 @@ int CreateDTICohort( itk::ants::CommandLineParser *parser )
     if( applyISV )
       {
       vnl_vector<RealType> R( ISV.cols() );
-      for( unsigned int d = 0; d < R.size(); d++ )
+      for(float & d : R)
         {
-        R[d] = randomizer->GetNormalVariate( 0.0, 1.0 );
+        d = randomizer->GetNormalVariate( 0.0, 1.0 );
         }
       eigenISVProjection = ISV * R;
       }

diff --git a/Examples/ImageMath_Templates.hxx b/Examples/ImageMath_Templates.hxx
@@ -10365,9 +10365,9 @@ int LabelThickness(      int argc, char *argv[])
         }
       }
     }
-  for( unsigned int i = 0; i < surface.size(); i++ )
+  for(double i : surface)
     {
-    if( surface[i] > 0 )
+    if( i > 0 )
       {
       // std::cout << " S " << surface[i] << " V " << volume[i] << " T " << volume[i] / surface[i] * 2.0 <<  std::endl;
       }
@@ -11647,12 +11647,12 @@ int MajorityVoting( int argc, char *argv[] )
     fileExtensions.emplace_back(".nrrd" );
 
     std::string outputMaskName;
-    for( unsigned int i = 0; i < fileExtensions.size(); i++ )
+    for(auto & fileExtension : fileExtensions)
       {
-      if( outputName.find_last_of( fileExtensions[i] ) != std::string::npos )
+      if( outputName.find_last_of( fileExtension ) != std::string::npos )
         {
         outputMaskName = outputName.insert(
-          outputName.find_last_of( fileExtensions[i] ) - ( fileExtensions[i] ).length(), "_Mask" );
+          outputName.find_last_of( fileExtension ) - fileExtension.length(), "_Mask" );
         break;
         }
       }
@@ -13226,9 +13226,9 @@ void getBlobCorrespondenceMatrix( unsigned int radval, typename TImage::Pointer
       weightsum += ( wt );
       }
     }
-  for( unsigned int ii = 0; ii < weights.size(); ii++ )
+  for(float & weight : weights)
     {
-    weights[ii] = weights[ii] / weightsum;
+    weight = weight / weightsum;
     }
   BlobPointer bestblob = nullptr;
   if( ( !blobs2.empty() ) && ( !blobs1.empty() ) )

diff --git a/Examples/LabelOverlapMeasures.cxx b/Examples/LabelOverlapMeasures.cxx
@@ -157,10 +157,8 @@ int LabelOverlapMeasures( int argc, char * argv[] )
               << std::setw( 17 ) << "False negative"
               << std::setw( 17 ) << "False positive"
               << std::endl;
-    for( unsigned int i = 0; i < allLabels.size(); i++ )
+    for(int label : allLabels)
       {
-      int label = allLabels[i];
-
       std::cout << std::setw( 10 ) << label;
       std::cout << std::setw( 17 ) << filter->GetTargetOverlap( label );
       std::cout << std::setw( 17 ) << filter->GetUnionOverlap( label );

diff --git a/Examples/PrintHeader.cxx b/Examples/PrintHeader.cxx
@@ -284,11 +284,11 @@ int PrintHeader(int argc, char *argv[])
         // For some weird reason, some of the strings returned by this method
         // contain '\0' characters. We will replace them by spaces
         std::ostringstream sout("");
-        for( unsigned int i = 0; i < v_string.length(); i++ )
+        for(char i : v_string)
           {
-          if( v_string[i] >= ' ' )
+          if( i >= ' ' )
             {
-            sout << v_string[i];
+            sout << i;
             }
           }
         v_string = sout.str();

diff --git a/Examples/antsAI.cxx b/Examples/antsAI.cxx
@@ -370,9 +370,9 @@ void GetBlobCorrespondenceMatrix( typename TImage::Pointer fixedImage,  typename
       weightSum += ( weight );
       }
     }
-  for( unsigned int i = 0; i < weights.size(); i++ )
+  for(float & weight : weights)
     {
-    weights[i] /= weightSum;
+    weight /= weightSum;
     }
 
   typedef itk::LinearInterpolateImageFunction<ImageType, RealType> ScalarInterpolatorType;

diff --git a/Examples/antsMotionCorr.cxx b/Examples/antsMotionCorr.cxx
@@ -410,13 +410,13 @@ AverageTimeImages( typename TImageIn::Pointer image_in,  typename TImageOut::Poi
     typename OutImageType::PixelType  fval = 0;
     typename ImageType::IndexType ind;
     typename OutImageType::IndexType spind = vfIter2.GetIndex();
-    for( unsigned int xx = 0; xx < timelist.size(); xx++ )
+    for(unsigned int & xx : timelist)
       {
       for( unsigned int yy = 0; yy < ImageDimension - 1; yy++ )
         {
         ind[yy] = spind[yy];
         }
-      ind[ImageDimension - 1] = timelist[xx];
+      ind[ImageDimension - 1] = xx;
       fval += image_in->GetPixel(ind);
       }
     fval /= (double)timelist.size();

diff --git a/Examples/antsSliceRegularizedRegistration.cxx b/Examples/antsSliceRegularizedRegistration.cxx
@@ -550,9 +550,9 @@ int ants_slice_regularized_registration( itk::ants::CommandLineParser *parser )
     {
     polydegree.resize( nparams, polydegree[ 0 ] );
     }
-  for ( unsigned int pind = 0; pind < polydegree.size(); pind++ )
+  for (unsigned int & pind : polydegree)
     {
-    if ( polydegree[pind] > (timedims-2) ) polydegree[pind] = timedims-2;
+    if ( pind > (timedims-2) ) pind = timedims-2;
     }
 
     // the fixed image slice is a reference image in 2D while the moving is a 2D slice image

diff --git a/Examples/sccan.cxx b/Examples/sccan.cxx
@@ -1039,13 +1039,13 @@ void ConvertImageVecListToProjection( std::string veclist, std::string imagelist
   std::string   fnmp = outname + std::string(".csv");
   myfile.open(fnmp.c_str(), std::ios::out );
   typedef itk::ImageRegionIteratorWithIndex<ImageType> Iterator;
-  for( unsigned int j = 0; j < image_fn_list.size(); j++ )
+  for(auto & j : image_fn_list)
     {
     for( unsigned int k = 0; k < vec_fn_list.size(); k++ )
       {
       double proj = 0, dotSum = 0, dotCounter = 0, dotTotal = 0;
       typename ReaderType::Pointer reader1 = ReaderType::New();
-      reader1->SetFileName( image_fn_list[j] );
+      reader1->SetFileName( j );
       reader1->Update();
       typename ReaderType::Pointer reader2 = ReaderType::New();
       reader2->SetFileName( vec_fn_list[k] );

diff --git a/ImageRegistration/itkCrossCorrelationRegistrationFunction.cxx b/ImageRegistration/itkCrossCorrelationRegistrationFunction.cxx
@@ -63,9 +63,9 @@ ::CrossCorrelationRegistrationFunction()
 
   m_MovingImageInterpolator = static_cast<InterpolatorType *>(
       interp.GetPointer() );
-  for( int i = 0; i < 5; i++ )
+  for(auto & finitediffimage : finitediffimages)
     {
-    finitediffimages[i] = nullptr;
+    finitediffimage = nullptr;
     }
 
   m_NumberOfHistogramBins = 32;

diff --git a/ImageRegistration/itkProbabilisticRegistrationFunction.cxx b/ImageRegistration/itkProbabilisticRegistrationFunction.cxx
@@ -60,9 +60,9 @@ ::ProbabilisticRegistrationFunction()
 
   m_MovingImageInterpolator = static_cast<InterpolatorType *>(
       interp.GetPointer() );
-  for( int i = 0; i < 5; i++ )
+  for(auto & finitediffimage : finitediffimages)
     {
-    finitediffimages[i] = nullptr;
+    finitediffimage = nullptr;
     }
 
   m_NumberOfHistogramBins = 32;

diff --git a/ImageSegmentation/itkWeightedVotingFusionImageFilter.hxx b/ImageSegmentation/itkWeightedVotingFusionImageFilter.hxx
@@ -625,11 +625,11 @@ WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
         W = vnl_svd<RealType>( MxBar ).solve( ones );
         }
 
-      for( SizeValueType i = 0; i < W.size(); i++ )
+      for(double & i : W)
         {
-        if( W[i] < 0.0 )
+        if( i < 0.0 )
           {
-          W[i] = 0.0;
+          i = 0.0;
           }
         }
       }

diff --git a/Utilities/itkMultiScaleLaplacianBlobDetectorImageFilter.hxx b/Utilities/itkMultiScaleLaplacianBlobDetectorImageFilter.hxx
@@ -91,13 +91,13 @@ void MultiScaleLaplacianBlobDetectorImageFilter<TInputImage>
 
   typedef itk::LaplacianRecursiveGaussianImageFilter<InputImageType, RealImageType> LaplacianFilterType;
   typename LaplacianFilterType::Pointer laplacianFilter[3];
-  for( unsigned int i = 0; i < 3; ++i )
+  for(auto & i : laplacianFilter)
     {
-    laplacianFilter[i] = LaplacianFilterType::New();
+    i = LaplacianFilterType::New();
 //    laplacianFilter[i]->SetNumberOfThreads( this->GetNumberOfThreads() );
-    laplacianFilter[i]->SetInput( inputImage );
-    laplacianFilter[i]->SetNormalizeAcrossScale( true );
-    progress->RegisterInternalFilter( laplacianFilter[i],   1.0 / numberOfScales );
+    i->SetInput( inputImage );
+    i->SetNormalizeAcrossScale( true );
+    progress->RegisterInternalFilter( i,   1.0 / numberOfScales );
     }
 
   BlobHeapType blobs;