remove trailing spaces from .cpp and .h files

BayesTest.cpp

@@ -41,7 +41,7 @@ double BayesTest::getGridParams(const std::vector<double>& aVars, const std::vec
 	// Initialize the probability list. Only the fg branch needs to be set
 	mBEBset.initializeFgBranch(mForest.adjustFgBranchIdx(aFgBranch));
 
-	// Calculating f(x_h|w) 
+	// Calculating f(x_h|w)
 	// Order of site classes for iw or f(x_h|w):
 	//					   back	  fore	   #sets
 	//	 site class 0:		w0	   w0		 10
@@ -62,13 +62,13 @@ double BayesTest::getGridParams(const std::vector<double>& aVars, const std::vec
 			omega_fg_is_one = omega_bg_is_one = true;
 		}
 		else if(iw < (BEB_N1D+1+BEB_N1D*BEB_N1D))				// class 2a: w0 w2
-		{								   
+		{
 			omega_bg = prior_params[2][(iw-BEB_N1D-1) / BEB_N1D];
 			omega_fg = prior_params[3][(iw-BEB_N1D-1) % BEB_N1D];
 			omega_fg_is_one = omega_bg_is_one = false;
 		}
 		else													// class 2b: w1 w2
-		{														
+		{
 			omega_bg = 1.;
 			omega_fg = prior_params[3][iw-BEB_N1D-1-BEB_N1D*BEB_N1D];
 			omega_fg_is_one = false; omega_bg_is_one = true;
@@ -89,7 +89,7 @@ double BayesTest::getGridParams(const std::vector<double>& aVars, const std::vec
 					q_fg.fillMatrix(omega_fg, kappa);
 
 				q_fg.eigenQREV();
-			} 
+			}
 			#pragma omp section
 			{
 				if(omega_bg_is_one)
@@ -171,10 +171,10 @@ void BayesTest::computeBEB(const std::vector<double>& aVars, size_t aFgBranch, c
 		// In the original code instead of BEB_DIMS there is nclassM. Both values are 4.
 		for(unsigned int codon_class=0; codon_class < BEB_DIMS; ++codon_class)
 		{
-			// Given the point on the grid ip[] and codon_class, this returns iw and codon_class_proportion, 
-			// where iw locates the correct f(x_h|w) stored in com.fhK[], and codon_class_proportion is 
+			// Given the point on the grid ip[] and codon_class, this returns iw and codon_class_proportion,
+			// where iw locates the correct f(x_h|w) stored in com.fhK[], and codon_class_proportion is
 			// the proportion of the site class under the model.
-			// The BEB_N1D*BEB_N1D grid for p0-p1 is mapped onto the ternary graph for p0-p1-p2.  
+			// The BEB_N1D*BEB_N1D grid for p0-p1 is mapped onto the ternary graph for p0-p1-p2.
 			//
 			unsigned int idx;
 			switch(codon_class)
@@ -326,7 +326,7 @@ void BayesTest::computeBEB(const std::vector<double>& aVars, size_t aFgBranch, c
 				// t is log of term on grid
 				double t = log(fhk[codon_class]) + lnfXs[igrid];
 				if(t > scale1 + 50)
-				{ 
+				{
 					// Change scale factor scale1
 					for(unsigned int j=0; j < BEB_DIMS; ++j) mSiteClassProb[j*mNumSites+site] *= exp(scale1-t);
 					scale1 = t;

CodeMLoptimizer.cpp

@@ -17,13 +17,13 @@ inline static double square(double a) {return a*a;}
 inline static void	 zero(double x[], int n) {memset(x, 0, n*sizeof(double));}
 inline static void	 xtoy(const double x[], double y[], int n) {memcpy(y, x, n*sizeof(double));}
 
-inline static void identityMatrix(double* x, int n) 
+inline static void identityMatrix(double* x, int n)
 {
 	memset(x, 0, n*n*sizeof(double));
 	for(int i = 0; i < n; i++) x[i*n + i] = 1.0;
 }
 
-static inline double norm(const double x[], int n) 
+static inline double norm(const double x[], int n)
 {
 #ifdef USE_LAPACK
 	return dnrm2_(&n, x, &I1);
@@ -36,7 +36,7 @@ static inline double norm(const double x[], int n)
 #endif
 }
 
-static inline double innerp(const double x[], const double y[], int n) 
+static inline double innerp(const double x[], const double y[], int n)
 {
 #ifdef USE_LAPACK
 	return ddot_(&n, x, &I1, y, &I1);
@@ -49,7 +49,7 @@ static inline double innerp(const double x[], const double y[], int n)
 #endif
 }
 
-static inline double distance(const double* RESTRICT x, const double* RESTRICT y, int n) 
+static inline double distance(const double* RESTRICT x, const double* RESTRICT y, int n)
 {
 	double t = 0;
 

CodonFrequencies.cpp

@@ -107,7 +107,7 @@ void CodonFrequencies::updateCodonCount(const std::vector<std::vector<unsigned i
 
 	// Count again
 	size_t cnt = aCodons.size();
-	for(size_t i=0; i < cnt; ++i) 
+	for(size_t i=0; i < cnt; ++i)
 	{
 		//if no ambiguities count as usual (add site multiplicity)
 		if(aCodons[i].size() == 2)

DAGScheduler.cpp

@@ -72,14 +72,14 @@ void DAGScheduler::dumpDAG(std::ostream& aOut) const
 		aOut << std::hex << *ind << ' ' << std::setw(6) << v << std::endl;
 	}
 	aOut << '#' << std::endl;
-	
+
 	// Then output all edges
 	std::vector<std::pair<const void*, const void*> >::const_iterator ied = mEdges.begin();
 	for(; ied != mEdges.end(); ++ied)
 	{
 		aOut << ied->first << ' ' << ied->second << std::endl;
 	}
-	
+
 	// For debug print the reference counts
 	std::map<const void*, int>::const_iterator irc = mRefCounter.begin();
 	for(; irc != mRefCounter.end(); ++irc)

FatVectorTransform.cpp

@@ -404,7 +404,7 @@ void FatVectorTransform::postCompact(CacheAlignedDoubleVector& aStepResults, Cac
 				}
 			}
 
-			// Reuse values 
+			// Reuse values
 			VectorOfRangesNoCnt::const_iterator icr=mReuseCmds[branch].begin();
 			const VectorOfRangesNoCnt::const_iterator endr=mReuseCmds[branch].end();
 			for(; icr != endr; ++icr)

Forest.cpp

@@ -285,7 +285,7 @@ bool Forest::getBranchRange(const CmdLine& aCmdLine, size_t& aBranchStart, size_
 	else if(aCmdLine.mBranchStart < UINT_MAX && aCmdLine.mBranchStart >= num_branches)
 	{
 		// Invalid start value, ignoring, do all branches
-		if(aCmdLine.mVerboseLevel >= VERBOSE_INFO_OUTPUT) std::cout << std::endl << "Invalid branch requested. Ignoring" << std::endl; 
+		if(aCmdLine.mVerboseLevel >= VERBOSE_INFO_OUTPUT) std::cout << std::endl << "Invalid branch requested. Ignoring" << std::endl;
 		aBranchStart = 0;
 		aBranchEnd	 = num_branches-1;
 	}
@@ -302,7 +302,7 @@ bool Forest::getBranchRange(const CmdLine& aCmdLine, size_t& aBranchStart, size_
 		aBranchStart = static_cast<size_t>(aCmdLine.mBranchStart);
 		if(aCmdLine.mBranchEnd >= num_branches)
 		{
-			if(aCmdLine.mVerboseLevel >= VERBOSE_INFO_OUTPUT) std::cout << std::endl << "Invalid end branch requested. Ignoring" << std::endl; 
+			if(aCmdLine.mVerboseLevel >= VERBOSE_INFO_OUTPUT) std::cout << std::endl << "Invalid end branch requested. Ignoring" << std::endl;
 			aBranchEnd = num_branches-1;
 			if(aBranchStart > 0) do_all = false;
 		}
@@ -622,7 +622,7 @@ void Forest::addAggressiveReduction(ForestNode* aNode)
 
 
 #ifdef NON_RECURSIVE_VISIT
-	
+
 void Forest::prepareNonRecursiveVisit(void)
 {
 	// Clean the list for non-recursive visit to the trees. Clear also the list of respective parents
@@ -673,7 +673,7 @@ void Forest::prepareNonRecursiveVisitWalker(ForestNode* aNode, ForestNode* aPare
 	// Store the nodes in the visit order except the root that should not be visited
 	// Store also the respective parent node
 	if(aParentNode)
-	{	
+	{
 		aVisitList.push_back(aNode);
 		aParentList.push_back(aParentNode);
 	}

Genes.cpp

@@ -14,10 +14,10 @@
 
 Genes::~Genes()
 {
-	mDnaSpecies.clear();		
-	mDnaGene.clear();			
-	mSiteMultiplicity.clear();	
-	mMapSiteToDnaGene.clear();	
+	mDnaSpecies.clear();
+	mDnaGene.clear();
+	mSiteMultiplicity.clear();
+	mMapSiteToDnaGene.clear();
 	mMapSpecieToDnaGene.clear();
 	mSitesMappingToOriginal.clear();
 	mMapCodonToPosition.clear();
@@ -186,7 +186,7 @@ void Genes::readFile(const char* aFilename, bool aCleanData)
 			}
 
 			// It is a gap if the codon is completely ambiguous
-			if(len < 61) break; 
+			if(len < 61) break;
 		}
 		if(i == nspecies)
 		{
@@ -241,7 +241,7 @@ void Genes::readFile(const char* aFilename, bool aCleanData)
 		std::cout << "Num. species: " << std::setw(6) << nspecies << std::endl;
 		std::cout << "Num. basis:	" << std::setw(6) << nbasis << std::endl;
 		std::cout << "Valid codons: " << std::setw(6) << valid_codons << "/" << ncodons << std::endl;
-		if(num_ambiguous) std::cout 
+		if(num_ambiguous) std::cout
 				  << "Ambiguous:	" << std::setw(6) << num_ambiguous << "/" << ncodons << std::endl;
 		if(num_gaps) std::cout
 				  << "Gaps removed: " << std::setw(6) << num_gaps << std::endl;
@@ -402,9 +402,9 @@ void Genes::initFullCodonMap(void)
 			for(k=1; k < 16; ++k)
 			{
 				// Build one of the possible codons (valid and ambiguous)
-				codona[0] = amb[i];					
-				codona[1] = amb[j];					
-				codona[2] = amb[k];					
+				codona[0] = amb[i];
+				codona[1] = amb[j];
+				codona[2] = amb[k];
 
 				std::vector<int> pos;
 				bool valid = false;

HighLevelCoordinator.cpp

@@ -142,7 +142,7 @@ struct HighLevelCoordinator::WorkTable
 	/// @param[in] aOut The stream on which the print should be done.
 	///
 	void printVariables(size_t aBranch, unsigned int aHyp, std::ostream& aOut=std::cout) const;
-	
+
 	/// Mark as job to be skipped branches outside the given range
 	///
 	/// @param[in] aBranchStart The first branch to be processed
@@ -282,7 +282,7 @@ void HighLevelCoordinator::WorkTable::checkAllJobsDone(void) const
 			any_error = true;
 		}
 	}
-	
+
 	if(any_error) {std::cout << std::endl; throw FastCodeMLFatal();}
 }
 
@@ -327,7 +327,7 @@ void HighLevelCoordinator::WorkTable::printVariables(size_t aBranch, unsigned in
 	unsigned int count_per_line = 0;
 	static const std::streamsize VARS_PRECISION = 7;
 	static const std::streamsize VARS_WIDTH		= 11;
-	
+
 	// Write the data with uniform precision
 	std::streamsize prec = aOut.precision(VARS_PRECISION);
 	aOut.setf(std::ios::fixed, std::ios::floatfield);
@@ -784,7 +784,7 @@ void HighLevelCoordinator::doWorker(Forest& aForest, const CmdLine& aCmdLine)
 	// Initialize the two hypothesis
 	BranchSiteModelNullHyp h0(aForest, aCmdLine);
 	BranchSiteModelAltHyp  h1(aForest, aCmdLine);
-		
+
 	// Initialize the BEB (no verbose at all)
 	BayesTest beb(aForest, 0, aCmdLine.mDoNotReduceForest);
 
@@ -846,7 +846,7 @@ void HighLevelCoordinator::doWorker(Forest& aForest, const CmdLine& aCmdLine)
 
 			// Compute H0
 			double lnl = h0(static_cast<size_t>(job[1]), aCmdLine.mStopIfNotLRT && job[2] > 0, threshold);
-	
+
 			// Assemble the results to be passed to the master
 			h0.getVariables(values_double);
 			values_double.push_back(lnl);

ParseParameters.cpp

@@ -48,7 +48,7 @@ void ParseParameters::addParameter(const char* aParamValuePair)
 	mDictionary[name] = atof(p+1);
 }
 
-	
+
 double ParseParameters::getParameter(const char* aParamName) const
 {
 	// The name should exist in the dictionary

Phylip.cpp

@@ -107,7 +107,7 @@ void Phylip::loadData(const char* aFilename, std::vector<std::string>& aSpecies,
 			throw FastCodeMLFatal(o);
 		}
 	}
-	
+
 	// Other sanity checks
 	if(nbasis % 3)
 	{

PhyloTree.cpp

@@ -100,8 +100,8 @@ unsigned int PhyloTree::cloneTree(ForestNode* aForestNode, unsigned int aTreeId,
 	// Set the pointers (best). The sequence is: Branch -> Site -> Set -> 1:N
 	for(int i=0; i < Nt; ++i)
 	{
-		//aForestNode->mProb[i] = &aProbVectors[VECTOR_SLOT*(aNumSites*Nt*id+aNumSites*i+aTreeId)]; 
-		aForestNode->mProb[i] = &aProbVectors[VECTOR_SLOT*(aNumSites*Nt*id+aTreeId*Nt+i)]; 
+		//aForestNode->mProb[i] = &aProbVectors[VECTOR_SLOT*(aNumSites*Nt*id+aNumSites*i+aTreeId)];
+		aForestNode->mProb[i] = &aProbVectors[VECTOR_SLOT*(aNumSites*Nt*id+aTreeId*Nt+i)];
 	}
 #endif
 

ProbabilityMatrixSet.cpp

@@ -86,7 +86,7 @@ const double** ProbabilityMatrixSetH0::getChangedMatrices(size_t aBranch)
 
 	return mMatricesPtr;
 }
-	
+
 void ProbabilityMatrixSetH0::setMatrices(size_t aBranch, const double** aMatricesPtr)
 {
 	memcpy(&mMatrixSpace[0*mNumMatrices*MATRIX_SLOT+aBranch*MATRIX_SLOT], aMatricesPtr[0], N*N*sizeof(double));

TransitionMatrix.cpp

@@ -368,7 +368,7 @@ void TransitionMatrix::eigenRealSymm(double* aU, int aDim, double* aR, double* /
 	{
 		work = new double[lwork];
 		//std::cout << "Optimal work:  " << lwork << " (" << lfwork << ")" << std::endl;
-		
+
 	}
 	if(liwork > lfiwork)
 	{

TreeAndSetsDependencies.cpp

@@ -97,7 +97,7 @@ void TreeAndSetsDependencies::computeDependencies(unsigned int aNumSets, bool aN
 
 	// Number of dependencies classes
 	size_t nc = tree_groups_dependencies.size();
-	
+
 	// One dependency classes
 	std::vector<unsigned int> one_class;
 

TreeAndSetsDependencies.h

@@ -2,7 +2,7 @@
 #ifndef TREEANDSETSDEPENDENCIES_H
 #define TREEANDSETSDEPENDENCIES_H
 
-/// List (each list depends on the previous) of list (sites to be executed in parallel) of pairs (site, site class) stored as site*4+site_class 
+/// List (each list depends on the previous) of list (sites to be executed in parallel) of pairs (site, site class) stored as site*4+site_class
 typedef std::vector< std::vector<unsigned int> > ListDependencies;
 
 #include "Forest.h"

WriteResults.cpp

@@ -42,7 +42,7 @@ void WriteResults::outputResults(void)
 	}
 
 	// Write the log-likelihood values (if a value is not present, write NA)
-	
+
 
 	for(size_t branch = min_branch; branch <= max_branch; ++branch)
 	{
@@ -57,7 +57,7 @@ void WriteResults::outputResults(void)
 		else
 		{
 			out << std::setw(22) << std::setprecision(15) << std::fixed << im->second;
-			
+
 			ims = mParamStr[0].find(branch);
 			// This output does not work on BG/Q.
 						//out << std::endl<< std::fixed <<"Branch lengths:" << ims->second << std::endl;

blas.h

@@ -38,7 +38,7 @@
 
 
 #ifdef __cplusplus
-extern "C" { 
+extern "C" {
 #endif
 
 /// DSYRK  performs one of the symmetric rank k operations
@@ -297,15 +297,15 @@ double ddot_(const int *n,
 
 
 /// Computes the Euclidean norm of a vector.
-/// 
+///
 /// DNRM2 computes the Euclidean (L2) norm of a double precision real vector
 ///
 /// @param[in]		n  Number of elements in the operand vector.
 /// @param[in]		dx	Array of dimension (n-1) * |incx| + 1. Array x contains the operand vector.
 /// @param[in]		incx  Increment between elements of x.
-/// 
+///
 /// @return Resulting Euclidean norm.
-/// 
+///
 double dnrm2_(const int *n,
 				const double *dx,
 				const int *incx);

fast.cpp

@@ -114,7 +114,7 @@ int main(int aRgc, char **aRgv)
 	// Write out command line parameters (if not quiet i.e. if verbose level > 0)
 	if(cmd.mVerboseLevel >= VERBOSE_INFO_OUTPUT)
 	{
-	
+
 		std::cout << "------------------------------------" << std::endl;
 		std::cout << "FastCodeML V"<<version << std::endl;
 		std::cout << "------------------------------------"<<std::endl;

lapack.h

@@ -126,7 +126,7 @@
 ///			 returns this value as the first entry of the IWORK array, and
 ///			 no error message related to LIWORK is issued by XERBLA.
 ///
-///	 @param[out] info 
+///	 @param[out] info
 ///			 = 0:  successful exit
 ///			 < 0:  if INFO = -i, the i-th argument had an illegal value
 ///			 > 0:  Internal error
@@ -158,31 +158,31 @@ extern "C" void dsyevr_(const char *jobz,
 ///	  DSYEVD computes all eigenvalues and, optionally, eigenvectors of a
 ///	  real symmetric matrix A. If eigenvectors are desired, it uses a
 ///	  divide and conquer algorithm.
-///	
+///
 ///	  The divide and conquer algorithm makes very mild assumptions about
 ///	  floating point arithmetic. It will work on machines with a guard
 ///	  digit in add/subtract, or on those binary machines without guard
 ///	  digits which subtract like the Cray X-MP, Cray Y-MP, Cray C-90, or
 ///	  Cray-2. It could conceivably fail on hexadecimal or decimal machines
 ///	  without guard digits, but we know of none.
-///	
+///
 ///	  Because of large use of BLAS of level 3, DSYEVD needs N**2 more
 ///	  workspace than DSYEVX.
-///	
+///
 ///	  Arguments
 ///	  =========
-///	
+///
 ///	  @param[in] jobz	 (input) CHARACTER*1
 ///			  = 'N':  Compute eigenvalues only;
 ///			  = 'V':  Compute eigenvalues and eigenvectors.
-///	
+///
 ///	  @param[in] uplo	 (input) CHARACTER*1
 ///			  = 'U':  Upper triangle of A is stored;
 ///			  = 'L':  Lower triangle of A is stored.
-///	
+///
 ///	  @param[in] n		 (input) INTEGER
 ///			  The order of the matrix A.  N >= 0.
-///	
+///
 ///	  @param[in,out] a		 (input/output) DOUBLE PRECISION array, dimension (LDA, N)
 ///			  On entry, the symmetric matrix A.	 If UPLO = 'U', the
 ///			  leading N-by-N upper triangular part of A contains the
@@ -194,16 +194,16 @@ extern "C" void dsyevr_(const char *jobz,
 ///			  If JOBZ = 'N', then on exit the lower triangle (if UPLO='L')
 ///			  or the upper triangle (if UPLO='U') of A, including the
 ///			  diagonal, is destroyed.
-///	
+///
 ///	  @param[in] lda	 (input) INTEGER
 ///			  The leading dimension of the array A.	 LDA >= max(1,N).
-///	
+///
 ///	  @param[out] w		  (output) DOUBLE PRECISION array, dimension (N)
 ///			  If INFO = 0, the eigenvalues in ascending order.
-///	
+///
 ///	  @param[out] work	  (workspace/output) DOUBLE PRECISION array, dimension (LWORK)
 ///			  On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
-///	
+///
 ///	  @param[in] lwork	 (input) INTEGER
 ///			  The dimension of the array WORK.
 ///			  If N <= 1,			   LWORK must be at least 1.
@@ -214,10 +214,10 @@ extern "C" void dsyevr_(const char *jobz,
 ///			  arrays, returns these values as the first entries of the WORK
 ///			  and IWORK arrays, and no error message related to LWORK or
 ///			  LIWORK is issued by XERBLA.
-///	
+///
 ///	  @param[out] iwork	  (workspace/output) INTEGER array, dimension (MAX(1,LIWORK))
 ///			  On exit, if INFO = 0, IWORK(1) returns the optimal LIWORK.
-///	
+///
 ///	  @param[in] liwork	 (input) INTEGER
 ///			  The dimension of the array IWORK.
 ///			  If N <= 1,				LIWORK must be at least 1.
@@ -228,7 +228,7 @@ extern "C" void dsyevr_(const char *jobz,
 ///			  IWORK arrays, returns these values as the first entries of
 ///			  the WORK and IWORK arrays, and no error message related to
 ///			  LWORK or LIWORK is issued by XERBLA.
-///	
+///
 ///	  @param[out] info	  (output) INTEGER
 ///			  = 0:	successful exit
 ///			  < 0:	if INFO = -i, the i-th argument had an illegal value
@@ -239,7 +239,7 @@ extern "C" void dsyevr_(const char *jobz,
 ///					to compute an eigenvalue while working on the submatrix
 ///					lying in rows and columns INFO/(N+1) through
 ///					mod(INFO,N+1).
-///	
+///
 extern "C" void dsyevd_(const char *jobz,
 						const char *uplo,
 						const int *n,

nlopt.h

@@ -7,17 +7,17 @@
  * distribute, sublicense, and/or sell copies of the Software, and to
  * permit persons to whom the Software is furnished to do so, subject to
  * the following conditions:
- * 
+ *
  * The above copyright notice and this permission notice shall be
  * included in all copies or substantial portions of the Software.
- * 
+ *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
  * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
  * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
  * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
  * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
  * OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 
+ * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  */
 
 #ifndef NLOPT_H
@@ -68,7 +68,7 @@ typedef void (*nlopt_mfunc)(unsigned m, double *result,
 				 double *gradient, /* NULL if not needed */
 				 void *func_data);
 
-/* A preconditioner, which preconditions v at x to return vpre. 
+/* A preconditioner, which preconditions v at x to return vpre.
    (The meaning of "preconditioning" is algorithm-dependent.) */
 typedef void (*nlopt_precond)(unsigned n, const double *x, const double *v,
 				  double *vpre, void *data);
@@ -76,10 +76,10 @@ typedef void (*nlopt_precond)(unsigned n, const double *x, const double *v,
 typedef enum {
 	 /* Naming conventions:
 
-		NLOPT_{G/L}{D/N}_* 
-		= global/local derivative/no-derivative optimization, 
-			  respectively 
- 
+		NLOPT_{G/L}{D/N}_*
+		= global/local derivative/no-derivative optimization,
+			  respectively
+
 	*_RAND algorithms involve some randomization.
 
 	*_NOSCAL algorithms are *not* scaled to a unit hypercube
@@ -197,9 +197,9 @@ NLOPT_EXTERN(nlopt_opt) nlopt_copy(const nlopt_opt opt);
 NLOPT_EXTERN(nlopt_result) nlopt_optimize(nlopt_opt opt, double *x,
 					 double *opt_f);
 
-NLOPT_EXTERN(nlopt_result) nlopt_set_min_objective(nlopt_opt opt, nlopt_func f, 
+NLOPT_EXTERN(nlopt_result) nlopt_set_min_objective(nlopt_opt opt, nlopt_func f,
 						  void *f_data);
-NLOPT_EXTERN(nlopt_result) nlopt_set_max_objective(nlopt_opt opt, nlopt_func f, 
+NLOPT_EXTERN(nlopt_result) nlopt_set_max_objective(nlopt_opt opt, nlopt_func f,
 						  void *f_data);
 
 NLOPT_EXTERN(nlopt_result) nlopt_set_precond_min_objective(nlopt_opt opt, nlopt_func f, nlopt_precond pre, void *f_data);
@@ -210,12 +210,12 @@ NLOPT_EXTERN(unsigned) nlopt_get_dimension(const nlopt_opt opt);
 
 /* constraints: */
 
-NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds(nlopt_opt opt, 
+NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds(nlopt_opt opt,
 						 const double *lb);
 NLOPT_EXTERN(nlopt_result) nlopt_set_lower_bounds1(nlopt_opt opt, double lb);
-NLOPT_EXTERN(nlopt_result) nlopt_get_lower_bounds(const nlopt_opt opt, 
+NLOPT_EXTERN(nlopt_result) nlopt_get_lower_bounds(const nlopt_opt opt,
 						 double *lb);
-NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds(nlopt_opt opt, 
+NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds(nlopt_opt opt,
 						 const double *ub);
 NLOPT_EXTERN(nlopt_result) nlopt_set_upper_bounds1(nlopt_opt opt, double ub);
 NLOPT_EXTERN(nlopt_result) nlopt_get_upper_bounds(const nlopt_opt opt,
@@ -278,7 +278,7 @@ NLOPT_EXTERN(int) nlopt_get_force_stop(const nlopt_opt opt);
 
 /* more algorithm-specific parameters */
 
-NLOPT_EXTERN(nlopt_result) nlopt_set_local_optimizer(nlopt_opt opt, 
+NLOPT_EXTERN(nlopt_result) nlopt_set_local_optimizer(nlopt_opt opt,
 							const nlopt_opt local_opt);
 
 NLOPT_EXTERN(nlopt_result) nlopt_set_population(nlopt_opt opt, unsigned pop);
@@ -287,12 +287,12 @@ NLOPT_EXTERN(unsigned) nlopt_get_population(const nlopt_opt opt);
 NLOPT_EXTERN(nlopt_result) nlopt_set_vector_storage(nlopt_opt opt, unsigned dim);
 NLOPT_EXTERN(unsigned) nlopt_get_vector_storage(const nlopt_opt opt);
 
-NLOPT_EXTERN(nlopt_result) nlopt_set_default_initial_step(nlopt_opt opt, 
+NLOPT_EXTERN(nlopt_result) nlopt_set_default_initial_step(nlopt_opt opt,
 							 const double *x);
-NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step(nlopt_opt opt, 
+NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step(nlopt_opt opt,
 						 const double *dx);
 NLOPT_EXTERN(nlopt_result) nlopt_set_initial_step1(nlopt_opt opt, double dx);
-NLOPT_EXTERN(nlopt_result) nlopt_get_initial_step(const nlopt_opt opt, 
+NLOPT_EXTERN(nlopt_result) nlopt_get_initial_step(const nlopt_opt opt,
 						 const double *x, double *dx);
 
 /* the following are functions mainly designed to be used internally
@@ -315,7 +315,7 @@ NLOPT_EXTERN(void) nlopt_set_munge(nlopt_opt opt,
 #if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__==3 && __GNUC_MINOR__ > 0))
 #  define NLOPT_DEPRECATED __attribute__((deprecated))
 #else
-#  define NLOPT_DEPRECATED 
+#  define NLOPT_DEPRECATED
 #endif
 
 typedef double (*nlopt_func_old)(int n, const double *x,