/**
 * @file matrixNormalization.hpp
 * @brief Matrix normalization operations for HDF5 matrices
 * @note 2026-03-07 Output datasets now inherit compression level from input datasets
 *         via setCompressionLevel() called before every createDataset() invocation.
 * @details This header file provides implementations for matrix normalization
 * operations on matrices stored in HDF5 format. The implementation includes:
 * 
 * Key features:
 * - Column-wise normalization
 * - Row-wise normalization
 * - Mean centering
 * - Standard deviation scaling
 * - Block-based processing
 * 
 * Supported operations:
 * - Z-score normalization
 * - Mean centering only
 * - Standard deviation scaling only
 * - Custom normalization parameters
 * - Parallel processing support
 * 
 * Performance features:
 * - Cache-friendly algorithms
 * - Block-based computation
 * - Multi-threaded processing
 * - Memory-efficient algorithms
 * - I/O optimization
 * 
 * The implementation uses:
 * - Efficient statistical computations
 * - Block algorithms
 * - HDF5 chunked storage
 * - Parallel I/O
 * - Vectorized operations
 */

#ifndef BIGDATASTATMETH_HDF5_MATRIXSNORMALIZATION_HPP
#define BIGDATASTATMETH_HDF5_MATRIXSNORMALIZATION_HPP

#include "Utilities/system-utils.hpp"

namespace BigDataStatMeth {


    // Function declaration
    template< typename M> inline Eigen::MatrixXd RcppNormalizeColwise ( M  X, bool bc, bool bs, Eigen::MatrixXd normdata );
    template< typename M> inline Eigen::MatrixXd RcppNormalizeColwise ( M  X, bool bc, bool bs);
    
    template< typename M> inline Eigen::MatrixXd RcppNormalizeRowwise ( M  X, bool bc, bool bs, Eigen::MatrixXd normdata );
    template< typename M> inline Eigen::MatrixXd RcppNormalizeRowwise ( M  X, bool bc, bool bs );
    
    template< typename M> inline M RcppNormalize_Data ( M  X, bool bc, bool bs, bool btransp, Eigen::MatrixXd normdata );
    template< typename M> inline M RcppNormalize_Data ( M  X, bool bc, bool bs, bool bRowMajor );
    
    
    
    // Internal call - 
    //   To be used when we have SD and Mean computed for each row/column and we 
    //   use this data to compute normalized matrix
    
    //.. ORIGINAL name ..// Eigen::MatrixXd RcppNormalize_Data_hdf5 ( Eigen::MatrixXd  X, bool bc, bool bs, bool btransp, Eigen::MatrixXd normdata )
    /**
     * @brief Template function for normalizing data with pre-computed statistics
     * @details Normalizes matrix data using pre-computed mean and standard deviation.
     * Supports both row-wise and column-wise normalization.
     * 
     * @tparam M Matrix type (Eigen::MatrixXd or mapped matrix)
     * @param X Input matrix to normalize
     * @param bc Whether to center the data
     * @param bs Whether to scale the data
     * @param btransp Whether to transpose before normalization
     * @param normdata Pre-computed normalization parameters (mean and std)
     * @return Normalized matrix
     */
    template< typename M>
    inline M RcppNormalize_Data ( M  X, bool bc, bool bs, bool btransp, Eigen::MatrixXd normdata )
    {
        
        static_assert(std::is_same<M, Eigen::MatrixXd >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>> >::value ,
                      "Error - type not allowed");
        
        Eigen::MatrixXd rX = X;

        if( btransp == true)
        {
            rX = RcppNormalizeRowwise(X, bc, bs, normdata );

        } else {
            rX = RcppNormalizeColwise(X, bc, bs, normdata );
        }

        return(rX);
    }
    

    /**
     * @brief Column-wise normalization with pre-computed statistics
     * @details Normalizes matrix columns using pre-computed mean and standard deviation.
     * 
     * @tparam M Matrix type (Eigen::MatrixXd or mapped matrix)
     * @param X Input matrix to normalize
     * @param bc Whether to center the data
     * @param bs Whether to scale the data
     * @param normdata Pre-computed normalization parameters (mean and std)
     * @return Column-wise normalized matrix
     */
    template< typename M>
    inline Eigen::MatrixXd RcppNormalizeColwise ( M  X, bool bc, bool bs, Eigen::MatrixXd normdata )
    {
        
        static_assert(std::is_same<M, Eigen::MatrixXd >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>> >::value,
                      "Error - type not allowed");
        
        Eigen::MatrixXd rX = X;
        
        Eigen::RowVectorXd std = normdata.row(1);
        Eigen::RowVectorXd mean = normdata.row(0);
        
        if( bc==true && bs==true )  {
            
            rX = (X.rowwise() - mean).array().rowwise() / std.array();
            
        }   else if (bc == true  && bs==false)   {
            
            rX = (X.rowwise() - mean);
            
        }  else if ( bc == false && bs == true)   {
            
            rX = X.array().rowwise() / std.array();
        }
        
        return(rX);
    }
    
    
    
    template< typename M>
    inline Eigen::MatrixXd RcppNormalizeColwise ( M  X, bool bc, bool bs )
    {
        
        static_assert(std::is_same<M, Eigen::MatrixXd >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>> >::value,
                      "Error - type not allowed");
        
        Eigen::MatrixXd rX = X;
        
        if( bc==true && bs==true )  {
            
            Eigen::RowVectorXd mean = X.colwise().mean();
            Eigen::RowVectorXd std = ((X.rowwise() - mean).array().square().colwise().sum() / (X.rows() - 1)).sqrt();
            rX = (X.rowwise() - mean).array().rowwise() / std.array();
            
        }   else if (bc == true  && bs==false)   {
            
            Eigen::RowVectorXd mean = X.colwise().mean();
            rX = (X.rowwise() - mean);
            
        }  else if ( bc == false && bs == true)   {
            
            Eigen::RowVectorXd mean = X.colwise().mean();
            Eigen::RowVectorXd std = (X.array().square().colwise().sum() / (X.rows() - 1)).sqrt();
            rX = X.array().rowwise() / std.array();
        }
        
        return(rX);
    }
    
    
    template< typename M>
    inline Eigen::MatrixXd RcppNormalizeRowwise ( M  X, bool bc, bool bs )
    {
        
        static_assert(std::is_same<M, Eigen::MatrixXd >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>> >::value,
                      "Error - type not allowed");
        
        Eigen::MatrixXd rX = X;
        
        Eigen::VectorXd mean = X.rowwise().mean();
        if( bc==true && bs==true )  {

            Eigen::VectorXd std = ((X.colwise() - mean).array().square().rowwise().sum() / (X.cols() - 1)).sqrt();
            rX = (X.colwise() - mean).array().colwise() / std.array();
            
        }   else if (bc == true  && bs==false)   {
            rX = (X.colwise() - mean);
            
        }  else if ( bc == false && bs == true)   {
            
            Eigen::VectorXd std = ((X.colwise() - mean).array().square().rowwise().sum() / (X.cols() - 1)).sqrt();
            rX = X.array().colwise() / std.array();
        }
        
        return(rX);
    }
    
    
    template< typename M>
    inline Eigen::MatrixXd RcppNormalizeRowwise ( M  X, bool bc, bool bs, Eigen::MatrixXd normdata )
    {

        static_assert(std::is_same<M, Eigen::MatrixXd >::value ||
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> >::value ||
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>> >::value,
                      "Error - type not allowed");

        Eigen::MatrixXd rX = X;

        Eigen::VectorXd std = normdata.row(1);
        Eigen::VectorXd mean = normdata.row(0);
        
        if( bc==true && bs==true )  {

            rX = (X.colwise() - mean).array().colwise() / std.array();

        }   else if (bc == true  && bs==false)   {

            rX = (X.colwise() - mean);

        }  else if ( bc == false && bs == true)   {

            rX = X.array().colwise() / std.array();
        }

        return(rX);
    }
    

    // Internal call - 
    //   To be used when we don't have SD and Mean computed and we need
    //   to compute this data to get normalized matrix
    template< typename M>
    inline M RcppNormalize_Data ( M  X, bool bc, bool bs, bool bRowMajor )
    {
        static_assert(std::is_same<M, Eigen::MatrixXd >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> >::value || 
                      std::is_same<M, Eigen::Map< Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>> >::value,
                      "Error - type not allowed");
        
        
        X = RcppNormalizeColwise(X, bc, bs );
        
        return(X); // Return data as initial type
    };


    inline Eigen::MatrixXd RcppNormalize_Data_R_hdf5( Eigen::MatrixXd  X, bool bc, bool bs,
                                               bool btransp, Eigen::MatrixXd normdata)
    {
        Eigen::MatrixXd rX;

        if( btransp == true) {
            if( bc==true && bs==true )  {
                rX = (X.colwise() - normdata.row(0).transpose() ).array().colwise() / normdata.row(1).transpose().array();
            }   else if (bc == true  && bs==false)   {
                rX = (X.colwise() - normdata.row(0).transpose());
            }  else if ( bc == false && bs == true)   {
                rX = X.array().colwise() / normdata.row(1).transpose().array();
            } else {
                rX = X;   // bc=FALSE, bs=FALSE: identity — return unchanged
            }
        } else {
            if( bc==true && bs==true )  {
                rX = (X.rowwise() - normdata.row(0)).array().rowwise() / normdata.row(1).array();
            } else if (bc == true  && bs==false) {
                rX = (X.rowwise() - normdata.row(0));
            }  else if ( bc == false && bs == true)   {
                rX = X.array().rowwise() / normdata.row(1).array();
            } else {
                rX = X;   // bc=FALSE, bs=FALSE: identity — return unchanged
            }
        }

        return(rX);
    }
    
    
    
    /**
     * @brief HDF5 matrix normalization with pre-computed statistics
     * @details Normalizes an HDF5 matrix dataset using pre-computed statistics.
     * Supports both row-wise and column-wise normalization.
     *
     * When the input matrix fits within 20% of available RAM and @p bparal is
     * false, a single HDF5 read + vectorised Eigen expression + single write is
     * used (PATH 1). Otherwise, block-wise streaming with optional OpenMP
     * parallelism is applied (PATH 2). Setting @p bparal = true forces PATH 2
     * regardless of matrix size, giving the caller full control over thread count.
     *
     * @param dsA       Input matrix dataset
     * @param dsNormal  Output normalized dataset
     * @param datanormal Pre-computed normalization parameters (mean and std)
     * @param wsize     Block size for processing
     * @param bc        Whether to center the data
     * @param bs        Whether to scale the data
     * @param bbyrows   Whether to normalize by rows
     * @param bcorrected Whether to use corrected standard deviation
     * @param bparal    Enable OpenMP parallelism in PATH 2 (default false).
     *                  When true, forces block-wise streaming regardless of
     *                  matrix size, so thread count is respected.
     * @param threads   Number of OpenMP threads (NULL = system default,
     *                  always capped by OMP_THREAD_LIMIT for CRAN compliance).
     *                  Ignored when bparal is false.
     */
    /*.. 2026/06/01 .. Parallel execution
    inline void RcppNormalizeHdf5( BigDataStatMeth::hdf5Dataset* dsA,
                                          BigDataStatMeth::hdf5Dataset* dsNormal,
                                          Eigen::MatrixXd datanormal,
                                          Rcpp::Nullable<int> wsize, 
                                          bool bc, bool bs, bool bbyrows, bool bcorrected)
    */
    inline void RcppNormalizeHdf5( BigDataStatMeth::hdf5Dataset* dsA,
                                   BigDataStatMeth::hdf5Dataset* dsNormal,
                                   Eigen::MatrixXd datanormal,
                                   Rcpp::Nullable<int> wsize, 
                                   bool bc, bool bs, bool bbyrows, bool bcorrected,
                                   bool bparal = false,
                                   Rcpp::Nullable<int> threads = R_NilValue)
    {
        
        try{
            
            std::vector<hsize_t> stride = {1, 1},
                                 block = {1, 1};
            
            bool bgetTransposed;
            hsize_t nrows = dsA->nrows(), 
                    ncols = dsA->ncols(), 
                    nRowsCols, blocksize;
            double correction = 1;
            
            blocksize = BigDataStatMeth::get_block_size(wsize, nrows, ncols);
            
            if(bbyrows == false ) {
                nRowsCols = nrows;
                bgetTransposed = true;
            } else {
                nRowsCols = ncols;
                bgetTransposed = false;
            }
            
            if(bcorrected){
                if(bbyrows == false) {
                    correction = 1/sqrt(ncols - 1 );
                } else {
                    correction = 1/sqrt(nrows - 1 );
                }    
            }
            
            // PRELOAD
            // If A fits within 20% of available RAM, read once, normalize with
            // a single vectorised Eigen expression, write once.  Eliminates the
            // per-block HDF5 gzip decompress + compress overhead of block-wise
            const double mem_MB   = static_cast<double>(nrows) * ncols * 8.0 / (1024.0 * 1024.0);
            const double avail_MB = std::max(512.0, static_cast<double>(getAvailableMemoryMB()));
            
            //.. 2026/06/01 omp paral..// if (mem_MB <= avail_MB * 0.20) 
            if (!bparal && mem_MB <= avail_MB * 0.20)
            {
                std::vector<double> vdFull( static_cast<std::size_t>(nrows) * ncols);
                dsA->readDatasetBlock( {0, 0}, {nrows, ncols}, stride, block, vdFull.data());
                
                Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> A_full(vdFull.data(), static_cast<int>(nrows), static_cast<int>(ncols));
                
                A_full = RcppNormalize_Data_R_hdf5( A_full, bc, bs, bgetTransposed, datanormal);
                
                if (bcorrected) A_full *= correction;
                
                dsNormal->writeRowMajorDatasetBlock( A_full, {0, 0}, {nrows, ncols}, stride, block);
                
                return void();
            }
            
            
            // block-wise (matrix too large for preload) 
            //.. 20260224 ..// for(hsize_t i=0; i*blocksize <= nRowsCols ; i++)
        /*..  2026/06/01 omp paral ..
            for(hsize_t i=0; i*blocksize < nRowsCols ; i++)
            {
                std::vector<hsize_t> offset, 
                count;
                hsize_t sizetoread = 0;
                
                if( (i+1) * blocksize < nRowsCols ) {
                    sizetoread = blocksize;
                } else {
                    sizetoread = nRowsCols - ( i * blocksize );
                }
                
                if(bbyrows == false) {
                    offset = { i*blocksize, 0 };
                    count = { sizetoread, ncols };    
                } else {
                    offset = {  0, i*blocksize };
                    count = { nrows, sizetoread };
                }
                
                // Normalize and write data
                std::vector<double> vdA( count[0] * count[1] ); 
                dsA->readDatasetBlock( {offset[0], offset[1]}, {count[0], count[1]}, stride, block, vdA.data() );
                Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> X (vdA.data(), count[0], count[1] );
                
                if(bbyrows == false) {
                    X = BigDataStatMeth::RcppNormalize_Data_R_hdf5(X, bc, bs, bgetTransposed, datanormal.block(0,offset[0], 2, count[0]));    
                } else {
                    X = BigDataStatMeth::RcppNormalize_Data_R_hdf5(X, bc, bs, bgetTransposed, datanormal.block(0,offset[1], 2, count[1]));
                }
                
                if(bcorrected){
                    X = X*correction;
                }
                
                dsNormal->writeRowMajorDatasetBlock( X, offset, count, stride, block);
                
            }
        */
        
            const hsize_t nBlocks = (nRowsCols + blocksize - 1) / blocksize;
            
            #pragma omp parallel for num_threads( get_threads(bparal, threads) ) schedule(dynamic)
            for(hsize_t i = 0; i < nBlocks; i++)
            {
                std::vector<hsize_t> offset, count;
                hsize_t sizetoread;
                
                if( (i + 1) * blocksize < nRowsCols ) {
                    sizetoread = blocksize;
                } else {
                    sizetoread = nRowsCols - ( i * blocksize );
                }
                
                if(bbyrows == false) {
                    offset = { i * blocksize, 0 };
                    count  = { sizetoread, ncols };
                } else {
                    offset = { 0, i * blocksize };
                    count  = { nrows, sizetoread };
                }
                
                std::vector<double> vdA( count[0] * count[1] );
                #pragma omp critical(accessFile)
                { 
                    dsA->readDatasetBlock( {offset[0], offset[1]}, {count[0], count[1]}, stride, block, vdA.data() ); 
                }
                
                Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> X(vdA.data(), count[0], count[1]);
                
                if(bbyrows == false) {
                    X = BigDataStatMeth::RcppNormalize_Data_R_hdf5(X, bc, bs, bgetTransposed, datanormal.block(0, offset[0], 2, count[0]));
                } else {
                    X = BigDataStatMeth::RcppNormalize_Data_R_hdf5(X, bc, bs, bgetTransposed, datanormal.block(0, offset[1], 2, count[1]));
                }
                
                if(bcorrected) { X = X * correction; }
                
                #pragma omp critical(accessFile)
                { 
                    dsNormal->writeRowMajorDatasetBlock( X, offset, count, stride, block); 
                }
            }
            
        }catch( H5::FileIException& error ) {
            throw std::runtime_error("c++ exception RcppNormalizeHdf5 (File IException)");
        } catch( H5::DataSetIException& error ) { // catch failure caused by the DataSet operations
            throw std::runtime_error("c++ exception RcppNormalizeHdf5 (DataSet IException)");
        } catch( H5::DataSpaceIException& error ) { // catch failure caused by the DataSpace operations
            throw std::runtime_error("c++ exception RcppNormalizeHdf5 (DataSpace IException)");
        } catch( H5::DataTypeIException& error ) { // catch failure caused by the DataSpace operations
            throw std::runtime_error("c++ exception RcppNormalizeHdf5 (DataType IException)");
        } catch(std::exception &ex) {
            throw std::runtime_error(std::string("C++ exception RcppNormalizeHdf5: ") + ex.what());
        } catch (...) {
            throw std::runtime_error("C++ exception RcppNormalizeHdf5 (unknown reason)");
        }
        
        return void();
    }
    
    
    
    // inline void RcppTypifyNormalizeHdf5( std::string filename, std::string strgroup, std::string strdataset,
    //                                             bool bc, bool bs, bool bbyrows)
    inline void RcppNormalizeHdf5( BigDataStatMeth::hdf5Dataset* dsA,
                                                bool bc, bool bs, bool bbyrows)
    {
        
        
        
        try{

            std::unique_ptr<BigDataStatMeth::hdf5Dataset> dsmean(nullptr);
            std::unique_ptr<BigDataStatMeth::hdf5Dataset> dssd(nullptr);
            std::unique_ptr<BigDataStatMeth::hdf5Dataset> dsNormal(nullptr);
            
            Rcpp::Nullable<int> wsize = R_NilValue;
            Eigen::MatrixXd datanormal;
            hsize_t nrows, ncols;
            std::string strgroupout;
            bool bcorrected = false;
            
            nrows = dsA->nrows();
            ncols = dsA->ncols();
            
            strgroupout = "NORMALIZED/" + dsA->getGroupName();
            std::string strdatasetmean = "mean." + dsA->getDatasetName();
            std::string strdatasetsd = "sd." + dsA->getDatasetName();
            
            // Define blocksize atending number of elements in rows and cols
            if( bbyrows == false) {
                datanormal = Eigen::MatrixXd::Zero( 2, nrows);
                get_HDF5_mean_sd_by_column( dsA, datanormal, true, true, wsize);
            } else {
                datanormal = Eigen::MatrixXd::Zero( 2, ncols);
                get_HDF5_mean_sd_by_row( dsA, datanormal, true, true, wsize);
            }
            
            // dsmean = new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, strdatasetmean, true);
            dsmean.reset(new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, strdatasetmean, true));
            dsmean->inheritCompressionLevel(dsA->getCompressionLevel());
            dsmean->createDataset( datanormal.cols(), 1, "real");
            dsmean->writeDataset( Rcpp::wrap(datanormal.row(0)) );
            dsmean.reset(nullptr);
            // delete dsmean; dsmean = nullptr;
            
            // dssd = new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, strdatasetsd, true);
            dssd.reset(new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, strdatasetsd, true));
            dssd->inheritCompressionLevel(dsA->getCompressionLevel());
            dssd->createDataset( datanormal.cols(), 1, "real");
            dssd->writeDataset( Rcpp::wrap(datanormal.row(1)) );
            dssd.reset(nullptr);
            // delete dssd; dssd = nullptr;
            
            // dsNormal = new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, dsA->getDatasetName(), true);
            dsNormal.reset(new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, dsA->getDatasetName(), true));
            dsNormal->inheritCompressionLevel(dsA->getCompressionLevel());
            dsNormal->createDataset( dsA, "real");
            
            if( dsA->getDatasetptr() != nullptr && dsNormal->getDatasetptr() != nullptr){
                BigDataStatMeth::RcppNormalizeHdf5( dsA, dsNormal.get(), datanormal, wsize, bc, bs, bbyrows, bcorrected);
            } else {
                Rcpp::stop("C++ exception bdNormalize_hdf5: failed to create output dataset");
            }
            
            // delete dsNormal; dsNormal = nullptr;
            
        } catch( H5::FileIException& error ) {
            throw std::runtime_error("c++ exception RcppNormalizeHdf5_F (File IException)");
        } catch( H5::DataSetIException& error ) { // catch failure caused by the DataSet operations
            throw std::runtime_error("c++ exception RcppNormalizeHdf5_F (DataSet IException)");
        } catch( H5::DataSpaceIException& error ) { // catch failure caused by the DataSpace operations
            throw std::runtime_error("c++ exception RcppNormalizeHdf5_F (DataSpace IException)");
        } catch( H5::DataTypeIException& error ) { // catch failure caused by the DataSpace operations
            throw std::runtime_error("c++ exception RcppNormalizeHdf5_F (DataType IException)");
        } catch(std::exception &ex) {
            throw std::runtime_error(std::string("C++ exception RcppNormalizeHdf5_F: ") + ex.what());
        } catch (...) {
            throw std::runtime_error("C++ exception RcppNormalizeHdf5_F (unknown reason)");
        }
        
        return void();
    }
    
    
    
    // inline void RcppTypifyNormalizeHdf5( std::string filename, std::string strgroup, std::string strdataset,
    //                                             bool bc, bool bs, bool bbyrows)
    inline void RcppTypifyNormalizeHdf5( BigDataStatMeth::hdf5Dataset* dsA,
                                                bool bc, bool bs, bool bbyrows)
    {
        
        try{

            std::unique_ptr<BigDataStatMeth::hdf5Dataset> dsmean(nullptr);
            std::unique_ptr<BigDataStatMeth::hdf5Dataset> dssd(nullptr);
            std::unique_ptr<BigDataStatMeth::hdf5Dataset> dsNormal(nullptr);
            
            Rcpp::Nullable<int> wsize = R_NilValue;
            Eigen::MatrixXd datanormal;
            hsize_t nrows, ncols;
            std::string strgroupout;
            bool corrected = true;
            
            nrows = dsA->nrows();
            ncols = dsA->ncols();
            
            strgroupout = "NORMALIZED_T/" + dsA->getGroupName();
            std::string strgroupout_ms = strgroupout + "/mean_sd";
            std::string strdatasetmean = "mean." + dsA->getDatasetName();
            std::string strdatasetsd = "sd." + dsA->getDatasetName();
            
            // Define blocksize atending number of elements in rows and cols
            if( bbyrows == false) {
                datanormal = Eigen::MatrixXd::Zero(2,nrows);
                get_HDF5_mean_sd_by_column( dsA, datanormal, true, true, wsize);
            } else {
                datanormal = Eigen::MatrixXd::Zero(2,ncols);
                get_HDF5_mean_sd_by_row( dsA, datanormal, true, true, wsize);
            }
            
            // dsmean = new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout_ms, strdatasetmean, true);
            dsmean.reset(new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout_ms, strdatasetmean, true));
            dsmean->inheritCompressionLevel(dsA->getCompressionLevel());
            dsmean->createDataset( datanormal.cols(), 1, "real");
            dsmean->writeDataset( Rcpp::wrap(datanormal.row(0)) );
            // delete dsmean; dsmean = nullptr;
            
            // dssd = new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout_ms, strdatasetsd, true);
            dssd.reset(new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout_ms, strdatasetsd, true));
            dssd->inheritCompressionLevel(dsA->getCompressionLevel());
            dssd->createDataset( datanormal.cols(), 1, "real");
            dssd->writeDataset( Rcpp::wrap(datanormal.row(1)) );
            // delete dssd; dssd = nullptr;
            
            // dsNormal = new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, dsA->getDatasetName(), true);
            dsNormal.reset(new BigDataStatMeth::hdf5Dataset(dsA->getFullPath(), strgroupout, dsA->getDatasetName(), true));
            dsNormal->inheritCompressionLevel(dsA->getCompressionLevel());
            dsNormal->createDataset( dsA, "real");
            
            if( dsA->getDatasetptr() != nullptr && dsNormal->getDatasetptr() != nullptr){
                BigDataStatMeth::RcppNormalizeHdf5( dsA, dsNormal.get(), datanormal, wsize, bc, bs, bbyrows, corrected);    
            }
            // delete dsNormal; dsNormal = nullptr;
            
        } catch( H5::FileIException& error ) {
            throw std::runtime_error("c++ exception RcppTypifyNormalizeHdf5 (File IException)");
        } catch( H5::DataSetIException& error ) { // catch failure caused by the DataSet operations
            throw std::runtime_error("c++ exception RcppTypifyNormalizeHdf5 (DataSet IException)");
        } catch( H5::DataSpaceIException& error ) { // catch failure caused by the DataSpace operations
            throw std::runtime_error("c++ exception RcppTypifyNormalizeHdf5 (DataSpace IException)");
        } catch( H5::DataTypeIException& error ) { // catch failure caused by the DataSpace operations
            throw std::runtime_error("c++ exception RcppTypifyNormalizeHdf5 (DataType IException)");
        } catch(std::exception &ex) {
            throw std::runtime_error(std::string("C++ exception RcppTypifyNormalizeHdf5: ") + ex.what());
        } catch (...) {
            throw std::runtime_error("C++ exception RcppTypifyNormalizeHdf5 (unknown reason)");
        }
        
        return void();
    }
    
    
    

}

#endif // BIGDATASTATMETH_HDF5_MATRIXSNORMALIZATION_HPP

