void test_scalar_generic(int nfft)
{
typedef typename FFT<T>::Complex Complex;
typedef typename FFT<T>::Scalar Scalar;
typedef typename VectorType<Container, Scalar>::type ScalarVector;
typedef typename VectorType<Container, Complex>::type ComplexVector;
FFT<T> fft;
ScalarVector tbuf(nfft);
ComplexVector freqBuf;
for (int k = 0; k < nfft; ++k)
tbuf[k] = (T)(rand() / (double)RAND_MAX - .5);
// make sure it DOESN'T give the right full spectrum answer
// if we've asked for half-spectrum
fft.SetFlag(fft.HalfSpectrum);
fft.fwd(freqBuf, tbuf);
VERIFY((size_t)freqBuf.size() == (size_t)((nfft >> 1) + 1));
VERIFY(fft_rmse(freqBuf, tbuf) < test_precision<T>()); // gross check
fft.ClearFlag(fft.HalfSpectrum);
fft.fwd(freqBuf, tbuf);
VERIFY((size_t)freqBuf.size() == (size_t)nfft);
VERIFY(fft_rmse(freqBuf, tbuf) < test_precision<T>()); // gross check
if (nfft & 1)
return; // odd FFTs get the wrong size inverse FFT
ScalarVector tbuf2;
fft.inv(tbuf2, freqBuf);
VERIFY(dif_rmse(tbuf, tbuf2) < test_precision<T>()); // gross check
// verify that the Unscaled flag takes effect
ScalarVector tbuf3;
fft.SetFlag(fft.Unscaled);
fft.inv(tbuf3, freqBuf);
for (int k = 0; k < nfft; ++k)
tbuf3[k] *= T(1. / nfft);
// for (size_t i=0;i<(size_t) tbuf.size();++i)
// cout << "freqBuf=" << freqBuf[i] << " in2=" << tbuf3[i] << " - in=" << tbuf[i] << " => " << (tbuf3[i] - tbuf[i] ) << endl;
VERIFY(dif_rmse(tbuf, tbuf3) < test_precision<T>()); // gross check
// verify that ClearFlag works
fft.ClearFlag(fft.Unscaled);
fft.inv(tbuf2, freqBuf);
VERIFY(dif_rmse(tbuf, tbuf2) < test_precision<T>()); // gross check
}
void bench(int nfft,bool fwd,bool unscaled=false, bool halfspec=false)
{
typedef typename NumTraits<T>::Real Scalar;
typedef typename std::complex<Scalar> Complex;
int nits = NDATA/nfft;
vector<T> inbuf(nfft);
vector<Complex > outbuf(nfft);
FFT< Scalar > fft;
if (unscaled) {
fft.SetFlag(fft.Unscaled);
cout << "unscaled ";
}
if (halfspec) {
fft.SetFlag(fft.HalfSpectrum);
cout << "halfspec ";
}
std::fill(inbuf.begin(),inbuf.end(),0);
fft.fwd( outbuf , inbuf);
BenchTimer timer;
timer.reset();
for (int k=0;k<8;++k) {
timer.start();
if (fwd)
for(int i = 0; i < nits; i++)
fft.fwd( outbuf , inbuf);
else
for(int i = 0; i < nits; i++)
fft.inv(inbuf,outbuf);
timer.stop();
}
cout << nameof<Scalar>() << " ";
double mflops = 5.*nfft*log2((double)nfft) / (1e6 * timer.value() / (double)nits );
if ( NumTraits<T>::IsComplex ) {
cout << "complex";
}else{
cout << "real ";
mflops /= 2;
}
if (fwd)
cout << " fwd";
else
cout << " inv";
cout << " NFFT=" << nfft << " " << (double(1e-6*nfft*nits)/timer.value()) << " MS/s " << mflops << "MFLOPS\n";
}
void test_return_by_value(int len)
{
VectorXf in;
VectorXf in1;
in.setRandom( len );
VectorXcf out1,out2;
FFT<float> fft;
fft.SetFlag(fft.HalfSpectrum );
fft.fwd(out1,in);
out2 = fft.fwd(in);
VERIFY( (out1-out2).norm() < test_precision<float>() );
in1 = fft.inv(out1);
VERIFY( (in1-in).norm() < test_precision<float>() );
}
void test_complex_generic(int nfft)
{
typedef typename FFT<T>::Complex Complex;
typedef typename VectorType<Container,Complex>::type ComplexVector;
FFT<T> fft;
ComplexVector inbuf(nfft);
ComplexVector outbuf;
ComplexVector buf3;
for (int k=0;k<nfft;++k)
inbuf[k]= Complex( (T)(rand()/(double)RAND_MAX - .5), (T)(rand()/(double)RAND_MAX - .5) );
fft.fwd( outbuf , inbuf);
VERIFY( fft_rmse(outbuf,inbuf) < test_precision<T>() );// gross check
fft.inv( buf3 , outbuf);
VERIFY( dif_rmse(inbuf,buf3) < test_precision<T>() );// gross check
// verify that the Unscaled flag takes effect
ComplexVector buf4;
fft.SetFlag(fft.Unscaled);
fft.inv( buf4 , outbuf);
for (int k=0;k<nfft;++k)
buf4[k] *= T(1./nfft);
VERIFY( dif_rmse(inbuf,buf4) < test_precision<T>() );// gross check
// verify that ClearFlag works
fft.ClearFlag(fft.Unscaled);
fft.inv( buf3 , outbuf);
VERIFY( dif_rmse(inbuf,buf3) < test_precision<T>() );// gross check
}
void fft_Z(MultiArray<complex<float>, 3> &a) {
FFT<float> fft;
int nz = a.dims()[2];
for (int x = 0; x < a.dims()[0]; x++) {
for (int y = 0; y < a.dims()[1]; y++) {
VectorXcf fft_in = a.slice<1>({x,y,0},{0,0,nz}).asArray();
VectorXcf fft_out(nz);
fft.fwd(fft_out, fft_in);
a.slice<1>({x,y,0},{0,0,nz}).asArray() = fft_out;
}
}
}
void fft_Y(MultiArray<complex<float>, 3> &a) {
FFT<float> fft;
int ny = a.dims()[1];
for (int z = 0; z < a.dims()[2]; z++) {
for (int x = 0; x < a.dims()[0]; x++) {
VectorXcf fft_in = a.slice<1>({x,0,z},{0,ny,0}).asArray();
VectorXcf fft_out(ny);
fft.fwd(fft_out, fft_in);
a.slice<1>({x,0,z},{0,ny,0}).asArray() = fft_out;
}
}
}
void fft_X(MultiArray<complex<float>, 3> &a) {
FFT<float> fft;
int nx = a.dims()[0];
for (int z = 0; z < a.dims()[2]; z++) {
for (int y = 0; y < a.dims()[1]; y++) {
VectorXcf fft_in = a.slice<1>({0,y,z},{nx,0,0}).asArray();
VectorXcf fft_out(nx);
fft.fwd(fft_out, fft_in);
a.slice<1>({0,y,z},{nx,0,0}).asArray() = fft_out;
}
}
}
void fwd_inv(size_t nfft)
{
typedef typename NumTraits<T_freq>::Real Scalar;
vector<T_time> timebuf(nfft);
RandomFill(timebuf);
vector<T_freq> freqbuf;
static FFT<Scalar> fft;
fft.fwd(freqbuf,timebuf);
vector<T_time> timebuf2;
fft.inv(timebuf2,freqbuf);
long double rmse = mag2(timebuf - timebuf2) / mag2(timebuf);
cout << "roundtrip rmse: " << rmse << endl;
}
int main(int argc, char*argv[])
{
// create FFT class implementation
// each implementation has its own plan map.
FFT<double> fft;
// create some data
size_t N = 1024 * 1024;
std::vector<complex<double>,fftalloc<complex<double> > > data(N);
std::vector<complex<double>,fftalloc<complex<double> > > dataFourier(N);
std::vector<complex<double>,fftalloc<complex<double> > > dataCalc(N);
std::cout << "1) --- original data ---" << endl;
for(std::vector<complex<double> >::size_type i = 0; i != data.size(); i++) {
data[i] = polar(sin(double(i)/N * M_PI * 2), 0.0);
// std::cout << i << data[i] << endl;
}
// fft
std::cout << "2) --- fft data ---" << endl;
fft.fwd(dataFourier, data);
std::cout << "speed / ms: " << fft.speed()/1000 << endl;
// for(std::vector<complex<double> >::size_type i = 0; i != dataFourier.size(); i++) {
// std::cout << i << dataFourier[i] << endl;
// }
// ifft
std::cout << "3) --- ifft data ---" << endl;
fft.inv(dataCalc, dataFourier);
std::cout << "speed / ms: " << fft.speed()/1000 << endl;
// for(std::vector<complex<double> >::size_type i = 0; i != dataCalc.size(); i++) {
// std::cout << i << dataCalc[i] << endl;
// }
// std::cout << "4) --- comparison data ---" << endl;
// for(std::vector<complex<double> >::size_type i = 0; i != dataCalc.size(); i++) {
// std::cout << i << data[i] - dataCalc[i] << endl;
// }
std::getchar();
return 0;
}
void TMSI::run()
{
while(m_bIsRunning)
{
//std::cout<<"TMSI::run(s)"<<std::endl;
//pop matrix only if the producer thread is running
if(m_pTMSIProducer->isRunning())
{
MatrixXf matValue = m_pRawMatrixBuffer_In->pop();
// Set Beep trigger (if activated)
if(m_bBeepTrigger && m_qTimerTrigger.elapsed() >= m_iTriggerInterval)
{
QFuture<void> future = QtConcurrent::run(Beep, 450, 700);
//Set trigger in received data samples - just for one sample, so that this event is easy to detect
matValue(136, m_iSamplesPerBlock-1) = 252;
m_qTimerTrigger.restart();
Q_UNUSED(future);
}
// Set keyboard trigger (if activated and !=0)
if(m_bUseKeyboardTrigger && m_iTriggerType!=0)
matValue(136, m_iSamplesPerBlock-1) = m_iTriggerType;
//Write raw data to fif file
if(m_bWriteToFile)
m_pOutfid->write_raw_buffer(matValue.cast<double>(), m_cals);
// TODO: Use preprocessing if wanted by the user
if(m_bUseFiltering)
{
MatrixXf temp = matValue;
matValue = matValue - m_matOldMatrix;
m_matOldMatrix = temp;
// //Check filter class - will be removed in the future - testing purpose only!
// FilterTools* filterObject = new FilterTools();
// //kaiser window testing
// qint32 numberCoeff = 51;
// QVector<float> impulseResponse(numberCoeff);
// filterObject->createDynamicFilter(QString('LP'), numberCoeff, (float)0.3, impulseResponse);
// ofstream outputFileStream("mne_x_plugins/resources/tmsi/filterToolsTest.txt", ios::out);
// outputFileStream << "impulseResponse:\n";
// for(int i=0; i<impulseResponse.size(); i++)
// outputFileStream << impulseResponse[i] << " ";
// outputFileStream << endl;
// //convolution testing
// QVector<float> in (12, 2);
// QVector<float> kernel (4, 2);
// QVector<float> out = filterObject->convolve(in, kernel);
// outputFileStream << "convolution result:\n";
// for(int i=0; i<out.size(); i++)
// outputFileStream << out[i] << " ";
// outputFileStream << endl;
}
// TODO: Perform a fft if wanted by the user
if(m_bUseFFT)
{
QElapsedTimer timer;
timer.start();
FFT<float> fft;
Matrix<complex<float>, 138, 16> freq;
for(qint32 i = 0; i < matValue.rows(); ++i)
fft.fwd(freq.row(i), matValue.row(i));
// cout<<"FFT postprocessing done in "<<timer.nsecsElapsed()<<" nanosec"<<endl;
// cout<<"matValue before FFT:"<<endl<<matValue<<endl;
// cout<<"freq after FFT:"<<endl<<freq<<endl;
// matValue = freq.cwiseAbs();
// cout<<"matValue after FFT:"<<endl<<matValue<<endl;
}
//Change values of the trigger channel for better plotting - this change is not saved in the produced fif file
if(m_iNumberOfChannels>137)
{
for(int i = 0; i<matValue.row(137).cols(); i++)
{
// Left keyboard or capacitive
if(matValue.row(136)[i] == 254)
matValue.row(136)[i] = 4000;
// Right keyboard
if(matValue.row(136)[i] == 253)
matValue.row(136)[i] = 8000;
// Beep
if(matValue.row(136)[i] == 252)
matValue.row(136)[i] = 2000;
}
}
//emit values to real time multi sample array
for(qint32 i = 0; i < matValue.cols(); ++i)
m_pRMTSA_TMSI->data()->setValue(matValue.col(i).cast<double>());
// Reset keyboard trigger
m_iTriggerType = 0;
}
}
//Close the fif output stream
if(m_bWriteToFile)
m_pOutfid->finish_writing_raw();
//std::cout<<"EXITING - TMSI::run()"<<std::endl;
}
void UnbiasedSquaredPhaseLagIndex::compute(ConnectivitySettings::IntermediateTrialData& inputData,
QVector<QPair<int,MatrixXcd> >& vecPairCsdSum,
QVector<QPair<int,MatrixXd> >& vecPairCsdImagSignSum,
QMutex& mutex,
int iNRows,
int iNFreqs,
int iNfft,
const QPair<MatrixXd, VectorXd>& tapers)
{
if(inputData.vecPairCsdImagSign.size() == iNRows) {
//qDebug() << "UnbiasedSquaredPhaseLagIndex::compute - vecPairCsdImagSign was already computed for this trial.";
return;
}
inputData.vecPairCsdImagSign.clear();
int i,j;
// Calculate tapered spectra if not available already
// This code was copied and changed modified Utils/Spectra since we do not want to call the function due to time loss.
if(inputData.vecTapSpectra.size() != iNRows) {
inputData.vecTapSpectra.clear();
RowVectorXd vecInputFFT, rowData;
RowVectorXcd vecTmpFreq;
MatrixXcd matTapSpectrum(tapers.first.rows(), iNFreqs);
QVector<Eigen::MatrixXcd> vecTapSpectra;
FFT<double> fft;
fft.SetFlag(fft.HalfSpectrum);
for (i = 0; i < iNRows; ++i) {
// Substract mean
rowData.array() = inputData.matData.row(i).array() - inputData.matData.row(i).mean();
// Calculate tapered spectra if not available already
for(j = 0; j < tapers.first.rows(); j++) {
vecInputFFT = rowData.cwiseProduct(tapers.first.row(j));
// FFT for freq domain returning the half spectrum and multiply taper weights
fft.fwd(vecTmpFreq, vecInputFFT, iNfft);
matTapSpectrum.row(j) = vecTmpFreq * tapers.second(j);
}
inputData.vecTapSpectra.append(matTapSpectrum);
}
}
// Compute CSD
if(inputData.vecPairCsd.isEmpty()) {
double denomCSD = sqrt(tapers.second.cwiseAbs2().sum()) * sqrt(tapers.second.cwiseAbs2().sum()) / 2.0;
bool bNfftEven = false;
if (iNfft % 2 == 0){
bNfftEven = true;
}
MatrixXcd matCsd = MatrixXcd(iNRows, iNFreqs);
for (i = 0; i < iNRows; ++i) {
for (j = i; j < iNRows; ++j) {
// Compute CSD (average over tapers if necessary)
matCsd.row(j) = inputData.vecTapSpectra.at(i).cwiseProduct(inputData.vecTapSpectra.at(j).conjugate()).colwise().sum() / denomCSD;
// Divide first and last element by 2 due to half spectrum
matCsd.row(j)(0) /= 2.0;
if(bNfftEven) {
matCsd.row(j).tail(1) /= 2.0;
}
}
inputData.vecPairCsd.append(QPair<int,MatrixXcd>(i,matCsd));
inputData.vecPairCsdImagSign.append(QPair<int,MatrixXd>(i,matCsd.imag().cwiseSign()));
}
mutex.lock();
if(vecPairCsdSum.isEmpty()) {
vecPairCsdSum = inputData.vecPairCsd;
vecPairCsdImagSignSum = inputData.vecPairCsdImagSign;
} else {
for (int j = 0; j < vecPairCsdSum.size(); ++j) {
vecPairCsdSum[j].second += inputData.vecPairCsd.at(j).second;
vecPairCsdImagSignSum[j].second += inputData.vecPairCsdImagSign.at(j).second;
}
}
mutex.unlock();
} else {
if(inputData.vecPairCsdImagSign.isEmpty()) {
for (i = 0; i < inputData.vecPairCsd.size(); ++i) {
inputData.vecPairCsdImagSign.append(QPair<int,MatrixXd>(i,inputData.vecPairCsd.at(i).second.imag().cwiseSign()));
}
mutex.lock();
if(vecPairCsdImagSignSum.isEmpty()) {
vecPairCsdImagSignSum = inputData.vecPairCsdImagSign;
} else {
for (int j = 0; j < vecPairCsdImagSignSum.size(); ++j) {
vecPairCsdImagSignSum[j].second += inputData.vecPairCsdImagSign.at(j).second;
}
}
mutex.unlock();
}
}
}
boolean CAlgorithmHilbertTransform::process(void)
{
uint32 l_ui32ChannelCount = ip_pMatrix->getDimensionSize(0);
uint32 l_ui32SamplesPerChannel = ip_pMatrix->getDimensionSize(1);
IMatrix* l_pInputMatrix = ip_pMatrix;
IMatrix* l_pOutputEnvelopeMatrix = op_pEnvelopeMatrix;
IMatrix* l_pOutputPhaseMatrix = op_pPhaseMatrix;
FFT< double, internal::kissfft_impl<double > > fft;
if(this->isInputTriggerActive(OVP_Algorithm_HilbertTransform_InputTriggerId_Process))
{
//Computing Hilbert transform for all channels
for(uint32 channel=0; channel<l_ui32ChannelCount; channel++)
{
//Initialization of buffer vectors
m_vecXcdSignalBuffer = RowVectorXcd::Zero(l_ui32SamplesPerChannel);
m_vecXcdSignalFourier = RowVectorXcd::Zero(l_ui32SamplesPerChannel);
//Initialization of vector h used to compute analytic signal
m_vecXdHilbert.resize(l_ui32SamplesPerChannel);
m_vecXdHilbert(0) = 1.0;
if(l_ui32SamplesPerChannel%2 == 0)
{
m_vecXdHilbert(l_ui32SamplesPerChannel/2) = 1.0;
for(uint32 i=1; i<l_ui32SamplesPerChannel/2; i++)
{
m_vecXdHilbert(i) = 2.0;
}
for(uint32 i=(l_ui32SamplesPerChannel/2)+1; i<l_ui32SamplesPerChannel; i++)
{
m_vecXdHilbert(i) = 0.0;
}
}
else
{
m_vecXdHilbert((l_ui32SamplesPerChannel/2)+1) = 1.0;
for(uint32 i=1; i<(l_ui32SamplesPerChannel/2)+1; i++)
{
m_vecXdHilbert(i) = 2.0;
}
for(uint32 i=(l_ui32SamplesPerChannel/2)+2; i<l_ui32SamplesPerChannel; i++)
{
m_vecXdHilbert(i) = 0.0;
}
}
//Copy input signal chunk on buffer
for(uint32 samples=0; samples<l_ui32SamplesPerChannel;samples++)
{
m_vecXcdSignalBuffer(samples).real(l_pInputMatrix->getBuffer()[samples + channel * (l_ui32SamplesPerChannel)]);
m_vecXcdSignalBuffer(samples).imag(0.0);
}
//Fast Fourier Transform of input signal
fft.fwd(m_vecXcdSignalFourier, m_vecXcdSignalBuffer);
//Apply Hilbert transform by element-wise multiplying fft vector by h
m_vecXcdSignalFourier = m_vecXcdSignalFourier * m_vecXdHilbert;
//Inverse Fast Fourier transform
fft.inv(m_vecXcdSignalBuffer, m_vecXcdSignalFourier); //m_vecXcdSignalBuffer is now the analytical signal of the initial input signal
//Compute envelope and phase and pass it to the corresponding output
for(uint32 samples=0; samples<l_ui32SamplesPerChannel;samples++)
{
l_pOutputEnvelopeMatrix->getBuffer()[samples + channel*samples] = abs(m_vecXcdSignalBuffer(samples));
l_pOutputPhaseMatrix->getBuffer()[samples + channel*samples] = arg(m_vecXcdSignalBuffer(samples));
}
}
}
return true;
}
boolean CAlgorithmHilbertTransform::process(void)
{
uint32 l_ui32ChannelCount = ip_pMatrix->getDimensionSize(0);
uint32 l_ui32SamplesPerChannel = ip_pMatrix->getDimensionSize(1);
IMatrix* l_pInputMatrix = ip_pMatrix;
IMatrix* l_pOutputHilbertMatrix = op_pHilbertMatrix;
IMatrix* l_pOutputEnvelopeMatrix = op_pEnvelopeMatrix;
IMatrix* l_pOutputPhaseMatrix = op_pPhaseMatrix;
FFT< double, internal::kissfft_impl<double > > fft; //create instance of fft transform
if(this->isInputTriggerActive(OVP_Algorithm_HilbertTransform_InputTriggerId_Initialize)) //Check if the input is correct
{
if( l_pInputMatrix->getDimensionCount() != 2)
{
this->getLogManager() << LogLevel_Error << "The input matrix must have 2 dimensions, here the dimension is ";
std::cout<<l_pInputMatrix->getDimensionCount()<<std::endl;
return false;
}
//Setting size of outputs
l_pOutputHilbertMatrix->setDimensionCount(2);
l_pOutputHilbertMatrix->setDimensionSize(0,l_ui32ChannelCount);
l_pOutputHilbertMatrix->setDimensionSize(1,l_ui32SamplesPerChannel);
l_pOutputEnvelopeMatrix->setDimensionCount(2);
l_pOutputEnvelopeMatrix->setDimensionSize(0,l_ui32ChannelCount);
l_pOutputEnvelopeMatrix->setDimensionSize(1,l_ui32SamplesPerChannel);
l_pOutputPhaseMatrix->setDimensionCount(2);
l_pOutputPhaseMatrix->setDimensionSize(0,l_ui32ChannelCount);
l_pOutputPhaseMatrix->setDimensionSize(1,l_ui32SamplesPerChannel);
}
if(this->isInputTriggerActive(OVP_Algorithm_HilbertTransform_InputTriggerId_Process))
{
//Computing Hilbert transform for all channels
for(uint32 channel=0; channel<l_ui32ChannelCount; channel++)
{
//Initialization of buffer vectors
m_vecXcdSignalBuffer = VectorXcd::Zero(l_ui32SamplesPerChannel);
m_vecXcdSignalFourier = VectorXcd::Zero(l_ui32SamplesPerChannel);
//Initialization of vector h used to compute analytic signal
m_vecXdHilbert.resize(l_ui32SamplesPerChannel);
m_vecXdHilbert(0) = 1.0;
if(l_ui32SamplesPerChannel%2 == 0)
{
m_vecXdHilbert(l_ui32SamplesPerChannel/2) = 1.0;
for(uint32 i=1; i<l_ui32SamplesPerChannel/2; i++)
{
m_vecXdHilbert(i) = 2.0;
}
for(uint32 i=(l_ui32SamplesPerChannel/2)+1; i<l_ui32SamplesPerChannel; i++)
{
m_vecXdHilbert(i) = 0.0;
}
}
else
{
m_vecXdHilbert((l_ui32SamplesPerChannel+1)/2) = 1.0;
for(uint32 i=1; i<(l_ui32SamplesPerChannel+1); i++)
{
m_vecXdHilbert(i) = 2.0;
}
for(uint32 i=(l_ui32SamplesPerChannel+1)/2+1; i<l_ui32SamplesPerChannel; i++)
{
m_vecXdHilbert(i) = 0.0;
}
}
//Copy input signal chunk on buffer
for(uint32 samples=0; samples<l_ui32SamplesPerChannel;samples++)
{
m_vecXcdSignalBuffer(samples).real(l_pInputMatrix->getBuffer()[samples + channel * (l_ui32SamplesPerChannel)]);
m_vecXcdSignalBuffer(samples).imag(0.0);
}
//Fast Fourier Transform of input signal
fft.fwd(m_vecXcdSignalFourier, m_vecXcdSignalBuffer);
//Apply Hilbert transform by element-wise multiplying fft vector by h
for(uint32 samples=0; samples<l_ui32SamplesPerChannel;samples++)
{
m_vecXcdSignalFourier(samples) = m_vecXcdSignalFourier(samples)*m_vecXdHilbert(samples);
}
//Inverse Fast Fourier transform
fft.inv(m_vecXcdSignalBuffer, m_vecXcdSignalFourier); // m_vecXcdSignalBuffer is now the analytical signal of the initial input signal
//Compute envelope and phase and pass it to the corresponding output
for(uint32 samples=0; samples<l_ui32SamplesPerChannel;samples++)
{
l_pOutputHilbertMatrix->getBuffer()[samples + channel*l_ui32SamplesPerChannel] = m_vecXcdSignalBuffer(samples).imag();
l_pOutputEnvelopeMatrix->getBuffer()[samples + channel*l_ui32SamplesPerChannel] = abs(m_vecXcdSignalBuffer(samples));
l_pOutputPhaseMatrix->getBuffer()[samples + channel*l_ui32SamplesPerChannel] = arg(m_vecXcdSignalBuffer(samples));
}
}
}
return true;
}
