static void
PyGSL_transform_space_dealloc(PyGSL_transform_space * self)
{
FUNC_MESS_BEGIN();
assert(PyGSL_transform_space_check(self));
assert(self->space.v);
switch(self->type){
case COMPLEX_WORKSPACE: gsl_fft_complex_workspace_free(self->space.cws); break;
case COMPLEX_WAVETABLE: gsl_fft_complex_wavetable_free(self->space.cwt); break;
case REAL_WORKSPACE: gsl_fft_real_workspace_free(self->space.rws); break;
case REAL_WAVETABLE: gsl_fft_real_wavetable_free(self->space.rwt); break;
case HALFCOMPLEX_WAVETABLE: gsl_fft_halfcomplex_wavetable_free(self->space.hcwt); break;
case COMPLEX_WORKSPACE_FLOAT: gsl_fft_complex_workspace_float_free(self->space.cwsf); break;
case COMPLEX_WAVETABLE_FLOAT: gsl_fft_complex_wavetable_float_free(self->space.cwtf); break;
case REAL_WORKSPACE_FLOAT: gsl_fft_real_workspace_float_free(self->space.rwsf); break;
case REAL_WAVETABLE_FLOAT: gsl_fft_real_wavetable_float_free(self->space.rwtf); break;
case HALFCOMPLEX_WAVETABLE_FLOAT: gsl_fft_halfcomplex_wavetable_float_free(self->space.hcwtf); break;
#ifdef _PYGSL_GSL_HAS_WAVELET
case WAVELET_WORKSPACE : gsl_wavelet_workspace_free(self->space.wws); break;
#endif
default: pygsl_error("Got unknown switch", filename, __LINE__, GSL_ESANITY); break;
}
self->space.v = NULL;
FUNC_MESS_END();
}
double *
get_spectrum_data(double *data, int ns, int nfft, int ntarget, int nslices, int *chunks)
{
//compute the spectrum that will be the target
int nchunks = ns / nfft;
if (ns % nfft != 0)
nchunks += -1;
*chunks = nchunks;
double *spectrum = (double *) calloc(nslices * nchunks * ntarget, sizeof(double));
double buffer[nfft];
int i, offset = 0;
gsl_fft_real_wavetable *real;
gsl_fft_real_workspace *work;
work = gsl_fft_real_workspace_alloc(nfft);
real = gsl_fft_real_wavetable_alloc(nfft);
for (i = 0; i < nchunks * nfft; i += nfft / nslices) {
memcpy(buffer, data + i, nfft * sizeof(double));
gsl_fft_real_transform(buffer, 1, nfft, real, work);
memcpy(spectrum + offset, buffer, ntarget * sizeof(double));
offset += ntarget;
}
gsl_fft_real_workspace_free(work);
gsl_fft_real_wavetable_free(real);
return spectrum;
}
/* calculates the fast Fourier transform of signal data, stores it into fft_results */
int fft (size_t N, double *data, double *fft_results) {
size_t i;
double *fft_data = (double *) calloc (N, sizeof (double));
int retcode;
/* initialize the data for fft */
for (i=0; i<N; i++) fft_data [i] = data [i];
/* use the corresponding routine if N is power of 2 */
if (is_power_of_n (N,2)) {
/* perform the fft */
retcode = gsl_fft_real_radix2_transform (fft_data, 1, N);
gsl_fft_halfcomplex_radix2_unpack (fft_data, fft_results, 1, N);
}
else {
/* alloc memory for real and half-complex wavetables, and workspace */
gsl_fft_real_wavetable *real_wavetable = gsl_fft_real_wavetable_alloc (N);
gsl_fft_real_workspace *ws = gsl_fft_real_workspace_alloc (N);
/* perform the fft */
retcode = gsl_fft_real_transform (fft_data, 1, N, real_wavetable, ws);
gsl_fft_halfcomplex_unpack (fft_data, fft_results, 1, N);
/* free memory */
gsl_fft_real_wavetable_free (real_wavetable);
gsl_fft_real_workspace_free (ws);
}
free (fft_data);
return retcode;
}
void SLItarget(double *x, double * slice, int nfft)
{
double buffer[3*nfft];
double * data = slice;
int i, offset = 0;
gsl_fft_real_wavetable *real;
gsl_fft_real_workspace *work;
work = gsl_fft_real_workspace_alloc(nfft);
real = gsl_fft_real_wavetable_alloc(nfft);
for (i = 0; i < 3 ; i++) {
memcpy(buffer+i*nfft, data + i*nfft/2, nfft * sizeof(double));
gsl_fft_real_transform(buffer+i*nfft, 1, nfft, real, work);
}
gsl_fft_real_workspace_free(work);
gsl_fft_real_wavetable_free(real);
int k;
double m=0;
for(k=0;k<nfft;k++)
{
x[k] = (buffer[k]+buffer[k+nfft]+buffer[k+2*nfft])/3.0;
// m+=x[k]*x[k]a;
}
// scaleData(1/m,x,nfft);
}
void
Fft<std::complex<double> >::forward(
const std::vector<std::complex<double> >& data,
unsigned int fftSize,
std::vector<std::complex<double> >& forwardResult)
{
queso_not_implemented();
std::complex<double> z = data[0]; z += 0.; // just to avoid icpc warnings
unsigned int f = fftSize; f += 1; // just to avoid icpc warnings
forwardResult[0] = 0.; // just to avoid icpc warnings
#if 0
if (forwardResult.size() != fftSize) {
forwardResult.resize(fftSize,std::complex<double>(0.,0.));
std::vector<std::complex<double> >(forwardResult).swap(forwardResult);
}
std::vector<double> internalData(fftSize,0.);
unsigned int minSize = std::min((unsigned int) data.size(),fftSize);
for (unsigned int j = 0; j < minSize; ++j) {
internalData[j] = data[j];
}
gsl_fft_real_workspace* realWkSpace = gsl_fft_real_workspace_alloc(fftSize);
gsl_fft_real_wavetable* realWvTable = gsl_fft_real_wavetable_alloc(fftSize);
gsl_fft_real_transform(&internalData[0],
1,
fftSize,
realWvTable,
realWkSpace);
gsl_fft_real_wavetable_free(realWvTable);
gsl_fft_real_workspace_free(realWkSpace);
unsigned int halfFFTSize = fftSize/2;
double realPartOfFFT = 0.;
double imagPartOfFFT = 0.;
for (unsigned int j = 0; j < internalData.size(); ++j) {
if (j == 0) {
realPartOfFFT = internalData[j];
imagPartOfFFT = 0.;
}
else if (j < halfFFTSize) {
realPartOfFFT = internalData[2*j-1];
imagPartOfFFT = internalData[2*j ];
}
else if (j == halfFFTSize) {
realPartOfFFT = internalData[2*j-1];
imagPartOfFFT = 0.;
}
else {
realPartOfFFT = internalData[2*(fftSize-j)-1];
imagPartOfFFT = -internalData[2*(fftSize-j) ];
}
forwardResult[j] = std::complex<double>(realPartOfFFT,imagPartOfFFT);
}
#endif
return;
}
void
Fft<T>::freeTables()
{
if (m_fftSize != 0) {
//gsl_fft_halfcomplex_wavetable_free(m_hcWvTable);
gsl_fft_real_wavetable_free (m_realWvTable);
gsl_fft_real_workspace_free (m_realWkSpace);
m_fftSize = 0;
}
return;
}
void
ODEfilter(double *data, int nsample, int nfft, ODEparams * pa)
{
int i, k;
gsl_fft_real_wavetable *real;
gsl_fft_halfcomplex_wavetable *hc;
gsl_fft_real_workspace *work;
work = gsl_fft_real_workspace_alloc(nfft);
real = gsl_fft_real_wavetable_alloc(nfft);
hc = gsl_fft_halfcomplex_wavetable_alloc(nfft);
double *filter = (double *) calloc(nfft, sizeof(double));
double *vdata = (double *) calloc(nsample + nfft / 2, sizeof(double));
memcpy(vdata, data, nsample * sizeof(double));
for (i = 0; i < nfft; i++) {
double ff = i * 44100.0 / nfft / 2;
double fil = gsl_spline_eval(pa->f_spline, ff, pa->f_acc);
if (ff > 8000.)
fil = 0.0;
filter[i] = fil;
//printf("%e %e\n", ff, fil);
}
for (i = 0; i < nsample; i += nfft) {
for (k = 0; k < nfft; k++) {
(data + i)[k] *= (1 - k / (1. * nfft)) * k / (1. * nfft); /* hamming window */
(vdata + i + nfft / 2)[k] *= (1 - k / (1. * nfft)) * k / (1. * nfft);
}
gsl_fft_real_transform(data + i, 1, nfft, real, work);
gsl_fft_real_transform(vdata + i + nfft / 2, 1, nfft, real, work);
for (k = 0; k < nfft; k++) {
(data + i)[k] = (data + i)[k] * filter[k];
(vdata + i + nfft / 2)[k] = (vdata + i + nfft / 2)[k] * filter[k];
}
gsl_fft_halfcomplex_inverse(data + i, 1, nfft, hc, work);
gsl_fft_halfcomplex_inverse(vdata + i + nfft / 2, 1, nfft, hc, work);
for (k = 0; k < nfft; k++)
(data + i)[k] = ((data + i)[k] + (vdata + i)[k]) / 2;
}
gsl_fft_real_workspace_free(work);
gsl_fft_real_wavetable_free(real);
gsl_fft_halfcomplex_wavetable_free(hc);
}
void
SLIfilter(double *data, int nfft, double * filter)
{
int i, k;
gsl_fft_real_wavetable *real;
gsl_fft_halfcomplex_wavetable *hc;
gsl_fft_real_workspace *work;
work = gsl_fft_real_workspace_alloc(nfft);
real = gsl_fft_real_wavetable_alloc(nfft);
hc = gsl_fft_halfcomplex_wavetable_alloc(nfft);
double *vdata = (double *) calloc(2*nfft+nfft/2, sizeof(double));
memcpy(vdata, data, 2* nfft * sizeof(double));
for (i = 0; i < 2; i ++)
{
for (k = 0; k < nfft; k++) {
(data + i*nfft)[k] *= (1 - k / (1. * nfft)) * k / (1. * nfft); /* hamming window */
(vdata + i*nfft + nfft / 2)[k] *= (1 - k / (1. * nfft)) * k / (1. * nfft);
}
gsl_fft_real_transform(data + i*nfft, 1, nfft, real, work);
gsl_fft_real_transform(vdata + i*nfft + nfft / 2, 1, nfft, real, work);
for (k = 0; k < nfft; k++) {
(data + i*nfft)[k] = (data + i)[k] * filter[k];
(vdata + i*nfft + nfft / 2)[k] = (vdata + i + nfft / 2)[k] * filter[k];
}
gsl_fft_halfcomplex_inverse(data + i*nfft, 1, nfft, hc, work);
gsl_fft_halfcomplex_inverse(vdata + i*nfft + nfft / 2, 1, nfft, hc, work);
for (k = 0; k < nfft; k++)
(data + i*nfft)[k] = ((data + i*nfft)[k] + (vdata + i*nfft)[k]) / 2;
}
gsl_fft_real_workspace_free(work);
gsl_fft_real_wavetable_free(real);
gsl_fft_halfcomplex_wavetable_free(hc);
}
void SmoothFilter::smoothFFT(double *x, double *y)
{
gsl_fft_real_workspace *work = gsl_fft_real_workspace_alloc(d_n);
gsl_fft_real_wavetable *real = gsl_fft_real_wavetable_alloc(d_n);
gsl_fft_real_transform (y, 1, d_n, real, work);//FFT forward
gsl_fft_real_wavetable_free (real);
double df = 1.0/(double)(x[1] - x[0]);
double lf = df/(double)d_smooth_points;//frequency cutoff
df = 0.5*df/(double)d_n;
for (int i = 0; i < d_n; i++){
x[i] = d_x[i];
y[i] = i*df > lf ? 0 : y[i];//filtering frequencies
}
gsl_fft_halfcomplex_wavetable *hc = gsl_fft_halfcomplex_wavetable_alloc (d_n);
gsl_fft_halfcomplex_inverse (y, 1, d_n, hc, work);//FFT inverse
gsl_fft_halfcomplex_wavetable_free (hc);
gsl_fft_real_workspace_free (work);
}
void FFTFilter::calculateOutputData(double *x, double *y)
{
// interpolate y to even spacing
double delta=(d_x[d_n-1]-d_x[0])/d_n;
double xi=d_x[0];
for (int i=0, j=0; j<d_n && xi<=d_x[d_n-1]; j++)
{
x[j]=xi;
if (i<d_n-1)
y[j]=((d_x[i+1]-xi)*d_y[i] + (xi-d_x[i])*d_y[i+1])/(d_x[i+1]-d_x[i]);
else
y[j]=d_y[d_n-1];
xi+=delta;
while (i<d_n && xi>d_x[i]) i++;
}
double df = 1.0/(x[d_n-1]-x[0]);
gsl_fft_real_workspace *work = gsl_fft_real_workspace_alloc(d_n);
gsl_fft_real_wavetable *real = gsl_fft_real_wavetable_alloc(d_n);
gsl_fft_real_transform (y, 1, d_n, real, work);
gsl_fft_real_wavetable_free (real);
d_explanation = QLocale().toString(d_low_freq) + " ";
if (d_filter_type > 2)
d_explanation += tr("to") + " " + QLocale().toString(d_high_freq) + " ";
d_explanation += tr("Hz") + " ";
switch ((int)d_filter_type)
{
case 1://low pass
d_explanation += tr("Low Pass FFT Filter");
for (int i = d_n-1; i >= 0 && ((i+1)/2)*df > d_low_freq; i--)
y[i] = 0;
break;
case 2://high pass
d_explanation += tr("High Pass FFT Filter");
for (int i = 0; i < d_n && ((i+1)/2)*df < d_low_freq; i++)
y[i] = 0;
break;
case 3://band pass
d_explanation += tr("Band Pass FFT Filter");
for (int i = d_offset ? 1 : 0; i < d_n; i++)
if ((((i+1)/2)*df <= d_low_freq ) || (((i+1)/2)*df >= d_high_freq ))
y[i] = 0;
break;
case 4://band block
d_explanation += tr("Band Block FFT Filter");
if(!d_offset)
y[0] = 0;//substract DC offset
for (int i = 1; i < d_n; i++)
if ((((i+1)/2)*df > d_low_freq ) && (((i+1)/2)*df < d_high_freq ))
y[i] = 0;
break;
}
gsl_fft_halfcomplex_wavetable *hc = gsl_fft_halfcomplex_wavetable_alloc (d_n);
gsl_fft_halfcomplex_inverse (y, 1, d_n, hc, work);
gsl_fft_halfcomplex_wavetable_free (hc);
gsl_fft_real_workspace_free (work);
}
void FFTRealTransform::CleanUp()
{
if(work!=NULL) { gsl_fft_real_workspace_free (work); work=NULL; }
if(real!=NULL) { gsl_fft_real_wavetable_free (real); real=NULL; }
if(hc!=NULL) { gsl_fft_halfcomplex_wavetable_free (hc); hc=NULL; }
}
void
Fft<double>::forward(
const std::vector<double>& data,
unsigned int fftSize,
std::vector<std::complex<double> >& forwardResult)
{
if (forwardResult.size() != fftSize) {
forwardResult.resize(fftSize,std::complex<double>(0.,0.));
std::vector<std::complex<double> >(forwardResult).swap(forwardResult);
}
std::vector<double> internalData(fftSize,0.);
unsigned int minSize = std::min((unsigned int) data.size(),fftSize);
for (unsigned int j = 0; j < minSize; ++j) {
internalData[j] = data[j];
}
//double sumOfAllTerms = 0.;
//for (unsigned int j = 0; j < fftSize; ++j) {
// sumOfAllTerms += internalData[j];
//}
//allocTables(fftSize);
gsl_fft_real_workspace* realWkSpace = gsl_fft_real_workspace_alloc(fftSize);
gsl_fft_real_wavetable* realWvTable = gsl_fft_real_wavetable_alloc(fftSize);
gsl_fft_real_transform(&internalData[0],
1,
fftSize,
realWvTable,
realWkSpace);
gsl_fft_real_wavetable_free(realWvTable);
gsl_fft_real_workspace_free(realWkSpace);
//freeTables();
//std::cout << "After FFT"
// << ", sumOfAllTerms = " << sumOfAllTerms
// << ", sumOfAllTerms - dft[0] = " << sumOfAllTerms - internalData[0]
// << std::endl;
unsigned int halfFFTSize = fftSize/2;
double realPartOfFFT = 0.;
double imagPartOfFFT = 0.;
for (unsigned int j = 0; j < internalData.size(); ++j) {
if (j == 0) {
realPartOfFFT = internalData[j];
imagPartOfFFT = 0.;
}
else if (j < halfFFTSize) {
realPartOfFFT = internalData[2*j-1];
imagPartOfFFT = internalData[2*j ];
}
else if (j == halfFFTSize) {
realPartOfFFT = internalData[2*j-1];
imagPartOfFFT = 0.;
}
else {
realPartOfFFT = internalData[2*(fftSize-j)-1];
imagPartOfFFT = -internalData[2*(fftSize-j) ];
}
forwardResult[j] = std::complex<double>(realPartOfFFT,imagPartOfFFT);
}
return;
}
QList<Column *> FFT::fftCurve() {
int i, i2;
int n2 = d_n / 2;
double *amp = new double[d_n];
double *result = new double[2 * d_n];
if (!amp || !result) {
QMessageBox::critical((ApplicationWindow *)parent(),
tr("AlphaPlot") + " - " + tr("Error"),
tr("Could not allocate memory, operation aborted!"));
d_init_err = true;
return QList<Column *>();
}
double df = 1.0 / (double)(d_n * d_sampling); // frequency sampling
double aMax = 0.0; // max amplitude
QList<Column *> columns;
if (!d_inverse) {
d_explanation = tr("Forward") + " " + tr("FFT") + " " + tr("of") + " " +
d_curve->title().text();
columns << new Column(tr("Frequency"), AlphaPlot::Numeric);
gsl_fft_real_workspace *work = gsl_fft_real_workspace_alloc(d_n);
gsl_fft_real_wavetable *real = gsl_fft_real_wavetable_alloc(d_n);
if (!work || !real) {
QMessageBox::critical(
(ApplicationWindow *)parent(), tr("AlphaPlot") + " - " + tr("Error"),
tr("Could not allocate memory, operation aborted!"));
d_init_err = true;
return QList<Column *>();
}
gsl_fft_real_transform(d_y, 1, d_n, real, work);
gsl_fft_halfcomplex_unpack(d_y, result, 1, d_n);
gsl_fft_real_wavetable_free(real);
gsl_fft_real_workspace_free(work);
} else {
d_explanation = tr("Inverse") + " " + tr("FFT") + " " + tr("of") + " " +
d_curve->title().text();
columns << new Column(tr("Time"), AlphaPlot::Numeric);
gsl_fft_real_unpack(d_y, result, 1, d_n);
gsl_fft_complex_wavetable *wavetable = gsl_fft_complex_wavetable_alloc(d_n);
gsl_fft_complex_workspace *workspace = gsl_fft_complex_workspace_alloc(d_n);
if (!workspace || !wavetable) {
QMessageBox::critical(
(ApplicationWindow *)parent(), tr("AlphaPlot") + " - " + tr("Error"),
tr("Could not allocate memory, operation aborted!"));
d_init_err = true;
return QList<Column *>();
}
gsl_fft_complex_inverse(result, 1, d_n, wavetable, workspace);
gsl_fft_complex_wavetable_free(wavetable);
gsl_fft_complex_workspace_free(workspace);
}
if (d_shift_order) {
for (i = 0; i < d_n; i++) {
d_x[i] = (i - n2) * df;
int j = i + d_n;
double aux = result[i];
result[i] = result[j];
result[j] = aux;
}
} else {
for (i = 0; i < d_n; i++) d_x[i] = i * df;
}
for (i = 0; i < d_n; i++) {
i2 = 2 * i;
double real_part = result[i2];
double im_part = result[i2 + 1];
double a = sqrt(real_part * real_part + im_part * im_part);
amp[i] = a;
if (a > aMax) aMax = a;
}
// ApplicationWindow *app = (ApplicationWindow *)parent();
columns << new Column(tr("Real"), AlphaPlot::Numeric);
columns << new Column(tr("Imaginary"), AlphaPlot::Numeric);
columns << new Column(tr("Amplitude"), AlphaPlot::Numeric);
columns << new Column(tr("Angle"), AlphaPlot::Numeric);
for (i = 0; i < d_n; i++) {
i2 = 2 * i;
columns.at(0)->setValueAt(i, d_x[i]);
columns.at(1)->setValueAt(i, result[i2]);
columns.at(2)->setValueAt(i, result[i2 + 1]);
if (d_normalize)
columns.at(3)->setValueAt(i, amp[i] / aMax);
else
columns.at(3)->setValueAt(i, amp[i]);
columns.at(4)->setValueAt(i, atan(result[i2 + 1] / result[i2]));
}
delete[] amp;
delete[] result;
columns.at(0)->setPlotDesignation(AlphaPlot::X);
columns.at(1)->setPlotDesignation(AlphaPlot::Y);
columns.at(2)->setPlotDesignation(AlphaPlot::Y);
columns.at(3)->setPlotDesignation(AlphaPlot::Y);
columns.at(4)->setPlotDesignation(AlphaPlot::Y);
return columns;
}
void FFT::fftCurve()
{
int n2 = d_n/2;
double *amp = (double *)malloc(d_n*sizeof(double));
double *result = (double *)malloc(2*d_n*sizeof(double));
if(!amp || !result){
memoryErrorMessage();
return;
}
double df = 1.0/(double)(d_n*d_sampling);//frequency sampling
double aMax = 0.0;//max amplitude
if(!d_inverse){
gsl_fft_real_workspace *work = gsl_fft_real_workspace_alloc(d_n);
gsl_fft_real_wavetable *real = gsl_fft_real_wavetable_alloc(d_n);
if(!work || !real){
memoryErrorMessage();
return;
}
gsl_fft_real_transform(d_y, 1, d_n, real, work);
gsl_fft_halfcomplex_unpack (d_y, result, 1, d_n);
gsl_fft_real_wavetable_free(real);
gsl_fft_real_workspace_free(work);
} else {
gsl_fft_real_unpack (d_y, result, 1, d_n);
gsl_fft_complex_wavetable *wavetable = gsl_fft_complex_wavetable_alloc (d_n);
gsl_fft_complex_workspace *workspace = gsl_fft_complex_workspace_alloc (d_n);
if(!workspace || !wavetable){
memoryErrorMessage();
return;
}
gsl_fft_complex_inverse (result, 1, d_n, wavetable, workspace);
gsl_fft_complex_wavetable_free (wavetable);
gsl_fft_complex_workspace_free (workspace);
}
if (d_shift_order){
for(int i = 0; i < d_n; i++){
d_x[i] = (i - n2)*df;
int j = i + d_n;
double aux = result[i];
result[i] = result[j];
result[j] = aux;
}
} else {
for(int i = 0; i < d_n; i++)
d_x[i] = i*df;
}
for(int i = 0; i < d_n; i++) {
int i2 = 2*i;
double real_part = result[i2];
double im_part = result[i2+1];
double a = sqrt(real_part*real_part + im_part*im_part);
amp[i]= a;
if (a > aMax)
aMax = a;
}
ApplicationWindow *app = (ApplicationWindow *)parent();
QLocale locale = app->locale();
int prec = app->d_decimal_digits;
for (int i = 0; i < d_n; i++){
int i2 = 2*i;
d_result_table->setText(i, 0, locale.toString(d_x[i], 'g', prec));
d_result_table->setText(i, 1, locale.toString(result[i2], 'g', prec));
d_result_table->setText(i, 2, locale.toString(result[i2 + 1], 'g', prec));
if (d_normalize)
d_result_table->setText(i, 3, locale.toString(amp[i]/aMax, 'g', prec));
else
d_result_table->setText(i, 3, locale.toString(amp[i], 'g', prec));
d_result_table->setText(i, 4, locale.toString(atan(result[i2 + 1]/result[i2]), 'g', prec));
}
free(amp);
free(result);
}
QString FFT::fftCurve()
{
int i, i2;
int n2 = d_n/2;
double *amp = new double[d_n];
double *result = new double[2*d_n];
if(!amp || !result){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot") + " - " + tr("Error"),
tr("Could not allocate memory, operation aborted!"));
d_init_err = true;
return "";
}
double df = 1.0/(double)(d_n*d_sampling);//frequency sampling
double aMax = 0.0;//max amplitude
QString text;
if(!d_inverse){
d_explanation = tr("Forward") + " " + tr("FFT") + " " + tr("of") + " " + d_curve->title().text();
text = tr("Frequency");
gsl_fft_real_workspace *work=gsl_fft_real_workspace_alloc(d_n);
gsl_fft_real_wavetable *real=gsl_fft_real_wavetable_alloc(d_n);
if(!work || !real){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot") + " - " + tr("Error"),
tr("Could not allocate memory, operation aborted!"));
d_init_err = true;
return "";
}
gsl_fft_real_transform(d_y, 1, d_n, real,work);
gsl_fft_halfcomplex_unpack (d_y, result, 1, d_n);
gsl_fft_real_wavetable_free(real);
gsl_fft_real_workspace_free(work);
} else {
d_explanation = tr("Inverse") + " " + tr("FFT") + " " + tr("of") + " " + d_curve->title().text();
text = tr("Time");
gsl_fft_real_unpack (d_y, result, 1, d_n);
gsl_fft_complex_wavetable *wavetable = gsl_fft_complex_wavetable_alloc (d_n);
gsl_fft_complex_workspace *workspace = gsl_fft_complex_workspace_alloc (d_n);
if(!workspace || !wavetable){
QMessageBox::critical((ApplicationWindow *)parent(), tr("MantidPlot") + " - " + tr("Error"),
tr("Could not allocate memory, operation aborted!"));
d_init_err = true;
return "";
}
gsl_fft_complex_inverse (result, 1, d_n, wavetable, workspace);
gsl_fft_complex_wavetable_free (wavetable);
gsl_fft_complex_workspace_free (workspace);
}
if (d_shift_order){
for(i=0; i<d_n; i++){
d_x[i] = (i-n2)*df;
int j = i + d_n;
double aux = result[i];
result[i] = result[j];
result[j] = aux;
}
} else {
for(i=0; i<d_n; i++)
d_x[i] = i*df;
}
for(i=0;i<d_n;i++) {
i2 = 2*i;
double real_part = result[i2];
double im_part = result[i2+1];
double a = sqrt(real_part*real_part + im_part*im_part);
amp[i]= a;
if (a > aMax)
aMax = a;
}
ApplicationWindow *app = (ApplicationWindow *)parent();
QLocale locale = app->locale();
int prec = app->d_decimal_digits;
text += "\t"+tr("Real")+"\t"+tr("Imaginary")+"\t"+ tr("Amplitude")+"\t"+tr("Angle")+"\n";
for (i=0; i<d_n; i++){
i2 = 2*i;
text += locale.toString(d_x[i], 'g', prec)+"\t";
text += locale.toString(result[i2], 'g', prec)+"\t";
text += locale.toString(result[i2+1], 'g', prec)+"\t";
if (d_normalize)
text += locale.toString(amp[i]/aMax, 'g', prec)+"\t";
else
text += locale.toString(amp[i], 'g', prec)+"\t";
text += locale.toString(atan(result[i2+1]/result[i2]), 'g', prec)+"\n";
}
delete[] amp;
delete[] result;
return text;
}
int main( int argc , char * argv[] )
{
int do_norm=0 , qdet=2 , have_freq=0 , do_automask=0 ;
float dt=0.0f , fbot=0.0f,ftop=999999.9f , blur=0.0f ;
MRI_IMARR *ortar=NULL ; MRI_IMAGE *ortim=NULL ;
THD_3dim_dataset **ortset=NULL ; int nortset=0 ;
THD_3dim_dataset *inset=NULL , *outset=NULL;
char *prefix="RSFC" ;
byte *mask=NULL ;
int mask_nx=0,mask_ny=0,mask_nz=0,nmask , verb=1 ,
nx,ny,nz,nvox , nfft=0 , kk ;
float **vec , **ort=NULL ; int nort=0 , vv , nopt , ntime ;
MRI_vectim *mrv ;
float pvrad=0.0f ; int nosat=0 ;
int do_despike=0 ;
// @@ non-BP variables
float fbotALL=0.0f, ftopALL=999999.9f; // do full range version
int NumDen = 0; // switch for doing numerator or denom
THD_3dim_dataset *outsetALL=NULL ;
int m, mm;
float delf; // harmonics
int ind_low,ind_high,N_ny, ctr;
float sqnt,nt_fac;
gsl_fft_real_wavetable *real1, *real2; // GSL stuff
gsl_fft_real_workspace *work;
double *series1, *series2;
double *xx1,*xx2;
float numer,denom,val;
float *alff=NULL,*malff=NULL,*falff=NULL,
*rsfa=NULL,*mrsfa=NULL,*frsfa=NULL; // values
float meanALFF=0.0f,meanRSFA=0.0f; // will be for mean in brain region
THD_3dim_dataset *outsetALFF=NULL;
THD_3dim_dataset *outsetmALFF=NULL;
THD_3dim_dataset *outsetfALFF=NULL;
THD_3dim_dataset *outsetRSFA=NULL;
THD_3dim_dataset *outsetmRSFA=NULL;
THD_3dim_dataset *outsetfRSFA=NULL;
char out_lff[300];
char out_alff[300];
char out_malff[300];
char out_falff[300];
char out_rsfa[300];
char out_mrsfa[300];
char out_frsfa[300];
char out_unBP[300];
int SERIES_OUT = 1;
int UNBP_OUT = 0;
int DO_RSFA = 1;
int BP_LAST = 0; // option for only doing filter to LFFs at very end of proc
float de_rsfa=0.0f,nu_rsfa=0.0f;
double pow1=0.0,pow2=0.0;
/*-- help? --*/
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"\n Program to calculate common resting state functional connectivity (RSFC)\n"
" parameters (ALFF, mALFF, fALFF, RSFA, etc.) for resting state time\n"
" series. This program is **heavily** based on the existing\n"
" 3dBandPass by RW Cox, with the amendments to calculate RSFC\n"
" parameters written by PA Taylor (July, 2012).\n"
" This program is part of FATCAT (Taylor & Saad, 2013) in AFNI. Importantly,\n"
" its functionality can be included in the `afni_proc.py' processing-script \n"
" generator; see that program's help file for an example including RSFC\n"
" and spectral parameter calculation via the `-regress_RSFC' option.\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"\n"
" All options of 3dBandPass may be used here (with a couple other\n"
" parameter options, as well): essentially, the motivation of this\n"
" program is to produce ALFF, etc. values of the actual RSFC time\n"
" series that you calculate. Therefore, all the 3dBandPass processing\n"
" you normally do en route to making your final `resting state time\n"
" series' is done here to generate your LFFs, from which the\n"
" amplitudes in the LFF band are calculated at the end. In order to\n"
" calculate fALFF, the same initial time series are put through the\n"
" same processing steps which you have chosen but *without* the\n"
" bandpass part; the spectrum of this second time series is used to\n"
" calculate the fALFF denominator.\n"
" \n"
" For more information about each RSFC parameter, see, e.g.: \n"
" ALFF/mALFF -- Zang et al. (2007),\n"
" fALFF -- Zou et al. (2008),\n"
" RSFA -- Kannurpatti & Biswal (2008).\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"\n"
" + USAGE: 3dRSFC [options] fbot ftop dataset\n"
"\n"
"* One function of this program is to prepare datasets for input\n"
" to 3dSetupGroupInCorr. Other uses are left to your imagination.\n"
"\n"
"* 'dataset' is a 3D+time sequence of volumes\n"
" ++ This must be a single imaging run -- that is, no discontinuities\n"
" in time from 3dTcat-ing multiple datasets together.\n"
"\n"
"* fbot = lowest frequency in the passband, in Hz\n"
" ++ fbot can be 0 if you want to do a lowpass filter only;\n"
" HOWEVER, the mean and Nyquist freq are always removed.\n"
"\n"
"* ftop = highest frequency in the passband (must be > fbot)\n"
" ++ if ftop > Nyquist freq, then it's a highpass filter only.\n"
"\n"
"* Set fbot=0 and ftop=99999 to do an 'allpass' filter.\n"
" ++ Except for removal of the 0 and Nyquist frequencies, that is.\n"
"\n"
"* You cannot construct a 'notch' filter with this program!\n"
" ++ You could use 3dRSFC followed by 3dcalc to get the same effect.\n"
" ++ If you are understand what you are doing, that is.\n"
" ++ Of course, that is the AFNI way -- if you don't want to\n"
" understand what you are doing, use Some other PrograM, and\n"
" you can still get Fine StatisticaL maps.\n"
"\n"
"* 3dRSFC will fail if fbot and ftop are too close for comfort.\n"
" ++ Which means closer than one frequency grid step df,\n"
" where df = 1 / (nfft * dt) [of course]\n"
"\n"
"* The actual FFT length used will be printed, and may be larger\n"
" than the input time series length for the sake of efficiency.\n"
" ++ The program will use a power-of-2, possibly multiplied by\n"
" a power of 3 and/or 5 (up to and including the 3rd power of\n"
" each of these: 3, 9, 27, and 5, 25, 125).\n"
"\n"
"* Note that the results of combining 3dDetrend and 3dRSFC will\n"
" depend on the order in which you run these programs. That's why\n"
" 3dRSFC has the '-ort' and '-dsort' options, so that the\n"
" time series filtering can be done properly, in one place.\n"
"\n"
"* The output dataset is stored in float format.\n"
"\n"
"* The order of processing steps is the following (most are optional), and\n"
" for the LFFs, the bandpass is done between the specified fbot and ftop,\n"
" while for the `whole spectrum' (i.e., fALFF denominator) the bandpass is:\n"
" done only to exclude the time series mean and the Nyquist frequency:\n"
" (0) Check time series for initial transients [does not alter data]\n"
" (1) Despiking of each time series\n"
" (2) Removal of a constant+linear+quadratic trend in each time series\n"
" (3) Bandpass of data time series\n"
" (4) Bandpass of -ort time series, then detrending of data\n"
" with respect to the -ort time series\n"
" (5) Bandpass and de-orting of the -dsort dataset,\n"
" then detrending of the data with respect to -dsort\n"
" (6) Blurring inside the mask [might be slow]\n"
" (7) Local PV calculation [WILL be slow!]\n"
" (8) L2 normalization [will be fast.]\n"
" (9) Calculate spectrum and amplitudes, for RSFC parameters.\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"--------\n"
"OPTIONS:\n"
"--------\n"
" -despike = Despike each time series before other processing.\n"
" ++ Hopefully, you don't actually need to do this,\n"
" which is why it is optional.\n"
" -ort f.1D = Also orthogonalize input to columns in f.1D\n"
" ++ Multiple '-ort' options are allowed.\n"
" -dsort fset = Orthogonalize each voxel to the corresponding\n"
" voxel time series in dataset 'fset', which must\n"
" have the same spatial and temporal grid structure\n"
" as the main input dataset.\n"
" ++ At present, only one '-dsort' option is allowed.\n"
" -nodetrend = Skip the quadratic detrending of the input that\n"
" occurs before the FFT-based bandpassing.\n"
" ++ You would only want to do this if the dataset\n"
" had been detrended already in some other program.\n"
" -dt dd = set time step to 'dd' sec [default=from dataset header]\n"
" -nfft N = set the FFT length to 'N' [must be a legal value]\n"
" -norm = Make all output time series have L2 norm = 1\n"
" ++ i.e., sum of squares = 1\n"
" -mask mset = Mask dataset\n"
" -automask = Create a mask from the input dataset\n"
" -blur fff = Blur (inside the mask only) with a filter\n"
" width (FWHM) of 'fff' millimeters.\n"
" -localPV rrr = Replace each vector by the local Principal Vector\n"
" (AKA first singular vector) from a neighborhood\n"
" of radius 'rrr' millimiters.\n"
" ++ Note that the PV time series is L2 normalized.\n"
" ++ This option is mostly for Bob Cox to have fun with.\n"
"\n"
" -input dataset = Alternative way to specify input dataset.\n"
" -band fbot ftop = Alternative way to specify passband frequencies.\n"
"\n"
" -prefix ppp = Set prefix name of output dataset. Name of filtered time\n"
" series would be, e.g., ppp_LFF+orig.*, and the parameter\n"
" outputs are named with obvious suffices.\n"
" -quiet = Turn off the fun and informative messages. (Why?)\n"
" -no_rs_out = Don't output processed time series-- just output\n"
" parameters (not recommended, since the point of\n"
" calculating RSFC params here is to have them be quite\n"
" related to the time series themselves which are used for\n"
" further analysis)."
" -un_bp_out = Output the un-bandpassed series as well (default is not \n"
" to). Name would be, e.g., ppp_unBP+orig.* .\n"
" with suffix `_unBP'.\n"
" -no_rsfa = If you don't want RSFA output (default is to do so).\n"
" -bp_at_end = A (probably unnecessary) switch to have bandpassing be \n"
" the very last processing step that is done in the\n"
" sequence of steps listed above; at Step 3 above, only \n"
" the time series mean and nyquist are BP'ed out, and then\n"
" the LFF series is created only after Step 9. NB: this \n"
" probably makes only very small changes for most\n"
" processing sequences (but maybe not, depending usage).\n"
"\n"
" -notrans = Don't check for initial positive transients in the data:\n"
" *OR* ++ The test is a little slow, so skipping it is OK,\n"
" -nosat if you KNOW the data time series are transient-free.\n"
" ++ Or set AFNI_SKIP_SATCHECK to YES.\n"
" ++ Initial transients won't be handled well by the\n"
" bandpassing algorithm, and in addition may seriously\n"
" contaminate any further processing, such as inter-\n"
" voxel correlations via InstaCorr.\n"
" ++ No other tests are made [yet] for non-stationary \n"
" behavior in the time series data.\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"\n"
" If you use this program, please reference the introductory/description\n"
" paper for the FATCAT toolbox:\n"
" Taylor PA, Saad ZS (2013). FATCAT: (An Efficient) Functional\n"
" And Tractographic Connectivity Analysis Toolbox. Brain \n"
" Connectivity 3(5):523-535.\n"
"____________________________________________________________________________\n"
);
PRINT_AFNI_OMP_USAGE(
" 3dRSFC" ,
" * At present, the only part of 3dRSFC that is parallelized is the\n"
" '-blur' option, which processes each sub-brick independently.\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*-- startup --*/
mainENTRY("3dRSFC"); machdep();
AFNI_logger("3dRSFC",argc,argv);
PRINT_VERSION("3dRSFC (from 3dBandpass by RW Cox): version THETA");
AUTHOR("PA Taylor");
nosat = AFNI_yesenv("AFNI_SKIP_SATCHECK") ;
nopt = 1 ;
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcmp(argv[nopt],"-despike") == 0 ){ /* 08 Oct 2010 */
do_despike++ ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-nfft") == 0 ){
int nnup ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -nfft!") ;
nfft = (int)strtod(argv[nopt],NULL) ;
nnup = csfft_nextup_even(nfft) ;
if( nfft < 16 || nfft != nnup )
ERROR_exit("value %d after -nfft is illegal! Next legal value = %d",nfft,nnup) ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-blur") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -blur!") ;
blur = strtod(argv[nopt],NULL) ;
if( blur <= 0.0f ) WARNING_message("non-positive blur?!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-localPV") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -localpv!") ;
pvrad = strtod(argv[nopt],NULL) ;
if( pvrad <= 0.0f ) WARNING_message("non-positive -localpv?!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-prefix") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -prefix!") ;
prefix = strdup(argv[nopt]) ;
if( !THD_filename_ok(prefix) ) ERROR_exit("bad -prefix option!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-automask") == 0 ){
if( mask != NULL ) ERROR_exit("Can't use -mask AND -automask!") ;
do_automask = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mask") == 0 ){
THD_3dim_dataset *mset ;
if( ++nopt >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || do_automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[nopt] ) ;
CHECK_OPEN_ERROR(mset,argv[nopt]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[nopt]) ;
nmask = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
if( verb ) INFO_message("Number of voxels in mask = %d",nmask) ;
if( nmask < 1 ) ERROR_exit("Mask is too small to process") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-norm") == 0 ){
do_norm = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-quiet") == 0 ){
verb = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-no_rs_out") == 0 ){ // @@
SERIES_OUT = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-un_bp_out") == 0 ){ // @@
UNBP_OUT = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-no_rsfa") == 0 ){ // @@
DO_RSFA = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-bp_at_end") == 0 ){ // @@
BP_LAST = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-notrans") == 0 || strcmp(argv[nopt],"-nosat") == 0 ){
nosat = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-ort") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -ort!") ;
if( ortar == NULL ) INIT_IMARR(ortar) ;
ortim = mri_read_1D( argv[nopt] ) ;
if( ortim == NULL ) ERROR_exit("can't read from -ort '%s'",argv[nopt]) ;
mri_add_name(argv[nopt],ortim) ;
ADDTO_IMARR(ortar,ortim) ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-dsort") == 0 ){
THD_3dim_dataset *qset ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -dsort!") ;
if( nortset > 0 ) ERROR_exit("only 1 -dsort option is allowed!") ;
qset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(qset,argv[nopt]) ;
ortset = (THD_3dim_dataset **)realloc(ortset,
sizeof(THD_3dim_dataset *)*(nortset+1)) ;
ortset[nortset++] = qset ;
nopt++ ; continue ;
}
if( strncmp(argv[nopt],"-nodetrend",6) == 0 ){
qdet = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-dt") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -dt!") ;
dt = (float)strtod(argv[nopt],NULL) ;
if( dt <= 0.0f ) WARNING_message("value after -dt illegal!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-input") == 0 ){
if( inset != NULL ) ERROR_exit("Can't have 2 -input options!") ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -input!") ;
inset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(inset,argv[nopt]) ;
nopt++ ; continue ;
}
if( strncmp(argv[nopt],"-band",5) == 0 ){
if( ++nopt >= argc-1 ) ERROR_exit("need 2 arguments after -band!") ;
if( have_freq ) WARNING_message("second -band option replaces first one!") ;
fbot = strtod(argv[nopt++],NULL) ;
ftop = strtod(argv[nopt++],NULL) ;
have_freq = 1 ; continue ;
}
ERROR_exit("Unknown option: '%s'",argv[nopt]) ;
}
/** check inputs for reasonablositiness **/
if( !have_freq ){
if( nopt+1 >= argc )
ERROR_exit("Need frequencies on command line after options!") ;
fbot = (float)strtod(argv[nopt++],NULL) ;
ftop = (float)strtod(argv[nopt++],NULL) ;
}
if( inset == NULL ){
if( nopt >= argc )
ERROR_exit("Need input dataset name on command line after options!") ;
inset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(inset,argv[nopt]) ;
nopt++ ;
}
DSET_UNMSEC(inset) ;
if( fbot < 0.0f ) ERROR_exit("fbot value can't be negative!") ;
if( ftop <= fbot ) ERROR_exit("ftop value %g must be greater than fbot value %g!",ftop,fbot) ;
ntime = DSET_NVALS(inset) ;
if( ntime < 9 ) ERROR_exit("Input dataset is too short!") ;
if( nfft <= 0 ){
nfft = csfft_nextup_even(ntime) ;
if( verb ) INFO_message("Data length = %d FFT length = %d",ntime,nfft) ;
(void)THD_bandpass_set_nfft(nfft) ;
} else if( nfft < ntime ){
ERROR_exit("-nfft %d is less than data length = %d",nfft,ntime) ;
} else {
kk = THD_bandpass_set_nfft(nfft) ;
if( kk != nfft && verb )
INFO_message("Data length = %d FFT length = %d",ntime,kk) ;
}
if( dt <= 0.0f ){
dt = DSET_TR(inset) ;
if( dt <= 0.0f ){
WARNING_message("Setting dt=1.0 since input dataset lacks a time axis!") ;
dt = 1.0f ;
}
}
ftopALL = 1./dt ;// Aug,2016: should solve problem of a too-large
// value for THD_bandpass_vectors(), while still
// being >f_{Nyquist}
if( !THD_bandpass_OK(ntime,dt,fbot,ftop,1) ) ERROR_exit("Can't continue!") ;
nx = DSET_NX(inset); ny = DSET_NY(inset); nz = DSET_NZ(inset); nvox = nx*ny*nz;
/* check mask, or create it */
if( verb ) INFO_message("Loading input dataset time series" ) ;
DSET_load(inset) ;
if( mask != NULL ){
if( mask_nx != nx || mask_ny != ny || mask_nz != nz )
ERROR_exit("-mask dataset grid doesn't match input dataset") ;
} else if( do_automask ){
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset?") ;
nmask = THD_countmask( DSET_NVOX(inset) , mask ) ;
if( verb ) INFO_message("Number of voxels in automask = %d",nmask);
if( nmask < 1 ) ERROR_exit("Automask is too small to process") ;
} else {
mask = (byte *)malloc(sizeof(byte)*nvox) ; nmask = nvox ;
memset(mask,1,sizeof(byte)*nvox) ;
// if( verb ) // @@ alert if aaaalllllll vox are going to be analyzed!
INFO_message("No mask ==> processing all %d voxels",nvox);
}
/* A simple check of dataset quality [08 Feb 2010] */
if( !nosat ){
float val ;
INFO_message(
"Checking dataset for initial transients [use '-notrans' to skip this test]") ;
val = THD_saturation_check(inset,mask,0,0) ; kk = (int)(val+0.54321f) ;
if( kk > 0 )
ININFO_message(
"Looks like there %s %d non-steady-state initial time point%s :-(" ,
((kk==1) ? "is" : "are") , kk , ((kk==1) ? " " : "s") ) ;
else if( val > 0.3210f ) /* don't ask where this threshold comes from! */
ININFO_message(
"MAYBE there's an initial positive transient of 1 point, but it's hard to tell\n") ;
else
ININFO_message("No widespread initial positive transient detected :-)") ;
}
/* check -dsort inputs for match to inset */
for( kk=0 ; kk < nortset ; kk++ ){
if( DSET_NX(ortset[kk]) != nx ||
DSET_NY(ortset[kk]) != ny ||
DSET_NZ(ortset[kk]) != nz ||
DSET_NVALS(ortset[kk]) != ntime )
ERROR_exit("-dsort %s doesn't match input dataset grid" ,
DSET_BRIKNAME(ortset[kk]) ) ;
}
/* convert input dataset to a vectim, which is more fun */
// @@ convert BP'ing ftop/bot into indices for the DFT (below)
delf = 1.0/(ntime*dt);
ind_low = (int) rint(fbot/delf);
ind_high = (int) rint(ftop/delf);
if( ntime % 2 ) // nyquist number
N_ny = (ntime-1)/2;
else
N_ny = ntime/2;
sqnt = sqrt(ntime);
nt_fac = sqrt(ntime*(ntime-1));
// @@ if BP_LAST==0:
// now we go through twice, doing LFF bandpass for NumDen==0 and
// `full spectrum' processing for NumDen==1.
// if BP_LAST==1:
// now we go through once, doing only `full spectrum' processing
for( NumDen=0 ; NumDen<2 ; NumDen++) {
//if( NumDen==1 ){ // full spectrum
// fbot = fbotALL;
// ftop = ftopALL;
//}
// essentially, just doesn't BP here, and the perfect filtering at end
// is used for both still; this makes the final output spectrum
// contain only frequencies in range of 0.01-0.08
if( BP_LAST==1 )
INFO_message("Only doing filtering to LFFs at end!");
mrv = THD_dset_to_vectim( inset , mask , 0 ) ;
if( mrv == NULL ) ERROR_exit("Can't load time series data!?") ;
if( NumDen==1 )
DSET_unload(inset) ; // @@ only unload on 2nd pass
/* similarly for the ort vectors */
if( ortar != NULL ){
for( kk=0 ; kk < IMARR_COUNT(ortar) ; kk++ ){
ortim = IMARR_SUBIM(ortar,kk) ;
if( ortim->nx < ntime )
ERROR_exit("-ort file %s is shorter than input dataset time series",
ortim->name ) ;
ort = (float **)realloc( ort , sizeof(float *)*(nort+ortim->ny) ) ;
for( vv=0 ; vv < ortim->ny ; vv++ )
ort[nort++] = MRI_FLOAT_PTR(ortim) + ortim->nx * vv ;
}
}
/* all the real work now */
if( do_despike ){
int_pair nsp ;
if( verb ) INFO_message("Testing data time series for spikes") ;
nsp = THD_vectim_despike9( mrv ) ;
if( verb ) ININFO_message(" -- Squashed %d spikes from %d voxels",nsp.j,nsp.i) ;
}
if( verb ) INFO_message("Bandpassing data time series") ;
if( (BP_LAST==0) && (NumDen==0) )
(void)THD_bandpass_vectim( mrv , dt,fbot,ftop , qdet , nort,ort ) ;
else
(void)THD_bandpass_vectim( mrv , dt,fbotALL,ftopALL, qdet,nort,ort ) ;
/* OK, maybe a little more work */
if( nortset == 1 ){
MRI_vectim *orv ;
orv = THD_dset_to_vectim( ortset[0] , mask , 0 ) ;
if( orv == NULL ){
ERROR_message("Can't load -dsort %s",DSET_BRIKNAME(ortset[0])) ;
} else {
float *dp , *mvv , *ovv , ff ;
if( verb ) INFO_message("Orthogonalizing to bandpassed -dsort") ;
//(void)THD_bandpass_vectim( orv , dt,fbot,ftop , qdet , nort,ort ) ; //@@
if( (BP_LAST==0) && (NumDen==0) )
(void)THD_bandpass_vectim(orv,dt,fbot,ftop,qdet,nort,ort);
else
(void)THD_bandpass_vectim(orv,dt,fbotALL,ftopALL,qdet,nort,ort);
THD_vectim_normalize( orv ) ;
dp = malloc(sizeof(float)*mrv->nvec) ;
THD_vectim_vectim_dot( mrv , orv , dp ) ;
for( vv=0 ; vv < mrv->nvec ; vv++ ){
ff = dp[vv] ;
if( ff != 0.0f ){
mvv = VECTIM_PTR(mrv,vv) ; ovv = VECTIM_PTR(orv,vv) ;
for( kk=0 ; kk < ntime ; kk++ ) mvv[kk] -= ff*ovv[kk] ;
}
}
VECTIM_destroy(orv) ; free(dp) ;
}
}
if( blur > 0.0f ){
if( verb )
INFO_message("Blurring time series data spatially; FWHM=%.2f",blur) ;
mri_blur3D_vectim( mrv , blur ) ;
}
if( pvrad > 0.0f ){
if( verb )
INFO_message("Local PV-ing time series data spatially; radius=%.2f",pvrad) ;
THD_vectim_normalize( mrv ) ;
THD_vectim_localpv( mrv , pvrad ) ;
}
if( do_norm && pvrad <= 0.0f ){
if( verb ) INFO_message("L2 normalizing time series data") ;
THD_vectim_normalize( mrv ) ;
}
/* create output dataset, populate it, write it, then quit */
if( (NumDen==0) ) { // @@ BP'ed version; will do filt if BP_LAST
if(BP_LAST) // do bandpass here for BP_LAST
(void)THD_bandpass_vectim(mrv,dt,fbot,ftop,qdet,0,NULL);
if( verb ) INFO_message("Creating output dataset in memory, then writing it") ;
outset = EDIT_empty_copy(inset) ;
if(SERIES_OUT){
sprintf(out_lff,"%s_LFF",prefix);
EDIT_dset_items( outset , ADN_prefix,out_lff , ADN_none ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dBandpass" , argc,argv , outset ) ;
}
for( vv=0 ; vv < ntime ; vv++ )
EDIT_substitute_brick( outset , vv , MRI_float , NULL ) ;
#if 1
THD_vectim_to_dset( mrv , outset ) ;
#else
AFNI_OMP_START ;
#pragma omp parallel
{ float *far , *var ; int *ivec=mrv->ivec ; int vv,kk ;
#pragma omp for
for( vv=0 ; vv < ntime ; vv++ ){
far = DSET_BRICK_ARRAY(outset,vv) ; var = mrv->fvec + vv ;
for( kk=0 ; kk < nmask ; kk++ ) far[ivec[kk]] = var[kk*ntime] ;
}
}
AFNI_OMP_END ;
#endif
VECTIM_destroy(mrv) ;
if(SERIES_OUT){ // @@
DSET_write(outset) ; if( verb ) WROTE_DSET(outset) ;
}
}
else{ // @@ non-BP'ed version
if( verb ) INFO_message("Creating output dataset 2 in memory") ;
// do this here because LFF version was also BP'ed at end.
if(BP_LAST) // do bandpass here for BP_LAST
(void)THD_bandpass_vectim(mrv,dt,fbotALL,ftopALL,qdet,0,NULL);
outsetALL = EDIT_empty_copy(inset) ;
if(UNBP_OUT){
sprintf(out_unBP,"%s_unBP",prefix);
EDIT_dset_items( outsetALL, ADN_prefix, out_unBP, ADN_none );
tross_Copy_History( inset , outsetALL ) ;
tross_Make_History( "3dRSFC" , argc,argv , outsetALL ) ;
}
for( vv=0 ; vv < ntime ; vv++ )
EDIT_substitute_brick( outsetALL , vv , MRI_float , NULL ) ;
#if 1
THD_vectim_to_dset( mrv , outsetALL ) ;
#else
AFNI_OMP_START ;
#pragma omp parallel
{ float *far , *var ; int *ivec=mrv->ivec ; int vv,kk ;
#pragma omp for
for( vv=0 ; vv < ntime ; vv++ ){
far = DSET_BRICK_ARRAY(outsetALL,vv) ; var = mrv->fvec + vv ;
for( kk=0 ; kk < nmask ; kk++ ) far[ivec[kk]] = var[kk*ntime] ;
}
}
AFNI_OMP_END ;
#endif
VECTIM_destroy(mrv) ;
if(UNBP_OUT){
DSET_write(outsetALL) ; if( verb ) WROTE_DSET(outsetALL) ;
}
}
}// end of NumDen loop
// @@
INFO_message("Starting the (f)ALaFFel calcs") ;
// allocations
series1 = (double *)calloc(ntime,sizeof(double));
series2 = (double *)calloc(ntime,sizeof(double));
xx1 = (double *)calloc(2*ntime,sizeof(double));
xx2 = (double *)calloc(2*ntime,sizeof(double));
alff = (float *)calloc(nvox,sizeof(float));
malff = (float *)calloc(nvox,sizeof(float));
falff = (float *)calloc(nvox,sizeof(float));
if( (series1 == NULL) || (series2 == NULL)
|| (xx1 == NULL) || (xx2 == NULL)
|| (alff == NULL) || (malff == NULL) || (falff == NULL)) {
fprintf(stderr, "\n\n MemAlloc failure.\n\n");
exit(122);
}
if(DO_RSFA) {
rsfa = (float *)calloc(nvox,sizeof(float));
mrsfa = (float *)calloc(nvox,sizeof(float));
frsfa = (float *)calloc(nvox,sizeof(float));
if( (rsfa == NULL) || (mrsfa == NULL) || (frsfa == NULL)) {
fprintf(stderr, "\n\n MemAlloc failure.\n\n");
exit(123);
}
}
work = gsl_fft_real_workspace_alloc (ntime);
real1 = gsl_fft_real_wavetable_alloc (ntime);
real2 = gsl_fft_real_wavetable_alloc (ntime);
gsl_complex_packed_array compl_freqs1 = xx1;
gsl_complex_packed_array compl_freqs2 = xx2;
// *********************************************************************
// *********************************************************************
// ************** Falafelling = ALFF/fALFF calcs *****************
// *********************************************************************
// *********************************************************************
// Be now have the BP'ed data set (outset) and the non-BP'ed one
// (outsetALL). now we'll FFT both, get amplitudes in appropriate
// ranges, and calculate: ALFF, mALFF, fALFF,
ctr = 0;
for( kk=0; kk<nvox ; kk++) {
if(mask[kk]) {
// BP one, and unBP one, either for BP_LAST or !BP_LAST
for( m=0 ; m<ntime ; m++ ) {
series1[m] = THD_get_voxel(outset,kk,m);
series2[m] = THD_get_voxel(outsetALL,kk,m);
}
mm = gsl_fft_real_transform(series1, 1, ntime, real1, work);
mm = gsl_fft_halfcomplex_unpack(series1, compl_freqs1, 1, ntime);
mm = gsl_fft_real_transform(series2, 1, ntime, real2, work);
mm = gsl_fft_halfcomplex_unpack(series2, compl_freqs2, 1, ntime);
numer = 0.0f;
denom = 0.0f;
de_rsfa = 0.0f;
nu_rsfa = 0.0f;
for( m=1 ; m<N_ny ; m++ ) {
mm = 2*m;
pow2 = compl_freqs2[mm]*compl_freqs2[mm] +
compl_freqs2[mm+1]*compl_freqs2[mm+1]; // power
//pow2*=2;// factor of 2 since ampls are even funcs
denom+= (float) sqrt(pow2); // amplitude
de_rsfa+= (float) pow2;
if( ( m>=ind_low ) && ( m<=ind_high ) ){
pow1 = compl_freqs1[mm]*compl_freqs1[mm]+
compl_freqs1[mm+1]*compl_freqs1[mm+1];
//pow1*=2;
numer+= (float) sqrt(pow1);
nu_rsfa+= (float) pow1;
}
}
if( denom>0.000001 )
falff[kk] = numer/denom;
else
falff[kk] = 0.;
alff[kk] = 2*numer/sqnt;// factor of 2 since ampl is even funct
meanALFF+= alff[kk];
if(DO_RSFA){
nu_rsfa = sqrt(2*nu_rsfa); // factor of 2 since ampls
de_rsfa = sqrt(2*de_rsfa); // are even funcs
if( de_rsfa>0.000001 )
frsfa[kk] = nu_rsfa/de_rsfa;
else
frsfa[kk]=0.;
rsfa[kk] = nu_rsfa/nt_fac;
meanRSFA+= rsfa[kk];
}
ctr+=1;
}
}
meanALFF/= ctr;
meanRSFA/= ctr;
gsl_fft_real_wavetable_free(real1);
gsl_fft_real_wavetable_free(real2);
gsl_fft_real_workspace_free(work);
// ALFFs divided by mean of brain value
for( kk=0 ; kk<nvox ; kk++ )
if(mask[kk]){
malff[kk] = alff[kk]/meanALFF;
if(DO_RSFA)
mrsfa[kk] = rsfa[kk]/meanRSFA;
}
// **************************************************************
// **************************************************************
// Store and output
// **************************************************************
// **************************************************************
outsetALFF = EDIT_empty_copy( inset ) ;
sprintf(out_alff,"%s_ALFF",prefix);
EDIT_dset_items( outsetALFF,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_alff,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetALFF)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetALFF));
EDIT_substitute_brick(outsetALFF, 0, MRI_float, alff);
alff=NULL;
THD_load_statistics(outsetALFF);
tross_Make_History("3dRSFC", argc, argv, outsetALFF);
THD_write_3dim_dataset(NULL, NULL, outsetALFF, True);
outsetfALFF = EDIT_empty_copy( inset ) ;
sprintf(out_falff,"%s_fALFF",prefix);
EDIT_dset_items( outsetfALFF,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_falff,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetfALFF)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetfALFF));
EDIT_substitute_brick(outsetfALFF, 0, MRI_float, falff);
falff=NULL;
THD_load_statistics(outsetfALFF);
tross_Make_History("3dRSFC", argc, argv, outsetfALFF);
THD_write_3dim_dataset(NULL, NULL, outsetfALFF, True);
outsetmALFF = EDIT_empty_copy( inset ) ;
sprintf(out_malff,"%s_mALFF",prefix);
EDIT_dset_items( outsetmALFF,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_malff,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetmALFF)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetmALFF));
EDIT_substitute_brick(outsetmALFF, 0, MRI_float, malff);
malff=NULL;
THD_load_statistics(outsetmALFF);
tross_Make_History("3dRSFC", argc, argv, outsetmALFF);
THD_write_3dim_dataset(NULL, NULL, outsetmALFF, True);
if(DO_RSFA){
outsetRSFA = EDIT_empty_copy( inset ) ;
sprintf(out_rsfa,"%s_RSFA",prefix);
EDIT_dset_items( outsetRSFA,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_rsfa,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetRSFA)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetRSFA));
EDIT_substitute_brick(outsetRSFA, 0, MRI_float, rsfa);
rsfa=NULL;
THD_load_statistics(outsetRSFA);
tross_Make_History("3dRSFC", argc, argv, outsetRSFA);
THD_write_3dim_dataset(NULL, NULL, outsetRSFA, True);
outsetfRSFA = EDIT_empty_copy( inset ) ;
sprintf(out_frsfa,"%s_fRSFA",prefix);
EDIT_dset_items( outsetfRSFA,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_frsfa,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetfRSFA)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetfRSFA));
EDIT_substitute_brick(outsetfRSFA, 0, MRI_float, frsfa);
frsfa=NULL;
THD_load_statistics(outsetfRSFA);
tross_Make_History("3dRSFC", argc, argv, outsetfRSFA);
THD_write_3dim_dataset(NULL, NULL, outsetfRSFA, True);
outsetmRSFA = EDIT_empty_copy( inset ) ;
sprintf(out_mrsfa,"%s_mRSFA",prefix);
EDIT_dset_items( outsetmRSFA,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_mrsfa,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetmRSFA)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetmRSFA));
EDIT_substitute_brick(outsetmRSFA, 0, MRI_float, mrsfa);
mrsfa=NULL;
THD_load_statistics(outsetmRSFA);
tross_Make_History("3dRSFC", argc, argv, outsetmRSFA);
THD_write_3dim_dataset(NULL, NULL, outsetmRSFA, True);
}
// ************************************************************
// ************************************************************
// Freeing
// ************************************************************
// ************************************************************
DSET_delete(inset);
DSET_delete(outsetALL);
DSET_delete(outset);
DSET_delete(outsetALFF);
DSET_delete(outsetmALFF);
DSET_delete(outsetfALFF);
DSET_delete(outsetRSFA);
DSET_delete(outsetmRSFA);
DSET_delete(outsetfRSFA);
free(inset);
free(outsetALL);
free(outset);
free(outsetALFF);
free(outsetmALFF);
free(outsetfALFF);
free(outsetRSFA);
free(outsetmRSFA);
free(outsetfRSFA);
free(rsfa);
free(mrsfa);
free(frsfa);
free(alff);
free(malff);
free(falff);
free(mask);
free(series1);
free(series2);
free(xx1);
free(xx2);
exit(0) ;
}
void FFTFilter::calculateOutputData(double *x, double *y) {
for (int i = 0; i < d_points; i++) {
x[i] = d_x[i];
y[i] = d_y[i];
}
double df =
0.5 /
(double)(d_n *
(x[1] - x[0])); // half frequency sampling due to GSL storing
gsl_fft_real_workspace *work = gsl_fft_real_workspace_alloc(d_n);
gsl_fft_real_wavetable *real = gsl_fft_real_wavetable_alloc(d_n);
gsl_fft_real_transform(y, 1, d_n, real, work);
gsl_fft_real_wavetable_free(real);
ApplicationWindow *app = dynamic_cast<ApplicationWindow *>(parent());
QLocale locale = app->locale();
d_explanation = locale.toString(d_low_freq) + " ";
if (d_filter_type > 2)
d_explanation += tr("to") + " " + locale.toString(d_high_freq) + " ";
d_explanation += tr("Hz") + " ";
switch ((int)d_filter_type) {
case 1: // low pass
d_explanation += tr("Low Pass FFT Filter");
for (int i = 0; i < d_n; i++)
y[i] = i * df > d_low_freq ? 0 : y[i];
break;
case 2: // high pass
d_explanation += tr("High Pass FFT Filter");
for (int i = 0; i < d_n; i++)
y[i] = i * df < d_low_freq ? 0 : y[i];
break;
case 3: // band pass
d_explanation += tr("Band Pass FFT Filter");
if (d_offset) { // keep DC offset
for (int i = 1; i < d_n; i++)
y[i] = ((i * df > d_low_freq) && (i * df < d_high_freq)) ? y[i] : 0;
} else {
for (int i = 0; i < d_n; i++)
y[i] = ((i * df > d_low_freq) && (i * df < d_high_freq)) ? y[i] : 0;
}
break;
case 4: // band block
d_explanation += tr("Band Block FFT Filter");
if (!d_offset)
y[0] = 0; // substract DC offset
for (int i = 1; i < d_n; i++)
y[i] = ((i * df > d_low_freq) && (i * df < d_high_freq)) ? 0 : y[i];
break;
}
gsl_fft_halfcomplex_wavetable *hc = gsl_fft_halfcomplex_wavetable_alloc(d_n);
gsl_fft_halfcomplex_inverse(y, 1, d_n, hc, work);
gsl_fft_halfcomplex_wavetable_free(hc);
gsl_fft_real_workspace_free(work);
}
