QString PSD::descriptionTip() const {
QString tip;
tip = tr("Spectrum: %1\n FFT Length: 2^%2").arg(Name()).arg(length());
if (average() || apodize() || removeMean()) {
tip += "\n ";
if (average()) tip += tr("Average; ");
if (apodize()) tip += tr("Apodize; ");
if (removeMean()) tip += tr("Remove Mean;");
}
tip += tr("\nInput: %1").arg(_inputVectors[INVECTOR]->descriptionTip());
return tip;
}
// store the current state of the widget as the default
void FFTOptions::setWidgetDefaults() {
_dialogDefaults->setValue("spectrum/freq", sampleRate());
_dialogDefaults->setValue("spectrum/average", interleavedAverage());
_dialogDefaults->setValue("spectrum/len", FFTLength());
_dialogDefaults->setValue("spectrum/apodize", apodize());
_dialogDefaults->setValue("spectrum/removeMean", removeMean());
_dialogDefaults->setValue("spectrum/vUnits", vectorUnits());
_dialogDefaults->setValue("spectrum/rUnits", rateUnits());
_dialogDefaults->setValue("spectrum/apodizeFxn", apodizeFunction());
_dialogDefaults->setValue("spectrum/gaussianSigma", sigma());
_dialogDefaults->setValue("spectrum/output", output());
_dialogDefaults->setValue("spectrum/interpolateHoles", interpolateOverHoles());
}
void Mesh :: normalizeSolution( void )
{
// center vertices around the origin
removeMean( newVertices );
// find the vertex with the largest norm
double r = 0.;
for( size_t i = 0; i < vertices.size(); i++ )
{
r = max( r, newVertices[i].norm2() );
}
r = sqrt(r);
// rescale so that vertices have norm at most one
for( size_t i = 0; i < vertices.size(); i++ )
{
newVertices[i] /= r;
}
}
KstObject::UpdateType KstPSDCurve::update(int update_counter) {
int i_subset, i_samp;
int n_subsets;
int v_len;
int copyLen;
double mean;
double y;
bool force = false;
KstVectorPtr iv = _inputVectors[INVECTOR];
double *psd;
if (KstObject::checkUpdateCounter(update_counter))
return NO_CHANGE;
if (update_counter <= 0) {
force = true;
} else {
iv->update(update_counter);
}
v_len = iv->sampleCount();
n_subsets = v_len/PSDLen+1;
last_n_new += iv->numNew();
if ((last_n_new < PSDLen/16) && (n_subsets - last_n_subsets < 1) && !force) {
return NO_CHANGE;
}
psd = (*_sVector)->value();
for (i_samp = 0; i_samp < PSDLen; i_samp++) {
psd[i_samp] = 0;
}
for (i_subset = 0; i_subset < n_subsets; i_subset++) {
/* copy each chunk into a[] and find mean */
if (i_subset*PSDLen + ALen <= v_len) {
copyLen = ALen;
} else {
copyLen = v_len - i_subset*PSDLen;
}
mean = 0;
for (i_samp = 0; i_samp < copyLen; i_samp++) {
mean += (
a[i_samp] =
iv->interpolate(i_samp + i_subset*PSDLen, v_len)
);
}
if (copyLen>1) mean/=(double)copyLen;
/* Remove Mean and apodize */
if (removeMean() && appodize()) {
for (i_samp=0; i_samp<copyLen; i_samp++) {
a[i_samp]= (a[i_samp]-mean)*w[i_samp];
}
} else if (removeMean()) {
for (i_samp=0; i_samp<copyLen; i_samp++) {
a[i_samp] -= mean;
}
} else if (appodize()) {
for (i_samp=0; i_samp<copyLen; i_samp++) {
a[i_samp] *= w[i_samp];
}
}
for (;i_samp < ALen; i_samp++) a[i_samp] = 0.0;
/* fft a */
rdft(ALen, 1, a);
/* sum each bin into psd[] */
psd[0]+=a[0];
psd[PSDLen-1] += a[1];
for (i_samp=1; i_samp<PSDLen-1; i_samp++) {
psd[i_samp]+= cabs(a[i_samp*2], a[i_samp*2+1]);
}
}
last_f0 = 0;
last_n_subsets = n_subsets;
last_n_new = 0;
norm_factor = 1.0/(sqrt(double(Freq)*double(PSDLen))*double(n_subsets));
psd[0]*=norm_factor;
MaxY = MinY = mean = psd[0];
if (psd[0]>0)
MinPosY = psd[0];
else (MinPosY = 1.0e300);
/* normalize psd */
for (i_samp=1; i_samp<PSDLen; i_samp++) {
y = (psd[i_samp]*=norm_factor);
if (y>MaxY)
MaxY=y;
if (y<MinY)
MinY=y;
if ((y>0) && (y<MinPosY))
MinPosY = y;
mean +=y;
}
if (PSDLen > 0)
MeanY = mean/PSDLen;
else MeanY = 0; // should never ever happen...
NS = PSDLen;
if (Freq <= 0)
Freq = 1.0;
MaxX = Freq/2.0;
MinX = 0;
MinPosX = 1.0/double(NS) * MaxX;
MeanX = MaxX/2.0;
double *f = (*_fVector)->value();
f[0] = 0;
f[1] = Freq/2.0;
(*_sVector)->update(update_counter);
(*_fVector)->update(update_counter);
return UPDATE;
}
void KstPSDGenerator::updateNow() {
int i_subset, i_samp;
int n_subsets;
int v_len;
int copyLen;
QValueVector<double> psd, f;
double mean;
double nf;
bool done;
adjustLengths();
v_len = _inputVector->size();
n_subsets = v_len/_PSDLen+1;
if (v_len%_PSDLen==0) n_subsets--;
// always update when asked
_last_n_new = _inputVector->size();
for (i_samp = 0; i_samp < _PSDLen; i_samp++) {
(*_powerVector)[i_samp] = 0;
(*_frequencyVector)[i_samp] = i_samp*0.5*_Freq/( _PSDLen-1 );
}
_frequencyVectorStep = 0.5*_Freq/(_PSDLen - 1);
nf = 0;
done = false;
for (i_subset = 0; !done; i_subset++) {
// copy each chunk into a[] and find mean
if (i_subset*_PSDLen + _ALen < v_len) {
copyLen = _ALen;
} else {
copyLen = v_len - i_subset*_PSDLen;
done = true;
}
mean = 0;
for (i_samp = 0; i_samp < copyLen; i_samp++) {
mean += (
_a[i_samp] =
_inputVector->at(i_samp + i_subset*_PSDLen)
);
}
if (copyLen > 1) {
mean /= (double)copyLen;
}
if (apodize()) {
GenW(copyLen);
}
// remove mean and apodize
if (removeMean() && apodize()) {
for (i_samp=0; i_samp<copyLen; i_samp++) {
_a[i_samp]= (_a[i_samp]-mean)*_w[i_samp];
}
} else if (removeMean()) {
for (i_samp=0; i_samp<copyLen; i_samp++) {
_a[i_samp] -= mean;
}
} else if (apodize()) {
for (i_samp=0; i_samp<copyLen; i_samp++) {
_a[i_samp] *= _w[i_samp];
}
}
nf += copyLen;
for (;i_samp < _ALen; i_samp++) {
_a[i_samp] = 0.0;
}
// fft a
rdft(_ALen, 1, _a);
(*_powerVector)[0] += _a[0]*_a[0];
(*_powerVector)[_PSDLen-1] += _a[1]*_a[1];
for (i_samp=1; i_samp<_PSDLen-1; i_samp++) {
(*_powerVector)[i_samp]+= cabs2(_a[i_samp*2], _a[i_samp*2+1]);
}
}
_last_f0 = 0;
_last_n_subsets = n_subsets;
_last_n_new = 0;
nf = 1.0/(double(_Freq)*double(nf/2.0));
for ( i_samp = 0; i_samp<_PSDLen; i_samp++ ) {
(*_powerVector)[i_samp] = sqrt((*_powerVector)[i_samp]*nf);
}
if (_Freq <= 0.0) {
_Freq = 1.0;
}
}
