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#ifndef NORMALIZE_PASSBAND_H
#define NORMALIZE_PASSBAND_H
#include "../algorithms/thresholdtools.h"
#include <aocommon/uvector.h>
#include <vector>
#ifdef __SSE__
#define USE_INTRINSICS
#endif
#ifdef USE_INTRINSICS
#include <xmmintrin.h>
#endif
class NormalizePassband
{
public:
static void NormalizeStepwise(TimeFrequencyData& data, size_t steps)
{
const size_t height = data.ImageHeight();
aocommon::UVector<num_t> stddev(steps);
for(size_t step=0; step!=steps; ++step)
{
const size_t startY = step*height/steps, endY = (step+1)*height/steps;
std::vector<num_t> dataVector((1+endY-startY) * data.ImageWidth() * data.ImageCount());
std::vector<num_t>::iterator vecIter = dataVector.begin();
const Mask2DCPtr mask = data.GetSingleMask();
for(size_t i=0; i!=data.ImageCount(); ++i)
{
const Image2D &image = *data.GetImage(i);
for(size_t y=startY; y!=endY; ++y)
{
const num_t* inputPtr = image.ValuePtr(0, y);
const bool* maskPtr = mask->ValuePtr(0, y);
for(size_t x=0; x!=image.Width(); ++x)
{
if(!*maskPtr && std::isfinite(*inputPtr))
{
*vecIter = *inputPtr;
++vecIter;
}
++inputPtr;
++maskPtr;
}
}
}
dataVector.resize(vecIter - dataVector.begin());
num_t mean;
ThresholdTools::WinsorizedMeanAndStdDev<num_t>(dataVector, mean, stddev[step]);
}
for(size_t i=0; i!=data.ImageCount(); ++i)
{
const Image2D &image = *data.GetImage(i);
Image2DPtr destImage = Image2D::CreateUnsetImagePtr(image.Width(), image.Height());
for(size_t step=0; step!=steps; ++step)
{
const size_t startY = step*height/steps, endY = (step+1)*height/steps;
float correctionFactor;
if(stddev[step] == 0.0)
correctionFactor = 0.0;
else
correctionFactor = 1.0 / stddev[step];
#ifdef USE_INTRINSICS
const __m128 corrFact4 = _mm_set_ps(correctionFactor, correctionFactor, correctionFactor, correctionFactor);
#endif
for(size_t y=startY; y!=endY; ++y)
{
const float *inputPtr = image.ValuePtr(0, y);
float *destPtr = destImage->ValuePtr(0, y);
#ifdef USE_INTRINSICS
for(size_t x=0;x<image.Width();x+=4)
{
_mm_store_ps(destPtr, _mm_mul_ps(corrFact4, _mm_load_ps(inputPtr)));
inputPtr += 4;
destPtr += 4;
}
#else
for(size_t x=0;x<image.Width();x++)
{
*destPtr = correctionFactor * *inputPtr;
inputPtr ++;
destPtr ++;
}
#endif
}
}
data.SetImage(i, std::move(destImage));
}
}
static void NormalizeSmooth(TimeFrequencyData& data)
{
TimeFrequencyData
real = data.Make(TimeFrequencyData::RealPart),
imag = data.Make(TimeFrequencyData::ImaginaryPart);
const size_t height = data.ImageHeight();
aocommon::UVector<double> rms(height);
std::vector<std::complex<num_t>> dataVector;
for(size_t y=0; y!=height; ++y)
{
dataVector.resize(data.ImageWidth() * data.ImageCount());
auto vecIter = dataVector.begin();
const Mask2DCPtr mask = data.GetSingleMask();
for(size_t i=0; i!=real.ImageCount(); ++i)
{
const Image2D& realImage = *real.GetImage(i);
const Image2D& imagImage = *imag.GetImage(i);
const num_t* realPtr = realImage.ValuePtr(0, y);
const num_t* imagPtr = imagImage.ValuePtr(0, y);
const bool* maskPtr = mask->ValuePtr(0, y);
for(size_t x=0; x!=realImage.Width(); ++x)
{
if(!*maskPtr && std::isfinite(*realPtr) && std::isfinite(*imagPtr))
{
*vecIter = std::complex<num_t>(*realPtr, *imagPtr);
++vecIter;
}
++realPtr;
++imagPtr;
++maskPtr;
}
}
dataVector.resize(vecIter - dataVector.begin());
rms[y] = ThresholdTools::WinsorizedRMS<num_t>(dataVector);
}
for(size_t i=0; i!=data.ImageCount(); ++i)
{
const Image2D &image = *data.GetImage(i);
Image2DPtr destImage = Image2D::CreateUnsetImagePtr(image.Width(), image.Height());
for(size_t y=0; y!=height; ++y)
{
num_t correctionFactor;
if(rms[y] == 0.0)
correctionFactor = 0.0;
else
correctionFactor = 1.0 / rms[y];
const num_t *inputPtr = image.ValuePtr(0, y);
num_t *destPtr = destImage->ValuePtr(0, y);
for(size_t x=0;x<image.Width();x++)
{
*destPtr = correctionFactor * *inputPtr;
++inputPtr;
++destPtr;
}
}
data.SetImage(i, std::move(destImage));
}
}
};
#endif
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