void Mask2D::EnlargeVerticallyAndSet(Mask2DCPtr smallMask, int factor)
{
for(size_t y=0;y<smallMask->Height();++y)
{
size_t binSize = factor;
if(binSize + y*factor > _height)
binSize = _height - y*factor;
for(size_t x=0;x<_width;++x)
{
for(size_t binY=0;binY<binSize;++binY)
{
size_t curY = y*factor + binY;
SetValue(x, curY, smallMask->Value(x, y));
}
}
}
}
void Mask2D::EnlargeHorizontallyAndSet(Mask2DCPtr smallMask, int factor)
{
for(size_t x=0;x<smallMask->Width();++x)
{
size_t binSize = factor;
if(binSize + x*factor > _width)
binSize = _width - x*factor;
for(size_t y=0;y<_height;++y)
{
for(size_t binX=0;binX<binSize;++binX)
{
size_t curX = x*factor + binX;
SetValue(curX, y, smallMask->Value(x, y));
}
}
}
}
void StatisticalFlagger::ThresholdTime(Mask2DCPtr mask, int **flagMarks, int **sums, int thresholdLevel, int width)
{
int halfWidthL = (width-1) / 2;
int halfWidthR = (width-1) / 2;
for(size_t y=0;y<mask->Height();++y)
{
const int *column = sums[y];
for(size_t x=halfWidthL;x<mask->Width() - halfWidthR;++x)
{
if(column[x] > thresholdLevel)
{
const unsigned right = x+halfWidthR+1;
++flagMarks[y][x-halfWidthL];
if(right < mask->Width())
--flagMarks[y][right];
}
}
}
}
void StatisticalFlagger::ThresholdFrequency(Mask2DCPtr mask, int **flagMarks, int **sums, int thresholdLevel, int width)
{
int halfWidthT = (width-1) / 2;
int halfWidthB = (width-1) / 2;
for(size_t y=halfWidthT;y<mask->Height() - halfWidthB;++y)
{
int *column = sums[y];
for(size_t x=0;x<mask->Width();++x)
{
if(column[x] > thresholdLevel)
{
const unsigned bottom = y+halfWidthB+1;
++flagMarks[y-halfWidthT][x];
if(bottom < mask->Height())
--flagMarks[bottom][x];
}
}
}
}
bool StatisticalFlagger::SquareContainsFlag(Mask2DCPtr mask, size_t xLeft, size_t yTop, size_t xRight, size_t yBottom)
{
for(size_t y=yTop;y<=yBottom;++y)
{
for(size_t x=xLeft;x<=xRight;++x)
{
if(mask->Value(x, y))
return true;
}
}
return false;
}
void StatisticalFlagger::SumToTop(Mask2DCPtr mask, int **sums, size_t width, size_t step, bool reverse)
{
if(reverse)
{
for(size_t y=width;y<mask->Height();++y)
{
int *column = sums[y];
for(size_t x=0;x<mask->Width();++x)
{
if(mask->Value(x, y - width/2))
column[x] += step;
}
}
} else {
for(size_t y=0;y<mask->Height() - width;++y)
{
int *column = sums[y];
for(size_t x=0;x<mask->Width();++x)
{
if(mask->Value(x, y + width/2))
column[x] += step;
}
}
}
}
void ChangeResolutionAction::DecreaseTimeWithMask(TimeFrequencyData &data)
{
size_t polCount = data.PolarisationCount();
for(size_t i=0;i<polCount;++i)
{
TimeFrequencyData *polData = data.CreateTFDataFromPolarisationIndex(i);
Mask2DCPtr mask = polData->GetSingleMask();
for(unsigned j=0;j<polData->ImageCount();++j)
{
Image2DCPtr image = polData->GetImage(j);
polData->SetImage(j, ThresholdTools::ShrinkHorizontally(_timeDecreaseFactor, image, mask));
}
delete polData;
}
size_t maskCount = data.MaskCount();
for(size_t i=0;i<maskCount;++i)
{
Mask2DCPtr mask = data.GetMask(i);
Mask2DPtr newMask = mask->ShrinkHorizontallyForAveraging(_timeDecreaseFactor);
data.SetMask(i, newMask);
}
}
void UVImager::Image(const TimeFrequencyData &data, TimeFrequencyMetaDataCPtr metaData, unsigned frequencyIndex)
{
if(_uvReal == 0)
Empty();
Image2DCPtr
real = data.GetRealPart(),
imaginary = data.GetImaginaryPart();
Mask2DCPtr
flags = data.GetSingleMask();
for(unsigned i=0;i<data.ImageWidth();++i) {
switch(_imageKind) {
case Homogeneous:
if(flags->Value(i, frequencyIndex)==0.0L) {
num_t
vr = real->Value(i, frequencyIndex),
vi = imaginary->Value(i, frequencyIndex);
if(std::isfinite(vr) && std::isfinite(vi))
{
num_t u,v;
GetUVPosition(u, v, i, frequencyIndex, metaData);
SetUVValue(u, v, vr, vi, 1.0);
SetUVValue(-u, -v, vr, -vi, 1.0);
}
}
break;
case Flagging:
if((flags->Value(i, frequencyIndex)!=0.0L && !_invertFlagging) ||
(flags->Value(i, frequencyIndex)==0.0L && _invertFlagging)) {
num_t u,v;
GetUVPosition(u, v, i, frequencyIndex, metaData);
SetUVValue(u, v, 1, 0, 1.0);
SetUVValue(-u, -v, 1, 0, 1.0);
}
break;
}
}
}
void ChangeResolutionAction::DecreaseTimeWithMask(TimeFrequencyData& data)
{
size_t polCount = data.PolarizationCount();
for(size_t i=0;i<polCount;++i)
{
TimeFrequencyData polData(data.MakeFromPolarizationIndex(i));
const Mask2DCPtr mask = polData.GetSingleMask();
for(unsigned j=0;j<polData.ImageCount();++j)
{
const Image2DCPtr image = polData.GetImage(j);
polData.SetImage(j, ThresholdTools::ShrinkHorizontally(_timeDecreaseFactor, image.get(), mask.get()));
}
data.SetPolarizationData(i, std::move(polData));
}
size_t maskCount = data.MaskCount();
for(size_t i=0;i<maskCount;++i)
{
Mask2DCPtr mask = data.GetMask(i);
Mask2DPtr newMask(new Mask2D(mask->ShrinkHorizontallyForAveraging(_timeDecreaseFactor)));
data.SetMask(i, std::move(newMask));
}
}
void FrequencyPowerPlot::Add(class TimeFrequencyData &data, TimeFrequencyMetaDataCPtr meta)
{
Image2DCPtr image = data.GetSingleImage();
Mask2DCPtr mask = data.GetSingleMask();
for(size_t y=0;y<image->Height();++y)
{
double frequency = meta->Band().channels[y].frequencyHz;
size_t count = 0;
long double value = 0.0L;
for(size_t x=0;x<image->Width();++x)
{
if(!mask->Value(x, y))
{
++count;
value += image->Value(x, y);
}
}
MapItem &item = _values[frequency];
item.total += value;
item.count += count;
}
}
void ChangeResolutionAction::DecreaseTime(TimeFrequencyData &timeFrequencyData)
{
if(_useMaskInAveraging)
{
DecreaseTimeWithMask(timeFrequencyData);
}
else {
size_t imageCount = timeFrequencyData.ImageCount();
for(size_t i=0;i<imageCount;++i)
{
Image2DCPtr image = timeFrequencyData.GetImage(i);
Image2DPtr newImage(new Image2D(image->ShrinkHorizontally(_timeDecreaseFactor)));
timeFrequencyData.SetImage(i, std::move(newImage));
}
size_t maskCount = timeFrequencyData.MaskCount();
for(size_t i=0;i<maskCount;++i)
{
Mask2DCPtr mask = timeFrequencyData.GetMask(i);
Mask2DPtr newMask(new Mask2D(mask->ShrinkHorizontally(_timeDecreaseFactor)));
timeFrequencyData.SetMask(i, std::move(newMask));
}
}
}
void Morphology::floodFill(Mask2DCPtr mask, SegmentedImagePtr output, const int *const *lengthWidthValues, size_t x, size_t y, size_t value)
{
std::stack<MorphologyPoint2D> points;
MorphologyPoint2D startPoint;
startPoint.x = x;
startPoint.y = y;
points.push(startPoint);
do {
MorphologyPoint2D p = points.top();
points.pop();
output->SetValue(p.x, p.y, value);
int z = lengthWidthValues[p.y][p.x];
if(p.x > 0 && output->Value(p.x-1, p.y) == 0 && mask->Value(p.x-1,p.y))
{
int zl = lengthWidthValues[p.y][p.x-1];
if((zl > 0 && z > 0) || (zl < 0 && z < 0))
{
MorphologyPoint2D newP;
newP.x = p.x-1; newP.y = p.y;
points.push(newP);
}
}
if(p.x < mask->Width()-1 && output->Value(p.x+1, p.y)==0 && mask->Value(p.x+1,p.y))
{
int zr = lengthWidthValues[p.y][p.x+1];
if((zr > 0 && z > 0) || (zr < 0 && z < 0))
{
MorphologyPoint2D newP;
newP.x = p.x+1; newP.y = p.y;
points.push(newP);
}
}
if(p.y > 0 && output->Value(p.x, p.y-1)==0 && mask->Value(p.x,p.y-1))
{
int zt = lengthWidthValues[p.y-1][p.x];
if((zt > 0 && z > 0) || (zt < 0 && z < 0))
{
MorphologyPoint2D newP;
newP.x = p.x; newP.y = p.y-1;
points.push(newP);
}
}
if(p.y < mask->Height()-1 && output->Value(p.x, p.y+1)==0 && mask->Value(p.x,p.y+1))
{
int zb = lengthWidthValues[p.y+1][p.x];
if((zb > 0 && z > 0) || (zb < 0 && z < 0))
{
MorphologyPoint2D newP;
newP.x = p.x; newP.y = p.y+1;
points.push(newP);
}
}
} while(points.size() != 0);
}
void HighPassFilter::setFlaggedValuesToZeroAndMakeWeights(const Image2DCPtr &inputImage, const Image2DPtr &outputImage, const Mask2DCPtr &inputMask, const Image2DPtr &weightsOutput)
{
const size_t width = inputImage->Width();
for(size_t y=0;y<inputImage->Height();++y)
{
for(size_t x=0;x<width;++x)
{
if(inputMask->Value(x, y) || !isfinite(inputImage->Value(x, y)))
{
outputImage->SetValue(x, y, 0.0);
weightsOutput->SetValue(x, y, 0.0);
} else {
outputImage->SetValue(x, y, inputImage->Value(x, y));
weightsOutput->SetValue(x, y, 1.0);
}
}
}
}
void HighPassFilter::setFlaggedValuesToZeroAndMakeWeightsSSE(const Image2DCPtr &inputImage, const Image2DPtr &outputImage, const Mask2DCPtr &inputMask, const Image2DPtr &weightsOutput)
{
const size_t width = inputImage->Width();
const __m128i zero4i = _mm_set_epi32(0, 0, 0, 0);
const __m128 zero4 = _mm_set_ps(0.0, 0.0, 0.0, 0.0);
const __m128 one4 = _mm_set_ps(1.0, 1.0, 1.0, 1.0);
for(size_t y=0;y<inputImage->Height();++y)
{
const bool *rowPtr = inputMask->ValuePtr(0, y);
const float *inputPtr = inputImage->ValuePtr(0, y);
float *outputPtr = outputImage->ValuePtr(0, y);
float *weightsPtr = weightsOutput->ValuePtr(0, y);
const float *end = inputPtr + width;
while(inputPtr < end)
{
// Assign each integer to one bool in the mask
// Convert false to 0xFFFFFFFF and true to 0
__m128 conditionMask = _mm_castsi128_ps(
_mm_cmpeq_epi32(_mm_set_epi32(rowPtr[3] || !isfinite(inputPtr[3]), rowPtr[2] || !isfinite(inputPtr[2]),
rowPtr[1] || !isfinite(inputPtr[1]), rowPtr[0] || !isfinite(inputPtr[0])),
zero4i));
_mm_store_ps(weightsPtr, _mm_or_ps(
_mm_and_ps(conditionMask, one4),
_mm_andnot_ps(conditionMask, zero4)
));
_mm_store_ps(outputPtr, _mm_or_ps(
_mm_and_ps(conditionMask, _mm_load_ps(inputPtr)),
_mm_andnot_ps(conditionMask, zero4)
));
rowPtr += 4;
outputPtr += 4;
inputPtr += 4;
weightsPtr += 4;
}
}
}
void Compress::WriteSubtractFrequencies(std::ofstream &stream, Image2DCPtr image, Mask2DCPtr mask)
{
const num_t
max = ThresholdTools::MaxValue(image, mask),
min = ThresholdTools::MinValue(image, mask);
const num_t normalizeFactor = (num_t) ((2<<22) + ((2<<22)-1)) / (max - min);
//const num_t normalizeFactor = 256.0;
const uint32_t
width = image->Width(),
height = image->Height();
const char mode = 1;
stream.write(reinterpret_cast<const char*>(&max), sizeof(max));
stream.write(reinterpret_cast<const char*>(&min), sizeof(min));
stream.write(reinterpret_cast<const char*>(&width), sizeof(width));
stream.write(reinterpret_cast<const char*>(&height), sizeof(height));
stream.write(&mode, sizeof(mode));
std::vector<int32_t> basis(width);
for(size_t x=0;x<width;++x)
{
SampleRowPtr row = SampleRow::CreateFromColumn(image, x);
basis[x] = (int32_t) round(row->Median() * normalizeFactor);
}
stream.write(reinterpret_cast<char*>(&basis[0]), sizeof(basis));
for(unsigned y=0;y<height;++y)
{
for(unsigned x=0;x<width;++x)
{
if(!mask->Value(x, y))
{
int32_t value = (int32_t) (round(image->Value(x, y) * normalizeFactor) - basis[x]);
stream.write(reinterpret_cast<char*>(&value)+1, 3);
}
}
}
}
Image2DPtr Compress::Read(std::ifstream &stream, Image2DPtr image, Mask2DCPtr mask)
{
num_t max = 0.0, min = 0.0;
size_t width = 0, height = 0;
char mode = 0;
stream.read(reinterpret_cast<char*>(&max), sizeof(max));
stream.read(reinterpret_cast<char*>(&min), sizeof(min));
stream.read(reinterpret_cast<char*>(&width), sizeof(width));
stream.read(reinterpret_cast<char*>(&height), sizeof(height));
stream.read(&mode, sizeof(mode));
num_t normalizeFactor = (max - min) / (num_t) ((2<<22) + ((2<<22)-1));
for(unsigned y=0;y<height;++y)
{
for(unsigned x=0;x<width;++x)
{
if(!mask->Value(x, y))
{
int32_t value;
stream.read(reinterpret_cast<char*>(&value), 3);
value >>= 8;
image->SetValue(x, y, value / normalizeFactor + min);
}
}
}
std::pair<TimeFrequencyData,TimeFrequencyMetaDataPtr> RSPReader::ReadChannelBeamlet(unsigned long timestepStart, unsigned long timestepEnd, unsigned beamletCount, unsigned beamletIndex)
{
const unsigned width = timestepEnd - timestepStart;
std::pair<TimeFrequencyData,TimeFrequencyMetaDataPtr> data = ReadSingleBeamlet(timestepStart*(unsigned long) 256, timestepEnd*(unsigned long) 256, beamletCount, beamletIndex);
TimeFrequencyData allX = data.first.Make(Polarization::XX);
TimeFrequencyData allY = data.first.Make(Polarization::YY);
Image2DCPtr xr = allX.GetRealPart();
Image2DCPtr xi = allX.GetImaginaryPart();
Image2DCPtr yr = allY.GetRealPart();
Image2DCPtr yi = allY.GetImaginaryPart();
Mask2DCPtr mask = data.first.GetSingleMask();
Image2DPtr
outXR = Image2D::CreateUnsetImagePtr(width, 256),
outXI = Image2D::CreateUnsetImagePtr(width, 256),
outYR = Image2D::CreateUnsetImagePtr(width, 256),
outYI = Image2D::CreateUnsetImagePtr(width, 256);
Mask2DPtr
outMask = Mask2D::CreateUnsetMaskPtr(width, 256);
std::vector<double> observationTimes;
for(unsigned long timestep = 0;timestep < timestepEnd-timestepStart;++timestep)
{
unsigned long timestepIndex = timestep * 256;
SampleRow
realX = SampleRow::MakeFromRow(xr.get(), timestepIndex, 256, 0),
imaginaryX = SampleRow::MakeFromRow(xi.get(), timestepIndex, 256, 0),
realY = SampleRow::MakeFromRow(yr.get(), timestepIndex, 256, 0),
imaginaryY = SampleRow::MakeFromRow(yi.get(), timestepIndex, 256, 0);
FFTTools::FFT(realX, imaginaryX);
FFTTools::FFT(realY, imaginaryY);
realX.SetVerticalImageValues(outXR.get(), timestep);
imaginaryX.SetVerticalImageValues(outXI.get(), timestep);
realY.SetVerticalImageValues(outYR.get(), timestep);
imaginaryY.SetVerticalImageValues(outYI.get(), timestep);
observationTimes.push_back(data.second->ObservationTimes()[timestepIndex + 256/2]);
size_t validValues = 0;
for(unsigned y=0;y<256;++y)
{
if(!mask->Value(timestepIndex + y, 0))
++validValues;
}
for(unsigned y=0;y<256;++y)
{
outMask->SetValue(timestep, y , validValues == 0);
}
}
data.first = TimeFrequencyData(Polarization::XX, outXR, outXI, Polarization::YY, outYR, outYI);
data.first.SetGlobalMask(outMask);
BandInfo band = data.second->Band();
band.channels.clear();
for(unsigned i=0;i<256;++i)
{
ChannelInfo channel;
channel.frequencyHz = i+1;
channel.frequencyIndex = i;
band.channels.push_back(channel);
}
data.second->SetBand(band);
data.second->SetObservationTimes(observationTimes);
return data;
}
void Morphology::SegmentByLengthRatio(Mask2DCPtr mask, SegmentedImagePtr output)
{
Mask2DPtr maskCopy = Mask2D::CreateCopy(mask);
//StatisticalFlagger::EnlargeFlags(maskCopy, 2, 2);
Mask2DPtr matrices[3];
for(size_t i=0;i<3;++i)
matrices[i] = Mask2D::CreateUnsetMaskPtr(mask->Width(), mask->Height());
int
**hCounts = new int*[mask->Height()],
**vCounts = new int*[mask->Height()];
for(size_t y=0;y<mask->Height();++y)
{
hCounts[y] = new int[mask->Width()];
vCounts[y] = new int[mask->Width()];
}
// Calculate convolved counts
calculateHorizontalCounts(maskCopy, hCounts);
calculateVerticalCounts(maskCopy, vCounts);
calculateOpenings(maskCopy, matrices, hCounts, vCounts);
for(size_t y=0;y<mask->Height();++y)
{
for(size_t x=0;x<mask->Width();++x)
output->SetValue(x, y, 0);
}
StatisticalFlagger::EnlargeFlags(matrices[0], _hLineEnlarging, 0);
StatisticalFlagger::EnlargeFlags(matrices[2], 0, _vLineEnlarging);
StatisticalFlagger::DensityTimeFlagger(matrices[0], _hDensityEnlargeRatio);
StatisticalFlagger::DensityFrequencyFlagger(matrices[2], _vDensityEnlargeRatio);
// Calculate counts again with new matrices
calculateHorizontalCounts(matrices[0], hCounts);
calculateVerticalCounts(matrices[2], vCounts);
for(size_t z=0;z<3;z+=2)
{
for(size_t y=0;y<mask->Height();++y)
{
for(size_t x=0;x<mask->Width();++x)
{
if(matrices[z]->Value(x, y) && output->Value(x, y)==0)
{
floodFill(mask, output, matrices, x, y, z, output->NewSegmentValue(), hCounts, vCounts);
}
}
}
}
for(size_t y=0;y<mask->Height();++y)
{
delete[] hCounts[y];
delete[] vCounts[y];
}
delete[] hCounts;
delete[] vCounts;
}
void ImageWidget::update(Cairo::RefPtr<Cairo::Context> cairo, unsigned width, unsigned height)
{
Image2DCPtr image = _image;
Mask2DCPtr mask = GetActiveMask(), originalMask = _originalMask, alternativeMask = _alternativeMask;
unsigned int
startX = (unsigned int) round(_startHorizontal * image->Width()),
startY = (unsigned int) round(_startVertical * image->Height()),
endX = (unsigned int) round(_endHorizontal * image->Width()),
endY = (unsigned int) round(_endVertical * image->Height());
size_t
imageWidth = endX - startX,
imageHeight = endY - startY;
if(imageWidth > 30000)
{
int shrinkFactor = (imageWidth + 29999) / 30000;
image = image->ShrinkHorizontally(shrinkFactor);
mask = mask->ShrinkHorizontally(shrinkFactor);
if(originalMask != 0)
originalMask = originalMask->ShrinkHorizontally(shrinkFactor);
if(alternativeMask != 0)
alternativeMask = alternativeMask->ShrinkHorizontally(shrinkFactor);
startX /= shrinkFactor;
endX /= shrinkFactor;
imageWidth = endX - startX;
}
num_t min, max;
findMinMax(image, mask, min, max);
// If these are not yet created, they are 0, so ok to delete.
delete _horiScale;
delete _vertScale;
delete _colorScale;
delete _plotTitle;
if(_showXYAxes)
{
_vertScale = new VerticalPlotScale();
_vertScale->SetDrawWithDescription(_showYAxisDescription);
_horiScale = new HorizontalPlotScale();
_horiScale->SetDrawWithDescription(_showXAxisDescription);
} else {
_vertScale = 0;
_horiScale = 0;
}
if(_showColorScale)
{
_colorScale = new ColorScale();
_colorScale->SetDrawWithDescription(_showZAxisDescription);
} else {
_colorScale = 0;
}
if(_showXYAxes)
{
if(_metaData != 0 && _metaData->HasBand()) {
_vertScale->InitializeNumericTicks(_metaData->Band().channels[startY].frequencyHz / 1e6, _metaData->Band().channels[endY-1].frequencyHz / 1e6);
_vertScale->SetUnitsCaption("Frequency (MHz)");
} else {
_vertScale->InitializeNumericTicks(-0.5 + startY, 0.5 + endY - 1.0);
}
if(_metaData != 0 && _metaData->HasObservationTimes())
{
_horiScale->InitializeTimeTicks(_metaData->ObservationTimes()[startX], _metaData->ObservationTimes()[endX-1]);
_horiScale->SetUnitsCaption("Time");
} else {
_horiScale->InitializeNumericTicks(-0.5 + startX, 0.5 + endX - 1.0);
}
if(_manualXAxisDescription)
_horiScale->SetUnitsCaption(_xAxisDescription);
if(_manualYAxisDescription)
_vertScale->SetUnitsCaption(_yAxisDescription);
}
if(_metaData != 0) {
if(_showColorScale && _metaData->ValueDescription()!="")
{
if(_metaData->ValueUnits()!="")
_colorScale->SetUnitsCaption(_metaData->ValueDescription() + " (" + _metaData->ValueUnits() + ")");
else
_colorScale->SetUnitsCaption(_metaData->ValueDescription());
}
}
if(_showColorScale)
{
if(_scaleOption == LogScale)
_colorScale->InitializeLogarithmicTicks(min, max);
else
_colorScale->InitializeNumericTicks(min, max);
if(_manualZAxisDescription)
_colorScale->SetUnitsCaption(_zAxisDescription);
}
if(_showTitle && !actualTitleText().empty())
{
_plotTitle = new Title();
_plotTitle->SetText(actualTitleText());
_plotTitle->SetPlotDimensions(width, height, 0.0);
_topBorderSize = _plotTitle->GetHeight(cairo);
} else {
_plotTitle = 0;
_topBorderSize = 10.0;
}
// The scale dimensions are depending on each other. However, since the height of the horizontal scale is practically
// not dependent on other dimensions, we give the horizontal scale temporary width/height, so that we can calculate its height:
if(_showXYAxes)
{
_horiScale->SetPlotDimensions(width, height, 0.0, 0.0);
_bottomBorderSize = _horiScale->GetHeight(cairo);
_rightBorderSize = _horiScale->GetRightMargin(cairo);
_vertScale->SetPlotDimensions(width - _rightBorderSize + 5.0, height - _topBorderSize - _bottomBorderSize, _topBorderSize);
_leftBorderSize = _vertScale->GetWidth(cairo);
} else {
_bottomBorderSize = 0.0;
_rightBorderSize = 0.0;
_leftBorderSize = 0.0;
}
if(_showColorScale)
{
_colorScale->SetPlotDimensions(width - _rightBorderSize, height - _topBorderSize, _topBorderSize);
_rightBorderSize += _colorScale->GetWidth(cairo) + 5.0;
}
if(_showXYAxes)
{
_horiScale->SetPlotDimensions(width - _rightBorderSize + 5.0, height -_topBorderSize - _bottomBorderSize, _topBorderSize, _vertScale->GetWidth(cairo));
}
class ColorMap *colorMap = createColorMap();
const double
minLog10 = min>0.0 ? log10(min) : 0.0,
maxLog10 = max>0.0 ? log10(max) : 0.0;
if(_showColorScale)
{
for(unsigned x=0;x<256;++x)
{
num_t colorVal = (2.0 / 256.0) * x - 1.0;
num_t imageVal;
if(_scaleOption == LogScale)
imageVal = exp10((x / 256.0) * (log10(max) - minLog10) + minLog10);
else
imageVal = (max-min) * x / 256.0 + min;
double
r = colorMap->ValueToColorR(colorVal),
g = colorMap->ValueToColorG(colorVal),
b = colorMap->ValueToColorB(colorVal);
_colorScale->SetColorValue(imageVal, r/255.0, g/255.0, b/255.0);
}
}
_imageSurface.clear();
_imageSurface =
Cairo::ImageSurface::create(Cairo::FORMAT_ARGB32, imageWidth, imageHeight);
_imageSurface->flush();
unsigned char *data = _imageSurface->get_data();
size_t rowStride = _imageSurface->get_stride();
Mask2DPtr highlightMask;
if(_highlighting)
{
highlightMask = Mask2D::CreateSetMaskPtr<false>(image->Width(), image->Height());
_highlightConfig->Execute(image, highlightMask, true, 10.0);
}
const bool
originalActive = _showOriginalMask && originalMask != 0,
altActive = _showAlternativeMask && alternativeMask != 0;
for(unsigned long y=startY;y<endY;++y) {
guint8* rowpointer = data + rowStride * (endY - y - 1);
for(unsigned long x=startX;x<endX;++x) {
int xa = (x-startX) * 4;
unsigned char r,g,b,a;
if(_highlighting && highlightMask->Value(x, y) != 0) {
r = 255; g = 0; b = 0; a = 255;
} else if(originalActive && originalMask->Value(x, y)) {
r = 255; g = 0; b = 255; a = 255;
} else if(altActive && alternativeMask->Value(x, y)) {
r = 255; g = 255; b = 0; a = 255;
} else {
num_t val = image->Value(x, y);
if(val > max) val = max;
else if(val < min) val = min;
if(_scaleOption == LogScale)
{
if(image->Value(x, y) <= 0.0)
val = -1.0;
else
val = (log10(image->Value(x, y)) - minLog10) * 2.0 / (maxLog10 - minLog10) - 1.0;
}
else
val = (image->Value(x, y) - min) * 2.0 / (max - min) - 1.0;
if(val < -1.0) val = -1.0;
else if(val > 1.0) val = 1.0;
r = colorMap->ValueToColorR(val);
g = colorMap->ValueToColorG(val);
b = colorMap->ValueToColorB(val);
a = colorMap->ValueToColorA(val);
}
rowpointer[xa]=b;
rowpointer[xa+1]=g;
rowpointer[xa+2]=r;
rowpointer[xa+3]=a;
}
}
delete colorMap;
if(_segmentedImage != 0)
{
for(unsigned long y=startY;y<endY;++y) {
guint8* rowpointer = data + rowStride * (y - startY);
for(unsigned long x=startX;x<endX;++x) {
if(_segmentedImage->Value(x,y) != 0)
{
int xa = (x-startX) * 4;
rowpointer[xa]=IntMap::R(_segmentedImage->Value(x,y));
rowpointer[xa+1]=IntMap::G(_segmentedImage->Value(x,y));
rowpointer[xa+2]=IntMap::B(_segmentedImage->Value(x,y));
rowpointer[xa+3]=IntMap::A(_segmentedImage->Value(x,y));
}
}
}
}
_imageSurface->mark_dirty();
while(_imageSurface->get_width() > (int) width || _imageSurface->get_height() > (int) height)
{
unsigned
newWidth = _imageSurface->get_width(),
newHeight = _imageSurface->get_height();
if(newWidth > width)
newWidth = width;
if(newHeight > height)
newHeight = height;
downsampleImageBuffer(newWidth, newHeight);
}
_isInitialized = true;
_initializedWidth = width;
_initializedHeight = height;
redrawWithoutChanges(cairo, width, height);
}
void TimeFrequencyImager::WriteNewFlags(Mask2DCPtr newXX, Mask2DCPtr newXY, Mask2DCPtr newYX, Mask2DCPtr newYY)
{
WriteNewFlagsPart(newXX, newXY, newYX, newYY, _antenna1Select, _antenna2Select, _spectralWindowSelect, 0, newXX->Width());
}
void TimeFrequencyImager::WriteNewFlags(Mask2DCPtr newXX, Mask2DCPtr newXY, Mask2DCPtr newYX, Mask2DCPtr newYY, int antenna1, int antenna2, int spectralWindow)
{
WriteNewFlagsPart(newXX, newXY, newYX, newYY, antenna1, antenna2, spectralWindow, 0, newXX->Width());
}
void TimeFrequencyImager::WriteNewFlagsPart(Mask2DCPtr newXX, Mask2DCPtr newXY, Mask2DCPtr newYX, Mask2DCPtr newYY, int antenna1, int antenna2, int spectralWindow, size_t timeOffset, size_t timeEnd, size_t leftBorder, size_t rightBorder)
{
initializePolarizations();
checkPolarizations();
size_t frequencyCount = _measurementSet->FrequencyCount();
std::map<double,size_t> observationTimes;
setObservationTimes(*_measurementSet, observationTimes);
casa::Table *table = _measurementSet->OpenTable(true);
casa::ScalarColumn<int> antenna1Column(*table, "ANTENNA1");
casa::ScalarColumn<int> antenna2Column(*table, "ANTENNA2");
casa::ScalarColumn<int> windowColumn(*table, "DATA_DESC_ID");
casa::ScalarColumn<double> timeColumn(*table, "TIME");
casa::ArrayColumn<bool> flagColumn(*table, "FLAG");
ScalarColumnIterator<int> antenna1Iter = ScalarColumnIterator<int>::First(antenna1Column);
ScalarColumnIterator<int> antenna2Iter = ScalarColumnIterator<int>::First(antenna2Column);
ScalarColumnIterator<int> windowIter = ScalarColumnIterator<int>::First(windowColumn);
ScalarColumnIterator<double> timeIter = ScalarColumnIterator<double>::First(timeColumn);
ArrayColumnIterator<bool> flagIter = ArrayColumnIterator<bool>::First(flagColumn);
if(frequencyCount != newXX->Height())
{
std::cerr << "The frequency count in the measurement set (" << frequencyCount << ") does not match the image!" << std::endl;
}
if(timeEnd - timeOffset != newXX->Width())
{
std::cerr << "The number of time scans to write in the measurement set (" << (timeEnd - timeOffset) << ") does not match the image (" << newXX->Width() << ") !" << std::endl;
}
size_t rowsWritten = 0;
for(size_t i=0;i<table->nrow();++i) {
if((*antenna1Iter) == (int) antenna1 &&
(*antenna2Iter) == (int) antenna2 &&
(*windowIter) == (int) spectralWindow)
{
double time = *timeIter;
size_t timeIndex = observationTimes.find(time)->second;
if(timeIndex >= timeOffset + leftBorder && timeIndex < timeEnd - rightBorder)
{
casa::Array<bool> flag = *flagIter;
casa::Array<bool>::iterator j = flag.begin();
for(size_t f=0;f<(size_t) frequencyCount;++f) {
if(_stokesIIndex >= 0)
{
if(_readStokesIDirectly) *j = newXX->Value(timeIndex - timeOffset, f);
++j;
}
if(_xxIndex >= 0)
{
if(_readXX) *j = newXX->Value(timeIndex - timeOffset, f);
++j;
}
if(_xyIndex >= 0)
{
if(_readXY) *j = newXY->Value(timeIndex - timeOffset, f);
++j;
}
if(_yxIndex >= 0)
{
if(_readYX) *j = newYX->Value(timeIndex - timeOffset, f);
++j;
}
if(_yyIndex >= 0)
{
if(_readYY) *j = newYY->Value(timeIndex - timeOffset, f);
++j;
}
}
flagIter.Set(flag);
++rowsWritten;
}
}
++antenna1Iter;
++antenna2Iter;
++timeIter;
++windowIter;
++flagIter;
}
AOLogger::Debug << "Rows written: " << rowsWritten << '\n';
delete table;
}
