//Read a line and fill parameter
void ExperimentalPoint::ReadLineAndTreat(int MagnetQuadrant, TString Grid, int Location, int Level, double Bx, double By, double Bz){
fMagnetQuadrant = MagnetQuadrant ;
fLocation = Location ;
fLevel = Level;
fGrid = Grid ;
TString key = Grid + Form("%d%d",MagnetQuadrant,Level);
CheckList(Grid, MagnetQuadrant, Level);
if(fSetBackground) SubtractBackground(Grid, Location, Bx,By,Bz);
CalculateCentralPosition() ;
//Correct for probe rotation
if (Grid=="B" && Location>=4 && Location<=7) {
// when the probe is tilted in the B grid, the sensorX receives most of the magnetic field in contrast to sensorY
// to keep things in order, the sensorX value could be split into (x-component an y-component)
fBField.SetXYZ(Bx/10.,0,-Bz/10.); //ignore By, it will be filled by rotation
fBField.RotateZ(-45*TMath::DegToRad());
TVector3 SensorOffsetX = fSensorOffsetX; // copy the offset and rotate 45 degrees
SensorOffsetX.RotateZ(-45*TMath::DegToRad());
fSensorPositionX = fPosition + SensorOffsetX;
fSensorPositionY = fPosition + SensorOffsetX; // yes it is X!
fSensorPositionZ = fPosition + fSensorOffsetZ;
}
else if (Grid=="D" && Location>=5 && Location<=7) {
// when the probe is tilted in the D grid, the sensorY receives most of the magnetic field in contrast to sensorX
// again, the sensorY value could be split into (x-component an y-component)
fBField.SetXYZ(0.,-By/10.,-Bz/10.); //ignore Bx, it will be filled by rotation
fBField.RotateZ(-45*TMath::DegToRad());
TVector3 SensorOffsetY = fSensorOffsetY; // copy the offset and rotate 45 degrees
SensorOffsetY.RotateZ(-45*TMath::DegToRad());
fSensorPositionX = fPosition + SensorOffsetY;
fSensorPositionY = fPosition + SensorOffsetY; // yes it is Y!
fSensorPositionZ = fPosition + fSensorOffsetZ;
}
else {
fBField.SetXYZ(Bx/10.,-By/10.,-Bz/10.); // turn to millitesla, By need to be multiplied by (-) to account for the axis change between simulation/experiment and the way the mapper is set
fSensorPositionX = fPosition + fSensorOffsetX;
fSensorPositionY = fPosition + fSensorOffsetY;
fSensorPositionZ = fPosition + fSensorOffsetZ;
}
//correct for Lens rotations
double angle = CalculateRotationAngle(MagnetQuadrant, Grid);
fPosition.RotateZ(angle*TMath::DegToRad());
fSensorPositionX.RotateZ(angle*TMath::DegToRad());
fSensorPositionY.RotateZ(angle*TMath::DegToRad());
fSensorPositionZ.RotateZ(angle*TMath::DegToRad());
fBField.RotateZ(angle*TMath::DegToRad());
//ShowParameters();
//cin.get();
}
Image2D TestSetGenerator::MakeTestSet(int number, Mask2D& rfi, unsigned width, unsigned height, int gaussianNoise)
{
Image2D image;
switch(number)
{
case 0: // Image of all zero's
return Image2D::MakeZeroImage(width, height);
case 1: // Image of all ones
image = Image2D::MakeUnsetImage(width, height);
image.SetAll(1.0);
break;
case 2: // Noise
return MakeNoise(width, height, gaussianNoise);
case 3: { // Several broadband lines
image = MakeNoise(width, height, gaussianNoise);
AddBroadbandToTestSet(image, rfi, 1.0);
} break;
case 4: { // Several broadband lines
image = MakeNoise(width, height, gaussianNoise);
AddBroadbandToTestSet(image, rfi, 0.5);
} break;
case 5: { // Several broadband lines of random length
image = MakeNoise(width, height, gaussianNoise);
AddVarBroadbandToTestSet(image, rfi);
} break;
case 6: { // Different broadband lines + low freq background
image = MakeNoise(width, height, gaussianNoise);
AddVarBroadbandToTestSet(image, rfi);
for(unsigned y=0;y<image.Height();++y) {
for(unsigned x=0;x<image.Width();++x) {
image.AddValue(x, y, sinn(num_t(x)*M_PIn*5.0 / image.Width()) + 0.1);
}
}
} break;
case 7: { // Different broadband lines + high freq background
image = MakeNoise(width, height, gaussianNoise);
for(unsigned y=0;y<image.Height();++y) {
for(unsigned x=0;x<image.Width();++x) {
image.AddValue(x, y, sinn((long double) (x+y*0.1)*M_PIn*5.0L / image.Width() + 0.1));
image.AddValue(x, y, sinn((long double) (x+pown(y, 1.1))*M_PIn*50.0L / image.Width() + 0.1));
}
}
AddVarBroadbandToTestSet(image, rfi);
for(unsigned y=0;y<image.Height();++y) {
for(unsigned x=0;x<image.Width();++x) {
image.AddValue(x, y, 1.0);
}
}
} break;
case 8: { // Different droadband lines + smoothed&subtracted high freq background
image = MakeNoise(width, height, gaussianNoise);
for(unsigned y=0;y<image.Height();++y) {
for(unsigned x=0;x<image.Width();++x) {
image.AddValue(x, y, sinn((num_t) (x+y*0.1)*M_PIn*5.0 / image.Width() + 0.1));
image.AddValue(x, y, sinn((num_t) (x+pown(y, 1.1))*M_PIn*50.0 / image.Width() + 0.1));
}
}
SubtractBackground(image);
AddVarBroadbandToTestSet(image, rfi);
} break;
case 9: { //FFT of 7
image = MakeTestSet(7, rfi, width, height);
Image2D copy(image);
FFTTools::CreateHorizontalFFTImage(image, copy, false);
for(unsigned y=0;y<rfi.Height();++y) {
for(unsigned x=0;x<rfi.Width();++x) {
image.SetValue(x, y, image.Value(x, y) / sqrtn(image.Width()));
}
}
} break;
case 10: { // Identity matrix
image = Image2D::MakeZeroImage(width, height);
unsigned min = width < height ? width : height;
for(unsigned i=0;i<min;++i) {
image.SetValue(i, i, 1.0);
rfi.SetValue(i, i, true);
}
} break;
case 11: { // FFT of identity matrix
image = MakeTestSet(10, rfi, width, height);
Image2D copy(image);
FFTTools::CreateHorizontalFFTImage(image, copy, false);
for(unsigned y=0;y<rfi.Height();++y) {
for(unsigned x=0;x<rfi.Width();++x) {
image.SetValue(x, y, image.Value(x, y) / sqrtn(width));
}
}
} break;
case 12: { // Broadband contaminating all channels
image = MakeNoise(width, height, gaussianNoise);
for(unsigned y=0;y<image.Height();++y) {
for(unsigned x=0;x<image.Width();++x) {
image.AddValue(x, y, sinn((num_t) (x+y*0.1)*M_PIn*5.0 / image.Width() + 0.1));
image.AddValue(x, y, sinn((num_t) (x+powl(y, 1.1))*M_PIn*50.0 / image.Width() + 0.1));
}
}
AddBroadbandToTestSet(image, rfi, 1.0);
} break;
case 13: { // Model of three point sources with broadband RFI
SetModelData(image, rfi, 3);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
AddBroadbandToTestSet(image, rfi, 1.0);
} break;
case 14: { // Model of five point sources with broadband RFI
SetModelData(image, rfi, 5);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
AddBroadbandToTestSet(image, rfi, 1.0);
} break;
case 15: { // Model of five point sources with partial broadband RFI
SetModelData(image, rfi, 5);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
AddBroadbandToTestSet(image, rfi, 0.5);
} break;
case 16: { // Model of five point sources with random broadband RFI
SetModelData(image, rfi, 5);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
AddVarBroadbandToTestSet(image, rfi);
} break;
case 17: { // Background-fitted model of five point sources with random broadband RFI
SetModelData(image, rfi, 5);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
SubtractBackground(image);
AddVarBroadbandToTestSet(image, rfi);
} break;
case 18: { // Model of three point sources with random RFI
SetModelData(image, rfi, 3);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
AddVarBroadbandToTestSet(image, rfi);
} break;
case 19: { // Model of three point sources with noise
SetModelData(image, rfi, 3);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
} break;
case 20: { // Model of five point sources with noise
SetModelData(image, rfi, 5);
Image2D noise = MakeNoise(image.Width(), image.Height(), gaussianNoise);
image += noise;
} break;
case 21: { // Model of three point sources
SetModelData(image, rfi, 3);
} break;
case 22: { // Model of five point sources
image = Image2D::MakeZeroImage(width, height);
SetModelData(image, rfi, 5);
} break;
case 23:
image = MakeNoise(width, height, gaussianNoise);
AddBroadbandToTestSet(image, rfi, 0.5, 0.1, true);
break;
case 24:
image = MakeNoise(width, height, gaussianNoise);
AddBroadbandToTestSet(image, rfi, 0.5, 10.0, true);
break;
case 25:
image = MakeNoise(width, height, gaussianNoise);
AddBroadbandToTestSet(image, rfi, 0.5, 1.0, true);
break;
case 26: { // Several Gaussian broadband lines
image = MakeNoise(width, height, gaussianNoise);
AddBroadbandToTestSet(image, rfi, 1.0, 1.0, false, GaussianShape);
} break;
case 27: { // Several Sinusoidal broadband lines
image = MakeNoise(width, height, gaussianNoise);
AddBroadbandToTestSet(image, rfi, 1.0, 1.0, false, SinusoidalShape);
} break;
case 28: { // Several slewed Gaussian broadband lines
image = MakeNoise(width, height, gaussianNoise);
AddSlewedBroadbandToTestSet(image, rfi, 1.0);
} break;
case 29: { // Several bursty broadband lines
image = MakeNoise(width, height, gaussianNoise);
AddBurstBroadbandToTestSet(image, rfi);
} break;
case 30: { // noise + RFI ^-2 distribution
image = sampleRFIDistribution(width, height, 1.0);
rfi.SetAll<true>();
} break;
case 31: { // noise + RFI ^-2 distribution
image = sampleRFIDistribution(width, height, 0.1);
rfi.SetAll<true>();
} break;
case 32: { // noise + RFI ^-2 distribution
image = sampleRFIDistribution(width, height, 0.01);
rfi.SetAll<true>();
} break;
}
return image;
}
