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used liqings FAS linear reg with fake data

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This commit is contained in:
Blake Leverington 2023-09-12 19:55:07 +02:00
parent 89258a816f
commit 25b19b6b68
3 changed files with 193 additions and 15 deletions
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16
hit2023v2/eventbuilder.cpp
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@ -64,7 +64,7 @@ void EventBuilder::onNewData(DataReceiver* receiver)
//1. Background subtraction.
frame_counter++;
/*
while (frame_counter<10000){
for (unsigned int dev_nr = 0; dev_nr < nrReceivers; dev_nr++){
for (unsigned int ch = 0; ch < channelCounts[dev_nr]; ch++)
@ -84,7 +84,7 @@ void EventBuilder::onNewData(DataReceiver* receiver)
currentFrame[dev_nr].sensor_data[ch]-=backgroundFrame[dev_nr].sensor_data[ch] ;
}
}
*/
lastFrameMutex.lock();
@ -108,13 +108,13 @@ void EventBuilder::onNewData(DataReceiver* receiver)
//log data
if (loggingData) logDataToFile();
//HIT_ANALYSE_V2 hit_analyse_v2;//create the object
// QString dataString = hit_analyse_v2.analyseBeamData(currentFrame);
HIT_ANALYSE_V2 hit_analyse_v2;//create the object
QString dataString = hit_analyse_v2.analyseBeamData(currentFrame);
std::cerr << dataString.toStdString() << std::endl;
// Call sendData method of the UDP server
QString dataString = QString::number(intensity) + ',' + QString::number(position) + ',' + QString::number(focus);
QByteArray data = dataString.toUtf8();
udpServer.sendData(data);
// QString dataString = QString::number(intensity) + ',' + QString::number(position) + ',' + QString::number(focus);
//QByteArray data = dataString.toUtf8();
// udpServer.sendData(data);
}

192
hit2023v2/hit_analyse_v2.cpp
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@ -8,6 +8,32 @@ HIT_ANALYSE_V2::HIT_ANALYSE_V2(QObject *parent) : QObject(parent)
}
// Define your own functions for matrix operations
struct Matrix2x2 {
double data[2][2];
};
Matrix2x2 InvertMatrix2x2(const Matrix2x2& mat) {
Matrix2x2 result;
double det = mat.data[0][0] * mat.data[1][1] - mat.data[0][1] * mat.data[1][0];
if (det != 0.0) {
double invDet = 1.0 / det;
result.data[0][0] = mat.data[1][1] * invDet;
result.data[0][1] = -mat.data[0][1] * invDet;
result.data[1][0] = -mat.data[1][0] * invDet;
result.data[1][1] = mat.data[0][0] * invDet;
} else {
// Handle the case when the matrix is not invertible
// You might want to implement error handling here.
std::cerr << "Matrix not invertible! " << std::endl;
}
return result;
}
struct Vector2 {
double data[2];
};
QString HIT_ANALYSE_V2::analyseBeamData(QVector<BufferData> dataframe){
double position=0.1;
@ -20,7 +46,8 @@ QString HIT_ANALYSE_V2::analyseBeamData(QVector<BufferData> dataframe){
std::vector<double> signal_list(vector_length);
std::vector<double> channel_list(vector_length);
std::vector<double> short_signal_list;
std::vector<double> short_channel_list;
// Create a random number generator with a Gaussian distribution
std::random_device rd;
@ -31,20 +58,24 @@ QString HIT_ANALYSE_V2::analyseBeamData(QVector<BufferData> dataframe){
std::vector<short int> result(vector_length);
// Fill the vector with random values
for (int i = 0; i < vector_length; ++i) {
result[i] = static_cast<short int>(dist(gen));
signal_list.push_back(result[i]);
channel_list.push_back(i);
for (int i = 0; i < vector_length; i++) {
double randomValue = dist(gen);
result[i] = static_cast<short int>(std::round(randomValue));
signal_list[i] = result[i];
channel_list[i] = i;
//std::cerr << vector_length<< " " << channel_list[i] << " " << signal_list[i] <<std::endl;
}
//add a gaussian profile, focus is FWHM, position is random between 50 and 250
position = 50 + (rand() % (int)(250 - 50 + 1));
for (int i = 0; i < vector_length; ++i) {
for (int i = 0; i < vector_length; i++) {
signal_list[i] += intensity*exp(-4*log(2)*pow((channel_list[i]-position)/focus,2));
// std::cerr << vector_length<< " " << channel_list[i] << " " << signal_list[i] <<std::endl;
}
/*
// Fill signal_list and channel_list with your data
double SumT = 0.0, SumS = 0.0, SumS2 = 0.0, SumST = 0.0, SumT2 = 0.0, SumY = 0.0, SumYS = 0.0, SumYT = 0.0;
@ -138,6 +169,151 @@ QString HIT_ANALYSE_V2::analyseBeamData(QVector<BufferData> dataframe){
//sigma = sqrt(1.0 / (2.0 * ABC_0));
focus = 2.3548/sqrt(2*p);
intensity = b;
*/
double SumArea = 0.0, SumY2 = 0.0, SumXY2 = 0.0, SumX2Y2 = 0.0, SumX3Y2 = 0.0;
double SumY2LnY = 0.0, SumXY2LnY = 0.0, Ymax = 0.0, Pomax = 0.0;
double fac_c = 0.0, Yn = 0.0, sigma = 0.0, amp = 0.0;
double SumYYP = 0.0, SumYYM = 0.0, MeanY = 0.0, window_start = 0.0, window_end = 0.0;
// ...
Matrix2x2 M1, M1inv;
Vector2 ABC, M2;
for (int i = 0; i < vector_length; i++) {
if (signal_list[i] > Ymax) {
Ymax = signal_list[i];
Pomax = channel_list[i];
}
if (i > 0 && signal_list[i] > 20) {
SumArea += signal_list[i] * (channel_list[i] - channel_list[i - 1]);
}
}
// Estimate sigma
sigma = SumArea / Ymax / 2.5066;
// Set a +-3 sigma window
window_start = Pomax - 3 * sigma;
window_end = Pomax + 3 * sigma;
// std::cerr<< Pomax << " " << Ymax << " " << sigma << std::endl;
for (int i = 0; i < vector_length; i++) {
if (signal_list[i] > 20 && channel_list[i] > window_start && channel_list[i] < window_end) {
short_signal_list.push_back(signal_list[i]);
short_channel_list.push_back(channel_list[i]);
}
}
signal_list.clear();
channel_list.clear();
// Recalculate SumArea using the sieved data
SumArea = 0.0;
for (int i = 1; i < short_signal_list.size(); i++) {
SumArea += short_signal_list[i] * (short_channel_list[i] - short_channel_list[i - 1]);
}
const int shortlist_length = short_channel_list.size();
if (shortlist_length <= 3) {
intensity = -1;
focus = -1;
position = -128;
dataString += QString::number(intensity) + ',' + QString::number(position) + ',' + QString::number(focus)
+ ',' + QString::number(0);
return dataString;
}
// Re-Estimate sigma
sigma = SumArea / Ymax / 2.5066;
fac_c = -1.0 / (2.0 * sigma * sigma);
// std::cerr << sigma << std::endl;
for(int k=0; k<shortlist_length;k++){
SumY2 += short_signal_list[k]*short_signal_list[k];
SumXY2 += short_signal_list[k]*short_signal_list[k]*short_channel_list[k];
SumX2Y2 += short_signal_list[k]*short_signal_list[k]*short_channel_list[k]*short_channel_list[k];
SumX3Y2 += short_signal_list[k]*short_signal_list[k]*short_channel_list[k]*short_channel_list[k]*short_channel_list[k];
SumY2LnY += short_signal_list[k]*short_signal_list[k]*log(short_signal_list[k]);
SumXY2LnY += short_channel_list[k]*short_signal_list[k]*short_signal_list[k]*log(short_signal_list[k]);
// std::cerr<< shortlist_length << " " << short_channel_list[k] << " " << short_signal_list[k] << " " << short_signal_list[k] << " " << log(short_signal_list[k]) << std::endl;
MeanY+=short_signal_list[k];
}
MeanY/=shortlist_length;
// Use custom matrix and vector functions for calculations
M1.data[0][0] = SumY2;
M1.data[0][1] = SumXY2;
M1.data[1][0] = SumXY2;
M1.data[1][1] = SumX2Y2;
// std::cerr << M1.data[0][0] << " " << M1.data[0][1] << " " << M1.data[1][0] << " " << M1.data[1][1] << std::endl;
M2.data[0] = SumY2LnY - fac_c * SumX2Y2;
M2.data[1] = SumXY2LnY - fac_c * SumX3Y2;
// std::cerr << M2.data[0] << " " << M2.data[1] << std::endl;
M1inv = InvertMatrix2x2(M1);
ABC.data[0] = M1inv.data[0][0] * M2.data[0] + M1inv.data[0][1] * M2.data[1];
ABC.data[1] = M1inv.data[1][0] * M2.data[0] + M1inv.data[1][1] * M2.data[1];
// std::cerr << ABC.data[0] << " " << ABC.data[1] << std::endl;
//iterate to improve the fit.
int N_iter = 1;
for (int i = 0; i < N_iter; i++) {
SumY2 = 0.0;
SumXY2 = 0.0;
SumX2Y2 = 0.0;
SumX3Y2 = 0.0;
SumY2LnY = 0.0;
SumXY2LnY = 0.0;
for (int k = 0; k < shortlist_length; k++) {
Yn = exp(ABC.data[0] + ABC.data[1] * short_channel_list[k] + fac_c * short_channel_list[k] * short_channel_list[k]);
SumY2 += Yn * Yn;
SumXY2 += Yn * Yn * short_channel_list[k];
SumX2Y2 += Yn * Yn * short_channel_list[k] * short_channel_list[k];
SumX3Y2 += Yn * Yn * short_channel_list[k] * short_channel_list[k] * short_channel_list[k];
SumY2LnY += Yn * Yn * log(short_signal_list[k]);
SumXY2LnY += short_channel_list[k] * Yn * Yn * log(short_signal_list[k]);
}
M1.data[0][0] = SumY2;
M1.data[0][1] = SumXY2;
M1.data[1][0] = SumXY2;
M1.data[1][1] = SumX2Y2;
M2.data[0] = SumY2LnY - fac_c * SumX2Y2;
M2.data[1] = SumXY2LnY - fac_c * SumX3Y2;
M1inv = InvertMatrix2x2(M1);
ABC.data[0] = M1inv.data[0][0] * M2.data[0] + M1inv.data[0][1] * M2.data[1];
ABC.data[1] = M1inv.data[1][0] * M2.data[0] + M1inv.data[1][1] * M2.data[1];
}
position = -ABC.data[1]/fac_c/2;
amp = exp(ABC.data[0]-ABC.data[1]*ABC.data[1]/4/fac_c);
sigma=SumArea/amp/2.5066;
// cout << sigma << " " << mean << " " << amp << endl;
for(int k=0; k<shortlist_length;k++){
SumYYM+= (short_signal_list[k]-MeanY)*(short_signal_list[k]-MeanY);
SumYYP+= (amp*exp(-(short_channel_list[k]-position)*(short_channel_list[k]-position)/2/(sigma*sigma)) - MeanY )*(amp*exp(-(short_channel_list[k]-position)*(short_channel_list[k]-position)/2/(sigma*sigma)) - MeanY );
}
focus = 2.3548*sigma;
intensity = amp;
double R_squared = SumYYP/SumYYM;
dataString += QString::number(intensity) + ',' + QString::number(position) + ',' + QString::number(focus)
+ ',' + QString::number(R_squared);
@ -148,6 +324,8 @@ QString HIT_ANALYSE_V2::analyseBeamData(QVector<BufferData> dataframe){
}
HIT_ANALYSE_V2::~HIT_ANALYSE_V2()
{

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hit2023v2/release/hit2023v2.exe
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