/** Calculates the normalization constant for the exponential decay
* @param ws :: [input] Workspace containing the spectra to remove exponential
* from
* @return :: Vector containing the normalization constants, N0, for each
* spectrum
*/
std::vector<double>
PhaseQuadMuon::getExponentialDecay(const API::MatrixWorkspace_sptr &ws) {
size_t nspec = ws->getNumberHistograms();
size_t npoints = ws->blocksize();
// Muon life time in microseconds
double muLife = PhysicalConstants::MuonLifetime * 1e6;
std::vector<double> n0(nspec, 0.);
for (size_t h = 0; h < ws->getNumberHistograms(); h++) {
const auto &X = ws->getSpectrum(h).x();
const auto &Y = ws->getSpectrum(h).y();
const auto &E = ws->getSpectrum(h).e();
double s, sx, sy;
s = sx = sy = 0;
for (size_t i = 0; i < npoints; i++) {
if (Y[i] > 0) {
double sig = E[i] * E[i] / Y[i] / Y[i];
s += 1. / sig;
sx += (X[i] - X[0]) / sig;
sy += log(Y[i]) / sig;
}
}
n0[h] = exp((sy + sx / muLife) / s);
}
return n0;
}
void SaveFITS::writeFITSImageMatrix(const API::MatrixWorkspace_sptr img,
std::ofstream &file) {
const size_t sizeX = img->blocksize();
const size_t sizeY = img->getNumberHistograms();
int bitDepth = getProperty(PROP_BIT_DEPTH);
const size_t bytespp = static_cast<size_t>(bitDepth) / 8;
for (size_t row = 0; row < sizeY; ++row) {
const auto &dataY = img->readY(row);
for (size_t col = 0; col < sizeX; ++col) {
int32_t pixelVal;
if (8 == bitDepth) {
pixelVal = static_cast<uint8_t>(dataY[col]);
} else if (16 == bitDepth) {
pixelVal = static_cast<uint16_t>(dataY[col]);
} else if (32 == bitDepth) {
pixelVal = static_cast<uint32_t>(dataY[col]);
}
// change endianness: to sequence of bytes in big-endian
// this needs revisiting (similarly in LoadFITS)
// See https://github.com/mantidproject/mantid/pull/15964
std::array<uint8_t, g_maxBytesPP> bytesPixel;
uint8_t *iter = reinterpret_cast<uint8_t *>(&pixelVal);
std::reverse_copy(iter, iter + bytespp, bytesPixel.data());
file.write(reinterpret_cast<const char *>(bytesPixel.data()), bytespp);
}
}
}
/** Reads in the data corresponding to a single spectrum
* @param speFile :: The file handle
* @param workspace :: The output workspace
* @param index :: The index of the current spectrum
*/
void LoadSPE::readHistogram(FILE *speFile, API::MatrixWorkspace_sptr workspace,
size_t index) {
// First, there should be a comment line
char comment[100];
fgets(comment, 100, speFile);
if (comment[0] != '#')
reportFormatError(std::string(comment));
// Then it's the Y values
MantidVec &Y = workspace->dataY(index);
const size_t nbins = workspace->blocksize();
int retval;
for (size_t i = 0; i < nbins; ++i) {
retval = fscanf(speFile, "%10le", &Y[i]);
// g_log.error() << Y[i] << std::endl;
if (retval != 1) {
std::stringstream ss;
ss << "Reading data value" << i << " of histogram " << index;
reportFormatError(ss.str());
}
// -10^30 is the flag for not a number used in SPE files (from
// www.mantidproject.org/images/3/3d/Spe_file_format.pdf)
if (Y[i] == SaveSPE::MASK_FLAG) {
Y[i] = std::numeric_limits<double>::quiet_NaN();
}
}
// Read to EOL
fgets(comment, 100, speFile);
// Another comment line
fgets(comment, 100, speFile);
if (comment[0] != '#')
reportFormatError(std::string(comment));
// And then the error values
MantidVec &E = workspace->dataE(index);
for (size_t i = 0; i < nbins; ++i) {
retval = fscanf(speFile, "%10le", &E[i]);
if (retval != 1) {
std::stringstream ss;
ss << "Reading error value" << i << " of histogram " << index;
reportFormatError(ss.str());
}
}
// Read to EOL
fgets(comment, 100, speFile);
return;
}
/** Returns a given spectrum as a complex number
* @param inWS :: [input] The input workspace containing all the spectra
* @param spec :: [input] The spectrum of interest
* @param errors :: [input] If true, returns the errors, otherwise returns the
* counts
* @return : Spectrum 'spec' as a complex vector
*/
std::vector<double> MaxEnt::toComplex(const API::MatrixWorkspace_sptr &inWS,
size_t spec, bool errors) {
std::vector<double> result(inWS->blocksize() * 2);
if (inWS->getNumberHistograms() % 2)
throw std::invalid_argument(
"Cannot convert input workspace to complex data");
size_t nspec = inWS->getNumberHistograms() / 2;
if (!errors) {
for (size_t i = 0; i < inWS->blocksize(); i++) {
result[2 * i] = inWS->readY(spec)[i];
result[2 * i + 1] = inWS->readY(spec + nspec)[i];
}
} else {
for (size_t i = 0; i < inWS->blocksize(); i++) {
result[2 * i] = inWS->readE(spec)[i];
result[2 * i + 1] = inWS->readE(spec + nspec)[i];
}
}
return result;
}
void SaveFITS::writeFITSHeaderAxesSizes(const API::MatrixWorkspace_sptr img,
std::ofstream &file) {
const std::string sizeX = std::to_string(img->blocksize());
const std::string sizeY = std::to_string(img->getNumberHistograms());
const size_t fieldWidth = 20;
std::stringstream axis1;
axis1 << "NAXIS1 = " << std::setw(fieldWidth) << sizeX
<< " / length of data axis 1";
writeFITSHeaderEntry(axis1.str(), file);
std::stringstream axis2;
axis2 << "NAXIS2 = " << std::setw(fieldWidth) << sizeY
<< " / length of data axis 2";
writeFITSHeaderEntry(axis2.str(), file);
}
/**
* The main method to calculate the ring profile for workspaces based on
*instruments.
*
* It will iterate over all the spectrum inside the workspace.
* For each spectrum, it will use the RingProfile::getBinForPixel method to
*identify
* where, in the output_bins, the sum of all the spectrum values should be
*placed in.
*
* @param inputWS: pointer to the input workspace
* @param output_bins: the reference to the vector to be filled with the
*integration values
*/
void RingProfile::processInstrumentRingProfile(
const API::MatrixWorkspace_sptr inputWS, std::vector<double> &output_bins) {
for (int i = 0; i < static_cast<int>(inputWS->getNumberHistograms()); i++) {
m_progress->report("Computing ring bins positions for detectors");
// for the detector based, the positions will be taken from the detector
// itself.
try {
Mantid::Geometry::IDetector_const_sptr det = inputWS->getDetector(i);
// skip monitors
if (det->isMonitor()) {
continue;
}
// this part will be executed if the instrument is attached to the
// workspace
// get the bin position
int bin_n = getBinForPixel(det);
if (bin_n < 0) // -1 is the agreement for an invalid bin, or outside the
// ring being integrated
continue;
g_log.debug() << "Bin for the index " << i << " = " << bin_n
<< " Pos = " << det->getPos() << std::endl;
// get the reference to the spectrum
auto spectrum_pt = inputWS->getSpectrum(i);
const MantidVec &refY = spectrum_pt->dataY();
// accumulate the values of this spectrum inside this bin
for (size_t sp_ind = 0; sp_ind < inputWS->blocksize(); sp_ind++)
output_bins[bin_n] += refY[sp_ind];
} catch (Kernel::Exception::NotFoundError &ex) {
g_log.information() << "It found that detector for " << i
<< " is not valid. " << ex.what() << std::endl;
continue;
}
}
}
/** Carries out the bin-by-bin normalisation
* @param inputWorkspace The input workspace
* @param outputWorkspace The result workspace
*/
void NormaliseToMonitor::normaliseBinByBin(API::MatrixWorkspace_sptr inputWorkspace,
API::MatrixWorkspace_sptr& outputWorkspace)
{
EventWorkspace_sptr inputEvent = boost::dynamic_pointer_cast<EventWorkspace>(inputWorkspace);
EventWorkspace_sptr outputEvent;
// Only create output workspace if different to input one
if (outputWorkspace != inputWorkspace )
{
if (inputEvent)
{
//Make a brand new EventWorkspace
outputEvent = boost::dynamic_pointer_cast<EventWorkspace>(
API::WorkspaceFactory::Instance().create("EventWorkspace", inputEvent->getNumberHistograms(), 2, 1));
//Copy geometry and data
API::WorkspaceFactory::Instance().initializeFromParent(inputEvent, outputEvent, false);
outputEvent->copyDataFrom( (*inputEvent) );
outputWorkspace = boost::dynamic_pointer_cast<MatrixWorkspace>(outputEvent);
}
else
outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace);
}
// Get hold of the monitor spectrum
const MantidVec& monX = m_monitor->readX(0);
MantidVec& monY = m_monitor->dataY(0);
MantidVec& monE = m_monitor->dataE(0);
// Calculate the overall normalisation just the once if bins are all matching
if (m_commonBins) this->normalisationFactor(m_monitor->readX(0),&monY,&monE);
const size_t numHists = inputWorkspace->getNumberHistograms();
MantidVec::size_type specLength = inputWorkspace->blocksize();
Progress prog(this,0.0,1.0,numHists);
// Loop over spectra
PARALLEL_FOR3(inputWorkspace,outputWorkspace,m_monitor)
for (int64_t i = 0; i < int64_t(numHists); ++i)
{
PARALLEL_START_INTERUPT_REGION
prog.report();
const MantidVec& X = inputWorkspace->readX(i);
// If not rebinning, just point to our monitor spectra, otherwise create new vectors
MantidVec* Y = ( m_commonBins ? &monY : new MantidVec(specLength) );
MantidVec* E = ( m_commonBins ? &monE : new MantidVec(specLength) );
if (!m_commonBins)
{
// ConvertUnits can give X vectors of all zeroes - skip these, they cause problems
if (X.back() == 0.0 && X.front() == 0.0) continue;
// Rebin the monitor spectrum to match the binning of the current data spectrum
VectorHelper::rebinHistogram(monX,monY,monE,X,*Y,*E,false);
// Recalculate the overall normalisation factor
this->normalisationFactor(X,Y,E);
}
if (inputEvent)
{
// ----------------------------------- EventWorkspace ---------------------------------------
EventList & outEL = outputEvent->getEventList(i);
outEL.divide(X, *Y, *E);
}
else
{
// ----------------------------------- Workspace2D ---------------------------------------
const MantidVec& inY = inputWorkspace->readY(i);
const MantidVec& inE = inputWorkspace->readE(i);
MantidVec& YOut = outputWorkspace->dataY(i);
MantidVec& EOut = outputWorkspace->dataE(i);
outputWorkspace->dataX(i) = inputWorkspace->readX(i);
// The code below comes more or less straight out of Divide.cpp
for (MantidVec::size_type k = 0; k < specLength; ++k)
{
// Get references to the input Y's
const double& leftY = inY[k];
const double& rightY = (*Y)[k];
// Calculate result and store in local variable to avoid overwriting original data if
// output workspace is same as one of the input ones
const double newY = leftY/rightY;
if (fabs(rightY)>1.0e-12 && fabs(newY)>1.0e-12)
{
const double lhsFactor = (inE[k]<1.0e-12|| fabs(leftY)<1.0e-12) ? 0.0 : pow((inE[k]/leftY),2);
const double rhsFactor = (*E)[k]<1.0e-12 ? 0.0 : pow(((*E)[k]/rightY),2);
EOut[k] = std::abs(newY) * sqrt(lhsFactor+rhsFactor);
}
// Now store the result
YOut[k] = newY;
} // end Workspace2D case
} // end loop over current spectrum
if (!m_commonBins) { delete Y; delete E; }
PARALLEL_END_INTERUPT_REGION
} // end loop over spectra
PARALLEL_CHECK_INTERUPT_REGION
}
/**
* Execute smoothing of a single spectrum.
* @param inputWS :: A workspace to pick a spectrum from.
* @param wsIndex :: An index of a spectrum to smooth.
* @return :: A single-spectrum workspace with the smoothed data.
*/
API::MatrixWorkspace_sptr
WienerSmooth::smoothSingleSpectrum(API::MatrixWorkspace_sptr inputWS,
size_t wsIndex) {
size_t dataSize = inputWS->blocksize();
// it won't work for very small workspaces
if (dataSize < 4) {
g_log.debug() << "No smoothing, spectrum copied." << std::endl;
return copyInput(inputWS, wsIndex);
}
// Due to the way RealFFT works the input should be even-sized
const bool isOddSize = dataSize % 2 != 0;
if (isOddSize) {
// add a fake value to the end to make size even
inputWS = copyInput(inputWS, wsIndex);
wsIndex = 0;
auto &X = inputWS->dataX(wsIndex);
auto &Y = inputWS->dataY(wsIndex);
auto &E = inputWS->dataE(wsIndex);
double dx = X[dataSize - 1] - X[dataSize - 2];
X.push_back(X.back() + dx);
Y.push_back(Y.back());
E.push_back(E.back());
}
// the input vectors
auto &X = inputWS->readX(wsIndex);
auto &Y = inputWS->readY(wsIndex);
auto &E = inputWS->readE(wsIndex);
// Digital fourier transform works best for data oscillating around 0.
// Fit a spline with a small number of break points to the data.
// Make sure that the spline passes through the first and the last points
// of the data.
// The fitted spline will be subtracted from the data and the difference
// will be smoothed with the Wiener filter. After that the spline will be
// added to the smoothed data to produce the output.
// number of spline break points, must be smaller than the data size but
// between 2 and 10
size_t nbreak = 10;
if (nbreak * 3 > dataSize)
nbreak = dataSize / 3;
// NB. The spline mustn't fit too well to the data. If it does smoothing
// doesn't happen.
// TODO: it's possible that the spline is unnecessary and a simple linear
// function will
// do a better job.
g_log.debug() << "Spline break points " << nbreak << std::endl;
// define the spline
API::IFunction_sptr spline =
API::FunctionFactory::Instance().createFunction("BSpline");
auto xInterval = getStartEnd(X, inputWS->isHistogramData());
spline->setAttributeValue("StartX", xInterval.first);
spline->setAttributeValue("EndX", xInterval.second);
spline->setAttributeValue("NBreak", static_cast<int>(nbreak));
// fix the first and last parameters to the first and last data values
spline->setParameter(0, Y.front());
spline->fix(0);
size_t lastParamIndex = spline->nParams() - 1;
spline->setParameter(lastParamIndex, Y.back());
spline->fix(lastParamIndex);
// fit the spline to the data
auto fit = createChildAlgorithm("Fit");
fit->initialize();
fit->setProperty("Function", spline);
fit->setProperty("InputWorkspace", inputWS);
fit->setProperty("WorkspaceIndex", static_cast<int>(wsIndex));
fit->setProperty("CreateOutput", true);
fit->execute();
// get the fit output workspace; spectrum 2 contains the difference that is to
// be smoothed
API::MatrixWorkspace_sptr fitOut = fit->getProperty("OutputWorkspace");
// Fourier transform the difference spectrum
auto fourier = createChildAlgorithm("RealFFT");
fourier->initialize();
fourier->setProperty("InputWorkspace", fitOut);
fourier->setProperty("WorkspaceIndex", 2);
// we don't require bin linearity as we don't need the exact transform
fourier->setProperty("IgnoreXBins", true);
fourier->execute();
API::MatrixWorkspace_sptr fourierOut =
fourier->getProperty("OutputWorkspace");
// spectrum 2 of the transformed workspace has the transform modulus which is
// a square
// root of the power spectrum
auto &powerSpec = fourierOut->dataY(2);
// convert the modulus to power spectrum wich is the base of the Wiener filter
std::transform(powerSpec.begin(), powerSpec.end(), powerSpec.begin(),
PowerSpectrum());
// estimate power spectrum's noise as the average of its high frequency half
size_t n2 = powerSpec.size();
double noise =
std::accumulate(powerSpec.begin() + n2 / 2, powerSpec.end(), 0.0);
noise /= static_cast<double>(n2);
// index of the maximum element in powerSpec
const size_t imax = static_cast<size_t>(std::distance(
powerSpec.begin(), std::max_element(powerSpec.begin(), powerSpec.end())));
if (noise == 0.0) {
noise = powerSpec[imax] / guessSignalToNoiseRatio;
}
g_log.debug() << "Maximum signal " << powerSpec[imax] << std::endl;
g_log.debug() << "Noise " << noise << std::endl;
// storage for the Wiener filter, initialized with 0.0's
std::vector<double> wf(n2);
// The filter consists of two parts:
// 1) low frequency region, from 0 until the power spectrum falls to the
// noise level, filter is calculated
// from the power spectrum
// 2) high frequency noisy region, filter is a smooth function of frequency
// decreasing to 0
// the following code is an adaptation of a fortran routine
// noise starting index
size_t i0 = 0;
// intermediate variables
double xx = 0.0;
double xy = 0.0;
double ym = 0.0;
// low frequency filter values: the higher the power spectrum the closer the
// filter to 1.0
for (size_t i = 0; i < n2; ++i) {
double cd1 = powerSpec[i] / noise;
if (cd1 < 1.0 && i > imax) {
i0 = i;
break;
}
double cd2 = log(cd1);
wf[i] = cd1 / (1.0 + cd1);
double j = static_cast<double>(i + 1);
xx += j * j;
xy += j * cd2;
ym += cd2;
}
// i0 should always be > 0 but in case something goes wrong make a check
if (i0 > 0) {
g_log.debug() << "Noise start index " << i0 << std::endl;
// high frequency filter values: smooth decreasing function
double ri0f = static_cast<double>(i0 + 1);
double xm = (1.0 + ri0f) / 2;
ym /= ri0f;
double a1 = (xy - ri0f * xm * ym) / (xx - ri0f * xm * xm);
double b1 = ym - a1 * xm;
g_log.debug() << "(a1,b1) = (" << a1 << ',' << b1 << ')' << std::endl;
const double dblev = -20.0;
// cut-off index
double ri1 = floor((dblev / 4 - b1) / a1);
if (ri1 < static_cast<double>(i0)) {
g_log.warning() << "Failed to build Wiener filter: no smoothing."
<< std::endl;
ri1 = static_cast<double>(i0);
}
size_t i1 = static_cast<size_t>(ri1);
if (i1 > n2)
i1 = n2;
for (size_t i = i0; i < i1; ++i) {
double s = exp(a1 * static_cast<double>(i + 1) + b1);
wf[i] = s / (1.0 + s);
}
// wf[i] for i1 <= i < n2 are 0.0
g_log.debug() << "Cut-off index " << i1 << std::endl;
} else {
g_log.warning() << "Power spectrum has an unexpected shape: no smoothing"
<< std::endl;
return copyInput(inputWS, wsIndex);
}
// multiply the fourier transform by the filter
auto &re = fourierOut->dataY(0);
auto &im = fourierOut->dataY(1);
std::transform(re.begin(), re.end(), wf.begin(), re.begin(),
std::multiplies<double>());
std::transform(im.begin(), im.end(), wf.begin(), im.begin(),
std::multiplies<double>());
// inverse fourier transform
fourier = createChildAlgorithm("RealFFT");
fourier->initialize();
fourier->setProperty("InputWorkspace", fourierOut);
fourier->setProperty("IgnoreXBins", true);
fourier->setPropertyValue("Transform", "Backward");
fourier->execute();
API::MatrixWorkspace_sptr out = fourier->getProperty("OutputWorkspace");
auto &background = fitOut->readY(1);
auto &y = out->dataY(0);
if (y.size() != background.size()) {
throw std::logic_error("Logic error: inconsistent arrays");
}
// add the spline "background" to the smoothed data
std::transform(y.begin(), y.end(), background.begin(), y.begin(),
std::plus<double>());
// copy the x-values and errors from the original spectrum
// remove the last values for odd-sized inputs
if (isOddSize) {
out->dataX(0).assign(X.begin(), X.end() - 1);
out->dataE(0).assign(E.begin(), E.end() - 1);
out->dataY(0).resize(Y.size() - 1);
} else {
out->setX(0, X);
out->dataE(0).assign(E.begin(), E.end());
}
return out;
}
/** Forms the quadrature phase signal (squashogram)
* @param ws :: [input] workspace containing the measured spectra
* @param phase :: [input] table workspace containing the detector phases
* @param n0 :: [input] vector containing the normalization constants
* @return :: workspace containing the quadrature phase signal
*/
API::MatrixWorkspace_sptr
PhaseQuadMuon::squash(const API::MatrixWorkspace_sptr &ws,
const API::ITableWorkspace_sptr &phase,
const std::vector<double> &n0) {
// Poisson limit: below this number we consider we don't have enough
// statistics
// to apply sqrt(N). This is an arbitrary number used in the original code
// provided by scientists
double poissonLimit = 30.;
size_t nspec = ws->getNumberHistograms();
size_t npoints = ws->blocksize();
// Muon life time in microseconds
double muLife = PhysicalConstants::MuonLifetime * 1e6;
if (n0.size() != nspec) {
throw std::invalid_argument("Invalid normalization constants");
}
// Get the maximum asymmetry
double maxAsym = 0.;
for (size_t h = 0; h < nspec; h++) {
if (phase->Double(h, 1) > maxAsym) {
maxAsym = phase->Double(h, 1);
}
}
if (maxAsym == 0.0) {
throw std::invalid_argument("Invalid detector asymmetries");
}
std::vector<double> aj, bj;
{
// Calculate coefficients aj, bj
double sxx = 0;
double syy = 0;
double sxy = 0;
for (size_t h = 0; h < nspec; h++) {
double asym = phase->Double(h, 1) / maxAsym;
double phi = phase->Double(h, 2);
double X = n0[h] * asym * cos(phi);
double Y = n0[h] * asym * sin(phi);
sxx += X * X;
syy += Y * Y;
sxy += X * Y;
}
double lam1 = 2 * syy / (sxx * syy - sxy * sxy);
double mu1 = 2 * sxy / (sxy * sxy - sxx * syy);
double lam2 = 2 * sxy / (sxy * sxy - sxx * syy);
double mu2 = 2 * sxx / (sxx * syy - sxy * sxy);
for (size_t h = 0; h < nspec; h++) {
double asym = phase->Double(h, 1) / maxAsym;
double phi = phase->Double(h, 2);
double X = n0[h] * asym * cos(phi);
double Y = n0[h] * asym * sin(phi);
aj.push_back((lam1 * X + mu1 * Y) * 0.5);
bj.push_back((lam2 * X + mu2 * Y) * 0.5);
}
}
// First X value
double X0 = ws->x(0).front();
// Create and populate output workspace
API::MatrixWorkspace_sptr ows = API::WorkspaceFactory::Instance().create(
"Workspace2D", 2, npoints + 1, npoints);
// X
ows->setSharedX(0, ws->sharedX(0));
ows->setSharedX(1, ws->sharedX(0));
// Phase quadrature
auto &realY = ows->mutableY(0);
auto &imagY = ows->mutableY(1);
auto &realE = ows->mutableE(0);
auto &imagE = ows->mutableE(1);
for (size_t i = 0; i < npoints; i++) {
for (size_t h = 0; h < nspec; h++) {
// (X,Y,E) with exponential decay removed
const double X = ws->x(h)[i];
const double Y = ws->y(h)[i] - n0[h] * exp(-(X - X0) / muLife);
const double E = (ws->y(h)[i] > poissonLimit)
? ws->e(h)[i]
: sqrt(n0[h] * exp(-(X - X0) / muLife));
realY[i] += aj[h] * Y;
imagY[i] += bj[h] * Y;
realE[i] += aj[h] * aj[h] * E * E;
imagE[i] += bj[h] * bj[h] * E * E;
}
realE[i] = sqrt(realE[i]);
imagE[i] = sqrt(imagE[i]);
// Regain exponential decay
const double X = ws->getSpectrum(0).x()[i];
const double e = exp(-(X - X0) / muLife);
realY[i] /= e;
imagY[i] /= e;
realE[i] /= e;
imagE[i] /= e;
}
return ows;
}
/** Forms the quadrature phase signal (squashogram)
* @param ws :: [input] workspace containing the measured spectra
* @param phase :: [input] table workspace containing the detector phases
* @param n0 :: [input] vector containing the normalization constants
* @return :: workspace containing the quadrature phase signal
*/
API::MatrixWorkspace_sptr
PhaseQuadMuon::squash(const API::MatrixWorkspace_sptr &ws,
const API::ITableWorkspace_sptr &phase,
const std::vector<double> &n0) {
// Poisson limit: below this number we consider we don't have enough
// statistics
// to apply sqrt(N). This is an arbitrary number used in the original code
// provided by scientists
const double poissonLimit = 30.;
// Muon life time in microseconds
const double muLife = PhysicalConstants::MuonLifetime * 1e6;
const size_t nspec = ws->getNumberHistograms();
if (n0.size() != nspec) {
throw std::invalid_argument("Invalid normalization constants");
}
auto names = phase->getColumnNames();
for (auto &name : names) {
std::transform(name.begin(), name.end(), name.begin(), ::tolower);
}
auto phaseIndex = findName(phaseNames, names);
auto asymmetryIndex = findName(asymmNames, names);
// Get the maximum asymmetry
double maxAsym = 0.;
for (size_t h = 0; h < nspec; h++) {
if (phase->Double(h, asymmetryIndex) > maxAsym &&
phase->Double(h, asymmetryIndex) != ASYMM_ERROR) {
maxAsym = phase->Double(h, asymmetryIndex);
}
}
if (maxAsym == 0.0) {
throw std::invalid_argument("Invalid detector asymmetries");
}
std::vector<bool> emptySpectrum;
emptySpectrum.reserve(nspec);
std::vector<double> aj, bj;
{
// Calculate coefficients aj, bj
double sxx = 0.;
double syy = 0.;
double sxy = 0.;
for (size_t h = 0; h < nspec; h++) {
emptySpectrum.push_back(
std::all_of(ws->y(h).begin(), ws->y(h).end(),
[](double value) { return value == 0.; }));
if (!emptySpectrum[h]) {
const double asym = phase->Double(h, asymmetryIndex) / maxAsym;
const double phi = phase->Double(h, phaseIndex);
const double X = n0[h] * asym * cos(phi);
const double Y = n0[h] * asym * sin(phi);
sxx += X * X;
syy += Y * Y;
sxy += X * Y;
}
}
const double lam1 = 2 * syy / (sxx * syy - sxy * sxy);
const double mu1 = 2 * sxy / (sxy * sxy - sxx * syy);
const double lam2 = 2 * sxy / (sxy * sxy - sxx * syy);
const double mu2 = 2 * sxx / (sxx * syy - sxy * sxy);
for (size_t h = 0; h < nspec; h++) {
if (emptySpectrum[h]) {
aj.push_back(0.0);
bj.push_back(0.0);
} else {
const double asym = phase->Double(h, asymmetryIndex) / maxAsym;
const double phi = phase->Double(h, phaseIndex);
const double X = n0[h] * asym * cos(phi);
const double Y = n0[h] * asym * sin(phi);
aj.push_back((lam1 * X + mu1 * Y) * 0.5);
bj.push_back((lam2 * X + mu2 * Y) * 0.5);
}
}
}
const size_t npoints = ws->blocksize();
// Create and populate output workspace
API::MatrixWorkspace_sptr ows =
API::WorkspaceFactory::Instance().create(ws, 2, npoints + 1, npoints);
// X
ows->setSharedX(0, ws->sharedX(0));
ows->setSharedX(1, ws->sharedX(0));
// Phase quadrature
auto &realY = ows->mutableY(0);
auto &imagY = ows->mutableY(1);
auto &realE = ows->mutableE(0);
auto &imagE = ows->mutableE(1);
const auto xPointData = ws->histogram(0).points();
// First X value
const double X0 = xPointData.front();
// calculate exponential decay outside of the loop
std::vector<double> expDecay = xPointData.rawData();
std::transform(expDecay.begin(), expDecay.end(), expDecay.begin(),
[X0, muLife](double x) { return exp(-(x - X0) / muLife); });
for (size_t i = 0; i < npoints; i++) {
for (size_t h = 0; h < nspec; h++) {
if (!emptySpectrum[h]) {
// (X,Y,E) with exponential decay removed
const double X = ws->x(h)[i];
const double exponential = n0[h] * exp(-(X - X0) / muLife);
const double Y = ws->y(h)[i] - exponential;
const double E =
(ws->y(h)[i] > poissonLimit) ? ws->e(h)[i] : sqrt(exponential);
realY[i] += aj[h] * Y;
imagY[i] += bj[h] * Y;
realE[i] += aj[h] * aj[h] * E * E;
imagE[i] += bj[h] * bj[h] * E * E;
}
}
realE[i] = sqrt(realE[i]);
imagE[i] = sqrt(imagE[i]);
// Regain exponential decay
realY[i] /= expDecay[i];
imagY[i] /= expDecay[i];
realE[i] /= expDecay[i];
imagE[i] /= expDecay[i];
}
// New Y axis label
ows->setYUnit("Asymmetry");
return ows;
}
/** Carries out the bin-by-bin normalization
* @param inputWorkspace The input workspace
* @param outputWorkspace The result workspace
*/
void NormaliseToMonitor::normaliseBinByBin(
const API::MatrixWorkspace_sptr &inputWorkspace,
API::MatrixWorkspace_sptr &outputWorkspace) {
EventWorkspace_sptr inputEvent =
boost::dynamic_pointer_cast<EventWorkspace>(inputWorkspace);
// Only create output workspace if different to input one
if (outputWorkspace != inputWorkspace) {
if (inputEvent) {
outputWorkspace = inputWorkspace->clone();
} else
outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace);
}
auto outputEvent =
boost::dynamic_pointer_cast<EventWorkspace>(outputWorkspace);
// Get hold of the monitor spectrum
const auto &monX = m_monitor->binEdges(0);
auto monY = m_monitor->counts(0);
auto monE = m_monitor->countStandardDeviations(0);
// Calculate the overall normalization just the once if bins are all matching
if (m_commonBins)
this->normalisationFactor(monX, monY, monE);
const size_t numHists = inputWorkspace->getNumberHistograms();
auto specLength = inputWorkspace->blocksize();
// Flag set when a division by 0 is found
bool hasZeroDivision = false;
Progress prog(this, 0.0, 1.0, numHists);
// Loop over spectra
PARALLEL_FOR_IF(
Kernel::threadSafe(*inputWorkspace, *outputWorkspace, *m_monitor))
for (int64_t i = 0; i < int64_t(numHists); ++i) {
PARALLEL_START_INTERUPT_REGION
prog.report();
const auto &X = inputWorkspace->binEdges(i);
// If not rebinning, just point to our monitor spectra, otherwise create new
// vectors
auto Y = (m_commonBins ? monY : Counts(specLength));
auto E = (m_commonBins ? monE : CountStandardDeviations(specLength));
if (!m_commonBins) {
// ConvertUnits can give X vectors of all zeros - skip these, they cause
// problems
if (X.back() == 0.0 && X.front() == 0.0)
continue;
// Rebin the monitor spectrum to match the binning of the current data
// spectrum
VectorHelper::rebinHistogram(
monX.rawData(), monY.mutableRawData(), monE.mutableRawData(),
X.rawData(), Y.mutableRawData(), E.mutableRawData(), false);
// Recalculate the overall normalization factor
this->normalisationFactor(X, Y, E);
}
if (inputEvent) {
// ----------------------------------- EventWorkspace
// ---------------------------------------
EventList &outEL = outputEvent->getSpectrum(i);
outEL.divide(X.rawData(), Y.mutableRawData(), E.mutableRawData());
} else {
// ----------------------------------- Workspace2D
// ---------------------------------------
auto &YOut = outputWorkspace->mutableY(i);
auto &EOut = outputWorkspace->mutableE(i);
const auto &inY = inputWorkspace->y(i);
const auto &inE = inputWorkspace->e(i);
outputWorkspace->mutableX(i) = inputWorkspace->x(i);
// The code below comes more or less straight out of Divide.cpp
for (size_t k = 0; k < specLength; ++k) {
// Get the input Y's
const double leftY = inY[k];
const double rightY = Y[k];
if (rightY == 0.0) {
hasZeroDivision = true;
}
// Calculate result and store in local variable to avoid overwriting
// original data if
// output workspace is same as one of the input ones
const double newY = leftY / rightY;
if (fabs(rightY) > 1.0e-12 && fabs(newY) > 1.0e-12) {
const double lhsFactor = (inE[k] < 1.0e-12 || fabs(leftY) < 1.0e-12)
? 0.0
: pow((inE[k] / leftY), 2);
const double rhsFactor =
E[k] < 1.0e-12 ? 0.0 : pow((E[k] / rightY), 2);
EOut[k] = std::abs(newY) * sqrt(lhsFactor + rhsFactor);
}
// Now store the result
YOut[k] = newY;
} // end Workspace2D case
} // end loop over current spectrum
PARALLEL_END_INTERUPT_REGION
} // end loop over spectra
PARALLEL_CHECK_INTERUPT_REGION
if (hasZeroDivision) {
g_log.warning() << "Division by zero in some of the bins.\n";
}
}
/** Executes the algorithm
*
* @throw Exception::FileError If the grouping file cannot be opened or read successfully
* @throw runtime_error If unable to run one of the sub-algorithms successfully
*/
void DiffractionFocussing::exec()
{
// retrieve the properties
std::string groupingFileName=getProperty("GroupingFileName");
// Get the input workspace
MatrixWorkspace_sptr inputW = getProperty("InputWorkspace");
bool dist = inputW->isDistribution();
//do this first to check that a valid file is available before doing any work
std::multimap<int64_t,int64_t> detectorGroups;// <group, UDET>
if (!readGroupingFile(groupingFileName, detectorGroups))
{
throw Exception::FileError("Error reading .cal file",groupingFileName);
}
//Convert to d-spacing units
API::MatrixWorkspace_sptr tmpW = convertUnitsToDSpacing(inputW);
//Rebin to a common set of bins
RebinWorkspace(tmpW);
std::set<int64_t> groupNumbers;
for(std::multimap<int64_t,int64_t>::const_iterator d = detectorGroups.begin();d!=detectorGroups.end();d++)
{
if (groupNumbers.find(d->first) == groupNumbers.end())
{
groupNumbers.insert(d->first);
}
}
int iprogress = 0;
int iprogress_count = static_cast<int>(groupNumbers.size());
int iprogress_step = iprogress_count / 100;
if (iprogress_step == 0) iprogress_step = 1;
std::vector<int64_t> resultIndeces;
for(std::set<int64_t>::const_iterator g = groupNumbers.begin();g!=groupNumbers.end();g++)
{
if (iprogress++ % iprogress_step == 0)
{
progress(0.68 + double(iprogress)/iprogress_count/3);
}
std::multimap<int64_t,int64_t>::const_iterator from = detectorGroups.lower_bound(*g);
std::multimap<int64_t,int64_t>::const_iterator to = detectorGroups.upper_bound(*g);
std::vector<detid_t> detectorList;
for(std::multimap<int64_t,int64_t>::const_iterator d = from;d!=to;d++)
detectorList.push_back(static_cast<detid_t>(d->second));
// Want version 1 of GroupDetectors here
API::IAlgorithm_sptr childAlg = createSubAlgorithm("GroupDetectors",-1.0,-1.0,true,1);
childAlg->setProperty("Workspace", tmpW);
childAlg->setProperty< std::vector<detid_t> >("DetectorList",detectorList);
childAlg->executeAsSubAlg();
try
{
// get the index of the combined spectrum
int ri = childAlg->getProperty("ResultIndex");
if (ri >= 0)
{
resultIndeces.push_back(ri);
}
}
catch(...)
{
throw std::runtime_error("Unable to get Properties from GroupDetectors sub-algorithm");
}
}
// Discard left-over spectra, but print warning message giving number discarded
int discarded = 0;
const int64_t oldHistNumber = tmpW->getNumberHistograms();
API::Axis *spectraAxis = tmpW->getAxis(1);
for(int64_t i=0; i < oldHistNumber; i++)
if ( spectraAxis->spectraNo(i) >= 0 && find(resultIndeces.begin(),resultIndeces.end(),i) == resultIndeces.end())
{
++discarded;
}
g_log.warning() << "Discarded " << discarded << " spectra that were not assigned to any group" << std::endl;
// Running GroupDetectors leads to a load of redundant spectra
// Create a new workspace that's the right size for the meaningful spectra and copy them in
int64_t newSize = tmpW->blocksize();
API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(tmpW,resultIndeces.size(),newSize+1,newSize);
// Copy units
outputW->getAxis(0)->unit() = tmpW->getAxis(0)->unit();
outputW->getAxis(1)->unit() = tmpW->getAxis(1)->unit();
API::Axis *spectraAxisNew = outputW->getAxis(1);
for(int64_t hist=0; hist < static_cast<int64_t>(resultIndeces.size()); hist++)
{
int64_t i = resultIndeces[hist];
double spNo = static_cast<double>(spectraAxis->spectraNo(i));
MantidVec &tmpE = tmpW->dataE(i);
MantidVec &outE = outputW->dataE(hist);
MantidVec &tmpY = tmpW->dataY(i);
MantidVec &outY = outputW->dataY(hist);
MantidVec &tmpX = tmpW->dataX(i);
MantidVec &outX = outputW->dataX(hist);
outE.assign(tmpE.begin(),tmpE.end());
outY.assign(tmpY.begin(),tmpY.end());
outX.assign(tmpX.begin(),tmpX.end());
spectraAxisNew->setValue(hist,spNo);
spectraAxis->setValue(i,-1);
}
progress(1.);
outputW->isDistribution(dist);
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputW);
return;
}
