/*
* Sum up all the unmasked detector pixels.
*
* @param rebinnedWS: workspace where all the wavelength bins have been grouped
*together
* @param sum: sum of all the unmasked detector pixels (counts)
* @param error: error on sum (counts)
* @param nPixels: number of unmasked detector pixels that contributed to sum
*/
void CalculateEfficiency::sumUnmaskedDetectors(MatrixWorkspace_sptr rebinnedWS,
double &sum, double &error,
int &nPixels) {
// Number of spectra
const int numberOfSpectra =
static_cast<int>(rebinnedWS->getNumberHistograms());
sum = 0.0;
error = 0.0;
nPixels = 0;
const auto &spectrumInfo = rebinnedWS->spectrumInfo();
for (int i = 0; i < numberOfSpectra; i++) {
progress(0.2 + 0.2 * i / numberOfSpectra, "Computing sensitivity");
// Skip if we have a monitor or if the detector is masked.
if (spectrumInfo.isMonitor(i) || spectrumInfo.isMasked(i))
continue;
// Retrieve the spectrum into a vector
auto &YValues = rebinnedWS->y(i);
auto &YErrors = rebinnedWS->e(i);
sum += YValues[0];
error += YErrors[0] * YErrors[0];
nPixels++;
}
g_log.debug() << "sumUnmaskedDetectors: unmasked pixels = " << nPixels
<< " from a total of " << numberOfSpectra << "\n";
error = std::sqrt(error);
}
/**
* Sum counts from the input workspace in lambda along lines of constant Q by
* projecting to "virtual lambda" at a reference angle twoThetaR.
*
* @param detectorWS [in] :: the input workspace in wavelength
* @return :: the output workspace in wavelength
*/
MatrixWorkspace_sptr
ReflectometryReductionOne2::sumInQ(MatrixWorkspace_sptr detectorWS) {
// Construct the output array in virtual lambda
MatrixWorkspace_sptr IvsLam = constructIvsLamWS(detectorWS);
// Loop through each input group (and corresponding output spectrum)
const size_t numGroups = detectorGroups().size();
for (size_t groupIdx = 0; groupIdx < numGroups; ++groupIdx) {
auto &detectors = detectorGroups()[groupIdx];
auto &outputE = IvsLam->dataE(groupIdx);
// Loop through each spectrum in the detector group
for (auto spIdx : detectors) {
// Get the angle of this detector and its size in twoTheta
const double twoTheta = getDetectorTwoTheta(m_spectrumInfo, spIdx);
const double bTwoTheta = getDetectorTwoThetaRange(spIdx);
// Check X length is Y length + 1
const auto &inputX = detectorWS->x(spIdx);
const auto &inputY = detectorWS->y(spIdx);
const auto &inputE = detectorWS->e(spIdx);
if (inputX.size() != inputY.size() + 1) {
throw std::runtime_error(
"Expected input workspace to be histogram data (got X len=" +
std::to_string(inputX.size()) +
", Y len=" + std::to_string(inputY.size()) + ")");
}
// Create a vector for the projected errors for this spectrum.
// (Output Y values can simply be accumulated directly into the output
// workspace, but for error values we need to create a separate error
// vector for the projected errors from each input spectrum and then
// do an overall sum in quadrature.)
std::vector<double> projectedE(outputE.size(), 0.0);
// Process each value in the spectrum
const int ySize = static_cast<int>(inputY.size());
for (int inputIdx = 0; inputIdx < ySize; ++inputIdx) {
// Do the summation in Q
sumInQProcessValue(inputIdx, twoTheta, bTwoTheta, inputX, inputY,
inputE, detectors, groupIdx, IvsLam, projectedE);
}
// Sum errors in quadrature
const int eSize = static_cast<int>(outputE.size());
for (int outIdx = 0; outIdx < eSize; ++outIdx) {
outputE[outIdx] += projectedE[outIdx] * projectedE[outIdx];
}
}
// Take the square root of all the accumulated squared errors for this
// detector group. Assumes Gaussian errors
double (*rs)(double) = std::sqrt;
std::transform(outputE.begin(), outputE.end(), outputE.begin(), rs);
}
return IvsLam;
}
void CalculateEfficiency::normalizeDetectors(MatrixWorkspace_sptr rebinnedWS,
MatrixWorkspace_sptr outputWS,
double sum, double error,
int nPixels, double min_eff,
double max_eff) {
// Number of spectra
const size_t numberOfSpectra = rebinnedWS->getNumberHistograms();
// Empty vector to store the pixels that outside the acceptable efficiency
// range
std::vector<size_t> dets_to_mask;
const auto &spectrumInfo = rebinnedWS->spectrumInfo();
for (size_t i = 0; i < numberOfSpectra; i++) {
const double currProgress =
0.4 +
0.2 * (static_cast<double>(i) / static_cast<double>(numberOfSpectra));
progress(currProgress, "Computing sensitivity");
// If this spectrum is masked, skip to the next one
if (spectrumInfo.isMasked(i))
continue;
// Retrieve the spectrum into a vector
auto &YIn = rebinnedWS->y(i);
auto &EIn = rebinnedWS->e(i);
auto &YOut = outputWS->mutableY(i);
auto &EOut = outputWS->mutableE(i);
// If this detector is a monitor, skip to the next one
if (spectrumInfo.isMonitor(i)) {
YOut[0] = 1.0;
EOut[0] = 0.0;
continue;
}
// Normalize counts to get relative efficiency
YOut[0] = nPixels / sum * YIn[0];
const double err_sum = YIn[0] / sum * error;
EOut[0] = nPixels / std::abs(sum) *
std::sqrt(EIn[0] * EIn[0] + err_sum * err_sum);
// Mask this detector if the signal is outside the acceptable band
if (!isEmpty(min_eff) && YOut[0] < min_eff)
dets_to_mask.push_back(i);
if (!isEmpty(max_eff) && YOut[0] > max_eff)
dets_to_mask.push_back(i);
}
g_log.debug() << "normalizeDetectors: Masked pixels outside the acceptable "
"efficiency range [" << min_eff << "," << max_eff
<< "] = " << dets_to_mask.size()
<< " from a total of non masked = " << nPixels
<< " (from a total number of spectra in the ws = "
<< numberOfSpectra << ")\n";
// If we identified pixels to be masked, mask them now
if (!dets_to_mask.empty()) {
// Mask detectors that were found to be outside the acceptable efficiency
// band
try {
IAlgorithm_sptr mask = createChildAlgorithm("MaskDetectors", 0.8, 0.9);
// First we mask detectors in the output workspace
mask->setProperty<MatrixWorkspace_sptr>("Workspace", outputWS);
mask->setProperty<std::vector<size_t>>("WorkspaceIndexList",
dets_to_mask);
mask->execute();
mask = createChildAlgorithm("MaskDetectors", 0.9, 1.0);
// Then we mask the same detectors in the input workspace
mask->setProperty<MatrixWorkspace_sptr>("Workspace", rebinnedWS);
mask->setProperty<std::vector<size_t>>("WorkspaceIndexList",
dets_to_mask);
mask->execute();
} catch (std::invalid_argument &err) {
std::stringstream e;
e << "Invalid argument to MaskDetectors Child Algorithm: " << err.what();
g_log.error(e.str());
} catch (std::runtime_error &err) {
std::stringstream e;
e << "Unable to successfully run MaskDetectors Child Algorithm: "
<< err.what();
g_log.error(e.str());
}
}
}
void SofQWCentre::exec() {
using namespace Geometry;
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
// Do the full check for common binning
if (!WorkspaceHelpers::commonBoundaries(*inputWorkspace)) {
g_log.error(
"The input workspace must have common binning across all spectra");
throw std::invalid_argument(
"The input workspace must have common binning across all spectra");
}
std::vector<double> verticalAxis;
MatrixWorkspace_sptr outputWorkspace = setUpOutputWorkspace(
inputWorkspace, getProperty("QAxisBinning"), verticalAxis);
setProperty("OutputWorkspace", outputWorkspace);
// Holds the spectrum-detector mapping
std::vector<specnum_t> specNumberMapping;
std::vector<detid_t> detIDMapping;
m_EmodeProperties.initCachedValues(*inputWorkspace, this);
int emode = m_EmodeProperties.m_emode;
// Get a pointer to the instrument contained in the workspace
Instrument_const_sptr instrument = inputWorkspace->getInstrument();
// Get the distance between the source and the sample (assume in metres)
IComponent_const_sptr source = instrument->getSource();
IComponent_const_sptr sample = instrument->getSample();
V3D beamDir = sample->getPos() - source->getPos();
beamDir.normalize();
try {
double l1 = source->getDistance(*sample);
g_log.debug() << "Source-sample distance: " << l1 << '\n';
} catch (Exception::NotFoundError &) {
g_log.error("Unable to calculate source-sample distance");
throw Exception::InstrumentDefinitionError(
"Unable to calculate source-sample distance",
inputWorkspace->getTitle());
}
// Conversion constant for E->k. k(A^-1) = sqrt(energyToK*E(meV))
const double energyToK = 8.0 * M_PI * M_PI * PhysicalConstants::NeutronMass *
PhysicalConstants::meV * 1e-20 /
(PhysicalConstants::h * PhysicalConstants::h);
// Loop over input workspace bins, reassigning data to correct bin in output
// qw workspace
const size_t numHists = inputWorkspace->getNumberHistograms();
const size_t numBins = inputWorkspace->blocksize();
Progress prog(this, 0.0, 1.0, numHists);
for (int64_t i = 0; i < int64_t(numHists); ++i) {
try {
// Now get the detector object for this histogram
IDetector_const_sptr spectrumDet = inputWorkspace->getDetector(i);
if (spectrumDet->isMonitor())
continue;
const double efixed = m_EmodeProperties.getEFixed(*spectrumDet);
// For inelastic scattering the simple relationship q=4*pi*sinTheta/lambda
// does not hold. In order to
// be completely general we must calculate the momentum transfer by
// calculating the incident and final
// wave vectors and then use |q| = sqrt[(ki - kf)*(ki - kf)]
DetectorGroup_const_sptr detGroup =
boost::dynamic_pointer_cast<const DetectorGroup>(spectrumDet);
std::vector<IDetector_const_sptr> detectors;
if (detGroup) {
detectors = detGroup->getDetectors();
} else {
detectors.push_back(spectrumDet);
}
const size_t numDets = detectors.size();
// cache to reduce number of static casts
const double numDets_d = static_cast<double>(numDets);
const auto &Y = inputWorkspace->y(i);
const auto &E = inputWorkspace->e(i);
const auto &X = inputWorkspace->x(i);
// Loop over the detectors and for each bin calculate Q
for (size_t idet = 0; idet < numDets; ++idet) {
IDetector_const_sptr det = detectors[idet];
// Calculate kf vector direction and then Q for each energy bin
V3D scatterDir = (det->getPos() - sample->getPos());
scatterDir.normalize();
for (size_t j = 0; j < numBins; ++j) {
const double deltaE = 0.5 * (X[j] + X[j + 1]);
// Compute ki and kf wave vectors and therefore q = ki - kf
double ei(0.0), ef(0.0);
if (emode == 1) {
ei = efixed;
ef = efixed - deltaE;
if (ef < 0) {
std::string mess =
"Energy transfer requested in Direct mode exceeds incident "
"energy.\n Found for det ID: " +
std::to_string(idet) + " bin No " + std::to_string(j) +
" with Ei=" + boost::lexical_cast<std::string>(efixed) +
" and energy transfer: " +
boost::lexical_cast<std::string>(deltaE);
throw std::runtime_error(mess);
}
} else {
ei = efixed + deltaE;
ef = efixed;
if (ef < 0) {
std::string mess =
"Incident energy of a neutron is negative. Are you trying to "
"process Direct data in Indirect mode?\n Found for det ID: " +
std::to_string(idet) + " bin No " + std::to_string(j) +
" with efied=" + boost::lexical_cast<std::string>(efixed) +
" and energy transfer: " +
boost::lexical_cast<std::string>(deltaE);
throw std::runtime_error(mess);
}
}
if (ei < 0)
throw std::runtime_error(
"Negative incident energy. Check binning.");
const V3D ki = beamDir * sqrt(energyToK * ei);
const V3D kf = scatterDir * (sqrt(energyToK * (ef)));
const double q = (ki - kf).norm();
// Test whether it's in range of the Q axis
if (q < verticalAxis.front() || q > verticalAxis.back())
continue;
// Find which q bin this point lies in
const MantidVec::difference_type qIndex =
std::upper_bound(verticalAxis.begin(), verticalAxis.end(), q) -
verticalAxis.begin() - 1;
// Add this spectra-detector pair to the mapping
specNumberMapping.push_back(
outputWorkspace->getSpectrum(qIndex).getSpectrumNo());
detIDMapping.push_back(det->getID());
// And add the data and it's error to that bin, taking into account
// the number of detectors contributing to this bin
outputWorkspace->mutableY(qIndex)[j] += Y[j] / numDets_d;
// Standard error on the average
outputWorkspace->mutableE(qIndex)[j] =
sqrt((pow(outputWorkspace->e(qIndex)[j], 2) + pow(E[j], 2)) /
numDets_d);
}
}
} catch (Exception::NotFoundError &) {
// Get to here if exception thrown when calculating distance to detector
// Presumably, if we get to here the spectrum will be all zeroes anyway
// (from conversion to E)
continue;
}
prog.report();
}
// If the input workspace was a distribution, need to divide by q bin width
if (inputWorkspace->isDistribution())
this->makeDistribution(outputWorkspace, verticalAxis);
// Set the output spectrum-detector mapping
SpectrumDetectorMapping outputDetectorMap(specNumberMapping, detIDMapping);
outputWorkspace->updateSpectraUsing(outputDetectorMap);
// Replace any NaNs in outputWorkspace with zeroes
if (this->getProperty("ReplaceNaNs")) {
auto replaceNans = this->createChildAlgorithm("ReplaceSpecialValues");
replaceNans->setChild(true);
replaceNans->initialize();
replaceNans->setProperty("InputWorkspace", outputWorkspace);
replaceNans->setProperty("OutputWorkspace", outputWorkspace);
replaceNans->setProperty("NaNValue", 0.0);
replaceNans->setProperty("InfinityValue", 0.0);
replaceNans->setProperty("BigNumberThreshold", DBL_MAX);
replaceNans->execute();
}
}
/** Carries out the bin-by-bin normalization
* @param inputWorkspace The input workspace
* @param outputWorkspace The result workspace
*/
void NormaliseToMonitor::normaliseBinByBin(
const MatrixWorkspace_sptr &inputWorkspace,
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 = create<MatrixWorkspace>(*inputWorkspace);
}
auto outputEvent =
boost::dynamic_pointer_cast<EventWorkspace>(outputWorkspace);
const auto &inputSpecInfo = inputWorkspace->spectrumInfo();
const auto &monitorSpecInfo = m_monitor->spectrumInfo();
const auto specLength = inputWorkspace->blocksize();
for (auto &workspaceIndex : m_workspaceIndexes) {
// Get hold of the monitor spectrum
const auto &monX = m_monitor->binEdges(workspaceIndex);
auto monY = m_monitor->counts(workspaceIndex);
auto monE = m_monitor->countStandardDeviations(workspaceIndex);
size_t timeIndex = 0;
if (m_scanInput)
timeIndex = monitorSpecInfo.spectrumDefinition(workspaceIndex)[0].second;
// 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();
// 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 &specDef = inputSpecInfo.spectrumDefinition(i);
if (!spectrumDefinitionsMatchTimeIndex(specDef, timeIndex))
continue;
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->setSharedX(i, inputWorkspace->sharedX(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";
}
if (inputEvent)
outputEvent->clearMRU();
}
}
/**
* Set errors in Diff spectrum after a fit
* @param data :: [input/output] Workspace containing spectrum to set errors to
*/
void ALCBaselineModellingModel::setErrorsAfterFit(MatrixWorkspace_sptr data) {
data->mutableE(2) = data->e(0);
}
