/**
* Sum counts from the input workspace in lambda along lines of constant Q by
* projecting to "virtual lambda" at a reference angle.
*
* @param detectorWS [in] :: the input workspace in wavelength
* @param indices [in] :: an index set defining the foreground histograms
* @return :: the single histogram output workspace in wavelength
*/
API::MatrixWorkspace_sptr
ReflectometrySumInQ::sumInQ(const API::MatrixWorkspace &detectorWS,
const Indexing::SpectrumIndexSet &indices) {
const auto spectrumInfo = detectorWS.spectrumInfo();
const auto refAngles = referenceAngles(spectrumInfo);
// Construct the output workspace in virtual lambda
API::MatrixWorkspace_sptr IvsLam =
constructIvsLamWS(detectorWS, indices, refAngles);
auto &outputE = IvsLam->dataE(0);
// Loop through each spectrum in the detector group
for (auto spIdx : indices) {
if (spectrumInfo.isMasked(spIdx) || spectrumInfo.isMonitor(spIdx)) {
continue;
}
// Get the size of this detector in twoTheta
const auto twoThetaRange = twoThetaWidth(spIdx, spectrumInfo);
// Check X length is Y length + 1
const auto inputBinEdges = detectorWS.binEdges(spIdx);
const auto inputCounts = detectorWS.counts(spIdx);
const auto inputStdDevs = detectorWS.countStandardDeviations(spIdx);
// 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>(inputCounts.size());
for (int inputIdx = 0; inputIdx < ySize; ++inputIdx) {
// Do the summation in Q
processValue(inputIdx, twoThetaRange, refAngles, inputBinEdges,
inputCounts, inputStdDevs, *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;
}
/**
* Return a pair of (minimum Q, maximum Q) for given
* indirect geometry workspace. Estimates the Q range from all detectors.
* If workspace contains grouped detectors/not all detectors are linked
* to a spectrum, the returned interval may be larger than actually needed.
* @param ws a workspace
* @param minE minimum energy transfer in ws
* @param maxE maximum energy transfer in ws
* @return a pair containing global minimun and maximum Q
*/
std::pair<double, double>
SofQCommon::qBinHintsIndirect(const API::MatrixWorkspace &ws, const double minE,
const double maxE) const {
using namespace Mantid::PhysicalConstants;
auto minQ = std::numeric_limits<double>::max();
auto maxQ = std::numeric_limits<double>::lowest();
const auto &detectorInfo = ws.detectorInfo();
for (size_t i = 0; i < detectorInfo.size(); ++i) {
if (detectorInfo.isMasked(i) || detectorInfo.isMonitor(i)) {
continue;
}
const auto twoTheta = detectorInfo.twoTheta(i);
const auto &det = detectorInfo.detector(i);
const auto Q1 = indirectQ(minE, twoTheta, &det);
const auto Q2 = indirectQ(maxE, twoTheta, &det);
const auto minmaxQ = std::minmax(Q1, Q2);
if (minmaxQ.first < minQ) {
minQ = minmaxQ.first;
}
if (minmaxQ.second > maxQ) {
maxQ = minmaxQ.second;
}
}
if (minQ == std::numeric_limits<double>::max()) {
throw std::runtime_error("Could not determine Q binning: workspace does "
"not contain usable spectra.");
}
return std::make_pair(minQ, maxQ);
}
/**
* Return a pair of (minimum Q, maximum Q) for given
* direct geometry workspace.
* @param ws a workspace
* @param minE minimum energy transfer in ws
* @param maxE maximum energy transfer in ws
* @return a pair containing global minimun and maximum Q
*/
std::pair<double, double>
SofQCommon::qBinHintsDirect(const API::MatrixWorkspace &ws, const double minE,
const double maxE) const {
using namespace Mantid::PhysicalConstants;
auto minTwoTheta = std::numeric_limits<double>::max();
auto maxTwoTheta = std::numeric_limits<double>::lowest();
const auto &spectrumInfo = ws.spectrumInfo();
for (size_t i = 0; i < spectrumInfo.size(); ++i) {
if (spectrumInfo.isMasked(i) || spectrumInfo.isMonitor(i)) {
continue;
}
const auto twoTheta = spectrumInfo.twoTheta(i);
if (twoTheta < minTwoTheta) {
minTwoTheta = twoTheta;
}
if (twoTheta > maxTwoTheta) {
maxTwoTheta = twoTheta;
}
}
if (minTwoTheta == std::numeric_limits<double>::max()) {
throw std::runtime_error("Could not determine Q binning: workspace does "
"not contain usable spectra.");
}
std::array<double, 4> q;
q[0] = directQ(minE, minTwoTheta);
q[1] = directQ(minE, maxTwoTheta);
q[2] = directQ(maxE, minTwoTheta);
q[3] = directQ(maxE, maxTwoTheta);
const auto minmaxQ = std::minmax_element(q.cbegin(), q.cend());
return std::make_pair(*minmaxQ.first, *minmaxQ.second);
}
/**
* Return the wavelength range of the output histogram.
* @param detectorWS [in] :: the input workspace
* @param indices [in] :: the workspace indices of foreground histograms
* @param refAngles [in] :: the reference angles
* @return :: the minimum and maximum virtual wavelengths
*/
ReflectometrySumInQ::MinMax ReflectometrySumInQ::findWavelengthMinMax(
const API::MatrixWorkspace &detectorWS,
const Indexing::SpectrumIndexSet &indices, const Angles &refAngles) {
const API::SpectrumInfo &spectrumInfo = detectorWS.spectrumInfo();
// Get the new max and min X values of the projected (virtual) lambda range
const bool includePartialBins = getProperty(Prop::PARTIAL_BINS);
// Find minimum and maximum 2thetas and the corresponding indices.
// It cannot be assumed that 2theta increases with indices, check for example
// D17 at ILL
MinMax inputLambdaRange;
MinMax inputTwoThetaRange;
for (const auto i : indices) {
const auto twoThetas = twoThetaWidth(i, spectrumInfo);
inputTwoThetaRange.testAndSetMin(includePartialBins ? twoThetas.min
: twoThetas.max);
inputTwoThetaRange.testAndSetMax(includePartialBins ? twoThetas.max
: twoThetas.min);
const auto &edges = detectorWS.binEdges(i);
for (size_t xIndex = 0; xIndex < edges.size(); ++xIndex) {
// It is common for the wavelength to have negative values at ILL.
const auto x = edges[xIndex + (includePartialBins ? 0 : 1)];
if (x > 0.) {
inputLambdaRange.testAndSet(x);
break;
}
}
if (includePartialBins) {
inputLambdaRange.testAndSet(edges.back());
} else {
inputLambdaRange.testAndSet(edges[edges.size() - 2]);
}
}
MinMax outputLambdaRange;
outputLambdaRange.min = projectToReference(inputLambdaRange.min,
inputTwoThetaRange.max, refAngles);
outputLambdaRange.max = projectToReference(inputLambdaRange.max,
inputTwoThetaRange.min, refAngles);
if (outputLambdaRange.min > outputLambdaRange.max) {
throw std::runtime_error(
"Error projecting lambda range to reference line; projected range (" +
std::to_string(outputLambdaRange.min) + "," +
std::to_string(outputLambdaRange.max) + ") is negative.");
}
return outputLambdaRange;
}
/** Calculate the derivatives of the newly smoothed data using the spline
* Wraps CubicSpline derivative1D
*
* @param inputWorkspace :: The input workspace
* @param outputWorkspace :: The output workspace
* @param order :: The order of derivatives to calculate
* @param row :: The row of spectra to use
*/
void SplineSmoothing::calculateDerivatives(
const MatrixWorkspace &inputWorkspace,
API::MatrixWorkspace &outputWorkspace, const int order,
const size_t row) const {
const auto &xIn = inputWorkspace.x(row);
const double *xValues = &(xIn[0]);
double *yValues = &(outputWorkspace.mutableY(order - 1)[0]);
const size_t nData = xIn.size();
m_cspline->derivative1D(yValues, xValues, nData, order);
}
/** The procedure analyses emode and efixed properties provided to the algorithm
*and identify the energy analysis mode and the way the properties are defined
*@param workspace :: input workspace which may or may not have incident
*energy property (Ei) attached to it as the run log
*@param hostAlgorithm :: the pointer to SofQ algorithm hosting the base class.
*This algorithm expects to have EMode and EFixed properties attached to it.
*/
void SofQCommon::initCachedValues(const API::MatrixWorkspace &workspace,
API::Algorithm *const hostAlgorithm) {
// Retrieve the emode & efixed properties
const std::string emode = hostAlgorithm->getProperty("EMode");
// Convert back to an integer representation
m_emode = 0;
if (emode == "Direct")
m_emode = 1;
else if (emode == "Indirect")
m_emode = 2;
m_efixed = hostAlgorithm->getProperty("EFixed");
// Check whether they should have supplied an EFixed value
if (m_emode == 1) // Direct
{
// If GetEi was run then it will have been stored in the workspace, if not
// the user will need to enter one
if (m_efixed == 0.0) {
if (workspace.run().hasProperty("Ei")) {
Kernel::Property *p = workspace.run().getProperty("Ei");
Kernel::PropertyWithValue<double> *eiProp =
dynamic_cast<Kernel::PropertyWithValue<double> *>(p);
if (!eiProp)
throw std::runtime_error("Input workspace contains Ei but its "
"property type is not a double.");
m_efixed = (*eiProp)();
} else {
throw std::invalid_argument("Input workspace does not contain an "
"EFixed value. Please provide one or run "
"GetEi.");
}
} else {
m_efixedGiven = true;
}
} else {
if (m_efixed != 0.0) {
m_efixedGiven = true;
}
}
}
/* Convert spectra mask into det-id mask using workspace as source of
*spectra-detector maps
*
* @param sourceWS -- the workspace containing source spectra-detector map
* to use on masks
* @param maskedSpecID -- vector of spectra id to mask
* @param singleDetIds -- output vector of detector ids to mask
*/
void LoadMask::convertSpMasksToDetIDs(const API::MatrixWorkspace &sourceWS,
const std::vector<int32_t> &maskedSpecID,
std::vector<int32_t> &singleDetIds) {
spec2index_map s2imap = sourceWS.getSpectrumToWorkspaceIndexMap();
detid2index_map sourceDetMap =
sourceWS.getDetectorIDToWorkspaceIndexMap(false);
std::multimap<size_t, Mantid::detid_t> spectr2index_map;
for (auto it = sourceDetMap.begin(); it != sourceDetMap.end(); it++) {
spectr2index_map.insert(
std::pair<size_t, Mantid::detid_t>(it->second, it->first));
}
spec2index_map new_map;
for (size_t i = 0; i < maskedSpecID.size(); i++) {
// find spectra number from spectra ID for the source workspace
const auto itSpec = s2imap.find(maskedSpecID[i]);
if (itSpec == s2imap.end()) {
throw std::runtime_error(
"Can not find spectra with ID: " +
boost::lexical_cast<std::string>(maskedSpecID[i]) +
" in the workspace" + sourceWS.getName());
}
size_t specN = itSpec->second;
// find detector range related to this spectra id in the source workspace
const auto source_range = spectr2index_map.equal_range(specN);
if (source_range.first == spectr2index_map.end()) {
throw std::runtime_error("Can not find spectra N: " +
boost::lexical_cast<std::string>(specN) +
" in the workspace" + sourceWS.getName());
}
// add detectors to the masked det-id list
for (auto it = source_range.first; it != source_range.second; ++it) {
singleDetIds.push_back(it->second);
}
}
}
/**
* Construct an "empty" output workspace in virtual-lambda for summation in Q.
*
* @param detectorWS [in] :: the input workspace
* @param indices [in] :: the workspace indices of the foreground histograms
* @param refAngles [in] :: the reference angles
* @return :: a 1D workspace where y values are all zero
*/
API::MatrixWorkspace_sptr ReflectometrySumInQ::constructIvsLamWS(
const API::MatrixWorkspace &detectorWS,
const Indexing::SpectrumIndexSet &indices, const Angles &refAngles) {
// Calculate the number of bins based on the min/max wavelength, using
// the same bin width as the input workspace
const auto &edges = detectorWS.binEdges(refAngles.referenceWSIndex);
const double binWidth =
(edges.back() - edges.front()) / static_cast<double>(edges.size());
const auto wavelengthRange =
findWavelengthMinMax(detectorWS, indices, refAngles);
if (std::abs(wavelengthRange.max - wavelengthRange.min) < binWidth) {
throw std::runtime_error("Given wavelength range too small.");
}
const int numBins = static_cast<int>(
std::ceil((wavelengthRange.max - wavelengthRange.min) / binWidth));
// Construct the histogram with these X values. Y and E values are zero.
const HistogramData::BinEdges bins(
numBins + 1,
HistogramData::LinearGenerator(wavelengthRange.min, binWidth));
const HistogramData::Counts counts(numBins, 0.);
const HistogramData::Histogram modelHistogram(std::move(bins),
std::move(counts));
// Create the output workspace
API::MatrixWorkspace_sptr outputWS =
DataObjects::create<DataObjects::Workspace2D>(detectorWS, 1,
std::move(modelHistogram));
// Set the detector IDs and specturm number from the twoThetaR detector.
const auto &thetaSpec = detectorWS.getSpectrum(refAngles.referenceWSIndex);
auto &outSpec = outputWS->getSpectrum(0);
outSpec.clearDetectorIDs();
outSpec.addDetectorIDs(thetaSpec.getDetectorIDs());
outSpec.setSpectrumNo(thetaSpec.getSpectrumNo());
return outputWS;
}
/**
* Linearly interpolate between the points in integrFlux at xValues and save the
* results in yValues.
* @param xValues :: X-values at which to interpolate
* @param integrFlux :: A workspace with the spectra to interpolate
* @param sp :: A workspace index for a spectrum in integrFlux to interpolate.
* @param yValues :: A vector to save the results.
*/
void MDNormSCD::calcIntegralsForIntersections(
const std::vector<double> &xValues, const API::MatrixWorkspace &integrFlux,
size_t sp, std::vector<double> &yValues) const {
assert(xValues.size() == yValues.size());
// the x-data from the workspace
const auto &xData = integrFlux.readX(sp);
const double xStart = xData.front();
const double xEnd = xData.back();
// the values in integrFlux are expected to be integrals of a non-negative
// function
// ie they must make a non-decreasing function
const auto &yData = integrFlux.readY(sp);
size_t spSize = yData.size();
const double yMin = 0.0;
const double yMax = yData.back();
size_t nData = xValues.size();
// all integrals below xStart must be 0
if (xValues[nData - 1] < xStart) {
std::fill(yValues.begin(), yValues.end(), yMin);
return;
}
// all integrals above xEnd must be equal tp yMax
if (xValues[0] > xEnd) {
std::fill(yValues.begin(), yValues.end(), yMax);
return;
}
size_t i = 0;
// integrals below xStart must be 0
while (i < nData - 1 && xValues[i] < xStart) {
yValues[i] = yMin;
i++;
}
size_t j = 0;
for (; i < nData; i++) {
// integrals above xEnd must be equal tp yMax
if (j >= spSize - 1) {
yValues[i] = yMax;
} else {
double xi = xValues[i];
while (j < spSize - 1 && xi > xData[j])
j++;
// if x falls onto an interpolation point return the corresponding y
if (xi == xData[j]) {
yValues[i] = yData[j];
} else if (j == spSize - 1) {
// if we get above xEnd it's yMax
yValues[i] = yMax;
} else if (j > 0) {
// interpolate between the consecutive points
double x0 = xData[j - 1];
double x1 = xData[j];
double y0 = yData[j - 1];
double y1 = yData[j];
yValues[i] = y0 + (y1 - y0) * (xi - x0) / (x1 - x0);
} else // j == 0
{
yValues[i] = yMin;
}
}
}
}
/**
* Finds the median of values in single bin histograms rejecting spectra from
* masked
* detectors and the results of divide by zero (infinite and NaN).
* The median is an average that is less affected by small numbers of very large
* values.
* @param input :: A histogram workspace with one entry in each bin
* @param excludeZeroes :: If true then zeroes will not be included in the
* median calculation
* @param indexmap :: indexmap
* @return The median value of the histograms in the workspace that was passed
* to it
* @throw out_of_range if a value is negative
*/
std::vector<double> DetectorDiagnostic::calculateMedian(
const API::MatrixWorkspace &input, bool excludeZeroes,
const std::vector<std::vector<size_t>> &indexmap) {
std::vector<double> medianvec;
g_log.debug("Calculating the median count rate of the spectra");
bool checkForMask = false;
Geometry::Instrument_const_sptr instrument = input.getInstrument();
if (instrument != nullptr) {
checkForMask = ((instrument->getSource() != nullptr) &&
(instrument->getSample() != nullptr));
}
const auto &spectrumInfo = input.spectrumInfo();
for (const auto &hists : indexmap) {
std::vector<double> medianInput;
const int nhists = static_cast<int>(hists.size());
// The maximum possible length is that of workspace length
medianInput.reserve(nhists);
PARALLEL_FOR_IF(Kernel::threadSafe(input))
for (int i = 0; i < nhists; ++i) { // NOLINT
PARALLEL_START_INTERUPT_REGION
if (checkForMask && spectrumInfo.hasDetectors(hists[i])) {
if (spectrumInfo.isMasked(hists[i]) || spectrumInfo.isMonitor(hists[i]))
continue;
}
const double yValue = input.readY(hists[i])[0];
if (yValue < 0.0) {
throw std::out_of_range("Negative number of counts found, could be "
"corrupted raw counts or solid angle data");
}
if (!std::isfinite(yValue) ||
(excludeZeroes && yValue < DBL_EPSILON)) // NaNs/Infs
{
continue;
}
// Now we have a good value
PARALLEL_CRITICAL(DetectorDiagnostic_median_d) {
medianInput.push_back(yValue);
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
if (medianInput.empty()) {
g_log.information(
"some group has no valid histograms. Will use 0 for median.");
medianInput.push_back(0.);
}
Kernel::Statistics stats =
Kernel::getStatistics(medianInput, StatOptions::Median);
double median = stats.median;
if (median < 0 || median > DBL_MAX / 10.0) {
throw std::out_of_range("The calculated value for the median was either "
"negative or unreliably large");
}
medianvec.push_back(median);
}
return medianvec;
}
std::vector<std::vector<size_t>>
DetectorDiagnostic::makeInstrumentMap(const API::MatrixWorkspace &countsWS) {
return {
{boost::counting_iterator<std::size_t>(0),
boost::counting_iterator<std::size_t>(countsWS.getNumberHistograms())}};
}
