/** This is a slightly "clever" method as it makes some guesses about where is best
* to look for the right Q bin based on the fact that the input Qs (calcualted from wavelengths) tend
* to go down while the output Qs are always in accending order
* @param[in] OutQs the array of output Q bin boundaries, this finds the bin that contains the QIn value
* @param[in] QToFind the Q value to find the correct bin for
* @param[in, out] loc points to the bin boundary (in the OutQs array) whos Q is higher than QToFind and higher by the smallest amount. Algorithm starts by checking the value of loc passed and then all the bins _downwards_ through the array
*/
void Q1D2::getQBinPlus1(const MantidVec & OutQs, const double QToFind, MantidVec::const_iterator & loc) const
{
if ( loc != OutQs.end() )
{
while ( loc != OutQs.begin() )
{
if ( (QToFind >= *(loc-1)) && (QToFind < *loc) )
{
return;
}
--loc;
}
if ( QToFind < *loc )
{
//QToFind is outside the array leave loc == OutQs.begin()
return;
}
}
else //loc == OutQs.end()
{
if ( OutQs.empty() || QToFind > *(loc-1) )
{
//outside the array leave loc == OutQs.end()
return;
}
}
// we are lost, normally the order of the Q values means we only get here on the first iteration. It's slow
loc = std::lower_bound(OutQs.begin(), OutQs.end(), QToFind);
}
/** Calculates Sn as estimator of scale for given vector
*
* This method implements a naive calculation of Sn, as defined by Rousseeuw
*and Croux (http://dx.doi.org/10.2307%2F2291267).
* In contrast to standard deviation, this is more robust towards outliers.
*
* @param begin :: Beginning of vector.
* @param end :: End of vector.
* @return Sn of supplied data.
*/
double PoldiPeakSearch::getSn(MantidVec::const_iterator begin,
MantidVec::const_iterator end) const {
size_t numberOfPoints = std::distance(begin, end);
MantidVec absoluteDifferenceMedians(numberOfPoints);
PARALLEL_FOR_NO_WSP_CHECK()
for (int i = 0; i < static_cast<int>(numberOfPoints); ++i) {
double currentValue = *(begin + i);
MantidVec temp;
temp.reserve(numberOfPoints - 1);
for (int j = 0; j < static_cast<int>(numberOfPoints); ++j) {
if (j != i) {
temp.push_back(fabs(*(begin + j) - currentValue));
}
}
std::sort(temp.begin(), temp.end());
absoluteDifferenceMedians[i] =
getMedianFromSortedVector(temp.begin(), temp.end());
}
std::sort(absoluteDifferenceMedians.begin(), absoluteDifferenceMedians.end());
return 1.1926 * getMedianFromSortedVector(absoluteDifferenceMedians.begin(),
absoluteDifferenceMedians.end());
}
/** Computes a background estimation with an error
*
* This method computes an estimate of the average background along with its deviation. Since the background does not
* follow a normal distribution and may contain outliers, instead of computing the average and standard deviation,
* the median is used as location estimator and Sn is used as scale estimator. For details regarding the latter
* refer to PoldiPeakSearch::getSn.
*
* @param peakPositions :: Peak positions.
* @param correlationCounts :: Data from which the peak positions were extracted.
* @return Background estimation with error.
*/
UncertainValue PoldiPeakSearch::getBackgroundWithSigma(std::list<MantidVec::const_iterator> peakPositions, const MantidVec &correlationCounts) const
{
MantidVec background = getBackground(peakPositions, correlationCounts);
/* Instead of using Mean and Standard deviation, which are appropriate
* for data originating from a normal distribution (which is not the case
* for background of POLDI correlation spectra), the more robust measures
* Median and Sn are used.
*/
std::sort(background.begin(), background.end());
double meanBackground = getMedianFromSortedVector(background.begin(), background.end());
double sigmaBackground = getSn(background.begin(), background.end());
return UncertainValue(meanBackground, sigmaBackground);
}
int UnwrapSNS::unwrapX(const MantidVec &datain, MantidVec &dataout,
const double &Ld) {
MantidVec tempX_L; // lower half - to be frame wrapped
tempX_L.reserve(m_XSize);
tempX_L.clear();
MantidVec tempX_U; // upper half - to not be frame wrapped
tempX_U.reserve(m_XSize);
tempX_U.clear();
double filterVal = m_Tmin * Ld / m_LRef;
dataout.clear();
int specialBin = 0;
for (int bin = 0; bin < m_XSize; ++bin) {
// This is the time-of-flight value under consideration in the current
// iteration of the loop
const double tof = datain[bin];
if (tof < filterVal) {
tempX_L.push_back(tof + m_frameWidth);
// Record the bins that fall in this range for copying over the data &
// errors
if (specialBin < bin)
specialBin = bin;
} else {
tempX_U.push_back(tof);
}
} // loop over X values
// now put it back into the vector supplied
dataout.clear();
dataout.insert(dataout.begin(), tempX_U.begin(), tempX_U.end());
dataout.insert(dataout.end(), tempX_L.begin(), tempX_L.end());
assert(datain.size() == dataout.size());
return specialBin;
}
MantidVec
operator()(const MantidVec &_Left,
const MantidVec &_Right) const { // apply operator+ to operands
MantidVec v(_Left.size());
std::transform(_Left.begin(), _Left.end(), _Right.begin(), v.begin(),
SumGaussError<double>());
return (v);
}
/**
* load vectors onto a Workspace2D with 3 bins (the three components of the
* vectors)
* dataX for the origin of the vector (assumed (0,0,0) )
* dataY for the tip of the vector
* dataE is assumed (0,0,0), no errors
* @param h5file file identifier
* @param gws pointer to WorkspaceGroup being filled
* @param sorting_indexes permutation of qvmod indexes to render it in
* increasing order of momemtum transfer
*/
const MantidVec LoadSassena::loadQvectors(const hid_t &h5file,
API::WorkspaceGroup_sptr gws,
std::vector<int> &sorting_indexes) {
const std::string gwsName = this->getPropertyValue("OutputWorkspace");
const std::string setName("qvectors");
hsize_t dims[3];
if (dataSetInfo(h5file, setName, dims) < 0) {
throw Kernel::Exception::FileError(
"Unable to read " + setName + " dataset info:", m_filename);
}
int nq = static_cast<int>(dims[0]); // number of q-vectors
double *buf = new double[nq * 3];
this->dataSetDouble(h5file, "qvectors", buf);
MantidVec qvmod; // store the modulus of the vector
double *curr = buf;
for (int iq = 0; iq < nq; iq++) {
qvmod.push_back(
sqrt(curr[0] * curr[0] + curr[1] * curr[1] + curr[2] * curr[2]));
curr += 3;
}
if (getProperty("SortByQVectors")) {
std::vector<mypair> qvmodpair;
for (int iq = 0; iq < nq; iq++)
qvmodpair.push_back(mypair(qvmod[iq], iq));
std::sort(qvmodpair.begin(), qvmodpair.end(), compare);
for (int iq = 0; iq < nq; iq++)
sorting_indexes.push_back(qvmodpair[iq].second);
std::sort(qvmod.begin(), qvmod.end());
} else
for (int iq = 0; iq < nq; iq++)
sorting_indexes.push_back(iq);
DataObjects::Workspace2D_sptr ws =
boost::dynamic_pointer_cast<DataObjects::Workspace2D>(
API::WorkspaceFactory::Instance().create("Workspace2D", nq, 3, 3));
std::string wsName = gwsName + std::string("_") + setName;
ws->setTitle(wsName);
for (int iq = 0; iq < nq; iq++) {
MantidVec &Y = ws->dataY(iq);
const int index = sorting_indexes[iq];
curr = buf + 3 * index;
Y.assign(curr, curr + 3);
}
delete[] buf;
ws->getAxis(0)->unit() = Kernel::UnitFactory::Instance().create(
"MomentumTransfer"); // Set the Units
this->registerWorkspace(
gws, wsName, ws, "X-axis: origin of Q-vectors; Y-axis: tip of Q-vectors");
return qvmod;
}
/** Retrieves a vector with all counts that belong to the background
*
* In this method, a vector is assembled which contains all count data that is
*considered to be background.
* Whether a point is considered background depends on its distance to the
*given peak positions.
*
* @param peakPositions :: Peak positions.
* @param correlationCounts :: Vector with the complete correlation spectrum.
* @return Vector only with counts that belong to the background.
*/
MantidVec PoldiPeakSearch::getBackground(
std::list<MantidVec::const_iterator> peakPositions,
const MantidVec &correlationCounts) const {
size_t backgroundPoints =
getNumberOfBackgroundPoints(peakPositions, correlationCounts);
MantidVec background;
background.reserve(backgroundPoints);
for (MantidVec::const_iterator point = correlationCounts.begin() + 1;
point != correlationCounts.end() - 1; ++point) {
if (distanceToPeaksGreaterThanMinimum(peakPositions, point)) {
background.push_back(*point);
}
}
return background;
}
/**
* Calculate detector efficiency given a formula, the efficiency at the elastic line,
* and a vector with energies.
* Efficiency = f(Ei-DeltaE) / f(Ei)
* Hope all compilers supports the NRVO (otherwise will copy the output vector)
* @param eff0 :: calculated eff0
* @param formula :: formula to calculate efficiency (parsed from IDF)
* @param xIn :: Energy bins vector (X axis)
* @return a vector with the efficiencies
*/
MantidVec DetectorEfficiencyCorUser::calculateEfficiency(double eff0,
const std::string& formula, const MantidVec& xIn) {
MantidVec effOut(xIn.size() - 1); // x are bins and have more one value than y
try {
double e;
mu::Parser p;
p.DefineVar("e", &e);
p.SetExpr(formula);
// copied from Jaques Ollivier Code
bool conditionForEnergy = std::min( std::abs( *std::min_element(xIn.begin(), xIn.end()) ) , m_Ei) < m_Ei;
MantidVec::const_iterator xIn_it = xIn.begin(); // DeltaE
MantidVec::iterator effOut_it = effOut.begin();
for (; effOut_it != effOut.end(); ++xIn_it, ++effOut_it) {
if (conditionForEnergy ) {
// cppcheck cannot see that this is used by reference by muparser
e = std::fabs(m_Ei + *xIn_it);
}
else {
// cppcheck cannot see that this is used by reference by muparser
// cppcheck-suppress unreadVariable
e = std::fabs(m_Ei - *xIn_it);
}
double eff = p.Eval();
*effOut_it = eff / eff0;
}
return effOut;
} catch (mu::Parser::exception_type &e) {
throw Kernel::Exception::InstrumentDefinitionError(
"Error calculating formula from string. Muparser error message is: "
+ e.GetMsg());
}
}
/**
* Copy over the metadata from the input matrix workspace to output MDEventWorkspace
* @param mdEventWS :: The output MDEventWorkspace where metadata are copied to. The source of the metadata is the input matrix workspace
*
*/
void ConvertToMD::copyMetaData(API::IMDEventWorkspace_sptr &mdEventWS) const
{
// found detector which is not a monitor to get proper bin boundaries.
size_t spectra_index(0);
bool dector_found(false);
for(size_t i=0;i<m_InWS2D->getNumberHistograms(); ++i)
{
try
{
auto det=m_InWS2D->getDetector(i);
if (!det->isMonitor())
{
spectra_index=i;
dector_found = true;
g_log.debug()<<"Using spectra N "<<i<< " as the source of the bin boundaries for the resolution corrections \n";
break;
}
}
catch(...)
{}
}
if (!dector_found)
g_log.warning()<<"No detectors in the workspace are associated with spectra. Using spectrum 0 trying to retrieve the bin boundaries \n";
// retrieve representative bin boundaries
MantidVec binBoundaries = m_InWS2D->readX(spectra_index);
// check if the boundaries transformation is necessary
if (m_Convertor->getUnitConversionHelper().isUnitConverted())
{
if( !dynamic_cast<DataObjects::EventWorkspace *>(m_InWS2D.get()))
{
g_log.information()<<" ConvertToMD converts input workspace units, but the bin boundaries are copied from the first workspace spectra. The resolution estimates can be incorrect if unit conversion depends on spectra number.\n";
UnitsConversionHelper &unitConv = m_Convertor->getUnitConversionHelper();
unitConv.updateConversion(spectra_index);
for(size_t i=0;i<binBoundaries.size();i++)
{
binBoundaries[i] =unitConv.convertUnits(binBoundaries[i]);
}
}
// sort bin boundaries in case if unit transformation have swapped them.
if (binBoundaries[0]>binBoundaries[binBoundaries.size()-1])
{
g_log.information()<<"Bin boundaries are not arranged monotonously. Sorting performed\n";
std::sort(binBoundaries.begin(),binBoundaries.end());
}
}
// Replacement for SpectraDetectorMap::createIDGroupsMap using the ISpectrum objects instead
auto mapping = boost::make_shared<det2group_map>();
for ( size_t i = 0; i < m_InWS2D->getNumberHistograms(); ++i )
{
const auto& dets = m_InWS2D->getSpectrum(i)->getDetectorIDs();
if(!dets.empty())
{
std::vector<detid_t> id_vector;
std::copy(dets.begin(), dets.end(), std::back_inserter(id_vector));
mapping->insert(std::make_pair(id_vector.front(), id_vector));
}
}
uint16_t nexpts = mdEventWS->getNumExperimentInfo();
for(uint16_t i = 0; i < nexpts; ++i)
{
ExperimentInfo_sptr expt = mdEventWS->getExperimentInfo(i);
expt->mutableRun().storeHistogramBinBoundaries(binBoundaries);
expt->cacheDetectorGroupings(*mapping);
}
}
/** Subtracts a constant from the data values in the given workspace
* @param Y :: The vector from which to subtract
* @param value :: The value to subtract from each data point
*/
void IQTransform::subtractBackgroundValue(MantidVec& Y, const double value)
{
g_log.debug() << "Subtracting the background value " << value << " from the input workspace.\n";
std::transform(Y.begin(),Y.end(),Y.begin(),std::bind2nd(std::minus<double>(),value));
}
void PoldiPeakSearch::exec() {
g_log.information() << "PoldiPeakSearch:" << std::endl;
Workspace2D_sptr correlationWorkspace = getProperty("InputWorkspace");
MantidVec correlationQValues = correlationWorkspace->readX(0);
MantidVec correlatedCounts = correlationWorkspace->readY(0);
g_log.information() << " Auto-correlation data read." << std::endl;
Unit_sptr xUnit = correlationWorkspace->getAxis(0)->unit();
if (xUnit->caption() == "") {
g_log.information()
<< " Workspace does not have unit, defaulting to MomentumTransfer."
<< std::endl;
xUnit = UnitFactory::Instance().create("MomentumTransfer");
} else {
g_log.information() << " Unit of workspace is " << xUnit->caption() << "."
<< std::endl;
}
setMinimumDistance(getProperty("MinimumPeakSeparation"));
setMinimumPeakHeight(getProperty("MinimumPeakHeight"));
setMaximumPeakNumber(getProperty("MaximumPeakNumber"));
if (m_doubleMinimumDistance > static_cast<int>(correlatedCounts.size())) {
throw(std::runtime_error("MinimumPeakSeparation is smaller than number of "
"spectrum points - no peaks possible."));
}
g_log.information() << " Parameters set." << std::endl;
MantidVec summedNeighborCounts = getNeighborSums(correlatedCounts);
g_log.information() << " Neighboring counts summed, contains "
<< summedNeighborCounts.size() << " data points."
<< std::endl;
std::list<MantidVec::const_iterator> peakPositionsSummed =
findPeaks(summedNeighborCounts.begin(), summedNeighborCounts.end());
g_log.information() << " Peaks detected in summed spectrum: "
<< peakPositionsSummed.size() << std::endl;
/* This step is required because peaks are actually searched in the
* "sum-of-neighbors"-spectrum.
* The mapping removes the offset from the peak position which results from
* different beginning
* of this vector compared to the original correlation counts.
*/
std::list<MantidVec::const_iterator> peakPositionsCorrelation =
mapPeakPositionsToCorrelationData(peakPositionsSummed,
summedNeighborCounts.begin(),
correlatedCounts.begin());
g_log.information() << " Peak positions transformed to original spectrum."
<< std::endl;
/* Since intensities are required for filtering, they are extracted from the
* original count data,
* along with the Q-values.
*/
std::vector<PoldiPeak_sptr> peakCoordinates =
getPeaks(correlatedCounts.begin(), correlatedCounts.end(),
peakPositionsCorrelation, correlationQValues, xUnit);
g_log.information()
<< " Extracted peak positions in Q and intensity guesses." << std::endl;
UncertainValue backgroundWithSigma =
getBackgroundWithSigma(peakPositionsCorrelation, correlatedCounts);
g_log.information() << " Calculated average background and deviation: "
<< UncertainValueIO::toString(backgroundWithSigma)
<< std::endl;
if ((*getProperty("MinimumPeakHeight")).isDefault()) {
setMinimumPeakHeight(minimumPeakHeightFromBackground(backgroundWithSigma));
}
std::vector<PoldiPeak_sptr> intensityFilteredPeaks(peakCoordinates.size());
auto newEnd = std::remove_copy_if(
peakCoordinates.begin(), peakCoordinates.end(),
intensityFilteredPeaks.begin(),
boost::bind(&PoldiPeakSearch::isLessThanMinimum, this, _1));
intensityFilteredPeaks.resize(
std::distance(intensityFilteredPeaks.begin(), newEnd));
g_log.information() << " Peaks above minimum intensity ("
<< m_minimumPeakHeight
<< "): " << intensityFilteredPeaks.size() << std::endl;
std::sort(intensityFilteredPeaks.begin(), intensityFilteredPeaks.end(),
boost::bind<bool>(&PoldiPeak::greaterThan, _1, _2,
&PoldiPeak::intensity));
for (std::vector<PoldiPeak_sptr>::const_iterator peak =
intensityFilteredPeaks.begin();
peak != intensityFilteredPeaks.end(); ++peak) {
m_peaks->addPeak(*peak);
}
/* The derived background error is set as error in the workspace containing
* correlation data, so it may be used as weights for peak fitting later on.
*/
setErrorsOnWorkspace(correlationWorkspace, backgroundWithSigma.error());
setProperty("OutputWorkspace", m_peaks->asTableWorkspace());
}
void LoadDaveGrp::exec()
{
const std::string filename = this->getProperty("Filename");
int yLength = 0;
MantidVec *xAxis = new MantidVec();
MantidVec *yAxis = new MantidVec();
std::vector<MantidVec *> data;
std::vector<MantidVec *> errors;
this->ifile.open(filename.c_str());
if (this->ifile.is_open())
{
// Size of x axis
this->getAxisLength(this->xLength);
// Size of y axis
this->getAxisLength(yLength);
// This is also the number of groups (spectra)
this->nGroups = yLength;
// Read in the x axis values
this->getAxisValues(xAxis, static_cast<std::size_t>(this->xLength));
// Read in the y axis values
this->getAxisValues(yAxis, static_cast<std::size_t>(yLength));
// Read in the data
this->getData(data, errors);
}
this->ifile.close();
// Scale the x-axis if it is in micro-eV to get it to meV
const bool isUeV = this->getProperty("IsMicroEV");
if (isUeV)
{
MantidVec::iterator iter;
for (iter = xAxis->begin(); iter != xAxis->end(); ++iter)
{
*iter /= 1000.0;
}
}
// Create workspace
API::MatrixWorkspace_sptr outputWorkspace = \
boost::dynamic_pointer_cast<API::MatrixWorkspace>\
(API::WorkspaceFactory::Instance().create("Workspace2D", this->nGroups,
this->xLength, yLength));
// Force the workspace to be a distribution
outputWorkspace->isDistribution(true);
// Set the x-axis units
outputWorkspace->getAxis(0)->unit() = Kernel::UnitFactory::Instance().create(this->getProperty("XAxisUnits"));
API::Axis* const verticalAxis = new API::NumericAxis(yLength);
// Set the y-axis units
verticalAxis->unit() = Kernel::UnitFactory::Instance().create(this->getProperty("YAxisUnits"));
outputWorkspace->replaceAxis(1, verticalAxis);
for(int i = 0; i < this->nGroups; i++)
{
outputWorkspace->dataX(i) = *xAxis;
outputWorkspace->dataY(i) = *data[i];
outputWorkspace->dataE(i) = *errors[i];
verticalAxis->setValue(i, yAxis->at(i));
delete data[i];
delete errors[i];
}
delete xAxis;
delete yAxis;
outputWorkspace->mutableRun().addProperty("Filename",filename);
this->setProperty("OutputWorkspace", outputWorkspace);
}
/** Finds the index in an ordered vector which follows the given value
* @param value :: The value to search for
* @param vec :: The vector to search
* @return The index (will give vec.size()+1 if the value is past the end of the vector)
*/
int RemoveBins::findIndex(const double& value, const MantidVec& vec)
{
MantidVec::const_iterator pos = std::lower_bound(vec.begin(),vec.end(),value);
return static_cast<int>(pos-vec.begin());
}