/** Do the initial copy of the data from the input to the output workspace for
* histogram workspaces.
* Takes out the bin width if necessary.
* @param inputWS The input workspace
* @param outputWS The output workspace
*/
void ConvertUnitsUsingDetectorTable::fillOutputHist(
const API::MatrixWorkspace_const_sptr inputWS,
const API::MatrixWorkspace_sptr outputWS) {
const int size = static_cast<int>(inputWS->blocksize());
// Loop over the histograms (detector spectra)
Progress prog(this, 0.0, 0.2, m_numberOfSpectra);
int64_t numberOfSpectra_i =
static_cast<int64_t>(m_numberOfSpectra); // cast to make openmp happy
PARALLEL_FOR2(inputWS, outputWS)
for (int64_t i = 0; i < numberOfSpectra_i; ++i) {
PARALLEL_START_INTERUPT_REGION
// Take the bin width dependency out of the Y & E data
if (m_distribution) {
for (int j = 0; j < size; ++j) {
const double width =
std::abs(inputWS->dataX(i)[j + 1] - inputWS->dataX(i)[j]);
outputWS->dataY(i)[j] = inputWS->dataY(i)[j] * width;
outputWS->dataE(i)[j] = inputWS->dataE(i)[j] * width;
}
} else {
// Just copy over
outputWS->dataY(i) = inputWS->readY(i);
outputWS->dataE(i) = inputWS->readE(i);
}
// Copy over the X data
outputWS->setX(i, inputWS->refX(i));
prog.report("Convert to " + m_outputUnit->unitID());
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
/**
* Perform a call to nxgetslab, via the NexusClasses wrapped methods for a given blocksize. The xbins are read along with
* each call to the data/error loading
* @param data :: The NXDataSet object of y values
* @param errors :: The NXDataSet object of error values
* @param xbins :: The xbin NXDataSet
* @param blocksize :: The blocksize to use
* @param nchannels :: The number of channels for the block
* @param hist :: The workspace index to start reading into
* @param wsIndex :: The workspace index to save data into
* @param local_workspace :: A pointer to the workspace
*/
void LoadNexusProcessed::loadBlock(NXDataSetTyped<double> & data, NXDataSetTyped<double> & errors, NXDouble & xbins,
int64_t blocksize, int64_t nchannels, int64_t &hist, int64_t& wsIndex,
API::MatrixWorkspace_sptr local_workspace)
{
data.load(static_cast<int>(blocksize),static_cast<int>(hist));
double *data_start = data();
double *data_end = data_start + nchannels;
errors.load(static_cast<int>(blocksize),static_cast<int>(hist));
double *err_start = errors();
double *err_end = err_start + nchannels;
xbins.load(static_cast<int>(blocksize),static_cast<int>(hist));
const int64_t nxbins(nchannels + 1);
double *xbin_start = xbins();
double *xbin_end = xbin_start + nxbins;
int64_t final(hist + blocksize);
while( hist < final )
{
MantidVec& Y = local_workspace->dataY(wsIndex);
Y.assign(data_start, data_end);
data_start += nchannels; data_end += nchannels;
MantidVec& E = local_workspace->dataE(wsIndex);
E.assign(err_start, err_end);
err_start += nchannels; err_end += nchannels;
MantidVec& X = local_workspace->dataX(wsIndex);
X.assign(xbin_start, xbin_end);
xbin_start += nxbins; xbin_end += nxbins;
++hist;
++wsIndex;
}
}
void ConvertEmptyToTof::setTofInWS(const std::vector<double> &tofAxis,
API::MatrixWorkspace_sptr outputWS) {
const size_t numberOfSpectra = m_inputWS->getNumberHistograms();
int64_t numberOfSpectraInt64 =
static_cast<int64_t>(numberOfSpectra); // cast to make openmp happy
g_log.debug() << "Setting the TOF X Axis for numberOfSpectra="
<< numberOfSpectra << '\n';
Progress prog(this, 0.0, 0.2, numberOfSpectra);
PARALLEL_FOR2(m_inputWS, outputWS)
for (int64_t i = 0; i < numberOfSpectraInt64; ++i) {
PARALLEL_START_INTERUPT_REGION
// Just copy over
outputWS->dataY(i) = m_inputWS->readY(i);
outputWS->dataE(i) = m_inputWS->readE(i);
// copy
outputWS->setX(i, tofAxis);
prog.report();
PARALLEL_END_INTERUPT_REGION
} // end for i
PARALLEL_CHECK_INTERUPT_REGION
outputWS->getAxis(0)->unit() = UnitFactory::Instance().create("TOF");
}
/** loadData
* Load the counts data from an NXInt into a workspace
*/
void LoadMuonNexus2::loadData(const Mantid::NeXus::NXInt &counts,
const std::vector<double> &timeBins, int wsIndex,
int period, int spec,
API::MatrixWorkspace_sptr localWorkspace) {
MantidVec &X = localWorkspace->dataX(wsIndex);
MantidVec &Y = localWorkspace->dataY(wsIndex);
MantidVec &E = localWorkspace->dataE(wsIndex);
X.assign(timeBins.begin(), timeBins.end());
int nBins = 0;
int *data = nullptr;
if (counts.rank() == 3) {
nBins = counts.dim2();
data = &counts(period, spec, 0);
} else if (counts.rank() == 2) {
nBins = counts.dim1();
data = &counts(spec, 0);
} else {
throw std::runtime_error("Data have unsupported dimansionality");
}
assert(nBins + 1 == static_cast<int>(timeBins.size()));
Y.assign(data, data + nBins);
typedef double (*uf)(double);
uf dblSqrt = std::sqrt;
std::transform(Y.begin(), Y.end(), E.begin(), dblSqrt);
}
/**
* Read spectra from the DAE
* @param period :: Current period index
* @param index :: First spectrum index
* @param count :: Number of spectra to read
* @param workspace :: Workspace to store the data
* @param workspaceIndex :: index in workspace to store data
*/
void ISISHistoDataListener::getData(int period, int index, int count,
API::MatrixWorkspace_sptr workspace,
size_t workspaceIndex) {
const int numberOfBins = m_numberOfBins[m_timeRegime];
const size_t bufferSize = count * (numberOfBins + 1) * sizeof(int);
std::vector<int> dataBuffer(bufferSize);
// Read in spectra from DAE
int ndims = 2, dims[2];
dims[0] = count;
dims[1] = numberOfBins + 1;
int spectrumIndex = index + period * (m_totalNumberOfSpectra + 1);
if (IDCgetdat(m_daeHandle, spectrumIndex, count, dataBuffer.data(), dims,
&ndims) != 0) {
g_log.error("Unable to read DATA from DAE " + m_daeName);
throw Kernel::Exception::FileError("Unable to read DATA from DAE ",
m_daeName);
}
for (size_t i = 0; i < static_cast<size_t>(count); ++i) {
size_t wi = workspaceIndex + i;
workspace->setX(wi, m_bins[m_timeRegime]);
MantidVec &y = workspace->dataY(wi);
MantidVec &e = workspace->dataE(wi);
workspace->getSpectrum(wi)->setSpectrumNo(index + static_cast<specid_t>(i));
size_t shift = i * (numberOfBins + 1) + 1;
y.assign(dataBuffer.begin() + shift, dataBuffer.begin() + shift + y.size());
std::transform(y.begin(), y.end(), e.begin(), dblSqrt);
}
}
/** Execute the algorithm.
*/
void DampSq::exec()
{
// TODO Auto-generated execute stub
// 1. Generate new workspace
API::MatrixWorkspace_const_sptr isqspace = getProperty("InputWorkspace");
API::MatrixWorkspace_sptr osqspace = WorkspaceFactory::Instance().create(isqspace, 1, isqspace->size(), isqspace->size());
int mode = getProperty("Mode");
double qmax = getProperty("QMax");
if (mode < 1 || mode > 4) {
g_log.error("Damp mode can only be 1, 2, 3, or 4");
return;
}
// 2. Get access to all
const MantidVec& iQVec = isqspace->dataX(0);
const MantidVec& iSVec = isqspace->dataY(0);
const MantidVec& iEVec = isqspace->dataE(0);
MantidVec& oQVec = osqspace->dataX(0);
MantidVec& oSVec = osqspace->dataY(0);
MantidVec& oEVec = osqspace->dataE(0);
// 3. Calculation
double dqmax = qmax - iQVec[0];
double damp;
for (unsigned int i = 0; i < iQVec.size(); i ++) {
// a) calculate damp coefficient
switch (mode) {
case 1:
damp = dampcoeff1(iQVec[i], qmax, dqmax);
break;
case 2:
damp = dampcoeff2(iQVec[i], qmax, dqmax);;
break;
case 3:
damp = dampcoeff3(iQVec[i], qmax, dqmax);;
break;
case 4:
damp = dampcoeff4(iQVec[i], qmax, dqmax);;
break;
default:
damp = 0;
break;
}
// b) calculate new S(q)
oQVec[i] = iQVec[i];
oSVec[i] = 1 + damp*(iSVec[i]-1);
oEVec[i] = damp*iEVec[i];
} // i
// 4. Over
setProperty("OutputWorkspace", osqspace);
return;
}
/** Performs the Holtzer transformation: IQ v Q
* @param ws The workspace to be transformed
*/
void IQTransform::holtzer(API::MatrixWorkspace_sptr ws)
{
MantidVec& X = ws->dataX(0);
MantidVec& Y = ws->dataY(0);
MantidVec& E = ws->dataE(0);
std::transform(Y.begin(),Y.end(),X.begin(),Y.begin(),std::multiplies<double>());
std::transform(E.begin(),E.end(),X.begin(),E.begin(),std::multiplies<double>());
ws->setYUnitLabel("I x Q");
}
/** Performs the Porod transformation: IQ^4 v Q
* @param ws The workspace to be transformed
*/
void IQTransform::porod(API::MatrixWorkspace_sptr ws)
{
MantidVec& X = ws->dataX(0);
MantidVec& Y = ws->dataY(0);
MantidVec& E = ws->dataE(0);
MantidVec Q4(X.size());
std::transform(X.begin(),X.end(),X.begin(),Q4.begin(),VectorHelper::TimesSquares<double>());
std::transform(Y.begin(),Y.end(),Q4.begin(),Y.begin(),std::multiplies<double>());
std::transform(E.begin(),E.end(),Q4.begin(),E.begin(),std::multiplies<double>());
ws->setYUnitLabel("I x Q^4");
}
/** Performs a log-log transformation: Ln(I) v Ln(Q)
* @param ws The workspace to be transformed
* @throw std::range_error if an attempt is made to take log of a negative number
*/
void IQTransform::logLog(API::MatrixWorkspace_sptr ws)
{
MantidVec& X = ws->dataX(0);
MantidVec& Y = ws->dataY(0);
MantidVec& E = ws->dataE(0);
std::transform(X.begin(),X.end(),X.begin(),VectorHelper::Log<double>());
std::transform(E.begin(),E.end(),Y.begin(),E.begin(),std::divides<double>());
std::transform(Y.begin(),Y.end(),Y.begin(),VectorHelper::LogNoThrow<double>());
ws->setYUnitLabel("Ln(I)");
m_label->setLabel("Ln(Q)");
}
/** Performs the Guinier (sheets) transformation: Ln(IQ^2) v Q^2
* @param ws The workspace to be transformed
* @throw std::range_error if an attempt is made to take log of a negative number
*/
void IQTransform::guinierSheets(API::MatrixWorkspace_sptr ws)
{
MantidVec& X = ws->dataX(0);
MantidVec& Y = ws->dataY(0);
MantidVec& E = ws->dataE(0);
std::transform(E.begin(),E.end(),Y.begin(),E.begin(),std::divides<double>());
std::transform(X.begin(),X.end(),X.begin(),VectorHelper::Squares<double>());
std::transform(Y.begin(),Y.end(),X.begin(),Y.begin(),std::multiplies<double>());
std::transform(Y.begin(),Y.end(),Y.begin(),VectorHelper::LogNoThrow<double>());
ws->setYUnitLabel("Ln(I x Q^2)");
m_label->setLabel("Q^2");
}
/** Unwraps the Y & E vectors of a spectrum according to the ranges found in
* unwrapX.
* @param tempWS :: A pointer to the temporary workspace in which the
* results are being stored
* @param spectrum :: The workspace index
* @param rangeBounds :: The upper and lower ranges for the unwrapping
*/
void UnwrapMonitor::unwrapYandE(const API::MatrixWorkspace_sptr &tempWS,
const int &spectrum,
const std::vector<int> &rangeBounds) {
// Copy over the relevant ranges of Y & E data
MantidVec &Y = tempWS->dataY(spectrum);
MantidVec &E = tempWS->dataE(spectrum);
// Get references to the input data
const MantidVec &YIn = m_inputWS->dataY(spectrum);
const MantidVec &EIn = m_inputWS->dataE(spectrum);
if (rangeBounds[2] != -1) {
// Copy in the upper range
Y.assign(YIn.begin() + rangeBounds[2], YIn.end());
E.assign(EIn.begin() + rangeBounds[2], EIn.end());
// Propagate masking, if necessary
if (m_inputWS->hasMaskedBins(spectrum)) {
const MatrixWorkspace::MaskList &inputMasks =
m_inputWS->maskedBins(spectrum);
MatrixWorkspace::MaskList::const_iterator it;
for (it = inputMasks.begin(); it != inputMasks.end(); ++it) {
if (static_cast<int>((*it).first) >= rangeBounds[2])
tempWS->flagMasked(spectrum, (*it).first - rangeBounds[2],
(*it).second);
}
}
} else {
// Y & E are references to existing vector. Assign above clears them, so
// need to explicitly here
Y.clear();
E.clear();
}
if (rangeBounds[0] != -1 && rangeBounds[1] > 0) {
// Now append the lower range
MantidVec::const_iterator YStart = YIn.begin();
MantidVec::const_iterator EStart = EIn.begin();
Y.insert(Y.end(), YStart + rangeBounds[0], YStart + rangeBounds[1]);
E.insert(E.end(), EStart + rangeBounds[0], EStart + rangeBounds[1]);
// Propagate masking, if necessary
if (m_inputWS->hasMaskedBins(spectrum)) {
const MatrixWorkspace::MaskList &inputMasks =
m_inputWS->maskedBins(spectrum);
MatrixWorkspace::MaskList::const_iterator it;
for (it = inputMasks.begin(); it != inputMasks.end(); ++it) {
const int maskIndex = static_cast<int>((*it).first);
if (maskIndex >= rangeBounds[0] && maskIndex < rangeBounds[1])
tempWS->flagMasked(spectrum, maskIndex - rangeBounds[0],
(*it).second);
}
}
}
}
/** Remove background per pixel
* @brief ConvertCWSDExpToMomentum::removeBackground
* @param dataws
*/
void ConvertCWSDExpToMomentum::removeBackground(
API::MatrixWorkspace_sptr dataws) {
if (dataws->getNumberHistograms() != m_backgroundWS->getNumberHistograms())
throw std::runtime_error("Impossible to have this situation");
size_t numhist = dataws->getNumberHistograms();
for (size_t i = 0; i < numhist; ++i) {
double bkgd_y = m_backgroundWS->readY(i)[0];
if (fabs(bkgd_y) > 1.E-2) {
dataws->dataY(i)[0] -= bkgd_y;
dataws->dataE(i)[0] = std::sqrt(dataws->readY(i)[0]);
}
}
}
/** 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;
}
/** Divide each bin by the width of its q bin.
* @param outputWS :: The output workspace
* @param qAxis :: A vector of the q bin boundaries
*/
void SofQWCentre::makeDistribution(API::MatrixWorkspace_sptr outputWS,
const std::vector<double> qAxis) {
std::vector<double> widths(qAxis.size());
std::adjacent_difference(qAxis.begin(), qAxis.end(), widths.begin());
const size_t numQBins = outputWS->getNumberHistograms();
for (size_t i = 0; i < numQBins; ++i) {
MantidVec &Y = outputWS->dataY(i);
MantidVec &E = outputWS->dataE(i);
std::transform(Y.begin(), Y.end(), Y.begin(),
std::bind2nd(std::divides<double>(), widths[i + 1]));
std::transform(E.begin(), E.end(), E.begin(),
std::bind2nd(std::divides<double>(), widths[i + 1]));
}
}
/**
* 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;
}
/** Performs a transformation of the form: Q^A x I^B x Ln(Q^C x I^D x E) v Q^F x
* I^G x Ln(Q^H x I^I x J).
* Uses the 'GeneralFunctionConstants' property where A-J are the 10 (ordered)
* input constants.
* @param ws The workspace to be transformed
* @throw std::range_error if an attempt is made to take log of a negative
* number
*/
void IQTransform::general(API::MatrixWorkspace_sptr ws) {
MantidVec &X = ws->dataX(0);
MantidVec &Y = ws->dataY(0);
MantidVec &E = ws->dataE(0);
const std::vector<double> C = getProperty("GeneralFunctionConstants");
// Check for the correct number of elements
if (C.size() != 10) {
std::string mess(
"The General transformation requires 10 values to be provided.");
g_log.error(mess);
throw std::invalid_argument(mess);
}
for (size_t i = 0; i < Y.size(); ++i) {
double tmpX = std::pow(X[i], C[7]) * std::pow(Y[i], C[8]) * C[9];
if (tmpX <= 0.0)
throw std::range_error(
"Attempt to take log of a zero or negative number.");
tmpX = std::pow(X[i], C[5]) * std::pow(Y[i], C[6]) * std::log(tmpX);
const double tmpY = std::pow(X[i], C[2]) * std::pow(Y[i], C[3]) * C[4];
if (tmpY <= 0.0)
throw std::range_error(
"Attempt to take log of a zero or negative number.");
const double newY =
std::pow(X[i], C[0]) * std::pow(Y[i], C[1]) * std::log(tmpY);
E[i] *= std::pow(X[i], C[0]) *
(C[1] * std::pow(Y[i], C[1] - 1) * std::log(tmpY) +
((std::pow(Y[i], C[1]) * std::pow(X[i], C[2]) * C[4] * C[3] *
std::pow(Y[i], C[3] - 1)) /
tmpY));
X[i] = tmpX;
Y[i] = newY;
}
std::stringstream ylabel;
ylabel << "Q^" << C[0] << " x I^" << C[1] << " x Ln( Q^" << C[2] << " x I^"
<< C[3] << " x " << C[4] << ")";
ws->setYUnitLabel(ylabel.str());
std::stringstream xlabel;
xlabel << "Q^" << C[5] << " x I^" << C[6] << " x Ln( Q^" << C[7] << " x I^"
<< C[8] << " x " << C[9] << ")";
m_label->setLabel(xlabel.str());
}
/** Performs the Debye-Bueche transformation: 1/sqrt(I) v Q^2
* The output is set to zero for negative input Y values
* @param ws The workspace to be transformed
*/
void IQTransform::debyeBueche(API::MatrixWorkspace_sptr ws) {
MantidVec &X = ws->dataX(0);
MantidVec &Y = ws->dataY(0);
MantidVec &E = ws->dataE(0);
std::transform(X.begin(), X.end(), X.begin(),
VectorHelper::Squares<double>());
for (size_t i = 0; i < Y.size(); ++i) {
if (Y[i] > 0.0) {
Y[i] = 1.0 / std::sqrt(Y[i]);
E[i] *= std::pow(Y[i], 3);
} else {
Y[i] = 0.0;
E[i] = 0.0;
}
}
ws->setYUnitLabel("1/sqrt(I)");
m_label->setLabel("Q^2");
}
/** Zeroes all data points outside the X values given
* @param outputWorkspace :: The output workspace - data has already been
* copied
* @param inIndex :: The workspace index of the spectrum in the input
* workspace
* @param outIndex :: The workspace index of the spectrum in the output
* workspace
*/
void ExtractSpectra::cropRagged(API::MatrixWorkspace_sptr outputWorkspace,
int inIndex, int outIndex) {
MantidVec &Y = outputWorkspace->dataY(outIndex);
MantidVec &E = outputWorkspace->dataE(outIndex);
const size_t size = Y.size();
size_t startX = this->getXMin(inIndex);
if (startX > size)
startX = size;
for (size_t i = 0; i < startX; ++i) {
Y[i] = 0.0;
E[i] = 0.0;
}
size_t endX = this->getXMax(inIndex);
if (endX > 0)
endX -= m_histogram;
for (size_t i = endX; i < size; ++i) {
Y[i] = 0.0;
E[i] = 0.0;
}
}
/**
* Perform a call to nxgetslab, via the NexusClasses wrapped methods for a given blocksize. This assumes that the
* xbins have alread been cached
* @param data :: The NXDataSet object of y values
* @param errors :: The NXDataSet object of error values
* @param blocksize :: The blocksize to use
* @param nchannels :: The number of channels for the block
* @param hist :: The workspace index to start reading into
* @param local_workspace :: A pointer to the workspace
*/
void LoadNexusProcessed::loadBlock(NXDataSetTyped<double> & data, NXDataSetTyped<double> & errors,
int64_t blocksize, int64_t nchannels, int64_t &hist,
API::MatrixWorkspace_sptr local_workspace)
{
data.load(static_cast<int>(blocksize),static_cast<int>(hist));
errors.load(static_cast<int>(blocksize),static_cast<int>(hist));
double *data_start = data();
double *data_end = data_start + nchannels;
double *err_start = errors();
double *err_end = err_start + nchannels;
int64_t final(hist + blocksize);
while( hist < final )
{
MantidVec& Y = local_workspace->dataY(hist);
Y.assign(data_start, data_end);
data_start += nchannels; data_end += nchannels;
MantidVec& E = local_workspace->dataE(hist);
E.assign(err_start, err_end);
err_start += nchannels; err_end += nchannels;
local_workspace->setX(hist, m_xbins);
++hist;
}
}
/**
* Move the user selected spectra in the input workspace into groups in the output workspace
* @param inputWS :: user selected input workspace for the algorithm
* @param outputWS :: user selected output workspace for the algorithm
* @param prog4Copy :: the amount of algorithm progress to attribute to moving a single spectra
* @return number of new grouped spectra
*/
size_t GroupDetectors2::formGroupsEvent( DataObjects::EventWorkspace_const_sptr inputWS, DataObjects::EventWorkspace_sptr outputWS,
const double prog4Copy)
{
// get "Behaviour" string
const std::string behaviour = getProperty("Behaviour");
int bhv = 0;
if ( behaviour == "Average" ) bhv = 1;
API::MatrixWorkspace_sptr beh = API::WorkspaceFactory::Instance().create(
"Workspace2D", static_cast<int>(m_GroupSpecInds.size()), 1, 1);
g_log.debug() << name() << ": Preparing to group spectra into " << m_GroupSpecInds.size() << " groups\n";
// where we are copying spectra to, we start copying to the start of the output workspace
size_t outIndex = 0;
// Only used for averaging behaviour. We may have a 1:1 map where a Divide would be waste as it would be just dividing by 1
bool requireDivide(false);
for ( storage_map::const_iterator it = m_GroupSpecInds.begin(); it != m_GroupSpecInds.end() ; ++it )
{
// This is the grouped spectrum
EventList & outEL = outputWS->getEventList(outIndex);
// The spectrum number of the group is the key
outEL.setSpectrumNo(it->first);
// Start fresh with no detector IDs
outEL.clearDetectorIDs();
// the Y values and errors from spectra being grouped are combined in the output spectrum
// Keep track of number of detectors required for masking
size_t nonMaskedSpectra(0);
beh->dataX(outIndex)[0] = 0.0;
beh->dataE(outIndex)[0] = 0.0;
for( std::vector<size_t>::const_iterator wsIter = it->second.begin(); wsIter != it->second.end(); ++wsIter)
{
const size_t originalWI = *wsIter;
const EventList & fromEL=inputWS->getEventList(originalWI);
//Add the event lists with the operator
outEL += fromEL;
// detectors to add to the output spectrum
outEL.addDetectorIDs(fromEL.getDetectorIDs() );
try
{
Geometry::IDetector_const_sptr det = inputWS->getDetector(originalWI);
if( !det->isMasked() ) ++nonMaskedSpectra;
}
catch(Exception::NotFoundError&)
{
// If a detector cannot be found, it cannot be masked
++nonMaskedSpectra;
}
}
if( nonMaskedSpectra == 0 ) ++nonMaskedSpectra; // Avoid possible divide by zero
if(!requireDivide) requireDivide = (nonMaskedSpectra > 1);
beh->dataY(outIndex)[0] = static_cast<double>(nonMaskedSpectra);
// make regular progress reports and check for cancelling the algorithm
if ( outIndex % INTERVAL == 0 )
{
m_FracCompl += INTERVAL*prog4Copy;
if ( m_FracCompl > 1.0 )
m_FracCompl = 1.0;
progress(m_FracCompl);
interruption_point();
}
outIndex ++;
}
// Refresh the spectraDetectorMap
outputWS->doneAddingEventLists();
if ( bhv == 1 && requireDivide )
{
g_log.debug() << "Running Divide algorithm to perform averaging.\n";
Mantid::API::IAlgorithm_sptr divide = createChildAlgorithm("Divide");
divide->initialize();
divide->setProperty<API::MatrixWorkspace_sptr>("LHSWorkspace", outputWS);
divide->setProperty<API::MatrixWorkspace_sptr>("RHSWorkspace", beh);
divide->setProperty<API::MatrixWorkspace_sptr>("OutputWorkspace", outputWS);
divide->execute();
}
g_log.debug() << name() << " created " << outIndex << " new grouped spectra\n";
return outIndex;
}
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);
}
/** Calculate the integral asymmetry for a workspace (red & green).
* The calculation is done by MuonAsymmetryCalc and SimpleIntegration algorithms.
* @param ws_red :: The red workspace
* @param ws_green :: The green workspace
* @param Y :: Reference to a variable receiving the value of asymmetry
* @param E :: Reference to a variable receiving the value of the error
*/
void PlotAsymmetryByLogValue::calcIntAsymmetry(API::MatrixWorkspace_sptr ws_red,
API::MatrixWorkspace_sptr ws_green,double& Y, double& E)
{
if ( !m_autogroup )
{
groupDetectors(ws_red,m_backward_list);
groupDetectors(ws_red,m_forward_list);
groupDetectors(ws_green,m_backward_list);
groupDetectors(ws_green,m_forward_list);
}
Property* startXprop = getProperty("TimeMin");
Property* endXprop = getProperty("TimeMax");
bool setX = !startXprop->isDefault() && !endXprop->isDefault();
double startX(0.0),endX(0.0);
if (setX)
{
startX = getProperty("TimeMin");
endX = getProperty("TimeMax");
}
if (!m_int)
{ // "Differential asymmetry"
API::MatrixWorkspace_sptr tmpWS = API::WorkspaceFactory::Instance().create(
ws_red,1,ws_red->readX(0).size(),ws_red->readY(0).size());
for(size_t i=0; i<tmpWS->dataY(0).size(); i++)
{
double FNORM = ws_green->readY(0)[i] + ws_red->readY(0)[i];
FNORM = FNORM != 0.0 ? 1.0 / FNORM : 1.0;
double BNORM = ws_green->readY(1)[i] + ws_red->readY(1)[i];
BNORM = BNORM != 0.0 ? 1.0 / BNORM : 1.0;
double ZF = ( ws_green->readY(0)[i] - ws_red->readY(0)[i] ) * FNORM;
double ZB = ( ws_green->readY(1)[i] - ws_red->readY(1)[i] ) * BNORM;
tmpWS->dataY(0)[i] = ZB - ZF;
tmpWS->dataE(0)[i] = (1.0+ZF*ZF)*FNORM+(1.0+ZB*ZB)*BNORM;
}
IAlgorithm_sptr integr = createChildAlgorithm("Integration");
integr->setProperty("InputWorkspace",tmpWS);
integr->setPropertyValue("OutputWorkspace","tmp");
if (setX)
{
integr->setProperty("RangeLower",startX);
integr->setProperty("RangeUpper",endX);
}
integr->execute();
MatrixWorkspace_sptr out = integr->getProperty("OutputWorkspace");
Y = out->readY(0)[0] / static_cast<double>(tmpWS->dataY(0).size());
E = out->readE(0)[0] / static_cast<double>(tmpWS->dataY(0).size());
}
else
{
// "Integral asymmetry"
IAlgorithm_sptr integr = createChildAlgorithm("Integration");
integr->setProperty("InputWorkspace", ws_red);
integr->setPropertyValue("OutputWorkspace","tmp");
if (setX)
{
integr->setProperty("RangeLower",startX);
integr->setProperty("RangeUpper",endX);
}
integr->execute();
API::MatrixWorkspace_sptr intWS_red = integr->getProperty("OutputWorkspace");
integr = createChildAlgorithm("Integration");
integr->setProperty("InputWorkspace", ws_green);
integr->setPropertyValue("OutputWorkspace","tmp");
if (setX)
{
integr->setProperty("RangeLower",startX);
integr->setProperty("RangeUpper",endX);
}
integr->execute();
API::MatrixWorkspace_sptr intWS_green = integr->getProperty("OutputWorkspace");
double YIF = ( intWS_green->readY(0)[0] - intWS_red->readY(0)[0] ) / ( intWS_green->readY(0)[0] + intWS_red->readY(0)[0] );
double YIB = ( intWS_green->readY(1)[0] - intWS_red->readY(1)[0] ) / ( intWS_green->readY(1)[0] + intWS_red->readY(1)[0] );
Y = YIB - YIF;
double VARIF = (1.0 + YIF*YIF) / ( intWS_green->readY(0)[0] + intWS_red->readY(0)[0] );
double VARIB = (1.0 + YIB*YIB) / ( intWS_green->readY(1)[0] + intWS_red->readY(1)[0] );
E = sqrt( VARIF + VARIB );
}
}
/**
* Move the user selected spectra in the input workspace into groups in the output workspace
* @param inputWS :: user selected input workspace for the algorithm
* @param outputWS :: user selected output workspace for the algorithm
* @param prog4Copy :: the amount of algorithm progress to attribute to moving a single spectra
* @return number of new grouped spectra
*/
size_t GroupDetectors2::formGroups( API::MatrixWorkspace_const_sptr inputWS, API::MatrixWorkspace_sptr outputWS,
const double prog4Copy)
{
// get "Behaviour" string
const std::string behaviour = getProperty("Behaviour");
int bhv = 0;
if ( behaviour == "Average" ) bhv = 1;
API::MatrixWorkspace_sptr beh = API::WorkspaceFactory::Instance().create(
"Workspace2D", static_cast<int>(m_GroupSpecInds.size()), 1, 1);
g_log.debug() << name() << ": Preparing to group spectra into " << m_GroupSpecInds.size() << " groups\n";
// where we are copying spectra to, we start copying to the start of the output workspace
size_t outIndex = 0;
// Only used for averaging behaviour. We may have a 1:1 map where a Divide would be waste as it would be just dividing by 1
bool requireDivide(false);
for ( storage_map::const_iterator it = m_GroupSpecInds.begin(); it != m_GroupSpecInds.end() ; ++it )
{
// This is the grouped spectrum
ISpectrum * outSpec = outputWS->getSpectrum(outIndex);
// The spectrum number of the group is the key
outSpec->setSpectrumNo(it->first);
// Start fresh with no detector IDs
outSpec->clearDetectorIDs();
// Copy over X data from first spectrum, the bin boundaries for all spectra are assumed to be the same here
outSpec->dataX() = inputWS->readX(0);
// the Y values and errors from spectra being grouped are combined in the output spectrum
// Keep track of number of detectors required for masking
size_t nonMaskedSpectra(0);
beh->dataX(outIndex)[0] = 0.0;
beh->dataE(outIndex)[0] = 0.0;
for( std::vector<size_t>::const_iterator wsIter = it->second.begin(); wsIter != it->second.end(); ++wsIter)
{
const size_t originalWI = *wsIter;
// detectors to add to firstSpecNum
const ISpectrum * fromSpectrum = inputWS->getSpectrum(originalWI);
// Add up all the Y spectra and store the result in the first one
// Need to keep the next 3 lines inside loop for now until ManagedWorkspace mru-list works properly
MantidVec &firstY = outSpec->dataY();
MantidVec::iterator fYit;
MantidVec::iterator fEit = outSpec->dataE().begin();
MantidVec::const_iterator Yit = fromSpectrum->dataY().begin();
MantidVec::const_iterator Eit = fromSpectrum->dataE().begin();
for (fYit = firstY.begin(); fYit != firstY.end(); ++fYit, ++fEit, ++Yit, ++Eit)
{
*fYit += *Yit;
// Assume 'normal' (i.e. Gaussian) combination of errors
*fEit = std::sqrt( (*fEit)*(*fEit) + (*Eit)*(*Eit) );
}
// detectors to add to the output spectrum
outSpec->addDetectorIDs(fromSpectrum->getDetectorIDs() );
try
{
Geometry::IDetector_const_sptr det = inputWS->getDetector(originalWI);
if( !det->isMasked() ) ++nonMaskedSpectra;
}
catch(Exception::NotFoundError&)
{
// If a detector cannot be found, it cannot be masked
++nonMaskedSpectra;
}
}
if( nonMaskedSpectra == 0 ) ++nonMaskedSpectra; // Avoid possible divide by zero
if(!requireDivide) requireDivide = (nonMaskedSpectra > 1);
beh->dataY(outIndex)[0] = static_cast<double>(nonMaskedSpectra);
// make regular progress reports and check for cancelling the algorithm
if ( outIndex % INTERVAL == 0 )
{
m_FracCompl += INTERVAL*prog4Copy;
if ( m_FracCompl > 1.0 )
m_FracCompl = 1.0;
progress(m_FracCompl);
interruption_point();
}
outIndex ++;
}
// Refresh the spectraDetectorMap
outputWS->generateSpectraMap();
if ( bhv == 1 && requireDivide )
{
g_log.debug() << "Running Divide algorithm to perform averaging.\n";
Mantid::API::IAlgorithm_sptr divide = createChildAlgorithm("Divide");
divide->initialize();
divide->setProperty<API::MatrixWorkspace_sptr>("LHSWorkspace", outputWS);
divide->setProperty<API::MatrixWorkspace_sptr>("RHSWorkspace", beh);
divide->setProperty<API::MatrixWorkspace_sptr>("OutputWorkspace", outputWS);
divide->execute();
}
g_log.debug() << name() << " created " << outIndex << " new grouped spectra\n";
return outIndex;
}
/**
* Executes the algorithm
*/
void ScaleX::exec()
{
//Get input workspace and offset
const MatrixWorkspace_sptr inputW = getProperty("InputWorkspace");
factor = getProperty("Factor");
API::MatrixWorkspace_sptr outputW = createOutputWS(inputW);
//Get number of histograms
int histnumber = static_cast<int>(inputW->getNumberHistograms());
m_progress = new API::Progress(this, 0.0, 1.0, histnumber+1);
m_progress->report("Scaling X");
wi_min = 0;
wi_max = histnumber-1;
//check if workspace indexes have been set
int tempwi_min = getProperty("IndexMin");
int tempwi_max = getProperty("IndexMax");
if ( tempwi_max != Mantid::EMPTY_INT() )
{
if ((wi_min <= tempwi_min) && (tempwi_min <= tempwi_max) && (tempwi_max <= wi_max))
{
wi_min = tempwi_min;
wi_max = tempwi_max;
}
else
{
g_log.error("Invalid Workspace Index min/max properties");
throw std::invalid_argument("Inconsistent properties defined");
}
}
//Check if its an event workspace
EventWorkspace_const_sptr eventWS = boost::dynamic_pointer_cast<const EventWorkspace>(inputW);
if (eventWS != NULL)
{
this->execEvent();
return;
}
// do the shift in X
PARALLEL_FOR2(inputW, outputW)
for (int i=0; i < histnumber; ++i)
{
PARALLEL_START_INTERUPT_REGION
//Do the offsetting
for (int j=0; j < static_cast<int>(inputW->readX(i).size()); ++j)
{
//Change bin value by offset
if ((i >= wi_min) && (i <= wi_max)) outputW->dataX(i)[j] = inputW->readX(i)[j] * factor;
else outputW->dataX(i)[j] = inputW->readX(i)[j];
}
//Copy y and e data
outputW->dataY(i) = inputW->dataY(i);
outputW->dataE(i) = inputW->dataE(i);
if( (i >= wi_min) && (i <= wi_max) && factor<0 )
{
std::reverse( outputW->dataX(i).begin(), outputW->dataX(i).end() );
std::reverse( outputW->dataY(i).begin(), outputW->dataY(i).end() );
std::reverse( outputW->dataE(i).begin(), outputW->dataE(i).end() );
}
m_progress->report("Scaling X");
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Copy units
if (outputW->getAxis(0)->unit().get())
outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
try
{
if (inputW->getAxis(1)->unit().get())
outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
}
catch(Exception::IndexError &) {
// OK, so this isn't a Workspace2D
}
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputW);
}
/** Executes the regroup algorithm
*
* @throw runtime_error Thrown if
*/
void Regroup::exec()
{
// retrieve the properties
std::vector<double> rb_params=getProperty("Params");
// Get the input workspace
MatrixWorkspace_const_sptr inputW = getProperty("InputWorkspace");
// can work only if all histograms have the same boundaries
if (!API::WorkspaceHelpers::commonBoundaries(inputW))
{
g_log.error("Histograms with different boundaries");
throw std::runtime_error("Histograms with different boundaries");
}
bool dist = inputW->isDistribution();
int histnumber = static_cast<int>(inputW->getNumberHistograms());
MantidVecPtr XValues_new;
const MantidVec & XValues_old = inputW->readX(0);
std::vector<int> xoldIndex;// indeces of new x in XValues_old
// create new output X axis
int ntcnew = newAxis(rb_params,XValues_old,XValues_new.access(),xoldIndex);
// make output Workspace the same type is the input, but with new length of signal array
API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(inputW,histnumber,ntcnew,ntcnew-1);
int progress_step = histnumber / 100;
if (progress_step == 0) progress_step = 1;
for (int hist=0; hist < histnumber;hist++)
{
// get const references to input Workspace arrays (no copying)
const MantidVec& XValues = inputW->readX(hist);
const MantidVec& YValues = inputW->readY(hist);
const MantidVec& YErrors = inputW->readE(hist);
//get references to output workspace data (no copying)
MantidVec& YValues_new=outputW->dataY(hist);
MantidVec& YErrors_new=outputW->dataE(hist);
// output data arrays are implicitly filled by function
rebin(XValues,YValues,YErrors,xoldIndex,YValues_new,YErrors_new, dist);
outputW->setX(hist,XValues_new);
if (hist % progress_step == 0)
{
progress(double(hist)/histnumber);
interruption_point();
}
}
outputW->isDistribution(dist);
// Copy units
if (outputW->getAxis(0)->unit().get())
outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
try
{
if (inputW->getAxis(1)->unit().get())
outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
}
catch(Exception::IndexError) {
// OK, so this isn't a Workspace2D
}
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputW);
return;
}
/**
* 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;
}
/** Executes the rebin algorithm
*
* @throw runtime_error Thrown if
*/
void Rebunch::exec()
{
// retrieve the properties
int n_bunch=getProperty("NBunch");
// Get the input workspace
MatrixWorkspace_const_sptr inputW = getProperty("InputWorkspace");
bool dist = inputW->isDistribution();
// workspace independent determination of length
int histnumber = static_cast<int>(inputW->size()/inputW->blocksize());
/*
const std::vector<double>& Xold = inputW->readX(0);
const std::vector<double>& Yold = inputW->readY(0);
int size_x=Xold.size();
int size_y=Yold.size();
*/
int size_x = static_cast<int>(inputW->readX(0).size());
int size_y = static_cast<int>(inputW->readY(0).size());
//signal is the same length for histogram and point data
int ny=(size_y/n_bunch);
if(size_y%n_bunch >0)ny+=1;
// default is for hist
int nx=ny+1;
bool point=false;
if (size_x==size_y)
{
point=true;
nx=ny;
}
// make output Workspace the same type is the input, but with new length of signal array
API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(inputW,histnumber,nx,ny);
int progress_step = histnumber / 100;
if (progress_step == 0) progress_step = 1;
PARALLEL_FOR2(inputW,outputW)
for (int hist=0; hist < histnumber;hist++)
{
PARALLEL_START_INTERUPT_REGION
// Ensure that axis information are copied to the output workspace if the axis exists
try
{
outputW->getAxis(1)->spectraNo(hist)=inputW->getAxis(1)->spectraNo(hist);
}
catch( Exception::IndexError& )
{
// Not a Workspace2D
}
// get const references to input Workspace arrays (no copying)
const MantidVec& XValues = inputW->readX(hist);
const MantidVec& YValues = inputW->readY(hist);
const MantidVec& YErrors = inputW->readE(hist);
//get references to output workspace data (no copying)
MantidVec& XValues_new=outputW->dataX(hist);
MantidVec& YValues_new=outputW->dataY(hist);
MantidVec& YErrors_new=outputW->dataE(hist);
// output data arrays are implicitly filled by function
if(point)
{
rebunch_point(XValues,YValues,YErrors,XValues_new,YValues_new,YErrors_new,n_bunch);
}
else
{
rebunch_hist(XValues,YValues,YErrors,XValues_new,YValues_new,YErrors_new,n_bunch, dist);
}
if (hist % progress_step == 0)
{
progress(double(hist)/histnumber);
interruption_point();
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
outputW->isDistribution(dist);
// Copy units
if (outputW->getAxis(0)->unit().get())
outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
try
{
if (inputW->getAxis(1)->unit().get())
outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
}
catch(Exception::IndexError&) {
// OK, so this isn't a Workspace2D
}
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputW);
return;
}
/** Executes the algorithm
*
*/
void SplineBackground::exec()
{
API::MatrixWorkspace_sptr inWS = getProperty("InputWorkspace");
int spec = getProperty("WorkspaceIndex");
if (spec > static_cast<int>(inWS->getNumberHistograms()))
throw std::out_of_range("WorkspaceIndex is out of range.");
const MantidVec& X = inWS->readX(spec);
const MantidVec& Y = inWS->readY(spec);
const MantidVec& E = inWS->readE(spec);
const bool isHistogram = inWS->isHistogramData();
const int ncoeffs = getProperty("NCoeff");
const int k = 4; // order of the spline + 1 (cubic)
const int nbreak = ncoeffs - (k - 2);
if (nbreak <= 0)
throw std::out_of_range("Too low NCoeff");
gsl_bspline_workspace *bw;
gsl_vector *B;
gsl_vector *c, *w, *x, *y;
gsl_matrix *Z, *cov;
gsl_multifit_linear_workspace *mw;
double chisq;
int n = static_cast<int>(Y.size());
bool isMasked = inWS->hasMaskedBins(spec);
std::vector<int> masked(Y.size());
if (isMasked)
{
for(API::MatrixWorkspace::MaskList::const_iterator it=inWS->maskedBins(spec).begin();it!=inWS->maskedBins(spec).end();++it)
masked[it->first] = 1;
n -= static_cast<int>(inWS->maskedBins(spec).size());
}
if (n < ncoeffs)
{
g_log.error("Too many basis functions (NCoeff)");
throw std::out_of_range("Too many basis functions (NCoeff)");
}
/* allocate a cubic bspline workspace (k = 4) */
bw = gsl_bspline_alloc(k, nbreak);
B = gsl_vector_alloc(ncoeffs);
x = gsl_vector_alloc(n);
y = gsl_vector_alloc(n);
Z = gsl_matrix_alloc(n, ncoeffs);
c = gsl_vector_alloc(ncoeffs);
w = gsl_vector_alloc(n);
cov = gsl_matrix_alloc(ncoeffs, ncoeffs);
mw = gsl_multifit_linear_alloc(n, ncoeffs);
/* this is the data to be fitted */
int j = 0;
for (MantidVec::size_type i = 0; i < Y.size(); ++i)
{
if (isMasked && masked[i]) continue;
gsl_vector_set(x, j, (isHistogram ? (0.5*(X[i]+X[i+1])) : X[i])); // Middle of the bins, if a histogram
gsl_vector_set(y, j, Y[i]);
gsl_vector_set(w, j, E[i]>0.?1./(E[i]*E[i]):0.);
++j;
}
if (n != j)
{
gsl_bspline_free(bw);
gsl_vector_free(B);
gsl_vector_free(x);
gsl_vector_free(y);
gsl_matrix_free(Z);
gsl_vector_free(c);
gsl_vector_free(w);
gsl_matrix_free(cov);
gsl_multifit_linear_free(mw);
throw std::runtime_error("Assertion failed: n != j");
}
double xStart = X.front();
double xEnd = X.back();
/* use uniform breakpoints */
gsl_bspline_knots_uniform(xStart, xEnd, bw);
/* construct the fit matrix X */
for (int i = 0; i < n; ++i)
{
double xi=gsl_vector_get(x, i);
/* compute B_j(xi) for all j */
gsl_bspline_eval(xi, B, bw);
/* fill in row i of X */
for (j = 0; j < ncoeffs; ++j)
{
double Bj = gsl_vector_get(B, j);
gsl_matrix_set(Z, i, j, Bj);
}
}
/* do the fit */
gsl_multifit_wlinear(Z, w, y, c, cov, &chisq, mw);
/* output the smoothed curve */
API::MatrixWorkspace_sptr outWS = WorkspaceFactory::Instance().create(inWS,1,X.size(),Y.size());
{
outWS->getAxis(1)->setValue(0, inWS->getAxis(1)->spectraNo(spec));
double xi, yi, yerr;
for (MantidVec::size_type i=0;i<Y.size();i++)
{
xi = X[i];
gsl_bspline_eval(xi, B, bw);
gsl_multifit_linear_est(B, c, cov, &yi, &yerr);
outWS->dataY(0)[i] = yi;
outWS->dataE(0)[i] = yerr;
}
outWS->dataX(0) = X;
}
gsl_bspline_free(bw);
gsl_vector_free(B);
gsl_vector_free(x);
gsl_vector_free(y);
gsl_matrix_free(Z);
gsl_vector_free(c);
gsl_vector_free(w);
gsl_matrix_free(cov);
gsl_multifit_linear_free(mw);
setProperty("OutputWorkspace",outWS);
}
/** 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
}
/** Executes the algorithm
* @throw Exception::FileError If the calibration file cannot be opened and
* read successfully
* @throw Exception::InstrumentDefinitionError If unable to obtain the
* source-sample distance
*/
void AlignDetectors::exec() {
// Get the input workspace
MatrixWorkspace_sptr inputWS = getProperty("InputWorkspace");
this->getCalibrationWS(inputWS);
// Initialise the progress reporting object
m_numberOfSpectra = static_cast<int64_t>(inputWS->getNumberHistograms());
// Check if its an event workspace
EventWorkspace_const_sptr eventW =
boost::dynamic_pointer_cast<const EventWorkspace>(inputWS);
if (eventW != nullptr) {
this->execEvent();
return;
}
API::MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
// If input and output workspaces are not the same, create a new workspace for
// the output
if (outputWS != inputWS) {
outputWS = WorkspaceFactory::Instance().create(inputWS);
setProperty("OutputWorkspace", outputWS);
}
// Set the final unit that our output workspace will have
setXAxisUnits(outputWS);
ConversionFactors converter = ConversionFactors(m_calibrationWS);
Progress progress(this, 0.0, 1.0, m_numberOfSpectra);
// Loop over the histograms (detector spectra)
PARALLEL_FOR2(inputWS, outputWS)
for (int64_t i = 0; i < m_numberOfSpectra; ++i) {
PARALLEL_START_INTERUPT_REGION
try {
// Get the input spectrum number at this workspace index
auto inSpec = inputWS->getSpectrum(size_t(i));
auto toDspacing = converter.getConversionFunc(inSpec->getDetectorIDs());
// Get references to the x data
MantidVec &xOut = outputWS->dataX(i);
// Make sure reference to input X vector is obtained after output one
// because in the case
// where the input & output workspaces are the same, it might move if the
// vectors were shared.
const MantidVec &xIn = inSpec->readX();
std::transform(xIn.begin(), xIn.end(), xOut.begin(), toDspacing);
// Copy the Y&E data
outputWS->dataY(i) = inSpec->readY();
outputWS->dataE(i) = inSpec->readE();
} catch (Exception::NotFoundError &) {
// Zero the data in this case
outputWS->dataX(i).assign(outputWS->readX(i).size(), 0.0);
outputWS->dataY(i).assign(outputWS->readY(i).size(), 0.0);
outputWS->dataE(i).assign(outputWS->readE(i).size(), 0.0);
}
progress.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
