/** 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
}
void LoadNexusProcessed::loadBlock(NXDataSetTyped<double> & data, NXDataSetTyped<double> & errors,
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));
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(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;
local_workspace->setX(wsIndex, m_xbins);
++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");
}
/**
* 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);
}
}
/** Convert the workspace units according to a simple output = a * (input^b) relationship
* @param outputWS :: the output workspace
* @param factor :: the conversion factor a to apply
* @param power :: the Power b to apply to the conversion
*/
void ConvertUnits::convertQuickly(API::MatrixWorkspace_sptr outputWS, const double& factor, const double& power)
{
Progress prog(this,0.2,1.0,m_numberOfSpectra);
int64_t numberOfSpectra_i = static_cast<int64_t>(m_numberOfSpectra); // cast to make openmp happy
// See if the workspace has common bins - if so the X vector can be common
// First a quick check using the validator
CommonBinsValidator sameBins;
bool commonBoundaries = false;
if ( sameBins.isValid(outputWS) == "" )
{
commonBoundaries = WorkspaceHelpers::commonBoundaries(outputWS);
// Only do the full check if the quick one passes
if (commonBoundaries)
{
// Calculate the new (common) X values
MantidVec::iterator iter;
for (iter = outputWS->dataX(0).begin(); iter != outputWS->dataX(0).end(); ++iter)
{
*iter = factor * std::pow(*iter,power);
}
MantidVecPtr xVals;
xVals.access() = outputWS->dataX(0);
PARALLEL_FOR1(outputWS)
for (int64_t j = 1; j < numberOfSpectra_i; ++j)
{
PARALLEL_START_INTERUPT_REGION
outputWS->setX(j,xVals);
prog.report("Convert to " + m_outputUnit->unitID());
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
if (!m_inputEvents) // if in event mode the work is done
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 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;
}