/** Convert X units from nano-secs to micro-secs and shift to start at m_tMin
* @param inputWs :: [input/output] workspace to convert
*/
void PhaseQuadMuon::convertToMicroSecs (API::MatrixWorkspace_sptr inputWs)
{
for (size_t h=0; h<inputWs->getNumberHistograms(); h++)
{
auto spec = inputWs->getSpectrum(h);
for (int t=0; t<m_nData+1; t++)
{
spec->dataX()[t] = spec->dataX()[t]/1000+m_tMin;
}
}
}
/** Loads, checks and passes back the values passed to the algorithm
* @param whiteBeam1 :: A white beam vanadium spectrum that will be used to
* check detector efficiency variations
* @param whiteBeam2 :: The other white beam vanadium spectrum from the same
* instrument to use for comparison
* @param variation :: The maximum fractional variation above the median that is
* allowed for god detectors
* @param startWsIndex :: Index number of the first spectrum to use
* @param endWsIndex :: Index number of the last spectrum to use
* @throw invalid_argument if there is an incapatible property value and so the
* algorithm can't continue
*/
void DetectorEfficiencyVariation::retrieveProperties(
API::MatrixWorkspace_sptr &whiteBeam1,
API::MatrixWorkspace_sptr &whiteBeam2, double &variation, int &startWsIndex,
int &endWsIndex) {
whiteBeam1 = getProperty("WhiteBeamBase");
whiteBeam2 = getProperty("WhiteBeamCompare");
if (whiteBeam1->getInstrument()->getName() !=
whiteBeam2->getInstrument()->getName()) {
throw std::invalid_argument("The two input white beam vanadium workspaces "
"must be from the same instrument");
}
int maxWsIndex = static_cast<int>(whiteBeam1->getNumberHistograms()) - 1;
if (maxWsIndex !=
static_cast<int>(whiteBeam2->getNumberHistograms()) -
1) { // we would get a crash later on if this were not true
throw std::invalid_argument("The input white beam vanadium workspaces must "
"be have the same number of histograms");
}
variation = getProperty("Variation");
startWsIndex = getProperty("StartWorkspaceIndex");
if ((startWsIndex < 0) || (startWsIndex > maxWsIndex)) {
g_log.warning("StartWorkspaceIndex out of range, changed to 0");
startWsIndex = 0;
}
endWsIndex = getProperty("EndWorkspaceIndex");
if (endWsIndex == Mantid::EMPTY_INT())
endWsIndex = maxWsIndex;
if ((endWsIndex < 0) || (endWsIndex > maxWsIndex)) {
g_log.warning(
"EndWorkspaceIndex out of range, changed to max Workspace number");
endWsIndex = maxWsIndex;
}
if ((endWsIndex < startWsIndex)) {
g_log.warning(
"EndWorkspaceIndex can not be less than the StartWorkspaceIndex, "
"changed to max Workspace number");
endWsIndex = maxWsIndex;
}
}
std::vector<std::vector<size_t> > DetectorDiagnostic::makeInstrumentMap(API::MatrixWorkspace_sptr countsWS)
{
std::vector<std::vector<size_t> > mymap;
std::vector<size_t> single;
for(size_t i=0;i < countsWS->getNumberHistograms();i++)
{
single.push_back(i);
}
mymap.push_back(single);
return mymap;
}
/***
* This will ensure the spectrum numbers do not overlap by starting the second
*on at the first + 1
*
* @param ws1 The first workspace supplied to the algorithm.
* @param ws2 The second workspace supplied to the algorithm.
* @param output The workspace that is going to be returned by the algorithm.
*/
void ConjoinWorkspaces::fixSpectrumNumbers(API::MatrixWorkspace_const_sptr ws1,
API::MatrixWorkspace_const_sptr ws2,
API::MatrixWorkspace_sptr output) {
bool needsFix(false);
if (this->getProperty("CheckOverlapping")) {
// If CheckOverlapping is required, then either skip fixing spectrum number
// or get stopped by an exception
if (!m_overlapChecked)
checkForOverlap(ws1, ws2, true);
needsFix = false;
} else {
// It will be determined later whether spectrum number needs to be fixed.
needsFix = true;
}
if (!needsFix)
return;
// is everything possibly ok?
specid_t min;
specid_t max;
getMinMax(output, min, max);
if (max - min >= static_cast<specid_t>(
output->getNumberHistograms())) // nothing to do then
return;
// information for remapping the spectra numbers
specid_t ws1min;
specid_t ws1max;
getMinMax(ws1, ws1min, ws1max);
// change the axis by adding the maximum existing spectrum number to the
// current value
for (size_t i = ws1->getNumberHistograms(); i < output->getNumberHistograms();
i++) {
specid_t origid;
origid = output->getSpectrum(i)->getSpectrumNo();
output->getSpectrum(i)->setSpectrumNo(origid + ws1max);
}
}
/** Returns a given spectrum as a complex number
* @param inWS :: [input] The input workspace containing all the spectra
* @param spec :: [input] The spectrum of interest
* @param errors :: [input] If true, returns the errors, otherwise returns the
* counts
* @return : Spectrum 'spec' as a complex vector
*/
std::vector<double> MaxEnt::toComplex(const API::MatrixWorkspace_sptr &inWS,
size_t spec, bool errors) {
std::vector<double> result(inWS->blocksize() * 2);
if (inWS->getNumberHistograms() % 2)
throw std::invalid_argument(
"Cannot convert input workspace to complex data");
size_t nspec = inWS->getNumberHistograms() / 2;
if (!errors) {
for (size_t i = 0; i < inWS->blocksize(); i++) {
result[2 * i] = inWS->readY(spec)[i];
result[2 * i + 1] = inWS->readY(spec + nspec)[i];
}
} else {
for (size_t i = 0; i < inWS->blocksize(); i++) {
result[2 * i] = inWS->readE(spec)[i];
result[2 * i + 1] = inWS->readE(spec + nspec)[i];
}
}
return result;
}
/** 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) {
auto &Y = outputWS->mutableY(i);
auto &E = outputWS->mutableE(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]));
}
}
void SaveFITS::writeFITSHeaderAxesSizes(const API::MatrixWorkspace_sptr img,
std::ofstream &file) {
const std::string sizeX = std::to_string(img->blocksize());
const std::string sizeY = std::to_string(img->getNumberHistograms());
const size_t fieldWidth = 20;
std::stringstream axis1;
axis1 << "NAXIS1 = " << std::setw(fieldWidth) << sizeX
<< " / length of data axis 1";
writeFITSHeaderEntry(axis1.str(), file);
std::stringstream axis2;
axis2 << "NAXIS2 = " << std::setw(fieldWidth) << sizeY
<< " / length of data axis 2";
writeFITSHeaderEntry(axis2.str(), file);
}
/** Fits each spectrum in the workspace to f(x) = A * sin( w * x + p)
* @param ws :: [input] The workspace to fit
* @param freq :: [input] Hint for the frequency (w)
* @param groupName :: [input] The name of the output workspace group
* @param resTab :: [output] Table workspace storing the asymmetries and phases
* @param resGroup :: [output] Workspace group storing the fitting results
*/
void CalMuonDetectorPhases::fitWorkspace(const API::MatrixWorkspace_sptr &ws,
double freq, std::string groupName,
API::ITableWorkspace_sptr &resTab,
API::WorkspaceGroup_sptr &resGroup) {
int nhist = static_cast<int>(ws->getNumberHistograms());
// Create the fitting function f(x) = A * sin ( w * x + p )
// The same function and initial parameters are used for each fit
std::string funcStr = createFittingFunction(freq, true);
// Set up results table
resTab->addColumn("int", "Spectrum number");
resTab->addColumn("double", "Asymmetry");
resTab->addColumn("double", "Phase");
// Loop through fitting all spectra individually
const static std::string success = "success";
for (int wsIndex = 0; wsIndex < nhist; wsIndex++) {
reportProgress(wsIndex, nhist);
auto fit = createChildAlgorithm("Fit");
fit->initialize();
fit->setPropertyValue("Function", funcStr);
fit->setProperty("InputWorkspace", ws);
fit->setProperty("WorkspaceIndex", wsIndex);
fit->setProperty("CreateOutput", true);
fit->setPropertyValue("Output", groupName);
fit->execute();
std::string status = fit->getProperty("OutputStatus");
if (!fit->isExecuted() || status != success) {
std::ostringstream error;
error << "Fit failed for spectrum at workspace index " << wsIndex;
error << ": " << status;
throw std::runtime_error(error.str());
}
API::MatrixWorkspace_sptr fitOut = fit->getProperty("OutputWorkspace");
resGroup->addWorkspace(fitOut);
API::ITableWorkspace_sptr tab = fit->getProperty("OutputParameters");
// Now we have our fitting results stored in tab
// but we need to extract the relevant information, i.e.
// the detector phases (parameter 'p') and asymmetries ('A')
const auto &spectrum = ws->getSpectrum(static_cast<size_t>(wsIndex));
extractDetectorInfo(tab, resTab, spectrum.getSpectrumNo());
}
}
/**
* Validation of the inputs of the RingProfile algorithm.
*
* Inside this method, the Workspace is considered a 2D Matrix, where each spectrum is
* the rows of the matrix and have the variation in axis0. The columns of the matrix
* is the position of dataX(0)
*
* The main validation are:
* - the centre of the ring is inside the image it self.
* - The minimum ring is smaller than the limits of the image to allow
* @param inputWS: pointer to the input workspace
*/
void RingProfile::checkInputsForNumericWorkspace(const API::MatrixWorkspace_sptr inputWS){
g_log.notice() << "CheckingInputs For Numeric Workspace" << std::endl;
// The Axis0 is defined by the values of readX inside the spectra of the workspace.
// The limits of this axis will be get by inspection of the readX vector taking the first
// and the last value.
// check that centre is inside the range available for the instrument
const MantidVec & refX = inputWS->readX(inputWS->getNumberHistograms()/2);
// get the limits of the axis 0 (X)
double min_v_x, max_v_x;
min_v_x = std::min(refX[0], refX[refX.size() -1]);
max_v_x = std::max(refX[0], refX[refX.size() -1]);
g_log.notice() << "Limits X = " << min_v_x << " " << max_v_x << std::endl;
// check centre is inside the X domain
if ( centre_x < min_v_x || centre_x > max_v_x){
std::stringstream s;
s << "The input value for centre (X="<< centre_x << ") is outside the limits of the instrument ["
<< min_v_x << ", "<< max_v_x << "]" ;
throw std::invalid_argument(s.str());
}
// The Axis1 is defined by the spectra inside the workspace. Its limits and values are given by the
// ws->getAxis(1)
// get the limits of the axis1 (Y)
API::NumericAxis *oldAxis2 = dynamic_cast<API::NumericAxis*>(inputWS->getAxis(1) );
// we cannot have the positions in Y direction without a NumericAxis
if( !oldAxis2 )
throw std::invalid_argument("Vertical axis is not a numeric axis. If it is a spectra axis try running ConvertSpectrumAxis first.");
double min_v_y = std::min(oldAxis2->getMin(), oldAxis2->getMax());
double max_v_y = std::max(oldAxis2->getMin(), oldAxis2->getMax());
g_log.notice() << "Limits Y = " << min_v_y << " " << max_v_y << std::endl;
// check centre is inside the Y domain
if (centre_y < min_v_y || centre_y > max_v_y){
std::stringstream s;
s << "The input value for centre (Y=" << centre_y << ") is outside the limits of the instrument ["
<< min_v_y << ", " << max_v_y << "]" ;
throw std::invalid_argument(s.str());
}
g_log.notice() << "Centre: " << centre_x << " " << centre_y << std::endl;
// check minradius is inside the limits of the region of the instrument
if (centre_x - min_radius > max_v_x || centre_x + min_radius < min_v_x || centre_y - min_radius > max_v_y || centre_y + min_radius < min_v_y)
throw std::invalid_argument("The minimun radius is outside the region of the instrument");
}
/** This function will check how to group spectra when calculating median
*
*
*/
std::vector<std::vector<size_t>>
DetectorDiagnostic::makeMap(API::MatrixWorkspace_sptr countsWS) {
std::multimap<Mantid::Geometry::ComponentID, size_t> mymap;
Geometry::Instrument_const_sptr instrument = countsWS->getInstrument();
if (m_parents == 0) {
return makeInstrumentMap(*countsWS);
}
if (!instrument) {
g_log.warning("Workspace has no instrument. LevelsUP is ignored");
return makeInstrumentMap(*countsWS);
}
// check if not grouped. If grouped, it will throw
if (countsWS->hasGroupedDetectors()) {
throw std::runtime_error("Median detector test: not able to create "
"detector to spectra map. Try with LevelUp=0.");
}
for (size_t i = 0; i < countsWS->getNumberHistograms(); i++) {
detid_t d = (*(countsWS->getSpectrum(i).getDetectorIDs().begin()));
auto anc = instrument->getDetector(d)->getAncestors();
if (anc.size() < static_cast<size_t>(m_parents)) {
g_log.warning("Too many levels up. Will ignore LevelsUp");
m_parents = 0;
return makeInstrumentMap(*countsWS);
}
mymap.emplace(anc[m_parents - 1]->getComponentID(), i);
}
std::vector<std::vector<size_t>> speclist;
std::multimap<Mantid::Geometry::ComponentID, size_t>::iterator m_it, s_it;
for (m_it = mymap.begin(); m_it != mymap.end(); m_it = s_it) {
Mantid::Geometry::ComponentID theKey = m_it->first;
auto keyRange = mymap.equal_range(theKey);
// Iterate over all map elements with key == theKey
std::vector<size_t> speclistsingle;
for (s_it = keyRange.first; s_it != keyRange.second; ++s_it) {
speclistsingle.push_back(s_it->second);
}
speclist.push_back(std::move(speclistsingle));
}
return speclist;
}
/** Assuming that the input workspace is a Numeric Image where the pixel
*positions depend on their
* relative position inside the workspace, this function extracts the position
*of the first and last
* pixel of the image.
*
* It is important that the input workspace must be a numeric image, and not an
*instrument related workspace.
* The function will raise exception (std::invalid_argument) if an invalid
*input is give.
*
* @see RadiusSum::inputWorkspaceHasInstrumentAssociated for reference.
*
* @param inWS reference to the workspace
* @return a list of values that defines the limits of the image in this order:
*Xmin, Xmax, Ymin, Ymax
*/
std::vector<double>
RadiusSum::getBoundariesOfNumericImage(API::MatrixWorkspace_sptr inWS) {
// horizontal axis
// get the pixel position in the horizontal axis from the middle of the image.
const MantidVec &refX = inWS->readX(inWS->getNumberHistograms() / 2);
double min_x, max_x;
const double &first_x(refX[0]);
const double &last_x(refX[refX.size() - 1]);
if (first_x < last_x) {
min_x = first_x;
max_x = last_x;
} else {
min_x = last_x;
max_x = first_x;
}
// vertical axis
API::NumericAxis *verticalAxis =
dynamic_cast<API::NumericAxis *>(inWS->getAxis(1));
if (!verticalAxis)
throw std::invalid_argument("Vertical axis is not a numeric axis. Can not "
"find the limits of the image.");
double min_y, max_y;
min_y = verticalAxis->getMin();
max_y = verticalAxis->getMax();
// check the assumption made that verticalAxis will provide the correct
// answer.
if (min_y > max_y) {
throw std::logic_error("Failure to get the boundaries of this image. "
"Internal logic error. Please, inform MantidHelp");
}
std::vector<double> output(4); // output = {min_x, max_x, min_y, max_y}; not
// supported in all compilers
output[0] = min_x;
output[1] = max_x;
output[2] = min_y;
output[3] = max_y;
return output;
}
/** If there is an overlap in spectrum numbers between ws1 and ws2,
* then the spectrum numbers are reset as a simple 1-1 correspondence
* with the workspace index.
*
* @param ws1 The first workspace supplied to the algorithm.
* @param ws2 The second workspace supplied to the algorithm.
* @param output The workspace that is going to be returned by the algorithm.
*/
void AppendSpectra::fixSpectrumNumbers(API::MatrixWorkspace_const_sptr ws1, API::MatrixWorkspace_const_sptr ws2,
API::MatrixWorkspace_sptr output)
{
specid_t ws1min;
specid_t ws1max;
getMinMax(ws1, ws1min, ws1max);
specid_t ws2min;
specid_t ws2max;
getMinMax(ws2, ws2min, ws2max);
// is everything possibly ok?
if (ws2min > ws1max)
return;
// change the axis by adding the maximum existing spectrum number to the current value
for (size_t i = 0; i < output->getNumberHistograms(); i++)
output->getSpectrum(i)->setSpectrumNo( specid_t(i) );
}
/**
* The main method to calculate the ring profile for workspaces based on
*instruments.
*
* It will iterate over all the spectrum inside the workspace.
* For each spectrum, it will use the RingProfile::getBinForPixel method to
*identify
* where, in the output_bins, the sum of all the spectrum values should be
*placed in.
*
* @param inputWS: pointer to the input workspace
* @param output_bins: the reference to the vector to be filled with the
*integration values
*/
void RingProfile::processInstrumentRingProfile(
const API::MatrixWorkspace_sptr inputWS, std::vector<double> &output_bins) {
for (int i = 0; i < static_cast<int>(inputWS->getNumberHistograms()); i++) {
m_progress->report("Computing ring bins positions for detectors");
// for the detector based, the positions will be taken from the detector
// itself.
try {
Mantid::Geometry::IDetector_const_sptr det = inputWS->getDetector(i);
// skip monitors
if (det->isMonitor()) {
continue;
}
// this part will be executed if the instrument is attached to the
// workspace
// get the bin position
int bin_n = getBinForPixel(det);
if (bin_n < 0) // -1 is the agreement for an invalid bin, or outside the
// ring being integrated
continue;
g_log.debug() << "Bin for the index " << i << " = " << bin_n
<< " Pos = " << det->getPos() << std::endl;
// get the reference to the spectrum
auto spectrum_pt = inputWS->getSpectrum(i);
const MantidVec &refY = spectrum_pt->dataY();
// accumulate the values of this spectrum inside this bin
for (size_t sp_ind = 0; sp_ind < inputWS->blocksize(); sp_ind++)
output_bins[bin_n] += refY[sp_ind];
} catch (Kernel::Exception::NotFoundError &ex) {
g_log.information() << "It found that detector for " << i
<< " is not valid. " << ex.what() << std::endl;
continue;
}
}
}
/** 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 normalization
* @param inputWorkspace The input workspace
* @param outputWorkspace The result workspace
*/
void NormaliseToMonitor::normaliseBinByBin(
const API::MatrixWorkspace_sptr &inputWorkspace,
API::MatrixWorkspace_sptr &outputWorkspace) {
EventWorkspace_sptr inputEvent =
boost::dynamic_pointer_cast<EventWorkspace>(inputWorkspace);
// Only create output workspace if different to input one
if (outputWorkspace != inputWorkspace) {
if (inputEvent) {
outputWorkspace = inputWorkspace->clone();
} else
outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace);
}
auto outputEvent =
boost::dynamic_pointer_cast<EventWorkspace>(outputWorkspace);
// Get hold of the monitor spectrum
const auto &monX = m_monitor->binEdges(0);
auto monY = m_monitor->counts(0);
auto monE = m_monitor->countStandardDeviations(0);
// Calculate the overall normalization just the once if bins are all matching
if (m_commonBins)
this->normalisationFactor(monX, monY, monE);
const size_t numHists = inputWorkspace->getNumberHistograms();
auto specLength = inputWorkspace->blocksize();
// Flag set when a division by 0 is found
bool hasZeroDivision = false;
Progress prog(this, 0.0, 1.0, numHists);
// Loop over spectra
PARALLEL_FOR_IF(
Kernel::threadSafe(*inputWorkspace, *outputWorkspace, *m_monitor))
for (int64_t i = 0; i < int64_t(numHists); ++i) {
PARALLEL_START_INTERUPT_REGION
prog.report();
const auto &X = inputWorkspace->binEdges(i);
// If not rebinning, just point to our monitor spectra, otherwise create new
// vectors
auto Y = (m_commonBins ? monY : Counts(specLength));
auto E = (m_commonBins ? monE : CountStandardDeviations(specLength));
if (!m_commonBins) {
// ConvertUnits can give X vectors of all zeros - skip these, they cause
// problems
if (X.back() == 0.0 && X.front() == 0.0)
continue;
// Rebin the monitor spectrum to match the binning of the current data
// spectrum
VectorHelper::rebinHistogram(
monX.rawData(), monY.mutableRawData(), monE.mutableRawData(),
X.rawData(), Y.mutableRawData(), E.mutableRawData(), false);
// Recalculate the overall normalization factor
this->normalisationFactor(X, Y, E);
}
if (inputEvent) {
// ----------------------------------- EventWorkspace
// ---------------------------------------
EventList &outEL = outputEvent->getSpectrum(i);
outEL.divide(X.rawData(), Y.mutableRawData(), E.mutableRawData());
} else {
// ----------------------------------- Workspace2D
// ---------------------------------------
auto &YOut = outputWorkspace->mutableY(i);
auto &EOut = outputWorkspace->mutableE(i);
const auto &inY = inputWorkspace->y(i);
const auto &inE = inputWorkspace->e(i);
outputWorkspace->mutableX(i) = inputWorkspace->x(i);
// The code below comes more or less straight out of Divide.cpp
for (size_t k = 0; k < specLength; ++k) {
// Get the input Y's
const double leftY = inY[k];
const double rightY = Y[k];
if (rightY == 0.0) {
hasZeroDivision = true;
}
// Calculate result and store in local variable to avoid overwriting
// original data if
// output workspace is same as one of the input ones
const double newY = leftY / rightY;
if (fabs(rightY) > 1.0e-12 && fabs(newY) > 1.0e-12) {
const double lhsFactor = (inE[k] < 1.0e-12 || fabs(leftY) < 1.0e-12)
? 0.0
: pow((inE[k] / leftY), 2);
const double rhsFactor =
E[k] < 1.0e-12 ? 0.0 : pow((E[k] / rightY), 2);
EOut[k] = std::abs(newY) * sqrt(lhsFactor + rhsFactor);
}
// Now store the result
YOut[k] = newY;
} // end Workspace2D case
} // end loop over current spectrum
PARALLEL_END_INTERUPT_REGION
} // end loop over spectra
PARALLEL_CHECK_INTERUPT_REGION
if (hasZeroDivision) {
g_log.warning() << "Division by zero in some of the bins.\n";
}
}
std::vector<std::vector<size_t>>
DetectorDiagnostic::makeInstrumentMap(API::MatrixWorkspace_sptr countsWS) {
return {
{boost::counting_iterator<std::size_t>(0),
boost::counting_iterator<std::size_t>(countsWS->getNumberHistograms())}};
}
/** Forms the quadrature phase signal (squashogram)
* @param ws :: [input] workspace containing the measured spectra
* @param phase :: [input] table workspace containing the detector phases
* @param n0 :: [input] vector containing the normalization constants
* @return :: workspace containing the quadrature phase signal
*/
API::MatrixWorkspace_sptr
PhaseQuadMuon::squash(const API::MatrixWorkspace_sptr &ws,
const API::ITableWorkspace_sptr &phase,
const std::vector<double> &n0) {
// Poisson limit: below this number we consider we don't have enough
// statistics
// to apply sqrt(N). This is an arbitrary number used in the original code
// provided by scientists
const double poissonLimit = 30.;
// Muon life time in microseconds
const double muLife = PhysicalConstants::MuonLifetime * 1e6;
const size_t nspec = ws->getNumberHistograms();
if (n0.size() != nspec) {
throw std::invalid_argument("Invalid normalization constants");
}
auto names = phase->getColumnNames();
for (auto &name : names) {
std::transform(name.begin(), name.end(), name.begin(), ::tolower);
}
auto phaseIndex = findName(phaseNames, names);
auto asymmetryIndex = findName(asymmNames, names);
// Get the maximum asymmetry
double maxAsym = 0.;
for (size_t h = 0; h < nspec; h++) {
if (phase->Double(h, asymmetryIndex) > maxAsym &&
phase->Double(h, asymmetryIndex) != ASYMM_ERROR) {
maxAsym = phase->Double(h, asymmetryIndex);
}
}
if (maxAsym == 0.0) {
throw std::invalid_argument("Invalid detector asymmetries");
}
std::vector<bool> emptySpectrum;
emptySpectrum.reserve(nspec);
std::vector<double> aj, bj;
{
// Calculate coefficients aj, bj
double sxx = 0.;
double syy = 0.;
double sxy = 0.;
for (size_t h = 0; h < nspec; h++) {
emptySpectrum.push_back(
std::all_of(ws->y(h).begin(), ws->y(h).end(),
[](double value) { return value == 0.; }));
if (!emptySpectrum[h]) {
const double asym = phase->Double(h, asymmetryIndex) / maxAsym;
const double phi = phase->Double(h, phaseIndex);
const double X = n0[h] * asym * cos(phi);
const double Y = n0[h] * asym * sin(phi);
sxx += X * X;
syy += Y * Y;
sxy += X * Y;
}
}
const double lam1 = 2 * syy / (sxx * syy - sxy * sxy);
const double mu1 = 2 * sxy / (sxy * sxy - sxx * syy);
const double lam2 = 2 * sxy / (sxy * sxy - sxx * syy);
const double mu2 = 2 * sxx / (sxx * syy - sxy * sxy);
for (size_t h = 0; h < nspec; h++) {
if (emptySpectrum[h]) {
aj.push_back(0.0);
bj.push_back(0.0);
} else {
const double asym = phase->Double(h, asymmetryIndex) / maxAsym;
const double phi = phase->Double(h, phaseIndex);
const double X = n0[h] * asym * cos(phi);
const double Y = n0[h] * asym * sin(phi);
aj.push_back((lam1 * X + mu1 * Y) * 0.5);
bj.push_back((lam2 * X + mu2 * Y) * 0.5);
}
}
}
const size_t npoints = ws->blocksize();
// Create and populate output workspace
API::MatrixWorkspace_sptr ows =
API::WorkspaceFactory::Instance().create(ws, 2, npoints + 1, npoints);
// X
ows->setSharedX(0, ws->sharedX(0));
ows->setSharedX(1, ws->sharedX(0));
// Phase quadrature
auto &realY = ows->mutableY(0);
auto &imagY = ows->mutableY(1);
auto &realE = ows->mutableE(0);
auto &imagE = ows->mutableE(1);
const auto xPointData = ws->histogram(0).points();
// First X value
const double X0 = xPointData.front();
// calculate exponential decay outside of the loop
std::vector<double> expDecay = xPointData.rawData();
std::transform(expDecay.begin(), expDecay.end(), expDecay.begin(),
[X0, muLife](double x) { return exp(-(x - X0) / muLife); });
for (size_t i = 0; i < npoints; i++) {
for (size_t h = 0; h < nspec; h++) {
if (!emptySpectrum[h]) {
// (X,Y,E) with exponential decay removed
const double X = ws->x(h)[i];
const double exponential = n0[h] * exp(-(X - X0) / muLife);
const double Y = ws->y(h)[i] - exponential;
const double E =
(ws->y(h)[i] > poissonLimit) ? ws->e(h)[i] : sqrt(exponential);
realY[i] += aj[h] * Y;
imagY[i] += bj[h] * Y;
realE[i] += aj[h] * aj[h] * E * E;
imagE[i] += bj[h] * bj[h] * E * E;
}
}
realE[i] = sqrt(realE[i]);
imagE[i] = sqrt(imagE[i]);
// Regain exponential decay
realY[i] /= expDecay[i];
imagY[i] /= expDecay[i];
realE[i] /= expDecay[i];
imagE[i] /= expDecay[i];
}
// New Y axis label
ows->setYUnit("Asymmetry");
return ows;
}
/** Fits each spectrum in the workspace to f(x) = A * sin( w * x + p)
* @param ws :: [input] The workspace to fit
* @param freq :: [input] Hint for the frequency (w)
* @param groupName :: [input] The name of the output workspace group
* @param resTab :: [output] Table workspace storing the asymmetries and phases
* @param resGroup :: [output] Workspace group storing the fitting results
*/
void CalMuonDetectorPhases::fitWorkspace(const API::MatrixWorkspace_sptr &ws,
double freq, std::string groupName,
API::ITableWorkspace_sptr resTab,
API::WorkspaceGroup_sptr &resGroup) {
int nhist = static_cast<int>(ws->getNumberHistograms());
// Create the fitting function f(x) = A * sin ( w * x + p )
// The same function and initial parameters are used for each fit
std::string funcStr = createFittingFunction(freq, true);
// Set up results table
resTab->addColumn("int", "Spectrum number");
resTab->addColumn("double", "Asymmetry");
resTab->addColumn("double", "Phase");
const auto &indexInfo = ws->indexInfo();
// Loop through fitting all spectra individually
const static std::string success = "success";
for (int wsIndex = 0; wsIndex < nhist; wsIndex++) {
reportProgress(wsIndex, nhist);
const auto &yValues = ws->y(wsIndex);
auto emptySpectrum = std::all_of(yValues.begin(), yValues.end(),
[](double value) { return value == 0.; });
if (emptySpectrum) {
g_log.warning("Spectrum " + std::to_string(wsIndex) + " is empty");
TableWorkspace_sptr tab = boost::make_shared<TableWorkspace>();
tab->addColumn("str", "Name");
tab->addColumn("double", "Value");
tab->addColumn("double", "Error");
for (int j = 0; j < 4; j++) {
API::TableRow row = tab->appendRow();
if (j == PHASE_ROW) {
row << "dummy" << 0.0 << 0.0;
} else {
row << "dummy" << ASYMM_ERROR << 0.0;
}
}
extractDetectorInfo(*tab, *resTab, indexInfo.spectrumNumber(wsIndex));
} else {
auto fit = createChildAlgorithm("Fit");
fit->initialize();
fit->setPropertyValue("Function", funcStr);
fit->setProperty("InputWorkspace", ws);
fit->setProperty("WorkspaceIndex", wsIndex);
fit->setProperty("CreateOutput", true);
fit->setPropertyValue("Output", groupName);
fit->execute();
std::string status = fit->getProperty("OutputStatus");
if (!fit->isExecuted()) {
std::ostringstream error;
error << "Fit failed for spectrum at workspace index " << wsIndex;
error << ": " << status;
throw std::runtime_error(error.str());
} else if (status != success) {
g_log.warning("Fit failed for spectrum at workspace index " +
std::to_string(wsIndex) + ": " + status);
}
API::MatrixWorkspace_sptr fitOut = fit->getProperty("OutputWorkspace");
resGroup->addWorkspace(fitOut);
API::ITableWorkspace_sptr tab = fit->getProperty("OutputParameters");
// Now we have our fitting results stored in tab
// but we need to extract the relevant information, i.e.
// the detector phases (parameter 'p') and asymmetries ('A')
extractDetectorInfo(*tab, *resTab, indexInfo.spectrumNumber(wsIndex));
}
}
}
API::MatrixWorkspace_sptr
CreateFloodWorkspace::removeBackground(API::MatrixWorkspace_sptr ws) {
g_log.information() << "Remove background "
<< getPropertyValue(Prop::BACKGROUND) << '\n';
auto fitWS = transpose(ws);
auto const &x = fitWS->x(0);
// Define the fitting interval
double startX = getProperty(Prop::START_X);
double endX = getProperty(Prop::END_X);
std::vector<double> excludeFromFit;
if (isDefault(Prop::START_X)) {
startX = x.front();
} else {
excludeFromFit.push_back(x.front());
excludeFromFit.push_back(startX);
}
if (isDefault(Prop::END_X)) {
endX = x.back();
} else {
excludeFromFit.push_back(endX);
excludeFromFit.push_back(x.back());
}
// Exclude any bad detectors.
for (auto i : m_excludedSpectra) {
excludeFromFit.push_back(i);
excludeFromFit.push_back(i);
}
std::string const function = getBackgroundFunction();
// Fit the data to determine unwanted background
auto alg = createChildAlgorithm("Fit", 0.9, 0.99);
alg->setProperty("Function", function);
alg->setProperty("InputWorkspace", fitWS);
alg->setProperty("WorkspaceIndex", 0);
if (!excludeFromFit.empty()) {
alg->setProperty("Exclude", excludeFromFit);
}
alg->setProperty("Output", "fit");
alg->execute();
IFunction_sptr func = alg->getProperty("Function");
g_log.information() << "Background function parameters:\n";
for (size_t i = 0; i < func->nParams(); ++i) {
g_log.information() << " " << func->parameterName(i) << ": "
<< func->getParameter(i) << '\n';
}
// Divide the workspace by the fitted curve to remove the background
// and scale to values around 1
MatrixWorkspace_sptr bkgWS = alg->getProperty("OutputWorkspace");
auto const &bkg = bkgWS->y(1);
auto const nHisto = static_cast<int>(ws->getNumberHistograms());
PARALLEL_FOR_IF(Kernel::threadSafe(*ws, *bkgWS))
for (int i = 0; i < nHisto; ++i) {
PARALLEL_START_INTERUPT_REGION
auto const xVal = x[i];
if (isExcludedSpectrum(xVal)) {
ws->mutableY(i)[0] = VERY_BIG_VALUE;
ws->mutableE(i)[0] = 0.0;
} else if (xVal >= startX && xVal <= endX) {
auto const background = bkg[i];
if (background <= 0.0) {
throw std::runtime_error(
"Background is expected to be positive, found value " +
std::to_string(background) + " at spectrum with workspace index " +
std::to_string(i));
}
ws->mutableY(i)[0] /= background;
ws->mutableE(i)[0] /= background;
} else {
ws->mutableY(i)[0] = 1.0;
ws->mutableE(i)[0] = 0.0;
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Remove the logs
ws->setSharedRun(make_cow<Run>());
return ws;
}
/** Forms the quadrature phase signal (squashogram)
* @param ws :: [input] workspace containing the measured spectra
* @param phase :: [input] table workspace containing the detector phases
* @param n0 :: [input] vector containing the normalization constants
* @return :: workspace containing the quadrature phase signal
*/
API::MatrixWorkspace_sptr
PhaseQuadMuon::squash(const API::MatrixWorkspace_sptr &ws,
const API::ITableWorkspace_sptr &phase,
const std::vector<double> &n0) {
// Poisson limit: below this number we consider we don't have enough
// statistics
// to apply sqrt(N). This is an arbitrary number used in the original code
// provided by scientists
double poissonLimit = 30.;
size_t nspec = ws->getNumberHistograms();
size_t npoints = ws->blocksize();
// Muon life time in microseconds
double muLife = PhysicalConstants::MuonLifetime * 1e6;
if (n0.size() != nspec) {
throw std::invalid_argument("Invalid normalization constants");
}
// Get the maximum asymmetry
double maxAsym = 0.;
for (size_t h = 0; h < nspec; h++) {
if (phase->Double(h, 1) > maxAsym) {
maxAsym = phase->Double(h, 1);
}
}
if (maxAsym == 0.0) {
throw std::invalid_argument("Invalid detector asymmetries");
}
std::vector<double> aj, bj;
{
// Calculate coefficients aj, bj
double sxx = 0;
double syy = 0;
double sxy = 0;
for (size_t h = 0; h < nspec; h++) {
double asym = phase->Double(h, 1) / maxAsym;
double phi = phase->Double(h, 2);
double X = n0[h] * asym * cos(phi);
double Y = n0[h] * asym * sin(phi);
sxx += X * X;
syy += Y * Y;
sxy += X * Y;
}
double lam1 = 2 * syy / (sxx * syy - sxy * sxy);
double mu1 = 2 * sxy / (sxy * sxy - sxx * syy);
double lam2 = 2 * sxy / (sxy * sxy - sxx * syy);
double mu2 = 2 * sxx / (sxx * syy - sxy * sxy);
for (size_t h = 0; h < nspec; h++) {
double asym = phase->Double(h, 1) / maxAsym;
double phi = phase->Double(h, 2);
double X = n0[h] * asym * cos(phi);
double Y = n0[h] * asym * sin(phi);
aj.push_back((lam1 * X + mu1 * Y) * 0.5);
bj.push_back((lam2 * X + mu2 * Y) * 0.5);
}
}
// First X value
double X0 = ws->x(0).front();
// Create and populate output workspace
API::MatrixWorkspace_sptr ows = API::WorkspaceFactory::Instance().create(
"Workspace2D", 2, npoints + 1, npoints);
// X
ows->setSharedX(0, ws->sharedX(0));
ows->setSharedX(1, ws->sharedX(0));
// Phase quadrature
auto &realY = ows->mutableY(0);
auto &imagY = ows->mutableY(1);
auto &realE = ows->mutableE(0);
auto &imagE = ows->mutableE(1);
for (size_t i = 0; i < npoints; i++) {
for (size_t h = 0; h < nspec; h++) {
// (X,Y,E) with exponential decay removed
const double X = ws->x(h)[i];
const double Y = ws->y(h)[i] - n0[h] * exp(-(X - X0) / muLife);
const double E = (ws->y(h)[i] > poissonLimit)
? ws->e(h)[i]
: sqrt(n0[h] * exp(-(X - X0) / muLife));
realY[i] += aj[h] * Y;
imagY[i] += bj[h] * Y;
realE[i] += aj[h] * aj[h] * E * E;
imagE[i] += bj[h] * bj[h] * E * E;
}
realE[i] = sqrt(realE[i]);
imagE[i] = sqrt(imagE[i]);
// Regain exponential decay
const double X = ws->getSpectrum(0).x()[i];
const double e = exp(-(X - X0) / muLife);
realY[i] /= e;
imagY[i] /= e;
realE[i] /= e;
imagE[i] /= e;
}
return ows;
}
/**
* Validation of the inputs of the RingProfile algorithm.
*
* Inside this method, the Workspace is considered an instrument based
*instrument. Each spectrum
* has a detector associated which has a position in the 3D space.
*
* The main validation are:
* - the centre of the ring is inside the image it self.
* - The minimum ring is smaller than the limits of the image to allow
*
* @param inputWS: the input workspace
*/
void RingProfile::checkInputsForSpectraWorkspace(
const API::MatrixWorkspace_sptr inputWS) {
try {
// finding the limits of the instrument
double first_x, first_y, first_z;
size_t i = 0;
while (true) {
i++;
if (i >= inputWS->getNumberHistograms())
throw std::invalid_argument(
"Did not find any non monitor detector position");
auto det = inputWS->getDetector(i);
if (det->isMonitor())
continue;
first_x = det->getPos().X();
first_y = det->getPos().Y();
first_z = det->getPos().Z();
break;
}
double last_x, last_y, last_z;
i = inputWS->getNumberHistograms() - 1;
while (true) {
i--;
if (i == 0)
throw std::invalid_argument(
"There is no region defined for the instrument of this workspace");
auto det = inputWS->getDetector(i);
if (det->isMonitor())
continue;
last_x = det->getPos().X();
last_y = det->getPos().Y();
last_z = det->getPos().Z();
break;
}
double xMax, yMax, zMax;
double xMin, yMin, zMin;
xMax = std::max(first_x, last_x);
yMax = std::max(first_y, last_y);
zMax = std::max(first_z, last_z);
xMin = std::min(first_x, last_x);
yMin = std::min(first_y, last_y);
zMin = std::min(first_z, last_z);
std::stringstream limits_s;
limits_s << "([" << xMin << ", " << xMax << "], [" << yMin << ", " << yMax
<< "], [" << zMin << ", " << zMax << "])";
g_log.debug() << "The limits for the instrument is : " << limits_s.str()
<< std::endl;
int xOutside = 0, yOutside = 0, zOutside = 0;
if (centre_x < xMin || centre_x > xMax)
xOutside = 1;
if (centre_y < yMin || centre_y > yMax)
yOutside = 1;
if (centre_z < zMin || centre_z > zMax)
zOutside = 1;
int summed = xOutside + yOutside + zOutside;
// if at least 2 are outside, the centre is considered outside the box.
if (summed >= 2) {
std::stringstream s;
s << "The defined centre (" << centre_x << ", " << centre_y << ", "
<< centre_z
<< ") is outside the limits of the detectors inside this instrument: "
<< limits_s.str();
throw std::invalid_argument(s.str());
}
xOutside = yOutside = zOutside = 0;
if (centre_x - min_radius > xMax || centre_x + min_radius < xMin)
xOutside = 1;
if (centre_y - min_radius > yMax || centre_y + min_radius < yMin)
yOutside = 1;
if (centre_z - min_radius > zMax || centre_z + min_radius < zMin)
zOutside = 1;
summed = xOutside + yOutside + zOutside;
if (summed >= 2) {
std::stringstream s;
s << "The defined minRadius make the inner ring outside the limits of "
"the detectors inside this instrument: " << limits_s.str();
throw std::invalid_argument(s.str());
}
} catch (Kernel::Exception::NotFoundError &) {
throw std::invalid_argument("Invalid input workspace. This workspace does "
"not has detectors to get the positions "
"from. ");
}
}
/** Assuming that the workspace has an instrument associated with it from which
*the pixel positions has to be taken,
* this function extracts the position of the first and last valid pixel
*(detector) and return a list of values
* giving the boundaries of the instrument.
*
* @param inWS Input Workspace
* @return a list of values that defines the limits of the image in this order:
*Xmin, Xmax, Ymin, Ymax, Zmin, Zmax
*/
std::vector<double>
RadiusSum::getBoundariesOfInstrument(API::MatrixWorkspace_sptr inWS) {
// This function is implemented based in the following assumption:
// - The workspace is composed by spectrum with associated spectrum No which
// is associated to one detector or monitor
// - The first spectrum No (non monitor) is associated with one detector
// while the last spectrum No (non monitor)
// is associated with one detector.
// - They are in complete oposite direction.
//
// Consider the following 'image' (where the ID is the number and the
// position is where it is displayed)
//
// 1 2 3
// 4 5 6
// 7 8 9
// 10 11 12
//
// In this image, the assumption is true, because, we can derive the
// boundaries of the image looking just to the
// ids 1 and 12.
//
// But the following image:
//
// 1 2 3 6 5 4
// 6 5 4 1 2 3
// 7 8 9 12 11 10
// 12 11 12 7 8 9
//
// Although valid 'IDF' instrument, fail the assumption, and will return
// wrong values.
// Bear in mind these words if you face problems with the return of the
// boundaries of one instrument
//
double first_x, first_y, first_z;
size_t i = 0;
while (true) {
i++;
if (i >= inWS->getNumberHistograms())
throw std::invalid_argument("Did not find any non monitor detector. "
"Failed to identify the boundaries of this "
"instrument.");
auto det = inWS->getDetector(i);
if (det->isMonitor())
continue;
// get the position of the first valid (non-monitor) detector.
first_x = det->getPos().X();
first_y = det->getPos().Y();
first_z = det->getPos().Z();
break;
}
double last_x, last_y, last_z;
i = inWS->getNumberHistograms() - 1;
while (true) {
i--;
if (i == 0)
throw std::invalid_argument("There is no region defined for the "
"instrument of this workspace. Failed to "
"identify the boundaries of this instrument");
auto det = inWS->getDetector(i);
if (det->isMonitor())
continue;
// get the last valid detector position
last_x = det->getPos().X();
last_y = det->getPos().Y();
last_z = det->getPos().Z();
break;
}
// order the values
double xMax, yMax, zMax;
double xMin, yMin, zMin;
xMax = std::max(first_x, last_x);
yMax = std::max(first_y, last_y);
zMax = std::max(first_z, last_z);
xMin = std::min(first_x, last_x);
yMin = std::min(first_y, last_y);
zMin = std::min(first_z, last_z);
std::vector<double> output(6); // output = {xMin, xMax, yMin, yMax, zMin,
// zMax }; not supported in all compilers
output[0] = xMin;
output[1] = xMax;
output[2] = yMin;
output[3] = yMax;
output[4] = zMin;
output[5] = zMax;
return output;
}
/** 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 grouping file cannot be opened or read successfully
* @throw runtime_error If unable to run one of the sub-algorithms successfully
*/
void DiffractionFocussing::exec()
{
// retrieve the properties
std::string groupingFileName=getProperty("GroupingFileName");
// Get the input workspace
MatrixWorkspace_sptr inputW = getProperty("InputWorkspace");
bool dist = inputW->isDistribution();
//do this first to check that a valid file is available before doing any work
std::multimap<int64_t,int64_t> detectorGroups;// <group, UDET>
if (!readGroupingFile(groupingFileName, detectorGroups))
{
throw Exception::FileError("Error reading .cal file",groupingFileName);
}
//Convert to d-spacing units
API::MatrixWorkspace_sptr tmpW = convertUnitsToDSpacing(inputW);
//Rebin to a common set of bins
RebinWorkspace(tmpW);
std::set<int64_t> groupNumbers;
for(std::multimap<int64_t,int64_t>::const_iterator d = detectorGroups.begin();d!=detectorGroups.end();d++)
{
if (groupNumbers.find(d->first) == groupNumbers.end())
{
groupNumbers.insert(d->first);
}
}
int iprogress = 0;
int iprogress_count = static_cast<int>(groupNumbers.size());
int iprogress_step = iprogress_count / 100;
if (iprogress_step == 0) iprogress_step = 1;
std::vector<int64_t> resultIndeces;
for(std::set<int64_t>::const_iterator g = groupNumbers.begin();g!=groupNumbers.end();g++)
{
if (iprogress++ % iprogress_step == 0)
{
progress(0.68 + double(iprogress)/iprogress_count/3);
}
std::multimap<int64_t,int64_t>::const_iterator from = detectorGroups.lower_bound(*g);
std::multimap<int64_t,int64_t>::const_iterator to = detectorGroups.upper_bound(*g);
std::vector<detid_t> detectorList;
for(std::multimap<int64_t,int64_t>::const_iterator d = from;d!=to;d++)
detectorList.push_back(static_cast<detid_t>(d->second));
// Want version 1 of GroupDetectors here
API::IAlgorithm_sptr childAlg = createSubAlgorithm("GroupDetectors",-1.0,-1.0,true,1);
childAlg->setProperty("Workspace", tmpW);
childAlg->setProperty< std::vector<detid_t> >("DetectorList",detectorList);
childAlg->executeAsSubAlg();
try
{
// get the index of the combined spectrum
int ri = childAlg->getProperty("ResultIndex");
if (ri >= 0)
{
resultIndeces.push_back(ri);
}
}
catch(...)
{
throw std::runtime_error("Unable to get Properties from GroupDetectors sub-algorithm");
}
}
// Discard left-over spectra, but print warning message giving number discarded
int discarded = 0;
const int64_t oldHistNumber = tmpW->getNumberHistograms();
API::Axis *spectraAxis = tmpW->getAxis(1);
for(int64_t i=0; i < oldHistNumber; i++)
if ( spectraAxis->spectraNo(i) >= 0 && find(resultIndeces.begin(),resultIndeces.end(),i) == resultIndeces.end())
{
++discarded;
}
g_log.warning() << "Discarded " << discarded << " spectra that were not assigned to any group" << std::endl;
// Running GroupDetectors leads to a load of redundant spectra
// Create a new workspace that's the right size for the meaningful spectra and copy them in
int64_t newSize = tmpW->blocksize();
API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(tmpW,resultIndeces.size(),newSize+1,newSize);
// Copy units
outputW->getAxis(0)->unit() = tmpW->getAxis(0)->unit();
outputW->getAxis(1)->unit() = tmpW->getAxis(1)->unit();
API::Axis *spectraAxisNew = outputW->getAxis(1);
for(int64_t hist=0; hist < static_cast<int64_t>(resultIndeces.size()); hist++)
{
int64_t i = resultIndeces[hist];
double spNo = static_cast<double>(spectraAxis->spectraNo(i));
MantidVec &tmpE = tmpW->dataE(i);
MantidVec &outE = outputW->dataE(hist);
MantidVec &tmpY = tmpW->dataY(i);
MantidVec &outY = outputW->dataY(hist);
MantidVec &tmpX = tmpW->dataX(i);
MantidVec &outX = outputW->dataX(hist);
outE.assign(tmpE.begin(),tmpE.end());
outY.assign(tmpY.begin(),tmpY.end());
outX.assign(tmpX.begin(),tmpX.end());
spectraAxisNew->setValue(hist,spNo);
spectraAxis->setValue(i,-1);
}
progress(1.);
outputW->isDistribution(dist);
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputW);
return;
}
