void LoadDaveGrp::getData(std::vector<MantidVec *> &data,
std::vector<MantidVec *> &errs)
{
double data_val = 0.0;
double err_val = 0.0;
API::Progress progress(this, 0.0, 1.0, this->nGroups);
for(int j = 0; j < this->nGroups; j++)
{
// Skip the group comment line
this->readLine();
// Read the data block
MantidVec *d = new MantidVec();
MantidVec *e = new MantidVec();
for(std::size_t k = 0; k < static_cast<std::size_t>(this->xLength); k++)
{
this->readLine();
std::istringstream is(this->line);
is >> data_val >> err_val;
d->push_back(data_val);
e->push_back(err_val);
}
data.push_back(d);
errs.push_back(e);
progress.report();
}
}
/**
* Find the overlap of the inputWS with the given polygon
* @param oldAxis1 :: Axis 1 bin boundaries from the input grid
* @param oldAxis2 :: Axis 2 bin boundaries from the input grid
* @param newPoly :: The new polygon to test
* @returns A list of intersection locations with weights of the overlap
*/
std::vector<Rebin2D::BinWithWeight>
Rebin2D::findIntersections(const MantidVec & oldAxis1, const MantidVec & oldAxis2,
const Geometry::ConvexPolygon & newPoly) const
{
std::vector<BinWithWeight> overlaps;
overlaps.reserve(5); // Have a guess at a posible limit
const size_t nxpoints(oldAxis1.size()-1), nypoints(oldAxis2.size()-1);
const double yn_lo(newPoly[0].Y()), yn_hi(newPoly[1].Y());
const double xn_lo(newPoly[0].X()), xn_hi(newPoly[2].X());
for(size_t i = 0; i < nypoints; ++i)
{
const double yo_lo(oldAxis2[i]), yo_hi(oldAxis2[i+1]);
// Check if there is a possibility of overlap
if( yo_hi < yn_lo || yo_lo > yn_hi ) continue;
for(size_t j = 0; j < nxpoints; ++j)
{
const double xo_lo(oldAxis1[j]), xo_hi(oldAxis1[j+1]);
// Check if there is a possibility of overlap
if( xo_hi < xn_lo || xo_lo > xn_hi ) continue;
ConvexPolygon oldPoly(xo_lo, xo_hi, yo_lo, yo_hi);
try
{
ConvexPolygon overlap = intersectionByLaszlo(newPoly, oldPoly);
overlaps.push_back(BinWithWeight(i,j,overlap.area()/oldPoly.area()));
}
catch(Geometry::NoIntersectionException &)
{}
}
}
return overlaps;
}
/** This is a slightly "clever" method as it makes some guesses about where is best
* to look for the right Q bin based on the fact that the input Qs (calcualted from wavelengths) tend
* to go down while the output Qs are always in accending order
* @param[in] OutQs the array of output Q bin boundaries, this finds the bin that contains the QIn value
* @param[in] QToFind the Q value to find the correct bin for
* @param[in, out] loc points to the bin boundary (in the OutQs array) whos Q is higher than QToFind and higher by the smallest amount. Algorithm starts by checking the value of loc passed and then all the bins _downwards_ through the array
*/
void Q1D2::getQBinPlus1(const MantidVec & OutQs, const double QToFind, MantidVec::const_iterator & loc) const
{
if ( loc != OutQs.end() )
{
while ( loc != OutQs.begin() )
{
if ( (QToFind >= *(loc-1)) && (QToFind < *loc) )
{
return;
}
--loc;
}
if ( QToFind < *loc )
{
//QToFind is outside the array leave loc == OutQs.begin()
return;
}
}
else //loc == OutQs.end()
{
if ( OutQs.empty() || QToFind > *(loc-1) )
{
//outside the array leave loc == OutQs.end()
return;
}
}
// we are lost, normally the order of the Q values means we only get here on the first iteration. It's slow
loc = std::lower_bound(OutQs.begin(), OutQs.end(), QToFind);
}
/** Deals with the (rare) case where the flightpath is longer than the reference
* Note that in this case both T1 & T2 will be greater than Tmax
*/
std::pair<int, int>
UnwrapMonitor::handleFrameOverlapped(const MantidVec &xdata, const double &Ld,
std::vector<double> &tempX) {
// Calculate the interval to exclude
const double Dt = (m_Tmax - m_Tmin) * (1 - (m_LRef / Ld));
// This gives us new minimum & maximum tof values
const double minT = m_Tmin + Dt;
const double maxT = m_Tmax - Dt;
// Can have situation where Ld is so much larger than Lref that everything
// would be excluded.
// This is an invalid input - warn and leave spectrum unwrapped
if (minT > maxT) {
g_log.warning() << "Invalid input, Ld (" << Ld << ") >> Lref (" << m_LRef
<< "). Current spectrum will not be unwrapped.\n";
return std::make_pair(0, static_cast<int>(xdata.size() - 1));
}
int min = 0, max = static_cast<int>(xdata.size());
for (unsigned int j = 0; j < m_XSize; ++j) {
const double T = xdata[j];
if (T < minT) {
min = j + 1;
tempX.erase(tempX.begin());
} else if (T > maxT) {
tempX.erase(tempX.end() - (max - j), tempX.end());
max = j - 1;
break;
}
}
return std::make_pair(min, max);
}
/** Calculates the rebin parameters in the case where the range of the second
* workspace is
* entirely within that of the first workspace.
* 'Inclusion' is used in the sense of a set being included in anothre.
* @param X1 :: The bin boundaries from the first workspace
* @param i :: Indicates the index in X1 immediately before the overlap
* region starts
* @param X2 :: The bin boundaries from the second workspace
* @param params :: A reference to the vector of rebinning parameters
*/
void MergeRuns::inclusionParams(const MantidVec &X1, int64_t &i,
const MantidVec &X2,
std::vector<double> ¶ms) const {
// First calculate the number of bins in each workspace that are in the
// overlap region
int64_t overlapbins1, overlapbins2;
for (overlapbins1 = 1; X1[i + overlapbins1] < X2.back(); ++overlapbins1) {
}
//++overlapbins1;
overlapbins2 = X2.size() - 1;
// In the overlap region, we want to use whichever one has the larger bins (on
// average)
if (overlapbins1 + 1 <= overlapbins2) {
// In the case where the first workspace has larger bins it's easy
// - just add the rest of X1's bins
for (; i < static_cast<int64_t>(X1.size()); ++i) {
params.push_back(X1[i] - X1[i - 1]);
params.push_back(X1[i]);
}
} else {
// In this case we want all of X2's bins first (without the first and last
// boundaries)
for (size_t j = 1; j < X2.size() - 1; ++j) {
params.push_back(X2[j] - params.back());
params.push_back(X2[j]);
}
// And now those from X1 that lie above the overlap region
i += overlapbins1;
for (; i < static_cast<int64_t>(X1.size()); ++i) {
params.push_back(X1[i] - params.back());
params.push_back(X1[i]);
}
}
}
/** Calculates the rebin parameters in the case where the bins of the two
* workspaces intersect.
* 'Intersect' is used in the sense of two intersecting sets.
* @param X1 :: The bin boundaries from the first workspace
* @param i :: Indicates the index in X1 immediately before the overlap
* region starts
* @param X2 :: The bin boundaries from the second workspace
* @param params :: A reference to the vector of rebinning parameters
*/
void MergeRuns::intersectionParams(const MantidVec &X1, int64_t &i,
const MantidVec &X2,
std::vector<double> ¶ms) const {
// First calculate the number of bins in each workspace that are in the
// overlap region
int64_t overlapbins1, overlapbins2;
overlapbins1 = X1.size() - i;
for (overlapbins2 = 0; X2[overlapbins2] < X1.back(); ++overlapbins2) {
}
// We want to use whichever one has the larger bins (on average)
if (overlapbins1 < overlapbins2) {
// In this case we want the rest of the bins from the first workspace.....
for (; i < static_cast<int64_t>(X1.size()); ++i) {
params.push_back(X1[i] - X1[i - 1]);
params.push_back(X1[i]);
}
// Now remove the last bin & boundary
params.pop_back();
params.pop_back();
// ....and then the non-overlap ones from the second workspace
for (size_t j = overlapbins2; j < X2.size(); ++j) {
params.push_back(X2[j] - params.back());
params.push_back(X2[j]);
}
} else {
// In this case we just have to add all the bins from the second workspace
for (size_t j = 1; j < X2.size(); ++j) {
params.push_back(X2[j] - params.back());
params.push_back(X2[j]);
}
}
}
MantidVec
operator()(const MantidVec &_Left,
const MantidVec &_Right) const { // apply operator+ to operands
MantidVec v(_Left.size());
std::transform(_Left.begin(), _Left.end(), _Right.begin(), v.begin(),
SumGaussError<double>());
return (v);
}
/**
* load vectors onto a Workspace2D with 3 bins (the three components of the
* vectors)
* dataX for the origin of the vector (assumed (0,0,0) )
* dataY for the tip of the vector
* dataE is assumed (0,0,0), no errors
* @param h5file file identifier
* @param gws pointer to WorkspaceGroup being filled
* @param sorting_indexes permutation of qvmod indexes to render it in
* increasing order of momemtum transfer
*/
const MantidVec LoadSassena::loadQvectors(const hid_t &h5file,
API::WorkspaceGroup_sptr gws,
std::vector<int> &sorting_indexes) {
const std::string gwsName = this->getPropertyValue("OutputWorkspace");
const std::string setName("qvectors");
hsize_t dims[3];
if (dataSetInfo(h5file, setName, dims) < 0) {
throw Kernel::Exception::FileError(
"Unable to read " + setName + " dataset info:", m_filename);
}
int nq = static_cast<int>(dims[0]); // number of q-vectors
double *buf = new double[nq * 3];
this->dataSetDouble(h5file, "qvectors", buf);
MantidVec qvmod; // store the modulus of the vector
double *curr = buf;
for (int iq = 0; iq < nq; iq++) {
qvmod.push_back(
sqrt(curr[0] * curr[0] + curr[1] * curr[1] + curr[2] * curr[2]));
curr += 3;
}
if (getProperty("SortByQVectors")) {
std::vector<mypair> qvmodpair;
for (int iq = 0; iq < nq; iq++)
qvmodpair.push_back(mypair(qvmod[iq], iq));
std::sort(qvmodpair.begin(), qvmodpair.end(), compare);
for (int iq = 0; iq < nq; iq++)
sorting_indexes.push_back(qvmodpair[iq].second);
std::sort(qvmod.begin(), qvmod.end());
} else
for (int iq = 0; iq < nq; iq++)
sorting_indexes.push_back(iq);
DataObjects::Workspace2D_sptr ws =
boost::dynamic_pointer_cast<DataObjects::Workspace2D>(
API::WorkspaceFactory::Instance().create("Workspace2D", nq, 3, 3));
std::string wsName = gwsName + std::string("_") + setName;
ws->setTitle(wsName);
for (int iq = 0; iq < nq; iq++) {
MantidVec &Y = ws->dataY(iq);
const int index = sorting_indexes[iq];
curr = buf + 3 * index;
Y.assign(curr, curr + 3);
}
delete[] buf;
ws->getAxis(0)->unit() = Kernel::UnitFactory::Instance().create(
"MomentumTransfer"); // Set the Units
this->registerWorkspace(
gws, wsName, ws, "X-axis: origin of Q-vectors; Y-axis: tip of Q-vectors");
return qvmod;
}
/** Calculates the overall normalization factor.
* This multiplies result by (bin width * sum of monitor counts) / total frame
* width.
* @param X The X vector
* @param Y The data vector
* @param E The error vector
*/
void NormaliseToMonitor::normalisationFactor(const MantidVec &X, MantidVec *Y,
MantidVec *E) {
const double monitorSum = std::accumulate(Y->begin(), Y->end(), 0.0);
const double range = X.back() - X.front();
MantidVec::size_type specLength = Y->size();
for (MantidVec::size_type j = 0; j < specLength; ++j) {
const double factor = range / ((X[j + 1] - X[j]) * monitorSum);
(*Y)[j] *= factor;
(*E)[j] *= factor;
}
}
/// Returns vector with x-values of spectrum if MatrixWorkspace does not contain
/// histogram data.
MantidVec FunctionDomain1DSpectrumCreator::getVectorNonHistogram() const {
const MantidVec wsXData = m_matrixWorkspace->readX(m_workspaceIndex);
size_t wsXSize = wsXData.size();
if (wsXSize < 1) {
throw std::invalid_argument(
"Workspace2D with less than one x-value cannot be processed.");
}
return MantidVec(wsXData);
}
/**
* @param tofMin Return value for minimum tof to be masked
* @param tofMax Return value for maximum tof to be masked
* @param tofPeak time-of-flight of the single crystal peak
* @param tof tof-of-flight axis for the spectrum where the peak supposedly
* exists
*/
void MaskPeaksWorkspace::getTofRange(double &tofMin, double &tofMax,
const double tofPeak,
const MantidVec &tof) {
tofMin = tof.front();
tofMax = tof.back() - 1;
if (!isEmpty(m_tofMin)) {
tofMin = tofPeak + m_tofMin;
}
if (!isEmpty(m_tofMax)) {
tofMax = tofPeak + m_tofMax;
}
}
int UnwrapSNS::unwrapX(const MantidVec &datain, MantidVec &dataout,
const double &Ld) {
MantidVec tempX_L; // lower half - to be frame wrapped
tempX_L.reserve(m_XSize);
tempX_L.clear();
MantidVec tempX_U; // upper half - to not be frame wrapped
tempX_U.reserve(m_XSize);
tempX_U.clear();
double filterVal = m_Tmin * Ld / m_LRef;
dataout.clear();
int specialBin = 0;
for (int bin = 0; bin < m_XSize; ++bin) {
// This is the time-of-flight value under consideration in the current
// iteration of the loop
const double tof = datain[bin];
if (tof < filterVal) {
tempX_L.push_back(tof + m_frameWidth);
// Record the bins that fall in this range for copying over the data &
// errors
if (specialBin < bin)
specialBin = bin;
} else {
tempX_U.push_back(tof);
}
} // loop over X values
// now put it back into the vector supplied
dataout.clear();
dataout.insert(dataout.begin(), tempX_U.begin(), tempX_U.end());
dataout.insert(dataout.end(), tempX_L.begin(), tempX_L.end());
assert(datain.size() == dataout.size());
return specialBin;
}
/**
* Get the start and end of the x-interval.
* @param X :: The x-vector of a spectrum.
* @param isHistogram :: Is the x-vector comming form a histogram? If it's true
* the bin
* centres are used.
* @return :: A pair of start x and end x.
*/
std::pair<double, double> WienerSmooth::getStartEnd(const MantidVec &X,
bool isHistogram) const {
const size_t n = X.size();
if (n < 3) {
// 3 is the smallest number for this method to work without breaking
throw std::runtime_error(
"Number of bins/data points cannot be smaller than 3.");
}
if (isHistogram) {
return std::make_pair((X[0] + X[1]) / 2, (X[n - 1] + X[n - 2]) / 2);
}
// else
return std::make_pair(X.front(), X.back());
}
/// Retrieve and check the Start/EndX parameters, if set
void Linear::setRange(const MantidVec& X, const MantidVec& Y)
{
//read in the values that the user selected
double startX = getProperty("StartX");
double endX = getProperty("EndX");
//If the user didn't a start default to the start of the data
if ( isEmpty(startX) ) startX = X.front();
//the default for the end is the end of the data
if ( isEmpty(endX) ) endX = X.back();
// Check the validity of startX
if ( startX < X.front() )
{
g_log.warning("StartX out of range! Set to start of frame.");
startX = X.front();
}
// Now get the corresponding bin boundary that comes before (or coincides with) this value
for (m_minX = 0; X[m_minX+1] < startX; ++m_minX) {}
// Check the validity of endX and get the bin boundary that come after (or coincides with) it
if ( endX >= X.back() || endX < startX )
{
if ( endX != X.back() )
{
g_log.warning("EndX out of range! Set to end of frame");
endX = X.back();
}
m_maxX = static_cast<int>(Y.size());
}
else
{
for (m_maxX = m_minX; X[m_maxX] < endX; ++m_maxX) {}
}
}
/** Execute the algorithm.
*/
void WeightedMeanOfWorkspace::exec()
{
MatrixWorkspace_sptr inputWS = this->getProperty("InputWorkspace");
// Check if it is an event workspace
EventWorkspace_const_sptr eventW = boost::dynamic_pointer_cast<const EventWorkspace>(inputWS);
if (eventW != NULL)
{
throw std::runtime_error("WeightedMeanOfWorkspace cannot handle EventWorkspaces!");
}
// Create the output workspace
MatrixWorkspace_sptr singleValued = WorkspaceFactory::Instance().create("WorkspaceSingleValue", 1, 1, 1);
// Calculate weighted mean
std::size_t numHists = inputWS->getNumberHistograms();
double averageValue = 0.0;
double weightSum = 0.0;
for (std::size_t i = 0; i < numHists; ++i)
{
try
{
IDetector_const_sptr det = inputWS->getDetector(i);
if( det->isMonitor() || det->isMasked() )
{
continue;
}
}
catch (...)
{
// Swallow these if no instrument is found
;
}
MantidVec y = inputWS->dataY(i);
MantidVec e = inputWS->dataE(i);
double weight = 0.0;
for (std::size_t j = 0; j < y.size(); ++j)
{
if (!boost::math::isnan(y[j]) && !boost::math::isinf(y[j]) &&
!boost::math::isnan(e[j]) && !boost::math::isinf(e[j]))
{
weight = 1.0 / (e[j] * e[j]);
averageValue += (y[j] * weight);
weightSum += weight;
}
}
}
singleValued->dataX(0)[0] = 0.0;
singleValued->dataY(0)[0] = averageValue / weightSum;
singleValued->dataE(0)[0] = std::sqrt(weightSum);
this->setProperty("OutputWorkspace", singleValued);
}
/**
* Create workspace to store the structure factor.
* First spectrum is the real part, second spectrum is the imaginary part
* X values are the modulus of the Q-vectors
* @param h5file file identifier
* @param gws pointer to WorkspaceGroup being filled
* @param setName string name of dataset
* @param qvmod vector of Q-vectors' moduli
* @param sorting_indexes permutation of qvmod indexes to render it in increasing order of momemtum transfer
*/
void LoadSassena::loadFQ(const hid_t& h5file, API::WorkspaceGroup_sptr gws, const std::string setName, const MantidVec &qvmod, const std::vector<int> &sorting_indexes)
{
const std::string gwsName = this->getPropertyValue("OutputWorkspace");
int nq = static_cast<int>( qvmod.size() ); //number of q-vectors
DataObjects::Workspace2D_sptr ws = boost::dynamic_pointer_cast<DataObjects::Workspace2D>(API::WorkspaceFactory::Instance().create("Workspace2D", 2, nq, nq));
const std::string wsName = gwsName + std::string("_") + setName;
ws->setTitle(wsName);
double* buf = new double[nq*2];
this->dataSetDouble(h5file,setName,buf);
MantidVec& re = ws->dataY(0); // store the real part
ws->dataX(0) = qvmod; //X-axis values are the modulus of the q vector
MantidVec& im = ws->dataY(1); // store the imaginary part
ws->dataX(1) = qvmod;
double *curr = buf;
for(int iq=0; iq<nq; iq++){
const int index=sorting_indexes[iq];
re[index]=curr[0];
im[index]=curr[1];
curr+=2;
}
delete[] buf;
// Set the Units
ws->getAxis(0)->unit() = Kernel::UnitFactory::Instance().create("MomentumTransfer");
this->registerWorkspace(gws,wsName,ws, "X-axis: Q-vector modulus; Y-axis: intermediate structure factor");
}
void Divide::performBinaryOperation(const MantidVec &lhsX,
const MantidVec &lhsY,
const MantidVec &lhsE,
const MantidVec &rhsY,
const MantidVec &rhsE, MantidVec &YOut,
MantidVec &EOut) {
(void)lhsX; // Avoid compiler warning
const int bins = static_cast<int>(lhsE.size());
for (int j = 0; j < bins; ++j) {
// Get references to the input Y's
const double leftY = lhsY[j];
const double rightY = rhsY[j];
// error dividing two uncorrelated numbers, re-arrange so that you don't
// get infinity if leftY==0 (when rightY=0 the Y value and the result will
// both be infinity)
// (Sa/a)2 + (Sb/b)2 = (Sc/c)2
// (Sa c/a)2 + (Sb c/b)2 = (Sc)2
// = (Sa 1/b)2 + (Sb (a/b2))2
// (Sc)2 = (1/b)2( (Sa)2 + (Sb a/b)2 )
EOut[j] =
sqrt(pow(lhsE[j], 2) + pow(leftY * rhsE[j] / rightY, 2)) / fabs(rightY);
// Copy the result last in case one of the input workspaces is also any
// output
YOut[j] = leftY / rightY;
;
}
}
void WeightedMean::performBinaryOperation(const MantidVec& lhsX, const MantidVec& lhsY, const MantidVec& lhsE,
const double rhsY, const double rhsE, MantidVec& YOut, MantidVec& EOut)
{
UNUSED_ARG(lhsX);
assert( lhsX.size() == 1 );
// If we get here we've got two single column workspaces so it's easy.
if (lhsE[0] > 0.0 && rhsE > 0.0)
{
const double err1 = lhsE[0]*lhsE[0];
const double err2 = rhsE*rhsE;
YOut[0] = (lhsY[0]/err1)+(rhsY/err2);
EOut[0] = (err1*err2)/(err1+err2);
YOut[0] *= EOut[0];
EOut[0] = sqrt(EOut[0]);
}
else if (lhsE[0] > 0.0 && rhsE <= 0.0)
{
YOut[0] = lhsY[0];
EOut[0] = lhsE[0];
}
else if (lhsE[0] <= 0.0 && rhsE > 0.0)
{
YOut[0] = rhsY;
EOut[0] = rhsE;
}
else
{
YOut[0] = 0.0;
EOut[0] = 0.0;
}
}
/**
* @param xValues The X data for the fitted spectrum
*/
void ComptonScatteringCountRate::createPositivityCM(const MantidVec &xValues) {
// -- Constraint matrix for J(y) > 0 --
// The first N columns are filled with J(y) for each mass + N_h for the first
// mass hermite
// terms included + (n+1) for each termn the background of order n
// The background columns are filled with x^j/error where j=(1,n+1)
const size_t nrows(xValues.size());
size_t nColsCMatrix(m_fixedParamIndices.size());
m_cmatrix = Kernel::DblMatrix(nrows, nColsCMatrix);
// Fill background values as they don't change at all
if (m_bkgdPolyN > 0) {
size_t polyStartCol = nColsCMatrix - m_bkgdPolyN - 1;
for (size_t i = 0; i < nrows; ++i) // rows
{
double *row = m_cmatrix[i];
const double &xi = xValues[i];
const double &erri = m_errors[i];
size_t polyN = m_bkgdPolyN;
for (size_t j = polyStartCol; j < nColsCMatrix; ++j) // cols
{
row[j] = std::pow(xi, static_cast<double>(polyN)) / erri;
--polyN;
}
}
}
}
void Divide::performBinaryOperation(const MantidVec &lhsX,
const MantidVec &lhsY,
const MantidVec &lhsE, const double rhsY,
const double rhsE, MantidVec &YOut,
MantidVec &EOut) {
(void)lhsX; // Avoid compiler warning
if (rhsY == 0 && m_warnOnZeroDivide)
g_log.warning() << "Division by zero: the RHS is a single-valued vector "
"with value zero."
<< "\n";
// Do the right-hand part of the error calculation just once
const double rhsFactor = pow(rhsE / rhsY, 2);
const int bins = static_cast<int>(lhsE.size());
for (int j = 0; j < bins; ++j) {
// Get reference to input Y
const double leftY = lhsY[j];
// see comment in the function above for the error formula
EOut[j] = sqrt(pow(lhsE[j], 2) + pow(leftY, 2) * rhsFactor) / fabs(rhsY);
// Copy the result last in case one of the input workspaces is also any
// output
YOut[j] = leftY / rhsY;
}
}
/// Returns vector with x-values of spectrum if MatrixWorkspace contains
/// histogram data.
MantidVec FunctionDomain1DSpectrumCreator::getVectorHistogram() const {
const MantidVec wsXData = m_matrixWorkspace->readX(m_workspaceIndex);
size_t wsXSize = wsXData.size();
if (wsXSize < 2) {
throw std::invalid_argument("Histogram Workspace2D with less than two "
"x-values cannot be processed.");
}
MantidVec x(wsXSize - 1);
for (size_t i = 0; i < x.size(); ++i) {
x[i] = (wsXData[i] + wsXData[i + 1]) / 2.0;
}
return x;
}
/** Read the specified number of entries from input file into the
* the array that is passed
* @param[in] nEntries the number of numbers to read
* @param[out] output the contents of this will be replaced by the data read from the file
*/
void LoadRKH::readNumEntrys(const int nEntries, MantidVec & output)
{
output.resize(nEntries);
for( int i = 0; i < nEntries; ++i )
{
m_fileIn >> output[i];
}
}
/** Calculates Sn as estimator of scale for given vector
*
* This method implements a naive calculation of Sn, as defined by Rousseeuw
*and Croux (http://dx.doi.org/10.2307%2F2291267).
* In contrast to standard deviation, this is more robust towards outliers.
*
* @param begin :: Beginning of vector.
* @param end :: End of vector.
* @return Sn of supplied data.
*/
double PoldiPeakSearch::getSn(MantidVec::const_iterator begin,
MantidVec::const_iterator end) const {
size_t numberOfPoints = std::distance(begin, end);
MantidVec absoluteDifferenceMedians(numberOfPoints);
PARALLEL_FOR_NO_WSP_CHECK()
for (int i = 0; i < static_cast<int>(numberOfPoints); ++i) {
double currentValue = *(begin + i);
MantidVec temp;
temp.reserve(numberOfPoints - 1);
for (int j = 0; j < static_cast<int>(numberOfPoints); ++j) {
if (j != i) {
temp.push_back(fabs(*(begin + j) - currentValue));
}
}
std::sort(temp.begin(), temp.end());
absoluteDifferenceMedians[i] =
getMedianFromSortedVector(temp.begin(), temp.end());
}
std::sort(absoluteDifferenceMedians.begin(), absoluteDifferenceMedians.end());
return 1.1926 * getMedianFromSortedVector(absoluteDifferenceMedians.begin(),
absoluteDifferenceMedians.end());
}
/** Retrieves a vector with all counts that belong to the background
*
* In this method, a vector is assembled which contains all count data that is
*considered to be background.
* Whether a point is considered background depends on its distance to the
*given peak positions.
*
* @param peakPositions :: Peak positions.
* @param correlationCounts :: Vector with the complete correlation spectrum.
* @return Vector only with counts that belong to the background.
*/
MantidVec PoldiPeakSearch::getBackground(
std::list<MantidVec::const_iterator> peakPositions,
const MantidVec &correlationCounts) const {
size_t backgroundPoints =
getNumberOfBackgroundPoints(peakPositions, correlationCounts);
MantidVec background;
background.reserve(backgroundPoints);
for (MantidVec::const_iterator point = correlationCounts.begin() + 1;
point != correlationCounts.end() - 1; ++point) {
if (distanceToPeaksGreaterThanMinimum(peakPositions, point)) {
background.push_back(*point);
}
}
return background;
}
/** Write data in SLOG format
*/
void SaveGSS::writeSLOGdata(const int bank, const bool MultiplyByBinWidth,
std::stringstream &out, const MantidVec &X,
const MantidVec &Y, const MantidVec &E) const {
const size_t datasize = Y.size();
double bc1 = X.front(); // minimum TOF in microseconds
if (bc1 <= 0.) {
throw std::runtime_error(
"Cannot write out logarithmic data starting at zero");
}
double bc2 = 0.5 * (*(X.rbegin()) +
*(X.rbegin() + 1)); // maximum TOF (in microseconds?)
double bc3 = (*(X.begin() + 1) - bc1) / bc1; // deltaT/T
g_log.debug() << "SaveGSS(): Min TOF = " << bc1 << std::endl;
writeBankLine(out, "SLOG", bank, datasize);
out << std::fixed << " " << std::setprecision(0) << std::setw(10) << bc1
<< std::fixed << " " << std::setprecision(0) << std::setw(10) << bc2
<< std::fixed << " " << std::setprecision(7) << std::setw(10) << bc3
<< std::fixed << " 0 FXYE" << std::endl;
for (size_t i = 0; i < datasize; i++) {
double y = Y[i];
double e = E[i];
if (MultiplyByBinWidth) {
// Multiple by bin width as
double delta = X[i + 1] - X[i];
y *= delta;
e *= delta;
}
e = fixErrorValue(e);
out << " " << std::fixed << std::setprecision(9) << std::setw(20)
<< 0.5 * (X[i] + X[i + 1]) << " " << std::fixed << std::setprecision(9)
<< std::setw(20) << y << " " << std::fixed << std::setprecision(9)
<< std::setw(20) << e << std::setw(12) << " "
<< "\n"; // let it flush its own buffer
}
out << std::flush;
return;
}
void PoissonErrors::performBinaryOperation(const MantidVec& lhsX, const MantidVec& lhsY, const MantidVec& lhsE,
const double rhsY, const double rhsE, MantidVec& YOut, MantidVec& EOut)
{
(void) lhsE; (void) lhsX; //Avoid compiler warning
assert( lhsX.size() == 1 );
// If we get here we've got two single column workspaces so it's easy.
YOut[0] = lhsY[0];
const double fractional = rhsY ? rhsE/rhsY : 0.0;
EOut[0] = fractional*lhsY[0];
}
/**
* Returns the phase shift to apply
* If "AutoShift" is set, calculates this automatically as -X[N/2]
* Otherwise, returns user-supplied "Shift" (or zero if none set)
* @param xValues :: [input] Reference to X values of input workspace
* @returns :: Phase shift
*/
double FFT::getPhaseShift(const MantidVec &xValues) {
double shift = 0.0;
const bool autoshift = getProperty("AutoShift");
if (autoshift) {
const size_t mid = xValues.size() / 2;
shift = -xValues[mid];
} else {
shift = getProperty("Shift");
}
shift *= 2 * M_PI;
return shift;
}
/** Zeroes data (Y/E) at the end of a spectrum
* @param start :: The index to start zeroing at
* @param end :: The index to end zeroing at
* @param Y :: The data vector
* @param E :: The error vector
*/
void RemoveBins::RemoveFromEnds(int start, int end, MantidVec& Y, MantidVec& E)
{
if ( start ) --start;
int size = static_cast<int>(Y.size());
if ( end > size ) end = size;
for (int j = start; j < end; ++j)
{
Y[j] = 0.0;
E[j] = 0.0;
}
return;
}
/** Divides the number of counts in each output Q bin by the wrighting ("number that would expected to arrive")
* The errors are propogated using the uncorrolated error estimate for multiplication/division
* @param[in] normSum the weighting for each bin
* @param[in] normError2 square of the error on the normalization
* @param[in, out] counts counts in each bin
* @param[in, out] errors input the _square_ of the error on each bin, output the total error (unsquared)
*/
void Q1D2::normalize(const MantidVec & normSum, const MantidVec & normError2, MantidVec & counts, MantidVec & errors) const
{
for (size_t k = 0; k < counts.size(); ++k)
{
// the normalisation is a = b/c where b = counts c =normalistion term
const double c = normSum[k];
const double a = counts[k] /= c;
// when a = b/c, the formula for Da, the error on a, in terms of Db, etc. is (Da/a)^2 = (Db/b)^2 + (Dc/c)^2
//(Da)^2 = ((Db/b)^2 + (Dc/c)^2)*(b^2/c^2) = ((Db/c)^2 + (b*Dc/c^2)^2) = (Db^2 + (b*Dc/c)^2)/c^2 = (Db^2 + (Dc*a)^2)/c^2
//this will work as long as c>0, but then the above formula above can't deal with 0 either
const double aOverc = a/c;
errors[k] = std::sqrt(errors[k]/(c*c) + normError2[k]*aOverc*aOverc);
}
}
/**
* Calculate detector efficiency given a formula, the efficiency at the elastic line,
* and a vector with energies.
* Efficiency = f(Ei-DeltaE) / f(Ei)
* Hope all compilers supports the NRVO (otherwise will copy the output vector)
* @param eff0 :: calculated eff0
* @param formula :: formula to calculate efficiency (parsed from IDF)
* @param xIn :: Energy bins vector (X axis)
* @return a vector with the efficiencies
*/
MantidVec DetectorEfficiencyCorUser::calculateEfficiency(double eff0,
const std::string& formula, const MantidVec& xIn) {
MantidVec effOut(xIn.size() - 1); // x are bins and have more one value than y
try {
double e;
mu::Parser p;
p.DefineVar("e", &e);
p.SetExpr(formula);
// copied from Jaques Ollivier Code
bool conditionForEnergy = std::min( std::abs( *std::min_element(xIn.begin(), xIn.end()) ) , m_Ei) < m_Ei;
MantidVec::const_iterator xIn_it = xIn.begin(); // DeltaE
MantidVec::iterator effOut_it = effOut.begin();
for (; effOut_it != effOut.end(); ++xIn_it, ++effOut_it) {
if (conditionForEnergy ) {
// cppcheck cannot see that this is used by reference by muparser
e = std::fabs(m_Ei + *xIn_it);
}
else {
// cppcheck cannot see that this is used by reference by muparser
// cppcheck-suppress unreadVariable
e = std::fabs(m_Ei - *xIn_it);
}
double eff = p.Eval();
*effOut_it = eff / eff0;
}
return effOut;
} catch (mu::Parser::exception_type &e) {
throw Kernel::Exception::InstrumentDefinitionError(
"Error calculating formula from string. Muparser error message is: "
+ e.GetMsg());
}
}
