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;
}
/**
* Use the binning information to generate a x-axis.
*
* @param xValues The new x-axis.
* @param xmin The x-min to be used.
* @param xmax The x-max to be used.
*
* @return The final delta value (absolute value).
*/
double ResampleX::determineBinning(MantidVec &xValues, const double xmin,
const double xmax) {
xValues.clear(); // clear out the x-values
int numBoundaries(0);
int reqNumBoundaries(m_numBins);
int expNumBoundaries(m_numBins);
if (m_isDistribution)
reqNumBoundaries -= 1; // to get the VectorHelper to do the right thing
else
expNumBoundaries += 1; // should be one more bin boundary for histograms
vector<double> params; // xmin, delta, xmax
params.push_back(xmin);
params.push_back(0.); // dummy delta value
params.push_back(xmax);
// constant binning is easy
if (m_useLogBinning) {
if (xmin == 0)
throw std::invalid_argument("Cannot calculate log of xmin=0");
if (xmax == 0)
throw std::invalid_argument("Cannot calculate log of xmax=0");
const int MAX_ITER(100); // things went wrong if we get this far
// starting delta value assuming everything happens exactly
double delta = (log(xmax) - log(xmin)) / static_cast<double>(m_numBins);
double shift = .1;
int sign = 0;
for (int numIter = 0; numIter < MAX_ITER; ++numIter) {
params[1] = -1. * delta;
if (!m_isDistribution)
params[2] = xmax + delta;
numBoundaries =
VectorHelper::createAxisFromRebinParams(params, xValues, true);
if (numBoundaries == expNumBoundaries) {
double diff = (xmax - xValues.back());
if (diff != 0.) {
g_log.debug()
<< "Didn't get the exact xmax value: [xmax - xValues.back()="
<< diff << "] [relative diff = " << fabs(100. * diff / xmax)
<< "%]\n";
g_log.debug() << "Resetting final x-value to xmax\n";
*(xValues.rbegin()) = xmax;
}
break;
} else if (numBoundaries > expNumBoundaries) // too few points
{
delta *= (1. + shift);
if (sign < 0)
shift *= .9;
sign = 1;
} else // too many points
{
delta *= (1. - shift);
if (sign > 0)
shift *= .9;
sign = -1;
}
}
} else {
params[1] = (xmax - xmin) / static_cast<double>(reqNumBoundaries);
numBoundaries =
VectorHelper::createAxisFromRebinParams(params, xValues, true);
}
if (numBoundaries != expNumBoundaries) {
g_log.warning()
<< "Did not generate the requested number of bins: generated "
<< numBoundaries << " requested " << expNumBoundaries << "\n";
}
// return the delta value so the caller can do debug printing
return params[1];
}
/**
* Get a data array covering the specified range of data, at the specified
* resolution. NOTE: The calling code is responsible for deleting the
* DataArray that is constructed in and returned by this method.
*
* @param xMin Left edge of region to be covered.
* @param xMax Right edge of region to be covered.
* @param yMin Bottom edge of region to be covered.
* @param yMax Top edge of region to be covered.
* @param numRows Number of rows to return. If the number of rows is less
* than the actual number of data rows in [yMin,yMax], the
* data will be subsampled, and only the specified number
* of rows will be returned.
* @param numCols The specrum data will be rebinned using the specified
* number of colums.
* @param isLogX Flag indicating whether or not the data should be
* binned logarithmically.
*/
DataArray_const_sptr MatrixWSDataSource::getDataArray( double xMin, double xMax,
double yMin, double yMax,
size_t numRows, size_t numCols,
bool isLogX )
{
/* Since we're rebinning, the columns can be arbitrary */
/* but rows must be aligned to get whole spectra */
size_t first_row;
SVUtils::CalculateInterval( m_totalYMin, m_totalYMax, m_totalRows,
first_row, yMin, yMax, numRows );
std::vector<float> newData(numRows * numCols);
MantidVec xScale;
xScale.resize(numCols + 1);
if ( isLogX )
{
for ( size_t i = 0; i < numCols+1; i++ )
{
xScale[i] = xMin * exp ( (double)i / (double)numCols * log(xMax/xMin) );
}
}
else
{
double dx = (xMax - xMin) / ((double)numCols + 1.0);
for ( size_t i = 0; i < numCols+1; i++ )
{
xScale[i] = xMin + (double)i * dx;
}
}
// Choose spectra from required range of spectrum indexes
double yStep = (yMax - yMin) / (double)numRows;
double dYIndex;
MantidVec yVals;
MantidVec err;
yVals.resize(numCols);
err.resize(numCols);
size_t index = 0;
for ( size_t i = 0; i < numRows; i++ )
{
double midY = yMin + ((double)i + 0.5) * yStep;
SVUtils::Interpolate( m_totalYMin, m_totalYMax, midY,
0.0, (double)m_totalRows, dYIndex );
size_t sourceRow = (size_t)dYIndex;
yVals.clear();
err.clear();
yVals.resize(numCols, 0);
err.resize(numCols, 0);
m_matWs->generateHistogram( sourceRow, xScale, yVals, err, true );
for ( size_t col = 0; col < numCols; col++ )
{
newData[index] = (float)yVals[col];
index++;
}
}
// The calling code is responsible for deleting the DataArray when it is done with it
DataArray_const_sptr newDataArray( new DataArray( xMin, xMax, yMin, yMax,
isLogX,
numRows, numCols,
newData) );
return newDataArray;
}