/**
* Linearly interpolate between the points in integrFlux at xValues and save the
* results in yValues.
* @param xValues :: X-values at which to interpolate
* @param integrFlux :: A workspace with the spectra to interpolate
* @param sp :: A workspace index for a spectrum in integrFlux to interpolate.
* @param yValues :: A vector to save the results.
*/
void MDNormSCD::calcIntegralsForIntersections(
const std::vector<double> &xValues, const API::MatrixWorkspace &integrFlux,
size_t sp, std::vector<double> &yValues) const {
assert(xValues.size() == yValues.size());
// the x-data from the workspace
const auto &xData = integrFlux.readX(sp);
const double xStart = xData.front();
const double xEnd = xData.back();
// the values in integrFlux are expected to be integrals of a non-negative
// function
// ie they must make a non-decreasing function
const auto &yData = integrFlux.readY(sp);
size_t spSize = yData.size();
const double yMin = 0.0;
const double yMax = yData.back();
size_t nData = xValues.size();
// all integrals below xStart must be 0
if (xValues[nData - 1] < xStart) {
std::fill(yValues.begin(), yValues.end(), yMin);
return;
}
// all integrals above xEnd must be equal tp yMax
if (xValues[0] > xEnd) {
std::fill(yValues.begin(), yValues.end(), yMax);
return;
}
size_t i = 0;
// integrals below xStart must be 0
while (i < nData - 1 && xValues[i] < xStart) {
yValues[i] = yMin;
i++;
}
size_t j = 0;
for (; i < nData; i++) {
// integrals above xEnd must be equal tp yMax
if (j >= spSize - 1) {
yValues[i] = yMax;
} else {
double xi = xValues[i];
while (j < spSize - 1 && xi > xData[j])
j++;
// if x falls onto an interpolation point return the corresponding y
if (xi == xData[j]) {
yValues[i] = yData[j];
} else if (j == spSize - 1) {
// if we get above xEnd it's yMax
yValues[i] = yMax;
} else if (j > 0) {
// interpolate between the consecutive points
double x0 = xData[j - 1];
double x1 = xData[j];
double y0 = yData[j - 1];
double y1 = yData[j];
yValues[i] = y0 + (y1 - y0) * (xi - x0) / (x1 - x0);
} else // j == 0
{
yValues[i] = yMin;
}
}
}
}
/**
* Finds the median of values in single bin histograms rejecting spectra from
* masked
* detectors and the results of divide by zero (infinite and NaN).
* The median is an average that is less affected by small numbers of very large
* values.
* @param input :: A histogram workspace with one entry in each bin
* @param excludeZeroes :: If true then zeroes will not be included in the
* median calculation
* @param indexmap :: indexmap
* @return The median value of the histograms in the workspace that was passed
* to it
* @throw out_of_range if a value is negative
*/
std::vector<double> DetectorDiagnostic::calculateMedian(
const API::MatrixWorkspace &input, bool excludeZeroes,
const std::vector<std::vector<size_t>> &indexmap) {
std::vector<double> medianvec;
g_log.debug("Calculating the median count rate of the spectra");
bool checkForMask = false;
Geometry::Instrument_const_sptr instrument = input.getInstrument();
if (instrument != nullptr) {
checkForMask = ((instrument->getSource() != nullptr) &&
(instrument->getSample() != nullptr));
}
const auto &spectrumInfo = input.spectrumInfo();
for (const auto &hists : indexmap) {
std::vector<double> medianInput;
const int nhists = static_cast<int>(hists.size());
// The maximum possible length is that of workspace length
medianInput.reserve(nhists);
PARALLEL_FOR_IF(Kernel::threadSafe(input))
for (int i = 0; i < nhists; ++i) { // NOLINT
PARALLEL_START_INTERUPT_REGION
if (checkForMask && spectrumInfo.hasDetectors(hists[i])) {
if (spectrumInfo.isMasked(hists[i]) || spectrumInfo.isMonitor(hists[i]))
continue;
}
const double yValue = input.readY(hists[i])[0];
if (yValue < 0.0) {
throw std::out_of_range("Negative number of counts found, could be "
"corrupted raw counts or solid angle data");
}
if (!std::isfinite(yValue) ||
(excludeZeroes && yValue < DBL_EPSILON)) // NaNs/Infs
{
continue;
}
// Now we have a good value
PARALLEL_CRITICAL(DetectorDiagnostic_median_d) {
medianInput.push_back(yValue);
}
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
if (medianInput.empty()) {
g_log.information(
"some group has no valid histograms. Will use 0 for median.");
medianInput.push_back(0.);
}
Kernel::Statistics stats =
Kernel::getStatistics(medianInput, StatOptions::Median);
double median = stats.median;
if (median < 0 || median > DBL_MAX / 10.0) {
throw std::out_of_range("The calculated value for the median was either "
"negative or unreliably large");
}
medianvec.push_back(median);
}
return medianvec;
}
