//----------------------------------------------------------------------
// Includes
//----------------------------------------------------------------------
#include "MantidAlgorithms/CalculateEfficiency.h"
#include "MantidDataObjects/EventWorkspace.h"
#include "MantidDataObjects/EventList.h"
#include "MantidKernel/BoundedValidator.h"
#include <vector>
namespace Mantid {
namespace Algorithms {
// Register the class into the algorithm factory
DECLARE_ALGORITHM(CalculateEfficiency)
using namespace Kernel;
using namespace API;
using namespace Geometry;
using namespace DataObjects;
/** Initialization method.
*
*/
void CalculateEfficiency::init() {
declareProperty(
new WorkspaceProperty<>("InputWorkspace", "", Direction::Input),
"The workspace containing the flood data");
declareProperty(
new WorkspaceProperty<>("OutputWorkspace", "", Direction::Output),
"The name of the workspace to be created as the output of the algorithm");
auto positiveDouble = boost::make_shared<BoundedValidator<double>>();
positiveDouble->setLower(0);
declareProperty(
"MinEfficiency", EMPTY_DBL(), positiveDouble,
"Minimum efficiency for a pixel to be considered (default: no minimum).");
declareProperty(
"MaxEfficiency", EMPTY_DBL(), positiveDouble,
"Maximum efficiency for a pixel to be considered (default: no maximum).");
}
/** Executes the algorithm
*
*/
void CalculateEfficiency::exec() {
// Minimum efficiency. Pixels with lower efficiency will be masked
double min_eff = getProperty("MinEfficiency");
// Maximum efficiency. Pixels with higher efficiency will be masked
double max_eff = getProperty("MaxEfficiency");
// Get the input workspace
MatrixWorkspace_sptr inputWS = getProperty("InputWorkspace");
MatrixWorkspace_sptr rebinnedWS; // = inputWS;
// Now create the output workspace
MatrixWorkspace_sptr outputWS; // = getProperty("OutputWorkspace");
// DataObjects::EventWorkspace_const_sptr inputEventWS =
// boost::dynamic_pointer_cast<const EventWorkspace>(inputWS);
// Sum up all the wavelength bins
IAlgorithm_sptr childAlg = createChildAlgorithm("Integration", 0.0, 0.2);
childAlg->setProperty<MatrixWorkspace_sptr>("InputWorkspace", inputWS);
childAlg->executeAsChildAlg();
rebinnedWS = childAlg->getProperty("OutputWorkspace");
outputWS = WorkspaceFactory::Instance().create(rebinnedWS);
WorkspaceFactory::Instance().initializeFromParent(inputWS, outputWS, false);
for (int i = 0; i < static_cast<int>(rebinnedWS->getNumberHistograms());
i++) {
outputWS->dataX(i) = rebinnedWS->readX(i);
}
setProperty("OutputWorkspace", outputWS);
double sum = 0.0;
double err = 0.0;
int npixels = 0;
// Loop over spectra and sum all the counts to get normalization
// Skip monitors and masked detectors
sumUnmaskedDetectors(rebinnedWS, sum, err, npixels);
// Normalize each detector pixel by the sum we just found to get the
// relative efficiency. If the minimum and maximum efficiencies are
// provided, the pixels falling outside this range will be marked
// as 'masked' in both the input and output workspace.
// We mask detectors in the input workspace so that we can resum the
// counts to find a new normalization factor that takes into account
// the newly masked detectors.
normalizeDetectors(rebinnedWS, outputWS, sum, err, npixels, min_eff, max_eff);
if (!isEmpty(min_eff) || !isEmpty(max_eff)) {
// Recompute the normalization, excluding the pixels that were outside
// the acceptable efficiency range.
sumUnmaskedDetectors(rebinnedWS, sum, err, npixels);
// Now that we have a normalization factor that excludes bad pixels,
// recompute the relative efficiency.
// We pass EMPTY_DBL() to avoid masking pixels that might end up high or low
// after the new normalization.
normalizeDetectors(rebinnedWS, outputWS, sum, err, npixels, EMPTY_DBL(),
EMPTY_DBL());
}
return;
}
/*
* Sum up all the unmasked detector pixels.
*
* @param rebinnedWS: workspace where all the wavelength bins have been grouped
*together
* @param sum: sum of all the unmasked detector pixels (counts)
* @param error: error on sum (counts)
* @param nPixels: number of unmasked detector pixels that contributed to sum
*/
void CalculateEfficiency::sumUnmaskedDetectors(MatrixWorkspace_sptr rebinnedWS,
double &sum, double &error,
int &nPixels) {
// Number of spectra
const int numberOfSpectra =
static_cast<int>(rebinnedWS->getNumberHistograms());
sum = 0.0;
error = 0.0;
nPixels = 0;
for (int i = 0; i < numberOfSpectra; i++) {
progress(0.2 + 0.2 * i / numberOfSpectra, "Computing sensitivity");
// Get the detector object for this spectrum
IDetector_const_sptr det = rebinnedWS->getDetector(i);
// If this detector is masked, skip to the next one
if (det->isMasked())
continue;
// If this detector is a monitor, skip to the next one
if (det->isMonitor())
continue;
// Retrieve the spectrum into a vector
const MantidVec &YValues = rebinnedWS->readY(i);
const MantidVec &YErrors = rebinnedWS->readE(i);
sum += YValues[0];
error += YErrors[0] * YErrors[0];
nPixels++;
}
error = std::sqrt(error);
}
/*
* Normalize each detector to produce the relative detector efficiency.
* Pixels that fall outside those efficiency limits will be masked in both
* the input and output workspaces.
*
* @param rebinnedWS: input workspace
* @param outputWS: output workspace containing the relative efficiency
* @param sum: sum of all the unmasked detector pixels (counts)
* @param error: error on sum (counts)
* @param nPixels: number of unmasked detector pixels that contributed to sum
*/
void CalculateEfficiency::normalizeDetectors(MatrixWorkspace_sptr rebinnedWS,
MatrixWorkspace_sptr outputWS,
double sum, double error,
int nPixels, double min_eff,
double max_eff) {
// Number of spectra
const size_t numberOfSpectra = rebinnedWS->getNumberHistograms();
// Empty vector to store the pixels that outside the acceptable efficiency
// range
std::vector<size_t> dets_to_mask;
for (size_t i = 0; i < numberOfSpectra; i++) {
const double currProgress =
0.4 +
0.2 * (static_cast<double>(i) / static_cast<double>(numberOfSpectra));
progress(currProgress, "Computing sensitivity");
// Get the detector object for this spectrum
IDetector_const_sptr det = rebinnedWS->getDetector(i);
// If this detector is masked, skip to the next one
if (det->isMasked())
continue;
// Retrieve the spectrum into a vector
const MantidVec &YIn = rebinnedWS->readY(i);
const MantidVec &EIn = rebinnedWS->readE(i);
MantidVec &YOut = outputWS->dataY(i);
MantidVec &EOut = outputWS->dataE(i);
// If this detector is a monitor, skip to the next one
if (det->isMonitor()) {
YOut[0] = 1.0;
EOut[0] = 0.0;
continue;
}
// Normalize counts to get relative efficiency
YOut[0] = nPixels / sum * YIn[0];
const double err_sum = YIn[0] / sum * error;
EOut[0] = nPixels / std::abs(sum) *
std::sqrt(EIn[0] * EIn[0] + err_sum * err_sum);
// Mask this detector if the signal is outside the acceptable band
if (!isEmpty(min_eff) && YOut[0] < min_eff)
dets_to_mask.push_back(i);
if (!isEmpty(max_eff) && YOut[0] > max_eff)
dets_to_mask.push_back(i);
}
// If we identified pixels to be masked, mask them now
if (!dets_to_mask.empty()) {
// Mask detectors that were found to be outside the acceptable efficiency
// band
try {
IAlgorithm_sptr mask = createChildAlgorithm("MaskDetectors", 0.8, 0.9);
// First we mask detectors in the output workspace
mask->setProperty<MatrixWorkspace_sptr>("Workspace", outputWS);
mask->setProperty<std::vector<size_t>>("WorkspaceIndexList",
dets_to_mask);
mask->execute();
mask = createChildAlgorithm("MaskDetectors", 0.9, 1.0);
// Then we mask the same detectors in the input workspace
mask->setProperty<MatrixWorkspace_sptr>("Workspace", rebinnedWS);
mask->setProperty<std::vector<size_t>>("WorkspaceIndexList",
dets_to_mask);
mask->execute();
} catch (std::invalid_argument &err) {
std::stringstream e;
e << "Invalid argument to MaskDetectors Child Algorithm: " << err.what();
g_log.error(e.str());
} catch (std::runtime_error &err) {
std::stringstream e;
e << "Unable to successfully run MaskDetectors Child Algorithm: "
<< err.what();
g_log.error(e.str());
}
}
}
} // namespace Algorithms
} // namespace Mantid