GetEi2.cpp

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// Mantid Repository : https://github.com/mantidproject/mantid
//
// Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
//   NScD Oak Ridge National Laboratory, European Spallation Source,
//   Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
// SPDX - License - Identifier: GPL - 3.0 +
#include "MantidAlgorithms/GetEi2.h"
 
#include "MantidAPI/HistogramValidator.h"
#include "MantidAPI/IEventWorkspace.h"
#include "MantidAPI/InstrumentValidator.h"
#include "MantidAPI/Run.h"
#include "MantidAPI/WorkspaceUnitValidator.h"
#include "MantidGeometry/IDetector_fwd.h"
#include "MantidGeometry/Instrument.h"
#include "MantidGeometry/Instrument/DetectorGroup.h"
#include "MantidGeometry/muParser_Silent.h"
#include "MantidKernel/BoundedValidator.h"
#include "MantidKernel/CompositeValidator.h"
#include "MantidKernel/PhysicalConstants.h"
#include "MantidKernel/VectorHelper.h"
 
#include <algorithm>
#include <boost/lexical_cast.hpp>
#include <cmath>
#include <sstream>
#include <utility>
 
using namespace Mantid::Kernel;
using namespace Mantid::API;
using namespace Mantid::Geometry;
 
namespace Mantid::Algorithms {
 
// Register the algorithm into the algorithm factory
DECLARE_ALGORITHM(GetEi2)
 
 
GetEi2::GetEi2()
    : Algorithm(), m_input_ws(), m_peak1_pos(0, 0.0), m_fixedei(false), m_tof_window(0.1), m_peak_signif(2.0),
      m_peak_deriv(1.0), m_binwidth_frac(1.0 / 12.0), m_bkgd_frac(0.5) {
  // Conversion factor common for converting between micro seconds and energy in
  // meV
  m_t_to_mev = 5e11 * PhysicalConstants::NeutronMass / PhysicalConstants::meV;
}
 
void GetEi2::init()
 
{ // Declare required input parameters for algorithm and do some validation here
  auto validator = std::make_shared<CompositeValidator>();
  validator->add<WorkspaceUnitValidator>("TOF");
  validator->add<HistogramValidator>();
  validator->add<InstrumentValidator>();
 
  declareProperty(std::make_unique<WorkspaceProperty<>>("InputWorkspace", "", Direction::InOut, validator),
                  "The X units of this workspace must be time of flight with times in\n"
                  "microseconds");
  auto mustBePositive = std::make_shared<BoundedValidator<int>>();
  mustBePositive->setLower(0);
  declareProperty("Monitor1Spec", EMPTY_INT(), mustBePositive,
                  "The spectrum number of the output of the first monitor, "
                  "e.g. MAPS 41474, MARI 2, MERLIN 69634.\n If empty, it will "
                  "be read from the instrument file.\n");
  declareProperty("Monitor2Spec", EMPTY_INT(), mustBePositive,
                  "The spectrum number of the output of the second monitor "
                  "e.g. MAPS 41475, MARI 3, MERLIN 69638.\n If empty, it will "
                  "be read from the instrument file.\n");
  auto positiveDouble = std::make_shared<BoundedValidator<double>>();
  positiveDouble->setLower(0.0);
  declareProperty("EnergyEstimate", EMPTY_DBL(), positiveDouble,
                  "An approximate value for the typical incident energy, energy of\n"
                  "neutrons leaving the source (meV)\n Can be empty if there is a Sample "
                  "Log called EnergyRequest. Otherwise it is mandatory.");
  declareProperty("FixEi", false,
                  "If true, the incident energy will be set to the value of the \n"
                  "EnergyEstimate property.");
 
  declareProperty("IncidentEnergy", -1.0, "The energy of neutron in meV, it is also printed to the Mantid's log",
                  Direction::Output);
 
  declareProperty("FirstMonitorPeak", -1.0,
                  "The time in :math:`\\rm{\\mu s}` when the count rate of the "
                  "first monitor, which defaults to the last monitor the beam "
                  "hits before the sample, is greatest. It is the mean X value "
                  "for the bin with the highest number of counts per second "
                  "and is also writen to Mantid's log.",
                  Direction::Output);
 
  declareProperty("FirstMonitorIndex", 0, "The workspace index of the first monitor in the input workspace.",
                  Direction::Output);
 
  declareProperty("Tzero", 0.0, "", Direction::Output);
 
  auto inRange0toOne = std::make_shared<BoundedValidator<double>>();
  inRange0toOne->setLower(0.0);
  inRange0toOne->setUpper(1.0);
  declareProperty("PeakSearchRange", 0.1, inRange0toOne,
                  "Specifies the relative TOF range where the algorithm tries "
                  "to find the monitor peak. Search occurs within "
                  "PEAK_TOF_Guess * (1 +/- PeakSearchRange) ranges.\n"
                  "Defaults are almost always sufficient but decrease this "
                  "value for very narrow peaks and increase for wide.",
                  Direction::Input);
}
 
void GetEi2::exec() {
  m_input_ws = getProperty("InputWorkspace");
  m_fixedei = getProperty("FixEi");
  m_tof_window = getProperty("PeakSearchRange");
 
  double initial_guess = getProperty("EnergyEstimate");
  // check if incident energy guess is left empty, and try to find it as
  // EnergyRequest parameter
  if (initial_guess == EMPTY_DBL()) {
    if (m_input_ws->run().hasProperty("EnergyRequest")) {
      initial_guess = m_input_ws->getLogAsSingleValue("EnergyRequest");
    } else {
      throw std::invalid_argument("Could not find an energy guess");
    }
  }
  double incident_energy = calculateEi(initial_guess);
 
  storeEi(incident_energy);
 
  setProperty("InputWorkspace", m_input_ws);
  // Output properties
  setProperty("IncidentEnergy", incident_energy);
  setProperty("FirstMonitorIndex", m_peak1_pos.first);
  setProperty("FirstMonitorPeak", m_peak1_pos.second);
}
 
double GetEi2::calculateEi(const double initial_guess) {
  const specnum_t monitor1_spec = getProperty("Monitor1Spec");
  const specnum_t monitor2_spec = getProperty("Monitor2Spec");
  specnum_t mon1 = monitor1_spec, mon2 = monitor2_spec;
 
  const ParameterMap &pmap = m_input_ws->constInstrumentParameters();
  Instrument_const_sptr instrument = m_input_ws->getInstrument();
 
  // If we have a t0 formula, calculate t0 and return the initial guess
  Parameter_sptr par = pmap.getRecursive(instrument->getChild(0).get(), "t0_formula");
  if (par) {
    std::string formula = par->asString();
    std::stringstream guess;
    guess << initial_guess;
 
    while (formula.find("incidentEnergy") != std::string::npos) {
      // check if more than one 'value' in m_eq
      size_t found = formula.find("incidentEnergy");
      formula.replace(found, 14, guess.str());
    }
 
    try {
      mu::Parser p;
      p.SetExpr(formula);
      double tzero = p.Eval();
      setProperty("Tzero", tzero);
 
      g_log.debug() << "T0 = " << tzero << '\n';
      return initial_guess;
    } catch (mu::Parser::exception_type &e) {
      throw Kernel::Exception::InstrumentDefinitionError(
          "Equation attribute for t0 formula. Muparser error message is: " + e.GetMsg());
    }
  }
 
  if (mon1 == EMPTY_INT()) {
    par = pmap.getRecursive(instrument->getChild(0).get(), "ei-mon1-spec");
    if (par) {
      mon1 = boost::lexical_cast<specnum_t>(par->asString());
    }
  }
  if (mon2 == EMPTY_INT()) {
    par = pmap.getRecursive(instrument->getChild(0).get(), "ei-mon2-spec");
    if (par) {
      mon2 = boost::lexical_cast<specnum_t>(par->asString());
    }
  }
  if ((mon1 == EMPTY_INT()) || (mon2 == EMPTY_INT())) {
    throw std::invalid_argument("Could not determine spectrum number to use. Try to set it explicitly");
  }
  // Covert spectrum numbers to workspace indices
  std::vector<specnum_t> spec_nums(2, mon1);
  spec_nums[1] = mon2;
  // get the index number of the histogram for the first monitor
  auto mon_indices = m_input_ws->getIndicesFromSpectra(spec_nums);
 
  if (mon_indices.size() != 2) {
    g_log.error() << "Error retrieving monitor spectra from input workspace. "
                     "Check input properties.\n";
    throw std::runtime_error("Error retrieving monitor spectra spectra from input workspace.");
  }
 
  // Calculate actual peak postion for each monitor peak
  double peak_times[2] = {0.0, 0.0};
  double det_distances[2] = {0.0, 0.0};
  auto &spectrumInfo = m_input_ws->spectrumInfo();
  for (unsigned int i = 0; i < 2; ++i) {
    size_t ws_index = mon_indices[i];
    det_distances[i] = getDistanceFromSource(ws_index, spectrumInfo);
    const double peak_guess = det_distances[i] * std::sqrt(m_t_to_mev / initial_guess);
    const double t_min = (1.0 - m_tof_window) * peak_guess;
    const double t_max = (1.0 + m_tof_window) * peak_guess;
    g_log.information() << "Time-of-flight window for peak " << (i + 1) << ": tmin = " << t_min
                        << " microseconds, tmax = " << t_max << " microseconds\n";
    try {
      peak_times[i] = calculatePeakPosition(ws_index, t_min, t_max);
      g_log.information() << "Peak for monitor " << (i + 1) << " (at " << det_distances[i]
                          << " metres) = " << peak_times[i] << " microseconds\n";
    } catch (std::invalid_argument &) {
 
      if (!m_fixedei) {
        throw std::invalid_argument("No peak found for the monitor with spectra num: " + std::to_string(spec_nums[i]) +
                                    " (at " + boost::lexical_cast<std::string>(det_distances[i]) +
                                    "  metres from source).\n");
      } else {
        peak_times[i] = peak_guess;
        g_log.warning() << "No peak found for monitor with spectra num " << spec_nums[i] << " (at " << det_distances[i]
                        << " metres).\n";
        g_log.warning() << "Using guess time found from energy estimate of Peak = " << peak_times[i]
                        << " microseconds\n";
      }
    }
    if (i == 0) {
      m_peak1_pos = std::make_pair(static_cast<int>(ws_index), peak_times[i]);
      if (m_fixedei)
        break;
    }
  }
 
  if (m_fixedei) {
    g_log.notice() << "Incident energy fixed at Ei=" << initial_guess << " meV\n";
    return initial_guess;
  } else {
    double mean_speed = (det_distances[1] - det_distances[0]) / (peak_times[1] - peak_times[0]);
    double tzero = peak_times[1] - ((1.0 / mean_speed) * det_distances[1]);
    g_log.debug() << "T0 = " << tzero << '\n';
    g_log.debug() << "Mean Speed = " << mean_speed << '\n';
    setProperty("Tzero", tzero);
 
    const double energy = mean_speed * mean_speed * m_t_to_mev;
    g_log.notice() << "Incident energy calculated at Ei= " << energy << " meV from initial guess = " << initial_guess
                   << " meV\n";
    return energy;
  }
}
 
double GetEi2::getDistanceFromSource(size_t ws_index, const SpectrumInfo &spectrumInfo) const {
  g_log.debug() << "Computing distance between spectrum at index '" << ws_index << "' and the source\n";
 
  const auto &detector = spectrumInfo.detector(ws_index);
  const IComponent_const_sptr source = m_input_ws->getInstrument()->getSource();
  if (!spectrumInfo.hasDetectors(ws_index)) {
    std::ostringstream msg;
    msg << "A detector for monitor at workspace index " << ws_index << " cannot be found. ";
    throw std::runtime_error(msg.str());
  }
  if (g_log.is(Logger::Priority::PRIO_DEBUG)) {
    g_log.debug() << "Detector position = " << spectrumInfo.position(ws_index)
                  << ", Source position = " << source->getPos() << "\n";
  }
  const double dist = detector.getDistance(*source);
  g_log.debug() << "Distance = " << dist << " metres\n";
  return dist;
}
 
double GetEi2::calculatePeakPosition(size_t ws_index, double t_min, double t_max) {
  // Crop out the current monitor workspace to the min/max times defined
  MatrixWorkspace_sptr monitor_ws = extractSpectrum(ws_index, t_min, t_max);
  // Workspace needs to be a count rate for the fitting algorithm
  WorkspaceHelpers::makeDistribution(monitor_ws);
 
  const double prominence(4.0);
  std::vector<double> dummyX, dummyY, dummyE;
  double peak_width = calculatePeakWidthAtHalfHeight(monitor_ws, prominence, dummyX, dummyY, dummyE);
  monitor_ws = rebin(monitor_ws, t_min, peak_width * m_binwidth_frac, t_max);
 
  double t_mean(0.0);
  try {
    t_mean = calculateFirstMoment(monitor_ws, prominence);
  } catch (std::runtime_error &) {
    // Retry with smaller prominence factor
    t_mean = calculateFirstMoment(monitor_ws, 2.0);
  }
 
  return t_mean;
}
 
MatrixWorkspace_sptr GetEi2::extractSpectrum(size_t ws_index, const double start, const double end) {
  auto childAlg = createChildAlgorithm("CropWorkspace");
  childAlg->setProperty("InputWorkspace", m_input_ws);
  childAlg->setProperty<int>("StartWorkspaceIndex", static_cast<int>(ws_index));
  childAlg->setProperty<int>("EndWorkspaceIndex", static_cast<int>(ws_index));
  childAlg->setProperty("XMin", start);
  childAlg->setProperty("XMax", end);
  childAlg->executeAsChildAlg();
  MatrixWorkspace_sptr monitors = childAlg->getProperty("OutputWorkspace");
  if (std::dynamic_pointer_cast<API::IEventWorkspace>(m_input_ws)) {
    // Convert to a MatrixWorkspace representation
    childAlg = createChildAlgorithm("ConvertToMatrixWorkspace");
    childAlg->setProperty("InputWorkspace", monitors);
    childAlg->setProperty("OutputWorkspace", monitors);
    childAlg->executeAsChildAlg();
    monitors = childAlg->getProperty("OutputWorkspace");
  }
 
  return monitors;
}
 
double GetEi2::calculatePeakWidthAtHalfHeight(const API::MatrixWorkspace_sptr &data_ws, const double prominence,
                                              std::vector<double> &peak_x, std::vector<double> &peak_y,
                                              std::vector<double> &peak_e) const {
  // Use WS->points() to create a temporary vector of bin_centre values to work
  // with
  auto Xs = data_ws->points(0);
  const auto &Ys = data_ws->y(0);
  const auto &Es = data_ws->e(0);
 
  auto peakIt = std::max_element(Ys.cbegin(), Ys.cend());
  double bkg_val = *std::min_element(Ys.begin(), Ys.end());
  if (*peakIt == bkg_val) {
    throw std::invalid_argument("No peak in the range specified as minimal and "
                                "maximal values of the function are equal ");
  }
  auto iPeak = peakIt - Ys.begin();
  double peakY = Ys[iPeak] - bkg_val;
  double peakE = Es[iPeak];
 
  const std::vector<double>::size_type nxvals = Xs.size();
 
  // nearest points that satisfy this
  auto im = static_cast<int64_t>(iPeak - 1);
  for (; im >= 0; --im) {
    const double ratio = (Ys[im] - bkg_val) / peakY;
    const double ratio_err = std::sqrt(std::pow(Es[im], 2) + std::pow(ratio * peakE, 2)) / peakY;
    if (ratio < (1.0 / prominence - m_peak_signif * ratio_err)) {
      break;
    }
  }
 
  std::vector<double>::size_type ip = iPeak + 1;
  for (; ip < nxvals; ip++) {
    const double ratio = (Ys[ip] - bkg_val) / peakY;
    const double ratio_err = std::sqrt(std::pow(Es[ip], 2) + std::pow(ratio * peakE, 2)) / peakY;
    if (ratio < (1.0 / prominence - m_peak_signif * ratio_err)) {
      break;
    }
  }
 
  if (ip == nxvals || im < 0) {
    throw std::invalid_argument("No peak found in data that satisfies prominence criterion");
  }
 
  // We now have a peak, so can start filling output arguments
  // Determine extent of peak using derivatives
  // At this point 1 =< im < ipk < ip =< size(x)
  // After this section, new values will be given to im, ip that still satisfy
  // these inequalities.
  //
  // The algorithm for negative side skipped if im=1; positive side skipped if
  // ip=size(x);
  // if fails derivative criterion -> ip=size(x) (+ve)  im=1 (-ve)
  // In either case, we deem that the peak has a tail(s) that extend outside the
  // range of x
 
  if (ip < nxvals) {
    double deriv = -1000.0;
    double error = 0.0;
    while ((ip < nxvals - 1) && (deriv < -m_peak_deriv * error)) {
      double dtp = Xs[ip + 1] - Xs[ip];
      double dtm = Xs[ip] - Xs[ip - 1];
      deriv = 0.5 * (((Ys[ip + 1] - Ys[ip]) / dtp) + ((Ys[ip] - Ys[ip - 1]) / dtm));
      error = 0.5 * std::sqrt(((std::pow(Es[ip + 1], 2) + std::pow(Es[ip], 2)) / std::pow(dtp, 2)) +
                              ((std::pow(Es[ip], 2) + std::pow(Es[ip - 1], 2)) / std::pow(dtm, 2)) -
                              2.0 * (std::pow(Es[ip], 2) / (dtp * dtm)));
      ip++;
    }
    ip--;
 
    if (deriv < -error) {
      ip = nxvals - 1; //        ! derivative criterion not met
    }
  }
 
  if (im > 0) {
    double deriv = 1000.0;
    double error = 0.0;
    while ((im > 0) && (deriv > m_peak_deriv * error)) {
      double dtp = Xs[im + 1] - Xs[im];
      double dtm = Xs[im] - Xs[im - 1];
      deriv = 0.5 * (((Ys[im + 1] - Ys[im]) / dtp) + ((Ys[im] - Ys[im - 1]) / dtm));
      error = 0.5 * std::sqrt(((std::pow(Es[im + 1], 2) + std::pow(Es[im], 2)) / std::pow(dtp, 2)) +
                              ((std::pow(Es[im], 2) + std::pow(Es[im - 1], 2)) / std::pow(dtm, 2)) -
                              2.0 * std::pow(Es[im], 2) / (dtp * dtm));
      im--;
    }
    im++;
    if (deriv > error)
      im = 0; //  derivative criterion not met
  }
  double pk_min = Xs[im];
  double pk_max = Xs[ip];
  double pk_width = Xs[ip] - Xs[im];
 
  // Determine background from either side of peak.
  // At this point, im and ip define the extreme points of the peak
  // Assume flat background
  double bkgd = 0.0;
  double bkgd_range = 0.0;
  double bkgd_min = std::max(Xs.front(), pk_min - m_bkgd_frac * pk_width);
  double bkgd_max = std::min(Xs.back(), pk_max + m_bkgd_frac * pk_width);
 
  if (im > 0) {
    double bkgd_m, bkgd_err_m;
    integrate(bkgd_m, bkgd_err_m, Xs.rawData(), Ys, Es, bkgd_min, pk_min);
    bkgd = bkgd + bkgd_m;
    bkgd_range = bkgd_range + (pk_min - bkgd_min);
  }
 
  if (ip < nxvals - 1) {
    double bkgd_p, bkgd_err_p;
    integrate(bkgd_p, bkgd_err_p, Xs.rawData(), Ys, Es, pk_max, bkgd_max);
    bkgd = bkgd + bkgd_p;
    bkgd_range = bkgd_range + (bkgd_max - pk_max);
  }
 
  if (im > 0 || ip < nxvals - 1) // background from at least one side
  {
    bkgd = bkgd / bkgd_range;
  }
  // Perform moment analysis on the peak after subtracting the background
  // Fill arrays with peak only:
  const std::vector<double>::size_type nvalues = ip - im + 1;
  peak_x.resize(nvalues);
  std::copy(Xs.begin() + im, Xs.begin() + ip + 1, peak_x.begin());
  peak_y.resize(nvalues);
  using std::placeholders::_1;
  std::transform(Ys.begin() + im, Ys.begin() + ip + 1, peak_y.begin(), std::bind(std::minus<double>(), _1, bkgd));
  peak_e.resize(nvalues);
  std::copy(Es.begin() + im, Es.begin() + ip + 1, peak_e.begin());
 
  // FWHH:
  int64_t ipk_int = static_cast<int64_t>(iPeak) - im; //       ! peak position in internal array
  double hby2 = 0.5 * peak_y[ipk_int];
  double xp_hh(0);
 
  auto nyvals = static_cast<int64_t>(peak_y.size());
  if (peak_y[nyvals - 1] < hby2) {
    int64_t ip1(0), ip2(0);
    for (int64_t i = ipk_int; i < nyvals; ++i) {
      if (peak_y[i] < hby2) {
        // after this point the intensity starts to go below half-height
        ip1 = i - 1;
        break;
      }
    }
    for (int64_t i = nyvals - 1; i >= ipk_int; --i) {
      if (peak_y[i] > hby2) {
        ip2 = i + 1; //   ! point closest to peak after which the intensity is
                     //   always below half height
        break;
      }
    }
    // A broad peak with many local maxima on the side can cause the algorithm to give the same indices for the two
    // points (ip1 and ip2) either side of the half-width point. Noise may also result in equal y values for different
    // ip1 and ip2 points which would cause a divide-by-zero error in the xp_hh calculation below. We know the
    // algorithm isn't perfect so move one index until the y values are differnt.
    if (peak_y[ip1] == peak_y[ip2]) {
      g_log.warning() << "A peak with a local maxima on the trailing edge has "
                         "been found. The estimation of the "
                      << "half-height point will not be as accurate.\n";
      // Shift the index ip1 until the heights are no longer equal. This avoids the divide-by-zero.
      while (peak_y[ip1] == peak_y[ip2]) {
        ip1--;
        if (ip1 < ipk_int)
          throw std::invalid_argument("Trailing edge values are equal until up to the peak centre.");
      }
    }
 
    xp_hh = peak_x[ip2] + (peak_x[ip1] - peak_x[ip2]) * ((hby2 - peak_y[ip2]) / (peak_y[ip1] - peak_y[ip2]));
  } else {
    xp_hh = peak_x[nyvals - 1];
  }
 
  double xm_hh(0);
  if (peak_y[0] < hby2) {
    int64_t im1(0), im2(0);
    for (int64_t i = ipk_int; i >= 0; --i) {
      if (peak_y[i] < hby2) {
        im1 = i + 1; // ! before this point the intensity starts to go below
                     // half-height
        break;
      }
    }
    for (int64_t i = 0; i <= ipk_int; ++i) {
      if (peak_y[i] > hby2) {
        im2 = i - 1; // ! point closest to peak before which the intensity is
                     // always below half height
        break;
      }
    }
    // A broad peak with many local maxima on the side can cause the algorithm to give the same indices for the two
    // points (im1 and im2) either side of the half-width point. Noise may also result in equal y values for different
    // im1 and im2 points which would cause a divide-by-zero error in the xp_hh calculation below. We know the
    // algorithm isn't perfect so move one index until the y values are differnt.
    if (peak_y[im1] == peak_y[im2]) {
      g_log.warning() << "A peak with a local maxima on the rising edge has "
                         "been found. The estimation of the "
                      << "half-height point will not be as accurate.\n";
      // Shift the index ip1 until the heights are no longer equal. This avoids the divide-by-zero.
      while (peak_y[im1] == peak_y[im2]) {
        im1++;
        if (im1 >= ipk_int)
          throw std::invalid_argument("The rising edge values are equal up to the peak centre.");
      }
    }
 
    xm_hh = peak_x[im2] + (peak_x[im1] - peak_x[im2]) * ((hby2 - peak_y[im2]) / (peak_y[im1] - peak_y[im2]));
  } else {
    xm_hh = peak_x.front();
  }
  return (xp_hh - xm_hh);
}
 
double GetEi2::calculateFirstMoment(const API::MatrixWorkspace_sptr &monitor_ws, const double prominence) {
  std::vector<double> peak_x, peak_y, peak_e;
  calculatePeakWidthAtHalfHeight(monitor_ws, prominence, peak_x, peak_y, peak_e);
 
  // Area
  double area(0.0), dummy(0.0);
  double pk_xmin = peak_x.front();
  double pk_xmax = peak_x.back();
  integrate(area, dummy, peak_x, peak_y, peak_e, pk_xmin, pk_xmax);
  // First moment
  std::transform(peak_y.begin(), peak_y.end(), peak_x.begin(), peak_y.begin(), std::multiplies<double>());
  double xbar(0.0);
  integrate(xbar, dummy, peak_x, peak_y, peak_e, pk_xmin, pk_xmax);
  return xbar / area;
}
 
API::MatrixWorkspace_sptr GetEi2::rebin(const API::MatrixWorkspace_sptr &monitor_ws, const double first,
                                        const double width, const double end) {
  auto childAlg = createChildAlgorithm("Rebin");
  childAlg->setProperty("InputWorkspace", monitor_ws);
  std::ostringstream binParams;
  binParams << first << "," << width << "," << end;
  childAlg->setPropertyValue("Params", binParams.str());
  childAlg->setProperty("IgnoreBinErrors", true);
  childAlg->executeAsChildAlg();
  return childAlg->getProperty("OutputWorkspace");
}
 
void GetEi2::integrate(double &integral_val, double &integral_err, const HistogramData::HistogramX &x,
                       const HistogramData::HistogramY &s, const HistogramData::HistogramE &e, const double xmin,
                       const double xmax) const {
  // MG: Note that this is integration of a point data set from libisis
  // @todo: Move to Kernel::VectorHelper and improve performance
 
  auto lowit = std::lower_bound(x.cbegin(), x.cend(), xmin);
  auto ml = std::distance(x.cbegin(), lowit);
  auto highit = std::upper_bound(lowit, x.cend(), xmax);
  auto mu = std::distance(x.cbegin(), highit);
  if (mu > 0)
    --mu;
 
  auto nx(x.size());
  if (mu < ml) {
    // special case of no data points in the integration range
    unsigned int ilo = std::max<unsigned int>(static_cast<unsigned int>(ml) - 1, 0);
    unsigned int ihi = std::min<unsigned int>(static_cast<unsigned int>(mu) + 1, static_cast<unsigned int>(nx));
    double fraction = (xmax - xmin) / (x[ihi] - x[ilo]);
    integral_val =
        0.5 * fraction * (s[ihi] * ((xmax - x[ilo]) + (xmin - x[ilo])) + s[ilo] * ((x[ihi] - xmax) + (x[ihi] - xmin)));
    double err_hi = e[ihi] * ((xmax - x[ilo]) + (xmin - x[ilo]));
    double err_lo = e[ilo] * ((x[ihi] - xmax) + (x[ihi] - xmin));
    integral_err = 0.5 * fraction * std::sqrt(err_hi * err_hi + err_lo * err_lo);
    return;
  }
 
  double x1eff(0.0), s1eff(0.0), e1eff(0.0);
  if (ml > 0) {
    x1eff = (xmin * (xmin - x[ml - 1]) + x[ml - 1] * (x[ml] - xmin)) / (x[ml] - x[ml - 1]);
    double fraction = (x[ml] - xmin) / ((x[ml] - x[ml - 1]) + (xmin - x[ml - 1]));
    s1eff = s[ml - 1] * fraction;
    e1eff = e[ml - 1] * fraction;
  } else {
    x1eff = x[ml];
    s1eff = 0.0;
  }
 
  double xneff(0.0), sneff(0.0), eneff;
  if (mu < static_cast<int>(nx - 1)) {
    xneff = (xmax * (x[mu + 1] - xmax) + x[mu + 1] * (xmax - x[mu])) / (x[mu + 1] - x[mu]);
    const double fraction = (xmax - x[mu]) / ((x[mu + 1] - x[mu]) + (x[mu + 1] - xmax));
    sneff = s[mu + 1] * fraction;
    eneff = e[mu + 1] * fraction;
  } else {
    xneff = x.back();
    sneff = 0.0;
    eneff = 0.0; // Make sure to initialize to a value.
  }
 
  // xmin -> x[ml]
  integral_val = (x[ml] - x1eff) * (s[ml] + s1eff);
  integral_err = e1eff * (x[ml] - x1eff);
  integral_err *= integral_err;
 
  if (mu == ml) {
    double ierr = e[ml] * (xneff - x1eff);
    integral_err += ierr * ierr;
  } else if (mu == ml + 1) {
    integral_val += (s[mu] + s[ml]) * (x[mu] - x[ml]);
    double err_lo = e[ml] * (x[ml + 1] - x1eff);
    double err_hi = e[mu] * (x[mu - 1] - xneff);
    integral_err += err_lo * err_lo + err_hi * err_hi;
  } else {
    for (auto i = static_cast<int>(ml); i < mu; ++i) {
      integral_val += (s[i + 1] + s[i]) * (x[i + 1] - x[i]);
      if (i < mu - 1) {
        double ierr = e[i + 1] * (x[i + 2] - x[i]);
        integral_err += ierr * ierr;
      }
    }
  }
 
  // x[mu] -> xmax
  integral_val += (xneff - x[mu]) * (s[mu] + sneff);
  double err_tmp = eneff * (xneff - x[mu]);
  integral_err += err_tmp * err_tmp;
 
  integral_val *= 0.5;
  integral_err = 0.5 * sqrt(integral_err);
}
 
void GetEi2::storeEi(const double ei) const {
  Property *incident_energy = new PropertyWithValue<double>("Ei", ei, Direction::Input);
  m_input_ws->mutableRun().addProperty(incident_energy, true);
}
} // namespace Mantid::Algorithms