C++ IAlgorithm_sptr::execute方法代码示例

/**
 * Places the detector at the right sample_detector_distance
 */
void LoadSpice2D::moveDetector(double sample_detector_distance,
                               double translation_distance) {
  // Some tests fail if the detector is moved here.
  // TODO: Move the detector here and not the SANSLoad
  UNUSED_ARG(translation_distance);

  // Move the detector to the right position
  API::IAlgorithm_sptr mover = createChildAlgorithm("MoveInstrumentComponent");

  // Finding the name of the detector object.
  std::string detID =
      m_workspace->getInstrument()->getStringParameter("detector-name")[0];

  g_log.information("Moving " + detID);
  try {
    mover->setProperty<API::MatrixWorkspace_sptr>("Workspace", m_workspace);
    mover->setProperty("ComponentName", detID);
    mover->setProperty("Z", sample_detector_distance / 1000.0);
    // mover->setProperty("X", -translation_distance);
    mover->execute();
  } catch (std::invalid_argument &e) {
    g_log.error("Invalid argument to MoveInstrumentComponent Child Algorithm");
    g_log.error(e.what());
  } catch (std::runtime_error &e) {
    g_log.error(
        "Unable to successfully run MoveInstrumentComponent Child Algorithm");
    g_log.error(e.what());
  }
}

/**
 * Execute the algorithm.
 */
void FitResolutionConvolvedModel::exec() {
  API::IAlgorithm_sptr fit = createFittingAlgorithm();
  fit->setPropertyValue("Function", createFunctionString());
  fit->setProperty("InputWorkspace", getPropertyValue(INPUT_WS_NAME));
  fit->setProperty("DomainType",
                   "Simple"); // Parallel not quite giving correct answers
  fit->setProperty("Minimizer", "Levenberg-MarquardtMD");

  const int maxIter = niterations();
  fit->setProperty("MaxIterations", maxIter);
  fit->setProperty("CreateOutput", true);
  fit->setPropertyValue("Output", getPropertyValue(SIMULATED_NAME));

  try {
    fit->execute();
  } catch (std::exception &exc) {
    throw std::runtime_error(
        std::string("FitResolutionConvolvedModel - Error running Fit: ") +
        exc.what());
  }

  // Pass on the relevant properties
  IMDEventWorkspace_sptr simulatedData = fit->getProperty("OutputWorkspace");
  this->setProperty(SIMULATED_NAME, simulatedData);

  if (this->existsProperty(OUTPUT_PARS)) {
    ITableWorkspace_sptr outputPars = fit->getProperty("OutputParameters");
    setProperty(OUTPUT_PARS, outputPars);
  }
  if (this->existsProperty(OUTPUTCOV_MATRIX)) {
    ITableWorkspace_sptr covarianceMatrix =
        fit->getProperty("OutputNormalisedCovarianceMatrix");
    setProperty(OUTPUTCOV_MATRIX, covarianceMatrix);
  }
}

/**
 * Fit peak without background i.e, with background removed
 *  inspired from FitPowderDiffPeaks.cpp
 *  copied from PoldiPeakDetection2.cpp
 *
 @param workspaceindex :: indice of the row to use
 @param center :: gaussian parameter - center
 @param sigma :: gaussian parameter - width
 @param height :: gaussian parameter - height
 @param startX :: fit range - start X value
 @param endX :: fit range - end X value
 @returns A boolean status flag, true for fit success, false else
 */
bool ConvertEmptyToTof::doFitGaussianPeak(int workspaceindex, double &center,
                                          double &sigma, double &height,
                                          double startX, double endX) {

  g_log.debug("Calling doFitGaussianPeak...");

  // 1. Estimate
  sigma = sigma * 0.5;

  // 2. Use factory to generate Gaussian
  auto temppeak = API::FunctionFactory::Instance().createFunction("Gaussian");
  auto gaussianpeak = boost::dynamic_pointer_cast<API::IPeakFunction>(temppeak);
  gaussianpeak->setHeight(height);
  gaussianpeak->setCentre(center);
  gaussianpeak->setFwhm(sigma);

  // 3. Constraint
  double centerleftend = center - sigma * 0.5;
  double centerrightend = center + sigma * 0.5;
  std::ostringstream os;
  os << centerleftend << " < PeakCentre < " << centerrightend;
  auto *centerbound = API::ConstraintFactory::Instance().createInitialized(
      gaussianpeak.get(), os.str(), false);
  gaussianpeak->addConstraint(centerbound);

  g_log.debug("Calling createChildAlgorithm : Fit...");
  // 4. Fit
  API::IAlgorithm_sptr fitalg = createChildAlgorithm("Fit", -1, -1, true);
  fitalg->initialize();

  fitalg->setProperty(
      "Function", boost::dynamic_pointer_cast<API::IFunction>(gaussianpeak));
  fitalg->setProperty("InputWorkspace", m_inputWS);
  fitalg->setProperty("WorkspaceIndex", workspaceindex);
  fitalg->setProperty("Minimizer", "Levenberg-MarquardtMD");
  fitalg->setProperty("CostFunction", "Least squares");
  fitalg->setProperty("MaxIterations", 1000);
  fitalg->setProperty("Output", "FitGaussianPeak");
  fitalg->setProperty("StartX", startX);
  fitalg->setProperty("EndX", endX);

  // 5.  Result
  bool successfulfit = fitalg->execute();
  if (!fitalg->isExecuted() || !successfulfit) {
    // Early return due to bad fit
    g_log.warning() << "Fitting Gaussian peak for peak around "
                    << gaussianpeak->centre() << '\n';
    return false;
  }

  // 6. Get result
  center = gaussianpeak->centre();
  height = gaussianpeak->height();
  double fwhm = gaussianpeak->fwhm();

  return fwhm > 0.0;
}

/**  Group detectors in the workspace.
 *  @param ws :: A local workspace
 *  @param spectraList :: A list of spectra to group.
 */
void PlotAsymmetryByLogValue::groupDetectors(API::MatrixWorkspace_sptr& ws,const std::vector<int>& spectraList)
{
    API::IAlgorithm_sptr group = createChildAlgorithm("GroupDetectors");
    group->setProperty("InputWorkspace",ws);
    group->setProperty("SpectraList",spectraList);
    group->setProperty("KeepUngroupedSpectra",true);
    group->execute();
    ws = group->getProperty("OutputWorkspace");
}

  /** Fit function
    * Minimizer: "Levenberg-MarquardtMD"/"Simplex"
   */
  bool RefinePowderInstrumentParameters2::doFitFunction(IFunction_sptr function, Workspace2D_sptr dataws, int wsindex,
                                                        string minimizer, int numiters, double& chi2, string& fitstatus)
  {
    // 0. Debug output
    stringstream outss;
    outss << "Fit function: " << m_positionFunc->asString() << endl << "Data To Fit: \n";
    for (size_t i = 0; i < dataws->readX(0).size(); ++i)
      outss << dataws->readX(wsindex)[i] << "\t\t" << dataws->readY(wsindex)[i] << "\t\t"
            << dataws->readE(wsindex)[i] << "\n";
    g_log.information() << outss.str();

    // 1. Create and setup fit algorithm
    API::IAlgorithm_sptr fitalg = createChildAlgorithm("Fit", 0.0, 0.2, true);
    fitalg->initialize();

    fitalg->setProperty("Function", function);
    fitalg->setProperty("InputWorkspace", dataws);
    fitalg->setProperty("WorkspaceIndex", wsindex);
    fitalg->setProperty("Minimizer", minimizer);
    fitalg->setProperty("CostFunction", "Least squares");
    fitalg->setProperty("MaxIterations", numiters);
    fitalg->setProperty("CalcErrors", true);

    // 2. Fit
    bool successfulfit = fitalg->execute();
    if (!fitalg->isExecuted() || ! successfulfit)
    {
      // Early return due to bad fit
      g_log.warning("Fitting to instrument geometry function failed. ");
      chi2 = DBL_MAX;
      fitstatus = "Minimizer throws exception.";
      return false;
    }

    // 3. Understand solution
    chi2 = fitalg->getProperty("OutputChi2overDoF");
    string tempfitstatus = fitalg->getProperty("OutputStatus");
    fitstatus = tempfitstatus;

    bool goodfit = fitstatus.compare("success") == 0;

    stringstream dbss;
    dbss << "Fit Result (GSL):  Chi^2 = " << chi2
         << "; Fit Status = " << fitstatus << ", Return Bool = " << goodfit << std::endl;
    vector<string> funcparnames = function->getParameterNames();
    for (size_t i = 0; i < funcparnames.size(); ++i)
      dbss << funcparnames[i] << " = " << setw(20) << function->getParameter(funcparnames[i])
           << " +/- " << function->getError(i) << "\n";
    g_log.debug() << dbss.str();

    return goodfit;
  }

void LoadNexusMonitors2::runLoadLogs(const std::string filename,
                                     API::MatrixWorkspace_sptr localWorkspace) {
  // do the actual work
  API::IAlgorithm_sptr loadLogs = createChildAlgorithm("LoadNexusLogs");

  // Now execute the Child Algorithm. Catch and log any error, but don't stop.
  try {
    g_log.information() << "Loading logs from NeXus file..." << std::endl;
    loadLogs->setPropertyValue("Filename", filename);
    loadLogs->setProperty<API::MatrixWorkspace_sptr>("Workspace",
                                                     localWorkspace);
    loadLogs->execute();
  } catch (...) {
    g_log.error() << "Error while loading Logs from Nexus. Some sample logs "
                     "may be missing." << std::endl;
  }
}

/**
  * Gaussian fit to determine peak position if no user position given.
  *
  * @return :: detector position of the peak: Gaussian fit and position
  * of the maximum (serves as start value for the optimization)
  */
double LoadILLReflectometry::reflectometryPeak() {
  if (!isDefault("BeamCentre")) {
    return getProperty("BeamCentre");
  }
  size_t startIndex;
  size_t endIndex;
  std::tie(startIndex, endIndex) =
      fitIntegrationWSIndexRange(*m_localWorkspace);
  IAlgorithm_sptr integration = createChildAlgorithm("Integration");
  integration->initialize();
  integration->setProperty("InputWorkspace", m_localWorkspace);
  integration->setProperty("OutputWorkspace", "__unused_for_child");
  integration->setProperty("StartWorkspaceIndex", static_cast<int>(startIndex));
  integration->setProperty("EndWorkspaceIndex", static_cast<int>(endIndex));
  integration->execute();
  MatrixWorkspace_sptr integralWS = integration->getProperty("OutputWorkspace");
  IAlgorithm_sptr transpose = createChildAlgorithm("Transpose");
  transpose->initialize();
  transpose->setProperty("InputWorkspace", integralWS);
  transpose->setProperty("OutputWorkspace", "__unused_for_child");
  transpose->execute();
  integralWS = transpose->getProperty("OutputWorkspace");
  rebinIntegralWorkspace(*integralWS);
  // determine initial height: maximum value
  const auto maxValueIt =
      std::max_element(integralWS->y(0).cbegin(), integralWS->y(0).cend());
  const double height = *maxValueIt;
  // determine initial centre: index of the maximum value
  const size_t maxIndex = std::distance(integralWS->y(0).cbegin(), maxValueIt);
  const double centreByMax = static_cast<double>(maxIndex);
  g_log.debug() << "Peak maximum position: " << centreByMax << '\n';
  // determine sigma
  const auto &ys = integralWS->y(0);
  auto lessThanHalfMax = [height](const double x) { return x < 0.5 * height; };
  using IterType = HistogramData::HistogramY::const_iterator;
  std::reverse_iterator<IterType> revMaxValueIt{maxValueIt};
  auto revMinFwhmIt = std::find_if(revMaxValueIt, ys.crend(), lessThanHalfMax);
  auto maxFwhmIt = std::find_if(maxValueIt, ys.cend(), lessThanHalfMax);
  std::reverse_iterator<IterType> revMaxFwhmIt{maxFwhmIt};
  if (revMinFwhmIt == ys.crend() || maxFwhmIt == ys.cend()) {
    g_log.warning() << "Couldn't determine fwhm of beam, using position of max "
                       "value as beam center.\n";
    return centreByMax;
  }
  const double fwhm =
      static_cast<double>(std::distance(revMaxFwhmIt, revMinFwhmIt) + 1);
  g_log.debug() << "Initial fwhm (full width at half maximum): " << fwhm
                << '\n';
  // generate Gaussian
  auto func =
      API::FunctionFactory::Instance().createFunction("CompositeFunction");
  auto sum = boost::dynamic_pointer_cast<API::CompositeFunction>(func);
  func = API::FunctionFactory::Instance().createFunction("Gaussian");
  auto gaussian = boost::dynamic_pointer_cast<API::IPeakFunction>(func);
  gaussian->setHeight(height);
  gaussian->setCentre(centreByMax);
  gaussian->setFwhm(fwhm);
  sum->addFunction(gaussian);
  func = API::FunctionFactory::Instance().createFunction("LinearBackground");
  func->setParameter("A0", 0.);
  func->setParameter("A1", 0.);
  sum->addFunction(func);
  // call Fit child algorithm
  API::IAlgorithm_sptr fit = createChildAlgorithm("Fit");
  fit->initialize();
  fit->setProperty("Function",
                   boost::dynamic_pointer_cast<API::IFunction>(sum));
  fit->setProperty("InputWorkspace", integralWS);
  fit->setProperty("StartX", centreByMax - 3 * fwhm);
  fit->setProperty("EndX", centreByMax + 3 * fwhm);
  fit->execute();
  const std::string fitStatus = fit->getProperty("OutputStatus");
  if (fitStatus != "success") {
    g_log.warning("Fit not successful, using position of max value.\n");
    return centreByMax;
  }
  const auto centre = gaussian->centre();
  g_log.debug() << "Sigma: " << gaussian->fwhm() << '\n';
  g_log.debug() << "Estimated peak position: " << centre << '\n';
  return centre;
}

//.........这里部分代码省略.........
          continue;
        }

        if (data.i < 0 && data.indx.empty())
        {
          g_log.warning() << "Zero spectra selected for fitting in workspace " << wsNames[i].name << '\n';
          continue;
        }

        int j,jend;
        if (data.i >= 0)
        {
          j = data.i;
          jend = j + 1;
        }
        else
        {// no need to check data.indx.empty()
          j = data.indx.front();
          jend = data.indx.back() + 1;
        }

        dProg /= abs(jend - j);
        for(;j < jend;++j)
        {

          // Find the log value: it is either a log-file value or simply the workspace number
          double logValue;
          if (logName.empty())
          {
            API::Axis* axis = data.ws->getAxis(1);
            logValue = (*axis)(j);
          }
          else if (logName != "SourceName")
          {
            Kernel::Property* prop = data.ws->run().getLogData(logName);
            if (!prop)
            {
              throw std::invalid_argument("Log value "+logName+" does not exist");
            }
            TimeSeriesProperty<double>* logp = 
              dynamic_cast<TimeSeriesProperty<double>*>(prop); 
            logValue = logp->lastValue();
          }

          std::string resFun = fun;
          std::vector<double> errors;
          double chi2;

          try
          {
            // Fit the function
            API::IAlgorithm_sptr fit = createSubAlgorithm("Fit");
            fit->initialize();
            fit->setProperty("InputWorkspace",data.ws);
            //fit->setPropertyValue("InputWorkspace",data.ws->getName());
            fit->setProperty("WorkspaceIndex",j);
            fit->setPropertyValue("Function",fun);
            fit->setPropertyValue("StartX",getPropertyValue("StartX"));
            fit->setPropertyValue("EndX",getPropertyValue("EndX"));
            fit->setPropertyValue("Minimizer",getPropertyValue("Minimizer"));
            fit->setPropertyValue("CostFunction",getPropertyValue("CostFunction"));
            fit->execute();
            resFun = fit->getPropertyValue("Function");
            errors = fit->getProperty("Errors");
            chi2 = fit->getProperty("OutputChi2overDoF");
          }
          catch(...)
          {
            g_log.error("Error in Fit subalgorithm");
            throw;
          }

          if (sequential)
          {
            fun = resFun;
          }

          // Extract the fitted parameters and put them into the result table
          TableRow row = result->appendRow();
          if (isDataName)
          {
            row << wsNames[i].name;
          }
          else
          {
            row << logValue;
          }
          ifun = FunctionFactory::Instance().createInitialized(resFun);
          for(size_t iPar=0;iPar<ifun->nParams();++iPar)
          {
            row << ifun->getParameter(iPar) << errors[iPar];
          }
          row << chi2;
          delete ifun;
          Prog += dProg;
          progress(Prog);
          interruption_point();
        } // for(;j < jend;++j)
      }
    }

    /** Get a workspace identified by an InputData structure. 
      * @param data :: InputData with name and either spec or i fields defined. 
      * @return InputData structure with the ws field set if everything was OK.
      */
    PlotPeakByLogValue::InputData PlotPeakByLogValue::getWorkspace(const InputData& data)
    {
      InputData out(data);
      if (API::AnalysisDataService::Instance().doesExist(data.name))
      {
        DataObjects::Workspace2D_sptr ws = boost::dynamic_pointer_cast<DataObjects::Workspace2D>(
          API::AnalysisDataService::Instance().retrieve(data.name));
        if (ws)
        {
          out.ws = ws;
        }
        else
        {
          return data;
        }
      }
      else
      {
        std::ifstream fil(data.name.c_str());
        if (!fil)
        {
          g_log.warning() << "File "<<data.name<<" does not exist\n";
          return data;
        }
        fil.close();
        std::string::size_type i = data.name.find_last_of('.');
        if (i == std::string::npos)
        {
          g_log.warning() << "Cannot open file "<<data.name<<"\n";
          return data;
        }
        std::string ext = data.name.substr(i);
        try
        {
          API::IAlgorithm_sptr load = createSubAlgorithm("Load");
          load->initialize();
          load->setPropertyValue("FileName",data.name);
          load->execute();
          if (load->isExecuted())
          {
            API::Workspace_sptr rws = load->getProperty("OutputWorkspace");
            if (rws)
            {
              DataObjects::Workspace2D_sptr ws = boost::dynamic_pointer_cast<DataObjects::Workspace2D>(rws);
              if (ws) 
              {
                out.ws = ws;
              }
              else
              {
                API::WorkspaceGroup_sptr gws = boost::dynamic_pointer_cast<API::WorkspaceGroup>(rws);
                if (gws)
                {
                  std::vector<std::string> wsNames = gws->getNames();
                  std::string propName = "OUTPUTWORKSPACE_" + boost::lexical_cast<std::string>(data.period);
                  if (load->existsProperty(propName))
                  {
                    Workspace_sptr rws1 = load->getProperty(propName);
                    out.ws = boost::dynamic_pointer_cast<DataObjects::Workspace2D>(rws1);
                  }
                }
              }
            }
          }
        }
        catch(std::exception& e)
        {
          g_log.error(e.what());
          return data;
        }
      }

      if (!out.ws) return data;

      API::Axis* axis = out.ws->getAxis(1);
      if (axis->isSpectra())
      {// spectra axis
        if (out.spec < 0)
        {
          if (out.i >= 0)
          {
            out.spec = axis->spectraNo(out.i);
          }
          else
          {// i < 0 && spec < 0 => use start and end
            for(size_t i=0;i<axis->length();++i)
            {
              double s = double(axis->spectraNo(i));
              if (s >= out.start && s <= out.end)
              {
                out.indx.push_back(static_cast<int>(i));
              }
            }
          }
        }
        else
//.........这里部分代码省略.........

//.........这里部分代码省略.........
      ws->getAxis(0)->unit() = Kernel::UnitFactory::Instance().create("Wavelength");
      ws->setYUnit("");
      API::Workspace_sptr workspace = boost::static_pointer_cast<API::Workspace>(ws);
      setProperty("OutputWorkspace", workspace);

      // Parse out each pixel. Pixels can be separated by white space, a tab, or an end-of-line character
      Poco::StringTokenizer pixels(data_str, " \n\t", Poco::StringTokenizer::TOK_TRIM | Poco::StringTokenizer::TOK_IGNORE_EMPTY);
      Poco::StringTokenizer::Iterator pixel = pixels.begin();

      // Check that we don't keep within the size of the workspace
      size_t pixelcount = pixels.count();
      if( pixelcount != static_cast<size_t>(numberXPixels*numberYPixels) )
      {
        throw Kernel::Exception::FileError("Inconsistent data set: "
            "There were more data pixels found than declared in the Spice XML meta-data.", fileName);
      }
      if( numSpectra == 0 )
      {
        throw Kernel::Exception::FileError("Empty data set: the data file has no pixel data.", fileName);
      }

      // Go through all detectors/channels
      int ipixel = 0;

      // Store monitor count
      store_value(ws, ipixel++, monitorCounts, monitorCounts>0 ? sqrt(monitorCounts) : 0.0,
          wavelength, dwavelength);

      // Store counting time
      store_value(ws, ipixel++, countingTime, 0.0, wavelength, dwavelength);

      // Store detector pixels
      while (pixel != pixels.end())
      {
        //int ix = ipixel%npixelsx;
        //int iy = (int)ipixel/npixelsx;

        // Get the count value and assign it to the right bin
        double count = 0.0;
        from_string<double>(count, *pixel, std::dec);

        // Data uncertainties, computed according to the HFIR/IGOR reduction code
        // The following is what I would suggest instead...
        // error = count > 0 ? sqrt((double)count) : 0.0;
        double error = sqrt( 0.5 + fabs( count - 0.5 ));

        store_value(ws, ipixel, count, error, wavelength, dwavelength);

        // Set the spectrum number
        ws->getAxis(1)->setValue(ipixel, ipixel);

        ++pixel;
        ipixel++;
      }

      // run load instrument
      runLoadInstrument(instrument, ws);
      runLoadMappingTable(ws, numberXPixels, numberYPixels);

      // Set the run properties
      ws->mutableRun().addProperty("sample-detector-distance", distance, "mm", true);
      ws->mutableRun().addProperty("beam-trap-diameter", beam_trap_diam, "mm", true);
      ws->mutableRun().addProperty("number-of-guides", nguides, true);
      ws->mutableRun().addProperty("source-sample-distance", source_distance, "mm", true);
      ws->mutableRun().addProperty("source-aperture-diameter", source_apert, "mm", true);
      ws->mutableRun().addProperty("sample-aperture-diameter", sample_apert, "mm", true);
      ws->mutableRun().addProperty("sample-thickness", sample_thickness, "cm", true);
      ws->mutableRun().addProperty("wavelength", wavelength, "Angstrom", true);
      ws->mutableRun().addProperty("wavelength-spread", dwavelength, "Angstrom", true);
      ws->mutableRun().addProperty("timer", countingTime, "sec", true);
      ws->mutableRun().addProperty("monitor", monitorCounts, "", true);
      ws->mutableRun().addProperty("start_time", start_time, "", true);
      ws->mutableRun().addProperty("run_start", start_time, "", true);

      // Move the detector to the right position
      API::IAlgorithm_sptr mover = createChildAlgorithm("MoveInstrumentComponent");

      // Finding the name of the detector object.
      std::string detID = ws->getInstrument()->getStringParameter("detector-name")[0];

      g_log.information("Moving "+detID);
      try
      {
        mover->setProperty<API::MatrixWorkspace_sptr> ("Workspace", ws);
        mover->setProperty("ComponentName", detID);
        mover->setProperty("Z", distance/1000.0);
        mover->execute();
      } catch (std::invalid_argument& e)
      {
        g_log.error("Invalid argument to MoveInstrumentComponent Child Algorithm");
        g_log.error(e.what());
      } catch (std::runtime_error& e)
      {
        g_log.error("Unable to successfully run MoveInstrumentComponent Child Algorithm");
        g_log.error(e.what());
      }

      // Release the XML document memory
      pDoc->release();
    }

  void MaskBinsFromTable::exec()
  {
    MatrixWorkspace_sptr inputWS = getProperty("InputWorkspace");
    DataObjects::TableWorkspace_sptr paramWS = getProperty("MaskingInformation");

    // 1. Check input table workspace and column order
    g_log.debug() << "Lines of parameters workspace = " << paramWS->rowCount() << std::endl;

    bool colname_specx = false;
    if (!paramWS)
    {
      throw std::invalid_argument("Input table workspace is not accepted.");
    }
    else
    {
      std::vector<std::string> colnames = paramWS->getColumnNames();
      // check colum name order
      if (colnames.size() < 3)
      {
        g_log.error() << "Input MaskingInformation table workspace has fewer than 3 columns.  " << colnames.size()
                      << " columns indeed" << std::endl;
        throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
      }
      if (colnames[0].compare("XMin") == 0)
      {
          // 1. Style XMin, XMax, SpectraList. Check rest
          if (colnames[1].compare("XMax") != 0 || colnames[2].compare("SpectraList") != 0)
          {
              g_log.error() << "INput MaskingInformation table workspace has wrong column order. " << std::endl;
              throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
          }
      }
      else if (colnames[0].compare("SpectraList") == 0)
      {
          // 2. Style SpectraList, XMin, XMax
          colname_specx = true;
          if (colnames[1].compare("XMin") != 0 || colnames[2].compare("XMax") != 0)
          {
              g_log.error() << "INput MaskingInformation table workspace has wrong column order. " << std::endl;
              throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
          }
      }
      else
      {
          g_log.error() << "INput MaskingInformation table workspace has wrong column order. " << std::endl;
          throw std::invalid_argument("MaskingInformation (TableWorkspace) has too few columns.");
      }
    }

    // 2. Loop over all rows
    bool firstloop = true;
    API::MatrixWorkspace_sptr outputws = this->getProperty("OutputWorkspace");

    for (size_t ib = 0; ib < paramWS->rowCount(); ++ib)
    {
      API::TableRow therow = paramWS->getRow(ib);
      double xmin, xmax;
      std::string speclist;
      if (colname_specx)
      {
          therow >> speclist >> xmin >> xmax;
      }
      else
      {
          therow >> xmin >> xmax >> speclist;
      }

      g_log.debug() << "Row " << ib << " XMin = " << xmin << "  XMax = " << xmax << " SpectraList = " << speclist << std::endl;

      API::IAlgorithm_sptr maskbins = this->createChildAlgorithm("MaskBins", 0, 0.3, true);
      maskbins->initialize();
      if (firstloop)
      {
        maskbins->setProperty("InputWorkspace", inputWS);
        firstloop = false;
      }
      else
      {
        maskbins->setProperty("InputWorkspace", outputws);
      }
      maskbins->setProperty("OutputWorkspace", outputws);
      maskbins->setPropertyValue("SpectraList", speclist);
      maskbins->setProperty("XMin", xmin);
      maskbins->setProperty("XMax", xmax);

      bool isexec = maskbins->execute();
      if (!isexec)
      {
        g_log.error() << "MaskBins() is not executed for row " << ib << std::endl;
        throw std::runtime_error("MaskBins() is not executed");
      }

      outputws = maskbins->getProperty("OutputWorkspace");
      if (!outputws)
      {
        g_log.error() << "OutputWorkspace is not retrieved for row " << ib << ". " << std::endl;
        throw std::runtime_error("OutputWorkspace is not got from MaskBins");
      }
    }