Fit1D.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 +
//----------------------------------------------------------------------
// Includes
//----------------------------------------------------------------------
#include "MantidCurveFitting/Algorithms/Fit1D.h"
#include "MantidAPI/Axis.h"
#include "MantidAPI/ITableWorkspace.h"
#include "MantidAPI/TableRow.h"
#include "MantidAPI/WorkspaceFactory.h"
#include "MantidDataObjects/Workspace2D.h"
#include "MantidKernel/BoundedValidator.h"
#include "MantidKernel/Exception.h"
#include "MantidKernel/StringTokenizer.h"
#include "MantidKernel/UnitFactory.h"
 
#include <gsl/gsl_blas.h>
#include <gsl/gsl_multifit_nlin.h>
#include <gsl/gsl_multimin.h>
#include <gsl/gsl_statistics.h>
#include <gsl/gsl_version.h>
 
#include <cmath>
#include <numeric>
#include <sstream>
 
namespace Mantid::CurveFitting::Algorithms {
 
using namespace Kernel;
using API::Jacobian;
using API::MatrixWorkspace;
using API::MatrixWorkspace_const_sptr;
using API::Progress;
using API::WorkspaceProperty;
 
class JacobianImpl : public Jacobian {
public:
  JacobianImpl() : Jacobian(), m_J(nullptr) {};
 
  std::map<int, int> m_map;
  void set(size_t iY, size_t iP, double value) override {
    int j = m_map[static_cast<int>(iP)];
    if (j >= 0)
      gsl_matrix_set(m_J, iY, j, value);
  }
  double get(size_t iY, size_t iP) override {
    int j = m_map[static_cast<int>(iP)];
    if (j >= 0)
      return gsl_matrix_get(m_J, iY, j);
    return 0.0;
  }
  void zero() override { gsl_matrix_set_zero(m_J); }
  void setJ(gsl_matrix *J) { m_J = J; }
 
private:
  gsl_matrix *m_J;
};
 
struct FitData {
  FitData(Fit1D *fit, const std::string &fixed);
  size_t n;
  size_t p;
  double *X;
  const double *Y;
  double *sigmaData;
  Fit1D *fit1D;
  double *forSimplexLSwrap;
  double *parameters;
  std::vector<bool> active;
  JacobianImpl J;
};
 
static int gsl_f(const gsl_vector *x, void *params, gsl_vector *f) {
  auto fitParams = static_cast<FitData *>(params);
  for (size_t i = 0, j = 0; i < fitParams->active.size(); i++)
    if (fitParams->active[i])
      fitParams->parameters[i] = x->data[j++];
 
  fitParams->fit1D->function(fitParams->parameters, f->data, fitParams->X, fitParams->n);
 
  // function() return calculated data values. Need to convert this values into
  // calculated-observed devided by error values used by GSL
 
  for (size_t i = 0; i < fitParams->n; i++)
    f->data[i] = (f->data[i] - fitParams->Y[i]) / fitParams->sigmaData[i];
 
  return GSL_SUCCESS;
}
 
static int gsl_df(const gsl_vector *x, void *params, gsl_matrix *J) {
  auto fitParams = static_cast<FitData *>(params);
  for (size_t i = 0, j = 0; i < fitParams->active.size(); i++) {
    if (fitParams->active[i])
      fitParams->parameters[i] = x->data[j++];
  }
 
  fitParams->J.setJ(J);
 
  fitParams->fit1D->functionDeriv(fitParams->parameters, &fitParams->J, fitParams->X, fitParams->n);
 
  // functionDeriv() return derivatives of calculated data values. Need to
  // convert this values into derivatives of calculated-observed devided
  // by error values used by GSL
 
  for (size_t iY = 0; iY < fitParams->n; iY++)
    for (size_t iP = 0; iP < fitParams->p; iP++)
      J->data[iY * fitParams->p + iP] /= fitParams->sigmaData[iY];
 
  return GSL_SUCCESS;
}
 
static int gsl_fdf(const gsl_vector *x, void *params, gsl_vector *f, gsl_matrix *J) {
  gsl_f(x, params, f);
  gsl_df(x, params, J);
  return GSL_SUCCESS;
}
 
static double gsl_costFunction(const gsl_vector *x, void *params) {
  auto fitParams = static_cast<FitData *>(params);
  double *l_forSimplexLSwrap = fitParams->forSimplexLSwrap;
 
  for (size_t i = 0, j = 0; i < fitParams->active.size(); i++)
    if (fitParams->active[i])
      fitParams->parameters[i] = x->data[j++];
 
  fitParams->fit1D->function(fitParams->parameters, l_forSimplexLSwrap, fitParams->X, fitParams->n);
 
  // function() return calculated data values. Need to convert this values into
  // calculated-observed devided by error values used by GSL
  for (size_t i = 0; i < fitParams->n; i++)
    l_forSimplexLSwrap[i] = (l_forSimplexLSwrap[i] - fitParams->Y[i]) / fitParams->sigmaData[i];
 
  double retVal = 0.0;
 
  for (unsigned int i = 0; i < fitParams->n; i++)
    retVal += l_forSimplexLSwrap[i] * l_forSimplexLSwrap[i];
 
  return retVal;
}
 
void Fit1D::functionDeriv(const double *in, Jacobian *out, const double *xValues, const size_t nData) {
  UNUSED_ARG(in);
  UNUSED_ARG(out);
  UNUSED_ARG(xValues);
  UNUSED_ARG(nData);
  throw Exception::NotImplementedError("No derivative function provided");
}
 
void Fit1D::modifyInitialFittedParameters(std::vector<double> &fittedParameter) {
  (void)fittedParameter; // Avoid compiler warning
}
 
void Fit1D::modifyFinalFittedParameters(std::vector<double> &fittedParameter) {
  (void)fittedParameter; // Avoid compiler warning
}
 
void Fit1D::init() {
  declareProperty(std::make_unique<WorkspaceProperty<MatrixWorkspace>>("InputWorkspace", "", Direction::Input),
                  "Name of the input Workspace");
 
  auto mustBePositive = std::make_shared<BoundedValidator<int>>();
  mustBePositive->setLower(0);
  declareProperty("WorkspaceIndex", 0, mustBePositive,
                  "The Workspace to fit, uses the workspace numbering of the "
                  "spectra (default 0)");
  declareProperty("StartX", EMPTY_DBL(),
                  "A value of x in, or on the low x "
                  "boundary of, the first bin to "
                  "include in\n"
                  "the fit (default lowest value of x)");
  declareProperty("EndX", EMPTY_DBL(),
                  "A value in, or on the high x boundary "
                  "of, the last bin the fitting range\n"
                  "(default the highest value of x)");
 
  size_t i0 = getProperties().size();
 
  // declare parameters specific to a given fitting function
  declareParameters();
 
  // load the name of these specific parameter into a vector for later use
  const std::vector<Property *> props = getProperties();
  for (size_t i = i0; i < props.size(); i++) {
    m_parameterNames.emplace_back(props[i]->name());
  }
 
  declareProperty("Fix", "",
                  "A list of comma separated parameter names which "
                  "should be fixed in the fit");
  declareProperty("MaxIterations", 500, mustBePositive,
                  "Stop after this number of iterations if a good fit is not found");
  declareProperty("OutputStatus", "", Direction::Output);
  declareProperty("OutputChi2overDoF", 0.0, Direction::Output);
 
  // Disable default gsl error handler (which is to call abort!)
  gsl_set_error_handler_off();
 
  declareAdditionalProperties();
 
  declareProperty("Output", "",
                  "If not empty OutputParameters TableWorksace "
                  "and OutputWorkspace will be created.");
}
 
void Fit1D::exec() {
 
  // Custom initialization
  prepare();
 
  // check if derivative defined in derived class
  bool isDerivDefined = true;
  gsl_matrix *M = nullptr;
  try {
    const std::vector<double> inTest(m_parameterNames.size(), 1.0);
    std::vector<double> outTest(m_parameterNames.size());
    const double xValuesTest = 0;
    JacobianImpl J;
    M = gsl_matrix_alloc(m_parameterNames.size(), 1);
    J.setJ(M);
    // note nData set to zero (last argument) hence this should avoid further
    // memory problems
    functionDeriv(&(inTest.front()), &J, &xValuesTest, 0);
  } catch (Exception::NotImplementedError &) {
    isDerivDefined = false;
  }
  gsl_matrix_free(M);
 
  // Try to retrieve optional properties
  int histNumber = getProperty("WorkspaceIndex");
  const int maxInterations = getProperty("MaxIterations");
 
  // Get the input workspace
  MatrixWorkspace_const_sptr localworkspace = getProperty("InputWorkspace");
 
  // number of histogram is equal to the number of spectra
  const size_t numberOfSpectra = localworkspace->getNumberHistograms();
  // Check that the index given is valid
  if (histNumber >= static_cast<int>(numberOfSpectra)) {
    g_log.warning("Invalid Workspace index given, using first Workspace");
    histNumber = 0;
  }
 
  // Retrieve the spectrum into a vector
  Mantid::HistogramData::HistogramX const &XValues = localworkspace->x(histNumber);
  Mantid::HistogramData::HistogramY const &YValues = localworkspace->y(histNumber);
  Mantid::HistogramData::HistogramE const &YErrors = localworkspace->e(histNumber);
 
  // Read in the fitting range data that we were sent
  double startX = getProperty("StartX");
  double endX = getProperty("EndX");
  // check if the values had been set, otherwise use defaults
  if (isEmpty(startX)) {
    startX = XValues.front();
    modifyStartOfRange(startX); // does nothing by default but derived class may
                                // provide a more intelligent value
  }
  if (isEmpty(endX)) {
    endX = XValues.back();
    modifyEndOfRange(endX); // does nothing by default but derived class may
                            // previde a more intelligent value
  }
 
  int m_minX;
  int m_maxX;
 
  // Check the validity of startX
  if (startX < XValues.front()) {
    g_log.warning("StartX out of range! Set to start of frame.");
    startX = XValues.front();
  }
  // Get the corresponding bin boundary that comes before (or coincides with)
  // this value
  for (m_minX = 0; XValues[m_minX + 1] < startX; ++m_minX) {
  }
 
  // Check the validity of endX and get the bin boundary that come after (or
  // coincides with) it
  if (endX >= XValues.back() || endX < startX) {
    g_log.warning("EndX out of range! Set to end of frame");
    endX = XValues.back();
    m_maxX = static_cast<int>(YValues.size());
  } else {
    for (m_maxX = m_minX; XValues[m_maxX] < endX; ++m_maxX) {
    }
  }
 
  afterDataRangedDetermined(m_minX, m_maxX);
 
  // create and populate GSL data container warn user if l_data.n < l_data.p
  // since as a rule of thumb this is required as a minimum to obtained
  // 'accurate'
  // fitting parameter values.
 
  FitData l_data(this, getProperty("Fix"));
 
  l_data.n = m_maxX - m_minX; // m_minX and m_maxX are array index markers. I.e. e.g. 0 & 19.
  if (l_data.n == 0) {
    g_log.error("The data set is empty.");
    throw std::runtime_error("The data set is empty.");
  }
  if (l_data.n < l_data.p) {
    g_log.error("Number of data points less than number of parameters to be fitted.");
    throw std::runtime_error("Number of data points less than number of parameters to be fitted.");
  }
  l_data.X = new double[l_data.n];
  l_data.sigmaData = new double[l_data.n];
  l_data.forSimplexLSwrap = new double[l_data.n];
  l_data.parameters = new double[nParams()];
 
  // check if histogram data in which case use mid points of histogram bins
 
  const bool isHistogram = localworkspace->isHistogramData();
  for (unsigned int i = 0; i < l_data.n; ++i) {
    if (isHistogram)
      l_data.X[i] = 0.5 * (XValues[m_minX + i] + XValues[m_minX + i + 1]); // take mid-point if histogram bin
    else
      l_data.X[i] = XValues[m_minX + i];
  }
 
  l_data.Y = &YValues[m_minX];
 
  // check that no error is negative or zero
  for (unsigned int i = 0; i < l_data.n; ++i) {
    if (YErrors[m_minX + i] <= 0.0) {
      l_data.sigmaData[i] = 1.0;
    } else
      l_data.sigmaData[i] = YErrors[m_minX + i];
  }
 
  // create array of fitted parameter. Take these to those input by the user.
  // However, for doing the
  // underlying fitting it might be more efficient to actually perform the
  // fitting on some of other
  // form of the fitted parameters. For instance, take the Gaussian sigma
  // parameter. In practice it
  // in fact more efficient to perform the fitting not on sigma but 1/sigma^2.
  // The methods
  // modifyInitialFittedParameters() and modifyFinalFittedParameters() are used
  // to allow for this;
  // by default these function do nothing.
 
  m_fittedParameter.clear();
  for (size_t i = 0; i < nParams(); i++) {
    m_fittedParameter.emplace_back(getProperty(m_parameterNames[i]));
  }
  modifyInitialFittedParameters(m_fittedParameter); // does nothing except if overwritten by derived class
  for (size_t i = 0; i < nParams(); i++) {
    l_data.parameters[i] = m_fittedParameter[i];
  }
 
  // set-up initial guess for fit parameters
 
  gsl_vector *initFuncArg;
  initFuncArg = gsl_vector_alloc(l_data.p);
 
  for (size_t i = 0, j = 0; i < nParams(); i++) {
    if (l_data.active[i])
      gsl_vector_set(initFuncArg, j++, m_fittedParameter[i]);
  }
 
  // set-up GSL container to be used with GSL simplex algorithm
 
  gsl_multimin_function gslSimplexContainer;
  gslSimplexContainer.n = l_data.p; // n here refers to number of parameters
  gslSimplexContainer.f = &gsl_costFunction;
  gslSimplexContainer.params = &l_data;
 
  // set-up GSL least squares container
 
  gsl_multifit_function_fdf f;
  f.f = &gsl_f;
  f.df = &gsl_df;
  f.fdf = &gsl_fdf;
  f.n = l_data.n;
  f.p = l_data.p;
  f.params = &l_data;
 
  // set-up remaining GSL machinery for least squared
 
  const gsl_multifit_fdfsolver_type *T = gsl_multifit_fdfsolver_lmsder;
  gsl_multifit_fdfsolver *s = nullptr;
  if (isDerivDefined) {
    s = gsl_multifit_fdfsolver_alloc(T, l_data.n, l_data.p);
    gsl_multifit_fdfsolver_set(s, &f, initFuncArg);
  }
 
  // set-up remaining GSL machinery to use simplex algorithm
 
  const gsl_multimin_fminimizer_type *simplexType = gsl_multimin_fminimizer_nmsimplex;
  gsl_multimin_fminimizer *simplexMinimizer = nullptr;
  gsl_vector *simplexStepSize = nullptr;
  if (!isDerivDefined) {
    simplexMinimizer = gsl_multimin_fminimizer_alloc(simplexType, l_data.p);
    simplexStepSize = gsl_vector_alloc(l_data.p);
    gsl_vector_set_all(simplexStepSize,
                       1.0); // is this always a sensible starting step size?
    gsl_multimin_fminimizer_set(simplexMinimizer, &gslSimplexContainer, initFuncArg, simplexStepSize);
  }
 
  // finally do the fitting
 
  int iter = 0;
  int status;
  double finalCostFuncVal;
  auto dof = static_cast<double>(l_data.n - l_data.p); // dof stands for degrees of freedom
 
  // Standard least-squares used if derivative function defined otherwise
  // simplex
  Progress prog(this, 0.0, 1.0, maxInterations);
  if (isDerivDefined) {
 
    do {
      iter++;
      status = gsl_multifit_fdfsolver_iterate(s);
 
      if (status) // break if error
        break;
 
      status = gsl_multifit_test_delta(s->dx, s->x, 1e-4, 1e-4);
      prog.report();
    } while (status == GSL_CONTINUE && iter < maxInterations);
 
    double chi = gsl_blas_dnrm2(s->f);
    finalCostFuncVal = chi * chi / dof;
 
    // put final converged fitting values back into m_fittedParameter
    for (size_t i = 0, j = 0; i < nParams(); i++)
      if (l_data.active[i])
        m_fittedParameter[i] = gsl_vector_get(s->x, j++);
  } else {
    do {
      iter++;
      status = gsl_multimin_fminimizer_iterate(simplexMinimizer);
 
      if (status) // break if error
        break;
 
      double size = gsl_multimin_fminimizer_size(simplexMinimizer);
      status = gsl_multimin_test_size(size, 1e-2);
      prog.report();
    } while (status == GSL_CONTINUE && iter < maxInterations);
 
    finalCostFuncVal = simplexMinimizer->fval / dof;
 
    // put final converged fitting values back into m_fittedParameter
    for (unsigned int i = 0, j = 0; i < m_fittedParameter.size(); i++)
      if (l_data.active[i])
        m_fittedParameter[i] = gsl_vector_get(simplexMinimizer->x, j++);
  }
 
  modifyFinalFittedParameters(m_fittedParameter); // do nothing except if overwritten by derived class
 
  // Output summary to log file
 
  std::string reportOfFit = gsl_strerror(status);
 
  g_log.information() << "Iteration = " << iter << "\n"
                      << "Status = " << reportOfFit << "\n"
                      << "Chi^2/DoF = " << finalCostFuncVal << "\n";
  for (size_t i = 0; i < m_fittedParameter.size(); i++)
    g_log.information() << m_parameterNames[i] << " = " << m_fittedParameter[i] << "  \n";
 
  // also output summary to properties
 
  setProperty("OutputStatus", reportOfFit);
  setProperty("OutputChi2overDoF", finalCostFuncVal);
  for (size_t i = 0; i < m_fittedParameter.size(); i++)
    setProperty(m_parameterNames[i], m_fittedParameter[i]);
 
  std::string output = getProperty("Output");
  if (!output.empty()) {
    // calculate covariance matrix if derivatives available
 
    gsl_matrix *covar(nullptr);
    std::vector<double> standardDeviations;
    std::vector<double> sdExtended;
    if (isDerivDefined) {
      covar = gsl_matrix_alloc(l_data.p, l_data.p);
#if GSL_MAJOR_VERSION < 2
      gsl_multifit_covar(s->J, 0.0, covar);
#else
      gsl_matrix *J = gsl_matrix_alloc(l_data.n, l_data.p);
      gsl_multifit_fdfsolver_jac(s, J);
      gsl_multifit_covar(J, 0.0, covar);
      gsl_matrix_free(J);
#endif
 
      int iPNotFixed = 0;
      for (size_t i = 0; i < nParams(); i++) {
        sdExtended.emplace_back(1.0);
        if (l_data.active[i]) {
          sdExtended[i] = sqrt(gsl_matrix_get(covar, iPNotFixed, iPNotFixed));
          iPNotFixed++;
        }
      }
      modifyFinalFittedParameters(sdExtended);
      for (size_t i = 0; i < nParams(); i++)
        if (l_data.active[i])
          standardDeviations.emplace_back(sdExtended[i]);
 
      declareProperty(std::make_unique<WorkspaceProperty<API::ITableWorkspace>>("OutputNormalisedCovarianceMatrix", "",
                                                                                Direction::Output),
                      "The name of the TableWorkspace in which to store the final "
                      "covariance matrix");
      setPropertyValue("OutputNormalisedCovarianceMatrix", output + "_NormalisedCovarianceMatrix");
 
      Mantid::API::ITableWorkspace_sptr m_covariance =
          Mantid::API::WorkspaceFactory::Instance().createTable("TableWorkspace");
      m_covariance->addColumn("str", "Name");
      std::vector<std::string> paramThatAreFitted; // used for populating 1st "name" column
      for (size_t i = 0; i < nParams(); i++) {
        if (l_data.active[i]) {
          m_covariance->addColumn("double", m_parameterNames[i]);
          paramThatAreFitted.emplace_back(m_parameterNames[i]);
        }
      }
 
      for (size_t i = 0; i < l_data.p; i++) {
 
        Mantid::API::TableRow row = m_covariance->appendRow();
        row << paramThatAreFitted[i];
        for (size_t j = 0; j < l_data.p; j++) {
          if (j == i)
            row << 1.0;
          else {
            row << 100.0 * gsl_matrix_get(covar, i, j) /
                       sqrt(gsl_matrix_get(covar, i, i) * gsl_matrix_get(covar, j, j));
          }
        }
      }
 
      setProperty("OutputNormalisedCovarianceMatrix", m_covariance);
    }
 
    declareProperty(
        std::make_unique<WorkspaceProperty<API::ITableWorkspace>>("OutputParameters", "", Direction::Output),
        "The name of the TableWorkspace in which to store the "
        "final fit parameters");
    declareProperty(std::make_unique<WorkspaceProperty<MatrixWorkspace>>("OutputWorkspace", "", Direction::Output),
                    "Name of the output Workspace holding resulting simlated spectrum");
 
    setPropertyValue("OutputParameters", output + "_Parameters");
    setPropertyValue("OutputWorkspace", output + "_Workspace");
 
    // Save the final fit parameters in the output table workspace
    Mantid::API::ITableWorkspace_sptr m_result =
        Mantid::API::WorkspaceFactory::Instance().createTable("TableWorkspace");
    m_result->addColumn("str", "Name");
    m_result->addColumn("double", "Value");
    if (isDerivDefined)
      m_result->addColumn("double", "Error");
    Mantid::API::TableRow firstRow = m_result->appendRow();
    firstRow << "Chi^2/DoF" << finalCostFuncVal;
 
    for (size_t i = 0; i < nParams(); i++) {
      Mantid::API::TableRow row = m_result->appendRow();
      row << m_parameterNames[i] << m_fittedParameter[i];
      if (isDerivDefined && l_data.active[i]) {
        // perhaps want to scale standard deviations with sqrt(finalCostFuncVal)
        row << sdExtended[i];
      }
    }
    setProperty("OutputParameters", m_result);
 
    // Save the fitted and simulated spectra in the output workspace
    MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
    int iSpec = getProperty("WorkspaceIndex");
    Mantid::HistogramData::HistogramX const &inputX = inputWorkspace->x(iSpec);
    Mantid::HistogramData::HistogramY const &inputY = inputWorkspace->y(iSpec);
 
    int histN = isHistogram ? 1 : 0;
    Mantid::DataObjects::Workspace2D_sptr ws = std::dynamic_pointer_cast<Mantid::DataObjects::Workspace2D>(
        Mantid::API::WorkspaceFactory::Instance().create("Workspace2D", 3, l_data.n + histN, l_data.n));
    ws->setTitle("");
    ws->getAxis(0)->unit() = inputWorkspace->getAxis(0)->unit(); //    UnitFactory::Instance().create("TOF");
 
    for (int i = 0; i < 3; i++)
      ws->mutableX(i).assign(inputX.begin() + m_minX, inputX.begin() + m_maxX + histN);
 
    ws->mutableY(0).assign(inputY.begin() + m_minX, inputY.begin() + m_maxX);
 
    auto &Y = ws->mutableY(1);
    auto &E = ws->mutableY(2);
 
    auto lOut = new double[l_data.n];                 // to capture output from call to function()
    modifyInitialFittedParameters(m_fittedParameter); // does nothing except if
                                                      // overwritten by derived
                                                      // class
    function(&m_fittedParameter[0], lOut, l_data.X, l_data.n);
    modifyInitialFittedParameters(m_fittedParameter); // reverse the effect of
    // modifyInitialFittedParameters - if any
 
    for (unsigned int i = 0; i < l_data.n; i++) {
      Y[i] = lOut[i];
      E[i] = l_data.Y[i] - Y[i];
    }
 
    delete[] lOut;
 
    setProperty("OutputWorkspace", std::dynamic_pointer_cast<MatrixWorkspace>(ws));
 
    if (isDerivDefined)
      gsl_matrix_free(covar);
  }
 
  // clean up dynamically allocated gsl stuff
 
  if (isDerivDefined)
    gsl_multifit_fdfsolver_free(s);
  else {
    gsl_vector_free(simplexStepSize);
    gsl_multimin_fminimizer_free(simplexMinimizer);
  }
 
  delete[] l_data.X;
  delete[] l_data.sigmaData;
  delete[] l_data.forSimplexLSwrap;
  delete[] l_data.parameters;
  gsl_vector_free(initFuncArg);
}
 
FitData::FitData(Fit1D *fit, const std::string &fixed)
    : n(0), X(nullptr), Y(nullptr), sigmaData(nullptr), fit1D(fit), forSimplexLSwrap(nullptr), parameters(nullptr) {
  Mantid::Kernel::StringTokenizer namesStrTok(fixed, ",", Mantid::Kernel::StringTokenizer::TOK_TRIM);
  active.insert(active.begin(), fit1D->m_parameterNames.size(), true);
  for (const auto &name : namesStrTok) {
    std::vector<std::string>::const_iterator i =
        std::find(fit1D->m_parameterNames.begin(), fit1D->m_parameterNames.end(), name);
    if (i != fit1D->m_parameterNames.end()) {
      active[i - fit1D->m_parameterNames.begin()] = false;
    } else
      throw std::invalid_argument("Attempt to fix non-existing parameter " + name);
  }
  p = 0;
  for (int i = 0; i < int(active.size()); i++)
    if (active[i])
      J.m_map[i] = static_cast<int>(p++);
    else
      J.m_map[i] = -1;
}
 
} // namespace Mantid::CurveFitting::Algorithms