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FitPeaks.cpp
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FitPeaks.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 "MantidAlgorithms/FitPeaks.h"
#include "MantidAPI/Axis.h"
#include "MantidAPI/CompositeFunction.h"
#include "MantidAPI/CostFunctionFactory.h"
#include "MantidAPI/FrameworkManager.h"
#include "MantidAPI/FuncMinimizerFactory.h"
#include "MantidAPI/FunctionFactory.h"
#include "MantidAPI/FunctionProperty.h"
#include "MantidAPI/MultiDomainFunction.h"
#include "MantidAPI/TableRow.h"
#include "MantidAPI/WorkspaceProperty.h"
#include "MantidAlgorithms/FindPeakBackground.h"
#include "MantidDataObjects/TableWorkspace.h"
#include "MantidDataObjects/Workspace2D.h"
#include "MantidDataObjects/WorkspaceCreation.h"
#include "MantidGeometry/IDetector.h"
#include "MantidGeometry/Instrument/Detector.h"
#include "MantidHistogramData/EstimatePolynomial.h"
#include "MantidHistogramData/Histogram.h"
#include "MantidHistogramData/HistogramBuilder.h"
#include "MantidHistogramData/HistogramIterator.h"
#include "MantidKernel/ArrayProperty.h"
#include "MantidKernel/BoundedValidator.h"
#include "MantidKernel/IValidator.h"
#include "MantidKernel/ListValidator.h"
#include "MantidKernel/StartsWithValidator.h"
#include "boost/algorithm/string.hpp"
#include "boost/algorithm/string/trim.hpp"
#include <limits>
#include <utility>
using namespace Mantid;
using namespace Algorithms::PeakParameterHelper;
using namespace Mantid::API;
using namespace Mantid::DataObjects;
using namespace Mantid::HistogramData;
using namespace Mantid::Kernel;
using namespace Mantid::Geometry;
using Mantid::HistogramData::Histogram;
using namespace std;
namespace Mantid::Algorithms {
namespace {
namespace PropertyNames {
const std::string INPUT_WKSP("InputWorkspace");
const std::string OUTPUT_WKSP("OutputWorkspace");
const std::string START_WKSP_INDEX("StartWorkspaceIndex");
const std::string STOP_WKSP_INDEX("StopWorkspaceIndex");
const std::string PEAK_CENTERS("PeakCenters");
const std::string PEAK_CENTERS_WKSP("PeakCentersWorkspace");
const std::string PEAK_FUNC("PeakFunction");
const std::string BACK_FUNC("BackgroundType");
const std::string FIT_WINDOW_LIST("FitWindowBoundaryList");
const std::string FIT_WINDOW_WKSP("FitPeakWindowWorkspace");
const std::string PEAK_WIDTH_PERCENT("PeakWidthPercent");
const std::string PEAK_PARAM_NAMES("PeakParameterNames");
const std::string PEAK_PARAM_VALUES("PeakParameterValues");
const std::string PEAK_PARAM_TABLE("PeakParameterValueTable");
const std::string FIT_FROM_RIGHT("FitFromRight");
const std::string MINIMIZER("Minimizer");
const std::string COST_FUNC("CostFunction");
const std::string MAX_FIT_ITER("MaxFitIterations");
const std::string BACKGROUND_Z_SCORE("FindBackgroundSigma");
const std::string HIGH_BACKGROUND("HighBackground");
const std::string POSITION_TOL("PositionTolerance");
const std::string PEAK_MIN_HEIGHT("MinimumPeakHeight");
const std::string CONSTRAIN_PEAK_POS("ConstrainPeakPositions");
const std::string OUTPUT_WKSP_MODEL("FittedPeaksWorkspace");
const std::string OUTPUT_WKSP_PARAMS("OutputPeakParametersWorkspace");
const std::string OUTPUT_WKSP_PARAM_ERRS("OutputParameterFitErrorsWorkspace");
const std::string RAW_PARAMS("RawPeakParameters");
} // namespace PropertyNames
} // namespace
namespace FitPeaksAlgorithm {
//----------------------------------------------------------------------------------------------
/// Holds all of the fitting information for a single spectrum
PeakFitResult::PeakFitResult(size_t num_peaks, size_t num_params) : m_function_parameters_number(num_params) {
// check input
if (num_peaks == 0 || num_params == 0)
throw std::runtime_error("No peak or no parameter error.");
//
m_fitted_peak_positions.resize(num_peaks, std::numeric_limits<double>::quiet_NaN());
m_costs.resize(num_peaks, DBL_MAX);
m_function_parameters_vector.resize(num_peaks);
m_function_errors_vector.resize(num_peaks);
for (size_t ipeak = 0; ipeak < num_peaks; ++ipeak) {
m_function_parameters_vector[ipeak].resize(num_params, std::numeric_limits<double>::quiet_NaN());
m_function_errors_vector[ipeak].resize(num_params, std::numeric_limits<double>::quiet_NaN());
}
return;
}
//----------------------------------------------------------------------------------------------
size_t PeakFitResult::getNumberParameters() const { return m_function_parameters_number; }
size_t PeakFitResult::getNumberPeaks() const { return m_function_parameters_vector.size(); }
//----------------------------------------------------------------------------------------------
/** get the fitting error of a particular parameter
* @param ipeak :: index of the peak in given peak position vector
* @param iparam :: index of the parameter in its corresponding peak profile
* function
* @return :: fitting error/uncertain of the specified parameter
*/
double PeakFitResult::getParameterError(size_t ipeak, size_t iparam) const {
return m_function_errors_vector[ipeak][iparam];
}
//----------------------------------------------------------------------------------------------
/** get the fitted value of a particular parameter
* @param ipeak :: index of the peak in given peak position vector
* @param iparam :: index of the parameter in its corresponding peak profile
* function
* @return :: fitted value of the specified parameter
*/
double PeakFitResult::getParameterValue(size_t ipeak, size_t iparam) const {
return m_function_parameters_vector[ipeak][iparam];
}
//----------------------------------------------------------------------------------------------
double PeakFitResult::getPeakPosition(size_t ipeak) const { return m_fitted_peak_positions[ipeak]; }
//----------------------------------------------------------------------------------------------
double PeakFitResult::getCost(size_t ipeak) const { return m_costs[ipeak]; }
//----------------------------------------------------------------------------------------------
/// set the peak fitting record/parameter for one peak
void PeakFitResult::setRecord(size_t ipeak, const double cost, const double peak_position,
const FitFunction &fit_functions) {
// check input
if (ipeak >= m_costs.size())
throw std::runtime_error("Peak index is out of range.");
// set the values
m_costs[ipeak] = cost;
// set peak position
m_fitted_peak_positions[ipeak] = peak_position;
// transfer from peak function to vector
size_t peak_num_params = fit_functions.peakfunction->nParams();
for (size_t ipar = 0; ipar < peak_num_params; ++ipar) {
// peak function
m_function_parameters_vector[ipeak][ipar] = fit_functions.peakfunction->getParameter(ipar);
m_function_errors_vector[ipeak][ipar] = fit_functions.peakfunction->getError(ipar);
}
for (size_t ipar = 0; ipar < fit_functions.bkgdfunction->nParams(); ++ipar) {
// background function
m_function_parameters_vector[ipeak][ipar + peak_num_params] = fit_functions.bkgdfunction->getParameter(ipar);
m_function_errors_vector[ipeak][ipar + peak_num_params] = fit_functions.bkgdfunction->getError(ipar);
}
}
//----------------------------------------------------------------------------------------------
/** The peak postition should be negative and indicates what went wrong
* @param ipeak :: index of the peak in user-specified peak position vector
* @param peak_position :: bad peak position indicating reason of bad fit
*/
void PeakFitResult::setBadRecord(size_t ipeak, const double peak_position) {
// check input
if (ipeak >= m_costs.size())
throw std::runtime_error("Peak index is out of range");
if (peak_position >= 0.)
throw std::runtime_error("Can only set negative postion for bad record");
// set the values
m_costs[ipeak] = DBL_MAX;
// set peak position
m_fitted_peak_positions[ipeak] = peak_position;
// transfer from peak function to vector
for (size_t ipar = 0; ipar < m_function_parameters_number; ++ipar) {
m_function_parameters_vector[ipeak][ipar] = 0.;
m_function_errors_vector[ipeak][ipar] = std::numeric_limits<double>::quiet_NaN();
}
}
} // namespace FitPeaksAlgorithm
//----------------------------------------------------------------------------------------------
FitPeaks::FitPeaks()
: m_fitPeaksFromRight(true), m_fitIterations(50), m_numPeaksToFit(0), m_minPeakHeight(20.), m_bkgdSimga(1.),
m_peakPosTolCase234(false) {}
//----------------------------------------------------------------------------------------------
/** initialize the properties
*/
void FitPeaks::init() {
declareProperty(std::make_unique<WorkspaceProperty<MatrixWorkspace>>(PropertyNames::INPUT_WKSP, "", Direction::Input),
"Name of the input workspace for peak fitting.");
declareProperty(
std::make_unique<WorkspaceProperty<MatrixWorkspace>>(PropertyNames::OUTPUT_WKSP, "", Direction::Output),
"Name of the output workspace containing peak centers for "
"fitting offset."
"The output workspace is point data."
"Each workspace index corresponds to a spectrum. "
"Each X value ranges from 0 to N-1, where N is the number of "
"peaks to fit. "
"Each Y value is the peak position obtained by peak fitting. "
"Negative value is used for error signals. "
"-1 for data is zero; -2 for maximum value is smaller than "
"specified minimum value."
"and -3 for non-converged fitting.");
// properties about fitting range and criteria
declareProperty(PropertyNames::START_WKSP_INDEX, EMPTY_INT(), "Starting workspace index for fit");
declareProperty(PropertyNames::STOP_WKSP_INDEX, EMPTY_INT(),
"Last workspace index to fit (which is included). "
"If a value larger than the workspace index of last spectrum, "
"then the workspace index of last spectrum is used.");
// properties about peak positions to fit
declareProperty(std::make_unique<ArrayProperty<double>>(PropertyNames::PEAK_CENTERS),
"List of peak centers to fit against.");
declareProperty(std::make_unique<WorkspaceProperty<MatrixWorkspace>>(PropertyNames::PEAK_CENTERS_WKSP, "",
Direction::Input, PropertyMode::Optional),
"MatrixWorkspace containing peak centers");
const std::string peakcentergrp("Peak Positions");
setPropertyGroup(PropertyNames::PEAK_CENTERS, peakcentergrp);
setPropertyGroup(PropertyNames::PEAK_CENTERS_WKSP, peakcentergrp);
// properties about peak profile
const std::vector<std::string> peakNames = FunctionFactory::Instance().getFunctionNames<API::IPeakFunction>();
declareProperty(PropertyNames::PEAK_FUNC, "Gaussian", std::make_shared<StringListValidator>(peakNames),
"Use of a BackToBackExponential profile is only reccomended if the "
"coeficients to calculate A and B are defined in the instrument "
"Parameters.xml file.");
const vector<string> bkgdtypes{"Flat", "Linear", "Quadratic"};
declareProperty(PropertyNames::BACK_FUNC, "Linear", std::make_shared<StringListValidator>(bkgdtypes),
"Type of Background.");
const std::string funcgroup("Function Types");
setPropertyGroup(PropertyNames::PEAK_FUNC, funcgroup);
setPropertyGroup(PropertyNames::BACK_FUNC, funcgroup);
// properties about peak range including fitting window and peak width
// (percentage)
declareProperty(std::make_unique<ArrayProperty<double>>(PropertyNames::FIT_WINDOW_LIST),
"List of left boundaries of the peak fitting window corresponding to "
"PeakCenters.");
declareProperty(std::make_unique<WorkspaceProperty<MatrixWorkspace>>(PropertyNames::FIT_WINDOW_WKSP, "",
Direction::Input, PropertyMode::Optional),
"MatrixWorkspace for of peak windows");
auto min = std::make_shared<BoundedValidator<double>>();
min->setLower(1e-3);
// min->setUpper(1.); TODO make this a limit
declareProperty(PropertyNames::PEAK_WIDTH_PERCENT, EMPTY_DBL(), min,
"The estimated peak width as a "
"percentage of the d-spacing "
"of the center of the peak. Value must be less than 1.");
const std::string fitrangeegrp("Peak Range Setup");
setPropertyGroup(PropertyNames::PEAK_WIDTH_PERCENT, fitrangeegrp);
setPropertyGroup(PropertyNames::FIT_WINDOW_LIST, fitrangeegrp);
setPropertyGroup(PropertyNames::FIT_WINDOW_WKSP, fitrangeegrp);
// properties about peak parameters' names and value
declareProperty(std::make_unique<ArrayProperty<std::string>>(PropertyNames::PEAK_PARAM_NAMES),
"List of peak parameters' names");
declareProperty(std::make_unique<ArrayProperty<double>>(PropertyNames::PEAK_PARAM_VALUES),
"List of peak parameters' value");
declareProperty(std::make_unique<WorkspaceProperty<TableWorkspace>>(PropertyNames::PEAK_PARAM_TABLE, "",
Direction::Input, PropertyMode::Optional),
"Name of the an optional workspace, whose each column "
"corresponds to given peak parameter names"
", and each row corresponds to a subset of spectra.");
const std::string startvaluegrp("Starting Parameters Setup");
setPropertyGroup(PropertyNames::PEAK_PARAM_NAMES, startvaluegrp);
setPropertyGroup(PropertyNames::PEAK_PARAM_VALUES, startvaluegrp);
setPropertyGroup(PropertyNames::PEAK_PARAM_TABLE, startvaluegrp);
// optimization setup
declareProperty(PropertyNames::FIT_FROM_RIGHT, true,
"Flag for the order to fit peaks. If true, peaks are fitted "
"from rightmost;"
"Otherwise peaks are fitted from leftmost.");
const std::vector<std::string> minimizerOptions = API::FuncMinimizerFactory::Instance().getKeys();
declareProperty(PropertyNames::MINIMIZER, "Levenberg-Marquardt",
Kernel::IValidator_sptr(new Kernel::StartsWithValidator(minimizerOptions)),
"Minimizer to use for fitting.");
const std::array<string, 2> costFuncOptions = {{"Least squares", "Rwp"}};
declareProperty(PropertyNames::COST_FUNC, "Least squares",
Kernel::IValidator_sptr(new Kernel::ListValidator<std::string>(costFuncOptions)), "Cost functions");
auto min_max_iter = std::make_shared<BoundedValidator<int>>();
min_max_iter->setLower(49);
declareProperty(PropertyNames::MAX_FIT_ITER, 50, min_max_iter, "Maximum number of function fitting iterations.");
const std::string optimizergrp("Optimization Setup");
setPropertyGroup(PropertyNames::MINIMIZER, optimizergrp);
setPropertyGroup(PropertyNames::COST_FUNC, optimizergrp);
// other helping information
declareProperty(PropertyNames::BACKGROUND_Z_SCORE, 1.0,
"Multiplier of standard deviations of the variance for convergence of "
"peak elimination. Default is 1.0. ");
declareProperty(PropertyNames::HIGH_BACKGROUND, true,
"Flag whether the data has high background comparing to "
"peaks' intensities. "
"For example, vanadium peaks usually have high background.");
declareProperty(std::make_unique<ArrayProperty<double>>(PropertyNames::POSITION_TOL),
"List of tolerance on fitted peak positions against given peak positions."
"If there is only one value given, then ");
declareProperty(PropertyNames::PEAK_MIN_HEIGHT, 0.,
"Minimum peak height such that all the fitted peaks with "
"height under this value will be excluded.");
declareProperty(PropertyNames::CONSTRAIN_PEAK_POS, true,
"If true peak position will be constrained by estimated positions "
"(highest Y value position) and "
"the peak width either estimted by observation or calculate.");
// additional output for reviewing
declareProperty(std::make_unique<WorkspaceProperty<MatrixWorkspace>>(PropertyNames::OUTPUT_WKSP_MODEL, "",
Direction::Output, PropertyMode::Optional),
"Name of the output matrix workspace with fitted peak. "
"This output workspace have the same dimesion as the input workspace."
"The Y values belonged to peaks to fit are replaced by fitted value. "
"Values of estimated background are used if peak fails to be fit.");
declareProperty(std::make_unique<WorkspaceProperty<API::ITableWorkspace>>(PropertyNames::OUTPUT_WKSP_PARAMS, "",
Direction::Output),
"Name of table workspace containing all fitted peak parameters.");
// Optional output table workspace for each individual parameter's fitting
// error
declareProperty(std::make_unique<WorkspaceProperty<API::ITableWorkspace>>(PropertyNames::OUTPUT_WKSP_PARAM_ERRS, "",
Direction::Output, PropertyMode::Optional),
"Name of workspace containing all fitted peak parameters' fitting error."
"It must be used along with FittedPeaksWorkspace and RawPeakParameters "
"(True)");
declareProperty(PropertyNames::RAW_PARAMS, true,
"false generates table with effective centre/width/height "
"parameters. true generates a table with peak function "
"parameters");
const std::string addoutgrp("Analysis");
setPropertyGroup(PropertyNames::OUTPUT_WKSP_PARAMS, addoutgrp);
setPropertyGroup(PropertyNames::OUTPUT_WKSP_MODEL, addoutgrp);
setPropertyGroup(PropertyNames::OUTPUT_WKSP_PARAM_ERRS, addoutgrp);
setPropertyGroup(PropertyNames::RAW_PARAMS, addoutgrp);
}
//----------------------------------------------------------------------------------------------
/** Validate inputs
*/
std::map<std::string, std::string> FitPeaks::validateInputs() {
map<std::string, std::string> issues;
// check that the peak parameters are in parallel properties
bool haveCommonPeakParameters(false);
std::vector<string> suppliedParameterNames = getProperty(PropertyNames::PEAK_PARAM_NAMES);
std::vector<double> peakParamValues = getProperty(PropertyNames::PEAK_PARAM_VALUES);
if ((!suppliedParameterNames.empty()) || (!peakParamValues.empty())) {
haveCommonPeakParameters = true;
if (suppliedParameterNames.size() != peakParamValues.size()) {
issues[PropertyNames::PEAK_PARAM_NAMES] = "must have same number of values as PeakParameterValues";
issues[PropertyNames::PEAK_PARAM_VALUES] = "must have same number of values as PeakParameterNames";
}
}
// get the information out of the table
std::string partablename = getPropertyValue(PropertyNames::PEAK_PARAM_TABLE);
if (!partablename.empty()) {
if (haveCommonPeakParameters) {
const std::string msg = "Parameter value table and initial parameter "
"name/value vectors cannot be given "
"simultanenously.";
issues[PropertyNames::PEAK_PARAM_TABLE] = msg;
issues[PropertyNames::PEAK_PARAM_NAMES] = msg;
issues[PropertyNames::PEAK_PARAM_VALUES] = msg;
} else {
m_profileStartingValueTable = getProperty(PropertyNames::PEAK_PARAM_TABLE);
suppliedParameterNames = m_profileStartingValueTable->getColumnNames();
}
}
// check that the suggested peak parameter names exist in the peak function
if (!suppliedParameterNames.empty()) {
std::string peakfunctiontype = getPropertyValue(PropertyNames::PEAK_FUNC);
m_peakFunction =
std::dynamic_pointer_cast<IPeakFunction>(API::FunctionFactory::Instance().createFunction(peakfunctiontype));
// put the names in a vector
std::vector<string> functionParameterNames;
for (size_t i = 0; i < m_peakFunction->nParams(); ++i)
functionParameterNames.emplace_back(m_peakFunction->parameterName(i));
// check that the supplied names are in the function
// it is acceptable to be missing parameters
bool failed = false;
for (const auto &name : suppliedParameterNames) {
if (std::find(functionParameterNames.begin(), functionParameterNames.end(), name) ==
functionParameterNames.end()) {
failed = true;
break;
}
}
if (failed) {
std::string msg = "Specified invalid parameter for peak function";
if (haveCommonPeakParameters)
issues[PropertyNames::PEAK_PARAM_NAMES] = msg;
else
issues[PropertyNames::PEAK_PARAM_TABLE] = msg;
}
}
// check inputs for uncertainty (fitting error)
const std::string error_table_name = getPropertyValue(PropertyNames::OUTPUT_WKSP_PARAM_ERRS);
if (!error_table_name.empty()) {
const bool use_raw_params = getProperty(PropertyNames::RAW_PARAMS);
if (!use_raw_params) {
issues[PropertyNames::OUTPUT_WKSP_PARAM_ERRS] = "Cannot be used with " + PropertyNames::RAW_PARAMS + "=False";
issues[PropertyNames::RAW_PARAMS] =
"Cannot be False with " + PropertyNames::OUTPUT_WKSP_PARAM_ERRS + " specified";
}
}
return issues;
}
//----------------------------------------------------------------------------------------------
void FitPeaks::exec() {
// process inputs
processInputs();
// create output workspace: fitted peak positions
generateOutputPeakPositionWS();
// create output workspace: fitted peaks' parameters values
generateFittedParametersValueWorkspaces();
// create output workspace: calculated from fitted peak and background
generateCalculatedPeaksWS();
// fit peaks
auto fit_results = fitPeaks();
// set the output workspaces to properites
processOutputs(fit_results);
}
//----------------------------------------------------------------------------------------------
void FitPeaks::processInputs() {
// input workspaces
m_inputMatrixWS = getProperty(PropertyNames::INPUT_WKSP);
if (m_inputMatrixWS->getAxis(0)->unit()->unitID() == "dSpacing")
m_inputIsDSpace = true;
else
m_inputIsDSpace = false;
// spectra to fit
int start_wi = getProperty(PropertyNames::START_WKSP_INDEX);
if (isEmpty(start_wi))
m_startWorkspaceIndex = 0;
else
m_startWorkspaceIndex = static_cast<size_t>(start_wi);
// last spectrum's workspace index, which is included
int stop_wi = getProperty(PropertyNames::STOP_WKSP_INDEX);
if (isEmpty(stop_wi))
m_stopWorkspaceIndex = m_inputMatrixWS->getNumberHistograms() - 1;
else {
m_stopWorkspaceIndex = static_cast<size_t>(stop_wi);
if (m_stopWorkspaceIndex > m_inputMatrixWS->getNumberHistograms() - 1)
m_stopWorkspaceIndex = m_inputMatrixWS->getNumberHistograms() - 1;
}
// optimizer, cost function and fitting scheme
m_minimizer = getPropertyValue(PropertyNames::MINIMIZER);
m_costFunction = getPropertyValue(PropertyNames::COST_FUNC);
m_fitPeaksFromRight = getProperty(PropertyNames::FIT_FROM_RIGHT);
m_constrainPeaksPosition = getProperty(PropertyNames::CONSTRAIN_PEAK_POS);
m_fitIterations = getProperty(PropertyNames::MAX_FIT_ITER);
// Peak centers, tolerance and fitting range
processInputPeakCenters();
// check
if (m_numPeaksToFit == 0)
throw std::runtime_error("number of peaks to fit is zero.");
// about how to estimate the peak width
m_peakWidthPercentage = getProperty(PropertyNames::PEAK_WIDTH_PERCENT);
if (isEmpty(m_peakWidthPercentage))
m_peakWidthPercentage = -1;
if (m_peakWidthPercentage >= 1.) // TODO
throw std::runtime_error("PeakWidthPercent must be less than 1");
g_log.debug() << "peak width/value = " << m_peakWidthPercentage << "\n";
// set up background
m_highBackground = getProperty(PropertyNames::HIGH_BACKGROUND);
m_bkgdSimga = getProperty(PropertyNames::BACKGROUND_Z_SCORE);
// Set up peak and background functions
processInputFunctions();
// about peak width and other peak parameter estimating method
if (m_peakWidthPercentage > 0.)
m_peakWidthEstimateApproach = EstimatePeakWidth::InstrumentResolution;
else if (isObservablePeakProfile((m_peakFunction->name())))
m_peakWidthEstimateApproach = EstimatePeakWidth::Observation;
else
m_peakWidthEstimateApproach = EstimatePeakWidth::NoEstimation;
// m_peakWidthEstimateApproach = EstimatePeakWidth::NoEstimation;
g_log.debug() << "Process inputs [3] peak type: " << m_peakFunction->name()
<< ", background type: " << m_bkgdFunction->name() << "\n";
processInputPeakTolerance();
processInputFitRanges();
return;
}
//----------------------------------------------------------------------------------------------
/** process inputs for peak profile and background
*/
void FitPeaks::processInputFunctions() {
// peak functions
std::string peakfunctiontype = getPropertyValue(PropertyNames::PEAK_FUNC);
m_peakFunction =
std::dynamic_pointer_cast<IPeakFunction>(API::FunctionFactory::Instance().createFunction(peakfunctiontype));
// background functions
std::string bkgdfunctiontype = getPropertyValue(PropertyNames::BACK_FUNC);
std::string bkgdname;
if (bkgdfunctiontype == "Linear")
bkgdname = "LinearBackground";
else if (bkgdfunctiontype == "Flat") {
g_log.warning("There may be problems with Flat background");
bkgdname = "FlatBackground";
} else
bkgdname = bkgdfunctiontype;
m_bkgdFunction =
std::dynamic_pointer_cast<IBackgroundFunction>(API::FunctionFactory::Instance().createFunction(bkgdname));
if (m_highBackground)
m_linearBackgroundFunction = std::dynamic_pointer_cast<IBackgroundFunction>(
API::FunctionFactory::Instance().createFunction("LinearBackground"));
else
m_linearBackgroundFunction = nullptr;
// TODO check that both parameter names and values exist
// input peak parameters
std::string partablename = getPropertyValue(PropertyNames::PEAK_PARAM_TABLE);
m_peakParamNames = getProperty(PropertyNames::PEAK_PARAM_NAMES);
m_uniformProfileStartingValue = false;
if (partablename.empty() && (!m_peakParamNames.empty())) {
// use uniform starting value of peak parameters
m_initParamValues = getProperty(PropertyNames::PEAK_PARAM_VALUES);
// convert the parameter name in string to parameter name in integer index
convertParametersNameToIndex();
// m_uniformProfileStartingValue = true;
} else if ((!partablename.empty()) && m_peakParamNames.empty()) {
// use non-uniform starting value of peak parameters
m_profileStartingValueTable = getProperty(partablename);
} else if (peakfunctiontype != "Gaussian") {
// user specifies nothing
g_log.warning("Neither parameter value table nor initial "
"parameter name/value vectors is specified. Fitting might "
"not be reliable for peak profile other than Gaussian");
}
return;
}
//----------------------------------------------------------------------------------------------
/** process and check for inputs about peak fitting range (i.e., window)
* Note: What is the output of the method?
*/
void FitPeaks::processInputFitRanges() {
// get peak fit window
std::vector<double> peakwindow = getProperty(PropertyNames::FIT_WINDOW_LIST);
std::string peakwindowname = getPropertyValue(PropertyNames::FIT_WINDOW_WKSP);
API::MatrixWorkspace_const_sptr peakwindowws = getProperty(PropertyNames::FIT_WINDOW_WKSP);
// in most case, calculate window by instrument resolution is False
m_calculateWindowInstrument = false;
if ((!peakwindow.empty()) && peakwindowname.empty()) {
// Peak windows are uniform among spectra: use vector for peak windows
m_uniformPeakWindows = true;
// check peak positions
if (!m_uniformPeakPositions)
throw std::invalid_argument("Uniform peak range/window requires uniform peak positions.");
// check size
if (peakwindow.size() != m_numPeaksToFit * 2)
throw std::invalid_argument("Peak window vector must be twice as large as number of peaks.");
// set up window to m_peakWindowVector
m_peakWindowVector.resize(m_numPeaksToFit);
for (size_t i = 0; i < m_numPeaksToFit; ++i) {
std::vector<double> peakranges(2);
peakranges[0] = peakwindow[i * 2];
peakranges[1] = peakwindow[i * 2 + 1];
// check peak window (range) against peak centers
if ((peakranges[0] < m_peakCenters[i]) && (m_peakCenters[i] < peakranges[1])) {
// pass check: set
m_peakWindowVector[i] = peakranges;
} else {
// failed
std::stringstream errss;
errss << "Peak " << i << ": user specifies an invalid range and peak center against " << peakranges[0] << " < "
<< m_peakCenters[i] << " < " << peakranges[1];
throw std::invalid_argument(errss.str());
}
} // END-FOR
// END for uniform peak window
} else if (peakwindow.empty() && peakwindowws != nullptr) {
// use matrix workspace for non-uniform peak windows
m_peakWindowWorkspace = getProperty(PropertyNames::FIT_WINDOW_WKSP);
m_uniformPeakWindows = false;
// check size
if (m_peakWindowWorkspace->getNumberHistograms() == m_inputMatrixWS->getNumberHistograms())
m_partialWindowSpectra = false;
else if (m_peakWindowWorkspace->getNumberHistograms() == (m_stopWorkspaceIndex - m_startWorkspaceIndex + 1))
m_partialWindowSpectra = true;
else
throw std::invalid_argument("Peak window workspace has unmatched number of spectra");
// check range for peak windows and peak positions
size_t window_index_start(0);
if (m_partialWindowSpectra)
window_index_start = m_startWorkspaceIndex;
size_t center_index_start(0);
if (m_partialSpectra)
center_index_start = m_startWorkspaceIndex;
// check each spectrum whether the window is defined with the correct size
for (size_t wi = 0; wi < m_peakWindowWorkspace->getNumberHistograms(); ++wi) {
// check size
if (m_peakWindowWorkspace->y(wi).size() != m_numPeaksToFit * 2) {
std::stringstream errss;
errss << "Peak window workspace index " << wi << " has incompatible number of fit windows (x2) "
<< m_peakWindowWorkspace->y(wi).size() << " with the number of peaks " << m_numPeaksToFit << " to fit.";
throw std::invalid_argument(errss.str());
}
const auto &peakWindowX = m_peakWindowWorkspace->x(wi);
// check window range against peak center
size_t window_index = window_index_start + wi;
size_t center_index = window_index - center_index_start;
const auto &peakCenterX = m_peakCenterWorkspace->x(center_index);
for (size_t ipeak = 0; ipeak < m_numPeaksToFit; ++ipeak) {
double left_w_bound = peakWindowX[ipeak * 2]; // TODO getting on y
double right_w_bound = peakWindowX[ipeak * 2 + 1];
double center = peakCenterX[ipeak];
if (!(left_w_bound < center && center < right_w_bound)) {
std::stringstream errss;
errss << "Workspace index " << wi << " has incompatible peak window (" // <<<<<<< HERE!!!!!!!!!
<< left_w_bound << ", " << right_w_bound << ") with " << ipeak << "-th expected peak's center "
<< center;
throw std::runtime_error(errss.str());
}
}
}
} else if (peakwindow.empty()) {
// no peak window is defined, then the peak window will be estimated by
// delta(D)/D
if (m_inputIsDSpace && m_peakWidthPercentage > 0)
m_calculateWindowInstrument = true;
else
throw std::invalid_argument("Without definition of peak window, the "
"input workspace must be in unit of dSpacing "
"and Delta(D)/D must be given!");
} else {
// non-supported situation
throw std::invalid_argument("One and only one of peak window array and "
"peak window workspace can be specified.");
}
return;
}
//----------------------------------------------------------------------------------------------
/** Processing peaks centers and fitting tolerance information from input. the
* parameters that are
* set including
* 1. m_peakCenters/m_peakCenterWorkspace/m_uniformPeakPositions
* (bool)/m_partialSpectra (bool)
* 2. m_peakPosTolerances (vector)
* 3. m_numPeaksToFit
*/
void FitPeaks::processInputPeakCenters() {
// peak centers
m_peakCenters = getProperty(PropertyNames::PEAK_CENTERS);
API::MatrixWorkspace_const_sptr peakcenterws = getProperty(PropertyNames::PEAK_CENTERS_WKSP);
if (!peakcenterws)
g_log.notice("Peak centers are not specified by peak center workspace");
std::string peakpswsname = getPropertyValue(PropertyNames::PEAK_CENTERS_WKSP);
if ((!m_peakCenters.empty()) && peakcenterws == nullptr) {
// peak positions are uniform among all spectra
m_uniformPeakPositions = true;
// number of peaks to fit!
m_numPeaksToFit = m_peakCenters.size();
} else if (m_peakCenters.empty() && peakcenterws != nullptr) {
// peak positions can be different among spectra
m_uniformPeakPositions = false;
m_peakCenterWorkspace = getProperty(PropertyNames::PEAK_CENTERS_WKSP);
// number of peaks to fit!
m_numPeaksToFit = m_peakCenterWorkspace->x(0).size();
g_log.debug() << "Input peak center workspace: " << m_peakCenterWorkspace->x(0).size() << ", "
<< m_peakCenterWorkspace->y(0).size() << "\n";
// check matrix worksapce for peak positions
const size_t peak_center_ws_spectra_number = m_peakCenterWorkspace->getNumberHistograms();
if (peak_center_ws_spectra_number == m_inputMatrixWS->getNumberHistograms()) {
// full spectra
m_partialSpectra = false;
} else if (peak_center_ws_spectra_number == m_stopWorkspaceIndex - m_startWorkspaceIndex + 1) {
// partial spectra
m_partialSpectra = true;
} else {
// a case indicating programming error
g_log.error() << "Peak center workspace has " << peak_center_ws_spectra_number << " spectra;"
<< "Input workspace has " << m_inputMatrixWS->getNumberHistograms() << " spectra;"
<< "User specifies to fit peaks from " << m_startWorkspaceIndex << " to " << m_stopWorkspaceIndex
<< ". They are mismatched to each other.\n";
throw std::invalid_argument("Input peak center workspace has mismatched "
"number of spectra to selected spectra to "
"fit.");
}
} else {
std::stringstream errss;
errss << "One and only one in 'PeakCenters' (vector) and "
"'PeakCentersWorkspace' shall be given. "
<< "'PeakCenters' has size " << m_peakCenters.size() << ", and name of peak center workspace "
<< "is " << peakpswsname;
throw std::invalid_argument(errss.str());
}
return;
}
//----------------------------------------------------------------------------------------------
/** Processing peak fitting tolerance information from input. The parameters
* that are
* set including
* 2. m_peakPosTolerances (vector)
*/
void FitPeaks::processInputPeakTolerance() {
// check code integrity
if (m_numPeaksToFit == 0)
throw std::runtime_error("ProcessInputPeakTolerance() must be called after "
"ProcessInputPeakCenters()");
// peak tolerance
m_peakPosTolerances = getProperty(PropertyNames::POSITION_TOL);
if (m_peakPosTolerances.empty()) {
// case 2, 3, 4
m_peakPosTolerances.clear();
m_peakPosTolCase234 = true;
} else if (m_peakPosTolerances.size() == 1) {
// only 1 uniform peak position tolerance is defined: expand to all peaks
double peak_tol = m_peakPosTolerances[0];
m_peakPosTolerances.resize(m_numPeaksToFit, peak_tol);
} else if (m_peakPosTolerances.size() != m_numPeaksToFit) {
// not uniform but number of peaks does not match
g_log.error() << "number of peak position tolerance " << m_peakPosTolerances.size()
<< " is not same as number of peaks " << m_numPeaksToFit << "\n";
throw std::runtime_error("Number of peak position tolerances and number of "
"peaks to fit are inconsistent.");
}
// minimum peak height: set default to zero
m_minPeakHeight = getProperty(PropertyNames::PEAK_MIN_HEIGHT);
if (isEmpty(m_minPeakHeight) || m_minPeakHeight < 0.)
m_minPeakHeight = 0.;
return;
}
//----------------------------------------------------------------------------------------------
/** Convert the input initial parameter name/value to parameter index/value for
* faster access
* according to the parameter name and peak profile function
* Output: m_initParamIndexes will be set up
*/
void FitPeaks::convertParametersNameToIndex() {
// get a map for peak profile parameter name and parameter index
std::map<std::string, size_t> parname_index_map;
for (size_t iparam = 0; iparam < m_peakFunction->nParams(); ++iparam)
parname_index_map.insert(std::make_pair(m_peakFunction->parameterName(iparam), iparam));
// define peak parameter names (class variable) if using table
if (m_profileStartingValueTable)
m_peakParamNames = m_profileStartingValueTable->getColumnNames();
// map the input parameter names to parameter indexes
for (const auto ¶mName : m_peakParamNames) {
auto locator = parname_index_map.find(paramName);
if (locator != parname_index_map.end()) {
m_initParamIndexes.emplace_back(locator->second);
} else {
// a parameter name that is not defined in the peak profile function. An
// out-of-range index is thus set to this
g_log.warning() << "Given peak parameter " << paramName
<< " is not an allowed parameter of peak "
"function "
<< m_peakFunction->name() << "\n";
m_initParamIndexes.emplace_back(m_peakFunction->nParams() * 10);
}
}
return;
}
//----------------------------------------------------------------------------------------------
/** main method to fit peaks among all
*/
std::vector<std::shared_ptr<FitPeaksAlgorithm::PeakFitResult>> FitPeaks::fitPeaks() {
API::Progress prog(this, 0., 1., m_stopWorkspaceIndex - m_startWorkspaceIndex);
/// Vector to record all the FitResult (only containing specified number of
/// spectra. shift is expected)
size_t num_fit_result = m_stopWorkspaceIndex - m_startWorkspaceIndex + 1;
std::vector<std::shared_ptr<FitPeaksAlgorithm::PeakFitResult>> fit_result_vector(num_fit_result);
const int nThreads = FrameworkManager::Instance().getNumOMPThreads();
size_t chunkSize = num_fit_result / nThreads;
PRAGMA_OMP(parallel for schedule(dynamic, 1) )
for (int ithread = 0; ithread < nThreads; ithread++) {
PARALLEL_START_INTERRUPT_REGION
auto iws_begin = m_startWorkspaceIndex + chunkSize * static_cast<size_t>(ithread);
auto iws_end = (ithread == nThreads - 1) ? m_stopWorkspaceIndex + 1 : iws_begin + chunkSize;
// vector to store fit params for last good fit to each peak
std::vector<std::vector<double>> lastGoodPeakParameters(m_numPeaksToFit,
std::vector<double>(m_peakFunction->nParams(), 0.0));
for (auto wi = iws_begin; wi < iws_end; ++wi) {
// peaks to fit
std::vector<double> expected_peak_centers = getExpectedPeakPositions(static_cast<size_t>(wi));
// initialize output for this
size_t numfuncparams = m_peakFunction->nParams() + m_bkgdFunction->nParams();
std::shared_ptr<FitPeaksAlgorithm::PeakFitResult> fit_result =
std::make_shared<FitPeaksAlgorithm::PeakFitResult>(m_numPeaksToFit, numfuncparams);
fitSpectrumPeaks(static_cast<size_t>(wi), expected_peak_centers, fit_result, lastGoodPeakParameters);
PARALLEL_CRITICAL(FindPeaks_WriteOutput) {
writeFitResult(static_cast<size_t>(wi), expected_peak_centers, fit_result);
fit_result_vector[wi - m_startWorkspaceIndex] = fit_result;
}
prog.report();
}
PARALLEL_END_INTERRUPT_REGION
}
PARALLEL_CHECK_INTERRUPT_REGION
return fit_result_vector;
}
namespace {
/// Supported peak profiles for observation
std::vector<std::string> supported_peak_profiles{"Gaussian", "Lorentzian", "PseudoVoigt", "Voigt",
"BackToBackExponential"};
double numberCounts(const Histogram &histogram) {
double total = 0.;
for (const auto &value : histogram.y())
total += std::fabs(value);
return total;
}
//----------------------------------------------------------------------------------------------
/** Get number of counts in a specified range of a histogram
* @param histogram :: histogram instance
* @param xmin :: left boundary
* @param xmax :: right boundary
* @return :: counts
*/
double numberCounts(const Histogram &histogram, const double xmin, const double xmax) {
const auto &vector_x = histogram.points();
// determine left boundary
auto start_iter = vector_x.begin();
if (xmin > vector_x.front())
start_iter = std::lower_bound(vector_x.begin(), vector_x.end(), xmin);
if (start_iter == vector_x.end())
return 0.; // past the end of the data means nothing to integrate
// determine right boundary
auto stop_iter = vector_x.end();
if (xmax < vector_x.back()) // will set at end of vector if too large
stop_iter = std::lower_bound(start_iter, stop_iter, xmax);
// convert to indexes to sum over y
size_t start_index = static_cast<size_t>(start_iter - vector_x.begin());
size_t stop_index = static_cast<size_t>(stop_iter - vector_x.begin());
// integrate
double total = 0.;
for (size_t i = start_index; i < stop_index; ++i)
total += std::fabs(histogram.y()[i]);
return total;
}
} // namespace
//----------------------------------------------------------------------------------------------
/** Fit peaks across one single spectrum
*/
void FitPeaks::fitSpectrumPeaks(size_t wi, const std::vector<double> &expected_peak_centers,
const std::shared_ptr<FitPeaksAlgorithm::PeakFitResult> &fit_result,
std::vector<std::vector<double>> &lastGoodPeakParameters) {
// Spectrum contains very weak signal: do not proceed and return
if (numberCounts(m_inputMatrixWS->histogram(wi)) <= m_minPeakHeight) {
for (size_t i = 0; i < fit_result->getNumberPeaks(); ++i)
fit_result->setBadRecord(i, -1.);
return; // don't do anything
}
// Set up sub algorithm Fit for peak and background
IAlgorithm_sptr peak_fitter; // both peak and background (combo)
try {
peak_fitter = createChildAlgorithm("Fit", -1, -1, false);
} catch (Exception::NotFoundError &) {
std::stringstream errss;
errss << "The FitPeak algorithm requires the CurveFitting library";
g_log.error(errss.str());
throw std::runtime_error(errss.str());
}
// Clone background function
IBackgroundFunction_sptr bkgdfunction = std::dynamic_pointer_cast<API::IBackgroundFunction>(m_bkgdFunction->clone());
// set up properties of algorithm (reference) 'Fit'
peak_fitter->setProperty("Minimizer", m_minimizer);
peak_fitter->setProperty("CostFunction", m_costFunction);
peak_fitter->setProperty("CalcErrors", true);
const double x0 = m_inputMatrixWS->histogram(wi).x().front();
const double xf = m_inputMatrixWS->histogram(wi).x().back();
// index of previous peak in same spectrum (initially invalid)
size_t prev_peak_index = m_numPeaksToFit;
bool neighborPeakSameSpectrum = false;
for (size_t fit_index = 0; fit_index < m_numPeaksToFit; ++fit_index) {
// convert fit index to peak index (in ascending order)
size_t peak_index(fit_index);
if (m_fitPeaksFromRight)
peak_index = m_numPeaksToFit - fit_index - 1;
// reset the background function
for (size_t i = 0; i < bkgdfunction->nParams(); ++i)
bkgdfunction->setParameter(i, 0.);
double expected_peak_pos = expected_peak_centers[peak_index];
// clone peak function for each peak (need to do this so can
// set center and calc any parameters from xml)
auto peakfunction = std::dynamic_pointer_cast<API::IPeakFunction>(m_peakFunction->clone());
peakfunction->setCentre(expected_peak_pos);
peakfunction->setMatrixWorkspace(m_inputMatrixWS, wi, 0.0, 0.0);
std::map<size_t, double> keep_values;
for (size_t ipar = 0; ipar < peakfunction->nParams(); ++ipar) {
if (peakfunction->isFixed(ipar)) {
// save value of these parameters which have just been calculated
// if they were set to be fixed (e.g. for the B2Bexp this would
// typically be A and B but not Sigma)
keep_values[ipar] = peakfunction->getParameter(ipar);
// let them be free to fit as these are typically refined from a
// focussed bank
peakfunction->unfix(ipar);
}
}