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PDCalibration.cpp
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PDCalibration.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/PDCalibration.h"
#include "MantidAPI/FileProperty.h"
#include "MantidAPI/FuncMinimizerFactory.h"
#include "MantidAPI/FunctionFactory.h"
#include "MantidAPI/IEventList.h"
#include "MantidAPI/IPeakFunction.h"
#include "MantidAPI/MatrixWorkspace.h"
#include "MantidAPI/Run.h"
#include "MantidAPI/SpectrumInfo.h"
#include "MantidAPI/TableRow.h"
#include "MantidAPI/WorkspaceGroup.h"
#include "MantidDataObjects/EventWorkspace.h"
#include "MantidDataObjects/MaskWorkspace.h"
#include "MantidDataObjects/SpecialWorkspace2D.h"
#include "MantidDataObjects/TableWorkspace.h"
#include "MantidDataObjects/Workspace2D.h"
#include "MantidDataObjects/WorkspaceCreation.h"
#include "MantidGeometry/IDetector.h"
#include "MantidGeometry/Instrument.h"
#include "MantidGeometry/Instrument/DetectorInfo.h"
#include "MantidHistogramData/Histogram.h"
#include "MantidKernel/ArrayBoundedValidator.h"
#include "MantidKernel/ArrayProperty.h"
#include "MantidKernel/BoundedValidator.h"
#include "MantidKernel/CompositeValidator.h"
#include "MantidKernel/ListValidator.h"
#include "MantidKernel/MandatoryValidator.h"
#include "MantidKernel/RebinParamsValidator.h"
#include "MantidKernel/Unit.h"
#include <algorithm>
#include <cassert>
#include <gsl/gsl_multifit_nlin.h>
#include <gsl/gsl_multimin.h>
#include <limits>
#include <numeric>
namespace Mantid {
namespace Algorithms {
using namespace Mantid::DataObjects;
using namespace Mantid::HistogramData;
using Mantid::API::FileProperty;
using Mantid::API::MatrixWorkspace;
using Mantid::API::MatrixWorkspace_sptr;
using Mantid::API::WorkspaceProperty;
using Mantid::DataObjects::EventWorkspace;
using Mantid::DataObjects::MaskWorkspace_sptr;
using Mantid::Geometry::Instrument_const_sptr;
using Mantid::Kernel::ArrayBoundedValidator;
using Mantid::Kernel::ArrayProperty;
using Mantid::Kernel::BoundedValidator;
using Mantid::Kernel::CompositeValidator;
using Mantid::Kernel::Direction;
using Mantid::Kernel::MandatoryValidator;
using Mantid::Kernel::RebinParamsValidator;
using Mantid::Kernel::StringListValidator;
using std::vector;
// Register the algorithm into the AlgorithmFactory
DECLARE_ALGORITHM(PDCalibration)
namespace { // anonymous
const auto isNonZero = [](const double value) { return value != 0.; };
}
/// private inner class
class PDCalibration::FittedPeaks {
public:
/**
* Find the bins with non-zero counts and with minimum and maximum TOF
*
* @param wksp :: Input signal workspace
* @param wkspIndex :: workspace index
*
* @throws runtime_error :: more than one detector is associated to the
* workspace index
*/
FittedPeaks(const API::MatrixWorkspace_const_sptr &wksp, const std::size_t wkspIndex) {
this->wkspIndex = wkspIndex;
// convert workspace index into detector id
const auto &spectrum = wksp->getSpectrum(wkspIndex);
this->detid = spectrum.getDetectorIDs();
const auto &X = spectrum.x();
const auto &Y = spectrum.y();
tofMin = X.front();
tofMax = X.back();
// determine tof min supported by the workspace (non-zero counts)
size_t minIndex = 0; // want to store value
for (; minIndex < Y.size(); ++minIndex) {
if (isNonZero(Y[minIndex])) {
tofMin = X[minIndex];
break; // we found the first bin with non-zero counts
}
}
// determine tof max supported by the workspace (non-zero counts)
size_t maxIndex = Y.size() - 1;
for (; maxIndex > minIndex; --maxIndex) {
if (isNonZero(Y[maxIndex])) {
tofMax = X[maxIndex];
break; // we found the last bin with non-zero counts
}
}
}
/**
* Store the positions of the peak centers, as well as the left and right
* fit ranges for each peak, in TOF units
*
* @param peaksInD :: peak centers, in d-spacing
* @param peaksInDWindows :: left and right fit ranges for each peak
* @param difa :: difa diffractometer constant (quadratic term)
* @param difc :: difc diffractometer constant (linear term)
* @param tzero :: tzero diffractometer constant (constant term)
*/
void setPositions(const std::vector<double> &peaksInD, const std::vector<double> &peaksInDWindows, const double difa,
const double difc, const double tzero) {
// clear out old values
inDPos.clear();
inTofPos.clear();
inTofWindows.clear();
// assign things
inDPos.assign(peaksInD.begin(), peaksInD.end());
inTofPos.assign(peaksInD.begin(), peaksInD.end());
inTofWindows.assign(peaksInDWindows.begin(), peaksInDWindows.end());
// convert the bits that matter to TOF
Kernel::Units::dSpacing dSpacingUnit;
std::vector<double> yunused;
dSpacingUnit.toTOF(
inTofPos, yunused, -1, 0,
{{Kernel::UnitParams::difa, difa}, {Kernel::UnitParams::difc, difc}, {Kernel::UnitParams::tzero, tzero}});
dSpacingUnit.toTOF(
inTofWindows, yunused, -1, 0,
{{Kernel::UnitParams::difa, difa}, {Kernel::UnitParams::difc, difc}, {Kernel::UnitParams::tzero, tzero}});
}
std::size_t wkspIndex;
std::set<detid_t> detid;
double tofMin; // TOF of bin with minimum TOF and non-zero counts
double tofMax; // TOF of bin with maximum TOF and non-zero counts
std::vector<double> inTofPos; // peak centers, in TOF
// left and right fit ranges for each peak center, in TOF
std::vector<double> inTofWindows;
std::vector<double> inDPos; // peak centers, in d-spacing
};
//----------------------------------------------------------------------------------------------
/** Constructor
*/
PDCalibration::PDCalibration() {}
//----------------------------------------------------------------------------------------------
/** Destructor
*/
PDCalibration::~PDCalibration() {}
//----------------------------------------------------------------------------------------------
/// Algorithms name for identification. @see Algorithm::name
const std::string PDCalibration::name() const { return "PDCalibration"; }
/// Algorithm's version for identification. @see Algorithm::version
int PDCalibration::version() const { return 1; }
/// Algorithm's category for identification. @see Algorithm::category
const std::string PDCalibration::category() const { return "Diffraction\\Calibration"; }
/// Algorithm's summary for use in the GUI and help. @see Algorithm::summary
const std::string PDCalibration::summary() const {
return "Calibrate the detector pixels and create a calibration table";
}
//----------------------------------------------------------------------------------------------
/** Initialize the algorithm's properties.
*/
void PDCalibration::init() {
declareProperty(std::make_unique<WorkspaceProperty<MatrixWorkspace>>("InputWorkspace", "", Direction::InOut),
"Input signal workspace");
declareProperty(std::make_unique<ArrayProperty<double>>("TofBinning", std::make_shared<RebinParamsValidator>()),
"Min, Step, and Max of time-of-flight bins. "
"Logarithmic binning is used if Step is negative.");
const std::vector<std::string> exts2{".h5", ".cal"};
declareProperty(std::make_unique<FileProperty>("PreviousCalibrationFile", "", FileProperty::OptionalLoad, exts2),
"Previous calibration file");
declareProperty(std::make_unique<WorkspaceProperty<API::ITableWorkspace>>(
"PreviousCalibrationTable", "", Direction::Input, API::PropertyMode::Optional),
"Previous calibration table. This overrides results from previous file.");
// properties about peak positions to fit
std::vector<std::string> peaktypes{"BackToBackExponential", "Gaussian", "Lorentzian", "PseudoVoigt",
"IkedaCarpenterPV"};
declareProperty("PeakFunction", "Gaussian", std::make_shared<StringListValidator>(peaktypes));
vector<std::string> bkgdtypes{"Flat", "Linear", "Quadratic"};
declareProperty("BackgroundType", "Linear", std::make_shared<StringListValidator>(bkgdtypes), "Type of Background.");
auto peaksValidator = std::make_shared<CompositeValidator>();
auto mustBePosArr = std::make_shared<Kernel::ArrayBoundedValidator<double>>();
mustBePosArr->setLower(0.0);
peaksValidator->add(mustBePosArr);
peaksValidator->add(std::make_shared<MandatoryValidator<std::vector<double>>>());
declareProperty(std::make_unique<ArrayProperty<double>>("PeakPositions", peaksValidator),
"Comma delimited d-space positions of reference peaks.");
auto mustBePositive = std::make_shared<BoundedValidator<double>>();
mustBePositive->setLower(0.0);
declareProperty("PeakWindow", 0.1, mustBePositive,
"The maximum window (in d -pace) to the left and right of the "
"nominal peak center to look for the peak");
std::vector<std::string> modes{"DIFC", "DIFC+TZERO", "DIFC+TZERO+DIFA"};
auto min = std::make_shared<BoundedValidator<double>>();
min->setLower(1e-3);
declareProperty("PeakWidthPercent", EMPTY_DBL(), min,
"The estimated peak width as a percent of the peak"
"center value, in d-spacing or TOF units.");
declareProperty("MinimumPeakHeight", 2.,
"Minimum peak height such that all the fitted peaks with "
"height under this value will be excluded.");
declareProperty("MaxChiSq", 100., "Maximum chisq value for individual peak fit allowed. (Default: 100)");
declareProperty("ConstrainPeakPositions", false,
"If true, peak centers will be constrained by estimated positions "
"(highest Y value position) and "
"the peak width will either be estimated by observation or calculated.");
declareProperty("CalibrationParameters", "DIFC", std::make_shared<StringListValidator>(modes),
"Select calibration parameters to fit.");
declareProperty(std::make_unique<ArrayProperty<double>>("TZEROrange"),
"Range for allowable TZERO from calibration (default is all)");
declareProperty(std::make_unique<ArrayProperty<double>>("DIFArange"),
"Range for allowable DIFA from calibration (default "
"is all)");
declareProperty("UseChiSq", false,
"By default the square of the peak height is used as weights "
"in the least-squares fit to find the diffractometer "
"constants, if UseChiSq is true then the inverse square of "
"the error on the fitted peak centres will be used instead.");
declareProperty(
std::make_unique<WorkspaceProperty<API::ITableWorkspace>>("OutputCalibrationTable", "", Direction::Output),
"Output table workspace containing the calibration");
declareProperty(
std::make_unique<WorkspaceProperty<API::WorkspaceGroup>>("DiagnosticWorkspaces", "", Direction::Output),
"Workspaces to promote understanding of calibration results");
// make group for Input properties
std::string inputGroup("Input Options");
setPropertyGroup("InputWorkspace", inputGroup);
setPropertyGroup("TofBinning", inputGroup);
setPropertyGroup("PreviousCalibrationFile", inputGroup);
setPropertyGroup("PreviousCalibrationTable", inputGroup);
std::string funcgroup("Function Types");
setPropertyGroup("PeakFunction", funcgroup);
setPropertyGroup("BackgroundType", funcgroup);
// make group for FitPeaks properties
std::string fitPeaksGroup("Peak Fitting");
setPropertyGroup("PeakPositions", fitPeaksGroup);
setPropertyGroup("PeakWindow", fitPeaksGroup);
setPropertyGroup("PeakWidthPercent", fitPeaksGroup);
setPropertyGroup("MinimumPeakHeight", fitPeaksGroup);
setPropertyGroup("MaxChiSq", fitPeaksGroup);
setPropertyGroup("ConstrainPeakPositions", fitPeaksGroup);
// make group for type of calibration
std::string calGroup("Calibration Type");
setPropertyGroup("CalibrationParameters", calGroup);
setPropertyGroup("TZEROrange", calGroup);
setPropertyGroup("DIFArange", calGroup);
setPropertyGroup("UseChiSq", calGroup);
}
std::map<std::string, std::string> PDCalibration::validateInputs() {
std::map<std::string, std::string> messages;
vector<double> tzeroRange = getProperty("TZEROrange");
if (!tzeroRange.empty()) {
if (tzeroRange.size() != 2) {
messages["TZEROrange"] = "Require two values [min,max]";
} else if (tzeroRange[0] >= tzeroRange[1]) {
messages["TZEROrange"] = "min must be less than max";
}
}
vector<double> difaRange = getProperty("DIFArange");
if (!difaRange.empty()) {
if (difaRange.size() != 2) {
messages["DIFArange"] = "Require two values [min,max]";
} else if (difaRange[0] >= difaRange[1]) {
messages["DIFArange"] = "min must be less than max";
}
}
return messages;
}
namespace {
/// @return ``true`` if an input table contains a column termed "dasid"
bool hasDasIDs(const API::ITableWorkspace_const_sptr &table) {
const auto columnNames = table->getColumnNames();
return (std::find(columnNames.begin(), columnNames.end(), std::string("dasid")) != columnNames.end());
}
/**
* Conversion factor between fit-function width and FWHM. Factors are calculated
* for "Gaussian" and "Lorentzian" functions. For other functions, a value
* of 1.0 is returned
*
* @param peakshape :: name of the fitting function
*/
double getWidthToFWHM(const std::string &peakshape) {
// could we use actual peak function here?
if (peakshape == "Gaussian") {
return 2 * std::sqrt(2. * std::log(2.));
} else if (peakshape == "Lorentzian") {
return 2.;
} else if (peakshape == "BackToBackExponential") {
return 1.; // TODO the conversion isn't document in the function
} else {
return 1.;
}
}
} // end of anonymous namespace
//----------------------------------------------------------------------------------------------
/** Execute the algorithm.
*/
void PDCalibration::exec() {
vector<double> tofBinningParams = getProperty("TofBinning");
m_tofMin = tofBinningParams.front();
m_tofMax = tofBinningParams.back();
vector<double> tzeroRange = getProperty("TZEROrange");
if (tzeroRange.size() == 2) {
m_tzeroMin = tzeroRange[0];
m_tzeroMax = tzeroRange[1];
std::stringstream msg;
msg << "Using tzero range of " << m_tzeroMin << " <= "
<< "TZERO <= " << m_tzeroMax;
g_log.information(msg.str());
} else {
g_log.information("Using all TZERO values");
m_tzeroMin = std::numeric_limits<double>::lowest();
m_tzeroMax = std::numeric_limits<double>::max();
}
vector<double> difaRange = getProperty("DIFArange");
if (difaRange.size() == 2) {
m_difaMin = difaRange[0];
m_difaMax = difaRange[1];
std::stringstream msg;
msg << "Using difa range of " << m_difaMin << " <= "
<< "DIFA <= " << m_difaMax;
g_log.information(msg.str());
} else {
g_log.information("Using all DIFA values");
m_difaMin = std::numeric_limits<double>::lowest();
m_difaMax = std::numeric_limits<double>::max();
}
m_peaksInDspacing = getProperty("PeakPositions");
// Sort peak positions, required for correct peak window calculations
std::sort(m_peaksInDspacing.begin(), m_peaksInDspacing.end());
const double peakWindowMaxInDSpacing = getProperty("PeakWindow");
const double minPeakHeight = getProperty("MinimumPeakHeight");
const double maxChiSquared = getProperty("MaxChiSq");
const std::string calParams = getPropertyValue("CalibrationParameters");
if (calParams == std::string("DIFC"))
m_numberMaxParams = 1;
else if (calParams == std::string("DIFC+TZERO"))
m_numberMaxParams = 2;
else if (calParams == std::string("DIFC+TZERO+DIFA"))
m_numberMaxParams = 3;
else
throw std::runtime_error("Encountered impossible CalibrationParameters value");
m_uncalibratedWS = loadAndBin();
setProperty("InputWorkspace", m_uncalibratedWS);
auto uncalibratedEWS = std::dynamic_pointer_cast<EventWorkspace>(m_uncalibratedWS);
auto isEvent = bool(uncalibratedEWS);
// Load Previous Calibration or create calibration table from signal file
if ((!static_cast<std::string>(getProperty("PreviousCalibrationFile")).empty()) ||
(!getPropertyValue("PreviousCalibrationTable").empty())) { //"PreviousCalibrationTable"
createCalTableFromExisting();
} else {
createCalTableNew(); // calculates "difc" values from instrument geometry
}
createInformationWorkspaces();
// Initialize the mask workspace, will all detectors masked by default
std::string maskWSName = getPropertyValue("OutputCalibrationTable");
maskWSName += "_mask";
declareProperty(std::make_unique<WorkspaceProperty<>>("MaskWorkspace", maskWSName, Direction::Output),
"An output workspace containing the mask");
MaskWorkspace_sptr maskWS = std::make_shared<DataObjects::MaskWorkspace>(m_uncalibratedWS->getInstrument());
for (size_t i = 0; i < maskWS->getNumberHistograms(); ++i) // REMOVE
maskWS->setMaskedIndex(i, true); // mask everything to start
setProperty("MaskWorkspace", maskWS);
const std::string peakFunction = getProperty("PeakFunction");
const double WIDTH_TO_FWHM = getWidthToFWHM(peakFunction);
if (WIDTH_TO_FWHM == 1.) {
g_log.notice() << "Unknown conversion for \"" << peakFunction
<< "\", found peak widths and resolution should not be "
"directly compared to delta-d/d";
}
auto NUMHIST = static_cast<int>(m_uncalibratedWS->getNumberHistograms());
// A pair of workspaces, one containing the nominal peak centers in TOF units,
// the other containing the left and right fitting ranges around each nominal
// peak center, also in TOF units. This for each pixel of the instrument
auto matrix_pair = createTOFPeakCenterFitWindowWorkspaces(m_uncalibratedWS, peakWindowMaxInDSpacing);
API::MatrixWorkspace_sptr tof_peak_center_ws = matrix_pair.first;
API::MatrixWorkspace_sptr tof_peak_window_ws = matrix_pair.second;
// API::MatrixWorkspace_sptr peak_window_ws =
// createTOFPeakFitWindowWorkspace(m_uncalibratedWS, windowsInDSpacing);
double peak_width_percent = getProperty("PeakWidthPercent");
const std::string diagnostic_prefix = getPropertyValue("DiagnosticWorkspaces");
// Refine the position of the peak centers starting from the nominal peak
// centers and fitting them against a peak fit function (e.g. a Gaussian)
auto algFitPeaks = createChildAlgorithm("FitPeaks", .2, .7);
algFitPeaks->setLoggingOffset(3);
algFitPeaks->setProperty("InputWorkspace", m_uncalibratedWS);
// theoretical peak center
algFitPeaks->setProperty("PeakCentersWorkspace", tof_peak_center_ws);
// peak and background functions
algFitPeaks->setProperty<std::string>("PeakFunction", peakFunction);
algFitPeaks->setProperty<std::string>("BackgroundType", getProperty("BackgroundType"));
// peak range setup
algFitPeaks->setProperty("FitPeakWindowWorkspace", tof_peak_window_ws);
algFitPeaks->setProperty("PeakWidthPercent", peak_width_percent);
algFitPeaks->setProperty("MinimumPeakHeight", minPeakHeight);
// some fitting strategy
algFitPeaks->setProperty("FitFromRight", true);
algFitPeaks->setProperty("HighBackground", false);
bool constrainPeakPosition = getProperty("ConstrainPeakPositions");
algFitPeaks->setProperty("ConstrainPeakPositions",
constrainPeakPosition); // TODO Pete: need to test this option
// optimization setup // TODO : need to test LM or LM-MD
algFitPeaks->setProperty("Minimizer", "Levenberg-Marquardt");
algFitPeaks->setProperty("CostFunction", "Least squares");
// FitPeaks will abstract the peak parameters if you ask (if using chisq then
// need FitPeaks to output fitted params rather than height, width)
const bool useChiSq = getProperty("UseChiSq");
algFitPeaks->setProperty("RawPeakParameters", useChiSq);
// Analysis output
// If using a Gaussian peak shape plus a constant background, then
// OutputPeakParametersWorkspace is a table with columns:
// wsindex_0 peakindex_0 centre width height intensity A0 chi2
// ...
// wsindex_0 peakindex_N centre width height intensity A0 chi2
// ...
// ...
// wsindex_M peakindex_N centre width height intensity
algFitPeaks->setPropertyValue("OutputPeakParametersWorkspace", diagnostic_prefix + "_fitparam");
// contains the same intensities as input m_uncalibratedWS except within
// the fitting range of each successfully fitted peak. Within this range,
// the actual intensities are replaced with the values resulting from
// evaluating the peak function (e.g. a Gaussian peak function)
algFitPeaks->setPropertyValue("FittedPeaksWorkspace", diagnostic_prefix + "_fitted");
if (useChiSq) {
algFitPeaks->setPropertyValue("OutputParameterFitErrorsWorkspace", diagnostic_prefix + "_fiterrors");
}
// run and get the result
algFitPeaks->executeAsChildAlg();
g_log.information("finished FitPeaks");
// get the fit result
API::ITableWorkspace_sptr fittedTable = algFitPeaks->getProperty("OutputPeakParametersWorkspace");
API::MatrixWorkspace_sptr calculatedWS = algFitPeaks->getProperty("FittedPeaksWorkspace");
API::ITableWorkspace_sptr errorTable; // or nullptr as in FitPeaks L1997
if (useChiSq) {
errorTable = algFitPeaks->getProperty("OutputParameterFitErrorsWorkspace");
}
// check : for Pete
if (!fittedTable)
throw std::runtime_error("FitPeaks does not have output OutputPeakParametersWorkspace.");
if (fittedTable->rowCount() != NUMHIST * m_peaksInDspacing.size())
throw std::runtime_error("The number of rows in OutputPeakParametersWorkspace is not correct!");
// END-OF (FitPeaks)
const std::string backgroundType = getPropertyValue("BackgroundType");
API::Progress prog(this, 0.7, 1.0, NUMHIST);
// calculate fitting ranges to the left and right of each nominal peak
// center, in d-spacing units
const auto windowsInDSpacing = dSpacingWindows(m_peaksInDspacing, peakWindowMaxInDSpacing);
// get spectrum info to check workspace index correpsonds to a valid spectrum
const auto &spectrumInfo = m_uncalibratedWS->spectrumInfo();
// Scan the table containing the fit parameters for every peak, retrieve the
// parameters for peaks that were successfully fitting, then use this info
// to obtain difc, difa, and tzero for each pixel
// cppcheck-suppress syntaxError
PRAGMA_OMP(parallel for schedule(dynamic, 1))
for (int wkspIndex = 0; wkspIndex < NUMHIST; ++wkspIndex) {
PARALLEL_START_INTERUPT_REGION
if ((isEvent && uncalibratedEWS->getSpectrum(wkspIndex).empty()) || !spectrumInfo.hasDetectors(wkspIndex) ||
spectrumInfo.isMonitor(wkspIndex)) {
prog.report();
continue;
}
// object to hold the information about the peak positions, detid, and wksp
// index
PDCalibration::FittedPeaks peaks(m_uncalibratedWS, wkspIndex);
auto [difc, difa, tzero] = getDSpacingToTof(peaks.detid); // doesn't matter which one - all have same difc etc.
peaks.setPositions(m_peaksInDspacing, windowsInDSpacing, difa, difc, tzero);
// includes peaks that aren't used in the fit
// The following data structures will hold information for the peaks
// found in the current spectrum
const size_t numPeaks = m_peaksInDspacing.size();
// TOF of fitted peak centers, default `nan` for failed fitted peaks
std::vector<double> tof_vec_full(numPeaks, std::nan(""));
std::vector<double> d_vec; // nominal peak centers of fitted peaks
std::vector<double> tof_vec; // TOF of fitted peak centers only
// width of fitted peak centers, default `nan` for failed fitted peaks
std::vector<double> width_vec_full(numPeaks, std::nan(""));
// height of fitted peak centers, default `nan` for failed fitted peaks
std::vector<double> height_vec_full(numPeaks, std::nan(""));
std::vector<double> weights; // weights for diff const fits
// for (size_t i = 0; i < fittedTable->rowCount(); ++i) {
const size_t rowNumInFitTableOffset = wkspIndex * numPeaks;
// We assumed that the current spectrum contains peaks near the nominal
// peak centers. Now we check how many peaks we actually found
for (size_t peakIndex = 0; peakIndex < numPeaks; ++peakIndex) {
size_t rowIndexInFitTable = rowNumInFitTableOffset + peakIndex;
// check indices in PeaksTable
if (fittedTable->getRef<int>("wsindex", rowIndexInFitTable) != wkspIndex)
throw std::runtime_error("workspace index mismatch!");
if (fittedTable->getRef<int>("peakindex", rowIndexInFitTable) != static_cast<int>(peakIndex))
throw std::runtime_error("peak index mismatch but workspace index matched");
const double chi2 = fittedTable->getRef<double>("chi2", rowIndexInFitTable);
double centre = 0.0;
double centre_error = 0.0; // only used if useChiSq true
double width = 0.0;
double height = 0.0;
if (!useChiSq) {
// get the effective peak parameters from FitPeaks output
centre = fittedTable->getRef<double>("centre", rowIndexInFitTable);
width = fittedTable->getRef<double>("width", rowIndexInFitTable);
height = fittedTable->getRef<double>("height", rowIndexInFitTable);
} else {
// FitPeaks outputs actual fitting parameters
// extract these from the peak function (not efficient)
auto peakfunc = std::dynamic_pointer_cast<API::IPeakFunction>(
API::FunctionFactory::Instance().createFunction(peakFunction));
// set peak functio nparameters from fit
for (size_t ipar = 0; ipar < peakfunc->nParams(); ipar++) {
peakfunc->setParameter(ipar, fittedTable->getRef<double>(peakfunc->parameterName(ipar), rowIndexInFitTable));
}
centre = peakfunc->centre();
width = peakfunc->fwhm();
height = peakfunc->height();
centre_error = errorTable->getRef<double>(peakfunc->getCentreParameterName(), rowIndexInFitTable);
}
// check chi-square
if (chi2 > maxChiSquared || chi2 < 0.) {
continue; // peak fit deemed as failure
}
// rule out of peak with wrong position. `centre` should be within its
// left and right window ranges
if (peaks.inTofWindows[2 * peakIndex] >= centre || peaks.inTofWindows[2 * peakIndex + 1] <= centre) {
continue; // peak fit deemed as failure
}
// check height: make sure 0 is smaller than 0
if (height < minPeakHeight + 1.E-15) {
continue; // peak fit deemed as failure
}
// the peak fit was a success. Collect info
d_vec.emplace_back(m_peaksInDspacing[peakIndex]);
tof_vec.emplace_back(centre);
if (!useChiSq) {
weights.emplace_back(height * height);
} else {
weights.emplace_back(1 / (centre_error * centre_error));
}
tof_vec_full[peakIndex] = centre;
width_vec_full[peakIndex] = width;
height_vec_full[peakIndex] = height;
}
// mask a detector if less than two peaks were fitted successfully
maskWS->setMasked(peaks.detid, d_vec.size() < 2);
if (d_vec.size() < 2) { // not enough peaks were found
continue;
} else {
// obtain difc, difa, and t0 by fitting the nominal peak center
// positions, in d-spacing against the fitted peak center positions, in
// TOF units.
double difc = 0., t0 = 0., difa = 0.;
fitDIFCtZeroDIFA_LM(d_vec, tof_vec, weights, difc, t0, difa);
for (auto iter = peaks.detid.begin(); iter != peaks.detid.end(); ++iter) {
auto det = *iter;
const auto rowIndexOutputPeaks = m_detidToRow[det];
// chisq represent the deviations between the nominal peak positions
// and the peak positions using the GSAS formula with optimized difc,
// difa, and tzero
double chisq = 0.;
Mantid::Kernel::Units::dSpacing dSpacingUnit;
dSpacingUnit.initialize(-1., 0,
Kernel::UnitParametersMap{{Kernel::UnitParams::difa, difa},
{Kernel::UnitParams::difc, difc},
{Kernel::UnitParams::tzero, t0}});
for (std::size_t i = 0; i < numPeaks; ++i) {
if (std::isnan(tof_vec_full[i]))
continue;
// Find d-spacing using the GSAS formula with optimized difc, difa,
// t0 for the TOF of the current peak's center.
const double dspacing = dSpacingUnit.singleFromTOF(tof_vec_full[i]);
// `temp` is residual between the nominal position in d-spacing for
// the current peak, and the fitted position in d-spacing
const double temp = m_peaksInDspacing[i] - dspacing;
chisq += (temp * temp);
m_peakPositionTable->cell<double>(rowIndexOutputPeaks, i + 1) = dspacing;
m_peakWidthTable->cell<double>(rowIndexOutputPeaks, i + 1) =
WIDTH_TO_FWHM * (width_vec_full[i] / (2 * difa * dspacing + difc));
m_peakHeightTable->cell<double>(rowIndexOutputPeaks, i + 1) = height_vec_full[i];
}
m_peakPositionTable->cell<double>(rowIndexOutputPeaks, m_peaksInDspacing.size() + 1) = chisq;
m_peakPositionTable->cell<double>(rowIndexOutputPeaks, m_peaksInDspacing.size() + 2) =
chisq / static_cast<double>(numPeaks - 1);
setCalibrationValues(det, difc, difa, t0);
}
}
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// sort the calibration tables by increasing detector ID
m_calibrationTable = sortTableWorkspace(m_calibrationTable);
setProperty("OutputCalibrationTable", m_calibrationTable);
// fix-up the diagnostic workspaces
m_peakPositionTable = sortTableWorkspace(m_peakPositionTable);
m_peakWidthTable = sortTableWorkspace(m_peakWidthTable);
m_peakHeightTable = sortTableWorkspace(m_peakHeightTable);
// a derived table from the position and width
auto resolutionWksp = calculateResolutionTable();
// set the diagnostic workspaces out
auto diagnosticGroup = std::make_shared<API::WorkspaceGroup>();
// add workspaces calculated by FitPeaks
API::AnalysisDataService::Instance().addOrReplace(diagnostic_prefix + "_fitparam", fittedTable);
diagnosticGroup->addWorkspace(fittedTable);
API::AnalysisDataService::Instance().addOrReplace(diagnostic_prefix + "_fitted", calculatedWS);
diagnosticGroup->addWorkspace(calculatedWS);
if (useChiSq) {
API::AnalysisDataService::Instance().addOrReplace(diagnostic_prefix + "_fiterror", errorTable);
diagnosticGroup->addWorkspace(errorTable);
}
// add workspaces calculated by PDCalibration
API::AnalysisDataService::Instance().addOrReplace(diagnostic_prefix + "_dspacing", m_peakPositionTable);
diagnosticGroup->addWorkspace(m_peakPositionTable);
API::AnalysisDataService::Instance().addOrReplace(diagnostic_prefix + "_width", m_peakWidthTable);
diagnosticGroup->addWorkspace(m_peakWidthTable);
API::AnalysisDataService::Instance().addOrReplace(diagnostic_prefix + "_height", m_peakHeightTable);
diagnosticGroup->addWorkspace(m_peakHeightTable);
API::AnalysisDataService::Instance().addOrReplace(diagnostic_prefix + "_resolution", resolutionWksp);
diagnosticGroup->addWorkspace(resolutionWksp);
setProperty("DiagnosticWorkspaces", diagnosticGroup);
}
namespace { // anonymous namespace
/**
* Helper function for calculating costs in gsl.
* @param v vector of parameters that are being modified by gsl (difc, tzero,
* difa)
* @param params The parameters being used for the fit
* @return Sum of the errors
*/
double gsl_costFunction(const gsl_vector *v, void *peaks) {
// this array is [numPeaks, numParams, vector<tof>, vector<dspace>,
// vector<weights>]
// index as [0, 1, 2, , 2+n , 2+2n]
const std::vector<double> *peakVec = reinterpret_cast<std::vector<double> *>(peaks);
// number of peaks being fit
const auto numPeaks = static_cast<size_t>(peakVec->at(0));
// number of parameters
const auto numParams = static_cast<size_t>(peakVec->at(1));
// isn't strictly necessary, but makes reading the code much easier
const std::vector<double> tofObs(peakVec->begin() + 2, peakVec->begin() + 2 + numPeaks);
const std::vector<double> dspace(peakVec->begin() + (2 + numPeaks), peakVec->begin() + (2 + 2 * numPeaks));
const std::vector<double> weights(peakVec->begin() + (2 + 2 * numPeaks), peakVec->begin() + (2 + 3 * numPeaks));
// create the function to convert tof to dspacing
double difc = gsl_vector_get(v, 0);
double tzero = 0.;
double difa = 0.;
if (numParams > 1) {
tzero = gsl_vector_get(v, 1);
if (numParams > 2)
difa = gsl_vector_get(v, 2);
}
Mantid::Kernel::Units::dSpacing dSpacingUnit;
dSpacingUnit.initialize(-1., 0,
Kernel::UnitParametersMap{{Kernel::UnitParams::difa, difa},
{Kernel::UnitParams::difc, difc},
{Kernel::UnitParams::tzero, tzero}});
// calculate the sum of the residuals from observed peaks
double errsum = 0.0;
for (size_t i = 0; i < numPeaks; ++i) {
const double tofCalib = dSpacingUnit.singleToTOF(dspace[i]);
const double errsum_i = std::fabs(tofObs[i] - tofCalib) * weights[i];
errsum += errsum_i;
}
return errsum;
}
/**
* Linear regression of the nominal peak center positions, in d-spacing
* against the fitted peak center positions, in TOF units.
*
* The equation to carry out the fit depends on the number of fit parameter
* selected:
* one fit parameter: TOF = DIFC * d
* two fit parameter: TOF = DIFC * d + TZERO
* three fit parameters: TOF = DIFC * d + TZERO + DIFA * d^2
*
* @param peaks :: array with structure (numpeaks, numfitparms, tof_1,
* ...tof_numpeaks, d1,...d_numpeaks, weight_1,...weight_numpeaks)
* @param difc :: will store optimized DIFC fit parameter
* @param t0 :: will store optimized TZERO fit parameter
* @param difa :: will store optimized DIFA fit parameter
* @return cummulative error of the fit, zero if the fit fails
*/
double fitDIFCtZeroDIFA(std::vector<double> &peaks, double &difc, double &t0, double &difa) {
const auto numParams = static_cast<size_t>(peaks[1]);
// initial starting point as [DIFC, 0, 0]
gsl_vector *fitParams = gsl_vector_alloc(numParams);
gsl_vector_set_all(fitParams, 0.0); // set all parameters to zero
gsl_vector_set(fitParams, 0, difc);
if (numParams > 1) {
gsl_vector_set(fitParams, 1, t0);
if (numParams > 2) {
gsl_vector_set(fitParams, 2, difa);
}
}
// Set initial step sizes
gsl_vector *stepSizes = gsl_vector_alloc(numParams);
gsl_vector_set_all(stepSizes, 0.1);
gsl_vector_set(stepSizes, 0, 0.01);
// Initialize method and iterate
gsl_multimin_function minex_func;
minex_func.n = numParams;
minex_func.f = &gsl_costFunction;
minex_func.params = &peaks;
// Set up GSL minimzer - simplex is overkill
const gsl_multimin_fminimizer_type *minimizerType = gsl_multimin_fminimizer_nmsimplex;
gsl_multimin_fminimizer *minimizer = gsl_multimin_fminimizer_alloc(minimizerType, numParams);
gsl_multimin_fminimizer_set(minimizer, &minex_func, fitParams, stepSizes);
// Finally do the fitting
size_t iter = 0; // number of iterations
const size_t MAX_ITER = 75 * numParams;
int status = 0;
do {
iter++;
status = gsl_multimin_fminimizer_iterate(minimizer);
if (status)
break;
double size = gsl_multimin_fminimizer_size(minimizer);
status = gsl_multimin_test_size(size, 1e-4);
} while (status == GSL_CONTINUE && iter < MAX_ITER);
// only update calibration values on successful fit
double errsum = 0.; // return 0. if fit didn't work
std::string status_msg = gsl_strerror(status);
if (status_msg == "success") {
difc = gsl_vector_get(minimizer->x, 0);
if (numParams > 1) {
t0 = gsl_vector_get(minimizer->x, 1);
if (numParams > 2) {
difa = gsl_vector_get(minimizer->x, 2);
}
}
// return from gsl_costFunction can be accessed as fval
errsum = minimizer->fval;
}
// free memory
gsl_vector_free(fitParams);
gsl_vector_free(stepSizes);
gsl_multimin_fminimizer_free(minimizer);
return errsum;
}
} // end of anonymous namespace
/**
* Fit the nominal peak center positions, in d-spacing against the fitted
* peak center positions, in TOF units. We use the GSAS formula:
* TOF = DIFC∗d + DIFA∗d^2 + TZERO
*
* No fitting is performed if the number of peaks is less than two.
*
* @param d :: nominal peak center positions, in d-spacing units
* @param tof :: fitted peak center positions, in TOF units
* @param weights :: weights for leastsq fit
* @param difc :: output optimized DIFC parameter
* @param t0 :: output optimized TZERO parameter
* @param difa :: output optimized DIFA parameter
*/
void PDCalibration::fitDIFCtZeroDIFA_LM(const std::vector<double> &d, const std::vector<double> &tof,
const std::vector<double> &weights, double &difc, double &t0, double &difa) {
const size_t numPeaks = d.size();
if (numPeaks <= 1) {
return; // don't do anything
}
// number of fit parameters 1=[DIFC], 2=[DIFC,TZERO], 3=[DIFC,TZERO,DIFA]
// set the maximum number of parameters that will be used
// statistics doesn't support having too few peaks
size_t maxParams = std::min<size_t>(numPeaks - 1, m_numberMaxParams);
// this must have the same layout as the unpacking in gsl_costFunction above
// `peaks` has the following structure for a fit session with three peaks
// and two fit parameters:
// 3, 2, tof1, tof2, tof3, d1, d2, d3, h21, h22, h23
std::vector<double> peaks(numPeaks * 3 + 2, 0.);
peaks[0] = static_cast<double>(d.size());
peaks[1] = 1.; // number of parameters to fit. Initialize to just one
for (size_t i = 0; i < numPeaks; ++i) {
peaks[i + 2] = tof[i];
peaks[i + 2 + numPeaks] = d[i];
peaks[i + 2 + 2 * numPeaks] = weights[i];
}
// calculate a starting DIFC
double difc_start = difc;
if (difc_start == 0.) {
const double d_sum = std::accumulate(d.begin(), d.end(), 0.);
const double tof_sum = std::accumulate(tof.begin(), tof.end(), 0.);
difc_start = tof_sum / d_sum; // number of peaks falls out of division
}
// save the best values so far
double best_errsum = std::numeric_limits<double>::max();
double best_difc = difc_start;
double best_t0 = 0.;
double best_difa = 0.;
// loop over possible number of parameters, doing up to three sequential fits.
// We first start with equation TOF = DIFC * d and obtain
// optimized DIFC which serves as initial guess for next fit with equation
// TOF = DIFC * d + TZERO, obtaining optimized DIFC and TZERO which serve as
// initial guess for final fit TOF = DIFC * d + DIFA * d^2 + TZERO.
for (size_t numParams = 1; numParams <= maxParams; ++numParams) {
peaks[1] = static_cast<double>(numParams);
double difc_local = best_difc;
double t0_local = best_t0;
double difa_local = best_difa;
double errsum = fitDIFCtZeroDIFA(peaks, difc_local, t0_local, difa_local);
if (errsum > 0.) {
// normalize by degrees of freedom
errsum = errsum / static_cast<double>(numPeaks - numParams);
// save the best and forget the rest
// the selected (DIFC, DIFA, TZERO) correspond to those of the fit that
// minimizes errsum. It doesn't have to be the last fit.
if (errsum < best_errsum) {
if (difa_local > m_difaMax || difa_local < m_difaMin)
continue; // unphysical fit
if (t0_local > m_tzeroMax || t0_local < m_tzeroMin)
continue; // unphysical fit
best_errsum = errsum;
best_difc = difc_local;
best_t0 = t0_local;
best_difa = difa_local;
}
}
}
difc = best_difc;
// check that something actually fit and set to the best result
if (best_difc > 0. && best_errsum < std::numeric_limits<double>::max()) {
t0 = best_t0;
difa = best_difa;
}
}
/**
* Fitting ranges to the left and right of peak center
*
* Example: if centres = [1, 3, 11] and widthMax=3, then this function returns
* [(3-1)/2, (3-1)/2, (3-1)/2, 3, 3, 3]
*
* @param centres :: peak centers, in d-spacing
* @param widthMax :: maximum allowable fit range
* @return array containing left and right ranges for first peak, left and right
* for second peak, and so on.
*/
vector<double> PDCalibration::dSpacingWindows(const std::vector<double> ¢res, const double widthMax) {
if (widthMax <= 0. || isEmpty(widthMax)) {
return vector<double>(); // option is turned off
}
const std::size_t numPeaks = centres.size();
// assumes distance between peaks can be used for window sizes
assert(numPeaks >= 2);
vector<double> windows(2 * numPeaks);
double widthLeft;
double widthRight;
for (std::size_t i = 0; i < centres.size(); ++i) {
// calculate left
if (i == 0)
widthLeft = .5 * (centres[1] - centres[0]);
else
widthLeft = .5 * (centres[i] - centres[i - 1]);
widthLeft = std::min(widthLeft, widthMax);
// calculate right
if (i + 1 == numPeaks)
widthRight = .5 * (centres[numPeaks - 1] - centres[numPeaks - 2]);
else
widthRight = .5 * (centres[i + 1] - centres[i]);
widthRight = std::min(widthRight, widthMax);
// set the windows
windows[2 * i] = centres[i] - widthLeft;
windows[2 * i + 1] = centres[i] + widthRight;
}
return windows;
}
/**
* Return a function that converts from d-spacing to TOF units for a particular
* pixel, evaulating the GSAS conversion formula:
* TOF = DIFC∗d + DIFA∗d^2 + TZERO
*
* @param detIds :: set of detector IDs
*/
std::tuple<double, double, double> PDCalibration::getDSpacingToTof(const std::set<detid_t> &detIds) {
// to start this is the old calibration values