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LeBailFit.cpp
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LeBailFit.cpp
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/* COMMIT NOTES *
1. Rename calculateDiffractionPatternMC to calculateDiffractionPattern
2.
* COMMIT NOTES */
#include "MantidCurveFitting/Algorithms/LeBailFit.h"
#include "MantidKernel/ListValidator.h"
#include "MantidAPI/TableRow.h"
#include "MantidAPI/FunctionFactory.h"
#include "MantidAPI/FuncMinimizerFactory.h"
#include "MantidAPI/WorkspaceFactory.h"
#include "MantidCurveFitting/Algorithms/Fit.h"
#include "MantidKernel/ArrayProperty.h"
#include "MantidKernel/VisibleWhenProperty.h"
#include "MantidCurveFitting/Functions/BackgroundFunction.h"
#include "MantidAPI/TextAxis.h"
#include <boost/algorithm/string.hpp>
#include <boost/algorithm/string/split.hpp>
#include <fstream>
const int OBSDATAINDEX(0);
const int CALDATAINDEX(1);
const int DATADIFFINDEX(2);
const int CALPUREPEAKINDEX(3);
const int CALBKGDINDEX(4); // Output workspace background at ws index 4
const int INPUTBKGDINDEX(6); // Input background
const int INPUTPUREPEAKINDEX(
7); // Output workspace: pure peak (data with background removed)
const int SMOOTHEDBKGDINDEX(8); // Smoothed background
const double NOBOUNDARYLIMIT(1.0E10);
const double EPSILON(1.0E-10);
using namespace Mantid;
using namespace Mantid::CurveFitting;
using namespace Mantid::DataObjects;
using namespace Mantid::API;
using namespace Mantid::Kernel;
using namespace std;
namespace Mantid {
namespace CurveFitting {
namespace Algorithms {
const Rfactor badR(DBL_MAX, DBL_MAX);
DECLARE_ALGORITHM(LeBailFit)
//----------------------------------------------------------------------------------------------
/** Constructor
*/
LeBailFit::LeBailFit()
: m_lebailFunction(), m_dataWS(), m_outputWS(), parameterWS(),
reflectionWS(), m_wsIndex(0), m_startX(DBL_MAX), m_endX(DBL_MIN),
m_inputPeakInfoVec(), m_backgroundFunction(), m_funcParameters(),
m_origFuncParameters(), m_peakType(), m_backgroundType(),
m_backgroundParameters(), m_backgroundParameterNames(), m_bkgdorder(0),
mPeakGroupMap(), mPeakGroupFitChi2Map(), mPeakGroupFitStatusMap(),
mPeakRadius(0), m_lebailFitChi2(0.), m_lebailCalChi2(0.), mMinimizer(),
m_dampingFactor(0.), m_inputParameterPhysical(false), m_fitMode(),
m_indicatePeakHeight(0.), m_MCGroups(), m_numMCGroups(0), m_bestRwp(0.),
m_bestRp(0.), m_bestParameters(), m_bestBackgroundData(), m_bestMCStep(0),
m_numMinimizeSteps(0), m_Temperature(DBL_MIN), m_useAnnealing(false),
m_walkStyle(RANDOMWALK), m_minimumPeakHeight(DBL_MAX),
m_tolerateInputDupHKL2Peaks(false), m_bkgdParameterNames(),
m_numberBkgdParameters(0), m_bkgdParameterBuffer(), m_bestBkgdParams(),
m_roundBkgd(0), m_bkgdParameterStepVec(), m_peakCentreTol(0.) {}
//----------------------------------------------------------------------------------------------
/** Declare the input properties for this algorithm
*/
void LeBailFit::init() {
// -------------- Input and output Workspaces -----------------
// Input Data Workspace
this->declareProperty(
Kernel::make_unique<WorkspaceProperty<MatrixWorkspace>>(
"InputWorkspace", "", Direction::Input),
"Input workspace containing the data to fit by LeBail algorithm.");
// Output Result Data/Model Workspace
this->declareProperty(Kernel::make_unique<WorkspaceProperty<Workspace2D>>(
"OutputWorkspace", "", Direction::Output),
"Output workspace containing calculated pattern or "
"calculated background. ");
// Instrument profile Parameters
this->declareProperty(Kernel::make_unique<WorkspaceProperty<TableWorkspace>>(
"InputParameterWorkspace", "", Direction::Input),
"Input table workspace containing the parameters "
"required by LeBail fit. ");
// Output instrument profile parameters
auto tablewsprop1 = Kernel::make_unique<WorkspaceProperty<TableWorkspace>>(
"OutputParameterWorkspace", "", Direction::Output,
API::PropertyMode::Optional);
this->declareProperty(std::move(tablewsprop1),
"Input table workspace containing the "
"parameters required by LeBail fit. ");
// Single peak: Reflection (HKL) Workspace, PeaksWorkspace
this->declareProperty(
Kernel::make_unique<WorkspaceProperty<TableWorkspace>>(
"InputHKLWorkspace", "", Direction::Input),
"Input table workspace containing the list of reflections (HKL). ");
// Bragg peaks profile parameter output table workspace
auto tablewsprop2 = Kernel::make_unique<WorkspaceProperty<TableWorkspace>>(
"OutputPeaksWorkspace", "", Direction::Output,
API::PropertyMode::Optional);
this->declareProperty(std::move(tablewsprop2),
"Optional output table workspace "
"containing all peaks' peak "
"parameters. ");
// WorkspaceIndex
this->declareProperty("WorkspaceIndex", 0,
"Workspace index of the spectrum to fit by LeBail.");
// Interested region
this->declareProperty(
Kernel::make_unique<Kernel::ArrayProperty<double>>("FitRegion"),
"Region of data (TOF) for LeBail fit. Default is whole range. ");
// Functionality: Fit/Calculation/Background
std::vector<std::string> functions{"LeBailFit", "Calculation", "MonteCarlo",
"RefineBackground"};
auto validator = boost::make_shared<Kernel::StringListValidator>(functions);
this->declareProperty("Function", "LeBailFit", validator, "Functionality");
// Peak type
vector<string> peaktypes{"ThermalNeutronBk2BkExpConvPVoigt",
"NeutronBk2BkExpConvPVoigt"};
auto peaktypevalidator = boost::make_shared<StringListValidator>(peaktypes);
declareProperty("PeakType", "ThermalNeutronBk2BkExpConvPVoigt",
peaktypevalidator, "Peak profile type.");
/*------------------------ Background Related Properties
* ---------------------------------*/
// About background: Background type, input (table workspace or array)
std::vector<std::string> bkgdtype{"Polynomial", "Chebyshev",
"FullprofPolynomial"};
auto bkgdvalidator =
boost::make_shared<Kernel::StringListValidator>(bkgdtype);
declareProperty("BackgroundType", "Polynomial", bkgdvalidator,
"Background type");
// Input background parameters (array)
this->declareProperty(
Kernel::make_unique<Kernel::ArrayProperty<double>>(
"BackgroundParameters"),
"Optional: enter a comma-separated list of background order parameters "
"from order 0. ");
// Input background parameters (tableworkspace)
auto tablewsprop3 = Kernel::make_unique<WorkspaceProperty<TableWorkspace>>(
"BackgroundParametersWorkspace", "", Direction::InOut,
API::PropertyMode::Optional);
this->declareProperty(
std::move(tablewsprop3),
"Optional table workspace containing the fit result for background.");
// Peak Radius
this->declareProperty(
"PeakRadius", 5, "Range (multiplier relative to FWHM) for a full peak. ");
/*------------------------ Properties for Calculation Mode
* --------------------------------*/
// Output option to plot each individual peak
declareProperty("PlotIndividualPeaks", false,
"Option to output each individual peak in mode Calculation.");
setPropertySettings("PlotIndividualPeaks",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "Calculation"));
// Make each reflection visible
declareProperty("IndicationPeakHeight", 0.0,
"Heigh of peaks (reflections) if its calculated height is "
"smaller than user-defined minimum.");
setPropertySettings("IndicationPeakHeight",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "Calculation"));
// UseInputPeakHeights
declareProperty("UseInputPeakHeights", true,
"For 'Calculation' mode only, use peak heights specified in "
"ReflectionWorkspace. "
"Otherwise, calcualte peaks' heights. ");
setPropertySettings("UseInputPeakHeights",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "Calculation"));
/*--------------------------- Properties for Fitting Mode
* ---------------------------------*/
// Minimizer
std::vector<std::string> minimizerOptions =
API::FuncMinimizerFactory::Instance().getKeys(); // :Instance().getKeys();
declareProperty("Minimizer", "Levenberg-MarquardtMD",
Kernel::IValidator_sptr(
new Kernel::ListValidator<std::string>(minimizerOptions)),
"The minimizer method applied to do the fit, default is "
"Levenberg-Marquardt",
Kernel::Direction::InOut);
setPropertySettings("Minimizer", Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "LeBailFit"));
declareProperty("Damping", 1.0, "Damping factor if minizer is 'Damping'");
setPropertySettings("Damping", Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "LeBailFit"));
setPropertySettings("Damping", Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "MonteCarlo"));
declareProperty("NumberMinimizeSteps", 100,
"Number of Monte Carlo random walk steps.");
setPropertySettings("NumberMinimizeSteps",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "LeBailFit"));
setPropertySettings("NumberMinimizeSteps",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "MonteCarlo"));
setPropertySettings("NumberMinimizeSteps",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "RefineBackground"));
//----------------- Parameters for Monte Carlo Simulated Annealing
//--------------------------
auto mcwsprop = Kernel::make_unique<WorkspaceProperty<TableWorkspace>>(
"MCSetupWorkspace", "", Direction::Input, PropertyMode::Optional);
declareProperty(std::move(mcwsprop),
"Name of table workspace containing parameters' "
"setup for Monte Carlo simualted annearling. ");
setPropertySettings("MCSetupWorkspace",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "MonteCarlo"));
declareProperty("RandomSeed", 1, "Random number seed.");
setPropertySettings("RandomSeed", Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "MonteCarlo"));
declareProperty("AnnealingTemperature", 1.0,
"Temperature used Monte Carlo. "
"Negative temperature is for simulated annealing. ");
setPropertySettings("AnnealingTemperature",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "MonteCarlo"));
declareProperty("UseAnnealing", true,
"Allow annealing temperature adjusted automatically.");
setPropertySettings("UseAnnealing",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "MonteCarlo"));
declareProperty("DrunkenWalk", false,
"Flag to use drunken walk algorithm. "
"Otherwise, random walk algorithm is used. ");
setPropertySettings("DrunkenWalk",
Kernel::make_unique<VisibleWhenProperty>(
"Function", IS_EQUAL_TO, "MonteCarlo"));
declareProperty(
"MinimumPeakHeight", 0.01,
"Minimum height of a peak to be counted "
"during smoothing background by exponential smooth algorithm. ");
// Flag to allow input hkl file containing degenerated peaks
declareProperty(
"AllowDegeneratedPeaks", false,
"Flag to allow degenerated peaks in input .hkl file. "
"Otherwise, an exception will be thrown if this situation occurs.");
// Tolerance of imported peak's position comparing to data range
declareProperty("ToleranceToImportPeak", EMPTY_DBL(),
"Tolerance in TOF to import peak from Bragg "
"peaks list. If it specified, all peaks within Xmin-Tol and "
"Xmax+Tol will be imported. "
"It is used in the case that the geometry parameters are "
"close to true values. ");
}
//----------------------------------------------------------------------------------------------
/** Implement abstract Algorithm methods
*/
void LeBailFit::exec() {
// Process input properties
processInputProperties();
// Import parameters from table workspace
parseInstrumentParametersTable();
parseBraggPeaksParametersTable();
// Background function and calculation on it
processInputBackground();
// Create Le Bail function
createLeBailFunction();
// Create output workspace/workspace
createOutputDataWorkspace();
// 5. Adjust function mode according to input values
if (m_lebailFunction->isParameterValid()) {
// All peaks within range are physical and good to refine
m_inputParameterPhysical = true;
} else {
// Some peaks within range have unphysical parameters. Just calcualtion for
// reference
m_inputParameterPhysical = false;
g_log.warning()
<< "Input instrument parameters values cause some peaks to have "
"unphysical profile parameters.\n";
if (m_fitMode == FIT || m_fitMode == MONTECARLO) {
g_log.warning()
<< "Function mode FIT is disabled. Convert to Calculation mode.\n";
m_fitMode = CALCULATION;
}
}
// 7. Do calculation or fitting
m_lebailFitChi2 = -1; // Initialize
m_lebailCalChi2 = -1;
switch (m_fitMode) {
case CALCULATION:
// Calculation
g_log.notice() << "Function: Pattern Calculation.\n";
execPatternCalculation();
break;
case FIT:
// LeBail Fit
g_log.notice() << "Function: Do LeBail Fit ==> Monte Carlo.\n";
// fall through
case MONTECARLO:
// Monte carlo Le Bail refinement
g_log.notice("Function: Do LeBail Fit By Monte Carlo Random Walk.");
execRandomWalkMinimizer(m_numMinimizeSteps, m_funcParameters);
break;
case BACKGROUNDPROCESS:
// Calculating background
// FIXME : Determine later whether this functionality is kept or removed!
g_log.notice() << "Function: Refine Background (Precisely).\n";
execRefineBackground();
break;
default:
// Impossible
std::stringstream errmsg;
errmsg << "FunctionMode = " << m_fitMode << " is not supported in exec().";
g_log.error() << errmsg.str() << "\n";
throw std::runtime_error(errmsg.str());
break;
}
// 7. Output peak (table) and parameter workspace
exportBraggPeakParameterToTable();
exportInstrumentParameterToTable(m_funcParameters);
setProperty("OutputWorkspace", m_outputWS);
// 8. Final statistic
Rfactor finalR = getRFactor(m_outputWS->readY(0), m_outputWS->readY(1),
m_outputWS->readE(0));
g_log.notice() << "\nFinal R factor: Rwp = " << finalR.Rwp
<< ", Rp = " << finalR.Rp
<< ", Data points = " << m_outputWS->readY(1).size()
<< ", Range = " << m_outputWS->readX(0)[0] << ", "
<< m_outputWS->readX(0).back() << "\n";
}
//----------------------------------------------------------------------------------------------
/** Process input background properties and do the calculation upon it
* and also calculate the input data with (input) background reduced
*/
void LeBailFit::processInputBackground() {
// FIXME - Need to think of FullprofPolynomial
// Type
m_backgroundType = getPropertyValue("BackgroundType");
// Parameters
m_backgroundParameters = getProperty("BackgroundParameters");
TableWorkspace_sptr bkgdparamws =
getProperty("BackgroundParametersWorkspace");
// Determine where the background parameters are from
if (!bkgdparamws) {
// Set up parameter name
m_backgroundParameterNames.clear();
size_t i0 = 0;
if (m_backgroundType == "FullprofPolynomial") {
// TODO - Add this special case to Wiki
m_backgroundParameterNames.emplace_back("Bkpos");
if (m_backgroundParameters[0] < m_startX ||
m_backgroundParameters[0] > m_endX)
g_log.warning(
"Bkpos is out side of data range. It MIGHT NOT BE RIGHT. ");
i0 = 1;
}
size_t numparams = m_backgroundParameters.size();
for (size_t i = i0; i < numparams; ++i) {
stringstream parss;
parss << "A" << (i - i0);
m_backgroundParameterNames.push_back(parss.str());
}
g_log.information() << "[Input] Use background specified with vector with "
"input vector sized " << numparams << ".\n";
} else {
g_log.information()
<< "[Input] Use background specified by table workspace.\n";
parseBackgroundTableWorkspace(bkgdparamws, m_backgroundParameterNames,
m_backgroundParameters);
}
// Set up background order
m_bkgdorder = static_cast<unsigned int>(m_backgroundParameterNames.size());
if (m_backgroundType == "FullprofPolynomial") {
// Consider 1 extra Bkpos
if (m_bkgdorder == 0)
throw runtime_error("FullprofPolynomial: Bkpos must be given! ");
else if (m_bkgdorder <= 7)
m_bkgdorder = 6;
else if (m_bkgdorder <= 13)
m_bkgdorder = 12;
else
throw runtime_error(
"There is something wrong to set up FullprofPolynomial. ");
} else {
// order n will have n+1 parameters
if (m_bkgdorder == 0)
throw runtime_error(
"Polynomial and Chebyshev at least be order 0 (1 parameter). ");
--m_bkgdorder;
}
}
//=================================== Pattern Calculation & Minimizing
//=======================
//----------------------------------------------------------------------------------------------
/** Calcualte LeBail diffraction pattern:
* Output spectra:
* 0: data; 1: calculated pattern; 3: difference
* 4: input pattern w/o background
* 5~5+(N-1): optional individual peak
*/
void LeBailFit::execPatternCalculation() {
// Generate domain and values vectors
const MantidVec &vecX = m_dataWS->readX(m_wsIndex);
MantidVec &vecY = m_outputWS->dataY(CALDATAINDEX);
// Calculate diffraction pattern
Rfactor rfactor(-DBL_MAX, -DBL_MAX);
// FIXME - It should be a new ticket to turn on this option (use
// user-specified peak height)
bool useinputpeakheights = this->getProperty("UseInputPeakHeights");
if (useinputpeakheights)
g_log.warning("UseInputPeakHeights is temporarily turned off now. ");
// Set the parameters to LeBailFunction
map<string, double> profilemap = convertToDoubleMap(m_funcParameters);
m_lebailFunction->setProfileParameterValues(profilemap);
// Calculate peak intensities and diffraction pattern
vector<double> emptyvec;
bool resultphysical = calculateDiffractionPattern(
m_dataWS->readX(m_wsIndex), m_dataWS->readY(m_wsIndex), true, true,
emptyvec, vecY, rfactor);
size_t numpts = vecY.size();
// Calculate background
MantidVec &vec_bkgd = m_outputWS->dataY(INPUTBKGDINDEX);
m_lebailFunction->function(vec_bkgd, vecX, false, true);
for (size_t i = 0; i < numpts; ++i)
m_outputWS->dataY(INPUTPUREPEAKINDEX)[i] =
m_outputWS->readY(OBSDATAINDEX)[i] -
m_outputWS->readY(INPUTBKGDINDEX)[i];
// Set up output workspaces
for (size_t i = 0; i < numpts; ++i) {
m_outputWS->dataY(DATADIFFINDEX)[i] =
m_outputWS->readY(OBSDATAINDEX)[i] - m_outputWS->readY(CALDATAINDEX)[i];
}
// Calcualte individual peaks
bool ploteachpeak = this->getProperty("PlotIndividualPeaks");
g_log.information() << "Output individual peaks = " << ploteachpeak << ".\n";
if (ploteachpeak) {
for (size_t ipk = 0; ipk < m_lebailFunction->getNumberOfPeaks(); ++ipk) {
MantidVec &vecTemp = m_outputWS->dataY(9 + ipk);
m_lebailFunction->calPeak(ipk, vecTemp, vecX);
}
}
// Record for output
Parameter par_rwp;
par_rwp.name = "Rwp";
par_rwp.curvalue = rfactor.Rwp;
m_funcParameters["Rwp"] = par_rwp;
g_log.notice() << "Rwp = " << rfactor.Rwp << ", Rp = " << rfactor.Rp << "\n";
if (!resultphysical) {
g_log.warning()
<< "Input parameters are unable to generate peaks that are physical."
<< ".\n";
}
}
//==================================== Refine background
//====================================
//----------------------------------------------------------------------------------------------
/** Calculate background of the specified diffraction pattern
* by
* 1. fix the peak parameters but height;
* 2. fit only heights of the peaks in a peak-group and background coefficients
* (assumed order 2 or 3 polynomial)
* 3. remove peaks by the fitting result
*/
void LeBailFit::execRefineBackground() {
// Set up class variables for background
m_bkgdParameterNames = m_backgroundFunction->getParameterNames();
m_numberBkgdParameters = m_bkgdParameterNames.size();
m_bkgdParameterBuffer.resize(m_numberBkgdParameters);
m_bestBkgdParams.resize(m_numberBkgdParameters);
m_roundBkgd = 0;
m_bkgdParameterStepVec.resize(m_numberBkgdParameters, 0.01);
for (size_t i = 1; i < m_numberBkgdParameters; ++i) {
m_bkgdParameterStepVec[i] = m_bkgdParameterStepVec[i - 1] * 0.0001;
}
// 1. Generate domain and value
const MantidVec &vecX = m_dataWS->readX(m_wsIndex);
const MantidVec &vecY = m_dataWS->readY(m_wsIndex);
vector<double> valueVec(vecX.size(), 0);
size_t numpts = vecX.size();
API::FunctionDomain1DVector domain(vecX);
API::FunctionValues values(domain);
// 2. Calculate diffraction pattern
Rfactor currR(DBL_MAX, DBL_MAX);
m_backgroundFunction->function(domain, values);
vector<double> backgroundvalues(numpts);
for (size_t i = 0; i < numpts; ++i) {
backgroundvalues[i] = values[i];
m_outputWS->dataY(INPUTPUREPEAKINDEX)[i] =
m_dataWS->readY(m_wsIndex)[i] - values[i];
m_outputWS->dataE(INPUTPUREPEAKINDEX)[i] = m_dataWS->readE(m_wsIndex)[i];
}
map<string, double> parammap = convertToDoubleMap(m_funcParameters);
m_lebailFunction->setProfileParameterValues(parammap);
calculateDiffractionPattern(m_outputWS->readX(INPUTPUREPEAKINDEX),
m_outputWS->readY(INPUTPUREPEAKINDEX), false,
true, backgroundvalues, valueVec, currR);
Rfactor bestR = currR;
storeBackgroundParameters(m_bestBkgdParams);
stringstream bufss;
bufss << "Starting background parameter ";
for (size_t i = 0; i < m_bestBkgdParams.size(); ++i)
bufss << "[" << i << "] = " << m_bestBkgdParams[i] << ", ";
bufss << ". Starting Rwp = " << currR.Rwp;
g_log.notice(bufss.str());
for (size_t istep = 0; istep < m_numMinimizeSteps; ++istep) {
// a) Store current setup
storeBackgroundParameters(m_bkgdParameterBuffer);
// b) Propose new values and evalulate
proposeNewBackgroundValues();
Rfactor newR(DBL_MAX, DBL_MAX);
m_backgroundFunction->function(domain, values);
for (size_t i = 0; i < numpts; ++i) {
backgroundvalues[i] = values[i];
m_outputWS->dataY(INPUTPUREPEAKINDEX)[i] =
m_dataWS->readY(m_wsIndex)[i] - values[i];
}
map<string, double> parammap = convertToDoubleMap(m_funcParameters);
m_lebailFunction->setProfileParameterValues(parammap);
calculateDiffractionPattern(m_outputWS->readX(INPUTPUREPEAKINDEX),
m_outputWS->readY(INPUTPUREPEAKINDEX), false,
true, backgroundvalues, valueVec, newR);
g_log.information() << "[DBx800] New Rwp = " << newR.Rwp
<< ", Rp = " << newR.Rp << ".\n";
bool accept = acceptOrDeny(currR, newR);
// c) Process result
if (!accept) {
// Not accept. Restore original
recoverBackgroundParameters(m_bkgdParameterBuffer);
} else {
// Accept
currR = newR;
if (newR.Rwp < bestR.Rwp) {
// Is it the best?
bestR = newR;
storeBackgroundParameters(m_bestBkgdParams);
stringstream bufss;
bufss << "Temp best background parameter ";
for (size_t i = 0; i < m_bestBkgdParams.size(); ++i)
bufss << "[" << i << "] = " << m_bestBkgdParams[i] << ", ";
g_log.information(bufss.str());
}
}
// d) Progress
progress(static_cast<double>(istep) /
static_cast<double>(m_numMinimizeSteps));
}
// 3. Recover the best
recoverBackgroundParameters(m_bestBkgdParams);
stringstream bufss1;
bufss1 << "Best background parameter ";
for (size_t i = 0; i < m_bkgdParameterStepVec.size(); ++i)
bufss1 << "[" << i << "] = " << m_backgroundFunction->getParameter(i)
<< ", ";
g_log.notice(bufss1.str());
Rfactor outputR(-DBL_MAX, -DBL_MAX);
m_backgroundFunction->function(domain, values);
for (size_t i = 0; i < numpts; ++i) {
backgroundvalues[i] = values[i];
m_outputWS->dataY(INPUTPUREPEAKINDEX)[i] =
m_dataWS->readY(m_wsIndex)[i] - values[i];
}
parammap = convertToDoubleMap(m_funcParameters);
m_lebailFunction->setProfileParameterValues(parammap);
calculateDiffractionPattern(m_outputWS->readX(INPUTPUREPEAKINDEX),
m_outputWS->readY(INPUTPUREPEAKINDEX), false,
true, backgroundvalues, valueVec, outputR);
g_log.notice() << "[RefineBackground] Best Rwp = " << bestR.Rwp
<< ", vs. recovered best Rwp = " << outputR.Rwp << ".\n";
// 4. Add data (0: experimental, 1: calcualted, 2: difference)
for (size_t i = 0; i < numpts; ++i) {
m_outputWS->dataY(1)[i] = valueVec[i] + backgroundvalues[i];
m_outputWS->dataY(2)[i] = vecY[i] - (valueVec[i] + backgroundvalues[i]);
}
// (3: peak without background, 4: input background)
// m_backgroundFunction->function(domain, values);
for (size_t i = 0; i < values.size(); ++i) {
m_outputWS->dataY(CALBKGDINDEX)[i] = backgroundvalues[i];
m_outputWS->dataY(CALPUREPEAKINDEX)[i] = valueVec[i];
}
// 5. Output background to table workspace
auto outtablews = boost::make_shared<TableWorkspace>();
outtablews->addColumn("str", "Name");
outtablews->addColumn("double", "Value");
outtablews->addColumn("double", "Error");
for (const auto &parname : m_bkgdParameterNames) {
double parvalue = m_backgroundFunction->getParameter(parname);
TableRow newrow = outtablews->appendRow();
newrow << parname << parvalue << 1.0;
}
setProperty("BackgroundParametersWorkspace", outtablews);
}
//----------------------------------------------------------------------------------------------
/** Store/buffer current background parameters
* @param bkgdparamvec :: vector to save the background parameters whose order
* is same in background function
*/
void LeBailFit::storeBackgroundParameters(vector<double> &bkgdparamvec) {
for (size_t i = 0; i < m_numberBkgdParameters; ++i) {
bkgdparamvec[i] = m_backgroundFunction->getParameter(i);
}
}
/** Restore/recover the buffered background parameters to m_background function
* @param bkgdparamvec :: vector holding the background parameters whose order
* is same in background function
*/
void LeBailFit::recoverBackgroundParameters(
const vector<double> &bkgdparamvec) {
for (size_t i = 0; i < m_numberBkgdParameters; ++i) {
m_backgroundFunction->setParameter(i, bkgdparamvec[i]);
}
}
/** Propose new background parameters
*/
void LeBailFit::proposeNewBackgroundValues() {
int iparam = m_roundBkgd % static_cast<int>(m_numberBkgdParameters);
double currvalue =
m_backgroundFunction->getParameter(static_cast<int>(iparam));
double r =
2 * (static_cast<double>(rand()) / static_cast<double>(RAND_MAX) - 0.5);
double newvalue = currvalue + r * m_bkgdParameterStepVec[iparam];
g_log.information() << "[DBx804] Background " << iparam
<< " propose new value = " << newvalue << " from "
<< currvalue << ".\n";
m_backgroundFunction->setParameter(static_cast<size_t>(iparam), newvalue);
++m_roundBkgd;
}
//=================================== Set up the Le Bail Fit
//================================
//----------------------------------------------------------------------------------------------
/** Create LeBailFunction, including creating Le Bail function, add peaks and
* background
*/
void LeBailFit::createLeBailFunction() {
// Generate Le Bail function
m_lebailFunction =
boost::make_shared<LeBailFunction>(LeBailFunction(m_peakType));
// Set up profile parameters
if (m_funcParameters.empty())
throw runtime_error("Function parameters must be set up by this point.");
map<string, double> pardblmap = convertToDoubleMap(m_funcParameters);
m_lebailFunction->setProfileParameterValues(pardblmap);
// Add peaks
if (!isEmpty(m_peakCentreTol)) {
const MantidVec &vecx = m_dataWS->readX(m_wsIndex);
m_lebailFunction->setPeakCentreTolerance(m_peakCentreTol, vecx.front(),
vecx.back());
}
vector<vector<int>> vecHKL;
vector<pair<vector<int>, double>>::iterator piter;
for (piter = m_inputPeakInfoVec.begin(); piter != m_inputPeakInfoVec.end();
++piter)
vecHKL.push_back(piter->first);
m_lebailFunction->addPeaks(vecHKL);
// Add background
m_lebailFunction->addBackgroundFunction(
m_backgroundType, m_bkgdorder, m_backgroundParameterNames,
m_backgroundParameters, m_startX, m_endX);
}
//----------------------------------------------------------------------------------------------
/** Crop workspace if user required
* @param inpws : input workspace to crop
* @param wsindex: workspace index of the data to fit against
*/
API::MatrixWorkspace_sptr
LeBailFit::cropWorkspace(API::MatrixWorkspace_sptr inpws, size_t wsindex) {
// Process input property 'FitRegion' for range of data to fit/calculate
std::vector<double> fitrange = this->getProperty("FitRegion");
double tof_min, tof_max;
if (fitrange.empty()) {
tof_min = inpws->readX(wsindex)[0];
tof_max = inpws->readX(wsindex).back();
} else if (fitrange.size() == 2) {
tof_min = fitrange[0];
tof_max = fitrange[1];
} else {
g_log.warning() << "Input FitRegion has more than 2 entries. Using "
"default in stread.\n";
tof_min = inpws->readX(wsindex)[0];
tof_max = inpws->readX(wsindex).back();
}
// Crop workspace
API::IAlgorithm_sptr cropalg =
this->createChildAlgorithm("CropWorkspace", -1, -1, true);
cropalg->initialize();
cropalg->setProperty("InputWorkspace", inpws);
cropalg->setPropertyValue("OutputWorkspace", "MyData");
cropalg->setProperty("XMin", tof_min);
cropalg->setProperty("XMax", tof_max);
bool cropstatus = cropalg->execute();
if (!cropstatus) {
std::stringstream errmsg;
errmsg << "DBx309 Cropping workspace unsuccessful. Fatal Error. Quit!";
g_log.error() << errmsg.str() << "\n";
throw std::runtime_error(errmsg.str());
}
API::MatrixWorkspace_sptr cropws = cropalg->getProperty("OutputWorkspace");
if (!cropws) {
g_log.error("Unable to retrieve a Workspace2D object from ChildAlgorithm "
"CropWorkspace");
throw runtime_error("Unable to retrieve a Workspace2D object from "
"ChildAlgorithm CropWorkspace");
} else {
g_log.debug() << "DBx307: Cropped Workspace... Range From "
<< cropws->readX(wsindex)[0] << " To "
<< cropws->readX(wsindex).back() << " of size "
<< cropws->readX(wsindex).size() << "\n";
}
return cropws;
}
//================================= Import/Parse and Output
//===================================
//----------------------------------------------------------------------------------------------
/** Process input properties to class variables and do some initial check
*/
void LeBailFit::processInputProperties() {
// Peak type
m_peakType = getPropertyValue("PeakType");
// 1. Get input and perform some check
// a) Import data workspace and related, do crop
API::MatrixWorkspace_sptr inpWS = this->getProperty("InputWorkspace");
int tempindex = this->getProperty("WorkspaceIndex");
m_wsIndex = size_t(tempindex);
if (m_wsIndex >= inpWS->getNumberHistograms()) {
// throw if workspace index is not correct
stringstream errss;
errss << "Input WorkspaceIndex " << tempindex << " is out of boundary [0, "
<< inpWS->getNumberHistograms() << "). ";
g_log.error(errss.str());
throw runtime_error(errss.str());
}
m_dataWS = this->cropWorkspace(inpWS, m_wsIndex);
m_startX = m_dataWS->readX(0).front();
m_endX = m_dataWS->readX(0).back();
// b) Minimizer
std::string minim = getProperty("Minimizer");
mMinimizer = minim;
// c) Peak parameters and related.
parameterWS = this->getProperty("InputParameterWorkspace");
reflectionWS = this->getProperty("InputHKLWorkspace");
mPeakRadius = this->getProperty("PeakRadius");
// d) Determine Functionality (function mode)
std::string function = this->getProperty("Function");
m_fitMode = FIT; // Default: LeBailFit
if (function.compare("Calculation") == 0) {
// peak calculation
m_fitMode = CALCULATION;
} else if (function.compare("CalculateBackground") == 0) {
// automatic background points selection
m_fitMode = BACKGROUNDPROCESS;
} else if (function.compare("MonteCarlo") == 0) {
// Monte Carlo random walk refinement
m_fitMode = MONTECARLO;
} else if (function.compare("LeBailFit") == 0) {
// Le Bail Fit mode
m_fitMode = FIT;
} else if (function.compare("RefineBackground") == 0) {
// Refine background mode
m_fitMode = BACKGROUNDPROCESS;
} else {
stringstream errss;
errss << "Function mode " << function
<< " is not supported by LeBailFit().";
g_log.error(errss.str());
throw invalid_argument(errss.str());
}
m_dampingFactor = getProperty("Damping");
tempindex = getProperty("NumberMinimizeSteps");
if (tempindex > 0)
m_numMinimizeSteps = static_cast<size_t>(tempindex);
else {
m_numMinimizeSteps = 0;
stringstream errss;
errss << "Input number of random walk steps (" << m_numMinimizeSteps
<< ") cannot be less and equal to zero.";
g_log.error(errss.str());
throw invalid_argument(errss.str());
}
m_minimumPeakHeight = getProperty("MinimumPeakHeight");
m_indicatePeakHeight = getProperty("IndicationPeakHeight");
// Tolerate duplicated input peak or not?
m_tolerateInputDupHKL2Peaks = getProperty("AllowDegeneratedPeaks");
// Tolerance in peak positions to import peak
m_peakCentreTol = getProperty("ToleranceToImportPeak");
}
//----------------------------------------------------------------------------------------------
/** Parse the input TableWorkspace to some maps for easy access
* Output : m_functionParameters
*/
void LeBailFit::parseInstrumentParametersTable() {
// 1. Check column orders
if (parameterWS->columnCount() < 3) {
g_log.error() << "Input parameter table workspace does not have enough "
"number of columns. "
<< " Number of columns (Input =" << parameterWS->columnCount()
<< ") >= 3 as required.\n";
throw std::invalid_argument("Input parameter workspace is wrong. ");
} else {
g_log.information()
<< "[DB] Starting to parse instrument parameter table workspace "
<< parameterWS->name() << ".\n";
}
// 2. Import data to maps
size_t numrows = parameterWS->rowCount();
std::vector<std::string> colnames = parameterWS->getColumnNames();
size_t numcols = colnames.size();
std::map<std::string, double> tempdblmap;
std::map<std::string, std::string> tempstrmap;
std::map<std::string, double>::iterator dbliter;
std::map<string, string>::iterator striter;
std::string colname;
double dblvalue;
std::string strvalue;
for (size_t ir = 0; ir < numrows; ++ir) {
// a) Clear the map
tempdblmap.clear();
tempstrmap.clear();
// b) Get the row
API::TableRow trow = parameterWS->getRow(ir);
// c) Parse each term
for (size_t icol = 0; icol < numcols; ++icol) {
colname = colnames[icol];
if (colname.compare("FitOrTie") != 0 && colname.compare("Name") != 0) {
// double data
g_log.debug() << "Col-name = " << colname << ", ";
trow >> dblvalue;
g_log.debug() << "Value = " << dblvalue << ".\n";
;
tempdblmap.emplace(colname, dblvalue);
} else {
// string data
g_log.debug() << "Col-name = " << colname << ", ";
trow >> strvalue;
strvalue.erase(std::find_if(strvalue.rbegin(), strvalue.rend(),
std::not1(std::ptr_fun<int, int>(
std::isspace))).base(),
strvalue.end());
g_log.debug() << "Value = " << strvalue << ".\n";
tempstrmap.emplace(colname, strvalue);
}
}
// d) Construct a Parameter instance
Parameter newparameter;
// i. name
striter = tempstrmap.find("Name");
if (striter != tempstrmap.end()) {
newparameter.name = striter->second;
} else {
std::stringstream errmsg;
errmsg << "Parameter (table) workspace " << parameterWS->name()
<< " does not contain column 'Name'. It is not a valid input. "
"Quit ";
g_log.error() << errmsg.str() << "\n";
throw std::invalid_argument(errmsg.str());
}
// ii. fit
striter = tempstrmap.find("FitOrTie");
if (striter != tempstrmap.end()) {
std::string fitortie = striter->second;
bool tofit = true;
if (fitortie.length() > 0) {
char fc = fitortie.c_str()[0];
if (fc == 't' || fc == 'T') {
tofit = false;
}
}
newparameter.fit = tofit;
} else {
std::stringstream errmsg;
errmsg << "Parameter (table) workspace " << parameterWS->name()
<< " does not contain column 'FitOrTie'. It is not a valid "
"input. Quit ";
g_log.error() << errmsg.str() << "\n";
throw std::invalid_argument(errmsg.str());