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FindPeaks.cpp
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FindPeaks.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/FindPeaks.h"
#include "MantidAlgorithms/SmoothData.h"
#include "MantidAPI/CostFunctionFactory.h"
#include "MantidAPI/FuncMinimizerFactory.h"
#include "MantidAPI/FunctionFactory.h"
#include "MantidAPI/TableRow.h"
#include "MantidAlgorithms/FitPeak.h"
#include "MantidDataObjects/TableWorkspace.h"
#include "MantidDataObjects/Workspace2D.h"
#include "MantidIndexing/GlobalSpectrumIndex.h"
#include "MantidIndexing/IndexInfo.h"
#include "MantidKernel/ArrayProperty.h"
#include "MantidKernel/StartsWithValidator.h"
#include "MantidKernel/VectorHelper.h"
#include "MantidKernel/BoundedValidator.h"
#include "MantidKernel/ListValidator.h"
#include <boost/algorithm/string.hpp>
#include <boost/math/special_functions/round.hpp>
#include <numeric>
#include <fstream>
using namespace Mantid;
using namespace Mantid::Kernel;
using namespace Mantid::API;
using namespace Mantid::DataObjects;
using namespace Mantid::HistogramData;
using namespace Mantid::Indexing;
// const double MINHEIGHT = 2.00000001;
namespace Mantid::Algorithms {
// Register the algorithm into the AlgorithmFactory
DECLARE_ALGORITHM(FindPeaks)
//----------------------------------------------------------------------------------------------
/** Constructor
*/
FindPeaks::FindPeaks()
: API::ParallelAlgorithm(), m_peakParameterNames(), m_bkgdParameterNames(), m_bkgdOrder(0), m_outPeakTableWS(),
m_dataWS(), m_inputPeakFWHM(0), m_highBackground(false), m_rawPeaksTable(false), m_numTableParams(0),
m_centreIndex(1) /* for Gaussian */, m_peakFuncType(""), m_backgroundType(""), m_vecPeakCentre(),
m_vecFitWindows(), m_backgroundFunction(), m_peakFunction(), m_minGuessedPeakWidth(0), m_maxGuessedPeakWidth(0),
m_stepGuessedPeakWidth(0), m_usePeakPositionTolerance(false), m_peakPositionTolerance(0.0), m_fitFunctions(),
m_peakLeftIndexes(), m_peakRightIndexes(), m_minimizer("Levenberg-MarquardtMD"), m_costFunction(),
m_minHeight(0.0), m_leastMaxObsY(0.), m_useObsCentre(false) {}
//----------------------------------------------------------------------------------------------
/** Initialize and declare properties.
*/
void FindPeaks::init() {
declareProperty(std::make_unique<WorkspaceProperty<>>("InputWorkspace", "", Direction::Input),
"Name of the workspace to search");
auto mustBeNonNegative = std::make_shared<BoundedValidator<int>>();
mustBeNonNegative->setLower(0);
declareProperty("WorkspaceIndex", EMPTY_INT(), mustBeNonNegative,
"If set, only this spectrum will be searched for peaks "
"(otherwise all are)");
auto min = std::make_shared<BoundedValidator<int>>();
min->setLower(1);
// The estimated width of a peak in terms of number of channels
declareProperty("FWHM", 7, min, "Estimated number of points covered by the fwhm of a peak (default 7)");
// The tolerance allowed in meeting the conditions
declareProperty("Tolerance", 4, min,
"A measure of the strictness desired in "
"meeting the condition on peak "
"candidates,\n"
"Mariscotti recommends 2 (default 4)");
declareProperty(std::make_unique<ArrayProperty<double>>("PeakPositions"),
"Optional: enter a comma-separated list of the expected "
"X-position of the centre of the peaks. Only peaks near "
"these positions will be fitted.");
declareProperty(std::make_unique<ArrayProperty<double>>("FitWindows"),
"Optional: enter a comma-separated list of the expected "
"X-position of windows to fit. The number of values must be "
"exactly double the number of specified peaks.");
std::vector<std::string> peakNames = FunctionFactory::Instance().getFunctionNames<API::IPeakFunction>();
declareProperty("PeakFunction", "Gaussian", std::make_shared<StringListValidator>(peakNames));
std::vector<std::string> bkgdtypes{"Flat", "Linear", "Quadratic"};
declareProperty("BackgroundType", "Linear", std::make_shared<StringListValidator>(bkgdtypes), "Type of Background.");
declareProperty("HighBackground", true, "Relatively weak peak in high background");
auto mustBePositive = std::make_shared<BoundedValidator<int>>();
mustBePositive->setLower(1);
declareProperty("MinGuessedPeakWidth", 2, mustBePositive,
"Minimum guessed peak width for fit. It is in unit of number of pixels.");
declareProperty("MaxGuessedPeakWidth", 10, mustBePositive,
"Maximum guessed peak width for fit. It is in unit of number of pixels.");
declareProperty("GuessedPeakWidthStep", 2, mustBePositive,
"Step of guessed peak width. It is in unit of number of pixels.");
auto mustBePositiveDBL = std::make_shared<BoundedValidator<double>>();
declareProperty("PeakPositionTolerance", EMPTY_DBL(), mustBePositiveDBL,
"Tolerance on the found peaks' positions against the input "
"peak positions. Non-positive value indicates that this "
"option is turned off.");
// The found peaks in a table
declareProperty(std::make_unique<WorkspaceProperty<API::ITableWorkspace>>("PeaksList", "", Direction::Output),
"The name of the TableWorkspace in which to store the list "
"of peaks found");
declareProperty("RawPeakParameters", false,
"false generates table with effective centre/width/height "
"parameters. true generates a table with peak function "
"parameters");
declareProperty("MinimumPeakHeight", DBL_MIN, "Minimum allowed peak height. ");
declareProperty("MinimumPeakHeightObs", 0.0,
"Least value of the maximum observed Y value of a peak within "
"specified region. If any peak's maximum observed Y value is smaller, "
"then "
"this peak will not be fit. It is designed for EventWorkspace with "
"integer counts.");
std::array<std::string, 2> costFuncOptions = {{"Chi-Square", "Rwp"}};
declareProperty("CostFunction", "Chi-Square",
Kernel::IValidator_sptr(new Kernel::ListValidator<std::string>(costFuncOptions)), "Cost functions");
std::vector<std::string> minimizerOptions = API::FuncMinimizerFactory::Instance().getKeys();
declareProperty("Minimizer", "Levenberg-MarquardtMD",
Kernel::IValidator_sptr(new Kernel::StartsWithValidator(minimizerOptions)),
"Minimizer to use for fitting. Minimizers available are "
"\"Levenberg-Marquardt\", \"Simplex\","
"\"Conjugate gradient (Fletcher-Reeves imp.)\", \"Conjugate "
"gradient (Polak-Ribiere imp.)\", \"BFGS\", and "
"\"Levenberg-MarquardtMD\"");
declareProperty("StartFromObservedPeakCentre", true, "Use observed value as the starting value of peak centre. ");
}
//----------------------------------------------------------------------------------------------
/** Execute the findPeaks algorithm.
*/
void FindPeaks::exec() {
// Process input
processAlgorithmProperties();
// Create those functions to fit
createFunctions();
// Set up output table workspace
generateOutputPeakParameterTable();
// Fit
if (!m_vecPeakCentre.empty()) {
if (!m_vecFitWindows.empty()) {
if (m_vecFitWindows.size() != (m_vecPeakCentre.size() * 2)) {
throw std::invalid_argument("Number of FitWindows must be exactly "
"twice the number of PeakPositions");
}
}
// Perform fit with fixed start positions.
findPeaksGivenStartingPoints(m_vecPeakCentre, m_vecFitWindows);
} else {
// Use Mariscotti's method to find the peak centers
m_usePeakPositionTolerance = false;
this->findPeaksUsingMariscotti();
}
// Set output properties
g_log.information() << "Total " << m_outPeakTableWS->rowCount() << " peaks found and successfully fitted.\n";
setProperty("PeaksList", m_outPeakTableWS);
} // END: exec()
//----------------------------------------------------------------------------------------------
/** Process algorithm's properties
*/
void FindPeaks::processAlgorithmProperties() {
// Input workspace
m_dataWS = getProperty("InputWorkspace");
// WorkspaceIndex
int wsIndex = getProperty("WorkspaceIndex");
if (!isEmpty(wsIndex)) {
if (wsIndex >= static_cast<int>(m_dataWS->getNumberHistograms())) {
g_log.warning() << "The value of WorkspaceIndex provided (" << wsIndex
<< ") is larger than the size of this workspace (" << m_dataWS->getNumberHistograms() << ")\n";
throw Kernel::Exception::IndexError(wsIndex, m_dataWS->getNumberHistograms() - 1,
"FindPeaks WorkspaceIndex property");
}
m_indexSet = m_dataWS->indexInfo().makeIndexSet({static_cast<Indexing::GlobalSpectrumIndex>(wsIndex)});
} else {
m_indexSet = m_dataWS->indexInfo().makeIndexSet();
}
// Peak width
m_inputPeakFWHM = getProperty("FWHM");
int t1 = getProperty("MinGuessedPeakWidth");
int t2 = getProperty("MaxGuessedPeakWidth");
int t3 = getProperty("GuessedPeakWidthStep");
if (t1 > t2 || t1 <= 0 || t3 <= 0) {
std::stringstream errss;
errss << "User specified wrong guessed peak width parameters (must be "
"postive and make sense). "
<< "User inputs are min = " << t1 << ", max = " << t2 << ", step = " << t3;
g_log.warning(errss.str());
throw std::runtime_error(errss.str());
}
m_minGuessedPeakWidth = t1;
m_maxGuessedPeakWidth = t2;
m_stepGuessedPeakWidth = t3;
m_peakPositionTolerance = getProperty("PeakPositionTolerance");
m_usePeakPositionTolerance = true;
if (isEmpty(m_peakPositionTolerance))
m_usePeakPositionTolerance = false;
// Specified peak positions, which is optional
m_vecPeakCentre = getProperty("PeakPositions");
if (!m_vecPeakCentre.empty())
std::sort(m_vecPeakCentre.begin(), m_vecPeakCentre.end());
m_vecFitWindows = getProperty("FitWindows");
// Peak and ground type
m_peakFuncType = getPropertyValue("PeakFunction");
m_backgroundType = getPropertyValue("BackgroundType");
// Fit algorithm
m_highBackground = getProperty("HighBackground");
// Peak parameters are give via a table workspace
m_rawPeaksTable = getProperty("RawPeakParameters");
// Minimum peak height
m_minHeight = getProperty("MinimumPeakHeight");
// About Fit
m_minimizer = getPropertyValue("Minimizer");
m_costFunction = getPropertyValue("CostFunction");
m_useObsCentre = getProperty("StartFromObservedPeakCentre");
m_leastMaxObsY = getProperty("MinimumPeakHeightObs");
}
//----------------------------------------------------------------------------------------------
/** Generate a table workspace for output peak parameters
*/
void FindPeaks::generateOutputPeakParameterTable() {
m_outPeakTableWS = std::make_shared<TableWorkspace>();
m_outPeakTableWS->addColumn("int", "spectrum");
if (m_rawPeaksTable) {
// Output raw peak parameters
size_t numpeakpars = m_peakFunction->nParams();
size_t numbkgdpars = m_backgroundFunction->nParams();
m_numTableParams = numpeakpars + numbkgdpars;
if (m_peakFuncType == "Gaussian")
m_centreIndex = 1;
else if (m_peakFuncType == "LogNormal")
m_centreIndex = 1;
else if (m_peakFuncType == "Lorentzian")
m_centreIndex = 1;
else if (m_peakFuncType == "PseudoVoigt")
m_centreIndex = 2;
else
m_centreIndex = m_numTableParams; // bad value
for (size_t i = 0; i < numpeakpars; ++i)
m_outPeakTableWS->addColumn("double", m_peakParameterNames[i]);
for (size_t i = 0; i < numbkgdpars; ++i)
m_outPeakTableWS->addColumn("double", m_bkgdParameterNames[i]);
// m_outPeakTableWS->addColumn("double", "f1.A2");
} else {
// Output centre, weight, height, A0, A1 and A2
m_numTableParams = 6;
m_centreIndex = 0;
m_outPeakTableWS->addColumn("double", "centre");
m_outPeakTableWS->addColumn("double", "width");
m_outPeakTableWS->addColumn("double", "height");
m_outPeakTableWS->addColumn("double", "backgroundintercept");
m_outPeakTableWS->addColumn("double", "backgroundslope");
m_outPeakTableWS->addColumn("double", "A2");
}
m_outPeakTableWS->addColumn("double", "chi2");
}
//----------------------------------------------------------------------------------------------
/** Find the start positions to fit peaks with given estimated peak centres
* @param peakcentres :: vector of the center x-positions specified to perform
* fits.
* @param fitwindows :: vector of windows around each peak. Otherwise, windows
* will be determined automatically.
*/
void FindPeaks::findPeaksGivenStartingPoints(const std::vector<double> &peakcentres,
const std::vector<double> &fitwindows) {
bool useWindows = (!fitwindows.empty());
std::size_t numPeaks = peakcentres.size();
// Loop over the spectra searching for peaks
m_progress = std::make_unique<Progress>(this, 0.0, 1.0, m_indexSet.size());
for (const auto spec : m_indexSet) {
const auto &vecX = m_dataWS->x(spec);
double practical_x_min = vecX.front();
double practical_x_max = vecX.back();
g_log.information() << "actual x-range = [" << practical_x_min << " -> " << practical_x_max << "]\n";
{
const auto &vecY = m_dataWS->y(spec);
const auto &vecE = m_dataWS->e(spec);
const size_t numY = vecY.size();
size_t i_min = 1;
for (; i_min < numY; ++i_min) {
if ((vecY[i_min] != 0.) || (vecE[i_min] != 0)) {
--i_min; // bring it back one
break;
}
}
practical_x_min = vecX[i_min];
size_t i_max = numY - 2;
for (; i_max > i_min; --i_max) {
if ((vecY[i_max] != 0.) || (vecE[i_max] != 0)) {
++i_max; // bring it back one
break;
}
}
g_log.debug() << "Finding peaks from giving starting point, with interval i_min = " << i_min
<< " i_max = " << i_max << '\n';
practical_x_max = vecX[i_max];
}
g_log.information() << "practical x-range = [" << practical_x_min << " -> " << practical_x_max << "]\n";
for (std::size_t ipeak = 0; ipeak < numPeaks; ipeak++) {
// Try to fit at this center
double x_center = peakcentres[ipeak];
std::stringstream infoss;
infoss << "Spectrum " << spec << ": Fit peak @ d = " << x_center;
if (useWindows) {
infoss << " inside fit window [" << fitwindows[2 * ipeak] << ", " << fitwindows[2 * ipeak + 1] << "]";
}
g_log.information(infoss.str());
// Check whether it is the in data range
if (x_center > practical_x_min && x_center < practical_x_max) {
if (useWindows)
fitPeakInWindow(m_dataWS, static_cast<int>(spec), x_center, fitwindows[2 * ipeak], fitwindows[2 * ipeak + 1]);
else {
bool hasLeftPeak = (ipeak > 0);
double leftpeakcentre = 0.;
if (hasLeftPeak)
leftpeakcentre = peakcentres[ipeak - 1];
bool hasRightPeak = (ipeak < numPeaks - 1);
double rightpeakcentre = 0.;
if (hasRightPeak)
rightpeakcentre = peakcentres[ipeak + 1];
fitPeakGivenFWHM(m_dataWS, static_cast<int>(spec), x_center, m_inputPeakFWHM, hasLeftPeak, leftpeakcentre,
hasRightPeak, rightpeakcentre);
}
} else {
g_log.warning() << "Given peak centre " << x_center << " is out side of given data's range (" << practical_x_min
<< ", " << practical_x_max << ").\n";
addNonFitRecord(spec, x_center);
}
} // loop through the peaks specified
m_progress->report();
} // loop over spectra
}
//----------------------------------------------------------------------------------------------
/** Use the Mariscotti method to find the start positions and fit gaussian peaks
*/
void FindPeaks::findPeaksUsingMariscotti() {
// At this point the data has not been smoothed yet.
auto smoothedData = this->calculateSecondDifference(m_dataWS);
// The optimum number of points in the smoothing, according to Mariscotti, is
// 0.6*fwhm
auto w = static_cast<int>(0.6 * m_inputPeakFWHM);
// w must be odd
if (!(w % 2))
++w;
smoothData(smoothedData, w, g_z);
// Now calculate the errors on the smoothed data
this->calculateStandardDeviation(m_dataWS, smoothedData, w);
// Calculate n1 (Mariscotti eqn. 18)
const double kz = 1.22; // This kz corresponds to z=5 & w=0.6*fwhm - see Mariscotti Fig. 8
const int n1 = boost::math::iround(kz * m_inputPeakFWHM);
// Can't calculate n2 or n3 yet because they need i0
const int tolerance = getProperty("Tolerance");
// Loop over the spectra searching for peaks
m_progress = std::make_unique<Progress>(this, 0.0, 1.0, m_indexSet.size());
for (size_t k_out = 0; k_out < m_indexSet.size(); ++k_out) {
const size_t k = m_indexSet[k_out];
const auto &S = smoothedData[k_out].y();
const auto &F = smoothedData[k_out].e();
// This implements the flow chart given on page 320 of Mariscotti
int i0 = 0, i1 = 0, i2 = 0, i3 = 0, i4 = 0, i5 = 0;
for (int i = 1; i < static_cast<int>(S.size()); ++i) {
int M = 0;
if (S[i] > F[i])
M = 1;
else {
S[i] > 0 ? M = 2 : M = 3;
}
if (S[i - 1] > F[i - 1]) {
switch (M) {
case 3:
i3 = i;
/* no break */
// intentional fall-through
case 2:
i2 = i - 1;
break;
case 1:
// do nothing
break;
default:
assert(false);
// should never happen
break;
}
} else if (S[i - 1] > 0) {
switch (M) {
case 3:
i3 = i;
break;
case 2:
// do nothing
break;
case 1:
i1 = i;
break;
default:
assert(false);
// should never happen
break;
}
} else {
switch (M) {
case 3:
// do nothing
break;
case 2: // fall through (i.e. same action if M = 1 or 2)
case 1:
i5 = i - 1;
break;
default:
assert(false);
// should never happen
break;
}
}
if (i5 && i1 && i2 && i3) // If i5 has been set then we should have the
// full set and can check conditions
{
i4 = i3; // Starting point for finding i4 - calculated below
double num = 0.0, denom = 0.0;
for (int j = i3; j <= i5; ++j) {
// Calculate i4 - it's at the minimum value of Si between i3 & i5
if (S[j] <= S[i4])
i4 = j;
// Calculate sums for i0 (Mariscotti eqn. 27)
num += j * S[j];
denom += S[j];
}
i0 = static_cast<int>(num / denom);
// Check we have a correctly ordered set of points. If not, reset and
// continue
if (i1 > i2 || i2 > i3 || i3 > i4 || i5 <= i4) {
i5 = 0;
continue;
}
// Check if conditions are fulfilled - if any are not, loop onto the
// next i in the spectrum
// Mariscotti eqn. (14)
if (std::abs(S[i4]) < 2 * F[i4]) {
i5 = 0;
continue;
}
// Mariscotti eqn. (19)
if (abs(i5 - i3 + 1 - n1) > tolerance) {
i5 = 0;
continue;
}
// Calculate n2 (Mariscotti eqn. 20)
int n2 = std::abs(boost::math::iround(0.5 * (F[i0] / S[i0]) * (n1 + tolerance)));
const int n2b = std::abs(boost::math::iround(0.5 * (F[i0] / S[i0]) * (n1 - tolerance)));
if (n2b > n2)
n2 = n2b;
// Mariscotti eqn. (21)
const int testVal = n2 ? n2 : 1;
if (i3 - i2 - 1 > testVal) {
i5 = 0;
continue;
}
// Calculate n3 (Mariscotti eqn. 22)
int n3 = std::abs(boost::math::iround((n1 + tolerance) * (1 - 2 * (F[i0] / S[i0]))));
const int n3b = std::abs(boost::math::iround((n1 - tolerance) * (1 - 2 * (F[i0] / S[i0]))));
if (n3b < n3)
n3 = n3b;
// Mariscotti eqn. (23)
if (i2 - i1 + 1 < n3) {
i5 = 0;
continue;
}
// If we get to here then we've identified a peak
g_log.debug() << "Spectrum=" << k << " i0=" << i0 << " X=" << m_dataWS->x(k)[i0] << " i1=" << i1 << " i2=" << i2
<< " i3=" << i3 << " i4=" << i4 << " i5=" << i5 << '\n';
// Use i0, i2 and i4 to find out i_min and i_max, i0: right, i2: left,
// i4: centre
auto wssize = static_cast<int>(m_dataWS->x(k).size());
int iwidth = i0 - i2;
if (iwidth <= 0)
iwidth = 1;
int i_min = 1;
if (i4 > 5 * iwidth)
i_min = i4 - 5 * iwidth;
int i_max = i4 + 5 * iwidth;
if (i_max >= wssize)
i_max = wssize - 1;
this->fitSinglePeak(m_dataWS, static_cast<int>(k), i_min, i_max, i4);
// reset and go searching for the next peak
i1 = 0, i2 = 0, i3 = 0, i4 = 0, i5 = 0;
}
} // loop through a single spectrum
m_progress->report();
} // loop over spectra
}
//----------------------------------------------------------------------------------------------
/** Calculates the second difference of the data (Y values) in a workspace.
* Done according to equation (3) in Mariscotti: \f$ S_i = N_{i+1} - 2N_i +
* N_{i+1} \f$.
* In the output workspace, the 2nd difference is in Y, X is unchanged and E
* is zero.
* @param input :: The workspace to calculate the second difference of
* @return A workspace containing the second difference
*/
std::vector<Histogram> FindPeaks::calculateSecondDifference(const API::MatrixWorkspace_const_sptr &input) {
std::vector<Histogram> diffed;
// Loop over spectra
for (const auto i : m_indexSet) {
diffed.emplace_back(input->histogram(i));
diffed.back().mutableY() = 0.0;
diffed.back().mutableE() = 0.0;
const auto &Y = input->y(i);
auto &S = diffed.back().mutableY();
// Go through each spectrum calculating the second difference at each point
// First and last points in each spectrum left as zero (you'd never be able
// to find peaks that close to the edge anyway)
for (size_t j = 1; j < S.size() - 1; ++j) {
S[j] = Y[j - 1] - 2 * Y[j] + Y[j + 1];
}
}
return diffed;
}
//----------------------------------------------------------------------------------------------
/** Smooth data for Mariscotti.
* @param histograms :: Vector of histograms to be smoothed (inplace).
* @param w :: The number of data points which should contribute to each
* smoothed point
* @param g_z :: The number of smoothing steps given by g_z (should be 5)
*/
void FindPeaks::smoothData(std::vector<Histogram> &histograms, const int w, const int g_z) {
for (auto &histogram : histograms)
for (int i = 0; i < g_z; ++i)
histogram = smooth(histogram, w);
}
//----------------------------------------------------------------------------------------------
/** Calculates the statistical error on the smoothed data.
* Uses Mariscotti equation (11), amended to use errors of input data rather
* than sqrt(Y).
* @param input :: The input data to the algorithm
* @param smoothed :: The smoothed dataBackgroud type is not supported in
* FindPeak.cpp
* @param w :: The value of w (the size of the smoothing 'window')
* @throw std::invalid_argument if w is greater than 19
*/
void FindPeaks::calculateStandardDeviation(const API::MatrixWorkspace_const_sptr &input,
std::vector<HistogramData::Histogram> &smoothed, const int &w) {
// Guard against anyone changing the value of z, which would mean different
// phi values were needed (see Marriscotti p.312)
static_assert(g_z == 5, "Value of z has changed!");
// Have to adjust for fact that I normalise Si (unlike the paper)
const auto factor = static_cast<int>(std::pow(static_cast<double>(w), g_z));
const double constant = sqrt(static_cast<double>(this->computePhi(w))) / factor;
for (size_t i = 0; i < m_indexSet.size(); ++i) {
size_t i_in = m_indexSet[i];
smoothed[i].mutableE() = input->e(i_in) * constant;
}
}
//----------------------------------------------------------------------------------------------
/** Calculates the coefficient phi which goes into the calculation of the error
* on the smoothed data
* Uses Mariscotti equation (11). Pinched from the GeneralisedSecondDifference
* code.
* Can return a very big number, hence the type.
* @param w The value of w (the size of the smoothing 'window')
* @return The value of phi(g_z,w)
*/
long long FindPeaks::computePhi(const int &w) const {
const int m = (w - 1) / 2;
int zz = 0;
int max_index_prev = 1;
int n_el_prev = 3;
std::vector<long long> previous(n_el_prev);
previous[0] = 1;
previous[1] = -2;
previous[2] = 1;
// Can't happen at present
if (g_z == 0)
return std::accumulate(previous.begin(), previous.end(), static_cast<long long>(0),
VectorHelper::SumSquares<long long>());
std::vector<long long> next;
// Calculate the Cij iteratively.
do {
zz++;
int max_index = zz * m + 1;
int n_el = 2 * max_index + 1;
next.resize(n_el);
std::fill(next.begin(), next.end(), 0);
for (int i = 0; i < n_el; ++i) {
int delta = -max_index + i;
for (int l = delta - m; l <= delta + m; l++) {
int index = l + max_index_prev;
if (index >= 0 && index < n_el_prev)
next[i] += previous[index];
}
}
previous.resize(n_el);
std::copy(next.begin(), next.end(), previous.begin());
max_index_prev = max_index;
n_el_prev = n_el;
} while (zz != g_z);
const long long retval = std::accumulate(previous.begin(), previous.end(), static_cast<long long>(0),
VectorHelper::SumSquares<long long>());
g_log.debug() << "FindPeaks::computePhi - calculated value = " << retval << "\n";
return retval;
}
//----------------------------------------------------------------------------------------------
/** Find the index of a value (or nearest) in a given the X data
* @param vecX :: vector
* @param x :: value to search
* @return index of x in vector
*/
int FindPeaks::getIndex(const HistogramX &vecX, double x) {
int index;
if (x <= vecX.front()) {
// Left or equal to lower boundary
index = 0;
} else if (x >= vecX.back()) {
// Right or equal to upper boundary
index = static_cast<int>(vecX.size()) - 1;
} else {
// within the range
index = static_cast<int>(std::lower_bound(vecX.begin(), vecX.end(), x) - vecX.begin());
// check lower boundary
if (index == 0) {
std::stringstream errss;
errss << "Returned index = 0 for x = " << x << " with X[0] = " << vecX[0]
<< ". This situation is ruled out in this algorithm.";
g_log.warning(errss.str());
throw std::runtime_error(errss.str());
} else if (x < vecX[index - 1] || x > vecX[index]) {
std::stringstream errss;
errss << "Returned x = " << x << " is not between " << vecX[index - 1] << " and " << vecX[index]
<< ", which are returned by lower_bound.";
g_log.warning(errss.str());
throw std::runtime_error(errss.str());
}
// Find the index of the nearest value to return
if (x - vecX[index - 1] < vecX[index] - x)
--index;
}
return index;
}
//----------------------------------------------------------------------------------------------
/** Attempts to fit a candidate peak given a center and width guess.
* (This is not the CORE fit peak method)
*
* @param input :: The input workspace
* @param wsIndex :: The workspace index of the peak
* @param center_guess :: A guess of the X-value of the center of the peak, in
*whatever units of the X-axis of the workspace.
* @param fitWidth :: A guess of the full-width-half-max of the peak, in # of
*bins.
* @param hasleftpeak :: flag to show that there is a specified peak to its
*left
* @param leftpeakcentre :: centre of left peak if existed
* @param hasrightpeak :: flag to show that there is a specified peak to its
*right
* @param rightpeakcentre :: centre of the right peak if existed
*/
void FindPeaks::fitPeakGivenFWHM(const API::MatrixWorkspace_sptr &input, const int wsIndex, const double center_guess,
const int fitWidth, const bool hasleftpeak, const double leftpeakcentre,
const bool hasrightpeak, const double rightpeakcentre) {
// The X axis you are looking at
const auto &vecX = input->x(wsIndex);
const auto &vecY = input->y(wsIndex);
// Find i_center - the index of the center - The guess is within the X axis?
int i_centre = this->getIndex(vecX, center_guess);
// Set up lower fit boundary
int i_min = i_centre - 5 * fitWidth;
if (i_min < 1)
i_min = 1;
if (hasleftpeak) {
// Use 2/3 distance as the seperation for right peak
double xmin = vecX[i_min];
double peaksepline = center_guess - (center_guess - leftpeakcentre) * 0.66;
if (xmin < peaksepline)
i_min = getIndex(vecX, peaksepline);
}
// Set up upper boundary
int i_max = i_centre + 5 * fitWidth;
if (i_max >= static_cast<int>(vecX.size()) - 1)
i_max = static_cast<int>(vecY.size()) - 2;
if (hasrightpeak) {
// Use 2/3 distance as the separation for right peak
double xmax = vecX[i_max];
double peaksepline = center_guess + (rightpeakcentre - center_guess) * 0.66;
if (xmax > peaksepline)
i_max = getIndex(vecX, peaksepline);
}
// Check
if (i_max - i_min <= 0)
throw std::runtime_error("Impossible to i_min >= i_max.");
std::stringstream outss;
outss << "Fit peak with guessed FWHM: starting center = " << center_guess << ", FWHM = " << fitWidth
<< ". Estimated peak fit window from given FWHM: " << vecX[i_min] << ", " << vecX[i_max];
g_log.information(outss.str());
fitSinglePeak(input, wsIndex, i_min, i_max, i_centre);
}
//----------------------------------------------------------------------------------------------
/** Attempts to fit a candidate peak with a given window of where peak resides
*
* @param input The input workspace
* @param wsIndex The workspace index of the peak
* @param centre_guess :: Channel number of peak candidate i0 - the higher
*side of the peak (right side)
* @param xmin Minimum x value to find the peak
* @param xmax Maximum x value to find the peak
*/
void FindPeaks::fitPeakInWindow(const API::MatrixWorkspace_sptr &input, const int wsIndex, const double centre_guess,
const double xmin, const double xmax) {
// Check
g_log.information() << "Fit Peak with given window: Guessed center = " << centre_guess << " x-min = " << xmin
<< ", x-max = " << xmax << "\n";
if (xmin >= centre_guess || xmax <= centre_guess) {
g_log.warning("Peak centre is on the edge of Fit window. ");
addNonFitRecord(wsIndex, centre_guess);
return;
}
// The X axis you are looking at
const auto &vecX = input->x(wsIndex);
// The centre index
int i_centre = this->getIndex(vecX, centre_guess);
// The left index
int i_min = getIndex(vecX, xmin);
if (i_min >= i_centre) {
g_log.warning() << "Input peak centre @ " << centre_guess << " is out side of minimum x = " << xmin
<< ". Input X ragne = " << vecX.front() << ", " << vecX.back() << "\n";
addNonFitRecord(wsIndex, centre_guess);
return;
}
// The right index
int i_max = getIndex(vecX, xmax);
if (i_max < i_centre) {
g_log.warning() << "Input peak centre @ " << centre_guess << " is out side of maximum x = " << xmax << "\n";
addNonFitRecord(wsIndex, centre_guess);
return;
}
// finally do the actual fit
fitSinglePeak(input, wsIndex, i_min, i_max, i_centre);
}
//----------------------------------------------------------------------------------------------
/** Fit a single peak
* This is the fundametary peak fit function used by all kinds of input
*/
void FindPeaks::fitSinglePeak(const API::MatrixWorkspace_sptr &input, const int spectrum, const int i_min,
const int i_max, const int i_centre) {
const auto &vecX = input->x(spectrum);
const auto &vecY = input->y(spectrum);
// Exclude peak with peak counts
bool hasHighCounts = false;
for (int i = i_min; i <= i_max; ++i)
if (vecY[i] > m_leastMaxObsY) {
hasHighCounts = true;
break;
}
if (!hasHighCounts) {
std::stringstream ess;
ess << "Peak supposed at " << vecY[i_centre] << " does not have enough counts as " << m_leastMaxObsY;
g_log.debug(ess.str());
addNonFitRecord(spectrum, vecY[i_centre]);
return;
}
{
std::stringstream outss;
outss << "Fit single peak in X-range " << vecX[i_min] << ", " << vecX[i_max] << ", centre at " << vecX[i_centre]
<< " (index = " << i_centre << "). ";
g_log.information(outss.str());
}
// Estimate background
std::vector<double> vecbkgdparvalue(3, 0.);
std::vector<double> vecpeakrange(3, 0.);
int usefpdresult = findPeakBackground(input, spectrum, i_min, i_max, vecbkgdparvalue, vecpeakrange);
if (usefpdresult < 0) {
// Estimate background roughly for a failed case
estimateBackground(vecX, vecY, i_min, i_max, vecbkgdparvalue);
}
for (size_t i = 0; i < vecbkgdparvalue.size(); ++i)
if (i < m_bkgdOrder)
m_backgroundFunction->setParameter(i, vecbkgdparvalue[i]);
// Estimate peak parameters
double est_height(0.0), est_fwhm(0.0);
size_t i_obscentre(0);
double est_leftfwhm(0.0), est_rightfwhm(0.0);
std::string errmsg = estimatePeakParameters(vecX, vecY, i_min, i_max, vecbkgdparvalue, i_obscentre, est_height,
est_fwhm, est_leftfwhm, est_rightfwhm);
if (!errmsg.empty()) {
// Unable to estimate peak
i_obscentre = i_centre;
est_fwhm = 1.;
est_height = 1.;
g_log.warning(errmsg);
}
// Set peak parameters to
if (m_useObsCentre)
m_peakFunction->setCentre(vecX[i_obscentre]);
else
m_peakFunction->setCentre(vecX[i_centre]);
m_peakFunction->setHeight(est_height);
m_peakFunction->setFwhm(est_fwhm);
if (usefpdresult < 0) {
// Estimate peak range based on estimated linear background and peak
// parameter estimated from observation
if (!m_useObsCentre)
i_obscentre = i_centre;
estimatePeakRange(vecX, i_obscentre, i_min, i_max, est_leftfwhm, est_rightfwhm, vecpeakrange);
}
//-------------------------------------------------------------------------
// Fit Peak
//-------------------------------------------------------------------------
std::vector<double> fitwindow(2);
fitwindow[0] = vecX[i_min];
fitwindow[1] = vecX[i_max];
double costfuncvalue = callFitPeak(input, spectrum, m_peakFunction, m_backgroundFunction, fitwindow, vecpeakrange,
m_minGuessedPeakWidth, m_maxGuessedPeakWidth, m_stepGuessedPeakWidth);
bool fitsuccess = false;
if (costfuncvalue < DBL_MAX && costfuncvalue >= 0. && m_peakFunction->height() > m_minHeight) {
fitsuccess = true;
}
if (fitsuccess && m_usePeakPositionTolerance) {
fitsuccess = (fabs(m_peakFunction->centre() - vecX[i_centre]) < m_peakPositionTolerance);
}
//-------------------------------------------------------------------------
// Process Fit result
//-------------------------------------------------------------------------
// Update output
if (fitsuccess)
addInfoRow(spectrum, m_peakFunction, m_backgroundFunction, m_rawPeaksTable, costfuncvalue);
else
addNonFitRecord(spectrum, m_peakFunction->centre());
}
//----------------------------------------------------------------------------------------------
/** Find peak background given a certain range by
* calling algorithm "FindPeakBackground"
*/
int FindPeaks::findPeakBackground(const MatrixWorkspace_sptr &input, int spectrum, size_t i_min, size_t i_max,
std::vector<double> &vecBkgdParamValues, std::vector<double> &vecpeakrange) {
const auto &vecX = input->x(spectrum);
// Call FindPeakBackground
auto estimate = createChildAlgorithm("FindPeakBackground");
estimate->setLoggingOffset(1);
estimate->setProperty("InputWorkspace", input);
estimate->setProperty("WorkspaceIndex", spectrum);
// estimate->setProperty("SigmaConstant", 1.0);
std::vector<double> fwvec;
fwvec.emplace_back(vecX[i_min]);
fwvec.emplace_back(vecX[i_max]);
estimate->setProperty("BackgroundType", m_backgroundType);
estimate->setProperty("FitWindow", fwvec);
estimate->executeAsChildAlg();
// Get back the result
Mantid::API::ITableWorkspace_sptr peaklisttablews = estimate->getProperty("OutputWorkspace");
// Determine whether to use FindPeakBackground's result.
int fitresult = -1;
if (peaklisttablews->columnCount() < 7)
throw std::runtime_error("No 7th column for use FindPeakBackground result or not. ");
if (peaklisttablews->rowCount() > 0) {
/// @todo Allow setting of fitresult. Currently, fitresult is left
/// deliberately hidden by creating a new variable here with the same
/// name. This should be fixed but it causes different behaviour which
/// breaks several unit tests. The issue to deal with this is #13950. Other
/// related issues are #13667, #15978 and #19773.
const int hiddenFitresult = peaklisttablews->Int(0, 6);
g_log.information() << "fitresult=" << hiddenFitresult << "\n";
}
// Local check whether FindPeakBackground gives a reasonable value
vecpeakrange.resize(2);
if (fitresult > 0) {
// Use FitPeakBackgroud's result
size_t i_peakmin, i_peakmax;
i_peakmin = peaklisttablews->Int(0, 1);
i_peakmax = peaklisttablews->Int(0, 2);
g_log.information() << "FindPeakBackground successful. "
<< "iMin = " << i_min << ", iPeakMin = " << i_peakmin << ", iPeakMax = " << i_peakmax