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ConvertMDHistoToMatrixWorkspace.cpp
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ConvertMDHistoToMatrixWorkspace.cpp
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// Mantid Repository : https://github.com/mantidproject/mantid
//
// Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
// NScD Oak Ridge National Laboratory, European Spallation Source,
// Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
// SPDX - License - Identifier: GPL - 3.0 +
//----------------------------------------------------------------------
// Includes
//----------------------------------------------------------------------
#include "MantidMDAlgorithms/ConvertMDHistoToMatrixWorkspace.h"
#include "MantidAPI/BinEdgeAxis.h"
#include "MantidAPI/CoordTransform.h"
#include "MantidAPI/IMDHistoWorkspace.h"
#include "MantidAPI/MatrixWorkspace.h"
#include "MantidAPI/NullCoordTransform.h"
#include "MantidAPI/WorkspaceFactory.h"
#include "MantidHistogramData/LinearGenerator.h"
#include "MantidKernel/ListValidator.h"
#include "MantidKernel/Unit.h"
#include "MantidKernel/UnitFactory.h"
#include <sstream>
using namespace Mantid::Kernel;
using namespace Mantid::API;
namespace {
/**
* A shared pointer deleter that doesn't delete.
*/
struct NullDeleter {
void operator()(void const * /*unused*/) const { // Do nothing
}
};
/**
Find the dimension to use as the plot axis.
@param start : start point in final frame
@param end : end point in final frame
@param transform : transform to original frame
@param inputWorkspace : inputWorkspace
@param logger : log object
@param id : id, or current index for the dimension to use as the x-plot
dimension
@param xAxisLabel : in/out reference for text to use as the x-axis label.
@return id/index of the dimension with the longest span in the original
coordinate system.
*/
size_t findXAxis(const VMD &start, const VMD &end, CoordTransform const *const transform,
IMDHistoWorkspace const *const inputWorkspace, Logger &logger, const size_t id,
std::string &xAxisLabel) {
// Find the start and end points in the original workspace
VMD originalStart = transform->applyVMD(start);
VMD originalEnd = transform->applyVMD(end);
VMD diff = originalEnd - originalStart;
// Now we find the dimension with the biggest change
double largest = -1e30;
size_t dimIndex = id;
const size_t nOriginalWorkspaces = inputWorkspace->numOriginalWorkspaces();
if (nOriginalWorkspaces < 1) {
logger.information("No original workspaces. Assume X-axis is Dim0.");
return dimIndex;
}
auto originalWS =
std::dynamic_pointer_cast<IMDWorkspace>(inputWorkspace->getOriginalWorkspace(nOriginalWorkspaces - 1));
for (size_t d = 0; d < diff.getNumDims(); d++) {
if (fabs(diff[d]) > largest || (originalWS->getDimension(dimIndex)->getIsIntegrated())) {
// Skip over any integrated dimensions
if (originalWS && !originalWS->getDimension(d)->getIsIntegrated()) {
largest = fabs(diff[d]);
dimIndex = d;
}
}
}
// Use the x-axis label from the original workspace.
xAxisLabel = originalWS->getDimension(dimIndex)->getName();
return dimIndex;
}
} // namespace
namespace Mantid::MDAlgorithms {
// Register the algorithm into the AlgorithmFactory
DECLARE_ALGORITHM(ConvertMDHistoToMatrixWorkspace)
/// Decalare the properties
void ConvertMDHistoToMatrixWorkspace::init() {
declareProperty(std::make_unique<WorkspaceProperty<API::IMDHistoWorkspace>>("InputWorkspace", "", Direction::Input),
"An input IMDHistoWorkspace.");
declareProperty(std::make_unique<WorkspaceProperty<>>("OutputWorkspace", "", Direction::Output),
"An output Workspace2D.");
std::array<std::string, 3> normalizations = {{"NoNormalization", "VolumeNormalization", "NumEventsNormalization"}};
declareProperty("Normalization", normalizations[0],
Kernel::IValidator_sptr(new Kernel::ListValidator<std::string>(normalizations)),
"Signal normalization method");
declareProperty(std::make_unique<PropertyWithValue<bool>>("FindXAxis", true, Direction::Input),
"If True, tries to automatically determine the dimension to use as the "
"output x-axis. Applies to line cut MD workspaces.");
}
/// Execute the algorithm
void ConvertMDHistoToMatrixWorkspace::exec() {
IMDHistoWorkspace_sptr inputWorkspace = getProperty("InputWorkspace");
Mantid::Geometry::VecIMDDimension_const_sptr nonIntegDims = inputWorkspace->getNonIntegratedDimensions();
if (nonIntegDims.size() == 1) {
make1DWorkspace();
} else if (nonIntegDims.size() == 2) {
make2DWorkspace();
} else {
throw std::invalid_argument("Cannot convert MD workspace with more than 2 dimensions.");
}
}
/**
* Make 1D MatrixWorkspace
*/
void ConvertMDHistoToMatrixWorkspace::make1DWorkspace() {
IMDHistoWorkspace_sptr inputWorkspace = getProperty("InputWorkspace");
Mantid::Geometry::VecIMDDimension_const_sptr nonIntegDims = inputWorkspace->getNonIntegratedDimensions();
std::string alongDim;
if (!nonIntegDims.empty())
alongDim = nonIntegDims[0]->getDimensionId();
else
alongDim = inputWorkspace->getDimension(0)->getDimensionId();
size_t nd = inputWorkspace->getNumDims();
Mantid::Kernel::VMD start = VMD(nd);
Mantid::Kernel::VMD end = VMD(nd);
size_t id = 0;
for (size_t d = 0; d < nd; d++) {
Mantid::Geometry::IMDDimension_const_sptr dim = inputWorkspace->getDimension(d);
if (dim->getDimensionId() == alongDim) {
// All the way through in the single dimension
start[d] = dim->getMinimum();
end[d] = dim->getMaximum();
id = d; // We take the first non integrated dimension to be the diemnsion
// of interest.
} else {
// Mid point along each dimension
start[d] = (dim->getMaximum() + dim->getMinimum()) / 2.0f;
end[d] = start[d];
}
}
// Unit direction of the line
Mantid::Kernel::VMD dir = end - start;
dir.normalize();
std::string normProp = getPropertyValue("Normalization");
Mantid::API::MDNormalization normalization;
if (normProp == "NoNormalization") {
normalization = NoNormalization;
} else if (normProp == "VolumeNormalization") {
normalization = VolumeNormalization;
} else if (normProp == "NumEventsNormalization") {
normalization = NumEventsNormalization;
} else {
normalization = NoNormalization;
}
auto line = inputWorkspace->getLineData(start, end, normalization);
MatrixWorkspace_sptr outputWorkspace =
WorkspaceFactory::Instance().create("Workspace2D", 1, line.x.size(), line.y.size());
outputWorkspace->mutableY(0) = line.y;
outputWorkspace->mutableE(0) = line.e;
const size_t numberTransformsToOriginal = inputWorkspace->getNumberTransformsToOriginal();
CoordTransform_const_sptr transform = std::make_shared<NullCoordTransform>(inputWorkspace->getNumDims());
if (numberTransformsToOriginal > 0) {
const size_t indexToLastTransform = numberTransformsToOriginal - 1;
transform = CoordTransform_const_sptr(inputWorkspace->getTransformToOriginal(indexToLastTransform), NullDeleter());
}
assert(line.x.size() == outputWorkspace->x(0).size());
std::string xAxisLabel = inputWorkspace->getDimension(id)->getName();
const bool autoFind = this->getProperty("FindXAxis");
if (autoFind) {
// We look to the original workspace if possbible to find the dimension of
// interest to plot against.
id = findXAxis(start, end, transform.get(), inputWorkspace.get(), g_log, id, xAxisLabel);
}
auto &mutableXValues = outputWorkspace->mutableX(0);
// VMD inTargetCoord;
for (size_t i = 0; i < line.x.size(); ++i) {
// Coordinates in the workspace being plotted
VMD wsCoord = start + dir * line.x[i];
VMD inTargetCoord = transform->applyVMD(wsCoord);
mutableXValues[i] = inTargetCoord[id];
}
// outputWorkspace->mutableX(0) = inTargetCoord;
std::shared_ptr<Kernel::Units::Label> labelX =
std::dynamic_pointer_cast<Kernel::Units::Label>(Kernel::UnitFactory::Instance().create("Label"));
labelX->setLabel(xAxisLabel);
outputWorkspace->getAxis(0)->unit() = labelX;
outputWorkspace->setYUnitLabel("Signal");
setProperty("OutputWorkspace", outputWorkspace);
}
/**
* Make 2D MatrixWorkspace
*/
void ConvertMDHistoToMatrixWorkspace::make2DWorkspace() {
// get the input workspace
IMDHistoWorkspace_sptr inputWorkspace = getProperty("InputWorkspace");
// find the non-integrated dimensions
Mantid::Geometry::VecIMDDimension_const_sptr nonIntegDims = inputWorkspace->getNonIntegratedDimensions();
auto xDim = nonIntegDims[0];
auto yDim = nonIntegDims[1];
size_t nx = xDim->getNBins();
size_t ny = yDim->getNBins();
size_t xDimIndex = inputWorkspace->getDimensionIndexById(xDim->getDimensionId());
size_t xStride = calcStride(*inputWorkspace, xDimIndex);
size_t yDimIndex = inputWorkspace->getDimensionIndexById(yDim->getDimensionId());
size_t yStride = calcStride(*inputWorkspace, yDimIndex);
// get the normalization of the output
std::string normProp = getPropertyValue("Normalization");
Mantid::API::MDNormalization normalization;
if (normProp == "NoNormalization") {
normalization = NoNormalization;
} else if (normProp == "VolumeNormalization") {
normalization = VolumeNormalization;
} else if (normProp == "NumEventsNormalization") {
normalization = NumEventsNormalization;
} else {
normalization = NoNormalization;
}
auto inverseVolume = static_cast<signal_t>(inputWorkspace->getInverseVolume());
// create the output workspace
MatrixWorkspace_sptr outputWorkspace = WorkspaceFactory::Instance().create("Workspace2D", ny, nx + 1, nx);
// set the x-values
const size_t xValsSize = outputWorkspace->x(0).size();
const double dx = xDim->getBinWidth();
const double minX = xDim->getMinimum();
outputWorkspace->setBinEdges(0, xValsSize, HistogramData::LinearGenerator(minX, dx));
// set the y-values and errors
for (size_t i = 0; i < ny; ++i) {
if (i > 0)
outputWorkspace->setSharedX(i, outputWorkspace->sharedX(0));
size_t yOffset = i * yStride;
for (size_t j = 0; j < nx; ++j) {
size_t linearIndex = yOffset + j * xStride;
signal_t signal = inputWorkspace->getSignalArray()[linearIndex];
signal_t error = inputWorkspace->getErrorSquaredArray()[linearIndex];
// apply normalization
if (normalization != NoNormalization) {
if (normalization == VolumeNormalization) {
signal *= inverseVolume;
error *= inverseVolume;
} else // normalization == NumEventsNormalization
{
signal_t factor = inputWorkspace->getNumEventsArray()[linearIndex];
factor = factor != 0.0 ? 1.0 / factor : 1.0;
signal *= factor;
error *= factor;
}
}
outputWorkspace->mutableY(i)[j] = signal;
outputWorkspace->mutableE(i)[j] = sqrt(error);
}
}
// set the first axis
auto labelX = std::dynamic_pointer_cast<Kernel::Units::Label>(Kernel::UnitFactory::Instance().create("Label"));
labelX->setLabel(xDim->getName());
outputWorkspace->getAxis(0)->unit() = labelX;
// set the second axis
auto yAxis = std::make_unique<BinEdgeAxis>(ny + 1);
for (size_t i = 0; i <= ny; ++i) {
yAxis->setValue(i, yDim->getX(i));
}
auto labelY = std::dynamic_pointer_cast<Kernel::Units::Label>(Kernel::UnitFactory::Instance().create("Label"));
labelY->setLabel(yDim->getName());
yAxis->unit() = labelY;
outputWorkspace->replaceAxis(1, std::move(yAxis));
// set the "units" for the y values
outputWorkspace->setYUnitLabel("Signal");
// done
setProperty("OutputWorkspace", outputWorkspace);
}
/**
* Calculate the stride for a dimension.
* @param workspace :: An MD workspace.
* @param dim :: A dimension index to calculate the stride for.
*/
size_t ConvertMDHistoToMatrixWorkspace::calcStride(const API::IMDHistoWorkspace &workspace, size_t dim) const {
size_t stride = 1;
for (size_t i = 0; i < dim; ++i) {
auto dimension = workspace.getDimension(i);
stride *= dimension->getNBins();
}
return stride;
}
} // namespace Mantid::MDAlgorithms