C++ IFunction_sptr::fix方法代码示例

/**
 * Execute smoothing of a single spectrum.
 * @param inputWS :: A workspace to pick a spectrum from.
 * @param wsIndex :: An index of a spectrum to smooth.
 * @return :: A single-spectrum workspace with the smoothed data.
 */
API::MatrixWorkspace_sptr
WienerSmooth::smoothSingleSpectrum(API::MatrixWorkspace_sptr inputWS,
                                   size_t wsIndex) {
  size_t dataSize = inputWS->blocksize();

  // it won't work for very small workspaces
  if (dataSize < 4) {
    g_log.debug() << "No smoothing, spectrum copied." << std::endl;
    return copyInput(inputWS, wsIndex);
  }

  // Due to the way RealFFT works the input should be even-sized
  const bool isOddSize = dataSize % 2 != 0;
  if (isOddSize) {
    // add a fake value to the end to make size even
    inputWS = copyInput(inputWS, wsIndex);
    wsIndex = 0;
    auto &X = inputWS->dataX(wsIndex);
    auto &Y = inputWS->dataY(wsIndex);
    auto &E = inputWS->dataE(wsIndex);
    double dx = X[dataSize - 1] - X[dataSize - 2];
    X.push_back(X.back() + dx);
    Y.push_back(Y.back());
    E.push_back(E.back());
  }

  // the input vectors
  auto &X = inputWS->readX(wsIndex);
  auto &Y = inputWS->readY(wsIndex);
  auto &E = inputWS->readE(wsIndex);

  // Digital fourier transform works best for data oscillating around 0.
  // Fit a spline with a small number of break points to the data.
  // Make sure that the spline passes through the first and the last points
  // of the data.
  // The fitted spline will be subtracted from the data and the difference
  // will be smoothed with the Wiener filter. After that the spline will be
  // added to the smoothed data to produce the output.

  // number of spline break points, must be smaller than the data size but
  // between 2 and 10
  size_t nbreak = 10;
  if (nbreak * 3 > dataSize)
    nbreak = dataSize / 3;

  // NB. The spline mustn't fit too well to the data. If it does smoothing
  // doesn't happen.
  // TODO: it's possible that the spline is unnecessary and a simple linear
  // function will
  //       do a better job.

  g_log.debug() << "Spline break points " << nbreak << std::endl;

  // define the spline
  API::IFunction_sptr spline =
      API::FunctionFactory::Instance().createFunction("BSpline");
  auto xInterval = getStartEnd(X, inputWS->isHistogramData());
  spline->setAttributeValue("StartX", xInterval.first);
  spline->setAttributeValue("EndX", xInterval.second);
  spline->setAttributeValue("NBreak", static_cast<int>(nbreak));
  // fix the first and last parameters to the first and last data values
  spline->setParameter(0, Y.front());
  spline->fix(0);
  size_t lastParamIndex = spline->nParams() - 1;
  spline->setParameter(lastParamIndex, Y.back());
  spline->fix(lastParamIndex);

  // fit the spline to the data
  auto fit = createChildAlgorithm("Fit");
  fit->initialize();
  fit->setProperty("Function", spline);
  fit->setProperty("InputWorkspace", inputWS);
  fit->setProperty("WorkspaceIndex", static_cast<int>(wsIndex));
  fit->setProperty("CreateOutput", true);
  fit->execute();

  // get the fit output workspace; spectrum 2 contains the difference that is to
  // be smoothed
  API::MatrixWorkspace_sptr fitOut = fit->getProperty("OutputWorkspace");

  // Fourier transform the difference spectrum
  auto fourier = createChildAlgorithm("RealFFT");
  fourier->initialize();
  fourier->setProperty("InputWorkspace", fitOut);
  fourier->setProperty("WorkspaceIndex", 2);
  // we don't require bin linearity as we don't need the exact transform
  fourier->setProperty("IgnoreXBins", true);
  fourier->execute();

  API::MatrixWorkspace_sptr fourierOut =
      fourier->getProperty("OutputWorkspace");

  // spectrum 2 of the transformed workspace has the transform modulus which is
  // a square
//.........这里部分代码省略.........