/** Do the initial copy of the data from the input to the output workspace for
 * histogram workspaces.
 *  Takes out the bin width if necessary.
 *  @param inputWS  The input workspace
 *  @param outputWS The output workspace
 */
void ConvertUnitsUsingDetectorTable::fillOutputHist(
    const API::MatrixWorkspace_const_sptr inputWS,
    const API::MatrixWorkspace_sptr outputWS) {
  const int size = static_cast<int>(inputWS->blocksize());

  // Loop over the histograms (detector spectra)
  Progress prog(this, 0.0, 0.2, m_numberOfSpectra);
  int64_t numberOfSpectra_i =
      static_cast<int64_t>(m_numberOfSpectra); // cast to make openmp happy
  PARALLEL_FOR2(inputWS, outputWS)
  for (int64_t i = 0; i < numberOfSpectra_i; ++i) {
    PARALLEL_START_INTERUPT_REGION
    // Take the bin width dependency out of the Y & E data
    if (m_distribution) {
      for (int j = 0; j < size; ++j) {
        const double width =
            std::abs(inputWS->dataX(i)[j + 1] - inputWS->dataX(i)[j]);
        outputWS->dataY(i)[j] = inputWS->dataY(i)[j] * width;
        outputWS->dataE(i)[j] = inputWS->dataE(i)[j] * width;
      }
    } else {
      // Just copy over
      outputWS->dataY(i) = inputWS->readY(i);
      outputWS->dataE(i) = inputWS->readE(i);
    }
    // Copy over the X data
    outputWS->setX(i, inputWS->refX(i));

    prog.report("Convert to " + m_outputUnit->unitID());
    PARALLEL_END_INTERUPT_REGION
  }
  PARALLEL_CHECK_INTERUPT_REGION
}
Beispiel #2
0
/**
 * Perform a call to nxgetslab, via the NexusClasses wrapped methods for a given blocksize. The xbins are read along with
 * each call to the data/error loading
 * @param data :: The NXDataSet object of y values
 * @param errors :: The NXDataSet object of error values
 * @param xbins :: The xbin NXDataSet
 * @param blocksize :: The blocksize to use
 * @param nchannels :: The number of channels for the block
 * @param hist :: The workspace index to start reading into
 * @param wsIndex :: The workspace index to save data into
 * @param local_workspace :: A pointer to the workspace
 */
void LoadNexusProcessed::loadBlock(NXDataSetTyped<double> & data, NXDataSetTyped<double> & errors, NXDouble & xbins,
    int64_t blocksize, int64_t nchannels, int64_t &hist, int64_t& wsIndex,
    API::MatrixWorkspace_sptr local_workspace)
{
  data.load(static_cast<int>(blocksize),static_cast<int>(hist));
  double *data_start = data();
  double *data_end = data_start + nchannels;
  errors.load(static_cast<int>(blocksize),static_cast<int>(hist));
  double *err_start = errors();
  double *err_end = err_start + nchannels;
  xbins.load(static_cast<int>(blocksize),static_cast<int>(hist));
  const int64_t nxbins(nchannels + 1);
  double *xbin_start = xbins();
  double *xbin_end = xbin_start + nxbins;
  int64_t final(hist + blocksize);
  while( hist < final )
  {
    MantidVec& Y = local_workspace->dataY(wsIndex);
    Y.assign(data_start, data_end);
    data_start += nchannels; data_end += nchannels;
    MantidVec& E = local_workspace->dataE(wsIndex);
    E.assign(err_start, err_end);
    err_start += nchannels; err_end += nchannels;
    MantidVec& X = local_workspace->dataX(wsIndex);
    X.assign(xbin_start, xbin_end);
    xbin_start += nxbins; xbin_end += nxbins;
    ++hist;
    ++wsIndex;
  }
}
Beispiel #3
0
void ConvertEmptyToTof::setTofInWS(const std::vector<double> &tofAxis,
                                   API::MatrixWorkspace_sptr outputWS) {

  const size_t numberOfSpectra = m_inputWS->getNumberHistograms();
  int64_t numberOfSpectraInt64 =
      static_cast<int64_t>(numberOfSpectra); // cast to make openmp happy

  g_log.debug() << "Setting the TOF X Axis for numberOfSpectra="
                << numberOfSpectra << '\n';

  Progress prog(this, 0.0, 0.2, numberOfSpectra);
  PARALLEL_FOR2(m_inputWS, outputWS)
  for (int64_t i = 0; i < numberOfSpectraInt64; ++i) {
    PARALLEL_START_INTERUPT_REGION
    // Just copy over
    outputWS->dataY(i) = m_inputWS->readY(i);
    outputWS->dataE(i) = m_inputWS->readE(i);
    // copy
    outputWS->setX(i, tofAxis);

    prog.report();
    PARALLEL_END_INTERUPT_REGION
  } // end for i
  PARALLEL_CHECK_INTERUPT_REGION
  outputWS->getAxis(0)->unit() = UnitFactory::Instance().create("TOF");
}
Beispiel #4
0
/** loadData
*  Load the counts data from an NXInt into a workspace
*/
void LoadMuonNexus2::loadData(const Mantid::NeXus::NXInt &counts,
                              const std::vector<double> &timeBins, int wsIndex,
                              int period, int spec,
                              API::MatrixWorkspace_sptr localWorkspace) {
  MantidVec &X = localWorkspace->dataX(wsIndex);
  MantidVec &Y = localWorkspace->dataY(wsIndex);
  MantidVec &E = localWorkspace->dataE(wsIndex);
  X.assign(timeBins.begin(), timeBins.end());

  int nBins = 0;
  int *data = nullptr;

  if (counts.rank() == 3) {
    nBins = counts.dim2();
    data = &counts(period, spec, 0);
  } else if (counts.rank() == 2) {
    nBins = counts.dim1();
    data = &counts(spec, 0);
  } else {
    throw std::runtime_error("Data have unsupported dimansionality");
  }
  assert(nBins + 1 == static_cast<int>(timeBins.size()));

  Y.assign(data, data + nBins);
  typedef double (*uf)(double);
  uf dblSqrt = std::sqrt;
  std::transform(Y.begin(), Y.end(), E.begin(), dblSqrt);
}
/**
 * Read spectra from the DAE
 * @param period :: Current period index
 * @param index :: First spectrum index
 * @param count :: Number of spectra to read
 * @param workspace :: Workspace to store the data
 * @param workspaceIndex :: index in workspace to store data
 */
void ISISHistoDataListener::getData(int period, int index, int count,
                                    API::MatrixWorkspace_sptr workspace,
                                    size_t workspaceIndex) {
  const int numberOfBins = m_numberOfBins[m_timeRegime];
  const size_t bufferSize = count * (numberOfBins + 1) * sizeof(int);
  std::vector<int> dataBuffer(bufferSize);
  // Read in spectra from DAE
  int ndims = 2, dims[2];
  dims[0] = count;
  dims[1] = numberOfBins + 1;

  int spectrumIndex = index + period * (m_totalNumberOfSpectra + 1);
  if (IDCgetdat(m_daeHandle, spectrumIndex, count, dataBuffer.data(), dims,
                &ndims) != 0) {
    g_log.error("Unable to read DATA from DAE " + m_daeName);
    throw Kernel::Exception::FileError("Unable to read DATA from DAE ",
                                       m_daeName);
  }

  for (size_t i = 0; i < static_cast<size_t>(count); ++i) {
    size_t wi = workspaceIndex + i;
    workspace->setX(wi, m_bins[m_timeRegime]);
    MantidVec &y = workspace->dataY(wi);
    MantidVec &e = workspace->dataE(wi);
    workspace->getSpectrum(wi)->setSpectrumNo(index + static_cast<specid_t>(i));
    size_t shift = i * (numberOfBins + 1) + 1;
    y.assign(dataBuffer.begin() + shift, dataBuffer.begin() + shift + y.size());
    std::transform(y.begin(), y.end(), e.begin(), dblSqrt);
  }
}
Beispiel #6
0
/** Execute the algorithm.
 */
void DampSq::exec()
{
    // TODO Auto-generated execute stub

    // 1. Generate new workspace
    API::MatrixWorkspace_const_sptr isqspace = getProperty("InputWorkspace");
    API::MatrixWorkspace_sptr osqspace = WorkspaceFactory::Instance().create(isqspace, 1, isqspace->size(), isqspace->size());

    int mode = getProperty("Mode");
    double qmax = getProperty("QMax");

    if (mode < 1 || mode > 4) {
        g_log.error("Damp mode can only be 1, 2, 3, or 4");
        return;
    }

    // 2. Get access to all
    const MantidVec& iQVec = isqspace->dataX(0);
    const MantidVec& iSVec = isqspace->dataY(0);
    const MantidVec& iEVec = isqspace->dataE(0);

    MantidVec& oQVec = osqspace->dataX(0);
    MantidVec& oSVec = osqspace->dataY(0);
    MantidVec& oEVec = osqspace->dataE(0);

    // 3. Calculation
    double dqmax = qmax - iQVec[0];

    double damp;
    for (unsigned int i = 0; i < iQVec.size(); i ++) {
        // a) calculate damp coefficient
        switch (mode) {
        case 1:
            damp = dampcoeff1(iQVec[i], qmax, dqmax);
            break;
        case 2:
            damp = dampcoeff2(iQVec[i], qmax, dqmax);;
            break;
        case 3:
            damp = dampcoeff3(iQVec[i], qmax, dqmax);;
            break;
        case 4:
            damp = dampcoeff4(iQVec[i], qmax, dqmax);;
            break;
        default:
            damp = 0;
            break;
        }
        // b) calculate new S(q)
        oQVec[i] = iQVec[i];
        oSVec[i] = 1 + damp*(iSVec[i]-1);
        oEVec[i] = damp*iEVec[i];
    }  // i

    // 4. Over
    setProperty("OutputWorkspace", osqspace);

    return;
}
Beispiel #7
0
/** Performs the Holtzer transformation: IQ v Q
 *  @param ws The workspace to be transformed
 */
void IQTransform::holtzer(API::MatrixWorkspace_sptr ws)
{
  MantidVec& X = ws->dataX(0);
  MantidVec& Y = ws->dataY(0);
  MantidVec& E = ws->dataE(0);
  std::transform(Y.begin(),Y.end(),X.begin(),Y.begin(),std::multiplies<double>());
  std::transform(E.begin(),E.end(),X.begin(),E.begin(),std::multiplies<double>());

  ws->setYUnitLabel("I x Q");
}
Beispiel #8
0
/** Performs the Porod transformation: IQ^4 v Q
 *  @param ws The workspace to be transformed
 */
void IQTransform::porod(API::MatrixWorkspace_sptr ws)
{
  MantidVec& X = ws->dataX(0);
  MantidVec& Y = ws->dataY(0);
  MantidVec& E = ws->dataE(0);
  MantidVec Q4(X.size());
  std::transform(X.begin(),X.end(),X.begin(),Q4.begin(),VectorHelper::TimesSquares<double>());
  std::transform(Y.begin(),Y.end(),Q4.begin(),Y.begin(),std::multiplies<double>());
  std::transform(E.begin(),E.end(),Q4.begin(),E.begin(),std::multiplies<double>());

  ws->setYUnitLabel("I x Q^4");
}
Beispiel #9
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/** Performs a log-log transformation: Ln(I) v Ln(Q)
 *  @param ws The workspace to be transformed
 *  @throw std::range_error if an attempt is made to take log of a negative number
 */
void IQTransform::logLog(API::MatrixWorkspace_sptr ws)
{
  MantidVec& X = ws->dataX(0);
  MantidVec& Y = ws->dataY(0);
  MantidVec& E = ws->dataE(0);
  std::transform(X.begin(),X.end(),X.begin(),VectorHelper::Log<double>());
  std::transform(E.begin(),E.end(),Y.begin(),E.begin(),std::divides<double>());
  std::transform(Y.begin(),Y.end(),Y.begin(),VectorHelper::LogNoThrow<double>());

  ws->setYUnitLabel("Ln(I)");
  m_label->setLabel("Ln(Q)");
}
Beispiel #10
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/** Performs the Guinier (sheets) transformation: Ln(IQ^2) v Q^2
 *  @param ws The workspace to be transformed
 *  @throw std::range_error if an attempt is made to take log of a negative number
 */
void IQTransform::guinierSheets(API::MatrixWorkspace_sptr ws)
{
  MantidVec& X = ws->dataX(0);
  MantidVec& Y = ws->dataY(0);
  MantidVec& E = ws->dataE(0);
  std::transform(E.begin(),E.end(),Y.begin(),E.begin(),std::divides<double>());
  std::transform(X.begin(),X.end(),X.begin(),VectorHelper::Squares<double>());
  std::transform(Y.begin(),Y.end(),X.begin(),Y.begin(),std::multiplies<double>());
  std::transform(Y.begin(),Y.end(),Y.begin(),VectorHelper::LogNoThrow<double>());

  ws->setYUnitLabel("Ln(I x Q^2)");
  m_label->setLabel("Q^2");
}
Beispiel #11
0
/** Unwraps the Y & E vectors of a spectrum according to the ranges found in
 * unwrapX.
 *  @param tempWS ::      A pointer to the temporary workspace in which the
 * results are being stored
 *  @param spectrum ::    The workspace index
 *  @param rangeBounds :: The upper and lower ranges for the unwrapping
 */
void UnwrapMonitor::unwrapYandE(const API::MatrixWorkspace_sptr &tempWS,
                                const int &spectrum,
                                const std::vector<int> &rangeBounds) {
  // Copy over the relevant ranges of Y & E data
  MantidVec &Y = tempWS->dataY(spectrum);
  MantidVec &E = tempWS->dataE(spectrum);
  // Get references to the input data
  const MantidVec &YIn = m_inputWS->dataY(spectrum);
  const MantidVec &EIn = m_inputWS->dataE(spectrum);
  if (rangeBounds[2] != -1) {
    // Copy in the upper range
    Y.assign(YIn.begin() + rangeBounds[2], YIn.end());
    E.assign(EIn.begin() + rangeBounds[2], EIn.end());
    // Propagate masking, if necessary
    if (m_inputWS->hasMaskedBins(spectrum)) {
      const MatrixWorkspace::MaskList &inputMasks =
          m_inputWS->maskedBins(spectrum);
      MatrixWorkspace::MaskList::const_iterator it;
      for (it = inputMasks.begin(); it != inputMasks.end(); ++it) {
        if (static_cast<int>((*it).first) >= rangeBounds[2])
          tempWS->flagMasked(spectrum, (*it).first - rangeBounds[2],
                             (*it).second);
      }
    }
  } else {
    // Y & E are references to existing vector. Assign above clears them, so
    // need to explicitly here
    Y.clear();
    E.clear();
  }
  if (rangeBounds[0] != -1 && rangeBounds[1] > 0) {
    // Now append the lower range
    MantidVec::const_iterator YStart = YIn.begin();
    MantidVec::const_iterator EStart = EIn.begin();
    Y.insert(Y.end(), YStart + rangeBounds[0], YStart + rangeBounds[1]);
    E.insert(E.end(), EStart + rangeBounds[0], EStart + rangeBounds[1]);
    // Propagate masking, if necessary
    if (m_inputWS->hasMaskedBins(spectrum)) {
      const MatrixWorkspace::MaskList &inputMasks =
          m_inputWS->maskedBins(spectrum);
      MatrixWorkspace::MaskList::const_iterator it;
      for (it = inputMasks.begin(); it != inputMasks.end(); ++it) {
        const int maskIndex = static_cast<int>((*it).first);
        if (maskIndex >= rangeBounds[0] && maskIndex < rangeBounds[1])
          tempWS->flagMasked(spectrum, maskIndex - rangeBounds[0],
                             (*it).second);
      }
    }
  }
}
/** Remove background per pixel
 * @brief ConvertCWSDExpToMomentum::removeBackground
 * @param dataws
 */
void ConvertCWSDExpToMomentum::removeBackground(
    API::MatrixWorkspace_sptr dataws) {
  if (dataws->getNumberHistograms() != m_backgroundWS->getNumberHistograms())
    throw std::runtime_error("Impossible to have this situation");

  size_t numhist = dataws->getNumberHistograms();
  for (size_t i = 0; i < numhist; ++i) {
    double bkgd_y = m_backgroundWS->readY(i)[0];
    if (fabs(bkgd_y) > 1.E-2) {
      dataws->dataY(i)[0] -= bkgd_y;
      dataws->dataE(i)[0] = std::sqrt(dataws->readY(i)[0]);
    }
  }
}
Beispiel #13
0
/** Reads in the data corresponding to a single spectrum
 *  @param speFile ::   The file handle
 *  @param workspace :: The output workspace
 *  @param index ::     The index of the current spectrum
 */
void LoadSPE::readHistogram(FILE *speFile, API::MatrixWorkspace_sptr workspace,
                            size_t index) {
  // First, there should be a comment line
  char comment[100];
  fgets(comment, 100, speFile);
  if (comment[0] != '#')
    reportFormatError(std::string(comment));

  // Then it's the Y values
  MantidVec &Y = workspace->dataY(index);
  const size_t nbins = workspace->blocksize();
  int retval;
  for (size_t i = 0; i < nbins; ++i) {
    retval = fscanf(speFile, "%10le", &Y[i]);
    // g_log.error() << Y[i] << std::endl;
    if (retval != 1) {
      std::stringstream ss;
      ss << "Reading data value" << i << " of histogram " << index;
      reportFormatError(ss.str());
    }
    // -10^30 is the flag for not a number used in SPE files (from
    // www.mantidproject.org/images/3/3d/Spe_file_format.pdf)
    if (Y[i] == SaveSPE::MASK_FLAG) {
      Y[i] = std::numeric_limits<double>::quiet_NaN();
    }
  }
  // Read to EOL
  fgets(comment, 100, speFile);

  // Another comment line
  fgets(comment, 100, speFile);
  if (comment[0] != '#')
    reportFormatError(std::string(comment));

  // And then the error values
  MantidVec &E = workspace->dataE(index);
  for (size_t i = 0; i < nbins; ++i) {
    retval = fscanf(speFile, "%10le", &E[i]);
    if (retval != 1) {
      std::stringstream ss;
      ss << "Reading error value" << i << " of histogram " << index;
      reportFormatError(ss.str());
    }
  }
  // Read to EOL
  fgets(comment, 100, speFile);

  return;
}
Beispiel #14
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/** Divide each bin by the width of its q bin.
 *  @param outputWS :: The output workspace
 *  @param qAxis ::    A vector of the q bin boundaries
 */
void SofQWCentre::makeDistribution(API::MatrixWorkspace_sptr outputWS,
                                   const std::vector<double> qAxis) {
  std::vector<double> widths(qAxis.size());
  std::adjacent_difference(qAxis.begin(), qAxis.end(), widths.begin());

  const size_t numQBins = outputWS->getNumberHistograms();
  for (size_t i = 0; i < numQBins; ++i) {
    MantidVec &Y = outputWS->dataY(i);
    MantidVec &E = outputWS->dataE(i);
    std::transform(Y.begin(), Y.end(), Y.begin(),
                   std::bind2nd(std::divides<double>(), widths[i + 1]));
    std::transform(E.begin(), E.end(), E.begin(),
                   std::bind2nd(std::divides<double>(), widths[i + 1]));
  }
}
Beispiel #15
0
/**
 * Sum counts from the input workspace in lambda along lines of constant Q by
 * projecting to "virtual lambda" at a reference angle.
 *
 * @param detectorWS [in] :: the input workspace in wavelength
 * @param indices [in] :: an index set defining the foreground histograms
 * @return :: the single histogram output workspace in wavelength
 */
API::MatrixWorkspace_sptr
ReflectometrySumInQ::sumInQ(const API::MatrixWorkspace &detectorWS,
                            const Indexing::SpectrumIndexSet &indices) {

  const auto spectrumInfo = detectorWS.spectrumInfo();
  const auto refAngles = referenceAngles(spectrumInfo);
  // Construct the output workspace in virtual lambda
  API::MatrixWorkspace_sptr IvsLam =
      constructIvsLamWS(detectorWS, indices, refAngles);
  auto &outputE = IvsLam->dataE(0);
  // Loop through each spectrum in the detector group
  for (auto spIdx : indices) {
    if (spectrumInfo.isMasked(spIdx) || spectrumInfo.isMonitor(spIdx)) {
      continue;
    }
    // Get the size of this detector in twoTheta
    const auto twoThetaRange = twoThetaWidth(spIdx, spectrumInfo);
    // Check X length is Y length + 1
    const auto inputBinEdges = detectorWS.binEdges(spIdx);
    const auto inputCounts = detectorWS.counts(spIdx);
    const auto inputStdDevs = detectorWS.countStandardDeviations(spIdx);
    // Create a vector for the projected errors for this spectrum.
    // (Output Y values can simply be accumulated directly into the output
    // workspace, but for error values we need to create a separate error
    // vector for the projected errors from each input spectrum and then
    // do an overall sum in quadrature.)
    std::vector<double> projectedE(outputE.size(), 0.0);
    // Process each value in the spectrum
    const int ySize = static_cast<int>(inputCounts.size());
    for (int inputIdx = 0; inputIdx < ySize; ++inputIdx) {
      // Do the summation in Q
      processValue(inputIdx, twoThetaRange, refAngles, inputBinEdges,
                   inputCounts, inputStdDevs, *IvsLam, projectedE);
    }
    // Sum errors in quadrature
    const int eSize = static_cast<int>(outputE.size());
    for (int outIdx = 0; outIdx < eSize; ++outIdx) {
      outputE[outIdx] += projectedE[outIdx] * projectedE[outIdx];
    }
  }

  // Take the square root of all the accumulated squared errors for this
  // detector group. Assumes Gaussian errors
  double (*rs)(double) = std::sqrt;
  std::transform(outputE.begin(), outputE.end(), outputE.begin(), rs);

  return IvsLam;
}
Beispiel #16
0
/** Performs a transformation of the form: Q^A x I^B x Ln(Q^C x I^D x E) v Q^F x
 * I^G x Ln(Q^H x I^I x J).
 *  Uses the 'GeneralFunctionConstants' property where A-J are the 10 (ordered)
 * input constants.
 *  @param ws The workspace to be transformed
 *  @throw std::range_error if an attempt is made to take log of a negative
 * number
 */
void IQTransform::general(API::MatrixWorkspace_sptr ws) {
  MantidVec &X = ws->dataX(0);
  MantidVec &Y = ws->dataY(0);
  MantidVec &E = ws->dataE(0);
  const std::vector<double> C = getProperty("GeneralFunctionConstants");
  // Check for the correct number of elements
  if (C.size() != 10) {
    std::string mess(
        "The General transformation requires 10 values to be provided.");
    g_log.error(mess);
    throw std::invalid_argument(mess);
  }

  for (size_t i = 0; i < Y.size(); ++i) {
    double tmpX = std::pow(X[i], C[7]) * std::pow(Y[i], C[8]) * C[9];
    if (tmpX <= 0.0)
      throw std::range_error(
          "Attempt to take log of a zero or negative number.");
    tmpX = std::pow(X[i], C[5]) * std::pow(Y[i], C[6]) * std::log(tmpX);
    const double tmpY = std::pow(X[i], C[2]) * std::pow(Y[i], C[3]) * C[4];
    if (tmpY <= 0.0)
      throw std::range_error(
          "Attempt to take log of a zero or negative number.");
    const double newY =
        std::pow(X[i], C[0]) * std::pow(Y[i], C[1]) * std::log(tmpY);

    E[i] *= std::pow(X[i], C[0]) *
            (C[1] * std::pow(Y[i], C[1] - 1) * std::log(tmpY) +
             ((std::pow(Y[i], C[1]) * std::pow(X[i], C[2]) * C[4] * C[3] *
               std::pow(Y[i], C[3] - 1)) /
              tmpY));
    X[i] = tmpX;
    Y[i] = newY;
  }

  std::stringstream ylabel;
  ylabel << "Q^" << C[0] << " x I^" << C[1] << " x Ln( Q^" << C[2] << " x I^"
         << C[3] << " x " << C[4] << ")";
  ws->setYUnitLabel(ylabel.str());
  std::stringstream xlabel;
  xlabel << "Q^" << C[5] << " x I^" << C[6] << " x Ln( Q^" << C[7] << " x I^"
         << C[8] << " x " << C[9] << ")";
  m_label->setLabel(xlabel.str());
}
Beispiel #17
0
/** Performs the Debye-Bueche transformation: 1/sqrt(I) v Q^2
 *  The output is set to zero for negative input Y values
 *  @param ws The workspace to be transformed
 */
void IQTransform::debyeBueche(API::MatrixWorkspace_sptr ws) {
  MantidVec &X = ws->dataX(0);
  MantidVec &Y = ws->dataY(0);
  MantidVec &E = ws->dataE(0);
  std::transform(X.begin(), X.end(), X.begin(),
                 VectorHelper::Squares<double>());
  for (size_t i = 0; i < Y.size(); ++i) {
    if (Y[i] > 0.0) {
      Y[i] = 1.0 / std::sqrt(Y[i]);
      E[i] *= std::pow(Y[i], 3);
    } else {
      Y[i] = 0.0;
      E[i] = 0.0;
    }
  }

  ws->setYUnitLabel("1/sqrt(I)");
  m_label->setLabel("Q^2");
}
/** Zeroes all data points outside the X values given
 *  @param outputWorkspace :: The output workspace - data has already been
 * copied
 *  @param inIndex ::         The workspace index of the spectrum in the input
 * workspace
 *  @param outIndex ::        The workspace index of the spectrum in the output
 * workspace
 */
void ExtractSpectra::cropRagged(API::MatrixWorkspace_sptr outputWorkspace,
                                int inIndex, int outIndex) {
  MantidVec &Y = outputWorkspace->dataY(outIndex);
  MantidVec &E = outputWorkspace->dataE(outIndex);
  const size_t size = Y.size();
  size_t startX = this->getXMin(inIndex);
  if (startX > size)
    startX = size;
  for (size_t i = 0; i < startX; ++i) {
    Y[i] = 0.0;
    E[i] = 0.0;
  }
  size_t endX = this->getXMax(inIndex);
  if (endX > 0)
    endX -= m_histogram;
  for (size_t i = endX; i < size; ++i) {
    Y[i] = 0.0;
    E[i] = 0.0;
  }
}
Beispiel #19
0
/**
 * Perform a call to nxgetslab, via the NexusClasses wrapped methods for a given blocksize. This assumes that the
 * xbins have alread been cached
 * @param data :: The NXDataSet object of y values
 * @param errors :: The NXDataSet object of error values
 * @param blocksize :: The blocksize to use
 * @param nchannels :: The number of channels for the block
 * @param hist :: The workspace index to start reading into
 * @param local_workspace :: A pointer to the workspace
 */
void LoadNexusProcessed::loadBlock(NXDataSetTyped<double> & data, NXDataSetTyped<double> & errors,
    int64_t blocksize, int64_t nchannels, int64_t &hist,
    API::MatrixWorkspace_sptr local_workspace)
{
  data.load(static_cast<int>(blocksize),static_cast<int>(hist));
  errors.load(static_cast<int>(blocksize),static_cast<int>(hist));
  double *data_start = data();
  double *data_end = data_start + nchannels;
  double *err_start = errors();
  double *err_end = err_start + nchannels;
  int64_t final(hist + blocksize);
  while( hist < final )
  {
    MantidVec& Y = local_workspace->dataY(hist);
    Y.assign(data_start, data_end);
    data_start += nchannels; data_end += nchannels;
    MantidVec& E = local_workspace->dataE(hist);
    E.assign(err_start, err_end);
    err_start += nchannels; err_end += nchannels;
    local_workspace->setX(hist, m_xbins);
    ++hist;
  }
}
Beispiel #20
0
/**
*  Move the user selected spectra in the input workspace into groups in the output workspace
*  @param inputWS :: user selected input workspace for the algorithm
*  @param outputWS :: user selected output workspace for the algorithm
*  @param prog4Copy :: the amount of algorithm progress to attribute to moving a single spectra
*  @return number of new grouped spectra
*/
size_t GroupDetectors2::formGroupsEvent( DataObjects::EventWorkspace_const_sptr inputWS, DataObjects::EventWorkspace_sptr  outputWS,
            const double prog4Copy)
{
  // get "Behaviour" string
  const std::string behaviour = getProperty("Behaviour");
  int bhv = 0;
  if ( behaviour == "Average" ) bhv = 1;

  API::MatrixWorkspace_sptr beh = API::WorkspaceFactory::Instance().create(
    "Workspace2D", static_cast<int>(m_GroupSpecInds.size()), 1, 1);

  g_log.debug() << name() << ": Preparing to group spectra into " << m_GroupSpecInds.size() << " groups\n";


  // where we are copying spectra to, we start copying to the start of the output workspace
  size_t outIndex = 0;
  // Only used for averaging behaviour. We may have a 1:1 map where a Divide would be waste as it would be just dividing by 1
  bool requireDivide(false);
  for ( storage_map::const_iterator it = m_GroupSpecInds.begin(); it != m_GroupSpecInds.end() ; ++it )
  {
    // This is the grouped spectrum
    EventList & outEL = outputWS->getEventList(outIndex);

    // The spectrum number of the group is the key
    outEL.setSpectrumNo(it->first);
    // Start fresh with no detector IDs
    outEL.clearDetectorIDs();

    // the Y values and errors from spectra being grouped are combined in the output spectrum
    // Keep track of number of detectors required for masking
    size_t nonMaskedSpectra(0);
    beh->dataX(outIndex)[0] = 0.0;
    beh->dataE(outIndex)[0] = 0.0;
    for( std::vector<size_t>::const_iterator wsIter = it->second.begin(); wsIter != it->second.end(); ++wsIter)
    {
      const size_t originalWI = *wsIter;

      const EventList & fromEL=inputWS->getEventList(originalWI);
      //Add the event lists with the operator
      outEL += fromEL;


      // detectors to add to the output spectrum
      outEL.addDetectorIDs(fromEL.getDetectorIDs() );
      try
      {
        Geometry::IDetector_const_sptr det = inputWS->getDetector(originalWI);
        if( !det->isMasked() ) ++nonMaskedSpectra;
      }
      catch(Exception::NotFoundError&)
      {
        // If a detector cannot be found, it cannot be masked
        ++nonMaskedSpectra;
      }
    }
    if( nonMaskedSpectra == 0 ) ++nonMaskedSpectra; // Avoid possible divide by zero
    if(!requireDivide) requireDivide = (nonMaskedSpectra > 1);
    beh->dataY(outIndex)[0] = static_cast<double>(nonMaskedSpectra);

    // make regular progress reports and check for cancelling the algorithm
    if ( outIndex % INTERVAL == 0 )
    {
      m_FracCompl += INTERVAL*prog4Copy;
      if ( m_FracCompl > 1.0 )
        m_FracCompl = 1.0;
      progress(m_FracCompl);
      interruption_point();
    }
    outIndex ++;
  }

  // Refresh the spectraDetectorMap
  outputWS->doneAddingEventLists();

  if ( bhv == 1 && requireDivide )
  {
    g_log.debug() << "Running Divide algorithm to perform averaging.\n";
    Mantid::API::IAlgorithm_sptr divide = createChildAlgorithm("Divide");
    divide->initialize();
    divide->setProperty<API::MatrixWorkspace_sptr>("LHSWorkspace", outputWS);
    divide->setProperty<API::MatrixWorkspace_sptr>("RHSWorkspace", beh);
    divide->setProperty<API::MatrixWorkspace_sptr>("OutputWorkspace", outputWS);
    divide->execute();
  }


  g_log.debug() << name() << " created " << outIndex << " new grouped spectra\n";
  return outIndex;
}
Beispiel #21
0
void LoadDaveGrp::exec()
{
  const std::string filename = this->getProperty("Filename");

  int yLength = 0;

  MantidVec *xAxis = new MantidVec();
  MantidVec *yAxis = new MantidVec();

  std::vector<MantidVec *> data;
  std::vector<MantidVec *> errors;

  this->ifile.open(filename.c_str());
  if (this->ifile.is_open())
  {
    // Size of x axis
    this->getAxisLength(this->xLength);
    // Size of y axis
    this->getAxisLength(yLength);
    // This is also the number of groups (spectra)
    this->nGroups = yLength;
    // Read in the x axis values
    this->getAxisValues(xAxis, static_cast<std::size_t>(this->xLength));
    // Read in the y axis values
    this->getAxisValues(yAxis, static_cast<std::size_t>(yLength));
    // Read in the data
    this->getData(data, errors);
  }
  this->ifile.close();

  // Scale the x-axis if it is in micro-eV to get it to meV
  const bool isUeV = this->getProperty("IsMicroEV");
  if (isUeV)
  {
    MantidVec::iterator iter;
    for (iter = xAxis->begin(); iter != xAxis->end(); ++iter)
    {
      *iter /= 1000.0;
    }
  }

  // Create workspace
  API::MatrixWorkspace_sptr outputWorkspace = \
      boost::dynamic_pointer_cast<API::MatrixWorkspace>\
      (API::WorkspaceFactory::Instance().create("Workspace2D", this->nGroups,
      this->xLength, yLength));
  // Force the workspace to be a distribution
  outputWorkspace->isDistribution(true);

  // Set the x-axis units
  outputWorkspace->getAxis(0)->unit() = Kernel::UnitFactory::Instance().create(this->getProperty("XAxisUnits"));

  API::Axis* const verticalAxis = new API::NumericAxis(yLength);
  // Set the y-axis units
  verticalAxis->unit() = Kernel::UnitFactory::Instance().create(this->getProperty("YAxisUnits"));

  outputWorkspace->replaceAxis(1, verticalAxis);

  for(int i = 0; i < this->nGroups; i++)
  {
    outputWorkspace->dataX(i) = *xAxis;
    outputWorkspace->dataY(i) = *data[i];
    outputWorkspace->dataE(i) = *errors[i];
    verticalAxis->setValue(i, yAxis->at(i));

    delete data[i];
    delete errors[i];
  }

  delete xAxis;
  delete yAxis;

  outputWorkspace->mutableRun().addProperty("Filename",filename);
  this->setProperty("OutputWorkspace", outputWorkspace);
}
/**  Calculate the integral asymmetry for a workspace (red & green).
*   The calculation is done by MuonAsymmetryCalc and SimpleIntegration algorithms.
*   @param ws_red :: The red workspace
*   @param ws_green :: The green workspace
*   @param Y :: Reference to a variable receiving the value of asymmetry
*   @param E :: Reference to a variable receiving the value of the error
*/
void PlotAsymmetryByLogValue::calcIntAsymmetry(API::MatrixWorkspace_sptr ws_red,
        API::MatrixWorkspace_sptr ws_green,double& Y, double& E)
{
    if ( !m_autogroup )
    {
        groupDetectors(ws_red,m_backward_list);
        groupDetectors(ws_red,m_forward_list);
        groupDetectors(ws_green,m_backward_list);
        groupDetectors(ws_green,m_forward_list);
    }

    Property* startXprop = getProperty("TimeMin");
    Property* endXprop = getProperty("TimeMax");
    bool setX = !startXprop->isDefault() && !endXprop->isDefault();
    double startX(0.0),endX(0.0);
    if (setX)
    {
        startX = getProperty("TimeMin");
        endX = getProperty("TimeMax");
    }
    if (!m_int)
    {   //  "Differential asymmetry"

        API::MatrixWorkspace_sptr tmpWS = API::WorkspaceFactory::Instance().create(
                                              ws_red,1,ws_red->readX(0).size(),ws_red->readY(0).size());

        for(size_t i=0; i<tmpWS->dataY(0).size(); i++)
        {
            double FNORM = ws_green->readY(0)[i] + ws_red->readY(0)[i];
            FNORM = FNORM != 0.0 ? 1.0 / FNORM : 1.0;
            double BNORM = ws_green->readY(1)[i] + ws_red->readY(1)[i];
            BNORM = BNORM != 0.0 ? 1.0 / BNORM : 1.0;
            double ZF = ( ws_green->readY(0)[i] - ws_red->readY(0)[i] ) * FNORM;
            double ZB = ( ws_green->readY(1)[i] - ws_red->readY(1)[i] ) * BNORM;
            tmpWS->dataY(0)[i] = ZB - ZF;
            tmpWS->dataE(0)[i] = (1.0+ZF*ZF)*FNORM+(1.0+ZB*ZB)*BNORM;
        }

        IAlgorithm_sptr integr = createChildAlgorithm("Integration");
        integr->setProperty("InputWorkspace",tmpWS);
        integr->setPropertyValue("OutputWorkspace","tmp");
        if (setX)
        {
            integr->setProperty("RangeLower",startX);
            integr->setProperty("RangeUpper",endX);
        }
        integr->execute();
        MatrixWorkspace_sptr out = integr->getProperty("OutputWorkspace");

        Y = out->readY(0)[0] / static_cast<double>(tmpWS->dataY(0).size());
        E = out->readE(0)[0] / static_cast<double>(tmpWS->dataY(0).size());
    }
    else
    {
        //  "Integral asymmetry"
        IAlgorithm_sptr integr = createChildAlgorithm("Integration");
        integr->setProperty("InputWorkspace", ws_red);
        integr->setPropertyValue("OutputWorkspace","tmp");
        if (setX)
        {
            integr->setProperty("RangeLower",startX);
            integr->setProperty("RangeUpper",endX);
        }
        integr->execute();
        API::MatrixWorkspace_sptr intWS_red = integr->getProperty("OutputWorkspace");

        integr = createChildAlgorithm("Integration");
        integr->setProperty("InputWorkspace", ws_green);
        integr->setPropertyValue("OutputWorkspace","tmp");
        if (setX)
        {
            integr->setProperty("RangeLower",startX);
            integr->setProperty("RangeUpper",endX);
        }
        integr->execute();
        API::MatrixWorkspace_sptr intWS_green = integr->getProperty("OutputWorkspace");

        double YIF = ( intWS_green->readY(0)[0] - intWS_red->readY(0)[0] ) / ( intWS_green->readY(0)[0] + intWS_red->readY(0)[0] );
        double YIB = ( intWS_green->readY(1)[0] - intWS_red->readY(1)[0] ) / ( intWS_green->readY(1)[0] + intWS_red->readY(1)[0] );

        Y = YIB - YIF;

        double VARIF = (1.0 + YIF*YIF) / ( intWS_green->readY(0)[0] + intWS_red->readY(0)[0] );
        double VARIB = (1.0 + YIB*YIB) / ( intWS_green->readY(1)[0] + intWS_red->readY(1)[0] );

        E = sqrt( VARIF + VARIB );
    }

}
Beispiel #23
0
/**
*  Move the user selected spectra in the input workspace into groups in the output workspace
*  @param inputWS :: user selected input workspace for the algorithm
*  @param outputWS :: user selected output workspace for the algorithm
*  @param prog4Copy :: the amount of algorithm progress to attribute to moving a single spectra
*  @return number of new grouped spectra
*/
size_t GroupDetectors2::formGroups( API::MatrixWorkspace_const_sptr inputWS, API::MatrixWorkspace_sptr outputWS, 
            const double prog4Copy)
{
  // get "Behaviour" string
  const std::string behaviour = getProperty("Behaviour");
  int bhv = 0;
  if ( behaviour == "Average" ) bhv = 1;

  API::MatrixWorkspace_sptr beh = API::WorkspaceFactory::Instance().create(
    "Workspace2D", static_cast<int>(m_GroupSpecInds.size()), 1, 1);

  g_log.debug() << name() << ": Preparing to group spectra into " << m_GroupSpecInds.size() << " groups\n";

  // where we are copying spectra to, we start copying to the start of the output workspace
  size_t outIndex = 0;
  // Only used for averaging behaviour. We may have a 1:1 map where a Divide would be waste as it would be just dividing by 1
  bool requireDivide(false);
  for ( storage_map::const_iterator it = m_GroupSpecInds.begin(); it != m_GroupSpecInds.end() ; ++it )
  {
    // This is the grouped spectrum
    ISpectrum * outSpec = outputWS->getSpectrum(outIndex);

    // The spectrum number of the group is the key
    outSpec->setSpectrumNo(it->first);
    // Start fresh with no detector IDs
    outSpec->clearDetectorIDs();

    // Copy over X data from first spectrum, the bin boundaries for all spectra are assumed to be the same here
    outSpec->dataX() = inputWS->readX(0);

    // the Y values and errors from spectra being grouped are combined in the output spectrum
    // Keep track of number of detectors required for masking
    size_t nonMaskedSpectra(0);
    beh->dataX(outIndex)[0] = 0.0;
    beh->dataE(outIndex)[0] = 0.0;
    for( std::vector<size_t>::const_iterator wsIter = it->second.begin(); wsIter != it->second.end(); ++wsIter)
    {
      const size_t originalWI = *wsIter;

      // detectors to add to firstSpecNum
      const ISpectrum * fromSpectrum = inputWS->getSpectrum(originalWI);

      // Add up all the Y spectra and store the result in the first one
      // Need to keep the next 3 lines inside loop for now until ManagedWorkspace mru-list works properly
      MantidVec &firstY = outSpec->dataY();
      MantidVec::iterator fYit;
      MantidVec::iterator fEit = outSpec->dataE().begin();
      MantidVec::const_iterator Yit = fromSpectrum->dataY().begin();
      MantidVec::const_iterator Eit = fromSpectrum->dataE().begin();
      for (fYit = firstY.begin(); fYit != firstY.end(); ++fYit, ++fEit, ++Yit, ++Eit)
      {
        *fYit += *Yit;
        // Assume 'normal' (i.e. Gaussian) combination of errors
        *fEit = std::sqrt( (*fEit)*(*fEit) + (*Eit)*(*Eit) );
      }

      // detectors to add to the output spectrum
      outSpec->addDetectorIDs(fromSpectrum->getDetectorIDs() );
      try
      {
        Geometry::IDetector_const_sptr det = inputWS->getDetector(originalWI);
        if( !det->isMasked() ) ++nonMaskedSpectra;
      }
      catch(Exception::NotFoundError&)
      {
        // If a detector cannot be found, it cannot be masked
        ++nonMaskedSpectra;
      }
    }
    if( nonMaskedSpectra == 0 ) ++nonMaskedSpectra; // Avoid possible divide by zero
    if(!requireDivide) requireDivide = (nonMaskedSpectra > 1);
    beh->dataY(outIndex)[0] = static_cast<double>(nonMaskedSpectra);

    // make regular progress reports and check for cancelling the algorithm
    if ( outIndex % INTERVAL == 0 )
    {
      m_FracCompl += INTERVAL*prog4Copy;
      if ( m_FracCompl > 1.0 )
        m_FracCompl = 1.0;
      progress(m_FracCompl);
      interruption_point();
    }
    outIndex ++;
  }
  
  // Refresh the spectraDetectorMap
  outputWS->generateSpectraMap();

  if ( bhv == 1 && requireDivide )
  {
    g_log.debug() << "Running Divide algorithm to perform averaging.\n";
    Mantid::API::IAlgorithm_sptr divide = createChildAlgorithm("Divide");
    divide->initialize();
    divide->setProperty<API::MatrixWorkspace_sptr>("LHSWorkspace", outputWS);
    divide->setProperty<API::MatrixWorkspace_sptr>("RHSWorkspace", beh);
    divide->setProperty<API::MatrixWorkspace_sptr>("OutputWorkspace", outputWS);
    divide->execute();
  }

  g_log.debug() << name() << " created " << outIndex << " new grouped spectra\n";
  return outIndex;
}
Beispiel #24
0
    /**
     * Executes the algorithm
     */
    void ScaleX::exec()
    {
      //Get input workspace and offset
      const MatrixWorkspace_sptr inputW = getProperty("InputWorkspace");

      factor = getProperty("Factor");

      API::MatrixWorkspace_sptr outputW = createOutputWS(inputW);

      //Get number of histograms
      int histnumber = static_cast<int>(inputW->getNumberHistograms());

      m_progress = new API::Progress(this, 0.0, 1.0, histnumber+1);
      m_progress->report("Scaling X");

	    wi_min = 0;
      wi_max = histnumber-1;
      //check if workspace indexes have been set
      int tempwi_min = getProperty("IndexMin");
      int tempwi_max = getProperty("IndexMax");
      if ( tempwi_max != Mantid::EMPTY_INT() )
      {
        if ((wi_min <= tempwi_min) && (tempwi_min <= tempwi_max) && (tempwi_max <= wi_max))
        {
          wi_min = tempwi_min;
          wi_max = tempwi_max;
        }
        else
        {
          g_log.error("Invalid Workspace Index min/max properties");
          throw std::invalid_argument("Inconsistent properties defined");
        }
      }


      //Check if its an event workspace
      EventWorkspace_const_sptr eventWS = boost::dynamic_pointer_cast<const EventWorkspace>(inputW);
      if (eventWS != NULL)
      {
        this->execEvent();
        return;
      }

      // do the shift in X
      PARALLEL_FOR2(inputW, outputW)
      for (int i=0; i < histnumber; ++i)
      {
        PARALLEL_START_INTERUPT_REGION
        //Do the offsetting
        for (int j=0; j <  static_cast<int>(inputW->readX(i).size()); ++j)
        {
          //Change bin value by offset
          if ((i >= wi_min) && (i <= wi_max)) outputW->dataX(i)[j] = inputW->readX(i)[j] * factor;
          else outputW->dataX(i)[j] = inputW->readX(i)[j];
        }
        //Copy y and e data
        outputW->dataY(i) = inputW->dataY(i);
        outputW->dataE(i) = inputW->dataE(i);

        if( (i >= wi_min) && (i <= wi_max) && factor<0 )
        {
          std::reverse( outputW->dataX(i).begin(), outputW->dataX(i).end() );
          std::reverse( outputW->dataY(i).begin(), outputW->dataY(i).end() );
          std::reverse( outputW->dataE(i).begin(), outputW->dataE(i).end() );
        }

        m_progress->report("Scaling X");
        PARALLEL_END_INTERUPT_REGION
      }
      PARALLEL_CHECK_INTERUPT_REGION

      // Copy units
      if (outputW->getAxis(0)->unit().get())
          outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
      try
      {
          if (inputW->getAxis(1)->unit().get())
              outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
      }
      catch(Exception::IndexError &) {
          // OK, so this isn't a Workspace2D
      }

      // Assign it to the output workspace property
      setProperty("OutputWorkspace",outputW);
    }
Beispiel #25
0
/** Executes the regroup algorithm
 *
 *  @throw runtime_error Thrown if
 */
void Regroup::exec()
{
  // retrieve the properties
  std::vector<double> rb_params=getProperty("Params");

  // Get the input workspace
  MatrixWorkspace_const_sptr inputW = getProperty("InputWorkspace");

  // can work only if all histograms have the same boundaries
  if (!API::WorkspaceHelpers::commonBoundaries(inputW))
  {
    g_log.error("Histograms with different boundaries");
    throw std::runtime_error("Histograms with different boundaries");
  }

  bool dist = inputW->isDistribution();

  int histnumber = static_cast<int>(inputW->getNumberHistograms());
  MantidVecPtr XValues_new;
  const MantidVec & XValues_old = inputW->readX(0);
  std::vector<int> xoldIndex;// indeces of new x in XValues_old
  // create new output X axis
  int ntcnew = newAxis(rb_params,XValues_old,XValues_new.access(),xoldIndex);

  // make output Workspace the same type is the input, but with new length of signal array
  API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(inputW,histnumber,ntcnew,ntcnew-1);

  int progress_step = histnumber / 100;
  if (progress_step == 0) progress_step = 1;
  for (int hist=0; hist <  histnumber;hist++)
  {
    // get const references to input Workspace arrays (no copying)
    const MantidVec& XValues = inputW->readX(hist);
    const MantidVec& YValues = inputW->readY(hist);
    const MantidVec& YErrors = inputW->readE(hist);

    //get references to output workspace data (no copying)
    MantidVec& YValues_new=outputW->dataY(hist);
    MantidVec& YErrors_new=outputW->dataE(hist);

    // output data arrays are implicitly filled by function
    rebin(XValues,YValues,YErrors,xoldIndex,YValues_new,YErrors_new, dist);

    outputW->setX(hist,XValues_new);

    if (hist % progress_step == 0)
    {
        progress(double(hist)/histnumber);
        interruption_point();
    }
  }

  outputW->isDistribution(dist);

  // Copy units
  if (outputW->getAxis(0)->unit().get())
    outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
  try
  {
    if (inputW->getAxis(1)->unit().get())
      outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
   }
  catch(Exception::IndexError) {
    // OK, so this isn't a Workspace2D
  }

  // Assign it to the output workspace property
  setProperty("OutputWorkspace",outputW);

  return;
}
Beispiel #26
0
/**
 * 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
  // root of the power spectrum
  auto &powerSpec = fourierOut->dataY(2);
  // convert the modulus to power spectrum wich is the base of the Wiener filter
  std::transform(powerSpec.begin(), powerSpec.end(), powerSpec.begin(),
                 PowerSpectrum());

  // estimate power spectrum's noise as the average of its high frequency half
  size_t n2 = powerSpec.size();
  double noise =
      std::accumulate(powerSpec.begin() + n2 / 2, powerSpec.end(), 0.0);
  noise /= static_cast<double>(n2);

  // index of the maximum element in powerSpec
  const size_t imax = static_cast<size_t>(std::distance(
      powerSpec.begin(), std::max_element(powerSpec.begin(), powerSpec.end())));

  if (noise == 0.0) {
    noise = powerSpec[imax] / guessSignalToNoiseRatio;
  }

  g_log.debug() << "Maximum signal " << powerSpec[imax] << std::endl;
  g_log.debug() << "Noise          " << noise << std::endl;

  // storage for the Wiener filter, initialized with 0.0's
  std::vector<double> wf(n2);

  // The filter consists of two parts:
  //   1) low frequency region, from 0 until the power spectrum falls to the
  //   noise level, filter is calculated
  //      from the power spectrum
  //   2) high frequency noisy region, filter is a smooth function of frequency
  //   decreasing to 0

  // the following code is an adaptation of a fortran routine
  // noise starting index
  size_t i0 = 0;
  // intermediate variables
  double xx = 0.0;
  double xy = 0.0;
  double ym = 0.0;
  // low frequency filter values: the higher the power spectrum the closer the
  // filter to 1.0
  for (size_t i = 0; i < n2; ++i) {
    double cd1 = powerSpec[i] / noise;
    if (cd1 < 1.0 && i > imax) {
      i0 = i;
      break;
    }
    double cd2 = log(cd1);
    wf[i] = cd1 / (1.0 + cd1);
    double j = static_cast<double>(i + 1);
    xx += j * j;
    xy += j * cd2;
    ym += cd2;
  }

  // i0 should always be > 0 but in case something goes wrong make a check
  if (i0 > 0) {
    g_log.debug() << "Noise start index " << i0 << std::endl;

    // high frequency filter values: smooth decreasing function
    double ri0f = static_cast<double>(i0 + 1);
    double xm = (1.0 + ri0f) / 2;
    ym /= ri0f;
    double a1 = (xy - ri0f * xm * ym) / (xx - ri0f * xm * xm);
    double b1 = ym - a1 * xm;

    g_log.debug() << "(a1,b1) = (" << a1 << ',' << b1 << ')' << std::endl;

    const double dblev = -20.0;
    // cut-off index
    double ri1 = floor((dblev / 4 - b1) / a1);
    if (ri1 < static_cast<double>(i0)) {
      g_log.warning() << "Failed to build Wiener filter: no smoothing."
                      << std::endl;
      ri1 = static_cast<double>(i0);
    }
    size_t i1 = static_cast<size_t>(ri1);
    if (i1 > n2)
      i1 = n2;
    for (size_t i = i0; i < i1; ++i) {
      double s = exp(a1 * static_cast<double>(i + 1) + b1);
      wf[i] = s / (1.0 + s);
    }
    // wf[i] for i1 <= i < n2 are 0.0

    g_log.debug() << "Cut-off index " << i1 << std::endl;
  } else {
    g_log.warning() << "Power spectrum has an unexpected shape: no smoothing"
                    << std::endl;
    return copyInput(inputWS, wsIndex);
  }

  // multiply the fourier transform by the filter
  auto &re = fourierOut->dataY(0);
  auto &im = fourierOut->dataY(1);

  std::transform(re.begin(), re.end(), wf.begin(), re.begin(),
                 std::multiplies<double>());
  std::transform(im.begin(), im.end(), wf.begin(), im.begin(),
                 std::multiplies<double>());

  // inverse fourier transform
  fourier = createChildAlgorithm("RealFFT");
  fourier->initialize();
  fourier->setProperty("InputWorkspace", fourierOut);
  fourier->setProperty("IgnoreXBins", true);
  fourier->setPropertyValue("Transform", "Backward");
  fourier->execute();

  API::MatrixWorkspace_sptr out = fourier->getProperty("OutputWorkspace");
  auto &background = fitOut->readY(1);
  auto &y = out->dataY(0);

  if (y.size() != background.size()) {
    throw std::logic_error("Logic error: inconsistent arrays");
  }

  // add the spline "background" to the smoothed data
  std::transform(y.begin(), y.end(), background.begin(), y.begin(),
                 std::plus<double>());

  // copy the x-values and errors from the original spectrum
  // remove the last values for odd-sized inputs
  if (isOddSize) {
    out->dataX(0).assign(X.begin(), X.end() - 1);
    out->dataE(0).assign(E.begin(), E.end() - 1);
    out->dataY(0).resize(Y.size() - 1);
  } else {
    out->setX(0, X);
    out->dataE(0).assign(E.begin(), E.end());
  }

  return out;
}
Beispiel #27
0
		/** Executes the rebin algorithm
		*
		*  @throw runtime_error Thrown if
		*/
		void Rebunch::exec()
		{
			// retrieve the properties
			int n_bunch=getProperty("NBunch");

			// Get the input workspace
			MatrixWorkspace_const_sptr inputW = getProperty("InputWorkspace");

			bool dist = inputW->isDistribution();

			// workspace independent determination of length
                        int histnumber = static_cast<int>(inputW->size()/inputW->blocksize());

			/*
			const std::vector<double>& Xold = inputW->readX(0);
			const std::vector<double>& Yold = inputW->readY(0);
			int size_x=Xold.size();
			int size_y=Yold.size();
			*/
                        int size_x = static_cast<int>(inputW->readX(0).size());
                        int size_y = static_cast<int>(inputW->readY(0).size());

			//signal is the same length for histogram and point data
			int ny=(size_y/n_bunch);
			if(size_y%n_bunch >0)ny+=1;
			// default is for hist
			int nx=ny+1;
			bool point=false;
			if (size_x==size_y)
			{
				point=true;
				nx=ny;
			}

			// make output Workspace the same type is the input, but with new length of signal array
			API::MatrixWorkspace_sptr outputW = API::WorkspaceFactory::Instance().create(inputW,histnumber,nx,ny);

            int progress_step = histnumber / 100;
            if (progress_step == 0) progress_step = 1;
			PARALLEL_FOR2(inputW,outputW)
			for (int hist=0; hist <  histnumber;hist++)
			{
				PARALLEL_START_INTERUPT_REGION
				// Ensure that axis information are copied to the output workspace if the axis exists
			        try
				{
				  outputW->getAxis(1)->spectraNo(hist)=inputW->getAxis(1)->spectraNo(hist);
				}
				catch( Exception::IndexError& )
				{ 
				  // Not a Workspace2D
				}

				// get const references to input Workspace arrays (no copying)
				const MantidVec& XValues = inputW->readX(hist);
				const MantidVec& YValues = inputW->readY(hist);
				const MantidVec& YErrors = inputW->readE(hist);

				//get references to output workspace data (no copying)
				MantidVec& XValues_new=outputW->dataX(hist);
				MantidVec& YValues_new=outputW->dataY(hist);
				MantidVec& YErrors_new=outputW->dataE(hist);

				// output data arrays are implicitly filled by function
				if(point)
				{
					rebunch_point(XValues,YValues,YErrors,XValues_new,YValues_new,YErrors_new,n_bunch);
				}
				else
				{
					rebunch_hist(XValues,YValues,YErrors,XValues_new,YValues_new,YErrors_new,n_bunch, dist);
				}

				if (hist % progress_step == 0)
				{
				  progress(double(hist)/histnumber);
				  interruption_point();
				}
				PARALLEL_END_INTERUPT_REGION
			}
			PARALLEL_CHECK_INTERUPT_REGION
			outputW->isDistribution(dist);

			// Copy units
			if (outputW->getAxis(0)->unit().get())
			  outputW->getAxis(0)->unit() = inputW->getAxis(0)->unit();
			try
			{
			  if (inputW->getAxis(1)->unit().get())
			    outputW->getAxis(1)->unit() = inputW->getAxis(1)->unit();
			}
			catch(Exception::IndexError&) {
			  // OK, so this isn't a Workspace2D
			}

			// Assign it to the output workspace property
			setProperty("OutputWorkspace",outputW);

			return;
		}
Beispiel #28
0
/** Executes the algorithm
 *
 */
void SplineBackground::exec()
{

  API::MatrixWorkspace_sptr inWS = getProperty("InputWorkspace");
  int spec = getProperty("WorkspaceIndex");

  if (spec > static_cast<int>(inWS->getNumberHistograms()))
    throw std::out_of_range("WorkspaceIndex is out of range.");

  const MantidVec& X = inWS->readX(spec);
  const MantidVec& Y = inWS->readY(spec);
  const MantidVec& E = inWS->readE(spec);
  const bool isHistogram = inWS->isHistogramData();

  const int ncoeffs = getProperty("NCoeff");
  const int k = 4; // order of the spline + 1 (cubic)
  const int nbreak = ncoeffs - (k - 2);

  if (nbreak <= 0)
    throw std::out_of_range("Too low NCoeff");

  gsl_bspline_workspace *bw;
  gsl_vector *B;

  gsl_vector *c, *w, *x, *y;
  gsl_matrix *Z, *cov;
  gsl_multifit_linear_workspace *mw;
  double chisq;

  int n = static_cast<int>(Y.size());
  bool isMasked = inWS->hasMaskedBins(spec);
  std::vector<int> masked(Y.size());
  if (isMasked)
  {
    for(API::MatrixWorkspace::MaskList::const_iterator it=inWS->maskedBins(spec).begin();it!=inWS->maskedBins(spec).end();++it)
      masked[it->first] = 1;
    n -= static_cast<int>(inWS->maskedBins(spec).size());
  }

  if (n < ncoeffs)
  {
    g_log.error("Too many basis functions (NCoeff)");
    throw std::out_of_range("Too many basis functions (NCoeff)");
  }

  /* allocate a cubic bspline workspace (k = 4) */
  bw = gsl_bspline_alloc(k, nbreak);
  B = gsl_vector_alloc(ncoeffs);

  x = gsl_vector_alloc(n);
  y = gsl_vector_alloc(n);
  Z = gsl_matrix_alloc(n, ncoeffs);
  c = gsl_vector_alloc(ncoeffs);
  w = gsl_vector_alloc(n);
  cov = gsl_matrix_alloc(ncoeffs, ncoeffs);
  mw = gsl_multifit_linear_alloc(n, ncoeffs);

  /* this is the data to be fitted */
  int j = 0;
  for (MantidVec::size_type i = 0; i < Y.size(); ++i)
  {
    if (isMasked && masked[i]) continue;
    gsl_vector_set(x, j, (isHistogram ? (0.5*(X[i]+X[i+1])) : X[i])); // Middle of the bins, if a histogram
    gsl_vector_set(y, j, Y[i]);
    gsl_vector_set(w, j, E[i]>0.?1./(E[i]*E[i]):0.);

    ++j;
  }

  if (n != j)
  {
    gsl_bspline_free(bw);
    gsl_vector_free(B);
    gsl_vector_free(x);
    gsl_vector_free(y);
    gsl_matrix_free(Z);
    gsl_vector_free(c);
    gsl_vector_free(w);
    gsl_matrix_free(cov);
    gsl_multifit_linear_free(mw);

    throw std::runtime_error("Assertion failed: n != j");
  }

  double xStart = X.front();
  double xEnd =   X.back();

  /* use uniform breakpoints */
  gsl_bspline_knots_uniform(xStart, xEnd, bw);

  /* construct the fit matrix X */
  for (int i = 0; i < n; ++i)
  {
    double xi=gsl_vector_get(x, i);

    /* compute B_j(xi) for all j */
    gsl_bspline_eval(xi, B, bw);

    /* fill in row i of X */
    for (j = 0; j < ncoeffs; ++j)
    {
      double Bj = gsl_vector_get(B, j);
      gsl_matrix_set(Z, i, j, Bj);
    }
  }

  /* do the fit */
  gsl_multifit_wlinear(Z, w, y, c, cov, &chisq, mw);

  /* output the smoothed curve */
  API::MatrixWorkspace_sptr outWS = WorkspaceFactory::Instance().create(inWS,1,X.size(),Y.size());
  {
    outWS->getAxis(1)->setValue(0, inWS->getAxis(1)->spectraNo(spec));
    double xi, yi, yerr;
    for (MantidVec::size_type i=0;i<Y.size();i++)
    {
      xi = X[i];
      gsl_bspline_eval(xi, B, bw);
      gsl_multifit_linear_est(B, c, cov, &yi, &yerr);
      outWS->dataY(0)[i] = yi;
      outWS->dataE(0)[i] = yerr;
    }
    outWS->dataX(0) = X;
  }

  gsl_bspline_free(bw);
  gsl_vector_free(B);
  gsl_vector_free(x);
  gsl_vector_free(y);
  gsl_matrix_free(Z);
  gsl_vector_free(c);
  gsl_vector_free(w);
  gsl_matrix_free(cov);
  gsl_multifit_linear_free(mw);

  setProperty("OutputWorkspace",outWS);

}
Beispiel #29
0
/** Carries out the bin-by-bin normalisation
 *  @param inputWorkspace The input workspace
 *  @param outputWorkspace The result workspace
 */
void NormaliseToMonitor::normaliseBinByBin(API::MatrixWorkspace_sptr inputWorkspace,
                                           API::MatrixWorkspace_sptr& outputWorkspace)
{ 
  EventWorkspace_sptr inputEvent = boost::dynamic_pointer_cast<EventWorkspace>(inputWorkspace);
  EventWorkspace_sptr outputEvent;

  // Only create output workspace if different to input one
  if (outputWorkspace != inputWorkspace )
  {
    if (inputEvent)
    {
      //Make a brand new EventWorkspace
      outputEvent = boost::dynamic_pointer_cast<EventWorkspace>(
          API::WorkspaceFactory::Instance().create("EventWorkspace", inputEvent->getNumberHistograms(), 2, 1));
      //Copy geometry and data
      API::WorkspaceFactory::Instance().initializeFromParent(inputEvent, outputEvent, false);
      outputEvent->copyDataFrom( (*inputEvent) );
      outputWorkspace = boost::dynamic_pointer_cast<MatrixWorkspace>(outputEvent);
    }
    else
      outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace);
  }

  // Get hold of the monitor spectrum
  const MantidVec& monX = m_monitor->readX(0);
  MantidVec& monY = m_monitor->dataY(0);
  MantidVec& monE = m_monitor->dataE(0);
  // Calculate the overall normalisation just the once if bins are all matching
  if (m_commonBins) this->normalisationFactor(m_monitor->readX(0),&monY,&monE);


  const size_t numHists = inputWorkspace->getNumberHistograms();
  MantidVec::size_type specLength = inputWorkspace->blocksize();
  Progress prog(this,0.0,1.0,numHists);
  // Loop over spectra
  PARALLEL_FOR3(inputWorkspace,outputWorkspace,m_monitor)
  for (int64_t i = 0; i < int64_t(numHists); ++i)
  {
    PARALLEL_START_INTERUPT_REGION
    prog.report();

    const MantidVec& X = inputWorkspace->readX(i);
    // If not rebinning, just point to our monitor spectra, otherwise create new vectors
    MantidVec* Y = ( m_commonBins ? &monY : new MantidVec(specLength) );
    MantidVec* E = ( m_commonBins ? &monE : new MantidVec(specLength) );

    if (!m_commonBins)
    {
      // ConvertUnits can give X vectors of all zeroes - skip these, they cause problems
      if (X.back() == 0.0 && X.front() == 0.0) continue;
      // Rebin the monitor spectrum to match the binning of the current data spectrum
      VectorHelper::rebinHistogram(monX,monY,monE,X,*Y,*E,false);
      // Recalculate the overall normalisation factor
      this->normalisationFactor(X,Y,E);
    }

    if (inputEvent)
    {
      // ----------------------------------- EventWorkspace ---------------------------------------
      EventList & outEL = outputEvent->getEventList(i);
      outEL.divide(X, *Y, *E);
    }
    else
    {
      // ----------------------------------- Workspace2D ---------------------------------------
      const MantidVec& inY = inputWorkspace->readY(i);
      const MantidVec& inE = inputWorkspace->readE(i);
      MantidVec& YOut = outputWorkspace->dataY(i);
      MantidVec& EOut = outputWorkspace->dataE(i);
      outputWorkspace->dataX(i) = inputWorkspace->readX(i);
      // The code below comes more or less straight out of Divide.cpp
      for (MantidVec::size_type k = 0; k < specLength; ++k)
      {
        // Get references to the input Y's
        const double& leftY = inY[k];
        const double& rightY = (*Y)[k];

        // Calculate result and store in local variable to avoid overwriting original data if
        // output workspace is same as one of the input ones
        const double newY = leftY/rightY;

        if (fabs(rightY)>1.0e-12 && fabs(newY)>1.0e-12)
        {
          const double lhsFactor = (inE[k]<1.0e-12|| fabs(leftY)<1.0e-12) ? 0.0 : pow((inE[k]/leftY),2);
          const double rhsFactor = (*E)[k]<1.0e-12 ? 0.0 : pow(((*E)[k]/rightY),2);
          EOut[k] = std::abs(newY) * sqrt(lhsFactor+rhsFactor);
        }

        // Now store the result
        YOut[k] = newY;
      } // end Workspace2D case
    } // end loop over current spectrum

    if (!m_commonBins) { delete Y; delete E; }
    PARALLEL_END_INTERUPT_REGION
  } // end loop over spectra
  PARALLEL_CHECK_INTERUPT_REGION
}
/** Executes the algorithm
 *  @throw Exception::FileError If the calibration file cannot be opened and
 * read successfully
 *  @throw Exception::InstrumentDefinitionError If unable to obtain the
 * source-sample distance
 */
void AlignDetectors::exec() {
  // Get the input workspace
  MatrixWorkspace_sptr inputWS = getProperty("InputWorkspace");

  this->getCalibrationWS(inputWS);

  // Initialise the progress reporting object
  m_numberOfSpectra = static_cast<int64_t>(inputWS->getNumberHistograms());

  // Check if its an event workspace
  EventWorkspace_const_sptr eventW =
      boost::dynamic_pointer_cast<const EventWorkspace>(inputWS);
  if (eventW != nullptr) {
    this->execEvent();
    return;
  }

  API::MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
  // If input and output workspaces are not the same, create a new workspace for
  // the output
  if (outputWS != inputWS) {
    outputWS = WorkspaceFactory::Instance().create(inputWS);
    setProperty("OutputWorkspace", outputWS);
  }

  // Set the final unit that our output workspace will have
  setXAxisUnits(outputWS);

  ConversionFactors converter = ConversionFactors(m_calibrationWS);

  Progress progress(this, 0.0, 1.0, m_numberOfSpectra);

  // Loop over the histograms (detector spectra)
  PARALLEL_FOR2(inputWS, outputWS)
  for (int64_t i = 0; i < m_numberOfSpectra; ++i) {
    PARALLEL_START_INTERUPT_REGION
    try {
      // Get the input spectrum number at this workspace index
      auto inSpec = inputWS->getSpectrum(size_t(i));
      auto toDspacing = converter.getConversionFunc(inSpec->getDetectorIDs());

      // Get references to the x data
      MantidVec &xOut = outputWS->dataX(i);

      // Make sure reference to input X vector is obtained after output one
      // because in the case
      // where the input & output workspaces are the same, it might move if the
      // vectors were shared.
      const MantidVec &xIn = inSpec->readX();

      std::transform(xIn.begin(), xIn.end(), xOut.begin(), toDspacing);

      // Copy the Y&E data
      outputWS->dataY(i) = inSpec->readY();
      outputWS->dataE(i) = inSpec->readE();

    } catch (Exception::NotFoundError &) {
      // Zero the data in this case
      outputWS->dataX(i).assign(outputWS->readX(i).size(), 0.0);
      outputWS->dataY(i).assign(outputWS->readY(i).size(), 0.0);
      outputWS->dataE(i).assign(outputWS->readE(i).size(), 0.0);
    }
    progress.report();
    PARALLEL_END_INTERUPT_REGION
  }
  PARALLEL_CHECK_INTERUPT_REGION
}