Scalar AdaptiveSparseGrid<Scalar,UserVector>::refine_grid(
   	         typename std::multimap<Scalar,std::vector<int> >  & indexSet, 
		 UserVector & integralValue,
		 AdaptiveSparseGridInterface<Scalar,UserVector> & problem_data) {
  
  int dimension = problem_data.getDimension();
  std::vector<EIntrepidBurkardt> rule1D; problem_data.getRule(rule1D);
  std::vector<EIntrepidGrowth> growth1D; problem_data.getGrowth(growth1D);
  
  // Copy Multimap into a Set for ease of use
  typename std::multimap<Scalar,std::vector<int> >::iterator it;
  std::set<std::vector<int> > oldSet;
  std::set<std::vector<int> >::iterator it1(oldSet.begin());
  for (it=indexSet.begin(); it!=indexSet.end(); it++) {
    oldSet.insert(it1,it->second);
    it1++;
  }
  indexSet.clear();
  
  // Find Possible Active Points
  int flag = 1;
  std::vector<int> index(dimension,0);
  typename std::multimap<Scalar,std::vector<int> > activeSet;
  for (it1=oldSet.begin(); it1!=oldSet.end(); it1++) {
    index = *it1;
    for (int i=0; i<dimension; i++) {
      index[i]++;
      flag = (int)(!oldSet.count(index));
      index[i]--;
      if (flag) {
	activeSet.insert(std::pair<Scalar,std::vector<int> >(1.0,index));
	oldSet.erase(it1);
	break;
      }
    }
  }

  // Compute local and global error indicators for active set
  typename std::multimap<Scalar,std::vector<int> >::iterator it2;
  Scalar eta = 0.0;
  Scalar G   = 0.0;
  Teuchos::RCP<UserVector> s = integralValue.Create();
  for (it2=activeSet.begin(); it2!=activeSet.end(); it2++) {
    // Build Differential Quarature Rule
    index = it2->second;
    CubatureTensorSorted<Scalar> diffRule(0,dimension);
    build_diffRule(diffRule,index,problem_data);
    
    // Apply Rule to function
    problem_data.eval_cubature(*s,diffRule);
    
    // Update local error indicator and index set
    G  = problem_data.error_indicator(*s);
    activeSet.erase(it2);
    activeSet.insert(it2,std::pair<Scalar,std::vector<int> >(G,index));
    eta += G;
  }

  // Refine Sparse Grid
  eta = refine_grid(activeSet,oldSet,integralValue,eta,
		    dimension,rule1D,growth1D);

  // Insert New Active and Old Index sets into indexSet
  indexSet.insert(activeSet.begin(),activeSet.end());
  for (it1=oldSet.begin(); it1!=oldSet.end(); it1++) {
    index = *it1;
    indexSet.insert(std::pair<Scalar,std::vector<int> >(-1.0,index));
  }
    
  return eta;
}
Scalar AdaptiveSparseGrid<Scalar,UserVector>::refine_grid(
	typename std::multimap<Scalar,std::vector<int> > & activeIndex, 
	std::set<std::vector<int> > & oldIndex, 
	UserVector & integralValue,
	CubatureTensorSorted<Scalar> & cubRule,
	Scalar globalErrorIndicator,
	AdaptiveSparseGridInterface<Scalar,UserVector> & problem_data) {

  TEUCHOS_TEST_FOR_EXCEPTION((activeIndex.empty()),std::out_of_range,
              ">>> ERROR (AdaptiveSparseGrid): Active Index set is empty.");  

  int dimension = problem_data.getDimension();
  std::vector<EIntrepidBurkardt> rule1D; problem_data.getRule(rule1D);
  std::vector<EIntrepidGrowth> growth1D; problem_data.getGrowth(growth1D);

  // Initialize Flags
  bool maxLevelFlag     = true;
  bool isAdmissibleFlag = true;

  // Initialize Cubature Rule
  Teuchos::RCP<UserVector> s = integralValue.Create();

  // Initialize iterator at end of inOldIndex
  std::set<std::vector<int> >::iterator it1(oldIndex.end());  

  // Initialize iterator at end of inActiveIndex
  typename std::multimap<Scalar,std::vector<int> >::iterator it;

  // Obtain Global Error Indicator as sum of key values of inActiveIndex
  Scalar eta = globalErrorIndicator;

  // Select Index to refine
  it = activeIndex.end(); it--;        // Decrement to position of final value 
  Scalar G               = it->first;  // Largest Error Indicator is at End
  eta                   -= G;          // Update global error indicator
  std::vector<int> index = it->second; // Get Corresponding index
  activeIndex.erase(it);               // Erase Index from active index set
  // Insert Index into old index set
  oldIndex.insert(it1,index); it1 = oldIndex.end(); 
  
  // Refinement process
  for (int k=0; k<dimension; k++) {
    index[k]++; // index + ek
    // Check Max Level
    maxLevelFlag = problem_data.max_level(index);
    if (maxLevelFlag) {
      // Check Admissibility
      isAdmissibleFlag = isAdmissible(index,k,oldIndex,problem_data);
      if (isAdmissibleFlag) { // If admissible
	// Build Differential Quarature Rule
	CubatureTensorSorted<Scalar> diffRule(0,dimension);
	build_diffRule(diffRule,index,problem_data);
	
	// Apply Rule to function
	problem_data.eval_cubature(*s,diffRule);
	
	// Update integral value
	integralValue.Update(*s);
	
	// Update local error indicator and index set
	G  = problem_data.error_indicator(*s); 	
	if (activeIndex.end()!=activeIndex.begin()) 
	  activeIndex.insert(activeIndex.end()--,
			   std::pair<Scalar,std::vector<int> >(G,index));
	else
	  activeIndex.insert(std::pair<Scalar,std::vector<int> >(G,index));
		
	// Update global error indicators
	eta += G;

	// Update adapted quadrature rule nodes and weights
	cubRule.update(1.0,diffRule,1.0);
      }
    }
    else { // Max Level Exceeded 
      //std::cout << "Maximum Level Exceeded" << std::endl;
    }
    index[k]--;
  }
  return eta;
}
Esempio n. 3
0
  void FindPeaksMD::findPeaks(typename MDEventWorkspace<MDE, nd>::sptr ws)
  {
    if (nd < 3)
      throw std::invalid_argument("Workspace must have at least 3 dimensions.");

    progress(0.01, "Refreshing Centroids");

    // TODO: This might be slow, progress report?
    // Make sure all centroids are fresh
    ws->getBox()->refreshCentroid();

    typedef IMDBox<MDE,nd>* boxPtr;

    if (ws->getNumExperimentInfo() == 0)
      throw std::runtime_error("No instrument was found in the MDEventWorkspace. Cannot find peaks.");

    // TODO: Do we need to pick a different instrument info?
    ExperimentInfo_sptr ei = ws->getExperimentInfo(0);
    // Instrument associated with workspace
    Geometry::Instrument_const_sptr inst = ei->getInstrument();
    // Find the run number
    int runNumber = ei->getRunNumber();

    // Check that the workspace dimensions are in Q-sample-frame or Q-lab-frame.
    eDimensionType dimType;

    std::string dim0 = ws->getDimension(0)->getName();
    if (dim0 == "H")
    {
      dimType = HKL;
      throw std::runtime_error("Cannot find peaks in a workspace that is already in HKL space.");
    }
    else if (dim0 == "Q_lab_x")
    {
      dimType = QLAB;
    }
    else if (dim0 == "Q_sample_x")
      dimType = QSAMPLE;
    else
      throw std::runtime_error("Unexpected dimensions: need either Q_lab_x or Q_sample_x.");

    // Find the goniometer rotation matrix
    Mantid::Kernel::Matrix<double> goniometer(3,3, true); // Default IDENTITY matrix
    try
    {
      goniometer = ei->mutableRun().getGoniometerMatrix();
    }
    catch (std::exception & e)
    {
      g_log.warning() << "Error finding goniometer matrix. It will not be set in the peaks found." << std::endl;
      g_log.warning() << e.what() << std::endl;
    }

    /// Arbitrary scaling factor for density to make more manageable numbers, especially for older file formats.
    signal_t densityScalingFactor = 1e-6;

    // Calculate a threshold below which a box is too diffuse to be considered a peak.
    signal_t thresholdDensity = 0.0;
    thresholdDensity = ws->getBox()->getSignalNormalized() * DensityThresholdFactor * densityScalingFactor;
    g_log.notice() << "Threshold signal density: " << thresholdDensity << std::endl;

    // We will fill this vector with pointers to all the boxes (up to a given depth)
    typename std::vector<boxPtr> boxes;

    // Get all the MDboxes
    progress(0.10, "Getting Boxes");
    ws->getBox()->getBoxes(boxes, 1000, true);



    // TODO: Here keep only the boxes > e.g. 3 * mean.
    typedef std::pair<double, boxPtr> dens_box;

    // Map that will sort the boxes by increasing density. The key = density; value = box *.
    typename std::multimap<double, boxPtr> sortedBoxes;

    progress(0.20, "Sorting Boxes by Density");
    typename std::vector<boxPtr>::iterator it1;
    typename std::vector<boxPtr>::iterator it1_end = boxes.end();
    for (it1 = boxes.begin(); it1 != it1_end; it1++)
    {
      boxPtr box = *it1;
      double density = box->getSignalNormalized() * densityScalingFactor;
      // Skip any boxes with too small a signal density.
      if (density > thresholdDensity)
        sortedBoxes.insert(dens_box(density,box));
    }

    // List of chosen possible peak boxes.
    std::vector<boxPtr> peakBoxes;

    prog = new Progress(this, 0.30, 0.95, MaxPeaks);

    int64_t numBoxesFound = 0;
    // Now we go (backwards) through the map
    // e.g. from highest density down to lowest density.
    typename std::multimap<double, boxPtr>::reverse_iterator it2;
    typename std::multimap<double, boxPtr>::reverse_iterator it2_end = sortedBoxes.rend();
    for (it2 = sortedBoxes.rbegin(); it2 != it2_end; it2++)
    {
      signal_t density = it2->first;
      boxPtr box = it2->second;
#ifndef MDBOX_TRACK_CENTROID
      coord_t boxCenter[nd];
      box->calculateCentroid(boxCenter);
#else
      const coord_t * boxCenter = box->getCentroid();
#endif

      // Compare to all boxes already picked.
      bool badBox = false;
      for (typename std::vector<boxPtr>::iterator it3=peakBoxes.begin(); it3 != peakBoxes.end(); it3++)
      {

#ifndef MDBOX_TRACK_CENTROID
        coord_t otherCenter[nd];
        (*it3)->calculateCentroid(otherCenter);
#else
        const coord_t * otherCenter = (*it3)->getCentroid();
#endif

        // Distance between this box and a box we already put in.
        coord_t distSquared = 0.0;
        for (size_t d=0; d<nd; d++)
        {
          coord_t dist = otherCenter[d] - boxCenter[d];
          distSquared += (dist * dist);
        }

        // Reject this box if it is too close to another previously found box.
        if (distSquared < peakRadiusSquared)
        {
          badBox = true;
          break;
        }
      }

      // The box was not rejected for another reason.
      if (!badBox)
      {
        if (numBoxesFound++ >= MaxPeaks)
        {
          g_log.notice() << "Number of peaks found exceeded the limit of " << MaxPeaks << ". Stopping peak finding." << std::endl;
          break;
        }

        peakBoxes.push_back(box);
        g_log.information() << "Found box at ";
        for (size_t d=0; d<nd; d++)
          g_log.information() << (d>0?",":"") << boxCenter[d];
        g_log.information() << "; Density = " << density << std::endl;
        // Report progres for each box found.
        prog->report("Finding Peaks");
      }
    }

    prog->resetNumSteps(numBoxesFound, 0.95, 1.0);

    // Copy the instrument, sample, run to the peaks workspace.
    peakWS->copyExperimentInfoFrom(ei.get());

    // --- Convert the "boxes" to peaks ----
    for (typename std::vector<boxPtr>::iterator it3=peakBoxes.begin(); it3 != peakBoxes.end(); it3++)
    {
      // The center of the box = Q in the lab frame
      boxPtr box = *it3;
#ifndef MDBOX_TRACK_CENTROID
      coord_t boxCenter[nd];
      box->calculateCentroid(boxCenter);
#else
      const coord_t * boxCenter = box->getCentroid();
#endif

      V3D Q(boxCenter[0], boxCenter[1], boxCenter[2]);

      // Create a peak and add it
      // Empty starting peak.
      Peak p;
      try
      {
        if (dimType == QLAB)
        {
          // Build using the Q-lab-frame constructor
          p = Peak(inst, Q);
          // Save gonio matrix for later
          p.setGoniometerMatrix(goniometer);
        }
        else if (dimType == QSAMPLE)
        {
          // Build using the Q-sample-frame constructor
          p = Peak(inst, Q, goniometer);
        }
      }
      catch (std::exception &e)
      {
        g_log.notice() << "Error creating peak at " << Q << " because of '" << e.what() << "'. Peak will be skipped." << std::endl;
        continue;
      }

      try
      { // Look for a detector
        p.findDetector();
      }
      catch (...)
      { /* Ignore errors in ray-tracer TODO: Handle for WISH data later */ }

      // The "bin count" used will be the box density.
      p.setBinCount( box->getSignalNormalized() * densityScalingFactor);

      // Save the run number found before.
      p.setRunNumber(runNumber);

      peakWS->addPeak(p);

      // Report progres for each box found.
      prog->report("Adding Peaks");

    } // for each box found

  }