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//============================================================================
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//============================================================================

//  This code is based on the algorithm presented in the paper:
//  “Parallel Peak Pruning for Scalable SMP Contour Tree Computation.”
//  Hamish Carr, Gunther Weber, Christopher Sewell, and James Ahrens.
//  Proceedings of the IEEE Symposium on Large Data Analysis and Visualization
//  (LDAV), October 2016, Baltimore, Maryland.

//=======================================================================================
//
// COMMENTS:
//
//	If we have computed the merge max & merge saddles correctly, we have substantially
//	computed the merge tree already. However, it is not in the same format as we have
//	previously represented it - in particular, we have yet to define all the merge arcs
//      and the superarcs we have collected are not the same as before - i.e. they are already
//	partially collapsed, but not according to the same rule as branch decomposition
//	This unit is therefore to get the same result out as before so we can set up an
//	automated crosscheck on the computation
//
//	Compared to earlier versions, we have made a significant change - the merge tree
// 	is only computed on critical points, not on the full array.  We therefore have a
//	final step: to extend it to the full array. To do this, we will keep the initial
//	mergeArcs array which records a maximum for each vertex, as we need the information
//
//	Each maximum is now labelled with the saddle it is mapped to, or to the global min
//	We therefore transfer this information back to the mergeArcs array, so that maxima
//	(including saddles) are marked with the (lower) vertex that is the low end of their
//	arc

//	BIG CHANGE: we can actually reuse the mergeArcs array for the final merge arc, for the
//	chain maximum for each (regular) point, and for the merge saddle for maxima.  This is
//	slightly tricky and has some extra memory traffic, but it avoids duplicating arrays
//	unnecessarily
//
//	Initially, mergeArcs will be set to an outbound neighbour (or self for extrema), as the
//	chainMaximum array used to be.
//
//	After chains are built, then it will hold *AN* accessible extremum for each vertex.
//
//	During the main processing, when an extremum is assigned a saddle, it will be stored
//	here. Regular points will still store pointers to an extremum.
//
//	After this is done, if the mergeArc points lower/higher, it is pointing to a saddle.
//      Otherwise it is pointing to an extremum.
//
//	And after the final pass, it will always point to the next along superarcs.
//
//=======================================================================================

#ifndef vtkm_worklet_contourtree_mergetree_h
#define vtkm_worklet_contourtree_mergetree_h

#include <vtkm/cont/ArrayHandle.h>
#include <vtkm/cont/ArrayHandleConstant.h>
#include <vtkm/cont/DataSet.h>

#include <vtkm/worklet/contourtree/ChainDoubler.h>
#include <vtkm/worklet/contourtree/JoinArcConnector.h>
#include <vtkm/worklet/contourtree/JoinSuperArcFinder.h>
#include <vtkm/worklet/contourtree/PrintVectors.h>
#include <vtkm/worklet/contourtree/VertexMergeComparator.h>

//#define DEBUG_PRINT 1
//#define DEBUG_FUNCTION_ENTRY 1
//#define DEBUG_TIMING 1

namespace vtkm
{
namespace worklet
{
namespace contourtree
{

template <typename T, typename StorageType, typename DeviceAdapter>
class MergeTree
{
public:
  using DeviceAlgorithm = typename vtkm::cont::DeviceAdapterAlgorithm<DeviceAdapter>;

  // original data array
  const vtkm::cont::ArrayHandle<T, StorageType>& values;

  // size of mesh
  vtkm::Id nRows, nCols, nSlices, nVertices, nLogSteps;

  // whether it is join or split tree
  bool isJoinTree;

  // vector of arcs representing the merge tree
  vtkm::cont::ArrayHandle<vtkm::Id> mergeArcs;

  // vector storing an extremum for each vertex
  vtkm::cont::ArrayHandle<vtkm::Id> extrema;

  // vector storing a saddle for each vertex
  vtkm::cont::ArrayHandle<vtkm::Id> saddles;

  // merge tree constructor
  MergeTree(const vtkm::cont::ArrayHandle<T, StorageType>& Values,
            vtkm::Id NRows,
            vtkm::Id NCols,
            vtkm::Id NSlices,
            bool IsJoinTree);

  // routine that does pointer-doubling in the mergeArc array
  void BuildRegularChains();

  // routine that computes the augmented merge tree superarcs from the merge graph
  void ComputeAugmentedSuperarcs();

  // routine that computes the augmented merge arcs from the superarcs
  // this is separate from the previous routine because it also gets called separately
  // once saddle & extrema are set for a given set of vertices, the merge arcs can be
  // computed for any subset of those vertices that contains all of the critical points
  void ComputeAugmentedArcs(vtkm::cont::ArrayHandle<vtkm::Id>& vertices);

  // debug routine
  void DebugPrint(const char* message);
};

// creates merge tree
template <typename T, typename StorageType, typename DeviceAdapter>
MergeTree<T, StorageType, DeviceAdapter>::MergeTree(
  const vtkm::cont::ArrayHandle<T, StorageType>& Values,
  vtkm::Id NRows,
  vtkm::Id NCols,
  vtkm::Id NSlices,
  bool IsJoinTree)
  : values(Values)
  , nRows(NRows)
  , nCols(NCols)
  , nSlices(NSlices)
  , isJoinTree(IsJoinTree)
{
  nVertices = nRows * nCols * nSlices;
  nLogSteps = 1;
  for (vtkm::Id shifter = nVertices; shifter != 0; shifter >>= 1)
    nLogSteps++;

  vtkm::cont::ArrayHandleConstant<vtkm::Id> nullArray(0, nVertices);

  mergeArcs.Allocate(nVertices);
  extrema.Allocate(nVertices);
  saddles.Allocate(nVertices);

  DeviceAlgorithm::Copy(nullArray, mergeArcs);
  DeviceAlgorithm::Copy(nullArray, extrema);
  DeviceAlgorithm::Copy(nullArray, saddles);
}

// routine that does pointer-doubling in the saddles array
template <typename T, typename StorageType, typename DeviceAdapter>
void MergeTree<T, StorageType, DeviceAdapter>::BuildRegularChains()
{
#ifdef DEBUG_FUNCTION_ENTRY
  std::cout << std::endl;
  std::cout << "====================" << std::endl;
  std::cout << "Build Regular Chains" << std::endl;
  std::cout << "====================" << std::endl;
  std::cout << std::endl;
#endif
  // 2. Create a temporary array so that we can alternate writing between them
  vtkm::cont::ArrayHandle<vtkm::Id> temporaryArcs;
  temporaryArcs.Allocate(nVertices);

  vtkm::cont::ArrayHandleIndex vertexIndexArray(nVertices);
  ChainDoubler chainDoubler;
  vtkm::worklet::DispatcherMapField<ChainDoubler> chainDoublerDispatcher(chainDoubler);
  chainDoublerDispatcher.SetDevice(DeviceAdapter());

  // 3. Apply pointer-doubling to build chains to maxima, rocking between two arrays
  for (vtkm::Id logStep = 0; logStep < nLogSteps; logStep++)
  {
    chainDoublerDispatcher.Invoke(vertexIndexArray, // input
                                  extrema);         // i/o whole array
  }
} // BuildRegularChains()

// routine that computes the augmented merge tree from the merge graph
template <typename T, typename StorageType, typename DeviceAdapter>
void MergeTree<T, StorageType, DeviceAdapter>::ComputeAugmentedSuperarcs()
{
#ifdef DEBUG_FUNCTION_ENTRY
  std::cout << std::endl;
  std::cout << "=================================" << std::endl;
  std::cout << "Compute Augmented Merge Superarcs" << std::endl;
  std::cout << "=================================" << std::endl;
  std::cout << std::endl;
#endif

  // our first step is to assign every vertex to a pseudo-extremum based on how the
  // vertex ascends to a extremum, and the sequence of pruning for the extremum
  // to do this, we iterate as many times as pruning occurred

  // we run a little loop for each element until it finds its join superarc
  // expressed as a functor.
  vtkm::Id nExtrema = extrema.GetNumberOfValues();

  JoinSuperArcFinder<T> joinSuperArcFinder(isJoinTree);
  vtkm::worklet::DispatcherMapField<JoinSuperArcFinder<T>> joinSuperArcFinderDispatcher(
    joinSuperArcFinder);
  joinSuperArcFinderDispatcher.SetDevice(DeviceAdapter());
  vtkm::cont::ArrayHandleIndex vertexIndexArray(nExtrema);

  joinSuperArcFinderDispatcher.Invoke(vertexIndexArray, // input
                                      values,           // input (whole array)
                                      saddles,          // i/o (whole array)
                                      extrema);         // i/o (whole array)

// at the end of this, all vertices should have a pseudo-extremum in the extrema array
// and a pseudo-saddle in the saddles array
#ifdef DEBUG_PRINT
  DebugPrint("Merge Superarcs Set");
#endif
} // ComputeAugmentedSuperarcs()

// routine that computes the augmented merge arcs from the superarcs
// this is separate from the previous routine because it also gets called separately
// once saddle & extrema are set for a given set of vertices, the merge arcs can be
// computed for any subset of those vertices that contains all of the critical points
template <typename T, typename StorageType, typename DeviceAdapter>
void MergeTree<T, StorageType, DeviceAdapter>::ComputeAugmentedArcs(
  vtkm::cont::ArrayHandle<vtkm::Id>& vertices)
{
#ifdef DEBUG_FUNCTION_ENTRY
  std::cout << std::endl;
  std::cout << "============================" << std::endl;
  std::cout << "Compute Augmented Merge Arcs" << std::endl;
  std::cout << "============================" << std::endl;
  std::cout << std::endl;
#endif

  // create a vector of indices for sorting
  vtkm::Id nCriticalVerts = vertices.GetNumberOfValues();
  vtkm::cont::ArrayHandle<vtkm::Id> vertexSorter;
  DeviceAlgorithm::Copy(vertices, vertexSorter);

  // We sort by pseudo-maximum to establish the extents
  DeviceAlgorithm::Sort(
    vertexSorter,
    VertexMergeComparator<T, StorageType, DeviceAdapter>(values.PrepareForInput(DeviceAdapter()),
                                                         extrema.PrepareForInput(DeviceAdapter()),
                                                         isJoinTree));
#ifdef DEBUG_PRINT
  DebugPrint("Sorting Complete");
#endif

  vtkm::cont::ArrayHandleConstant<vtkm::Id> noVertArray(NO_VERTEX_ASSIGNED, nVertices);
  DeviceAlgorithm::Copy(noVertArray, mergeArcs);

  vtkm::cont::ArrayHandleIndex critVertexIndexArray(nCriticalVerts);
  JoinArcConnector joinArcConnector;
  vtkm::worklet::DispatcherMapField<JoinArcConnector> joinArcConnectorDispatcher(joinArcConnector);
  joinArcConnectorDispatcher.SetDevice(DeviceAdapter());

  joinArcConnectorDispatcher.Invoke(critVertexIndexArray, // input
                                    vertexSorter,         // input (whole array)
                                    extrema,              // input (whole array)
                                    saddles,              // input (whole array)
                                    mergeArcs);           // output (whole array)
#ifdef DEBUG_PRINT
  DebugPrint("Augmented Arcs Set");
#endif
} // ComputeAugmentedArcs()

// debug routine
template <typename T, typename StorageType, typename DeviceAdapter>
void MergeTree<T, StorageType, DeviceAdapter>::DebugPrint(const char* message)
{
  std::cout << "---------------------------" << std::endl;
  std::cout << std::string(message) << std::endl;
  std::cout << "---------------------------" << std::endl;
  std::cout << std::endl;

  printLabelledBlock("Values", values, nRows * nSlices, nCols);
  std::cout << std::endl;
  printLabelledBlock("MergeArcs", mergeArcs, nRows, nCols);
  std::cout << std::endl;
  printLabelledBlock("Extrema", extrema, nRows, nCols);
  std::cout << std::endl;
  printLabelledBlock("Saddles", saddles, nRows, nCols);
  std::cout << std::endl;
} // DebugPrint()
}
}
}
#endif
