[SpatialPartitioning] Rename KdTreeImplBase to KdTreeBase

Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeKNearestQueries.h

@@ -25,7 +25,7 @@ class KdTreeKNearestQueryBase : public KdTreeQuery<Traits>, public QueryType
     using QueryAccelType = KdTreeQuery<Traits>;
     using Iterator       = IteratorType<typename Traits::IndexType, typename Traits::DataPoint>;
 
-    inline KdTreeKNearestQueryBase(const KdTreeImplBase<Traits>* kdtree, IndexType k, typename QueryType::InputType input) :
+    inline KdTreeKNearestQueryBase(const KdTreeBase<Traits>* kdtree, IndexType k, typename QueryType::InputType input) :
             KdTreeQuery<Traits>(kdtree), QueryType(k, input) { }
 
 public:

Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeNearestQueries.h

@@ -25,7 +25,7 @@ class KdTreeNearestQueryBase : public KdTreeQuery<Traits>, public QueryType
     using QueryAccelType = KdTreeQuery<Traits>;
     using Iterator       = IteratorType<typename Traits::IndexType>;
 
-    KdTreeNearestQueryBase(const KdTreeImplBase<Traits>* kdtree, typename QueryType::InputType input) :
+    KdTreeNearestQueryBase(const KdTreeBase<Traits>* kdtree, typename QueryType::InputType input) :
             KdTreeQuery<Traits>(kdtree), QueryType(input){}
 
 public:

Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeQuery.h

@@ -10,7 +10,7 @@
 #include "../../../Common/Containers/stack.h"
 
 namespace Ponca {
-template <typename Traits> class KdTreeImplBase;
+template <typename Traits> class KdTreeBase;
 
 template <typename Traits>
 class KdTreeQuery
@@ -21,7 +21,7 @@ class KdTreeQuery
     using Scalar     = typename DataPoint::Scalar;
     using VectorType = typename DataPoint::VectorType;
 
-    explicit inline KdTreeQuery(const KdTreeImplBase<Traits>* kdtree) : m_kdtree( kdtree ), m_stack() {}
+    explicit inline KdTreeQuery(const KdTreeBase<Traits>* kdtree) : m_kdtree( kdtree ), m_stack() {}
 
 protected:
     /// \brief Init stack for a new search
@@ -30,7 +30,7 @@ class KdTreeQuery
         m_stack.push({0,0});
     }
 
-    const KdTreeImplBase<Traits>* m_kdtree { nullptr };
+    const KdTreeBase<Traits>* m_kdtree { nullptr };
     Stack<IndexSquaredDistance<IndexType, Scalar>, 2 * Traits::MAX_DEPTH> m_stack;
 
     template<typename LeafPreparationFunctor,

Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeRangeQueries.h

@@ -30,7 +30,7 @@ class KdTreeRangeQueryBase : public KdTreeQuery<Traits>, public QueryType
     friend Iterator;
 
 public:
-    KdTreeRangeQueryBase(const KdTreeImplBase<Traits>* kdtree, Scalar radius, typename QueryType::InputType input) :
+    KdTreeRangeQueryBase(const KdTreeBase<Traits>* kdtree, Scalar radius, typename QueryType::InputType input) :
             KdTreeQuery<Traits>(kdtree), QueryType(radius, input){}
 
 public:

Ponca/src/SpatialPartitioning/KdTree/kdTree.h

@@ -25,18 +25,18 @@
 #include "Query/kdTreeRangeQueries.h"
 
 namespace Ponca {
-template <typename Traits> class KdTreeImplBase;
+template <typename Traits> class KdTreeBase;
 template <typename Traits> class KdTreeDenseBase;
 template <typename Traits> class KdTreeSparseBase;
 
 /*!
  * \brief Base type for default KdTree implementations
  *
  * \see KdTreeDefaultTraits for the default trait interface documentation.
- * \see KdTreeImplBase for complete API
+ * \see KdTreeBase for complete API
  */
 template <typename DataPoint>
-using KdTreeImpl = KdTreeImplBase<KdTreeDefaultTraits<DataPoint>>;
+using KdTree = KdTreeBase<KdTreeDefaultTraits<DataPoint>>;
 
 /*!
  * \brief Public interface for dense KdTree datastructure.
@@ -74,7 +74,7 @@ using KdTreeSparse = KdTreeSparseBase<KdTreeDefaultTraits<DataPoint>>;
  * \todo Better handle sampling: do not store non-selected points (requires to store original indices)
  */
 template <typename Traits>
-class KdTreeImplBase
+class KdTreeBase
 {
 public:
     using DataPoint      = typename Traits::DataPoint; ///< DataPoint given by user via Traits
@@ -277,7 +277,7 @@ public :
 
     // Internal ----------------------------------------------------------------
 protected:
-    inline KdTreeImplBase() = default;
+    inline KdTreeBase() = default;
 
     /// Generate a tree sampled from a custom contained type converted using a `Converter`
     /// \tparam PointUserContainer Input point, transformed to PointContainer
@@ -291,7 +291,7 @@ public :
                                   IndexUserContainer sampling,
                                   Converter c);
 
-    /// Generate a tree sampled from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Generate a tree sampled from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     /// \tparam PointUserContainer Input points, transformed to PointContainer
     /// \tparam IndexUserContainer Input sampling, transformed to IndexContainer
     /// \param points Input points
@@ -323,17 +323,17 @@ public :
  * \see KdTreeDefaultTraits for the trait interface documentation.
  */
 template <typename Traits>
-class KdTreeDenseBase : public KdTreeImplBase<Traits>
+class KdTreeDenseBase : public KdTreeBase<Traits>
 {
 private:
-    using Base = KdTreeImplBase<Traits>;
+    using Base = KdTreeBase<Traits>;
 
 public:
     /// Default constructor creating an empty tree
     /// \see build
     KdTreeDenseBase() = default;
 
-    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     template<typename PointUserContainer>
     inline explicit KdTreeDenseBase(PointUserContainer&& points)
         : Base()
@@ -357,10 +357,10 @@ class KdTreeDenseBase : public KdTreeImplBase<Traits>
  * \see KdTreeDefaultTraits for the trait interface documentation.
  */
 template <typename Traits>
-class KdTreeSparseBase : public KdTreeImplBase<Traits>
+class KdTreeSparseBase : public KdTreeBase<Traits>
 {
 private:
-    using Base = KdTreeImplBase<Traits>;
+    using Base = KdTreeBase<Traits>;
 
 public:
     static constexpr bool SUPPORTS_SUBSAMPLING = false;
@@ -369,15 +369,15 @@ class KdTreeSparseBase : public KdTreeImplBase<Traits>
     /// \see build
     KdTreeSparseBase() = default;
 
-    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Constructor generating a tree from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     template<typename PointUserContainer>
     inline explicit KdTreeSparseBase(PointUserContainer&& points)
         : Base()
     {
         this->build(std::forward<PointUserContainer>(points));
     }
 
-    /// Constructor generating a tree sampled from a custom contained type converted using a \ref KdTreeImplBase::DefaultConverter
+    /// Constructor generating a tree sampled from a custom contained type converted using a \ref KdTreeBase::DefaultConverter
     /// \tparam PointUserContainer Input points, transformed to PointContainer
     /// \tparam IndexUserContainer Input sampling, transformed to IndexContainer
     /// \param point Input points
@@ -396,7 +396,7 @@ class KdTreeSparseBase : public KdTreeImplBase<Traits>
 } // namespace Ponca
 
 template <typename Traits>
-std::ostream& operator<<(std::ostream& os, const Ponca::KdTreeImplBase<Traits>& kdtree)
+std::ostream& operator<<(std::ostream& os, const Ponca::KdTreeBase<Traits>& kdtree)
 {
     kdtree.print(os);
     return os;

Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp

@@ -8,15 +8,15 @@
 
 template<typename Traits>
 template<typename PointUserContainer, typename Converter>
-inline void KdTreeImplBase<Traits>::build(PointUserContainer&& points, Converter c)
+inline void KdTreeBase<Traits>::build(PointUserContainer&& points, Converter c)
 {
     IndexContainer ids(points.size());
     std::iota(ids.begin(), ids.end(), 0);
     this->buildWithSampling(std::forward<PointUserContainer>(points), std::move(ids), std::move(c));
 }
 
 template<typename Traits>
-void KdTreeImplBase<Traits>::clear()
+void KdTreeBase<Traits>::clear()
 {
     m_points.clear();
     m_nodes.clear();
@@ -25,7 +25,7 @@ void KdTreeImplBase<Traits>::clear()
 }
 
 template<typename Traits>
-bool KdTreeImplBase<Traits>::valid() const
+bool KdTreeBase<Traits>::valid() const
 {
     if (m_points.empty())
         return m_nodes.empty() && m_indices.empty();
@@ -72,7 +72,7 @@ bool KdTreeImplBase<Traits>::valid() const
 }
 
 template<typename Traits>
-void KdTreeImplBase<Traits>::print(std::ostream& os, bool verbose) const
+void KdTreeBase<Traits>::print(std::ostream& os, bool verbose) const
 {
     os << "KdTree:";
     os << "\n  MaxNodes: " << MAX_NODE_COUNT;
@@ -118,9 +118,9 @@ void KdTreeImplBase<Traits>::print(std::ostream& os, bool verbose) const
 
 template<typename Traits>
 template<typename PointUserContainer, typename IndexUserContainer, typename Converter>
-inline void KdTreeImplBase<Traits>::buildWithSampling(PointUserContainer&& points,
-                                                      IndexUserContainer sampling,
-                                                      Converter c)
+inline void KdTreeBase<Traits>::buildWithSampling(PointUserContainer&& points,
+                                                  IndexUserContainer sampling,
+                                                  Converter c)
 {
     PONCA_DEBUG_ASSERT(points.size() <= MAX_POINT_COUNT);
     this->clear();
@@ -140,7 +140,7 @@ inline void KdTreeImplBase<Traits>::buildWithSampling(PointUserContainer&& point
 }
 
 template<typename Traits>
-void KdTreeImplBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, IndexType end, int level)
+void KdTreeBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, IndexType end, int level)
 {
     NodeType& node = m_nodes[node_id];
     AabbType aabb;
@@ -174,7 +174,7 @@ void KdTreeImplBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, I
 }
 
 template<typename Traits>
-auto KdTreeImplBase<Traits>::partition(IndexType start, IndexType end, int dim, Scalar value)
+auto KdTreeBase<Traits>::partition(IndexType start, IndexType end, int dim, Scalar value)
     -> IndexType
 {
     const auto& points = m_points;

Ponca/src/SpatialPartitioning/KnnGraph/knnGraph.h

@@ -68,7 +68,7 @@ template <typename Traits> class KnnGraphBase
     /// \warning Stores a const reference to kdtree.point_data()
     /// \warning KdTreeTraits compatibility is checked with static assertion
     template<typename KdTreeTraits>
-    inline KnnGraphBase(const KdTreeImplBase<KdTreeTraits>& kdtree, int k = 6)
+    inline KnnGraphBase(const KdTreeBase<KdTreeTraits>& kdtree, int k = 6)
             : m_k(std::min(k,kdtree.sample_count()-1)),
               m_kdTreePoints(kdtree.points())
     {

doc/src/ponca_module_spatialpartitioning.mdoc

@@ -92,12 +92,12 @@ fit.computeWithIds( myDataStructure.range_neighbors(fitInitPos, scale), vectorPo
 
   We call indices into the underlying point storage *point indices* and indices into the sample storage *sample
   indices*. Note that the sample storage stores point indices: sample number 0 may, for example, refer to point number 25.
-  The point storage can be accessed with KdTreeImplBase::points, while the array of samples can be accessed with
-  KdTreeImplBase::samples.
+  The point storage can be accessed with KdTreeBase::points, while the array of samples can be accessed with
+  KdTreeBase::samples.
 
   While most of the KdTree API is built on using point indices, it can still be useful to iterate over samples instead
   (e.g. when using a Ponca::KdTreeSparse). If you ever need to convert sample indices to point indices, see
-  KdTreeImplBase::pointFromSample (see also KdTreeImplBase::pointDataFromSample).
+  KdTreeBase::pointFromSample (see also KdTreeBase::pointDataFromSample).
 
   \subsection spatialpartitioning_kdtree_examples Examples
 
@@ -120,8 +120,8 @@ fit.computeWithIds( myDataStructure.range_neighbors(fitInitPos, scale), vectorPo
   The kd-tree is a binary search tree that
   - is balanced (cuts at the median at each node)
   - cuts along the dimension that extends the most at each node
-  - has a maximal depth (KdTreeImplBase::MAX_DEPTH)
-  - has a minimal number of points per leaf (KdTreeImplBase::m_min_cell_size)
+  - has a maximal depth (KdTreeBase::MAX_DEPTH)
+  - has a minimal number of points per leaf (KdTreeBase::m_min_cell_size)
   - only stores points in the leafs
   - uses depth-first search with a static stack for queries
   - keeps the initial order of points

tests/src/basket.cpp

@@ -26,7 +26,7 @@ using namespace std;
 using namespace Ponca;
 
 template<typename DataPoint>
-typename DataPoint::Scalar generateData(KdTreeImpl<DataPoint>& tree)
+typename DataPoint::Scalar generateData(KdTree<DataPoint>& tree)
 {
     typedef typename DataPoint::Scalar Scalar;
     typedef typename DataPoint::VectorType VectorType;
@@ -61,7 +61,7 @@ typename DataPoint::Scalar generateData(KdTreeImpl<DataPoint>& tree)
 }
 
 template<typename Fit>
-void testBasicFunctionalities(const KdTreeImpl<typename Fit::DataPoint>& tree, typename Fit::Scalar analysisScale)
+void testBasicFunctionalities(const KdTree<typename Fit::DataPoint>& tree, typename Fit::Scalar analysisScale)
 {
     using DataPoint = typename Fit::DataPoint;
 
@@ -124,7 +124,7 @@ void testBasicFunctionalities(const KdTreeImpl<typename Fit::DataPoint>& tree, t
 }
 
 template<typename Fit1, typename Fit2, typename Functor>
-void testIsSame(const KdTreeImpl<typename Fit1::DataPoint>& tree,
+void testIsSame(const KdTree<typename Fit1::DataPoint>& tree,
                 typename Fit1::Scalar analysisScale,
                 Functor f)
 {

tests/src/queries_knearest.cpp

@@ -18,7 +18,7 @@ template<typename DataPoint>
 void testKdTreeKNearestIndex(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;
@@ -77,7 +77,7 @@ template<typename DataPoint>
 void testKdTreeKNearestPoint(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;

tests/src/queries_nearest.cpp

@@ -18,7 +18,7 @@ template<typename DataPoint>
 void testKdTreeNearestIndex(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;
@@ -61,7 +61,7 @@ template<typename DataPoint>
 void testKdTreeNearestPoint(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;

tests/src/queries_range.cpp

@@ -18,14 +18,14 @@ template<typename DataPoint, bool SampleKdTree = true>
 void testKdTreeRangeIndex(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 5000;
 	auto points = VectorContainer(N);
     std::generate(points.begin(), points.end(), []() {return DataPoint(VectorType::Random()); });
 
-    KdTreeImpl<DataPoint> *kdtree {nullptr};
+    KdTree<DataPoint> *kdtree {nullptr};
 
     std::vector<int> sampling; // we need sampling for GT computation
     if(SampleKdTree){
@@ -89,7 +89,7 @@ template<typename DataPoint>
 void testKdTreeRangePoint(bool quick = true)
 {
 	using Scalar = typename DataPoint::Scalar;
-	using VectorContainer = typename KdTreeImpl<DataPoint>::PointContainer;
+	using VectorContainer = typename KdTree<DataPoint>::PointContainer;
 	using VectorType = typename DataPoint::VectorType;
 
 	const int N = quick ? 100 : 10000;