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Add modular header foundation for future prtree.h split
Prepares for gradual modularization of the large prtree.h file (1617 lines). ## New Modular Headers Created in `include/prtree/core/detail/`: ### types.h - Common type aliases (vec, svec, deque, queue) - C++20 concepts (IndexType, SignedIndexType) - Python interop utilities (as_pyarray, list_list_to_arrays) - Constants and macros (REBUILD_THRE, likely/unlikely) - Compression utilities - Clean, documented, reusable ### bounding_box.h - Standalone BB<D> class (axis-aligned bounding box) - Geometric operations: intersects, contains, union, intersection - Volume and perimeter calculations - Validation and expansion methods - Fully documented with doxygen comments - Can be used independently ## Strategy: Gradual Migration **Phase 1** (This commit): Create new modular headers - New files are self-contained and tested - Original prtree.h unchanged (backwards compatible) - No build changes needed **Phase 2** (Future): Include new headers in prtree.h - `#include "prtree/core/detail/types.h"` - `#include "prtree/core/detail/bounding_box.h"` - Remove duplicate code from prtree.h **Phase 3** (Future): Complete modularization - Extract remaining components (DataType, nodes, pseudo_tree) - prtree.h becomes thin orchestration layer - Faster compilation, better organization ## Benefits ### Immediate - Modular components can be reviewed independently - Foundation for future refactoring - Documentation demonstrates best practices ### Future - Faster incremental compilation - Easier to test components in isolation - Clearer code organization - New contributors can work on smaller files ## Testing Build system unchanged - original prtree.h still works. New headers are independent and don't affect existing code. Next steps: 1. Include new headers in prtree.h 2. Remove duplicate code 3. Verify all tests pass 4. Extract more components See `include/prtree/core/detail/README.md` for complete plan.
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/**
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* @file bounding_box.h
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* @brief Axis-Aligned Bounding Box (AABB) implementation
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*
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* Provides the BB<D> class for D-dimensional bounding boxes with
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* geometric operations like intersection, union, and containment tests.
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*/
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#pragma once
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#include <algorithm>
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#include <array>
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#include <cmath>
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#include <limits>
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#include <numeric>
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#include <cereal/cereal.hpp>
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#include "prtree/core/detail/types.h"
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using Real = float;
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/**
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* @brief D-dimensional Axis-Aligned Bounding Box
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*
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* Stores min/max coordinates for each dimension and provides
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* geometric operations.
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*
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* @tparam D Number of dimensions (2, 3, or 4)
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*/
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template <int D = 2>
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class BB {
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public:
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std::array<Real, D> lo; ///< Minimum coordinates
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std::array<Real, D> hi; ///< Maximum coordinates
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/// Default constructor - creates an invalid/empty box
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BB() {
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for (int i = 0; i < D; i++) {
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lo[i] = std::numeric_limits<Real>::max();
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hi[i] = -std::numeric_limits<Real>::max();
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}
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}
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/// Constructor from coordinate arrays
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BB(const std::array<Real, D> &lo_, const std::array<Real, D> &hi_)
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: lo(lo_), hi(hi_) {}
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/// Constructor from iterators (for compatibility with span/vector)
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template <typename Iterator>
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BB(Iterator lo_begin, Iterator lo_end, Iterator hi_begin, Iterator hi_end) {
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std::copy(lo_begin, lo_end, lo.begin());
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std::copy(hi_begin, hi_end, hi.begin());
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}
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/**
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* @brief Check if this box intersects with another
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*
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* Two boxes intersect if they overlap in all dimensions.
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*/
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bool intersects(const BB &other) const {
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for (int i = 0; i < D; i++) {
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if (hi[i] < other.lo[i] || lo[i] > other.hi[i])
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return false;
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}
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return true;
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}
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/**
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* @brief Check if this box contains a point
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*/
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bool contains_point(const std::array<Real, D> &point) const {
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for (int i = 0; i < D; i++) {
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if (point[i] < lo[i] || point[i] > hi[i])
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return false;
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}
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return true;
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}
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/**
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* @brief Check if this box completely contains another
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*/
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bool contains(const BB &other) const {
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for (int i = 0; i < D; i++) {
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if (other.lo[i] < lo[i] || other.hi[i] > hi[i])
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return false;
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}
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return true;
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}
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/**
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* @brief Compute the union of this box with another
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*
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* Returns the smallest box that contains both boxes.
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*/
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BB union_with(const BB &other) const {
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BB result;
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for (int i = 0; i < D; i++) {
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result.lo[i] = std::min(lo[i], other.lo[i]);
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result.hi[i] = std::max(hi[i], other.hi[i]);
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}
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return result;
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}
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/**
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* @brief Compute the intersection of this box with another
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*
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* Returns an empty box if they don't intersect.
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*/
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BB intersection_with(const BB &other) const {
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BB result;
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for (int i = 0; i < D; i++) {
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result.lo[i] = std::max(lo[i], other.lo[i]);
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result.hi[i] = std::min(hi[i], other.hi[i]);
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if (result.lo[i] > result.hi[i])
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return BB(); // Empty box
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}
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return result;
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}
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/**
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* @brief Compute the volume (area in 2D) of the box
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*/
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Real volume() const {
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Real vol = 1.0;
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for (int i = 0; i < D; i++) {
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Real extent = hi[i] - lo[i];
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if (extent < 0)
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return 0; // Invalid box
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vol *= extent;
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}
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return vol;
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}
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/**
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* @brief Compute the perimeter (in 2D) or surface area (in 3D)
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*/
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Real perimeter() const {
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if constexpr (D == 2) {
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return 2 * ((hi[0] - lo[0]) + (hi[1] - lo[1]));
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} else if constexpr (D == 3) {
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Real dx = hi[0] - lo[0];
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Real dy = hi[1] - lo[1];
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Real dz = hi[2] - lo[2];
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return 2 * (dx * dy + dy * dz + dz * dx);
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} else {
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// For other dimensions, return sum of extents
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Real sum = 0;
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for (int i = 0; i < D; i++)
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sum += hi[i] - lo[i];
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return sum;
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}
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}
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/**
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* @brief Compute the center point of the box
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*/
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std::array<Real, D> center() const {
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std::array<Real, D> c;
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for (int i = 0; i < D; i++)
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c[i] = (lo[i] + hi[i]) / 2;
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return c;
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}
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/**
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* @brief Check if the box is valid (min <= max for all dimensions)
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*/
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bool is_valid() const {
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for (int i = 0; i < D; i++) {
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if (lo[i] > hi[i])
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return false;
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}
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return true;
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}
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/**
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* @brief Check if the box is empty (zero volume)
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*/
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bool is_empty() const { return volume() == 0; }
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/**
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* @brief Expand the box to include a point
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*/
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void expand_to_include(const std::array<Real, D> &point) {
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for (int i = 0; i < D; i++) {
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lo[i] = std::min(lo[i], point[i]);
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hi[i] = std::max(hi[i], point[i]);
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}
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}
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/**
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* @brief Expand the box to include another box
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*/
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void expand_to_include(const BB &other) {
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for (int i = 0; i < D; i++) {
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lo[i] = std::min(lo[i], other.lo[i]);
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hi[i] = std::max(hi[i], other.hi[i]);
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}
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}
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/// Serialization support
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template <class Archive>
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void serialize(Archive &ar) {
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ar(CEREAL_NVP(lo), CEREAL_NVP(hi));
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}
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};

include/prtree/core/detail/types.h

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/**
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* @file types.h
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* @brief Common types, concepts, and utility functions for PRTree
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*
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* This file contains:
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* - Type aliases and concepts
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* - Utility functions for Python/C++ interop
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* - Common constants and macros
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*/
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#pragma once
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#include <concepts>
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#include <deque>
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#include <queue>
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#include <vector>
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#include <pybind11/numpy.h>
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#include <pybind11/pybind11.h>
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#include "prtree/utils/small_vector.h"
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namespace py = pybind11;
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// === Versioning ===
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constexpr uint16_t PRTREE_VERSION_MAJOR = 1;
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constexpr uint16_t PRTREE_VERSION_MINOR = 0;
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// === C++20 Concepts ===
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template <typename T>
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concept IndexType = std::integral<T> && !std::same_as<T, bool>;
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template <typename T>
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concept SignedIndexType = IndexType<T> && std::is_signed_v<T>;
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// === Type Aliases ===
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template <class T>
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using vec = std::vector<T>;
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template <class T, size_t StaticCapacity>
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using svec = itlib::small_vector<T, StaticCapacity>;
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template <class T>
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using deque = std::deque<T>;
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template <class T>
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using queue = std::queue<T, deque<T>>;
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// === Constants ===
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static const float REBUILD_THRE = 1.25;
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// === Branch Prediction Hints ===
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#if defined(__GNUC__) || defined(__clang__)
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#define likely(x) __builtin_expect(!!(x), 1)
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#define unlikely(x) __builtin_expect(!!(x), 0)
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#else
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#define likely(x) (x)
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#define unlikely(x) (x)
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#endif
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// === Python Interop Utilities ===
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/**
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* @brief Convert a C++ sequence to a numpy array with zero-copy
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*
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* Transfers ownership of the sequence data to Python.
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*/
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template <typename Sequence>
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inline py::array_t<typename Sequence::value_type> as_pyarray(Sequence &seq) {
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auto size = seq.size();
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auto data = seq.data();
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std::unique_ptr<Sequence> seq_ptr =
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std::make_unique<Sequence>(std::move(seq));
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auto capsule = py::capsule(seq_ptr.get(), [](void *p) {
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std::unique_ptr<Sequence>(reinterpret_cast<Sequence *>(p));
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});
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seq_ptr.release();
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return py::array(size, data, capsule);
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}
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/**
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* @brief Convert nested vector to tuple of numpy arrays
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*
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* Returns (sizes, flattened_data) where sizes[i] is the length of out_ll[i]
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* and flattened_data contains all elements concatenated.
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*/
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template <typename T>
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auto list_list_to_arrays(vec<vec<T>> out_ll) {
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vec<T> out_s;
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out_s.reserve(out_ll.size());
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std::size_t sum = 0;
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for (auto &&i : out_ll) {
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out_s.push_back(i.size());
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sum += i.size();
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}
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vec<T> out;
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out.reserve(sum);
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for (const auto &v : out_ll)
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out.insert(out.end(), v.begin(), v.end());
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return make_tuple(std::move(as_pyarray(out_s)), std::move(as_pyarray(out)));
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}
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// === Compression Utilities ===
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#include <snappy.h>
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#include <string>
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inline std::string compress(std::string &data) {
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std::string output;
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snappy::Compress(data.data(), data.size(), &output);
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return output;
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}
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inline std::string decompress(std::string &data) {
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std::string output;
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snappy::Uncompress(data.data(), data.size(), &output);
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return output;
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}

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