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- Minor formatting improvements across multiple files - Refactored `RTree` `knn_search` logic to fix redundant tuple syntax
…ection to main README for clarity.
- Introduced 2D and 3D point support with corresponding tests - Expanded `BoundingVolume` trait with `overlap` and `margin` methods
- Updated `kNN` and `range_search` methods to accept a generic `DistanceMetric`. - Unified benchmarks and tests to explicitly utilize `EuclideanDistance`. - Demonstrated `ManhattanDistance` implementation in examples.
- Implemented bulk insertion benchmarks for Quadtree, Octree, KdTree, RTree, and RStarTree in 2D and 3D. - Updated tests to validate bulk insertion for all supported spatial tree structures. - Refactored Python examples to use bulk insertion methods. - Adjusted benchmark parameters for consistency and added new criterion configuration.
- Implemented serialization and deserialization for Quadtree, Octree, KdTree, RTree, and RStarTree using `serde`. - Added serialization examples to Python and Rust examples. - Created comprehensive unit tests and benchmarks for serialization and deserialization. - Refactored code to ensure compatibility with the new serialization features.
…pand README examples - Implemented benchmark functions for bounding box range search using R*-tree in 2D and 3D. - Updated the `benches` group to include new benchmark tests. - Expanded README with detailed usage examples for bulk insertion, querying, and serialization across all spatial tree structures.
…nd serialization sections into the main README.
- Clarified installation and usage descriptions in the main README. - Updated `pyspart/README.md` to improve example references and reorganize content.
- Included Spart logo at the top of the README. - Added an acknowledgements section for logo attribution.
- Added details about Spart's Python bindings, including installation and usage instructions. - Enhanced README with notes on supported features, development status, and additional documentation references. - Improved formatting for concepts and debugging sections.
- Added extensive test cases for `knn_search`, `range_search`, and `delete` operations, including edge cases for various tree structures (Quadtree, Octree, KDTree, RTree, and RStarTree). - Documented new tree behaviors and edge cases in the main and PySpart READMEs. - Updated `knn_search` to handle `k=0` gracefully across all trees.
- Simplified and streamlined insertion process for clarity and reduced redundancy. - Added `unreachable!` checks to enforce boundary logic consistency.
- Implemented robust delete logic for managing node underflows by reinserting orphaned entries. - Introduced `min_entries` field to enforce minimum occupancy during deletions. - Expanded test suite to validate underflow scenarios and ensure correctness across edge cases.
- Introduced forced reinsertion mechanism to handle node overflows during insertion, improving tree balancing. - Refactored insertion logic to include recursive handling of overflows and node splitting. - Added a test case to validate forced reinsertion behavior and tree height stability.
…l errors with `unwrap` - Updated all `new` constructors in tree structures (RTree, RStarTree, Quadtree, Octree, and KdTree) to explicitly handle potential `Result` types using `.unwrap()`. - Adjusted benchmarks, tests, and Python bindings to match the new safety mechanism. - Simplified error management during tree initialization.
- Refactored delete and insert benchmarks to use explicit `iter_with_setup` for clearer setup and execution separation. - Removed intermediate `info!` logging from KNN and range search benchmarks for cleaner output. - Increased `BENCH_NUM_INSERT` to 10,000 for more comprehensive test coverage.
…_common` - Moved shared algorithms (delete, range search, KNN, MBR computation) into `rtree_common` module. - Unified `EntryAccess` and `NodeAccess` traits to abstract R-tree and R*-tree implementations. - Simplified RTree and RStarTree structures by delegating common functionality to `rtree_common`. - Removed duplicated code and added reusable trait implementations.
- Simplified `RTree` and `RStarTree` implementations by eliminating the need for the `Ord` trait on point data. - Replaced tuple-based k-NN heap with `HeapItem` struct to handle ties and maintain consistent ordering. - Adjusted k-NN search logic for both 2D and 3D variants to integrate with the new heap structure.
…es and streamline ownership semantics across spatial trees, tests, and benchmarks.
- Added `r_star_tree.py` example to demonstrate 2D and 3D R*-tree usage for k-NN and range searches. - Introduced `bench_serialization` module and integrated it into the benchmarking suite for testing serialization performance.
…nd RStarTree - Simplified k-NN heap logic by replacing the custom `OrdDist` implementation with `OrderedFloat<f64>`. - Reduced redundancy and improved consistency across spatial tree structures.
- Renamed `r_star_tree.py` to `rstar_tree.py` for consistency. - Added Python quick-start and per-example run instructions in `pyspart/examples/README.md`. - Updated Rust `examples/README.md` with an entry for R*-tree. - Updated root `README.md` to include Python R*-tree examples and steps. - Extended Makefile to include `rstar_tree.py` in `make run-py-examples`.
…DME.md` for detailed usage info
…-tree and R-tree - Moved `compute_group_mbr` and `search_node` functions to a shared module (`rtree_common`) to improve code reuse. - Updated `compute_group_mbr` to return `Option` for safer handling of empty inputs. - Replaced unwraps with safe handling (`if let` and `expect`) for bounding volume computations. - Simplified k-NN and range query implementations for readability.
… across spatial trees and update related tests and assertions
…nd Python GIL for safe cloning; simplify k-NN and range query mappings by removing redundant clones.
…icking - Updated `BSPBounds` trait to return `Result` for `center` and `extent` methods. - Replaced `panic!` with `Err` for invalid dimensions in `Rectangle`, `Cube`, and related implementations. - Adjusted `KdPoint` trait and implementations to return `Result` for `coord` method. - Replaced unwraps with proper error handling in R*-tree and k-d tree logic for coordinate and center access.
…andling and dimension inference - Removed `new(k)` constructor in favor of `new()` with inferred dimensions. - Added `DimensionMismatch` error for insertions with incompatible dimensions. - Updated insert and bulk insert methods to return `Result` instead of panicking. - Adjusted tests and examples to align with new KdTree API.
- Removed unused benchmark functions and `dead_code` warnings. - Simplified KdTree insert methods to improve error handling and comply with new API updates. - Streamlined benchmark definitions to remove unnecessary helpers and reduce verbosity.
- Removed dimension parameter from `KdTree::new()` for consistency with updated API. - Updated insert methods to use `Result` handling and suppress unused values. - Simplified geometry imports in benchmarks by removing unused types.
- Updated `insert_bulk` in Python bindings to suppress unused value warnings. - Added k-NN search functionality (`knn_search`) to 2D and 3D R-trees in examples. - Enhanced test cases with assertions for k-NN search in 2D and 3D R-trees. - Updated README to indicate missing features in Python bindings.
…, Manhattan distance, and structured initialization examples
- Added `.github/FUNDING.yml` with GitHub sponsor details. - Updated pre-commit configuration to include explicit `pre-commit` stages for format and lint hooks.
…tions for insertion and k-NN search logic
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This revision adds the R*-tree implementation. It also refactors existing spatial trees. The changes improve performance, safety, and the API.
Resulttypes for safer error handling.