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Data Visualization\n\nA companion webapp to this test suite exists where the results can be explored interactively, at [https://sdd.github.io/kd-tree-comparison-webapp/](https://sdd.github.io/kd-tree-comparison-webapp/).\n\nThe repository for the visualisation webapp is at [https://github.com/sdd/kd-tree-comparison-webapp](https://github.com/sdd/kd-tree-comparison-webapp)\n\n## Libraries tested\n(full disclosure: I'm the author of Kiddo)\n\n* [Kiddo v5.x](https://github.com/sdd/kiddo)\n* [Kiddo v3.x](https://github.com/sdd/kiddo)\n* [Kiddo v2.x](https://github.com/sdd/kiddo)\n* [Kiddo v1.x / v0.2.x](https://github.com/sdd/kiddo_v1)\n* [FNNTW](https://crates.io/crates/fnntw) v0.2.3\n* [nabo-rs](https://crates.io/crates/nabo) v0.2.1\n* [pykdtree](https://github.com/storpipfugl/pykdtree) v1.3.4\n* [sklearn.neighbours.KDTree](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html) v1.2.2\n* [scipy.spatial.KDTree](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html) v1.10.1\n\n## Running the benchmarks to generate NDJSON\n\n```bash\ncargo criterion --message-format json \u003e all-benchmarks.ndjson\n```\n\n\n## Converting the criterion NDJSON into a convenient JSON object\n```bash\njq -s '.[] | select(.reason == \"benchmark-complete\") | with_entries(select([.key] | inside([\"id\", \"mean\"])))'  \u003c all-benchmarks.ndjson | jq -s \u003e all-benchmarks.json\n```\n\n## Benchmark System Details\n\n* Processor: Ryzen 5900X (12/24 core)\n* Memory: 32Gb DDR4, 3600MHz\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsdd%2Fkd-tree-comparison","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsdd%2Fkd-tree-comparison","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsdd%2Fkd-tree-comparison/lists"}