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. 2020 Feb 3;17(3):261–272. doi: 10.1038/s41592-019-0686-2

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© The Author(s) 2020

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Fig. 3 — The benchmark generates a k-d tree from uniformly distributed points in an m-dimensional unit hypercube, then finds the nearest (Euclidean) neighbor in the tree for each of 1,000 query points. Each marker in the figure indicates the execution time of the benchmark for a commit in the master branch of SciPy.