The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.
It completed the assignment in one-shot, accounting for all of the many feature constraints specified. The “Python Jupyter Notebook” notebook command at the end is how I manually tested whether the pyo3 bridge worked, and it indeed worked like a charm. There was one mistake that’s my fault however: I naively chose the fontdue Rust crate as the renderer because I remember seeing a benchmark showing it was the fastest at text rendering. However, testing large icon generation exposed a flaw: fontdue achieves its speed by only partially rendering curves, which is a very big problem for icons, so I followed up:
。关于这个话题，下载安装 谷歌浏览器 开启极速安全的 上网之旅。提供了深入分析
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