There's a tradeoff: a lower capacity means you can skip more space during queries (you zoom in faster), but the tree has more nodes and uses more memory. A higher capacity means fewer nodes but each node requires checking more points linearly. As a starting point, capacities between 4 and 16 are reasonable defaults, though the best value depends on your data distribution and query patterns.
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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:
– Only modify the torn-paper interior areas.
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