{"url":"https://api.github.com/gists/3795544","forks_url":"https://api.github.com/gists/3795544/forks","commits_url":"https://api.github.com/gists/3795544/commits","id":"3795544","node_id":"MDQ6R2lzdDM3OTU1NDQ=","git_pull_url":"https://gist.github.com/3795544.git","git_push_url":"https://gist.github.com/3795544.git","html_url":"https://gist.github.com/mbostock/3795544","files":{".block":{"filename":".block","type":"text/plain","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/3795544/raw/536cd7065f9f6520c05457a09ba41c0464ac27f7/.block","size":74,"truncated":false,"content":"license: gpl-3.0\nredirect: https://observablehq.com/@d3/adaptive-sampling\n","encoding":"utf-8"},"README.md":{"filename":"README.md","type":"text/markdown","language":"Markdown","raw_url":"https://gist.githubusercontent.com/mbostock/3795544/raw/25e00ab8da1b8ff0dfd398416f876d3439dbba7a/README.md","size":668,"truncated":false,"content":"[D3 3.0](https://github.com/mbostock/d3/wiki/3.0)’s projections use **adaptive resampling** to increase the accuracy of projected lines and polygons while still performing efficiently. On the left is no resampling, where obvious polygonal artifacts are visible due to projected lines becoming curves. Uniform resampling improves the appearance, but is inefficient because most of the resampling points are wasted, and areas of extreme distortion still exhibit artifacts. Adaptive resampling takes an idea from [line simplification](http://bost.ocks.org/mike/simplify/) and resamples only where needed, producing high-quality results with low computational 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4sZUdgANmT6gAey3Vxai4a3rzC7iPuk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\n+vjFL2TIwEhefllKBpkrRkpWYGcAwYHchsEUATezjnGGAIY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