适用于大型数据集的良好算法方法
将您的棱镜存储在R-Tree 中。对于矩形同轴棱镜,搜索和插入的顺序应该是log(n)。
有一些用于 R-Trees 的 Python 包。使用Rtree 0.6.0,您的代码将非常简单:
>>> from rtree import Rtree
>>> idx = Rtree()
>>> minx, miny, maxx, maxy = (0.0, 0.0, 1.0, 1.0)
>>> idx.add(0, (minx, miny, maxx, maxy))
>>> list(idx.intersection((1.0, 1.0, 2.0, 2.0)))
[0L]
>>> list(idx.intersection((1.0000001, 1.0000001, 2.0, 2.0)))
[]
实用而快速的方法
将您的数据存储在sqlite 数据库中,可以使用很少的代码行在文件或内存中创建该数据库(有many java implementations)。创建名为prisms 的表,其列将是id、min_x、min_y、min_z、max_x、max_y、max_z。索引每一行。
插入是O(1),检查交叉点遵循Magnus Skog's approach,给定new_min_x, new_min_y, new_min_z, new_max_x, new_max_y, new_max_z:
SELECT COUNT(*) FROM prisms
WHERE (new_min_x BETWEEN min_x and max_x OR new_max_x BETWEEN min_x and max_x)
AND (new_min_y BETWEEN min_y and max_y OR new_max_y BETWEEN min_y and max_y)
AND (new_min_z BETWEEN min_z and max_z OR new_max_z BETWEEN min_z and max_z)