Displaying 1 to 5 from 5 results

geo - S2 geometry library in Go


This is a library for manipulating geometric shapes. Unlike many geometry libraries, S2 is primarily designed to work with spherical geometry, i.e., shapes drawn on a sphere rather than on a planar 2D map. (In fact, the name S2 is derived from the mathematical notation for the unit sphere.) This makes it especially suitable for working with geographic data.Basic representations of angles, intervals, latitude-longitude points, unit 3D vectors, and conversions among them.

Silverlight Sphere Control


This is a Silverlight spherical selection control that I built that uses the projection transformations in Silverlight. It includes several modes (including random, rows, columns, vertical carousel, horizontal carousel, and checkered) and events. Upon selecting one of the ...

icosphere - Generates icosphere meshes of varying levels of complexity


Generates icosphere meshes of varying levels of complexity – implementation sourced from this article.MIT. See LICENSE.md for details.

leaflet-geodesy - a leaflet plugin for the earth


A Leaflet plugin for the earth.Either via require('leaflet-geodesy') or the LGeo object when used without browserify.




static-kdtree - A static kdtree data structure


kd-trees are a compact data structure for answering orthogonal range and nearest neighbor queries on higher dimensional point data in linear time. While they are not as efficient at answering orthogonal range queries as range trees - especially in low dimensions - kdtrees consume exponentially less space, support k-nearest neighbor queries and are relatively cheap to construct. This makes them useful in small to medium dimensions for achieving a modest speed up over a linear scan. It is also worth mentioning that for approximate nearest neighbor queries or queries with a fixed size radius, grids and locality sensitive hashing are strictly better options. In these charts the transition between "Medium" and "Big" depends on how many points there are in the data structure. As the number of points grows larger, the dimension at which kdtrees become practical goes up.