ndsel — n-dimensional spatial selection

ndsel is a JSON-serializable representation of n-dimensional array indexing operations. A message denotes a subset of grid points (which points are selected from a source array) plus how those points are arranged in the result array (their output coordinate frame). The name stands for "n-dimensional spatial selection".

ndsel adapts the index model of TensorStore.

Status

ndsel is in draft. Anything can change without warning. Use at your own risk.

Message kinds

There are five message kinds, ranging from convenient shorthands to the full canonical form.

Kind	Description	Example
point	A single grid point	{"kind":"point","coords":[4,7]}
box	A contiguous hyperrectangle	{"kind":"box","inclusive_min":[0,0],"exclusive_max":[3,4]}
slice	A regular strided region	{"kind":"slice","start":[0],"stop":[10],"step":[2]}
points	An explicit set of points	{"kind":"points","coords":[[1,10],[2,20]]}
transform	Full canonical core (TensorStore IndexTransform)	{"kind":"transform","input_inclusive_min":[0,0],"input_exclusive_max":[3,4]}

Every message normalizes to the canonical transform form. The shorthands are coordinate-frame preserving: a box is an identity transform (output coordinates equal input coordinates); a slice keeps the source coordinate frame — re-basing the output to 0 is an explicit, separate step.

Repository layout

Path	Contents
spec/ndsel.md	Normative specification
schema/ndsel.schema.json	JSON Schema for all message kinds
conformance/	Conformance corpus (valid + invalid fixtures)
rust/ndsel/	Rust reference implementation
python/ndsel/	Python reference implementation
typescript/	TypeScript reference implementation

License

The specification (spec/), the JSON Schema (schema/), and the conformance corpus (conformance/) are dedicated to the public domain under CC0 1.0 Universal — implement them freely, no attribution required.

The reference implementations (rust/, python/, typescript/) are licensed under either of Apache License 2.0 or MIT license at your option (MIT OR Apache-2.0). Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work shall be dual licensed as above, without any additional terms or conditions.