ndshape

Simple, fast linearization of 2D, 3D, and 4D coordinates.

The canonical choice of linearization function is row-major, i.e. stepping linearly through an N dimensional array would step by X first, then Y, then Z, etc, assuming that [T; N] coordinates are provided as [X, Y, Z, ...]. More explicitly:

linearize([x, y, z, ...]) = x + X_SIZE * y + X_SIZE * Y_SIZE * z + ...

To achieve a different layout, one only needs to choose a different permutation of coordinates. For example, column-major layout would require coordinates specified as [..., Z, Y, X]. For a 3D layout where each Y level set is contiguous in memory, either layout [X, Z, Y] or [Z, X, Y] would work.

Example: Indexing Multidimensional Arrays

use ndshape::{Shape, ConstShape3u32, ConstShape4u32, ConstPow2Shape3u32, RuntimeShape};

// An arbitrary shape.
let shape = ConstShape3u32::<5, 6, 7>;
let index = shape.linearize([1, 2, 3]);
assert_eq!(index, 101);
assert_eq!(shape.delinearize(index), [1, 2, 3]);

// A shape with power-of-two dimensions
// This allows us to use bit shifting and masking for linearization.
let shape = ConstPow2Shape3u32::<1, 2, 3>; // These are number of bits per dimension.
let index = shape.linearize([1, 2, 3]);
assert_eq!(index, 0b011_10_1);
assert_eq!(shape.delinearize(index), [1, 2, 3]);

// A runtime shape.
let shape = RuntimeShape::<u32, 3>::new([5, 6, 7]);
let index = shape.linearize([1, 2, 3]);
assert_eq!(index, 101);
assert_eq!(shape.delinearize(index), [1, 2, 3]);

// Use a shape for indexing an array in 4D.
// Step X, then Y, then Z, since that results in monotonic increasing indices.
// (Believe it or not, Rust's N-dimensional array (e.g. `[[T; N]; M]`)
// indexing is significantly slower than this).
let shape = ConstShape4u32::<5, 6, 7, 8>;
let data = [0; 5 * 6 * 7 * 8];
for w in 0..8 {
    for z in 0..7 {
        for y in 0..6 {
            for x in 0..5 {
                let i = shape.linearize([x, y, z, w]);
                assert_eq!(0, data[i as usize]);
            }
        }
    }
}

Example: Negative Strides with Modular Arithmetic

It is often beneficial to linearize a negative vector that results in a negative linear "stride." But when using unsigned linear indices, a negative stride would require a modular arithmetic representation, where e.g. -1 maps to u32::MAX. This works fine with any Shape. You just need to be sure to use modular arithmetic with the resulting linear strides, e.g. u32::wrapping_add and u32::wrapping_mul. Also, it is not possible to delinearize a negative stride with modular arithmetic. For that, you must use signed integer coordinates.

use ndshape::{Shape, ConstShape3u32, ConstShape3i32};

let shape = ConstShape3u32::<10, 10, 10>;
let stride = shape.linearize([0, -1i32 as u32, 0]);
assert_eq!(stride, -10i32 as u32);

// Delinearize does not work with unsigned coordinates!
assert_ne!(shape.delinearize(stride), [0, -1i32 as u32, 0]);
assert_eq!(shape.delinearize(stride), [6, 8, 42949672]);

let shape = ConstShape3i32::<10, 10, 10>;
let stride = shape.linearize([0, -1, 0]);
assert_eq!(stride, -10);

// Delinearize works with signed coordinates.
assert_eq!(shape.delinearize(stride), [0, -1, 0]);

License: MIT OR Apache-2.0