ndarray-slice

Fast and robust slice-based algorithms (e.g., sorting, selection, search) for non-contiguous (sub)views into n-dimensional arrays. Reimplements algorithms in slice and rayon::slice for ndarray with arbitrary memory layout (e.g., non-contiguous).

Example

use ndarray_slice::{ndarray::arr2, Slice1Ext};

// 2-dimensional array of 4 rows and 5 columns.
let mut v = arr2(&[[-5, 4, 1, -3,  2],   // row 0, axis 0
                   [ 8, 3, 2,  4,  8],   // row 1, axis 0
                   [38, 9, 3,  0,  3],   // row 2, axis 0
                   [ 4, 9, 0,  8, -1]]); // row 3, axis 0
//                    \     \       \
//                  column 0 \    column 4         axis 1
//                         column 2                axis 1

// Mutable subview into the last column.
let mut column = v.column_mut(4);

// Due to row-major memory layout, columns are non-contiguous
// and hence cannot be sorted by viewing them as mutable slices.
assert_eq!(column.as_slice_mut(), None);

// Instead, sorting is specifically implemented for non-contiguous
//  mutable (sub)views.
column.sort_unstable();

assert!(v == arr2(&[[-5, 4, 1, -3, -1],
                    [ 8, 3, 2,  4,  2],
                    [38, 9, 3,  0,  3],
                    [ 4, 9, 0,  8,  8]]));
//                                   \
//                                 column 4 sorted, others untouched

Current Implementation

Complexities where n is the length of the (sub)view and m the count of indices to select.

Resource	Complexity	Sorting (stable)	Sorting (unstable)	Selection (unstable)	Bulk Selection (unstable)
Time	Best	O(n)	O(n)	O(n)	O(n log m)
Time	Expected	O(n log n)	O(n log n)	O(n)	O(n log m)
Time	Worst	O(n log n)	O(n log n)	O(n log n)	O(n log n log m)
Space	Best	O(1)	O(1)	O(1)	O(m)
Space	Expected	O(n/2)	O(log n)	O(log n)	O(m+log n)
Spoce	Worst	O(n/2)	O(log n)	O(log n)	O(m+log n)

Roadmap

Lower worst-case time complexity from O(n log n) to O(n) for selection algorithms.
Add SliceExt trait for n-dimensional array or (sub)view with methods expecting Axis as their first argument. Comparing methods will always be suffixed with _by or _by_key defining how to compare multi-dimensional elements (e.g., columns) along the provided axis of interest (e.g., rows).

See the release history to keep track of the development.

Features

alloc for stable sort/sort_by/sort_by_key. Enabled by std.
std for stable sort_by_cached_key. Enabled by default or rayon.
rayon for parallel par_sort*/par_select_many_nth_unstable*.

License

This project is licensed under either of

Apache License, Version 2.0, (LICENSES/Apache-2.0 or https://www.apache.org/licenses/LICENSE-2.0)
MIT license (LICENSES/MIT or https://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.