#array #data-structure #multidimensional #matrix #blas

ndarray

An n-dimensional array for general elements and for numerics. Lightweight array views and slicing; views support chunking and splitting.

79 releases

✓ Uses Rust 2018 edition

0.13.0 Sep 23, 2019
0.12.1 Nov 21, 2018
0.12.0 Sep 1, 2018
0.11.2 Mar 21, 2018
0.3.0-alpha.1 Dec 27, 2015

#14 in Data structures

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17,772 downloads per month
Used in 196 crates (152 directly)

MIT/Apache

630KB
12K SLoC

ndarray implements an n-dimensional container for general elements and for numerics.

In n-dimensional we include for example 1-dimensional rows or columns, 2-dimensional matrices, and higher dimensional arrays. If the array has n dimensions, then an element in the array is accessed by using that many indices. Each dimension is also called an axis.

Highlights

  • Generic n-dimensional array
  • Slicing, also with arbitrary step size, and negative indices to mean elements from the end of the axis.
  • Views and subviews of arrays; iterators that yield subviews.
  • Higher order operations and arithmetic are performant
  • Array views can be used to slice and mutate any [T] data using ArrayView::from and ArrayViewMut::from.
  • Zip for lock step function application across two or more arrays or other item producers (NdProducer trait).

lib.rs:

The ndarray crate provides an n-dimensional container for general elements and for numerics.

In n-dimensional we include for example 1-dimensional rows or columns, 2-dimensional matrices, and higher dimensional arrays. If the array has n dimensions, then an element in the array is accessed by using that many indices. Each dimension is also called an axis.

  • ArrayBase: The n-dimensional array type itself.
    It is used to implement both the owned arrays and the views; see its docs for an overview of all array features.
  • The main specific array type is Array, which owns its elements.

Highlights

  • Generic n-dimensional array
  • Slicing, also with arbitrary step size, and negative indices to mean elements from the end of the axis.
  • Views and subviews of arrays; iterators that yield subviews.
  • Higher order operations and arithmetic are performant
  • Array views can be used to slice and mutate any [T] data using ArrayView::from and ArrayViewMut::from.
  • Zip for lock step function application across two or more arrays or other item producers (NdProducer trait).

Crate Status

  • Still iterating on and evolving the crate
    • The crate is continuously developing, and breaking changes are expected during evolution from version to version. We adopt the newest stable rust features if we need them.
    • Note that functions/methods/traits/etc. hidden from the docs are not considered part of the public API, so changes to them are not considered breaking changes.
  • Performance:
    • Prefer higher order methods and arithmetic operations on arrays first, then iteration, and as a last priority using indexed algorithms.
    • The higher order functions like .map(), .map_inplace(), .zip_mut_with(), Zip and azip!() are the most efficient ways to perform single traversal and lock step traversal respectively.
    • Performance of an operation depends on the memory layout of the array or array view. Especially if it's a binary operation, which needs matching memory layout to be efficient (with some exceptions).
    • Efficient floating point matrix multiplication even for very large matrices; can optionally use BLAS to improve it further.
  • Requires Rust 1.37 or later

Crate Feature Flags

The following crate feature flags are available. They are configured in your Cargo.toml.

  • serde
    • Optional, compatible with Rust stable
    • Enables serialization support for serde 1.x
  • rayon
    • Optional, compatible with Rust stable
    • Enables parallel iterators, parallelized methods and [par_azip!].
  • approx
    • Optional, compatible with Rust stable
    • Enables implementations of traits from the [approx] crate.
  • blas
    • Optional and experimental, compatible with Rust stable
    • Enable transparent BLAS support for matrix multiplication. Uses blas-src for pluggable backend, which needs to be configured separately.

Documentation

  • The docs for ArrayBase provide an overview of the n-dimensional array type. Other good pages to look at are the documentation for the s![] and azip!() macros.

  • If you have experience with NumPy, you may also be interested in ndarray_for_numpy_users.

The ndarray ecosystem

ndarray provides a lot of functionality, but it's not a one-stop solution.

ndarray includes matrix multiplication and other binary/unary operations out of the box. More advanced linear algebra routines (e.g. SVD decomposition or eigenvalue computation) can be found in ndarray-linalg.

The same holds for statistics: ndarray provides some basic functionalities (e.g. mean) but more advanced routines can be found in ndarray-stats.

If you are looking to generate random arrays instead, check out ndarray-rand.

Dependencies

~0.6–1MB
~19K SLoC