Lib.rs

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 1.49 or later

Crate Feature Flags

The following crate feature flags are available. They are configured in your Cargo.toml. See doc::crate_feature_flags for more information.

• std: Rust standard library-using functionality (enabled by default)
• serde: serialization support for serde 1.x
• rayon: Parallel iterators, parallelized methods, the parallel module and par_azip!.
• approx Implementations of traits from version 0.4 of the approx crate.
• approx-0_5: Implementations of traits from version 0.5 of the approx crate.
• blas: transparent BLAS support for matrix multiplication, needs configuration.
• matrixmultiply-threading: Use threading from matrixmultiply.

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.

For conversion between ndarray, nalgebra and image check out nshare.