ndarray 0.4.0-alpha.8

The ndarray crate provides an N-dimensional container similar to numpy’s ndarray.

• ArrayBase: The N-dimensional array type itself.
• OwnedArray: An array where the data is owned uniquely.
• RcArray: An array where the data has shared ownership and is copy on write.
• ArrayView, ArrayViewMut: Lightweight array views.

Highlights

• Generic N-dimensional array
• Slicing, also with arbitrary step size, and negative indices to mean elements from the end of the axis.
• There is both a copy on write array (RcArray), or a regular uniquely owned array (OwnedArray), and both can use read-only and read-write array views.
• Iteration and most operations are efficient on arrays with contiguous innermost dimension.
• Array views can be used to slice and mutate any [T] data using ArrayView::from and ArrayViewMut::from.

Crate Status

• Still iterating on and evolving the API
  • The crate is continuously developing, and breaking changes are expected during evolution from version to version. We adhere to semver, but alpha releases break at will.
  • We adopt the newest stable rust features we need. In place methods like iadd will be deprecated when Rust supports += and similar in Rust 1.8.
  • We try to introduce more static checking gradually.
• Performance status:
  • Arithmetic involving arrays of contiguous inner dimension optimizes very well.
  • .fold() and .zip_mut_with() are the most efficient ways to perform single traversal and lock step traversal respectively.
  • .iter() and .iter_mut() are efficient for contiguous arrays.
• There is experimental bridging to the linear algebra package rblas.

Crate Feature Flags

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

• assign_ops
  • Requires Rust 1.8, will be default soon.
  • Enables the compound assignment operators
• rustc-serialize
  • Optional, stable
  • Enables serialization support
• rblas
  • Optional, stable
  • Enables rblas integration