rendarray 0.2.0-alpha.8

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

• ArrayBase: The N-dimensional array type itself.
• Array: An array where the data is shared and copy on write, it can act as both an owner of the data as well as a lightweight view.
• OwnedArray: An array where the data is owned uniquely.
• 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 an easy to use copy on write array (Array), or a regular uniquely owned array (OwnedArray), and both can use read-only and read-write array views.
• Iteration and most operations are very efficient on contiguous c-order arrays (the default layout, without any transposition or discontiguous subslicing), and on arrays where the lowest dimension is contiguous.
• Array views can be used to slice and mutate any [T] data.

Status and Lookout

• Still iterating on the API
• Performance status:

• Arithmetic involving contiguous c-order arrays and contiguous lowest dimension arrays optimizes very well.
• .fold() and .zip_mut_with() are the most efficient ways to perform single traversal and lock step traversal respectively.
• Transposed arrays where the lowest dimension is not c-contiguous is still a pain point.

• There is experimental bridging to the linear algebra package rblas.

Crate Feature Flags

• assign_ops
• Optional, requires nightly
• Enables the compound assignment operators
• rustc-serialize
• Optional, stable
• Enables serialization support
• rblas
• Optional, stable
• Enables rblas integration