rust-ndarray/ndarray

Features

• Multidimensional Arrays - Provides the core infrastructure for managing and manipulating n-dimensional arrays and matrices.
• Array View Creation - Produces read-only or mutable windows into existing data buffers without copying underlying elements.
• Strided - Maps multidimensional indices to flat memory buffers using axis-specific step sizes for efficient zero-copy slicing.
• Zero-Copy Array Views - Provides lightweight references to existing memory buffers to allow slicing and reshaping without data duplication.
• Tensor Slicing and Indexing - Extracts specific elements or sub-regions of arrays using coordinate-based indexing and slicing.
• Numeric Type Traits - Uses generics and numeric traits to apply mathematical operations across different numeric types and dimensions.
• Array Layout Management - Modifies shape and stride information independently of the underlying data using dedicated reference types.
• Array Slicing - Extracts sub-sections of arrays using arbitrary step sizes and negative indices.
• Element-wise Array Operations - Executes high-performance element-wise operations and mathematical functions across multidimensional arrays.
• Generalized Matrix Multiplications - Executes high-performance matrix product computations with support for scalar scaling.
• High-Performance Array Arithmetic - Executes high-performance mathematical operations across arrays of varying dimensions using optimized routines.
• Scientific Computing - Provides a computational framework for performing complex mathematical modeling and multi-dimensional array operations.
• Linear Algebra Libraries - Serves as a high-performance framework for vector and matrix operations.
• Linear Algebra Routines - Implements fundamental linear algebra operations including matrix multiplication and dot products.
• Multi-Dimensional Arrays - Implements data structures that organize numeric elements into grids or higher-dimensional spaces.
• Multidimensional Array Containers - Offers dense data structures supporting various memory layouts for numerical computing and data analysis.
• N-Dimensional Array Libraries - Provides a comprehensive Rust library for creating and manipulating multidimensional arrays with efficient memory views.
• Linear Algebra - Provides high-performance routines for vector and matrix operations, including dot products.
• Parallel Array Processing - Increases processing speed by distributing array iterations and methods across multiple CPU threads.
• Dot Product Computation - Calculates the inner product of two arrays, including those with dynamic dimensions.
• Ownership and Borrowing - Uses Rust's ownership and borrowing semantics to separate data ownership from data access via unified interfaces.
• Multidimensional Shape Handling - Implements runtime-defined vectors to support arrays with an arbitrary number of axes.
• Dimension Squeezing - Removes dimensions of size one from an array to simplify its overall shape.
• Scientific Array Serialization - Supports persisting and loading multidimensional arrays using scientific data formats.
• Axis Permutation - Changes the order of dimensions or reverses the direction of specific axes in-place.
• Parallel Iterators - Distributes element-wise operations and axis reductions across multiple CPU cores using parallel iterators.
• BLAS Backend Integration - Leverages optimized backend math libraries for high-speed floating-point matrix multiplication.
• Strided Window Generation - Generates overlapping windows of a specific size along an axis using a defined step size.
• Ownership-Based Memory Management - Implements memory management using Rust's ownership and borrowing model to handle owned arrays and non-owning views.
• Array Function Mapping - Applies custom functions to corresponding elements of multiple arrays in lock step with broadcasting support.
• Array Combinations - Joins arrays together by stacking them along new axes or concatenating them along existing ones.
• Array Collapsing - Collapses multidimensional arrays into one-dimensional sequences while preserving element order.
• Array Reshaping - Changes the dimensions of an array while preserving elements and specifying memory layout order.
• Array Splitting - Divides single arrays into multiple smaller views along a specified axis.
• Dimensional Reductions - Reduces arrays by computing sums and other aggregations along specified dimensions.
• BLAS and LAPACK Integrations - Provides interfaces to optimized BLAS libraries for high-performance linear algebra and matrix multiplication.
• Coordinate Grid Generation - Builds regular grids of values to evaluate functions over a specific coordinate space.
• Numerical Computation Accelerations - Accelerates mathematical processing by distributing array operations across multiple CPU threads.
• Parallel Array Processors - Distributes heavy mathematical iterations and array methods across multiple CPU threads to accelerate performance.
• Scientific Computing Libraries - N-dimensional array library for Rust.

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