Crate numrs2

Expand description

§NumRS2: High-Performance Numerical Computing in Rust

NumRS2 is a comprehensive numerical computing library for Rust, inspired by NumPy. It provides a powerful N-dimensional array object, sophisticated mathematical functions, and advanced linear algebra, statistical, and random number functionality.

§Quick Start

use numrs2::prelude::*;

let a = Array::from_vec(vec![1.0, 2.0, 3.0, 4.0]).reshape(&[2, 2]);
let b = Array::from_vec(vec![5.0, 6.0, 7.0, 8.0]).reshape(&[2, 2]);
let c = a.matmul(&b).unwrap();
println!("Matrix multiplication result: {}", c);

§Main Features

N-dimensional Array: Core Array type with efficient memory layout and broadcasting
Linear Algebra: Matrix operations, decompositions, solvers through BLAS/LAPACK integration
Mathematical Functions: Comprehensive set of element-wise mathematical operations
SIMD Acceleration: Vectorized math operations using SIMD instructions
Parallel Computing: Multi-threaded execution with Rayon
Random Number Generation: Modern interface for various distributions
GPU Acceleration: Optional GPU-accelerated array operations using WGPU
Type Safety: Leverage Rust’s type system for compile-time guarantees

Re-exports§

pub use error::NumRs2Error;
pub use error::Result;
pub use random::random_base;

Modules§

algorithms: High-performance numerical algorithms
array
array_ops
array_ops_legacy
arrays: Array operations and data structures
axis_ops
blas
char: String and character array operations (NumPy char module equivalent)
comparisons
conversions
error: NumRS2 Error System
financial: Financial Functions Module
indexing
interop: Interoperability with other Rust numerical libraries
io
linalg: Basic linear algebra operations with Array Includes matrix multiplication, dot product, matrix inversion, etc.
linalg_extended: Extended Linear Algebra Operations
linalg_optimized: Optimized linear algebra operations with enhanced performance algorithms
linalg_parallel: Parallel linear algebra operations with load balancing
linalg_stable: Numerically stable matrix decomposition algorithms
masked
math
math_extended: Extended Mathematical Functions
matrix
memory_alloc: Custom memory allocation for numerical workloads
memory_optimize: Memory layout optimization for cache efficiency
mmap
new_modulesDeprecated
parallel: Parallel processing enhancements and workload balancing
parallel_optimize: Parallel processing optimization
prelude: Core prelude that exports the most commonly used types and functions
printing: Array Printing and Display Module
random: Random Number Generation
set_ops: Set operations for arrays
signal: Signal Processing Module
simd
simd_optimize: CPU feature detection and SIMD optimization for NumRS
sparse: Sparse Matrix Operations
sparse_enhanced: Enhanced sparse matrix operations with advanced algorithms and optimizations
stats
stride_tricks
traits: Core Trait System for NumRS2
types: Advanced data types and type system for NumRS
ufuncs
unique
unique_optimized
util
views

Macros§

error_location: Macro for creating error location from current position
operation_context: Macro for creating operation context with current function
simd_array: Convenience macro for creating SIMD-optimized arrays