RMath: Silicon-Speed Numerical Computing for Python

RMath is a high-performance numerical toolkit backed by a high-concurrency Rust engine. It provides NumPy-equivalent APIs with significant speedups in multi-threaded environments, leveraging Rayon for data-parallelism and PyO3 for zero-overhead FFI.

🚀 Why RMath?

• Zero-GIL Parallelism: Perform heavy matrix operations and statistical calculations without blocking the Python Global Interpreter Lock.
• Rust Precision: Built on faer, matrixmultiply, and ndarray for industrial-grade accuracy.
• Triple-Tier Storage: Automatic selection between Inline-Stack, Heap, and Memory-Mapped storage based on data size.
• No Compiler Required: Distributed as pre-compiled wheels for all major platforms.

🛠 Features & Modules

1. rmath.array & rmath.vector

Core N-dimensional array processing engine.

import rmath as rm
import rmath.vector as rv

# SIMD-accelerated vector ops
v1 = rv.Vector([1.0, 2.0, 3.0])
v2 = v1.exp().sin() # Parallelized element-wise ops

2. rmath.linalg

Advanced matrix solvers and decompositions.

• Solvers: LU, QR, Cholesky, and SVD.
• Inversion: High-speed matrix inversion via partial pivoting.

3. rmath.stats

Real-time descriptive and inferential statistics.

• Uses Welford’s Algorithm for single-pass, numerically stable variance/mean calculation.
• Full support for regression, p-value approximations, and distributions (Normal, Gamma, Beta).

📦 Installation

Install the pre-compiled binaries via pip:

pip install rmath

📖 Modern Documentation Portal

For the full API reference, architectural deep-dives, and performance benchmarks, visit our premium documentation portal:

👉 https://Ay-developerweb.github.io/rmath/

⚡ Quick Start

import rmath as rm

# Generate a 1000x1000 matrix
arr = rm.Array.randn(1000, 1000)

# High-speed parallel stats
avg = arr.mean()
std = arr.std()

# Linear Algebra Solver
b = rm.Array.ones(1000, 1)
x = rm.linalg.solve(arr, b)

print(f"Residual Sum: {x.sum()}")

🤝 Contributing

RMath is built in Rust (src/) and exposed to Python via PyO3.

• Rust: Core logic, SIMD, and Parallelism.
• Python: High-level API and Type Stubs (.pyi).

📜 License

Dual-licensed under MIT and Apache 2.0.