arprax-algorithms 0.1.1

Industrial-Grade Algorithms by Arprax Lab

Arprax Algorithms

Industrial-grade algorithms, performance profilers, and data structures for Python.

Built by Arprax Lab, this toolkit is designed for the "Applied Data Intelligence" era—where understanding how code scales is as important as the code itself.

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🚀 Features

• ArpraxProfiler: High-precision analysis with GC control, warmup cycles, and OHPV2 (Doubling Test) complexity estimation.
• Industrial Generators: Data factories for random, sorted, and reversed datasets.
• Standard Library: High-performance implementations of classic algorithms (Merge Sort, Bubble Sort, etc.) with strict type hinting.

📦 Installation

pip install arprax-algorithms

🔬 Quick Start: Benchmarking

Once installed, you can immediately run a performance battle between algorithms.

from arprax_algorithms import ArpraxProfiler, generators, algorithms

# 1. Initialize the industrial profiler
profiler = ArpraxProfiler(mode="min", repeats=5)

# 2. Run a doubling test (OHPV2 Analysis)
# This measures how Merge Sort scales as data size (N) doubles
results = profiler.run_doubling_test(
    algorithms.sorting.merge_sort, 
    generators.random_array,
    start_n=500,
    rounds=5
)

# 3. Print the performance analysis
profiler.print_analysis("Merge Sort", results)

🏗️ The Arprax Philosophy

Applied Data Intelligence requires more than just code—it requires proof.

• Zero-Magic: Every algorithm is written for clarity and performance. We don't hide logic behind obscure abstractions or hidden standard library calls.
• Empirical Evidence: We don't just guess Big O complexity; we measure it using high-resolution timers and controlled environments.
• Industrial Scale: Our tools are designed to filter out background CPU noise, providing reliable benchmarks for real-world software engineering.

📚 Citation

To cite the Software: See the "Cite this repository" button on our GitHub.

To cite the Handbook (Documentation):

@manual{arprax_handbook,
  title        = {The Algorithm Engineering Handbook},
  author       = {Chowdhury, Tanmoy},
  organization = {Arprax LLC},
  year         = {2026},
  url          = {https://algorithms.arprax.com/book},
  note         = {Accessed: 2026-02-01}
}

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© 2026 Arprax Lab A core division of Arprax dedicated to Applied Data Intelligence.