pycalc-pro 2.0.1

PyCalc Pro – High-performance AI math engine with physics, units, and sequence operations.

PyCalc Pro v2.0.1

A high-performance mathematical computing engine written in Python — designed for AI systems and scientific computing with advanced mathematics, physics calculations, and sequence operations featuring multi-backend acceleration and memory optimization.

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Version Notes

v2.0.1 — November 2025

• Performance Upgrade: Phase 3 optimizations with C++ extensions and GPU acceleration
• Multi-Backend Support: C++, Numba JIT, and pure Python fallbacks
• Memory Optimization: Advanced memory pooling for reduced allocations
• Expanded Modules: Sequences, unit operations, and physics calculations
• License Change: Updated to BSD 3-Clause license
• Code Quality: Fixed Numba signatures and consistent error handling

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Features

Core Mathematical Operations

• Basic Arithmetic: Optimized addition, subtraction, multiplication, division
• Advanced Functions: Power, roots, logarithms, exponentials
• Trigonometric Functions: Sine, cosine, tangent with degree support
• Special Functions: Factorial, modulus, absolute value
• Batch Processing: Parallel execution for large datasets

Physics Engine

• Classical Mechanics: Kinetic energy, potential energy, centripetal force
• Relativity: Time dilation, length contraction, relativistic gamma
• Quantum Mechanics: de Broglie wavelength, Schwarzschild radius
• Thermodynamics: Ideal gas law calculations
• Projectile Motion: Range calculations with custom gravity

Sequence & Number Theory

• Mathematical Sequences: Fibonacci, arithmetic, geometric progressions
• Prime Operations: Prime checking, factorization, prime generation
• Combinatorics: Permutations, combinations, factorial operations
• Series Calculations: Summation, product sequences

Unit Operations

• Unit Conversion: Comprehensive unit system support
• Dimensional Analysis: Automatic unit validation and conversion
• Physical Constants: Extensive library of scientific constants

Performance Optimizations

• C++ Extensions: Critical operations accelerated with C++
• GPU Acceleration: CUDA support for large batch operations
• Memory Pooling: Reduced allocation overhead
• Numba JIT: Just-in-time compilation for numerical functions
• Smart Backend Selection: Automatic optimization based on operation size
• Error Handling: Comprehensive validation and error codes

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Architecture

pycalc-pro/
├── core/ # Core mathematical engines
│ ├── math_ops.py # Phase 3 optimized math operations
│ ├── calculator.py # Main calculator engine
│ ├── physics_ops.py # Physics calculations
│ ├── sequences.py # Sequence and number theory
│ ├── unit_ops.py # Unit operations and conversions
│ ├── cpp_bridge.py # C++ extensions interface
│ ├── cpp_extensions.cpp # C++ acceleration code
│ ├── gpu_accelerator.py # GPU acceleration
│ └── performance.py # Performance monitoring
├── interface/ # User interfaces
│ ├── cli.py # Command-line interface
│ └── ai_interface.py # Interface for AI systems
├── utils/ # Support utilities
│ ├── cache.py # Intelligent caching system
│ ├── memory_pool.py # Memory optimization
│ ├── constants.py # Physical and mathematical constants
│ └── evaluator.py # Expression evaluation
├── examples/ # Usage examples
│ └── ai_usage.py # AI system integration examples
└── dist/ # Distribution files

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Installation & Usage

Option 1: Install from PyPI (Recommended)

pip install pycalc-pro
pycalc

Option 2: Install from GitHub

pip install git+https://github.com/lw-xiong/pycalc-pro.git
pycalc

Option 3: Clone and Run

git clone https://github.com/lw-xiong/pycalc-pro.git
cd pycalc-pro
pip install -e .
pycalc

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AI System Integration

from pycalc_pro.core.math_ops import MathOperations
from pycalc_pro.core.physics_ops import PhysicsOperations

# High-performance math for AI systems
math_engine = MathOperations()
result = math_engine.power(2, 10)  # 1024

# Physics calculations for scientific AI
physics_engine = PhysicsOperations()
ke = physics_engine.kinetic_energy(10, 5)  # 125 J

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Performance Features

• Memory Optimization: Reduced allocations via memory pooling
• Multi-Backend: Automatic selection of C++ → Numba → Python backends
• Batch Processing: Parallel execution for large-scale computations
• Error Safety: Comprehensive error codes and validation
• GPU Support: Optional CUDA acceleration for large datasets

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Author

Li Wen Xiong (李文雄) GitHub: @lw-xiong

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License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

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PyCalc Pro v2.0.1 — Optimized for Scientific Computing