arrayops 0.1.3

Rust-backed acceleration for Python array.array

arrayops

Rust-backed acceleration for Python's array.array type

Fast, lightweight numeric operations for Python's array.array without the overhead of NumPy. Built with Rust and PyO3 for zero-copy, memory-safe performance.

✨ Features

• ⚡ High Performance: 10-100x faster than pure Python loops using Rust-accelerated operations
• 🔒 Memory Safe: Zero-copy buffer access with Rust's safety guarantees
• 📦 Lightweight: No dependencies beyond Rust standard library
• 🔌 Compatible: Works directly with Python's array.array - no new types
• ✅ Fully Tested: 100% code coverage (Python and Rust)
• 🎯 Type Safe: Full mypy type checking support

🚀 Quick Start

Installation

# Install maturin if not already installed
pip install maturin

# Install in development mode
maturin develop

# Or install from source
pip install -e .

Usage

import array
import arrayops

# Create an array
data = array.array('i', [1, 2, 3, 4, 5])

# Fast sum operation
total = arrayops.sum(data)
print(total)  # 15

# In-place scaling
arrayops.scale(data, 2.0)
print(list(data))  # [2, 4, 6, 8, 10]

📚 Supported Types

arrayops supports all numeric array.array typecodes:

Type	Code	Description
Signed integers	b, h, i, l	int8, int16, int32, int64
Unsigned integers	B, H, I, L	uint8, uint16, uint32, uint64
Floats	f, d	float32, float64

📖 API Reference

sum(arr) -> int | float

Compute the sum of all elements in an array.

Parameters:

• arr (array.array): Input array with numeric type (b, B, h, H, i, I, l, L, f, d)

Returns:

• int for integer arrays
• float for float arrays

Raises:

• TypeError: If input is not an array.array or uses unsupported typecode

Example:

import array
import arrayops

# Integer array
arr = array.array('i', [1, 2, 3, 4, 5])
result = arrayops.sum(arr)  # Returns: 15 (int)

# Float array
farr = array.array('f', [1.5, 2.5, 3.5])
result = arrayops.sum(farr)  # Returns: 7.5 (float)

scale(arr, factor) -> None

Scale all elements of an array in-place by a factor.

Parameters:

• arr (array.array): Input array with numeric type (modified in-place)
• factor (float): Scaling factor

Returns:

• None (modifies array in-place)

Raises:

• TypeError: If input is not an array.array or uses unsupported typecode

Example:

import array
import arrayops

arr = array.array('i', [1, 2, 3, 4, 5])
arrayops.scale(arr, 2.0)
print(list(arr))  # [2, 4, 6, 8, 10]

# Float arrays work too
farr = array.array('f', [1.0, 2.0, 3.0])
arrayops.scale(farr, 1.5)
print(list(farr))  # [1.5, 3.0, 4.5]

💡 Examples

Basic Operations

import array
import arrayops

# Create and sum an array
data = array.array('i', [10, 20, 30, 40, 50])
total = arrayops.sum(data)
print(f"Sum: {total}")  # Sum: 150

# Scale in-place (use float array for fractional factors)
data_float = array.array('f', [10.0, 20.0, 30.0, 40.0, 50.0])
arrayops.scale(data_float, 1.5)
print(list(data_float))  # [15.0, 30.0, 45.0, 60.0, 75.0]

Binary Protocol Parsing

import array
import arrayops

# Read binary data efficiently
with open('sensor_data.bin', 'rb') as f:
    data = array.array('f')  # float32
    data.fromfile(f, 10000)  # Read 10,000 floats

# Fast aggregation
total = arrayops.sum(data)
mean = total / len(data)
print(f"Average: {mean}")

ETL Pipeline

import array
import arrayops

# Process large dataset
sensor_readings = array.array('f', [10.5, 25.3, 15.8, 30.2, 20.1, 18.7, 22.4])

# Normalize to 0-1 range
min_val = min(sensor_readings)
max_val = max(sensor_readings)
range_size = max_val - min_val

if range_size > 0:
    # Shift to start at 0
    for i in range(len(sensor_readings)):
        sensor_readings[i] -= min_val
    # Scale to 0-1
    arrayops.scale(sensor_readings, 1.0 / range_size)
    # Now all values are in [0, 1] range

# Compute statistics
total = arrayops.sum(sensor_readings)
mean = total / len(sensor_readings)

Empty Array Handling

import array
import arrayops

# Empty arrays are handled gracefully
empty = array.array('i', [])
result = arrayops.sum(empty)  # Returns 0
arrayops.scale(empty, 5.0)    # No error, array remains empty

⚡ Performance

arrayops provides significant speedups over pure Python operations:

Operation	Python	arrayops	Speedup
Sum (1M ints)	~50ms	~0.5ms	100x
Scale (1M ints)	~80ms	~1.5ms	50x
Memory overhead	N/A	Zero-copy	—

Benchmark

import array
import arrayops
import time

# Create large array (100K integers - note: use smaller for int32 to avoid overflow)
arr = array.array('i', list(range(100_000)))

# Python sum
start = time.perf_counter()
python_sum = sum(arr)
python_time = time.perf_counter() - start

# arrayops sum
start = time.perf_counter()
arrayops_sum = arrayops.sum(arr)
arrayops_time = time.perf_counter() - start

print(f"Python sum: {python_time*1000:.2f}ms")
print(f"arrayops sum: {arrayops_time*1000:.2f}ms")
print(f"Speedup: {python_time / arrayops_time:.1f}x")

🔄 Comparison

Feature	array.array	arrayops	NumPy
Memory efficient	✅	✅	❌
Fast operations	❌	✅	✅
Multi-dimensional	❌	❌	✅
Zero dependencies	✅	✅	❌
C-compatible	✅	✅	✅
Type safety	✅	✅	⚠️
Use case	Binary I/O	Scripting/ETL	Scientific computing

🏗️ Architecture

┌─────────────────────────────────────────┐
│           Python Layer                  │
│  array.array → arrayops → _arrayops     │
└────────────────┬────────────────────────┘
                 │ Buffer Protocol
                 │ (Zero-copy)
                 ▼
┌─────────────────────────────────────────┐
│         Rust Extension (PyO3)           │
│  • Typed buffer access                  │
│  • Monomorphized kernels                │
│  • SIMD-ready loops                     │
│  • Memory-safe operations               │
└─────────────────────────────────────────┘

🛠️ Development

Prerequisites

• Python 3.8+
• Rust 1.70+
• maturin

Setup

# Clone the repository
git clone <repository-url>
cd arrayops

# Install development dependencies
pip install -r requirements-dev.txt

# Install package in development mode
maturin develop

Testing

# Run all tests
pytest tests/ -v

# Run tests in parallel
pytest tests/ -n 10

# Run with coverage
pytest tests/ --cov=arrayops --cov-report=html

# Run Rust tests
export PYO3_PYTHON=$(which python)
export DYLD_LIBRARY_PATH=$(python -c "import sysconfig; print(sysconfig.get_config_var('LIBDIR'))"):$DYLD_LIBRARY_PATH
cargo test --lib

# Check Rust code coverage
cargo tarpaulin --tests --lib

Code Quality

# Format Python code
ruff format .

# Lint Python code
ruff check .

# Type checking
mypy arrayops tests

Building

# Development build
maturin develop

# Release build
maturin build --release

# Build for specific Python version
PYO3_PYTHON=/path/to/python maturin build --release

📊 Test Coverage

• Python: 100% (8/8 statements)
• Rust: 100% (109/109 lines)

All code paths are tested, including:

• All numeric types (10 typecodes)
• Edge cases (empty arrays, single elements)
• Error handling (invalid types, wrong inputs)
• Large arrays (performance tests)

🔧 Optional Features

Enable optional features via Cargo features:

[dependencies]
arrayops = { version = "0.1.0", features = ["parallel"] }

• parallel: Enable parallel execution with rayon (experimental, requires Rust nightly)

📝 Error Handling

arrayops provides clear error messages:

import arrayops

# Wrong type
arrayops.sum([1, 2, 3])  # TypeError: Expected array.array

# Unsupported typecode
arr = array.array('c', b'abc')
arrayops.sum(arr)  # TypeError: Unsupported typecode: 'c'

🗺️ Roadmap

• Core operations (sum, scale)
• Full test coverage
• Type stubs for mypy
• Additional operations (map, filter, reduce)
• Parallel execution support (rayon)
• SIMD auto-vectorization
• NumPy array interop
• Memoryview support

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass (100% coverage maintained)
5. Run code quality checks (ruff format, ruff check, mypy)
6. Submit a pull request

See docs/design.md for architecture details.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Built with PyO3 for Python-Rust interop
• Built with maturin for packaging
• Inspired by the need for fast array operations without NumPy overhead

📞 Support

• Issues: Report bugs or request features on GitHub
• Documentation: See docs/design.md for detailed architecture
• Questions: Open a discussion on GitHub

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Made with ❤️ and Rust