astro-void-finder 0.2.0

High-performance astrophysics library for void finding, neutrino physics, and N-body simulations

AstroLib - Advanced Astrophysics Library

Overview

AstroLib is a high-performance Python library for astrophysical computations, focusing on void finding, neutrino physics, and N-body simulations. It's designed to work efficiently even on low-performance hardware through innovative algorithms and resource management.

Features

Core Components

• Void Finding: Advanced algorithms for void detection and analysis
• Neutrino Physics: Tools for neutrino mass estimation using void expansion
• N-Body Simulations: Optimized particle simulations
• Machine Learning Integration: ML-based analysis tools

Performance Optimizations

• Adaptive resource management
• Smart caching system
• Memory-efficient data structures
• Parallel processing capabilities

Installation

# Clone the repository
git clone https://github.com/yourusername/astrolib.git
cd astrolib

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install -e .

Requirements

• Python >= 3.8
• NumPy >= 1.20.0
• SciPy >= 1.7.0
• Astropy >= 4.0
• Numba >= 0.55.0
• h5py >= 3.0.0
• matplotlib >= 3.4.0
• pandas >= 1.3.0
• scikit-learn >= 1.0.0
• psutil >= 5.8.0

Quick Start

from astrolib.optimized_voids import StreamingVoidFinder
from astrolib.optimized_nbody import OptimizedNBody, SimulationConfig
from astrolib.neutrino_ml import NeutrinoMassEstimator

# Initialize void finder with resource-aware settings
finder = StreamingVoidFinder(chunk_size=100000, n_workers=2)

# Find voids in streaming mode
for void in finder.find_voids_streaming(data_iterator, box_size=500.0):
    print(f"Found void: radius = {void['radius']:.2f}")

# Set up N-body simulation
config = SimulationConfig(
    dt=0.01,
    softening=1e-4,
    theta=0.5,
    use_cache=True,
    n_workers=2
)
simulator = OptimizedNBody(config)

# Run simulation
positions, velocities = simulator.simulate_chunk(
    positions,
    velocities,
    masses,
    steps=100
)

# Estimate neutrino mass
estimator = NeutrinoMassEstimator()
mass, uncertainty = estimator.predict(voids)
print(f"Estimated neutrino mass: {mass:.3f} ± {uncertainty:.3f} eV")

Documentation

Void Finding

The library implements multiple void finding algorithms:

• Watershed Void Finder
• ZOBOV (Zones Bordering On Voidness)
• ML-enhanced void detection

from astrolib.optimized_voids import StreamingVoidFinder

# Initialize finder
finder = StreamingVoidFinder()

# Find voids
voids = finder.find_voids_streaming(
    data_iterator,
    box_size=500.0
)

N-Body Simulations

Efficient N-body simulations with adaptive time stepping:

from astrolib.optimized_nbody import OptimizedNBody

# Configure simulation
simulator = OptimizedNBody()

# Run simulation
positions, velocities = simulator.simulate_chunk(
    initial_positions,
    initial_velocities,
    masses
)

Neutrino Physics

Tools for neutrino mass estimation:

from astrolib.neutrino_ml import NeutrinoMassEstimator

# Initialize estimator
estimator = NeutrinoMassEstimator()

# Estimate mass
mass, uncertainty = estimator.predict(void_catalog)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this library in your research, please cite:

@software{astrolib2025,
  author = {Your Name},
  title = {AstroLib: Advanced Astrophysics Library},
  year = {2025},
  url = {https://github.com/yourusername/astrolib}
}