galform-analysis 0.1.7

A modular Python framework for reading and analyzing GALFORM HDF5 simulation outputs.

galform_analysis

A modular Python framework designed for the efficient reading and analysis of GALFORM HDF5 simulation outputs. This library provides standardized tools for processing large-scale galaxy formation data, from low-level HDF5 I/O to high-level astronomical probes.

Core Features

• Standardized I/O: Robust loaders for GALFORM galaxies.hdf5 files with support for different output versions.
• Data Aggregation: Tools to scan simulation directories and aggregate data across subvolumes using high-performance polars dataframes.
• Mass Functions: Computation of Stellar Mass Functions (SMF), Halo Mass Functions (HMF), and Halo Occupation Distribution (HOD).
• Correlation Functions: Estimators for 2-point, 3-point, and N-point correlation functions (2PCF/NPCF) including subvolume-weighted corrections for convergence analysis.
• Redshift-Space Distortions: Estimators for anisotropic clustering ($\xi(s, \mu)$) and multipoles ($\xi_0, \xi_2, \xi_4$).
• Simulation Management: Built-in configurations for major N-body simulations including L800, Millennium I/II, COLIBRE, and FLAMINGO.

Installation

Install the package in your Python environment:

uv pip install -e .

Dependencies

The library requires numpy, scipy, matplotlib, polars, h5py, seaborn, and Corrfunc. These are automatically managed during installation.

Quick Start

The following example demonstrates how to load a simulation configuration and read galaxy data:

from galform_analysis import SimulationConfig, config
from galform_analysis.readers.loaders import read_snapshot_data

# 1. Access simulation-specific constants (box size, cosmology, etc.)
sim = SimulationConfig('L800')
print(f"Simulation: {sim.name}, Box Size: {sim.box_size} Mpc/h")

# 2. Configure the data location
config.set_base_dir('/path/to/Galform_Out/L800/model_name')

# 3. Load snapshot data for a specific subvolume
data = read_snapshot_data('iz271', ivol=0)
mstar = data['mstar']  # Stellar masses in M_sun/h

Citation & Academic Credit

If you use galform_analysis in your research, please cite the software using the DOI provided above or the following BibTeX entry:

@software{galform_analysis,
  author = {Hickman, Oscar},
  title = {galform_analysis: A modular Python framework for analyzing GALFORM simulation outputs},
  version = {0.1.3},
  doi = {10.5281/zenodo.FIXME},
  url = {https://github.com/OscarHickman/galform_analysis}
}

Simulation Metadata

Configurations for supported simulations are stored centrally in galform_analysis/sim_configs/. This allows for dynamic access to cosmological parameters and volume metadata:

from galform_analysis import SimulationConfig

flamingo = SimulationConfig('FLAMINGO')
omega_m = flamingo.omega_m
h0 = flamingo.h0

Documentation & Examples

Refer to the examples/ directory for interactive Jupyter notebooks:

• examples/readers/load_snapshot.ipynb: Introduction to data loading.
• examples/analysis/mass_functions/smf_example.ipynb: Plotting Stellar Mass Functions.
• examples/analysis/correlation/correlation_example.ipynb: Computing clustering statistics.

Testing & Quality Standards

The project maintains high code quality through automated linting and comprehensive testing:

# Run the test suite
pytest tests

# Check code style
ruff check galform_analysis

Author

Oscar Hickman