InferAGNI 26.3.18

Infer planet properties using AGNI as a static structure model

AGNI Inference Package

Inferring planet properties using AGNI as a static structure model. This package provides a Bayesian inference framework for exoplanet characterization by coupling AGNI (a static interior structure model) with atmospheric retrieval via MCMC.

Contents

• Installation
• Quick Start
• Project Structure
• Getting Started with Development
• Running Tests
• Key Modules
• Resources

External links

• AGNI Model (atmosphere climate model)
• AGNI GitHub
• Zalmoxis (interior structure model)
• Research Paper

Citation

@article{nicholls_volatile_2026,
	author = {Nicholls, Harrison and Lichtenberg, Tim and Chatterjee, Richard D. and Guimond, Claire Marie and Postolec, Emma and Pierrehumbert, Raymond T.},
	title = {{Volatile-rich evolution of molten super-Earth L 98-59 d}},
	journal = {Nat. Astron.},
	pages = {1--9},
	year = {2026},
	month = mar,
	issn = {2397-3366},
	publisher = {Nature Publishing Group},
	doi = {10.1038/s41550-026-02815-8}
}

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Installation

Prerequisites

• Python 3.12 or later
• A conda distribution (e.g., Miniforge)

Setup

1. Clone the repository and navigate to the directory:

   git clone https://github.com/nichollsh/InferAGNI.git
   cd InferAGNI
   

2. Install the package and its dependencies:

   pip install -e .
   

3. Download required data files:

   inferagni update
   

Quick Start

Running Your First Inference

Once installed, retrieve properties for an exoplanet:

inferagni infer "L 98-59 d"

Available Commands

• inferagni infer <planet_name> - Run atmospheric retrieval for a planet
• inferagni listplanets - List all available planets in the database
• inferagni planet <planet_name> - Get observed parameters for a specific planet
• inferagni listvars - Display available variables in the AGNI grid

Running Tests

Quick Test Commands

# Run all tests
pytest tests/

# Run a specific test file
pytest tests/test_util.py

# Run a single test
pytest tests/test_util.py::test_calc_scaleheight_basic

# Run only unit tests (fast)
pytest -m unit tests/

# Run with coverage report
coverage run -m pytest tests/
coverage report

Test Organization

Tests are organized by functionality with pytest markers:

• @pytest.mark.unit - Fast unit tests (~seconds)
• @pytest.mark.integration - Integration tests requiring external resources
• @pytest.mark.slow - Long-running tests
• @pytest.mark.smoke - Quick validation tests

Exclude slow tests during development with pytest -m "not slow" tests/.

Code Quality

# Lint code
ruff check src/ tests/

# Format code
ruff format src/ tests/

# Run pre-commit hooks
pre-commit run --all-files

The project enforces a minimum 10% code coverage threshold in CI.

Project Structure

src/inferagni/
├── cli.py              # Command-line interface (Click-based)
├── planets.py          # Exoplanet database integration
├── retrieve.py         # MCMC atmospheric retrieval logic
├── grid.py             # AGNI grid data management
├── data.py             # Data loading and caching
├── plot.py             # Visualization utilities
├── util.py             # Helper functions
├── data/               # Grid data and other dependencies
└── exoatlas-data/      # Bundled exoplanet database

cli.py

The command-line interface providing user-facing commands for inferring planet properties, listing available exoplanets, and inspecting grid variables.

planets.py

Integrates with the exoatlas library to manage exoplanet data. Solar system planets are bundled separately; exoplanet data is filtered to include only those with measured radii.

retrieve.py

Implements Bayesian atmospheric retrieval using MCMC sampling. Uses emcee for efficient ensemble sampling.

grid.py

Manages static grid data from AGNI model outputs. Provides interpolation and data access for interior structure calculations.

util.py

Utility functions for common calculations (scale height, dimensionless parameter conversion, LaTeX formatting) and general helpers.

plot.py

Plotting utilities using matplotlib and cmcrameri colormaps for scientific visualization.

External Libraries

• emcee - MCMC sampling
• exoatlas - Exoplanet database
• Click - CLI framework
• netCDF4 - Data file format
• NumPy/SciPy - Scientific computing

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Available under GPLv3. Copyright (c) 2026 Harrison Nicholls.