ForMoSA 2.0.2

ForMoSA: Forward Modeling Tool for Spectral Analysis

ForMoSA — Forward Modeling Tool for Spectral Analysis

ForMoSA is an open-source Python package for modeling exoplanetary atmospheres using a forward modeling approach. It compares observed spectra and photometry against grids of atmospheric models via nested sampling to derive posterior distributions on physical parameters.

📖 Full documentation: https://formosa.readthedocs.io

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Features

• Class-based API centered around a single Analysis entry point
• Multi-instrument support (MOSAIC) — fit spectroscopic and photometric data simultaneously from multiple instruments
• Three nested-sampling back-ends — nestle, PyMultiNest, and UltraNest
• High-contrast mode — model stellar speckles and systematics alongside the companion signal
• Photometry filter service — automatic retrieval and caching of filter curves from the SVO Filter Profile Service
• Configurable prior distributions — uniform, log-uniform, Gaussian, and constant priors
• Comprehensive plotting — corner plots, chain diagnostics, radar diagrams, best-fit spectra, CCFs, and RV–v sin i maps
• Flexible configuration — INI-based config files or direct Python dataclass instantiation

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Installation

From PyPI

pip install ForMoSA
conda install dask netCDF4 bottleneck

From source

git clone https://github.com/exoAtmospheres/ForMoSA.git
cd ForMoSA
pip install -e .
conda install dask netCDF4 bottleneck

For developers

The optional dev extra adds the testing, packaging, and documentation-building tools on top of the runtime dependencies:

pip install -e ".[dev]"

A plain pip install ForMoSA installs only what is needed to run analyses — no test or documentation tooling.

ForMoSA requires Python ≥ 3.10. See the installation guide for PyMultiNest, GPU/torch, and macOS Apple Silicon instructions.

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Quick Start

from ForMoSA import Analysis
from ForMoSA.config.global_config import (
    ConfigPath, ConfigAdapt, ConfigInversion, ConfigParameters
)

# 1. Define paths
config_path = ConfigPath(
    observation_path=["path/to/observation.fits"],
    adapt_store_path="path/to/adapted_grid/",
    result_path="path/to/results/",
    model_path="path/to/model_grid.nc",
)

# 2. Initialize the analysis
analysis = Analysis(config_path)

# 3. Configure adaptation & inversion
config_adapt = ConfigAdapt(method="linear")
config_inversion = ConfigInversion(ns_algo="pymultinest", npoints=100)
config_parameters = ConfigParameters(
    par1=["uniform", "500", "3000"],   # e.g. Teff
    par2=["uniform", "2.5", "5.5"],    # e.g. log(g)
    r=["uniform", "0.5", "3.0"],       # radius in R_Jup
    d=["constant", "50"],              # distance in pc
)

# 4. Adapt the model grid to the observations
analysis.adapt(config_adapt, config_inversion)

# 5. Run nested sampling
analysis.nested_sampling(config_parameters, config_adapt, config_inversion)

# 6. Plot results
analysis.plot(analysis.ns.results)

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Package Structure

ForMoSA/
├── analysis.py              # Main Analysis class
├── config/                  # Configuration dataclasses & file I/O
├── core/                    # Enums, errors, logging, plot configs
├── filter/                  # SVO photometry filter interface
├── grid/                    # Model grids, subgrids, adaptation
├── nested_sampling/         # NS engine, post-processing, results, plotting
├── observation/             # Observation classes (spectral, photometric, set)
├── parameter/               # Parameters, priors, parameter sets
├── transform/               # Physics & observational effect pipelines
└── utils/                   # Spectral tools, likelihood & prior functions

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What's New in v2.0.0

v2.0.0 is a complete rewrite and is not backwards-compatible with v1.x. See the full migration guide in the docs.

Project updates since the v2.0.0 rewrite:

• Automated testing — every push and pull request is install- and import-checked and the test suite is run on Python 3.10, 3.11, and 3.12.
• One-tag releases — pushing a v* tag builds the package and publishes it to PyPI via a Trusted Publisher, with auto-generated GitHub release notes.
• JOSS paper — a software paper accompanying the v2.0.0 release is in preparation.

Area	v1.x	v2.0.0
Entry point	main.py script + launch_adapt() / launch_nested_sampling()	Single Analysis class
Configuration	config.ini + GlobFile	Python dataclasses (ConfigPath, ConfigAdapt, …)
Observations	Raw arrays from INI file paths	Typed ObservationSet loaded from .fits
Model grid handling	adapt/ functions	grid/ subgrid classes
Photometry filters	Bundled .npz files	Auto-downloaded from SVO and cached
CCF / RV–v sin i	✗	✓ new via analysis.plot_ccf() / analysis.plot_rv_vsini_map()
Logging	print statements	Python logging module
Error handling	Generic exceptions	ForMoSAError

Upgrading from v1.x

Replace the old script-based calls:

# v1.x (old)
from ForMoSA.global_file import GlobFile
from ForMoSA.adapt.adapt_obs_mod import launch_adapt
from ForMoSA.nested_sampling.nested_sampling import launch_nested_sampling

global_params = GlobFile("config.ini")
launch_adapt(global_params, adapt_model=True)
launch_nested_sampling(global_params)

with the new class-based API:

# v2.0.0 (new)
from ForMoSA import Analysis
from ForMoSA.config.global_config import ConfigPath, ConfigAdapt, ConfigInversion, ConfigParameters

analysis = Analysis(ConfigPath(
    observation_path=["obs.fits"],
    adapt_store_path="adapted_grid/",
    result_path="results/",
    model_path="model_grid.nc",
))
analysis.adapt(ConfigAdapt(), ConfigInversion())
analysis.nested_sampling(ConfigParameters(par1=["uniform","500","3000"], r=["uniform","0.5","3.0"], d=["constant","50"]))
analysis.plot(analysis.ns.results)

If you need the old behavior in the short term, pin to formosa==1.1.6.

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Attribution

If you use ForMoSA in your research, please cite Petrus et al. (2023).

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Contributing

Contributions are welcome — bug reports, fixes, new features, and documentation improvements alike. To set up a development environment:

git clone https://github.com/exoAtmospheres/ForMoSA.git
cd ForMoSA
pip install -e ".[dev]"
conda install dask netCDF4 bottleneck

Then:

1. Branch off ForMoSA_class (the integration branch), not main:

   git checkout ForMoSA_class
   git checkout -b my-feature
   

2. Run the test suite before opening a pull request:

   pytest
   

   Note: continuous integration checks that the package installs, imports, and passes the test suite cleanly on Python 3.10–3.12.

3. Build the documentation locally if your change touches the docs:

   cd docs && make html
   

4. Open a pull request against ForMoSA_class describing the change.

Releases are automated: once changes are merged and a maintainer pushes a v* tag (e.g. v2.0.0), the package is built and published to PyPI, and a GitHub release is created automatically.

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Issues

If you encounter any problems, please open an issue on GitHub.

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Acknowledgments

Our sincere thanks to Code/Astro, which provided the foundational training that enabled us to transform ForMoSA into a professional, open-source Python package.

We gratefully acknowledge the funding and support for the ForM-X workshops held in Nice (2023), Heidelberg (2024/2025), and Grenoble (2025). These collaborative sessions were instrumental in developing and refining the code. We also thank the various laboratories and institutions, especially IPAG, Lagrange, and MPIA, for their continued support.

Furthermore, this work has been supported by the French National Research Agency (ANR) through the MIRAGES project (PI: A. Vigan, ANR-20-CE31-0017). We also acknowledge support from the French National Research Agency (ANR) through the project grant ANR-20-CE31-0012.

S. Petrus was supported by an appointment to the NASA Postdoctoral Program at the NASA-Goddard Space Flight Center, administered by Oak Ridge Associated Universities under contract with NASA.

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Star History