pycosmix 1.4.0

A Bayesian Inference Pipeline for Modified Gravity

COSMIX — COSmological Modular Inference eXplorer

COSMIX is a modular, likelihood-driven Bayesian inference framework for testing modified gravity and dark energy cosmological models against observational data. It supports multiple samplers (emcee, Dynesty, PolyChord) and a growing library of likelihoods (CC, Pantheon+, DESI DR2 BAO, RSD, GW standard sirens, DES-SN5YR).

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Installation

From PyPI

pip install pycosmix

From source (for development)

git clone https://github.com/AmeyaKolhatkar/COSMIX.git
cd COSMIX
pip install -e .

For a specific tagged release (recommended for reproducible research):

git clone --branch v1.4.0 https://github.com/AmeyaKolhatkar/COSMIX.git
cd COSMIX
pip install -e .

Alternatively, a stable archived version is available via Zenodo:

Optional: build documentation locally

pip install -e ".[docs]"

Optional: PolyChord nested sampler

Warning! The PolyChord nested sampler has not been tested extensively. If you still wish to use it, do

pip install -e src/cosmix/data/PolyChordLite

On Windows you will need WinLibs/MinGW with gfortran and ensure <mingw64>/bin is on your PATH (or set POLYCHORD_DLL_DIR).

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Quick Start

1. Configure a run

Edit input.yaml (or copy it) to specify the model, datasets, sampler, and output options:

run:
  name: "MyModel"
  run_id: "my_model_test_1"

model:
  name: fQ_EHybrid          # LCDM | fQ_Hybrid | fQ_EHybrid | fQ_LSR | fQ_LSR_IDE | fQ_Hybrid_IDE

likelihoods:
  - name: CC                 # Cosmic Chronometers
  - name: DDTB               # DESI DR2 BAO
  - name: PP                 # Pantheon+ (SN Ia)
  - name: RSD                # Redshift Space Distortions

sampler:
  name: dynesty              # emcee | dynesty | polychord
  init:
    nlive: 500

outputs:
  plots:
    trace: true
    corner: true
    residual: true
  archive: true

2. Run

Once installed, the cosmix command is available on your PATH:

cosmix input.yaml

Results are saved to runs/<run_id>/ including the chain, diagnostics, information criteria (AIC, BIC, DIC), and publication-quality figures.

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Project Structure

COSMIX/
├── pyproject.toml            # Package metadata, dependencies, build config
├── input.yaml                # Template run configuration
├── src/
│   └── cosmix/
│       ├── run_cosmix.py     # Main entry point — reads input.yaml and runs the pipeline
│       ├── Constants.py      # Physical constants (c, Omegar0)
│       ├── core/             # Framework internals (pipeline, parameter management, caching)
│       ├── theory/           # Cosmological models (LCDM, f(Q) variants)
│       │   ├── Solvers_/     # ODE/root solvers (RK4, analytical, Numba JIT)
│       │   └── Background_/  # Background state layout
│       ├── likelihoods/      # Observational likelihoods
│       ├── samplers/         # Sampler wrappers (emcee, Dynesty, PolyChord)
│       ├── drivers/          # Multi-chain convergence strategies
│       ├── postprocessing/   # Results, diagnostics, visualization, archival
│       │   └── Archive_/     # Run manifest, serialization (YAML/JSON/NumPy)
│       └── data/             # Observational data files
│           └── PolyChordLite/  # Bundled PolyChord source (build separately)
└── runs/                     # Output directory (gitignored contents)

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Available Models

Key	Description
LCDM	Standard ΛCDM
fQ_Hybrid	f(Q) Hybrid model (analytical solution)
fQ_EHybrid	Extended f(Q) Hybrid model (ODE solver)
fQ_Hybrid_Curved	f(Q) Hybrid model with spatial curvature
fQ_LSR	f(Q) Log-Square-Root model
fQ_LSR_IDE	f(Q) LSR with Interacting Dark Energy
fQ_Hybrid_IDE	f(Q) Hybrid with Interacting Dark Energy
fQ_Squared_Curved	Squared f(Q) model with spatial curvature

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Available Likelihoods

Key	Dataset	Reference
cosmicchronometers	Cosmic Chronometers (correlated + uncorrelated)	Moresco et al.
pantheonplus	Pantheon+ (SN Ia, no SH0ES prior)	Brout et al. 2022
pantheonplusshoes	Pantheon+SH0ES (SN Ia + H₀ prior)	Brout et al. 2022
desidr2bao	DESI DR2 BAO (full GC combination)	DESI Collaboration 2025
rsd	Redshift Space Distortions (fσ₈)	Various
gw	GW Standard Sirens (GW170817)	LIGO/Virgo
desy5	DES-SN5YR (SN Ia with probabilistic classification)	DES Collaboration
desdovekie	DES-Dovekie Likelihood	DES Collaboration
shoes	SH0ES H₀ Gaussian prior	Riess et al.
trgb	TRGB H₀ prior	Freedman et al.
holicow	H0LiCOW H₀ prior	Wong et al.
compressedcmb	Compressed CMB	Z. Zhai and Y. Wang
egstatistic	Eg Statistic	G. Alestas et al.

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Adding a New Model

1. Create src/cosmix/theory/MyModel.py inheriting from cosmix.core.CosmologyModelBase.
2. Implement declare_parameters(), check_physicality(), and get_requirements().
3. Register it through the Registry.py utility

   from cosmix.core.Registry import cosmix_registry
   
   @cosmix_registry.register_model("MyModel")
   class MyModel(...)
   

4. Reference it in input.yaml as model: { name: MyModel }.

Adding a New Likelihood

1. Create src/cosmix/likelihoods/MyLikelihood.py inheriting from cosmix.core.LikelihoodBase_.
2. Implement declare_parameters(), get_requirements(), and lnlike().
3. Register it through the Registry.py utility

   from cosmix.core.Registry import cosmix_registry
   
   @cosmix_registry.register_likelihood("MyLikelihood")
   class MyLikelihood(...)
   

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Under Development

• The PolyChord sampler has not been fully tested yet. User discretion is adviced.
• The multi_auto mode for emcee sampler is not fully developed. The goal is to implement the auto stop feature found in most Bayesian inference codes like Cobaya.
• The MetropolisHastings.py sampler is premature. Avoid using it for serious projects.

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Coming Soon

• Full CAMB-based CMB power spectrum likelihoods (preliminary interface already in place).

Requirements

• Python ≥ 3.10
• See pyproject.toml for the full list of dependencies.
• A Fortran compiler (gfortran) is only needed if building PolyChordLite.

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License

This project is licensed under the GNU General Public License v3.0. See LICENSE for details.

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Citation

If you use COSMIX in your research, please cite the following:

@software{kolhatkar_cosmix_2026,
  author       = {Kolhatkar, Ameya},
  title        = {{COSMIX: Cosmological Modular Inference Explorer}},
  year         = 2026,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.19791571},
  url          = {https://doi.org/10.5281/zenodo.19791571}
}

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Contact

If there are any issues or suggestions for the code please feel free to contact me at
[e-mail]

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