ucdmcmc 1.2.1

MCMC fitting code for low temperature atmosphere spectra

UCDMCMC

Markov Chain Monte Carlo (MCMC) fitting code for low-temperature stars, brown dwarfs ande extrasolar planet spectra, tuned particularly to the near-infrared.

INSTALLATION NOTES

ucdmcmc can be installed from pip:

pip install ucdmcmc

or from git:

git clone
cd ucdmcmc
python -m setup.py install

It is recommended that you install in a conda environment to ensure the dependencies do not conflict with your own installation

conda create -n ucdmcmc python=3.13
conda activate ucdmcmc
pip install ucdmcmc

A check that this worked is that you can import ucdmcmc into python/jupyter noteobook, and that the ucdmcmc.MODEL_FOLDER points to the models folder that was downloaded

ucdmcmc uses the following extenal packages:

• astropy: https://www.astropy.org/
• astroquery: https://astroquery.readthedocs.io/en/latest/
• corner: https://corner.readthedocs.io/en/latest/
• emcee: https://emcee.readthedocs.io/en/stable/
• matplotlib: https://matplotlib.org/
• numpy<2.0: https://numpy.org/
• pandas: https://pandas.pydata.org/
• (py)tables: https://www.pytables.org/
• requests: https://requests.readthedocs.io/en/latest/
• scipy: https://scipy.org/
• spectres: https://spectres.readthedocs.io/en/latest/
• statsmodels: https://www.statsmodels.org/stable/index.html
• tqdm: https://tqdm.github.io/

Optionally install SPLAT

To generate new model sets using the built-in generateModels() function, you will need to install SPLAT (note: this is not necessary for the other functionality in this code). SPLAT is not automatically installed on setup. The instructions are essentially the same:

git clone https://github.com/aburgasser/splat.git
cd splat
python -m pip install .

See https://github.com/aburgasser/splat for additional instructions

Models

ucdmcmc comes with a starter set of models that play nicely with the code. An extended set can be downloaded from https://spexarchive.coolstarlab.ucsd.edu/ucdmcmc/. These should be placed in the folder .ucdmcmc_models in your home directory (i.e., /home/adam/.ucdmcmc.models). If it doesn't already exist, this directory will be created on the first call to ucdmcmcm. In addition, models that exist on this website and not present in this folder will be downloaded directly when getModelSet()`` is called. You can also generate your own set of models using the generateModels()` function (see note above).

Spectra

ucdmcmc comes with a starter set of spectra for the following instruments:

• EUCLID: TBD
• NIR: TRAPPIST1 spectrum from Davoudi et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...970L...4D/abstract
• SPEX-PRISM: 2MASS J0559-1404 from Burgasser et al. (2006) https://ui.adsabs.harvard.edu/abs/2006ApJ...637.1067B/abstract
• JWST-NIRSPEC-PRISM: UNCOVER 33436 from Burgasser et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...962..177B/abstract
• JWST-NIRSPEC-G395H: TBD
• JWST-MIRI-LRS: TBD
• JWST-NIRSPEC-MIRI: Combined NIRSpec/PRISM and MIRI/LRS of SDSS J1624+0029 from Beiler et al. (2024) https://ui.adsabs.harvard.edu/abs/2024arXiv240708518B/abstract

User spectra can be read in using ucdmcmc.Spectrum("filename"). Files can be .fits, .csv, .txt (space-delimited), or .tsv (tab-delimited), and should have wavelength, flux, and uncertainty arrays. You can also read in these files separately and create a Spectrum object using the call ucdmcmc.Spectrum(wave=[wave array,flux=[flux array],noise=[uncertainty array]). See the docstring for ucdmcmc.Spectrum for further details.

Usage

[TBD examples]

Opacities

[TBD]

Citing the code

If you use this code in your research, publications, or presentatinos, please include the following citation:

Adam Burgasser. (2025). aburgasser/ucdmcmc (vXXX). Zenodo. https://doi.org/10.5281/zenodo.16923762

or in bibtex:

@software{adam_burgasser_2025_16921711,
	author = {Adam Burgasser},
	doi = {10.5281/zenodo.16921711},
	month = aug,
	publisher = {Zenodo},
	title = {aburgasser/ucdmcmc},
	url = {https://doi.org/10.5281/zenodo.16921711},
	version = {vXXX},
	year = 2025,
	bdsk-url-1 = {https://doi.org/10.5281/zenodo.16921711}}

where (vXXX) corresponds to the version used.

ucdmcmc and its antecedents has been used in the following publications:

• Burgasser et al. (2024, ApJ 962, 177): https://ui.adsabs.harvard.edu/abs/2024ApJ...962..177B/abstract
• Burgasser et al. (2025, ApJ 982, 79): https://ui.adsabs.harvard.edu/abs/2025ApJ...982...79B/abstract
• Lueber & Burgasser (2025, ApJ 988, 31): https://ui.adsabs.harvard.edu/abs/2025ApJ...988...31L/abstract
• Morrissey et al. (2025, AJ, submitted)
• Burgasser et al. (2025, Science, submitted)

Please let me know if you make use of the code so we can include your publication in the list above!