ucdmcmc 1.4.2
MCMC fitting code for low temperature atmosphere spectra
Requires Python: >=3.10

# UCDMCMC
Markov Chain Monte Carlo (MCMC) fitting code for low-temperature stars, brown dwarfs and extrasolar planet spectra, tuned particularly to the near-infrared. This code is continually being updated, so please suggest fixes, features, and/or improvements.

## INSTALLATION NOTES

`ucdmcmc` can be installed from pip:

	pip install ucdmcmc

or from git:

	git clone https://github.com/aburgasser/ucdmcmc.git
	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 external 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 `ucdmcmc`. 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).

`ucdmcmc` has been successfully run with the following models:

* ATMO2020: Phillips et al. (2020) https://ui.adsabs.harvard.edu/abs/2020A%26A...637A..38P/abstract
* ATMO2020++: Meisner et al. (2023) https://ui.adsabs.harvard.edu/abs/2023AJ....166...57M/abstract
* BT Cond: Allard et al. (2012) https://ui.adsabs.harvard.edu/abs/2012RSPTA.370.2765A/abstract
* BT Dusty: Allard et al. (2012) https://ui.adsabs.harvard.edu/abs/2012RSPTA.370.2765A/abstract
* BT Settl 2008: Allard et al. (2012) https://ui.adsabs.harvard.edu/abs/2012RSPTA.370.2765A/abstract
* BT Settl 2015: Allard et al. (2015) https://ui.adsabs.harvard.edu/abs/2015A%26A...577A..42B/abstract
* Burrows et al. (2006) https://ui.adsabs.harvard.edu/abs/2006ApJ...640.1063B/abstract
* Drift: Witte et al. (2011) https://ui.adsabs.harvard.edu/abs/2011A%26A...529A..44W/abstract
* Exo-REM: Blain et al. (2021) https://ui.adsabs.harvard.edu/abs/2021A%26A...646A..15B/abstract
* Helios: Kitzmann et al. (2020) https://ui.adsabs.harvard.edu/abs/2020ApJ...890..174K/abstract
* Lacy & Burrows (2023) https://ui.adsabs.harvard.edu/abs/2023ApJ...950....8L/abstract
* LOWZ: Meisner et al. (2021) https://ui.adsabs.harvard.edu/abs/2021ApJ...915..120M/abstract
* Madhusudhan et al. (2011) https://ui.adsabs.harvard.edu/abs/2011ApJ...737...34M/abstract
* Morley et al. (2012) https://ui.adsabs.harvard.edu/abs/2012ApJ...756..172M/abstract
* Morley et al. (2014) https://ui.adsabs.harvard.edu/abs/2014ApJ...787...78M/abstract
* Phoenix New Era: Hauschildt et al. (2025) https://ui.adsabs.harvard.edu/abs/2025A%26A...698A..47H/abstract
* SAND: Alvarado et al. (2024) https://ui.adsabs.harvard.edu/abs/2024RNAAS...8..134A/abstract
* Saumon et al. (2012) https://ui.adsabs.harvard.edu/abs/2012ApJ...750...74S/abstract
* Sonora Bobcat: Marley et al. (2021) https://ui.adsabs.harvard.edu/abs/2021ApJ...920...85M/abstract
* Sonora Cholla: Karalidi et al. (2021) https://ui.adsabs.harvard.edu/abs/2021ApJ...923..269K/abstract
* Sonora Diamondback: Morley et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...975...59M/abstract
* Sonora Elfowl: Mukherjee et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...963...73M/abstract
* Sonora Elfowl + PH3: Beiler et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...973...60B/abstract
* Sonora Elfowl + CO2: Wogan et al. (2025) https://ui.adsabs.harvard.edu/abs/2025RNAAS...9..108W/abstract
* SPHINX: Iyer et al. (2023) https://ui.adsabs.harvard.edu/abs/2023ApJ...944...41I/abstract
* SPHINX 2: Iyer et al. (2025) https://ui.adsabs.harvard.edu/abs/2025arXiv251202269I/abstract
* Tremblin et al. (2015): https://ui.adsabs.harvard.edu/abs/2015ApJ...804L..17T/abstract

Additional models are continuously being added, please let us know if there are models you'd like to see included!

## Spectra

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

* OIR: Generic optical and infrared grid 0.3-30 µm at resolution = 300
* NIR: TRAPPIST1 spectrum from Davoudi et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...970L...4D/abstract
* EUCLID: EUCLID J0359-4740 from Dominguez-Tagle et al. (2025) https://ui.adsabs.harvard.edu/abs/2025ApJ...991...84D/abstract
* JWST-NIRSPEC-PRISM: Wolf 1130C from Burgasser et al. (2025) https://ui.adsabs.harvard.edu/abs/2025Sci...390..697B/abstract
* JWST-NIRSPEC-G395H: Wolf 1130C from Burgasser et al. (2025) https://ui.adsabs.harvard.edu/abs/2025Sci...390..697B/abstract
* JWST-MIRI-LRS: SDSS J1624+0029 from Beiler et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...973..107B/abstract
* JWST-NIRSPEC-MIRI: Combined NIRSpec/PRISM and MIRI/LRS of SDSS J1624+0029 from Beiler et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...973..107B/abstract
* KECK-NIRES: UGPC J0722-0540 from Theissen et al. (2022) https://ui.adsabs.harvard.edu/abs/2022RNAAS...6..151T/abstract
* FIRE-PRISM: Ross 458C from Burgasser et al. (2010) https://ui.adsabs.harvard.edu/abs/2010ApJ...725.1405B/abstract
* FIRE-PRISM: UGPC J0722-0540 from Bochanski et al. (2011) https://ui.adsabs.harvard.edu/abs/2011AJ....142..169B/abstract
* SPEX-PRISM: 2MASS J0559-1404 from Burgasser et al. (2006) https://ui.adsabs.harvard.edu/abs/2006ApJ...637.1067B/abstract
* SPEX-SXD: 2MASS J0559-1404 from Cushing et al. (2005) https://ui.adsabs.harvard.edu/abs/2005ApJ...623.1115C/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

`ucdmcmc` comes with samples to help you test the code which you can read in with the function `ucdmcmc.getSample()`:

	import ucdmcmc
	sp = ucdmcmc.getSample('JWST-NIRSPEC-PRISM')
	sp.plot()

<img width="804" height="488" alt="plot1" src="https://github.com/user-attachments/assets/81ab06f9-8341-47c6-89f2-bf61268d225d" />

Fitting requires reading in a model set; for existing sets specify the model name and the instrument:

	models,wave = ucdmcmc.getModelSet('elfowl24','JWST-NIRSPEC-PRISM')

We map our spectrum to the model wave grid, then we can conduct a grid fit to find the single best fitting model using `ucdmcmc.fitGrid()`:

	sp.toWavelengths(wave)
	par = ucdmcmc.fitGrid(sp,models)

Output: 

	Best parameters:
		model = elfowl24
		co = 0.5
		kzz = 7.0
		logg = 4.75
		teff = 600.0
		z = -1.0
		scale = 1.616424519232685e-20
		chis = 1290354.449246344
		radius = 0.056390540414855445
		dof = 853.0
		rchi = 1512.7250284247878
	
<img width="804" height="487" alt="plot2" src="https://github.com/user-attachments/assets/461ac995-0361-46bc-93cc-dd42ebcd58c5" />

These grid parameters can be used to initiate an MCMC fit using using `ucdmcmc.fitMCMC()`:

	npar = ucdmcmc.fitMCMC(sp,models,par,nstep=100,burn=0.25,absolute=True,verbose=False)

Output: 

	Best parameters:
		model: elfowl24
		co: 0.580211857460554
		kzz: 7.3919278336945515
		logg: 5.365954958490183
		teff: 633.8145781129864
		z: -0.9388765775373868
		scale: 1.328342456515958e-20
		chis: 1328254.9586724846
		radius: 0.05111914351717615
		dof: 848
		rchi: 1512.7250284247878
	
<img width="914" height="567" alt="plot3" src="https://github.com/user-attachments/assets/8a1053cf-22df-463f-a120-1c60dfbda799" />

(note: it is recommended to run with more than 100 steps!)

You can visualize the parameter distributions using `ucdmcmc.plotCorner()`:

	ucdmcmc.plotCorner(npar)
	
<img width="703" height="703" alt="plot4" src="https://github.com/user-attachments/assets/a23af421-4f0d-4552-965c-bef1ec7398bc" />

There are many variations to these fits, as well as secondary parameters (e.g., RV, vsini, line broadening, reddening, etc) that can be explored. Please see the help files associated with the specific functions.

## Opacities

[TBD]

## Citing the code

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

	Burgasser, Brooks, Morrissey, Haynes, & Liou (2025). aburgasser/ucdmcmc (vXXX). Zenodo. https://doi.org/10.5281/zenodo.16921710

or in bibtex:

	@software{adam_burgasser_2026_16921710,
	  author       = {Adam Burgasser and
	                  Brooks, Hunter and
	                  Morrissey, Sara and
	                  Haynes, Julia and
	                  Liou, Tiffany},
	  title        = {aburgasser/ucdmcmc: vXXXX},
	  month        = jan,
	  year         = 2026,
	  publisher    = {Zenodo},
	  version      = {v1.4},
	  doi          = {10.5281/zenodo.16921710},
	  url          = {https://doi.org/10.5281/zenodo.16921710},
	}

 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
* Burgasser et al. (2025, Science, 390, 697): https://ui.adsabs.harvard.edu/abs/2025Sci...390..697B/abstract
* Morrissey et al. (2026, AJ, in press): https://ui.adsabs.harvard.edu/abs/2025arXiv251101167M/abstract

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