PyIRoGlass: An Open-Source, Bayesian MCMC Algorithm for Fitting Baselines to FTIR Spectra of Basaltic-Andesitic Glasses

Quantifying volatile concentrations in magmas is critical for understanding magma storage, phase equilibria, and eruption processes. We present PyIRoGlass, an open-source Python package for quantifying H$_2$O and CO$_2$ species concentrations in the transmission FTIR spectra of basaltic to andesitic glasses. We leverage a database of naturally degassed melt inclusions and back-arc basin basalts to delineate the fundamental shape and variability of the baseline underlying the $\mathrm{CO_3^{2-}}$ and $\mathrm{H_2O_{m, 1635}}$ peaks, in the mid-infrared region. All Beer-Lambert Law parameters are examined to quantify associated uncertainties. PyIRoGlass employs Bayesian inference and Markov Chain Monte Carlo sampling to fit all probable baselines and peaks, solving for best-fit parameters and capturing covariance to offer robust uncertainty estimates. Results from PyIRoGlass agree with independent analysis of experimental devolatilized glasses (within 6\%) and interlaboratory standards (13\% for H$_2$O, 9\% for CO$_2$). The open-source nature of PyIRoGlass ensures its adaptability and evolution as more data become available.