BBPN (Bye Bye Pink Noise) reduces 1/f noise in JWST calibrated (cal.fits) images. It takes an input cal.fits file and (optionally) a segmentation mask to estimate and subtract correlated background structure, including calculations performed separately for each of four detector amplifiers. If no mask is supplied, BBPN attempts to locate a corresponding _seg.fits file. The software writes a corrected FITS image and includes switches to disable per-amplifier 1/f subtraction and/or per-amplifier background subtraction when masking is imperfect. BBPN can also produce diagnostic plots of intermediate processing steps.

gsf fits photometric data points, simultaneously with grism spectra if provided, to get posterior probability of galaxy physical properties, such as stellar mass, dust attenuation, metallicity, as well as star formation and metallicity enrichment histories. Designed for extra-galactic science, this flexible, python-based SED fitting code involves a Markov-Chain Monte-Carlo (MCMC) process, and may take more time (depending on the number of parameters and length of MCMC chains) than other SED fitting codes based on chi-square minimization.