globalemu emulates the Global or sky averaged 21-cm signal and the associated neutral fraction history. The code can train a network on your own Global 21-cm signal or neutral fraction simulations using the built-in globalemu pre-processing techniques. It also features a GUI that can be invoked from the command line and used to explore how the structure of the Global 21-cm signal varies with the values of the astrophysical inputs.

maxsmooth fits derivative constrained functions (DCF) such as Maximally Smooth Functions (MSFs) to data sets. MSFs are functions for which there are no zero crossings in derivatives of order m >= 2 within the domain of interest. They are designed to prevent the loss of signals when fitting out dominant smooth foregrounds or large magnitude signals that mask signals of interest. Here "smooth" means that the foregrounds follow power law structures and do not feature turning points in the band of interest. maxsmooth uses quadratic programming implemented with CVXOPT (ascl:2008.017) to fit data subject to a fixed linear constraint, Ga <= 0, where the product Ga is a matrix of derivatives. The code tests the <= 0 constraint multiplied by a positive or negative sign and can test every available sign combination but by default, it implements a sign navigating algorithm.