I am a fairly active open-source developer on GitHub. Most of my projects are related to astronomy in some way and I typically code in Julia and Python. Below are some projects I feel like highlighting.
JuliaHCI is a group of Julia packages for high-contrast imaging (HCI). ADI.jl was inspired by the vortex imaging pipeline (VIP) and contains algorithms and metrics used for removing speckles in angular differential imaging (ADI). Firefly.jl is still experimental, but will contain statistical models for exoplanets for use with ADI.jl
I’m particularly active around JuliaAstro; some notable packages I’ve worked on or written are
The main feature of this package is allocation-free aperture photometry. The performance compared to photutils is a couple orders of magnitude faster using the unique array represantation of the apertures.
Transits.jl provides fast and accurate analytical limb-darkened light curves, using the formalism from Agol et al. 2020. The goal of this package is to provide the orbits, light curves, and distributions to fit transiting light-curves with packages like Optim.jl and Turing.jl. Long-term I have visions for separating out the orbit code into its own package, adding more limb-darkening models, adding some radial-velocity specific distributions and code into its own package, and adding Gaussian process covariance modeling.
PSFModels.jl uses a similar design idea as the apertures Photometry.jl to provide fast, allocation-free PSF models such as Gaussians and Airy disks. Evaluating a Gaussian PSF is 4 orders of magnitude faster than AstroPy’s 2D Gaussian model, which makes fitting PSF models effortless with Optim.jl.
CCDReduction.jl was developed as part of Google Summer of Code (GSoC) 2020. I helped mentor an undergraduate student in developing this package, which provides convenient utilities and functions for reducing CCD data and was inspired by ccdproc.
NestedSamplers.jl provides implementations of static nested sampling algorithms. Similar to dynesty, it breaks up the nested sampling algorithm into three distinct pieces which work together to perform Bayesian inference. It supports single and multi-ellipsoidal bounding spaces as well as a variety of methods for proposing new points. NestedSamplers.jl implements the AbstractMCMC.jl interface, which greatly increases the flexibility and standardizes sampling with NestedSamplers.jl.