ExoPix is a collection of tutorials aimed at illustrating the imaging of exoplanets with the James Webb Space Telescope (JWST). ExoPix tutorials are meant to demonstrate the application of the PSF-subtraction algorithm pyKLIP (ascl:1506.001) to simulated JWST NIRCAM data. We provide simple walkthroughs of pyKLIP’s ability to reveal exoplanets, compute contrast curves, and measure exoplanet astrometry and photometry in imaged extrasolar systems.

kpic_pipeline reduces data taken with the Keck Planet Imager and Characterizer (KPIC). Written in Python, the code processes high resolution spectroscopy data taken with KPIC to study exoplanet atmospheres; it processes and calibrate the data to enable spectroscopic model fitting. kpic_pipeline can reduce the observed data into 1D spectra for one given science target or can be used to reduce the full nightly data.

The SPEDAS (Space Physics Environment Data Analysis Software) framework supports multi-mission data ingestion, analysis and visualization for the Space Physics community. It standardizes the retrieval of data from distributed repositories, the scientific processing with a powerful set of legacy routines, the quick visualization with full output control and the graph creation for use in papers and presentations. SPEDAS includes a GUI for ease of use by novice users, works on multiple platforms, and though based on IDL, can be used with or without an IDL license. The framework supports plugin modules for multiple projects such as THEMIS, MMS, and WIND, and provides interfaces for software modules developed by the individual teams of those missions. A Python implementation of the framework, PySPEDAS (ascl:2405.005), is also available.

whereistheplanet predicts the locations of directly imaged companions (mainly exoplanets and brown dwarfs) based on past orbital fits to the data. This tool helps coordinate follow-up observations to characterize their properties, as precise pointing of the instrument is often needed. It uses orbitize! (ascl:1910.009) as a backend. whereistheplanet is available as a Python API, a command line tool, and a web form at whereistheplanet.com.

centerRadon finds the center of stars based on Radon Transform to sub-pixel precision. For a coronagraphic image of a star, it starts from a given location, then for each sub-pixel position, it interpolates the image and sums the pixels along different angles, creating a cost function. The center of the star is expected to correspond with where the cost function maximizes. The default values are set for the STIS coronagraphic images of the Hubble Space Telescope by summing over the diagonals (i.e., 45° and 135°), but it can be generally applied to other high-contrast imaging instruments with or without Adaptive Optics systems such as HST-NICMOS, P1640, or GPI.

pyKLIP subtracts out the stellar PSF to search for directly-imaged exoplanets and disks using a Python implementation of the Karhunen-Loève Image Projection (KLIP) algorithm. pyKLIP supports ADI, SDI, and ADI+SDI to model the stellar PSF and offers a large array of PSF subtraction parameters to optimize the reduction. pyKLIP relies on a minimal amount of dependencies (numpy, scipy, and astropy) and parallelizes the KLIP algorithm to speed up the reduction. pyKLIP supports GPI and P1640 data and can interface with other data sources with the addition of new modules. It also can inject simulated planets and disks as well as automatically search for point sources in PSF-subtracted data.

orbitize fits the orbits of directly-imaged objects by packaging the Orbits for the Impatient (OFTI) algorithm and a parallel-tempered Markov Chain Monte Carlo (MCMC) algorithm into a consistent API. It accepts observations in three measurement formats, which can be mixed in the same input file, generates orbits, and plots the computed orbital parameters. orbitize offers numerous ways to visualize the data, including histograms, corner plots, and orbit plots. Generated orbits can be saved in HDF5 format for future use and analysis.