pyDIA performs star detection, kernel-based difference imaging, and photometry on astronomical images. Its modular design separates routines for image alignment, image subtraction, source detection, and photometric measurement, so users can run end-to-end difference-imaging pipelines or call individual components in custom workflows. pyDIA is written in Python with some C components.

Dazzle constructs high-precision difference images to detect and measure transients, such as microlensing events, in crowded stellar fields using the Nancy Grace Roman Space Telescope. It generates difference images by subtracting an oversampled reference image, with iterative masking to address outlier pixels. It also provides an analytic correction for small dither offset errors. Microlensing detection is achieved through a three-dimensional matched-filtering technique, optimized with Gaussian kernels to capture varying event durations, and verified through synthetic tests with high recovery rates. Dazzle performs transient photometry via PSF fitting on difference images, using Nelder–Mead optimization for subpixel accuracy.

CMDFitter fits a probabilistic generative model to a color–magnitude diagram (CMD) of a star cluster or stellar population. Developed in Python, it uses a definition file to specify input photometry, isochrone models, and parameter priors, then samples the posterior distribution of stellar-population parameters (e.g., age, distance, metallicity) via nested sampling. The output includes posterior estimates of model parameters as well as diagnostic plots of the fitted model over-laid on the CMD. CMDFitter is particularly suited for interpreting deep stellar-population photometry in crowded fields and supports flexible customization of priors, isochrone sets, and observational uncertainties.