PyTorchDIA is a Difference Image Analysis tool. It is built around the PyTorch machine learning framework and uses automatic differentiation and (optional) GPU support to perform fast optimizations of image models. The code offers quick results and is scalable and flexible.

VBBinaryLensing forward models gravitational microlensing events using the advanced contour integration method; it supports single and binary lenses. The lens map is inverted on a collection of points on the source boundary to obtain a corresponding collection of points on the boundaries of the images from which the area of the images can be recovered by use of Green’s theorem. The code takes advantage of a number of techniques to make contour integration much more efficient, including using a parabolic correction to increase the accuracy of the summation, introducing an error estimate on each arc of the boundary to enable defining an optimal sampling, and allowing the inclusion of limb darkening. The code is written as a C++ library and wrapped as a Python package, and can be called from either C++ or Python.

This package is superseded by VBMicrolensing (ascl:2506.005), which encompasses VBBinaryLensing. VBBinaryLensing will still be available as a legacy software, but will no longer be maintained.