prysm VS xrt

My current concept is to just combine zernike polynomials with a random factor and calculate the PSF from that, which can be somewhat easily be done with the prysm library. These PSFs can then be convolved with circular and gaussian kernels for modelling additional defocus and accounting for other stuff like the AA filter. Then I'd add chromatic aberration by offseting/scaling the PSFs for each channel. Some generated kernels already look pretty good when comparing them to stars in astrophotography images, but others not so much.

https://github.com/brandondube/prysm (caveat emptor: mine)

You can trace about 1 billion raysurfaces per second in pure python with CuPy, or a few million raysurfaces per second on CPU.

New paper from /u/BDube_Lensman using prysm to model NASA's Roman Coronagraph

This free book is what this free code is based on

Prysm Originally for diffraction type optics but seems to able to handle...everything? Performance as a priamary concern, GPU acceleration, proven JPL heritage :) Raytracing is however still experimental and without docs, generally whilst the library looks excellent if you're an optics person already I think I lack a bit of the base fundamental knowledge to really use it powerfully from just the API reference. I can see BDube has some raytracing example code in some of the issues I could probably adapt and muddle my way through at least. No guis is mildly annoying for a noob like myself, but I can work my way around matplotlib-ing just fine instead i'm sure.

You may want to steal my shim set since it lets you hot swap Numpy<-->cupy at runtime

I do computational diffraction with large manycore servers and GPUs at a FFRDC. The difference between MKL and not MKL is the difference between hitting enter and getting a result in an hour or two vs tomorrow.

I am working on simulations in the x-ray range, so fundamentals like propagators are the same, but approximations of optical elements and interactions are different. The x-rays are emitted by a synchrotron radiation source, i.e. an electron storage ring. The statistical properties of the wavefronts are determined by the phase space of the electrons in the storage ring, which is usually modeled by a 5D Gaussian distribution (space + momentum + energy). People are interested in the coherence properties of the emitted ensemble of wavefronts, so the simulation consists are drawing random parameters from the 5D Gaussian, calculating the wavefront emitted and propagating it to the points of interest. There, the wavefronts are collected and their coherence properties are evaluated. I use the coherence module of XRT for that: https://github.com/kklmn/xrt/blob/master/xrt/backends/raycing/coherence.py