prysm
JuliaAdviceForMatlabProgrammers
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prysm | JuliaAdviceForMatlabProgrammers | |
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28 | 1 | |
234 | 38 | |
- | - | |
8.3 | 0.0 | |
15 days ago | almost 2 years ago | |
Python | ||
MIT License | MIT License |
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prysm
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How to generate realistic PSFs for camera lenses?
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.
- Prysm is a Python 3.6 library for numerical optics
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Books/ other resources to learn about Fraunhofer diffraction farfield model using MATLAB/python?
https://github.com/brandondube/prysm (caveat emptor: mine)
- Demonstrations of laser optics/Fourier optics and diffraction simulations
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Python raytracer optimizations and improvements
You can trace about 1 billion raysurfaces per second in pure python with CuPy, or a few million raysurfaces per second on CPU.
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Exascale integrated modeling of low-order wavefront sensing and control for the Roman Coronagraph instrument
New paper from /u/BDube_Lensman using prysm to model NASA's Roman Coronagraph
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Reccomended textbooks/reading for learning Thin Films
This free book is what this free code is based on
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Options for free optical simulation?
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.
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Options for GPU accelerated python experiments?
You may want to steal my shim set since it lets you hot swap Numpy<-->cupy at runtime
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Anaconda is so fucking broken!
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.
JuliaAdviceForMatlabProgrammers
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Opticsim.jl: Optical Simulation Software
I think people often underestimate (or just plain don't know about) the degree to which a multiple-dispatch-based programming language like Julia effectively implies its whole own dispatch-oriented programming paradigm, with both some amazing advantages (composability [1], and an IMO excellent balance of speed and interactivity when combined with JAOT compilation), but also some entirely new pitfalls to watch out for (particularly, type-instability [2,3]). Meanwhile, some habits and code patterns that may be seen as "best practices" in Python, Matlab can be detrimental and lead to excess allocations in Julia [4], so it may almost be easier to switch to Julia (and get good performance from day 1) if you are coming from a language like C where you are used to thinking about allocations, in-place methods, and loops being fast.
Things are definitely stabilizing a bit post-1.0, but it's still a young language, so it'll take a while for documentation to fully catch up; in the meanwhile, the best option in my experience has been to lurk the various chat forums (slack/zulip/etc.) and pick up best-practices from the folks on the cutting edge by osmosis.
[1] https://www.youtube.com/watch?v=kc9HwsxE1OY
[2] https://www.johnmyleswhite.com/notebook/2013/12/06/writing-t...
[3] https://docs.julialang.org/en/v1.5/manual/performance-tips/#...
[4] https://github.com/brenhinkeller/JuliaAdviceForMatlabProgram...
What are some alternatives?
OpticSim.jl - Optical Simulation software
nogil - Multithreaded Python without the GIL
poppy - Physical Optics Propagation in Python
mypyc - Compile type annotated Python to fast C extensions
go-tfhe - 🐿️ Pure go implementation of TFHE Fully Homomorphic Encryption Scheme
pymae - Materials for the book "Python for Mechanical and Aerospace Engineering"
warp - A Python framework for high performance GPU simulation and graphics
raypier_optics - A raytracing toolkit for optical design
raytracing-in-python - Simple raytacer written in Python.
degradr - Python library for realistically degrading images.
hy - A dialect of Lisp that's embedded in Python