This organization and its repositories have been abandoned. The About section below is left intact. The work was abandoned for the following reasons:
- Julia does not offer significant acceleration over the numpy code of prysm -- only about 20%
- writing performant julia is significantly more difficult than writing performant numpy. The base language emits unvectorized code which is slower than numpy, the
@avxmacro is unstable and often crashes the interpreter, the allocator is much slower than numpy or even matlab, and.and!are quasi-white space symbols that are far too important to the performance of an algorithm.@.prefixes to a line are slower than hand placing the., so that is not a solution - the language itself, as well as its tooling, is too immature, with poor documentation and many bugs or sharp edges
- errors in Julia are severely cluttered by multiple line long type information which does not aid clarity
JuliaOptics is an organization to house a suite of libraries for physical optics in Julia. This suite has particular goals from the outset:
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To respect the prior art that exists in the enumerable physical optics codes written in matlab, python, and so on and learn from their API design.
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To provide a minimal API that is easy to learn with a reasonable balance between explicit and "fluent" design.
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To enable truly next generation computation in optics through automatic differentiation and other areas of significant development in Julia.
This provides a set of guiding performance marks:
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To be more than (5x required, 100x desired) faster than prysm, the fastest public physical optics code.
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To freely support computation on CPU or GPU
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To force as few allocations as possible, keeping GC time below 15% overall.
The goal at the outset is not to have all features, but to specialize in computational physical optics with emphasis on propagation from pupil to PSF or vice-versa, and between planes. A numerical model of propagation through a coronagraph (two plane to plane, then pupil => PSF => pupil => PSF) will demonstrate the performance efficacy (or lack thereof) of Julia for this domain.