BUbuescher·May 22, 2023Submission
Prysm is a Python 3.6 library for numerical optics
3 points · Open discussionbrandondube
prysm
Python2021–2023
brandondube / prysm
prysm
physical optics: integrated modeling, phase retrieval, segmented systems, polynomials and fitting, sequential raytracing...
2Distinct people
2Mentions
0Explicit recommenders
366GitHub stars
What to know before exploring
What it is
physical optics: integrated modeling, phase retrieval, segmented systems, polynomials and fitting, sequential raytracing...
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
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Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month2 dated mentions
Observed mentions: 2021-03-22 14:49:18 UTC to 2023-05-22 14:27:01 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 2021 | 2021-03-01 00:00:00 UTC | 2021-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2021 | 2021-04-01 00:00:00 UTC | 2021-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2021 | 2021-05-01 00:00:00 UTC | 2021-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2021 | 2021-06-01 00:00:00 UTC | 2021-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2021 | 2021-07-01 00:00:00 UTC | 2021-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2021 | 2021-08-01 00:00:00 UTC | 2021-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2021 | 2021-09-01 00:00:00 UTC | 2021-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2021 | 2021-10-01 00:00:00 UTC | 2021-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2021 | 2021-11-01 00:00:00 UTC | 2021-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2021 | 2021-12-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2022 | 2022-01-01 00:00:00 UTC | 2022-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2022 | 2022-02-01 00:00:00 UTC | 2022-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2022 | 2022-03-01 00:00:00 UTC | 2022-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2022 | 2022-04-01 00:00:00 UTC | 2022-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2022 | 2022-05-01 00:00:00 UTC | 2022-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2022 | 2022-06-01 00:00:00 UTC | 2022-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2022 | 2022-07-01 00:00:00 UTC | 2022-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2022 | 2022-08-01 00:00:00 UTC | 2022-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2022 | 2022-09-01 00:00:00 UTC | 2022-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2022 | 2022-10-01 00:00:00 UTC | 2022-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2022 | 2022-11-01 00:00:00 UTC | 2022-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2022 | 2022-12-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2023 | 2023-01-01 00:00:00 UTC | 2023-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2023 | 2023-02-01 00:00:00 UTC | 2023-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2023 | 2023-03-01 00:00:00 UTC | 2023-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2023 | 2023-04-01 00:00:00 UTC | 2023-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2023 | 2023-05-01 00:00:00 UTC | 2023-06-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
2 sourcesAh, that's too bad, but I can't really blame them. Going from a fully-featured Python codebase they already had in https://github.com/brandondube/prysm and translating into Julia would need to be motivated by much more than speed - after all the limiting factor here is FFT and heavy computations like cis, so I'm pleasantly surprised they reported gains from their simple port compared to numpy. It's also much easier to go from a C/C++ computational code to Julia than it is from a Python one, because the mental performance models are more similar.
For reference, we (metalenz.com) have a large Julia codebase centered around optical design, simulation, and analysis. The motivation for that is more along the lines of composability and clarity of abstractions (aided by multiple-dispatch). We can differentiate through our physical optics solver
Read the rest
code (forward or reverse) for optimization/ML, plug in our designs across a hierarchy of different E&M solvers, run on GPU, and write very efficient code when our profiling identifies a bottleneck. If we just had to perform one thing (physical optics simulations), then our investment wouldn't be as justified.