Interactive Solar Eclipse Simulator

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This is a demo site showing an interactive general purpose solar eclipse simulator. With two solar eclipses in 2023/24, I added eclipse calculations and maps to my photography planning app last year. After getting the basics in place I built this simulator. It tries to give a reasonable photo realistic view of any eclipse by showing phenomena such as the diamond ring, the corona and chromosphere, along with accurate geometry/timings etc. The simulator also accounts for the effect of the uneven lunar surface. It gives accurate simulations of Baily's Beads as they are seen around second and third contact times. These vary from one eclipse to the next and by location. The simulator calculates the lunar 'libration' (the orientation of the Moon) at maximum eclipse and then pulls the appropriate lunar limb profile from an API server. You can toggle between "photo realistic" and "outline" mode to see a vertically exaggerated lunar limb profile. The demo page is set up with three sample eclipses (TSE2024, ASE2024, HSE2031 - the next hybrid eclipse) and a few sample locations for each. You can pinch to zoom and pan (two fingers on mobile). The simulator is packaged as a (rather heavyweight) 'widget' and was built with Vite/React using three.js and some custom shaders. It pulls the data it needs from an astronomy and geospatial web service project I'm working on (radiantdrift.com). Performance in Firefox on Mac is horrible - not sure why as yet.

Visit product View on Hacker NewsMar 25, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, using · Missing: agents, macos, agent

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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