uP

uPlot.js v1.0 – A fast, small chart for time series, OHLC and bars

Hacker News

uPlot.js v1.0 – A fast, small chart for time series, OHLC and bars

Hello again! 5 months and ~400 commits since the initial prototype: https://news.ycombinator.com/item?id=21207132 v1.0 has shipped! - https://github.com/leeoniya/uPlot A non-exaustive list of features/concerns that have been addressed since v0.1.0: - A terse, consistent API, (w/ plugins & hooks) - Timezone & DST handling - Point rendering - Bi-directional zooming - Dynamic data updates (streaming) - Numeric x scales (non-temporal) - Area fills & high/low bands - Dependent scales (°C -> °F) - Cursor sync across charts - Highlight closest series - Axis labeling & positioning - Bars & OHLC (via tiny plugins) - Feature gates for even smaller custom builds A bunch of demos: https://leeoniya.github.io/uPlot/demos/index.html About a dozen brave early adopters helped me weed out bugs, add features, refine the API and contributed demo code. Without them there'd be no v1.0, so thank you. So far there are some cool use-cases. One user renders several hundred hundred-point charts per page - something that's impossible with WebGL or heavier charting libs. There's been a prototype for Grafana panel integration. The author of Phaser [1] is currently taking uPlot for a perf-monitoring test drive while working on the new engine: https://github.com/phaserjs/phaser4-dev :D Also, a shout out to the Chart.js guys, who recently tagged v3.0-alpha which performs 3x better than v2 on uPlot's benchmark, and is the only lib to have made any noticeable headway, let alone multiple factor! cheers! [1] https://github.com/photonstorm/phaser

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, user, new · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · 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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