[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70663":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":43,"readmeContent":44,"aiSummary":45,"trendingCount":16,"starSnapshotCount":16,"syncStatus":46,"lastSyncTime":47,"discoverSource":48},70663,"uPlot","leeoniya\u002FuPlot","leeoniya","📈 A small, fast chart for time series, lines, areas, ohlc & bars","",null,"JavaScript",10228,455,59,129,0,8,27,90,24,42.98,"MIT License",false,"master",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"analytics","chart","charts","data-visualization","graph","graphs","lightweight","line-chart","ohlc","performance","plot","plotting","streaming","time-series","timeseries","trend-analysis","2026-06-12 02:02:41","\u003Ch2>\u003Cimg src=\"uPlot.svg\" alt=\"uPlot logo\" height=\"36\" align=\"top\"> μPlot\u003C\u002Fh2>\n\nA small ([~50 KB min](https:\u002F\u002Fgithub.com\u002Fleeoniya\u002FuPlot\u002Ftree\u002Fmaster\u002Fdist\u002FuPlot.iife.min.js)), [fast](#performance) chart for time series, lines, areas, ohlc & bars _(MIT Licensed)_\n\n---\n### Introduction\n\nuPlot is a [fast, memory-efficient](#performance) [Canvas 2D](https:\u002F\u002Fdeveloper.mozilla.org\u002Fen-US\u002Fdocs\u002FWeb\u002FAPI\u002FCanvasRenderingContext2D)-based chart for plotting [time series](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTime_series), lines, areas, ohlc & bars. From a cold start it can create an [interactive chart containing 166,650 data points](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FuPlot.html) in 25ms, scaling linearly at [~100,000 pts\u002Fms](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FuPlot-10M.html) afterwards. In addition to fast initial render, the zooming and cursor performance is by far the best of any similar charting lib; at ~50 KB, it's likely the smallest and fastest time series plotter that doesn't make use of [context-limited](https:\u002F\u002Fbugs.chromium.org\u002Fp\u002Fchromium\u002Fissues\u002Fdetail?id=771792) WebGL shaders or WASM, both of which have much higher startup cost and code size.\n\nIn most sane cases, you can live-stream data with uPlot at 60fps, though it may begin to struggle beyond 100k in-view points.\nWhen [updating 3,600 points at 60fps](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fsine-stream.html), uPlot uses 10% CPU and 12.3MB RAM.\nThe next fastest Canvas-based libs (Chart.js and ECharts) use [40% \u002F 77MB](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FChart.js4-sine-stream.html) and [70% \u002F 85MB](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FECharts5-sine-stream.html), respectively.\nIf you need to stream, give [unclog your rendering pipeline](#unclog-your-rendering-pipeline) a try.\nIf that does not help, consider reducing the update frequency or switch to a WebGL\u002FWebGPU solution, like [danchitnis\u002Fwebgl-plot](https:\u002F\u002Fgithub.com\u002Fdanchitnis\u002Fwebgl-plot), [huww98\u002FTimeChart](https:\u002F\u002Fgithub.com\u002Fhuww98\u002FTimeChart), [epezent\u002Fimplot](https:\u002F\u002Fgithub.com\u002Fepezent\u002Fimplot).\n\n---\n![uPlot Chart](uPlot.png \"uPlot Chart\")\n\n---\n### Features\n\n- Multiple series w\u002Ftoggle\n- Multiple y-axes, scales & grids\n- Temporal or numeric x-axis\n- Linear, uniform or [logarithmic](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Flog-scales.html) scales\n- Line & Area styles (stroke, fill, width, dash)\n- Pluggable path renderers [linear, spline, stepped, bars](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fline-paths.html)\n- Zoom with auto-rescale\n- Legend with live values\n- Support for [IANA Time Zone Names](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FList_of_tz_database_time_zones) & DST\n- [Support for missing data](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fmissing-data.html)\n- [Cursor sync for multiple charts](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fsync-cursor.html)\n- [Focus closest series](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Ffocus-cursor.html)\n- [Data streaming (live update)](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fstream-data.html)\n- [High \u002F Low bands](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fhigh-low-bands.html)\n- A lean, consistent, and powerful API with hooks & plugins\n\n---\n### Non-Features\n\nIn order to stay lean, fast and focused the following features will not be added:\n\n- No data parsing, aggregation, summation or statistical processing - just do it in advance. e.g. [simples-statistics](https:\u002F\u002Fsimple-statistics.github.io\u002F), https:\u002F\u002Fgithub.com\u002Fleeoniya\u002FuDSV\n- No transitions or animations - they're always pure distractions.\n- No collision avoidance for axis tick labels, so may require manual tweaking of spacing metrics if label customization significiantly increases default label widths.\n- No stacked series: see [\"Stacked Area Graphs Are Not Your Friend\"](https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20221208193656\u002Fhttps:\u002F\u002Feverydayanalytics.ca\u002F2014\u002F08\u002Fstacked-area-graphs-are-not-your-friend.html) and a [horrific demo](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fstacked-series.html). While smooth spline interpolation is available, its use is strongly discouraged: [Your data is misrepresented!](http:\u002F\u002Fwww.vizwiz.com\u002F2011\u002F12\u002Fwhen-you-use-smoothed-line-chart-your.html). Both visualizations are terrible at accurately communicating information.\n- No built-in drag scrolling\u002Fpanning due to ambiguous native zoom\u002Fselection behavior. However, this can be added externally via the plugin\u002Fhooks API: [zoom-wheel](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fzoom-wheel.html), [zoom-touch](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fzoom-touch.html).\n\n---\n### Documentation (WIP)\n\nThe docs are a perpetual work in progress, it seems.\nStart with [\u002Fdocs\u002FREADME.md](https:\u002F\u002Fgithub.com\u002Fleeoniya\u002FuPlot\u002Ftree\u002Fmaster\u002Fdocs) for a conceptual overview.\nThe full API is further documented via comments in [\u002Fdist\u002FuPlot.d.ts](https:\u002F\u002Fgithub.com\u002Fleeoniya\u002FuPlot\u002Fblob\u002Fmaster\u002Fdist\u002FuPlot.d.ts).\nAdditionally, an ever-expanding collection of runnable [\u002Fdemos](https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Findex.html) covers the vast majority of uPlot's API.\n\n---\n### Third-party Integrations\n\n- [React, Vue.js and Svelte](https:\u002F\u002Fgithub.com\u002Fskalinichev\u002Fuplot-wrappers) (Sergey Kalinichev)\n- [Python](https:\u002F\u002Fgithub.com\u002Fstephane-caron\u002Fuplot-python) (Stéphane Caron)\n\n---\n### Performance\n\nBenchmarks done on this hardware:\n\n- Date: 2023-03-11\n- AMD Ryzen 7 PRO 5850U @ 1.9GHz, 32GB RAM\n- EndeavourOS\u002FArch (KDE\u002FPlasma), Chrome 113.0.5638.0 (64-bit)\n- 4K display scaled to 1440p (1.5 devicePixelRatio)\n\n![uPlot Performance](perf.png \"uPlot Performance\")\n\nFull size: https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fmulti-bars.html\n\nRaw data: https:\u002F\u002Fgithub.com\u002Fleeoniya\u002FuPlot\u002Fblob\u002Fmaster\u002Fbench\u002Fresults.json\n\n\u003Cpre>\n| lib                    | size    | done    | js,rend,paint,sys | heap peak,final | mousemove (10s)     |\n| ---------------------- | ------- | ------- | ----------------- | --------------- | ------------------- |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FuPlot.html\">uPlot v1.6.24\u003C\u002Fa>          | 47.9 KB |   34 ms |   51   2   1   34 |  21 MB   3 MB   |  218  360  146  196 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FChart.js4.html\">Chart.js v4.2.1\u003C\u002Fa>        |  254 KB |   38 ms |   90   2   1   40 |  29 MB  10 MB   | 1154   46  165  235 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FFlot.html\">Flot v3.0.0\u003C\u002Fa>            |  494 KB |   60 ms |  105   5   1   52 |  41 MB  21 MB   | ---                 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FECharts5.html\">ECharts v5.4.1\u003C\u002Fa>         | 1000 KB |   55 ms |  148   3   1   35 |  17 MB   3 MB   | 1943  444  203  208 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002Fdygraphs.html\">dygraphs v2.2.1\u003C\u002Fa>        |  132 KB |   90 ms |  163   2   1   33 |  88 MB  42 MB   | 1438  371  174  268 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FLightningChart.html\">LightningChart® v4.0.2\u003C\u002Fa> | 1300 KB |  --- ms |  250   2   1   33 |  33 MB  13 MB   | 5390  120  128  325 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FCanvasJS.html\">CanvasJS v3.7.5\u003C\u002Fa>        |  489 KB |  130 ms |  266   4   1   35 |  98 MB  69 MB   | 1030  445   90  246 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FdvxCharts.html\">dvxCharts v5.1.0\u003C\u002Fa>       |  373 KB |  160 ms |  264  23   1   62 | 100 MB  61 MB   |  687  779  206  197 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FHighcharts.html\">Highcharts v10.3.3\u003C\u002Fa>     |  413 KB |  --- ms |  416   7   1   38 |  97 MB  55 MB   | 1286  824  205  242 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FPlotly.js.html\">Plotly.js v2.18.2\u003C\u002Fa>      | 3600 KB |  310 ms |  655  14   1   40 | 104 MB  70 MB   | 1814  163   25  208 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FApexCharts.html\">ApexCharts v3.37.1\u003C\u002Fa>     |  503 KB |  685 ms |  694   9   1   33 | 175 MB  46 MB   | 1708  421  106  207 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FZingChart.html\">ZingChart v2.9.10\u003C\u002Fa>      |  871 KB |  681 ms |  717   7   1  105 | 290 MB 195 MB   | 9021  305   41   71 |\n| \u003Ca href=\"https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fbench\u002FamCharts5.html\">amCharts v5.3.7\u003C\u002Fa>        |  625 KB |  --- ms | 1601   3   3   46 | 147 MB 121 MB   | 9171   71  460  167 |\n\u003C\u002Fpre>\n\n- libs are sorted by their initial, cold-start, render performance (excluding network transfer time to download the lib)\n- `size` includes the lib itself plus any dependencies required to render the benchmark, e.g. Moment, jQuery, etc.\n- Flot does not make available any minified assets and all their examples use the uncompressed sources; they also use an uncompressed version of jQuery :\u002F\n\nSome libraries provide their own performance demos:\n\n- https:\u002F\u002Fecharts.apache.org\u002Fnext\u002Fexamples\u002Fen\u002Findex.html\n- https:\u002F\u002Fgithub.com\u002Fsveinn-steinarsson\u002Fflot-downsample\u002F\n- https:\u002F\u002Fdygraphs.com\u002Ftests\u002Fdygraph-many-points-benchmark.html\n- https:\u002F\u002Fwww.chartjs.org\u002Fdocs\u002Flatest\u002Fgeneral\u002Fperformance.html\n- https:\u002F\u002Fdash.plotly.com\u002Fperformance\n- https:\u002F\u002Fwww.highcharts.com\u002Fdocs\u002Fadvanced-chart-features\u002Fboost-module\n- https:\u002F\u002Fdanchitnis.github.io\u002Fwebgl-plot-examples\u002Fvanilla\u002F\n- https:\u002F\u002Fhuww98.github.io\u002FTimeChart\u002Fdocs\u002Fperformance\n- https:\u002F\u002Fwww.arction.com\u002Flightningchart-js-performance\u002F\n\nTODO (all of these use SVG, so performance should be similar to Highcharts):\n\n- Chartist.js\n- d3-based\n  - C3.js\n  - dc.js\n  - MetricsGraphics\n  - rickshaw\n\n---\n### Unclog your rendering pipeline\n\nYour browser's performance is highly dependent on your hardware, operating system, and GPU drivers.\n\nIf you're using a Chromium-based browser, there are some hidden settings that can unlock significant performance improvements for Canvas2D rendering.\nMost of these have to do with where and how the rasterization is performed.\n\nHead over to https:\u002F\u002Fleeoniya.github.io\u002FuPlot\u002Fdemos\u002Fsine-stream.html and open up Chrome's DevTools (F12), then toggle the Performance Monitor.\n\n![Chrome DevTools Peformance Monitor](img\u002Fchrome-perf-monitor.png \"Chrome DevTools Peformance Monitor\")\n\nFor me:\n\n- On Windows 10 Desktop, Core i7-8700, 16GB RAM, AMD RX480 GPU, 2048 x 1080 resolution = 57% CPU usage\n- On Manjaro Laptop (Arch Linux), AMD Ryzen 7 PRO 5850U, 48GB RAM, AMD Radeon RX Vega 8 (integrated GPU), 4K resolution = **99% CPU usage**\n\nIf your CPU is close to 100%, it may be rasterizing everything in the same CPU process.\n\nPop open `chrome:\u002F\u002Fgpu` and see what's orange or red.\n\n![Chrome gpu](img\u002Fchrome-gpu.png \"Chrome gpu\")\n\nThen open `chrome:\u002F\u002Fflags` and search for \"raster\" to see what can be force-enabled.\n\n![Chrome flags](img\u002Fchrome-flags.png \"Chrome flags\")\n\n- On my Manjaro\u002FRyzen\u002FIntegrated GPU setup, force-enabling `Canvas out-of-process rasterization` resulted in a dramatic framerate improvement.\n- On my Windows\u002Fi7\u002FDedicated GPU setup, toggling the same flags moved the work to another process (still good), but did not have a significant framerate impact.\n\nYMMV!\n\n---\n### Acknowledgements\n\n- Dan Vanderkam's [dygraphs](https:\u002F\u002Fgithub.com\u002Fdanvk\u002Fdygraphs) was a big inspiration; in fact, my stale [pull request #948](https:\u002F\u002Fgithub.com\u002Fdanvk\u002Fdygraphs\u002Fpull\u002F948) was a primary motivator for μPlot's inception.\n- Adam Pearce for [#15 - remove redundant lineTo commands](https:\u002F\u002Fgithub.com\u002Fleeoniya\u002FuPlot\u002Fissues\u002F15).\n","uPlot 是一个轻量级（约50KB）、高性能的时间序列、折线图、面积图、OHLC图和柱状图绘制库。它基于Canvas 2D技术，具有快速渲染和低内存占用的特点，能够在25毫秒内创建包含166,650个数据点的交互式图表，并且在缩放和平移时保持流畅。uPlot 支持多种图表样式与自定义选项，如多系列显示、多Y轴、时间或数值X轴、不同类型的缩放模式等。此外，它还提供了对数据流的支持，适用于需要实时更新图表的应用场景，比如金融数据分析、物联网监控系统或者任何需要高效展示大量时间序列数据的情况。",2,"2026-06-11 03:33:29","high_star"]