Download next.js/bench/vercel/chart.js from Devendra174/react-code-dataset: direct link, hf CLI and curl.
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https://huggingface.co/datasets/Devendra174/react-code-dataset/resolve/main/next.js/bench/vercel/chart.js
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2.82 kB
| import downsampler from 'downsample-lttb' | |
| import asciichart from 'asciichart' | |
| import terminalSize from 'term-size' | |
| const CHART_WIDTH = terminalSize().columns - 15 // space for the labels | |
| function getMetrics(data) { | |
| const sorted = [...data].sort((a, b) => a - b) | |
| const getPercentile = (percentile) => { | |
| const index = Math.floor((sorted.length - 1) * percentile) | |
| return sorted[index] | |
| } | |
| return { | |
| hits: sorted.length, | |
| confidenceInterval: round(getConfidenceInterval(sorted)), | |
| median: getPercentile(0.5), | |
| avg: sorted.reduce((a, b) => a + b, 0) / sorted.length, | |
| p75: getPercentile(0.75), | |
| p95: getPercentile(0.95), | |
| p99: getPercentile(0.99), | |
| p25: getPercentile(0.25), | |
| min: sorted[0], | |
| max: sorted[sorted.length - 1], | |
| } | |
| } | |
| function round(num) { | |
| return Math.round(num * 100) / 100 | |
| } | |
| // thanks Copilot | |
| function getConfidenceInterval(data) { | |
| const n = data.length | |
| const m = data.reduce((a, b) => a + b) / n | |
| const s = Math.sqrt( | |
| data.map((x) => Math.pow(x - m, 2)).reduce((a, b) => a + b) / n | |
| ) | |
| const z = 1.96 // 95% confidence | |
| const e = z * (s / Math.sqrt(n)) | |
| return e | |
| } | |
| export function downsample(data, maxPoints) { | |
| const sortedData = [...data].sort((a, b) => a - b) | |
| return downsampler | |
| .processData( | |
| // the downsampler expects a 2d array of [x, y] values, so we need to add an index | |
| sortedData.map((p, i) => [p, i]), | |
| maxPoints | |
| ) | |
| .map((p) => p[0]) | |
| } | |
| export function printBenchmarkResults({ origin, head }, metricSelector) { | |
| const [processedOriginData, processedHeadData] = [origin, head].map( | |
| (results) => results.map(metricSelector).filter(Boolean) | |
| ) | |
| if (processedHeadData.length === 0 || processedOriginData.length === 0) { | |
| console.log('No data to compare, skipping') | |
| return | |
| } | |
| const [originMetrics, headMetrics] = [ | |
| processedOriginData, | |
| processedHeadData, | |
| ].map(getMetrics) | |
| const deltaMetrics = { | |
| min: headMetrics.min - originMetrics.min, | |
| max: headMetrics.max - originMetrics.max, | |
| avg: headMetrics.avg - originMetrics.avg, | |
| median: headMetrics.median - originMetrics.median, | |
| p95: headMetrics.p95 - originMetrics.p95, | |
| p99: headMetrics.p99 - originMetrics.p99, | |
| p75: headMetrics.p75 - originMetrics.p75, | |
| p25: headMetrics.p25 - originMetrics.p25, | |
| } | |
| console.table({ | |
| origin: originMetrics, | |
| head: headMetrics, | |
| delta: deltaMetrics, | |
| }) | |
| const [originData, headData] = [processedOriginData, processedHeadData].map( | |
| (data) => | |
| downsample( | |
| data, | |
| Math.min( | |
| CHART_WIDTH, | |
| processedOriginData.length, | |
| processedHeadData.length | |
| ) | |
| ) | |
| ) | |
| console.log( | |
| asciichart.plot([originData, headData], { | |
| height: 15, | |
| colors: [asciichart.blue, asciichart.red], | |
| }) | |
| ) | |
| } | |