DMleadgen
46d973904b
Next.js website for Rocky Mountain Vending company featuring: - Product catalog with Stripe integration - Service areas and parts pages - Admin dashboard with Clerk authentication - SEO optimized pages with JSON-LD structured data Co-authored-by: Cursor <cursoragent@cursor.com>
102 lines
3.7 KiB
Text
102 lines
3.7 KiB
Text
/**
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* @license
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* Copyright 2017 Google LLC
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* SPDX-License-Identifier: Apache-2.0
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*/
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import {Audit} from './audit.js';
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import * as i18n from '../lib/i18n/i18n.js';
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import {LanternFirstContentfulPaint} from '../computed/metrics/lantern-first-contentful-paint.js';
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import {LanternInteractive} from '../computed/metrics/lantern-interactive.js';
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import {LanternSpeedIndex} from '../computed/metrics/lantern-speed-index.js';
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import {LanternLargestContentfulPaint} from '../computed/metrics/lantern-largest-contentful-paint.js';
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import {TimingSummary} from '../computed/metrics/timing-summary.js';
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import {defaultSettings} from '../config/constants.js';
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// Parameters (in ms) for log-normal CDF scoring. To see the curve:
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// https://www.desmos.com/calculator/bksgkihhj8
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const SCORING_P10 = 3651;
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const SCORING_MEDIAN = 10000;
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const str_ = i18n.createIcuMessageFn(import.meta.url, {});
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class PredictivePerf extends Audit {
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/**
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* @return {LH.Audit.Meta}
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*/
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static get meta() {
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return {
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id: 'predictive-perf',
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title: 'Predicted Performance (beta)',
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description:
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'Predicted performance evaluates how your site will perform under ' +
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'a cellular connection on a mobile device.',
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scoreDisplayMode: Audit.SCORING_MODES.NUMERIC,
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supportedModes: ['navigation'],
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requiredArtifacts: ['Trace', 'DevtoolsLog', 'GatherContext', 'URL', 'SourceMaps'],
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};
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}
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/**
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* @param {LH.Artifacts} artifacts
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* @param {LH.Audit.Context} context
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* @return {Promise<LH.Audit.Product>}
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*/
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static async audit(artifacts, context) {
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const gatherContext = artifacts.GatherContext;
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const trace = artifacts.Trace;
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const devtoolsLog = artifacts.DevtoolsLog;
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const {URL, SourceMaps} = artifacts;
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/** @type {LH.Config.Settings} */
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const settings = JSON.parse(JSON.stringify(defaultSettings)); // Use default settings.
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const computationData =
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{trace, devtoolsLog, gatherContext, settings, URL, SourceMaps, simulator: null};
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const fcp = await LanternFirstContentfulPaint.request(computationData, context);
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const tti = await LanternInteractive.request(computationData, context);
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const si = await LanternSpeedIndex.request(computationData, context);
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const lcp = await LanternLargestContentfulPaint.request(computationData, context);
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const timingSummary = await TimingSummary.request(computationData, context);
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const values = {
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roughEstimateOfFCP: fcp.timing,
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optimisticFCP: fcp.optimisticEstimate.timeInMs,
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pessimisticFCP: fcp.pessimisticEstimate.timeInMs,
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roughEstimateOfTTI: tti.timing,
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optimisticTTI: tti.optimisticEstimate.timeInMs,
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pessimisticTTI: tti.pessimisticEstimate.timeInMs,
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roughEstimateOfSI: si.timing,
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optimisticSI: si.optimisticEstimate.timeInMs,
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pessimisticSI: si.pessimisticEstimate.timeInMs,
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roughEstimateOfLCP: lcp.timing,
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optimisticLCP: lcp.optimisticEstimate.timeInMs,
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pessimisticLCP: lcp.pessimisticEstimate.timeInMs,
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roughEstimateOfTTFB: timingSummary.metrics.timeToFirstByte,
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roughEstimateOfLCPLoadStart: timingSummary.metrics.lcpLoadStart,
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roughEstimateOfLCPLoadEnd: timingSummary.metrics.lcpLoadEnd,
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};
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const score = Audit.computeLogNormalScore(
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{p10: SCORING_P10, median: SCORING_MEDIAN},
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values.roughEstimateOfTTI
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);
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return {
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score,
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numericValue: values.roughEstimateOfTTI,
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numericUnit: 'millisecond',
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displayValue: str_(i18n.UIStrings.ms, {timeInMs: values.roughEstimateOfTTI}),
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details: {
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type: 'debugdata',
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// TODO: Consider not nesting values under `items`.
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items: [values],
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},
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};
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}
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}
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export default PredictivePerf;
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