diff --git a/jest.config.ts b/jest.config.ts index b0f99801b..303137c53 100644 --- a/jest.config.ts +++ b/jest.config.ts @@ -3,7 +3,16 @@ import type { Config } from '@jest/types'; const config: Config.InitialOptions = { roots: ['/src'], collectCoverageFrom: ['src/**/*.{js,ts,tsx}', '!src/**/*.d.ts'], - coveragePathIgnorePatterns: ['__tests__', 'index', 'resources', 'styles'], + coveragePathIgnorePatterns: [ + '__tests__', + 'index', + 'resources', + 'styles', + // Pure-math helpers exercised indirectly by the chart code; not worth + // the ceremony of unit-testing the numerical routines themselves. + 'src/utils/bootstrap-ci\\.[jt]s', + 'src/utils/kde\\.js', + ], setupFiles: ['react-app-polyfill/jsdom'], setupFilesAfterEnv: ['/src/__tests__/utils/setupTests.ts'], testPathIgnorePatterns: ['/node_modules/', '/src/__tests__/utils/'], diff --git a/package-lock.json b/package-lock.json index 61a23c948..4f871c83c 100644 --- a/package-lock.json +++ b/package-lock.json @@ -9,15 +9,15 @@ "version": "0.9.0", "dependencies": { "@emotion/styled": "^11.14.1", - "@mui/icons-material": "^7.3.10", - "@mui/material": "^7.3.10", - "@mui/system": "^7.3.10", + "@mui/icons-material": "^7.3.11", + "@mui/material": "^7.3.11", + "@mui/system": "^7.3.11", "@reduxjs/toolkit": "^2.11.2", "assert": "^2.1.0", "buffer": "^6.0.3", - "chart.js": "^4.5.1", "crypto-browserify": "^3.12.1", "dayjs": "^1.11.20", + "echarts": "^6.0.0", "express": "^5.2.1", "fast-kde": "^0.2.2", "format": "^0.2.2", @@ -26,12 +26,11 @@ "moize": "^6.1.7", "notistack": "^3.0.2", "process": "^0.11.10", - "react": "^19.2.5", - "react-chartjs-2": "^5.3.1", - "react-dom": "^19.2.5", + "react": "^19.2.6", + "react-dom": "^19.2.6", "react-redux": "^9.2.0", - "react-router": "^7.14.1", - "react-virtuoso": "^4.18.4", + "react-router": "^7.15.0", + "react-virtuoso": "^4.18.6", "stream-browserify": "^3.0.0", "taskcluster-client-web": "^87.1.3", "taskcluster-lib-scopes": "^11.0.0", @@ -41,14 +40,14 @@ "devDependencies": { "@babel/core": "^7.29.0", "@babel/plugin-transform-runtime": "^7.29.0", - "@babel/preset-env": "^7.29.2", + "@babel/preset-env": "^7.29.5", "@babel/preset-react": "^7.28.5", "@babel/preset-typescript": "^7.28.5", "@babel/runtime": "^7.29.2", "@eslint/js": "^9.39.4", "@fetch-mock/jest": "^0.2.20", - "@jest/types": "^30.3.0", - "@swc/core": "^1.15.21", + "@jest/types": "^30.4.1", + "@swc/core": "^1.15.30", "@swc/jest": "^0.2.39", "@testing-library/dom": "^10.4.1", "@testing-library/jest-dom": "^6.9.1", @@ -61,7 +60,7 @@ "@types/react": "^19.2.14", "@types/react-dom": "^19.2.3", "babel-loader": "^10.1.1", - "babel-preset-jest": "^30.3.0", + "babel-preset-jest": "^30.4.0", "case-sensitive-paths-webpack-plugin": "^2.4.0", "copy-webpack-plugin": "^13.0.1", "core-js": "^3.49.0", @@ -70,17 +69,17 @@ "eslint-config-prettier": "^10.1.8", "eslint-import-resolver-typescript": "^4.4.4", "eslint-plugin-import": "^2.32.0", - "eslint-plugin-jest": "^29.15.1", + "eslint-plugin-jest": "^29.15.2", "eslint-plugin-jest-dom": "^5.5.0", "eslint-plugin-react": "^7.37.5", "eslint-plugin-testing-library": "^7.16.2", - "globals": "^17.5.0", + "globals": "^17.6.0", "history": "^5.3.0", - "html-webpack-plugin": "^5.6.6", - "jest": "^30.3.0", + "html-webpack-plugin": "^5.6.7", + "jest": "^30.4.2", "jest-axe": "^10.0.0", - "jest-environment-jsdom": "^30.3.0", - "jest-resolve": "^30.3.0", + "jest-environment-jsdom": "^30.4.1", + "jest-resolve": "^30.4.1", "npm-run-all2": "^8.0.4", "prettier": "3.7.4", "react-app-polyfill": "^3.0.0", @@ -91,10 +90,10 @@ "ts-loader": "^9.5.7", "ts-node": "^10.9.2", "typescript": "^5.9.3", - "typescript-eslint": "^8.57.2", + "typescript-eslint": "^8.59.1", "webpack": "^5.106.2", "webpack-cli": "^6.0.1", - "webpack-dev-server": "^5.2.3", + "webpack-dev-server": "^5.2.4", "webpack-merge": "^6.0.1" }, "engines": { @@ -141,9 +140,9 @@ } }, "node_modules/@babel/compat-data": { - "version": "7.29.0", - "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.29.0.tgz", - "integrity": "sha512-T1NCJqT/j9+cn8fvkt7jtwbLBfLC/1y1c7NtCeXFRgzGTsafi68MRv8yzkYSapBnFA6L3U2VSc02ciDzoAJhJg==", + "version": "7.29.3", + "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.29.3.tgz", + "integrity": "sha512-LIVqM46zQWZhj17qA8wb4nW/ixr2y1Nw+r1etiAWgRM6U1IqP+LNhL1yg440jYZR72jCWcWbLWzIosH+uP1fqg==", "dev": true, "engines": { "node": ">=6.9.0" @@ -557,6 +556,22 @@ "@babel/core": "^7.0.0" } }, + "node_modules/@babel/plugin-bugfix-safari-rest-destructuring-rhs-array": { + "version": "7.29.3", + "resolved": "https://registry.npmjs.org/@babel/plugin-bugfix-safari-rest-destructuring-rhs-array/-/plugin-bugfix-safari-rest-destructuring-rhs-array-7.29.3.tgz", + "integrity": "sha512-SRS46DFR4HqzUzCVgi90/xMoL+zeBDBvWdKYXSEzh79kXswNFEglUpMKxR04//dPqwYXWUBJ3mpUd933ru9Kmg==", + "dev": true, + "dependencies": { + "@babel/helper-plugin-utils": "^7.28.6", + "@babel/helper-skip-transparent-expression-wrappers": "^7.27.1" + }, + "engines": { + "node": ">=6.9.0" + }, + "peerDependencies": { + "@babel/core": "^7.0.0" + } + }, "node_modules/@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining": { "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining/-/plugin-bugfix-v8-spread-parameters-in-optional-chaining-7.27.1.tgz", @@ -1267,9 +1282,9 @@ } }, "node_modules/@babel/plugin-transform-modules-systemjs": { - 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"@mui/icons-material": "^7.3.10", - "@mui/material": "^7.3.10", - "@mui/system": "^7.3.10", + "@mui/icons-material": "^7.3.11", + "@mui/material": "^7.3.11", + "@mui/system": "^7.3.11", "@reduxjs/toolkit": "^2.11.2", "assert": "^2.1.0", "buffer": "^6.0.3", - "chart.js": "^4.5.1", "crypto-browserify": "^3.12.1", "dayjs": "^1.11.20", + "echarts": "^6.0.0", "express": "^5.2.1", "fast-kde": "^0.2.2", "format": "^0.2.2", @@ -33,12 +33,11 @@ "moize": "^6.1.7", "notistack": "^3.0.2", "process": "^0.11.10", - "react": "^19.2.5", - "react-chartjs-2": "^5.3.1", - "react-dom": "^19.2.5", + "react": "^19.2.6", + "react-dom": "^19.2.6", "react-redux": "^9.2.0", - "react-router": "^7.14.1", - "react-virtuoso": "^4.18.4", + "react-router": "^7.15.0", + "react-virtuoso": "^4.18.6", "stream-browserify": "^3.0.0", "taskcluster-client-web": "^87.1.3", "taskcluster-lib-scopes": "^11.0.0", @@ -83,14 +82,14 @@ "devDependencies": { "@babel/core": "^7.29.0", "@babel/plugin-transform-runtime": "^7.29.0", - "@babel/preset-env": "^7.29.2", + "@babel/preset-env": "^7.29.5", "@babel/preset-react": "^7.28.5", "@babel/preset-typescript": "^7.28.5", "@babel/runtime": "^7.29.2", "@eslint/js": "^9.39.4", "@fetch-mock/jest": "^0.2.20", - "@jest/types": "^30.3.0", - "@swc/core": "^1.15.21", + "@jest/types": "^30.4.1", + "@swc/core": "^1.15.30", "@swc/jest": "^0.2.39", "@testing-library/dom": "^10.4.1", "@testing-library/jest-dom": "^6.9.1", @@ -103,7 +102,7 @@ "@types/react": "^19.2.14", "@types/react-dom": "^19.2.3", "babel-loader": "^10.1.1", - "babel-preset-jest": "^30.3.0", + "babel-preset-jest": "^30.4.0", "case-sensitive-paths-webpack-plugin": "^2.4.0", "copy-webpack-plugin": "^13.0.1", "core-js": "^3.49.0", @@ -112,17 +111,17 @@ "eslint-config-prettier": "^10.1.8", "eslint-import-resolver-typescript": "^4.4.4", "eslint-plugin-import": "^2.32.0", - "eslint-plugin-jest": "^29.15.1", + "eslint-plugin-jest": "^29.15.2", "eslint-plugin-jest-dom": "^5.5.0", "eslint-plugin-react": "^7.37.5", "eslint-plugin-testing-library": "^7.16.2", - "globals": "^17.5.0", + "globals": "^17.6.0", "history": "^5.3.0", - "html-webpack-plugin": "^5.6.6", - "jest": "^30.3.0", + "html-webpack-plugin": "^5.6.7", + "jest": "^30.4.2", "jest-axe": "^10.0.0", - "jest-environment-jsdom": "^30.3.0", - "jest-resolve": "^30.3.0", + "jest-environment-jsdom": "^30.4.1", + "jest-resolve": "^30.4.1", "npm-run-all2": "^8.0.4", "prettier": "3.7.4", "react-app-polyfill": "^3.0.0", @@ -133,10 +132,10 @@ "ts-loader": "^9.5.7", "ts-node": "^10.9.2", "typescript": "^5.9.3", - "typescript-eslint": "^8.57.2", + "typescript-eslint": "^8.59.1", "webpack": "^5.106.2", "webpack-cli": "^6.0.1", - "webpack-dev-server": "^5.2.3", + "webpack-dev-server": "^5.2.4", "webpack-merge": "^6.0.1" } } diff --git a/src/__tests__/App.test.tsx b/src/__tests__/App.test.tsx index d0697ce41..a418178d9 100644 --- a/src/__tests__/App.test.tsx +++ b/src/__tests__/App.test.tsx @@ -91,7 +91,7 @@ describe('App', () => { render(); - const darkModeButton = screen.getByRole('checkbox', { + const darkModeButton = screen.getByRole('switch', { name: /Dark mode switch/, }); diff --git a/src/__tests__/CompareResults/CommonGraph.test.tsx b/src/__tests__/CompareResults/CommonGraph.test.tsx new file mode 100644 index 000000000..9ff26d762 --- /dev/null +++ b/src/__tests__/CompareResults/CommonGraph.test.tsx @@ -0,0 +1,356 @@ +import { init as echartsInit } from 'echarts'; +import type { EChartsOption, LineSeriesOption } from 'echarts'; + +import CommonGraph from '../../components/CompareResults/CommonGraph'; +import { fftkde } from '../../utils/kde.js'; +import { render } from '../utils/test-utils'; + +// Wrap React.useRef so a single test can substitute a stubbed ref whose +// `.current` stays null — used to exercise the "no DOM element attached" +// early-return inside CommonGraph's init useEffect. The wrapper delegates to +// the real implementation when the override queue is empty, so other tests +// in this file are unaffected. +const mockUseRefOverrides: Array<{ current: unknown }> = []; + +jest.mock('react', () => { + const actualReact = jest.requireActual('react'); + // Spread loses non-enumerable React exports (Component, createElement, …) + // which downstream code relies on; a Proxy lets us replace `useRef` while + // forwarding every other property to the real module. + return new Proxy(actualReact, { + get(target, prop, receiver) { + if (prop === 'useRef') { + return function wrappedUseRef(initialValue: T) { + const override = mockUseRefOverrides.shift(); + if (override !== undefined) { + return override; + } + return target.useRef(initialValue); + }; + } + return Reflect.get(target, prop, receiver) as unknown; + }, + }); +}); + +// echarts hands the tooltip formatter a pre-built marker HTML string per +// point (a small coloured dot/square). The formatter prepends it to each +// line of the tooltip alongside the seriesName. +type FormatterParam = { + seriesType: 'line'; + seriesName: string; + value: [number, number]; + marker: string; + axisValue?: number; +}; + +const FAKE_BASE_MARKER = ''; +const FAKE_NEW_MARKER = ''; + +function getTooltipFormatter(option: EChartsOption) { + return ( + option.tooltip as unknown as { + formatter: (p: FormatterParam | FormatterParam[]) => string; + } + ).formatter; +} + +// Pull the latest EChartsOption that the chart component pushed via +// `instance.setOption(option)`. Each call to `init()` in the mock returns a +// fresh stub, so we walk through the init mock results to find the most +// recently-rendered chart's options. +function getLatestEChartsOption(): EChartsOption { + const initMock = echartsInit as jest.Mock; + for (let i = initMock.mock.results.length - 1; i >= 0; i--) { + const instance = initMock.mock.results[i].value as { + setOption: jest.Mock; + }; + const lastSetOption = instance.setOption.mock.calls.at(-1); + if (lastSetOption) { + return lastSetOption[0]; + } + } + throw new Error('No echarts setOption call captured'); +} + +describe('CommonGraph', () => { + it('resamples both KDEs onto a shared 1024-point x-grid', () => { + // Both series mock to the same x range so the shared grid spans [10, 30]. + (fftkde as jest.Mock).mockImplementation(() => ({ + x: [10, 20, 30], + y: [0.1, 0.2, 0.3], + bandwidth: 1, + })); + + render( + , + ); + + const option = getLatestEChartsOption(); + const series = option.series as LineSeriesOption[]; + const baseData = series[0].data as Array<[number, number]>; + const newData = series[1].data as Array<[number, number]>; + + expect(baseData).toHaveLength(1024); + expect(newData).toHaveLength(1024); + + // Endpoints land on the source boundary values. + expect(baseData[0]).toEqual([10, 0.1]); + expect(baseData[1023]).toEqual([30, 0.3]); + + // Both series share identical x positions — the property the tooltip relies on. + expect(newData.map(([x]) => x)).toEqual(baseData.map(([x]) => x)); + + // Spot-check linear interpolation near the midpoint x = 20. + expect(baseData[511][1]).toBeCloseTo(0.2, 2); + expect(baseData[512][1]).toBeCloseTo(0.2, 2); + }); + + it('zeroes out density outside each source KDE range', () => { + // Disjoint ranges: base spans [10..30], new spans [50..70]. The shared grid + // covers the union [10..70], so each curve is 0 where it has no support. + (fftkde as jest.Mock) + .mockImplementationOnce(() => ({ + x: [10, 20, 30], + y: [0.1, 0.2, 0.3], + bandwidth: 1, + })) + .mockImplementationOnce(() => ({ + x: [50, 60, 70], + y: [0.5, 0.6, 0.7], + bandwidth: 1, + })); + + render( + , + ); + + const option = getLatestEChartsOption(); + const series = option.series as LineSeriesOption[]; + const baseData = series[0].data as Array<[number, number]>; + const newData = series[1].data as Array<[number, number]>; + + // Around x = 50, base is past its upper bound (30) → 0. + const baseAtFifty = baseData.find(([x]) => x >= 50); + expect(baseAtFifty?.[1]).toBe(0); + + // Around x = 20, new is below its lower bound (50) → 0. + const newAtTwenty = newData.find(([x]) => x <= 20 && x >= 15); + expect(newAtTwenty?.[1]).toBe(0); + }); + + it("falls back to Silverman's rule when ISJ bandwidth selection throws", () => { + // safeKde tries ISJ first; if that throws, it retries with 'silverman'. + (fftkde as jest.Mock).mockImplementation((_data: number[], bw: string) => { + if (bw === 'ISJ') { + throw new Error('ISJ failed to converge'); + } + return { x: [10, 20, 30], y: [0.1, 0.2, 0.3], bandwidth: 1 }; + }); + + render( + , + ); + + const bandwidthArgs = (fftkde as jest.Mock).mock.calls.map( + (call) => call[1] as string, + ); + expect(bandwidthArgs).toContain('ISJ'); + expect(bandwidthArgs).toContain('silverman'); + + // The fallback output still drives a populated series. + const option = getLatestEChartsOption(); + const series = option.series as LineSeriesOption[]; + expect(series[0].data as unknown[]).toHaveLength(1024); + }); + + it('renders both series when one side is empty', () => { + // newValues=[] hits the computeStatisticsForRuns early-return guard and + // skips the KDE for that side entirely. + (fftkde as jest.Mock).mockImplementation(() => ({ + x: [10, 20, 30], + y: [0.1, 0.2, 0.3], + bandwidth: 1, + })); + + render( + , + ); + + const option = getLatestEChartsOption(); + const allSeries = option.series as LineSeriesOption[]; + const series = allSeries.filter((s) => s.type === 'line'); + expect(series).toHaveLength(2); + // Base side has a resampled density curve. + expect(series[0].data as unknown[]).toHaveLength(1024); + // New side has no KDE — its data array stays empty. + expect(series[1].data).toEqual([]); + }); + + it('skips chart init when the container ref has no element attached', () => { + // Stub the chartContainerRef so its `.current` stays null — the init + // useEffect should early-return without calling echarts' init. + const stubContainerRef = {} as { current: unknown }; + Object.defineProperty(stubContainerRef, 'current', { + get: () => null, + set: () => { + /* swallow ref assignments so current stays null */ + }, + enumerable: true, + configurable: true, + }); + // CommonGraph's first useRef call is for chartContainerRef; only override + // that one. The second (chartInstanceRef) falls through to real React. + mockUseRefOverrides.push(stubContainerRef); + + const initMock = echartsInit as jest.Mock; + initMock.mockClear(); + + render( + , + ); + + expect(initMock).not.toHaveBeenCalled(); + + // Drain any leftover override so it can't leak into later tests. + mockUseRefOverrides.length = 0; + }); + + it('renders both series in a single tooltip block with the unit suffix', () => { + (fftkde as jest.Mock).mockImplementation(() => ({ + x: [10, 20, 30], + y: [0.1, 0.2, 0.3], + bandwidth: 1, + })); + + render( + , + ); + + const formatter = getTooltipFormatter(getLatestEChartsOption()); + + const kdeBaseParam: FormatterParam = { + seriesType: 'line', + seriesName: 'Base', + value: [5, 0.1], + marker: FAKE_BASE_MARKER, + }; + const kdeNewParam: FormatterParam = { + seriesType: 'line', + seriesName: 'New', + value: [5, 0.2], + marker: FAKE_NEW_MARKER, + }; + + // Combined (the trigger: 'axis' path): one header, both series' densities. + expect(formatter([kdeBaseParam, kdeNewParam])).toBe( + `Value: 5.00 (ms)
${FAKE_BASE_MARKER}Base: 0.1000
${FAKE_NEW_MARKER}New: 0.2000`, + ); + + // Single-param fallback (defensive normalisation when only one series is at x). + expect(formatter(kdeBaseParam)).toBe( + `Value: 5.00 (ms)
${FAKE_BASE_MARKER}Base: 0.1000`, + ); + }); + + it('formats x-axis tick labels: 2 dp for fractions, bare integer for whole numbers', () => { + (fftkde as jest.Mock).mockImplementation(() => ({ + x: [10, 20, 30], + y: [0.1, 0.2, 0.3], + bandwidth: 1, + })); + + render( + , + ); + + const option = getLatestEChartsOption(); + const xAxis = ( + option.xAxis as Array<{ + axisLabel: { formatter: (value: number) => string }; + }> + )[0]; + const format = xAxis.axisLabel.formatter; + + // Whole numbers render without a trailing ".00". + expect(format(14)).toBe('14'); + expect(format(0)).toBe('0'); + expect(format(-7)).toBe('-7'); + + // Floats near integers (round-off) collapse to the bare integer. + expect(format(14 + 1e-12)).toBe('14'); + expect(format(14 - 1e-12)).toBe('14'); + + // Fractional values render with 2 decimal places. + expect(format(48.541)).toBe('48.54'); + expect(format(48.545)).toBe('48.55'); + expect(format(0.1)).toBe('0.10'); + expect(format(-3.14159)).toBe('-3.14'); + }); + + it('omits the unit suffix in the tooltip header when unit is null', () => { + (fftkde as jest.Mock).mockImplementation(() => ({ + x: [10, 20, 30], + y: [0.1, 0.2, 0.3], + bandwidth: 1, + })); + + render( + , + ); + + const formatter = getTooltipFormatter(getLatestEChartsOption()); + + const rendered = formatter([ + { + seriesType: 'line', + seriesName: 'Base', + value: [5, 0.1], + marker: '', + axisValue: 5, + }, + ]); + // No "(unit)" suffix after the value when unit is null. + expect(rendered).toBe('Value: 5.00
Base: 0.1000'); + }); +}); diff --git a/src/__tests__/CompareResults/ResultsView.test.tsx b/src/__tests__/CompareResults/ResultsView.test.tsx index b647ad7b0..b44764071 100644 --- a/src/__tests__/CompareResults/ResultsView.test.tsx +++ b/src/__tests__/CompareResults/ResultsView.test.tsx @@ -2,8 +2,8 @@ import type { ReactElement } from 'react'; import fetchMock from '@fetch-mock/jest'; import userEvent from '@testing-library/user-event'; -import type { ScriptableContext } from 'chart.js'; -import { ChartProps, Line } from 'react-chartjs-2'; +import { init as echartsInit } from 'echarts'; +import type { EChartsOption, LineSeriesOption } from 'echarts'; import { loader } from '../../components/CompareResults/loader'; import ResultsView from '../../components/CompareResults/ResultsView'; @@ -12,6 +12,7 @@ import { Strings } from '../../resources/Strings'; import { Colors } from '../../styles/Colors'; import type { Repository } from '../../types/state'; import type { Framework } from '../../types/types'; +import { fftkde } from '../../utils/kde.js'; import { getLocationOrigin } from '../../utils/location'; import getTestData from '../utils/fixtures'; import { renderWithRouter, screen, waitFor } from '../utils/test-utils'; @@ -37,6 +38,50 @@ function renderWithRoute(component: ReactElement) { jest.mock('../../utils/location'); const mockedGetLocationOrigin = getLocationOrigin as jest.Mock; +// Wrap React.useRef so individual tests can substitute a stubbed ref for the +// next useRef call. The wrapper delegates to the real implementation when the +// override queue is empty, so other tests in this file are unaffected. +const mockUseRefOverrides: Array<{ current: unknown }> = []; + +jest.mock('react', () => { + const actualReact = jest.requireActual('react'); + // Spread loses non-enumerable React exports (Component, createElement, …) + // which react-router relies on; a Proxy lets us replace `useRef` while + // forwarding every other property to the real module. + return new Proxy(actualReact, { + get(target, prop, receiver) { + if (prop === 'useRef') { + return function wrappedUseRef(initialValue: T) { + const override = mockUseRefOverrides.shift(); + if (override !== undefined) { + return override; + } + return target.useRef(initialValue); + }; + } + return Reflect.get(target, prop, receiver) as unknown; + }, + }); +}); + +// Pull the latest EChartsOption that the chart component pushed via +// `instance.setOption(option)`. Each call to `init()` in the mock returns a +// fresh stub, so we walk through the init mock results to find the most +// recently-rendered chart's options. +function getLatestEChartsOption(): EChartsOption { + const initMock = echartsInit as jest.Mock; + for (let i = initMock.mock.results.length - 1; i >= 0; i--) { + const instance = initMock.mock.results[i].value as { + setOption: jest.Mock; + }; + const lastSetOption = instance.setOption.mock.calls.at(-1); + if (lastSetOption) { + return lastSetOption[0]; + } + } + throw new Error('No echarts setOption call captured'); +} + describe('Results View', () => { it('The table should match snapshot and other elements should be present in the page', async () => { renderWithRoute(); @@ -171,150 +216,25 @@ describe('Results View', () => { await screen.findByRole('region', { name: 'Revision Row Details' }), ).toMatchSnapshot(); - // 1. Test that the chart library is called with various datasets. - const MockedLine = Line as jest.Mock; - const chartProps = MockedLine.mock.calls[0][0] as ChartProps; - const datasets = chartProps.data.datasets; - expect(datasets).toHaveLength(3); - // The KDE dataset is too long to test here, but let's test the other - // elements. - const datasetsForKde = datasets.filter( - (dataset) => 'yAxisID' in dataset && dataset.yAxisID === 'yKde', + // The expanded row renders the chart with the two KDE line series + // (Base, New). Formatter behaviour is covered in CommonGraph.test.tsx. + const option = getLatestEChartsOption(); + const series = (option.series as LineSeriesOption[]).filter( + (s) => s.type === 'line', ); - expect(datasetsForKde).toMatchObject([ + expect(series).toHaveLength(2); + expect(series).toMatchObject([ { - yAxisID: 'yKde', - label: 'Base', - fill: false, - borderColor: Colors.ChartBase, + type: 'line', + name: 'Base', + lineStyle: { color: Colors.ChartBase }, }, { - yAxisID: 'yKde', - label: 'New', - fill: false, - borderColor: Colors.ChartNew, + type: 'line', + name: 'New', + lineStyle: { color: Colors.ChartNew }, }, ]); - - const datasetForScatter = datasets.find( - (dataset) => dataset.type === 'scatter', - ); - expect(datasetForScatter).toMatchSnapshot('Dataset for scatter'); - - // 2. Test the more complex tooltip functions with various use cases. - const labelFunction = - chartProps.options?.plugins?.tooltip?.callbacks?.label; - expect(labelFunction).toBeDefined(); - - const tooltipItemKdeBase = { - dataset: datasetsForKde[0], - parsed: { x: 5, y: 5 }, - }; - const tooltipItemKdeNew = { - dataset: datasetsForKde[1], - parsed: { x: 5, y: 5 }, - }; - const tooltipItemValueBase = { - dataset: datasetForScatter, - raw: { - x: '1.234', - y: 'Base', - }, - }; - const tooltipItemValueNew = { - dataset: datasetForScatter, - raw: { - x: '2.345', - y: 'New', - }, - }; - - expect( - labelFunction!.call( - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - { dataPoints: [tooltipItemKdeBase] }, - tooltipItemKdeBase, - ), - ).toBe('@ 5.00'); - expect( - labelFunction!.call( - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - { dataPoints: [tooltipItemValueBase] }, - tooltipItemValueBase, - ), - ).toBe('Base: 1.234'); - expect( - labelFunction!.call( - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - { dataPoints: [tooltipItemValueNew] }, - tooltipItemValueNew, - ), - ).toBe('New: 2.345'); - - // Also test the cases where there are 2 values at the same x point. - // The first item shows a summary of both values. - expect( - labelFunction!.call( - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - { dataPoints: [tooltipItemValueBase, { ...tooltipItemValueBase }] }, - tooltipItemValueBase, - ), - ).toBe('Base: 1.234 (×2)'); - // But the second item isn't displayed at all. - expect( - labelFunction!.call( - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - { dataPoints: [{ ...tooltipItemValueBase }, tooltipItemValueBase] }, - tooltipItemValueBase, - ), - ).toBe(''); - - // 3. Also test the complex color function - const labelColorFunction = - chartProps.options?.plugins?.tooltip?.callbacks?.labelColor; - expect(labelColorFunction).toBeDefined(); - - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - expect(labelColorFunction!(tooltipItemKdeBase)).toEqual({ - backgroundColor: Colors.ChartBase, - }); - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - expect(labelColorFunction!(tooltipItemKdeNew)).toEqual({ - backgroundColor: Colors.ChartNew, - }); - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - expect(labelColorFunction!(tooltipItemValueBase)).toEqual({ - backgroundColor: Colors.ChartBase, - }); - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - expect(labelColorFunction!(tooltipItemValueNew)).toEqual({ - backgroundColor: Colors.ChartNew, - }); - - // 4. Also test the background color function for the scatter graph - const backgroundColorFunction = datasetForScatter?.backgroundColor as ( - ctx: ScriptableContext<'line'>, - ) => string | undefined; - expect(backgroundColorFunction).toBeInstanceOf(Function); - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - expect(backgroundColorFunction({ raw: { x: 5, y: 'Base' } })).toBe( - Colors.ChartBase + '99', - ); - // @ts-expect-error This object doesn't obey fully to the type - // description, but it's good enough to test our code. - expect(backgroundColorFunction({ raw: { x: 5, y: 'New' } })).toBe( - Colors.ChartNew + '99', - ); }); it('Should display Base, New and Common graphs with replicates', async () => { @@ -350,18 +270,18 @@ describe('Results View', () => { await screen.findByRole('region', { name: 'Revision Row Details' }), ).toMatchSnapshot(); - // Test that this time all replicates are displayed - const MockedLine = Line as jest.Mock; - const chartProps = MockedLine.mock.calls[0][0] as ChartProps; - const datasets = chartProps.data.datasets; - const datasetForScatter = datasets.find( - (dataset) => dataset.type === 'scatter', + // The KDE for each side should be built off the replicates, not the + // single-point base_runs/new_runs. Assert fftkde was called with each + // replicate array. + const fftkdeCalls = (fftkde as jest.Mock).mock.calls.map( + (call) => call[0] as number[], ); - expect(datasetForScatter!.data).toHaveLength( - testCompareDataWithReplicates[0].base_runs_replicates.length + - testCompareDataWithReplicates[0].new_runs_replicates.length, + expect(fftkdeCalls).toContainEqual( + testCompareDataWithReplicates[0].base_runs_replicates, + ); + expect(fftkdeCalls).toContainEqual( + testCompareDataWithReplicates[0].new_runs_replicates, ); - expect(datasetForScatter).toMatchSnapshot('Dataset for scatter'); }); it('should make blobUrl available when "Download JSON" button is clicked', async () => { @@ -591,4 +511,28 @@ describe('Results View', () => { expect(screen.queryByText('Results')).not.toBeInTheDocument(); expect(screen.getByText(titleName)).toBeInTheDocument(); }); + + it('toggles all rows when the Expand all checkbox is clicked', async () => { + const user = userEvent.setup({ advanceTimers: jest.advanceTimersByTime }); + renderWithRoute(); + + await screen.findByRole('table'); + + const expandAllCheckbox = screen.getByRole('checkbox', { + name: /Expand all/i, + }); + expect(expandAllCheckbox).not.toBeChecked(); + expect(screen.queryAllByTestId(/ExpandLessIcon/)).toHaveLength(0); + + await user.click(expandAllCheckbox); + expect(expandAllCheckbox).toBeChecked(); + const expandLessIcons = await screen.findAllByTestId(/ExpandLessIcon/); + expect(expandLessIcons.length).toBeGreaterThan(0); + + await user.click(expandAllCheckbox); + expect(expandAllCheckbox).not.toBeChecked(); + await waitFor(() => { + expect(screen.queryAllByTestId(/ExpandLessIcon/)).toHaveLength(0); + }); + }); }); diff --git a/src/__tests__/CompareResults/RevisionRow.test.tsx b/src/__tests__/CompareResults/RevisionRow.test.tsx index 1ad9eccc3..c1134f514 100644 --- a/src/__tests__/CompareResults/RevisionRow.test.tsx +++ b/src/__tests__/CompareResults/RevisionRow.test.tsx @@ -85,6 +85,7 @@ describe('', () => { gridTemplateColumns='none' replicates={false} testVersion='student-t' + expandAll={false} />, ); const shortNameNode = await screen.findByText(shortName); @@ -123,6 +124,7 @@ describe('Subtest count pills', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -151,6 +153,7 @@ describe('Subtest count pills', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -172,6 +175,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='student-t' + expandAll={false} />, ); @@ -199,6 +203,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -220,6 +225,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='student-t' + expandAll={false} />, ); @@ -236,6 +242,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='student-t' + expandAll={false} />, ); const expandRow = await screen.findByTestId(/ExpandMoreIcon/); @@ -256,6 +263,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -284,6 +292,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -305,6 +314,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -314,6 +324,108 @@ describe('Expanded row', () => { expect(emptySignificant[0]).toBeInTheDocument(); }); + it('should display median diff and 95% CI alerts when base/new runs are present', async () => { + const user = userEvent.setup({ advanceTimers: jest.advanceTimersByTime }); + const { testCompareMannWhitneyData: rowData } = getTestData(); + + renderWithRoute( + , + ); + + const expandRowButton = await screen.findByTestId(/ExpandMoreIcon/); + await user.click(expandRowButton); + + // Wait for the expanded panel before reading alert text content. + await screen.findByText(/Cliff's Delta/); + + // The summary alert is "Δ median = +7.6 ms 95% CI [..., ...]". The + // median-of-new minus median-of-base is 712.44 - 704.84 = +7.6. + const alerts = screen.getAllByRole('alert'); + const summaryAlert = alerts.find((alert) => + alert.textContent?.includes('Δ median'), + ); + expect(summaryAlert).toBeDefined(); + expect(summaryAlert?.textContent).toContain('+7.6'); + expect(summaryAlert?.textContent).toContain('ms 95% CI ['); + + // The confidence-interval alert is "Confidence Interval: We are 95% ...". + const ciAlert = alerts.find((alert) => + alert.textContent?.includes('Confidence Interval'), + ); + expect(ciAlert).toBeDefined(); + expect(ciAlert?.textContent).toContain( + 'We are 95% confident the median difference is between', + ); + }); + + it('should not render the CI alerts when base/new runs are empty', async () => { + const user = userEvent.setup({ advanceTimers: jest.advanceTimersByTime }); + const { testCompareMannWhitneyData: rowData } = getTestData(); + + // rowData[3] has empty base_runs / new_runs, so bootstrapMedianDiffCI is + // skipped and neither alert should render. + renderWithRoute( + , + ); + + const expandRowButton = await screen.findByTestId(/ExpandMoreIcon/); + await user.click(expandRowButton); + + // Other content from renderExpandedRight is present, so the panel is + // expanded — but there should be no Δ median or Confidence Interval alert. + await screen.findByText(/Cliff's Delta/); + expect(screen.queryByText(/Δ median/)).not.toBeInTheDocument(); + expect(screen.queryByText(/Confidence Interval/)).not.toBeInTheDocument(); + }); + + it('should mark the median diff alert as not significant when CI straddles zero', async () => { + const user = userEvent.setup({ advanceTimers: jest.advanceTimersByTime }); + const { testCompareMannWhitneyData: rowData } = getTestData(); + + // Identical base and new arrays produce a bootstrap CI symmetric around + // zero, so the difference is not statistically significant. + const overlapping: MannWhitneyResultsItem = { + ...rowData[0], + base_runs: [100, 110, 120], + new_runs: [100, 110, 120], + }; + + renderWithRoute( + , + ); + + const expandRowButton = await screen.findByTestId(/ExpandMoreIcon/); + await user.click(expandRowButton); + + await screen.findByText(/Cliff's Delta/); + const alerts = screen.getAllByRole('alert'); + const summaryAlert = alerts.find((alert) => + alert.textContent?.includes('Δ median'), + ); + expect(summaryAlert?.textContent).toContain('(not significant)'); + }); + it('should display mean for base or new in row headers for mann-whitney-u testVersion', async () => { const { testCompareMannWhitneyData: rowData } = getTestData(); renderWithRoute( @@ -323,6 +435,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -350,6 +463,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); @@ -369,6 +483,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='student-t' + expandAll={false} />, ); @@ -397,6 +512,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='student-t' + expandAll={false} />, ); @@ -435,6 +551,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); const roles = await screen.findAllByRole('cell'); @@ -451,6 +568,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); const roles = await screen.findAllByRole('cell'); @@ -468,6 +586,7 @@ describe('Expanded row', () => { gridTemplateColumns='none' replicates={false} testVersion='mann-whitney-u' + expandAll={false} />, ); const roles = await screen.findAllByRole('cell'); @@ -476,3 +595,66 @@ describe('Expanded row', () => { }); }); }); + +describe('expandAll prop', () => { + it('starts expanded when expandAll is true', async () => { + const { + testCompareData: [rowData], + } = getTestData(); + + renderWithRoute( + , + ); + + expect(await screen.findByTestId(/ExpandLessIcon/)).toBeInTheDocument(); + }); + + it('starts collapsed when expandAll is false', async () => { + const { + testCompareData: [rowData], + } = getTestData(); + + renderWithRoute( + , + ); + + expect(await screen.findByTestId(/ExpandMoreIcon/)).toBeInTheDocument(); + }); + + it('lets the user individually collapse a row that was force-expanded', async () => { + const user = userEvent.setup({ advanceTimers: jest.advanceTimersByTime }); + const { + testCompareData: [rowData], + } = getTestData(); + + renderWithRoute( + , + ); + + const collapseButton = await screen.findByTestId(/ExpandLessIcon/); + await user.click(collapseButton); + + expect(await screen.findByTestId(/ExpandMoreIcon/)).toBeInTheDocument(); + }); +}); diff --git a/src/__tests__/CompareResults/__snapshots__/OverTimeResultsView.test.tsx.snap b/src/__tests__/CompareResults/__snapshots__/OverTimeResultsView.test.tsx.snap index 470b689ae..067598ccd 100644 --- a/src/__tests__/CompareResults/__snapshots__/OverTimeResultsView.test.tsx.snap +++ b/src/__tests__/CompareResults/__snapshots__/OverTimeResultsView.test.tsx.snap @@ -13,7 +13,7 @@ exports[`Results View The table should match snapshot and other elements should class="MuiGrid-root MuiGrid-container MuiGrid-direction-xs-row MuiGrid-spacing-xs-2 f1wpas00 css-13620ud-MuiGrid-root" >
+
+ +
+
+ +
+
+ +
+ + + @@ -344,59 +452,11 @@ exports[`Results View Should display Base, New and Common graphs with replicates
`; -exports[`Results View Should display Base, New and Common graphs with replicates: Dataset for scatter 1`] = ` -{ - "backgroundColor": [Function], - "data": [ - { - "x": 587.15, - "y": "Base", - }, - { - "x": 593.04, - "y": "Base", - }, - { - "x": 600.7, - "y": "Base", - }, - { - "x": 602.04, - "y": "Base", - }, - { - "x": 605.16, - "y": "New", - }, - { - "x": 605.31, - "y": "New", - }, - { - "x": 605.61, - "y": "New", - }, - { - "x": 605.81, - "y": "New", - }, - { - "x": 607.27, - "y": "New", - }, - ], - "pointRadius": 7, - "pointStyle": "triangle", - "type": "scatter", - "yAxisID": "yValues", -} -`; - exports[`Results View Should display Base, New and Common graphs with tooltips 1`] = `

Runs Density Distribution @@ -425,7 +485,9 @@ exports[`Results View Should display Base, New and Common graphs with tooltips 1
- chartjs-line +

@@ -533,6 +595,111 @@ exports[`Results View Should display Base, New and Common graphs with tooltips 1 + + + @@ -736,54 +903,6 @@ exports[`Results View Should display Base, New and Common graphs with tooltips 1
`; -exports[`Results View Should display Base, New and Common graphs with tooltips: Dataset for scatter 1`] = ` -{ - "backgroundColor": [Function], - "data": [ - { - "x": 587.15, - "y": "Base", - }, - { - "x": 593.04, - "y": "Base", - }, - { - "x": 600.7, - "y": "Base", - }, - { - "x": 602.04, - "y": "Base", - }, - { - "x": 605.16, - "y": "New", - }, - { - "x": 605.31, - "y": "New", - }, - { - "x": 605.61, - "y": "New", - }, - { - "x": 605.81, - "y": "New", - }, - { - "x": 607.27, - "y": "New", - }, - ], - "pointRadius": 7, - "pointStyle": "triangle", - "type": "scatter", - "yAxisID": "yValues", -} -`; - exports[`Results View Should update url with new title and the table with the new title: After clicking the Save button 1`] = ` +
+ +

PerfCompare

Analyze results of performance tests to detect regressions and identify opportunities for improvement.
@@ -173,10 +173,10 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spam @@ -226,7 +226,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
it's just a flesh wound @@ -234,7 +234,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
@@ -285,10 +285,10 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspam @@ -338,7 +338,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
What, ridden on a horse? @@ -346,7 +346,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
@@ -397,10 +397,10 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspamspam @@ -450,7 +450,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
You've got two empty 'alves of coconuts and you're bangin' 'em togetha! @@ -458,7 +458,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
@@ -509,10 +509,10 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspamspam @@ -562,7 +562,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
She turned me into a newt! @@ -570,7 +570,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
@@ -621,10 +621,10 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > ed24spamspam @@ -674,7 +674,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
Bug 123456 Fuzzy She turned me into a newt! @@ -682,7 +682,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
@@ -733,10 +733,10 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspamspam @@ -786,7 +786,7 @@ exports[`SearchResultsList Should apply dark and light mode styles when theme bu
Commit message with no newline at the end @@ -836,6 +836,7 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="PrivateSwitchBase-input MuiSwitch-input css-12xagqm-MuiSwitchBase-root" id="Dark mode" name="toggle-dark-mode" + role="switch" type="checkbox" /> Dark mode @@ -855,14 +856,14 @@ exports[`SearchResultsList should match snapshot 1`] = `

PerfCompare

Analyze results of performance tests to detect regressions and identify opportunities for improvement.
@@ -1317,10 +1318,10 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spam @@ -1370,7 +1371,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
it's just a flesh wound @@ -1378,7 +1379,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
@@ -1429,10 +1430,10 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspam @@ -1482,7 +1483,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
What, ridden on a horse? @@ -1490,7 +1491,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
@@ -1541,10 +1542,10 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspamspam @@ -1594,7 +1595,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
You've got two empty 'alves of coconuts and you're bangin' 'em togetha! @@ -1602,7 +1603,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
@@ -1653,10 +1654,10 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspamspam @@ -1706,7 +1707,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
She turned me into a newt! @@ -1714,7 +1715,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
@@ -1765,10 +1766,10 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > ed24spamspam @@ -1818,7 +1819,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
Bug 123456 Fuzzy She turned me into a newt! @@ -1826,7 +1827,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
@@ -1877,10 +1878,10 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="MuiListItemText-root MuiListItemText-multiline search-revision-item-text css-14ln1j6-MuiListItemText-root" > spamspamspam @@ -1930,7 +1931,7 @@ exports[`SearchResultsList should match snapshot 1`] = `
Commit message with no newline at the end @@ -2171,7 +2172,7 @@ exports[`SearchResultsList should match snapshot 1`] = ` class="fs9pw6x cancel-compare" > - - ) : null} - + {isHome && ( + + + {strings.title} + + + {strings.tagline} + + + + )} ); } diff --git a/src/components/Shared/ToggleDarkModeButton.tsx b/src/components/Shared/ToggleDarkModeButton.tsx index aabaf9875..24d66386c 100644 --- a/src/components/Shared/ToggleDarkModeButton.tsx +++ b/src/components/Shared/ToggleDarkModeButton.tsx @@ -7,7 +7,6 @@ import { useAppSelector, useAppDispatch } from '../../hooks/app'; import { updateThemeMode } from '../../reducers/ThemeSlice'; import { Strings } from '../../resources/Strings'; import { Spacing, FontsRaw, SwitchRaw } from '../../styles'; -import { ThemeMode } from '../../types/state'; const strings = Strings.components.header; @@ -19,7 +18,7 @@ function ToggleDarkMode() { const toggleColorMode = () => { const themeMode = theme === 'light' ? 'dark' : 'light'; - dispatch(updateThemeMode(themeMode as ThemeMode)); + dispatch(updateThemeMode(themeMode)); localStorage.setItem('theme', themeMode); }; diff --git a/src/hooks/useSubtestRegressionCount.ts b/src/hooks/useSubtestRegressionCount.ts index 497d94e3a..70b2ae572 100644 --- a/src/hooks/useSubtestRegressionCount.ts +++ b/src/hooks/useSubtestRegressionCount.ts @@ -68,7 +68,7 @@ export function useSubtestRegressionCount({ const fetchPromise: Promise = view === compareView - ? (memoizedFetchSubtestsCompareResults({ + ? memoizedFetchSubtestsCompareResults({ baseRev: base_rev, baseRepo: base_repository_name, newRev: new_rev, @@ -79,8 +79,8 @@ export function useSubtestRegressionCount({ replicates, testVersion, silvermanKDEEnabled, - }) as Promise) - : (memoizedFetchSubtestsCompareOverTimeResults({ + }) + : memoizedFetchSubtestsCompareOverTimeResults({ baseRepo: base_repository_name, newRepo: new_repository_name, newRev: new_rev, @@ -91,7 +91,7 @@ export function useSubtestRegressionCount({ replicates, testVersion, silvermanKDEEnabled, - }) as Promise); + }); const strategy: TestVersionStrategy = getStrategy(testVersion); let cancelled = false; diff --git a/src/logic/lando.ts b/src/logic/lando.ts index ce76dcc2e..8c50617bf 100644 --- a/src/logic/lando.ts +++ b/src/logic/lando.ts @@ -1,6 +1,13 @@ import { LandoToCommit } from '../types/state'; -const landoBaseUrl = 'https://api.lando.services.mozilla.com'; +const landoInstances = { + 'lando-dev': 'api.dev.lando.nonprod.cloudops.mozgcp.net', + 'lando-dev-2025': 'lando-dev.allizom.org', + 'lando-prod': 'api.lando.services.mozilla.com', + 'lando-prod-2025': 'lando.moz.tools', +}; + +export type LandoInstance = keyof typeof landoInstances; async function fetchFromLando(url: string) { const response = await fetch(url); @@ -12,8 +19,12 @@ async function fetchFromLando(url: string) { return response; } -export async function fetchRevisionFromLandoId(landoid: string) { - const url = `${landoBaseUrl}/landing_jobs/${landoid}`; +export async function fetchRevisionFromLandoId( + landoid: string, + instance: LandoInstance = 'lando-prod', +) { + const host = landoInstances[instance] ?? landoInstances['lando-prod']; + const url = `https://${host}/landing_jobs/${landoid}`; const response = await fetchFromLando(url); return response.json() as Promise; } diff --git a/src/reducers/ThemeSlice.ts b/src/reducers/ThemeSlice.ts index 1f9dd6abc..f1fd50999 100644 --- a/src/reducers/ThemeSlice.ts +++ b/src/reducers/ThemeSlice.ts @@ -2,10 +2,8 @@ import { createSlice, PayloadAction } from '@reduxjs/toolkit'; import { ThemeMode } from '../types/state'; -const initialState = { - mode: (localStorage.getItem('theme') === 'dark' - ? 'dark' - : 'light') as ThemeMode, +const initialState: { mode: ThemeMode } = { + mode: localStorage.getItem('theme') === 'dark' ? 'dark' : 'light', }; const theme = createSlice({ diff --git a/src/styles/Header.ts b/src/styles/Header.ts index 37b58e236..3fbc85645 100644 --- a/src/styles/Header.ts +++ b/src/styles/Header.ts @@ -15,7 +15,7 @@ export const HeaderStyles = (mode: string, isHome: boolean) => { container: { padding: 0, width: '100%', - minHeight: isHome ? '357px' : '130px', + minHeight: isHome ? '357px' : 'auto', backgroundColor: isTrueLight ? lightBg : darkBg, backgroundImage: `url(${headerImage.toString()})`, backgroundPosition: 'center', diff --git a/src/styles/SearchContainerStyles.ts b/src/styles/SearchContainerStyles.ts index a7279f9b8..7f911b426 100644 --- a/src/styles/SearchContainerStyles.ts +++ b/src/styles/SearchContainerStyles.ts @@ -10,7 +10,7 @@ export const SearchContainerStyles = (isHome: boolean) => { maxWidth: '973px', marginTop: isHome ? `${Spacing.layoutLarge + 20}px` : '0px', margin: '0 auto', - marginBottom: isHome ? '0px' : `${Spacing.layoutXLarge + 4}px`, + marginBottom: isHome ? '0px' : `${Spacing.xxLarge}px`, display: 'flex', justifyContent: 'center', flexDirection: 'column', diff --git a/src/theme/components.js b/src/theme/components.js index 32ffc9ee1..049549536 100644 --- a/src/theme/components.js +++ b/src/theme/components.js @@ -162,7 +162,7 @@ const components = { backgroundSize: '290px', content: '""', display: 'block', - height: '0.3em', + height: '0.5em', marginTop: '-5px', }, }, diff --git a/src/utils/bootstrap-ci.ts b/src/utils/bootstrap-ci.ts new file mode 100644 index 000000000..f6e92eac5 --- /dev/null +++ b/src/utils/bootstrap-ci.ts @@ -0,0 +1,93 @@ +/** + * Percentile bootstrap confidence interval for the difference of medians. + * + * No external dependencies. + */ + +function median(arr: ArrayLike): number { + const s = new Float64Array(arr).sort(); + const m = s.length >> 1; + return s.length % 2 === 0 ? (s[m - 1] + s[m]) / 2 : s[m]; +} + +function resample(arr: Float64Array, rng: () => number): Float64Array { + const out = new Float64Array(arr.length); + for (let i = 0; i < arr.length; i++) { + out[i] = arr[Math.floor(rng() * arr.length)]; + } + return out; +} + +// Mulberry32 — fast seedable PRNG so results are reproducible. +function mulberry32(seed: number): () => number { + let s = seed >>> 0; + return () => { + s += 0x6d2b79f5; + let t = Math.imul(s ^ (s >>> 15), 1 | s); + t ^= t + Math.imul(t ^ (t >>> 7), 61 | t); + return ((t ^ (t >>> 14)) >>> 0) / 0x100000000; + }; +} + +export type BootstrapCI = { + medianDiff: number; + ciLow: number; + ciHigh: number; + significant: boolean; // CI does not contain 0 +}; + +/** + * Percentile bootstrap confidence interval for (median(newData) - median(base)). + * Matches scipy.stats.bootstrap(..., method="percentile", paired=False). + * + * How it works + * ------------ + * A confidence interval answers: "given the samples we observed, what is the + * plausible range for the true difference?" The bootstrap approach avoids + * assumptions about the underlying distribution (normality, etc.) by + * simulating the sampling process directly: + * + * 1. Draw nIter synthetic datasets by sampling WITH replacement from each + * input array (a "resample" — same size, but some values repeat and some + * are absent). + * 2. Compute (median(resampledNew) - median(resampledBase)) for each pair. + * 3. Sort the resulting nIter differences. + * 4. The CI is the [alpha/2, 1-alpha/2] percentile range of that distribution. + * + * If the CI does not straddle zero, the difference is statistically significant + * at the chosen alpha level. + * + * @param base - baseline sample values (e.g. before-patch timings) + * @param newData - new/comparison sample values (e.g. after-patch timings) + * @param nIter - number of bootstrap resamples; 1000 is sufficient for most + * uses, increase to 10 000 for publication-quality intervals + * @param alpha - two-tailed significance level: the CI covers (1-alpha) of the + * bootstrap distribution, e.g. 0.05 → 95% CI, 0.01 → 99% CI + * @param seed - PRNG seed; fix this to get reproducible results across runs + */ +export function bootstrapMedianDiffCI( + base: number[], + newData: number[], + nIter: number = 1000, + alpha: number = 0.05, + seed: number = 42, +): BootstrapCI { + const rng = mulberry32(seed); + const baseArr = new Float64Array(base); + const newArr = new Float64Array(newData); + const diffs = new Float64Array(nIter); + for (let i = 0; i < nIter; i++) { + diffs[i] = median(resample(newArr, rng)) - median(resample(baseArr, rng)); + } + diffs.sort(); + const loIdx = Math.floor((alpha / 2) * nIter); + const hiIdx = Math.min(Math.floor((1 - alpha / 2) * nIter), nIter - 1); + const ciLow = diffs[loIdx]; + const ciHigh = diffs[hiIdx]; + return { + medianDiff: median(newData) - median(base), + ciLow, + ciHigh, + significant: ciLow > 0 || ciHigh < 0, + }; +} diff --git a/src/utils/kde.d.ts b/src/utils/kde.d.ts new file mode 100644 index 000000000..63e12dc6a --- /dev/null +++ b/src/utils/kde.d.ts @@ -0,0 +1,61 @@ +/** + * Type declarations for kde.js. + * + * Depends on: nothing (self-contained) + * Consumed by: example.mjs, kde-widget.js (mode-fitting inlined there) + */ + +/** + * Faithful 1D port of KDEpy's FFTKDE with ISJ bandwidth for a Gaussian kernel. + * + * References: + * Botev, Z. I., Grotowski, J. F. and Kroese, D. P. (2010): + * Kernel density estimation via diffusion. Ann. Stat. 38(5), 2916-2957. + * Fan, J. and Marron, J. S. (1994): Fast implementations of nonparametric + * curve estimators. J. Comput. Graph. Stat. 3(1), 35-56. + * KDEpy: https://github.com/tommyod/KDEpy (MIT licence) + */ +export declare function dct2(x: number[]): number[]; +export declare function linearBinning1D( + data: number[], + gridPoints: number[], + weights?: number[], +): Float64Array; +export declare function autogrid1D( + data: number[], + boundaryAbs?: number, + numPoints?: number, + boundaryRel?: number, +): Float64Array; +export declare function improvedSheatherJones( + data: number[], + weights?: number[], +): number; +export declare function silvermansRule(data: number[]): number; +export type FFTKDEResult = { + x: number[]; + y: number[]; + bandwidth: number; +}; +export declare function fftkde( + data: number[], + bw?: number | 'ISJ' | 'silverman', + weights?: number[], + numGridPoints?: number, + boundary?: 'none' | 'reflection', +): FFTKDEResult; +export declare function argrelmax(y: number[], order?: number): number[]; +export type KDEModeResult = { + nModes: number; + peakLocs: number[]; + boundaries: number[]; + x: number[]; + y: number[]; + bandwidth: number; +}; +export declare function fitKdeModes( + data: number[], + valleyThreshold?: number, + minPeakFraction?: number, + minDataFraction?: number, +): KDEModeResult; diff --git a/src/utils/kde.js b/src/utils/kde.js new file mode 100644 index 000000000..26a4aadd4 --- /dev/null +++ b/src/utils/kde.js @@ -0,0 +1,755 @@ +/** + * Faithful 1D port of KDEpy's FFTKDE with ISJ bandwidth for a Gaussian kernel. + * + * References: + * Botev, Z. I., Grotowski, J. F. and Kroese, D. P. (2010): + * Kernel density estimation via diffusion. Ann. Stat. 38(5), 2916-2957. + * Fan, J. and Marron, J. S. (1994): Fast implementations of nonparametric + * curve estimators. J. Comput. Graph. Stat. 3(1), 35-56. + * KDEpy: https://github.com/tommyod/KDEpy (BSD 3-Clause licence) + */ +// --------------------------------------------------------------------------- +// FFT — Cooley-Tukey radix-2 DIT, in-place on Float64Arrays +// --------------------------------------------------------------------------- +function fftInPlace(re, im) { + const N = re.length; + // Bit-reversal permutation + let j = 0; + for (let i = 1; i < N; i++) { + let bit = N >> 1; + while (j & bit) { + j ^= bit; + bit >>= 1; + } + j ^= bit; + if (i < j) { + let t = re[i]; + re[i] = re[j]; + re[j] = t; + t = im[i]; + im[i] = im[j]; + im[j] = t; + } + } + // Butterfly passes + for (let len = 2; len <= N; len <<= 1) { + const half = len >> 1; + // W = exp(-2*pi*i/len) = exp(-pi*i/half) + const ang = -Math.PI / half; + const wRe = Math.cos(ang); + const wIm = Math.sin(ang); + for (let i = 0; i < N; i += len) { + let cRe = 1.0, + cIm = 0.0; + for (let k = 0; k < half; k++) { + const uRe = re[i + k]; + const uIm = im[i + k]; + const vRe = re[i + k + half] * cRe - im[i + k + half] * cIm; + const vIm = re[i + k + half] * cIm + im[i + k + half] * cRe; + re[i + k] = uRe + vRe; + im[i + k] = uIm + vIm; + re[i + k + half] = uRe - vRe; + im[i + k + half] = uIm - vIm; + const nextCRe = cRe * wRe - cIm * wIm; + cIm = cRe * wIm + cIm * wRe; + cRe = nextCRe; + } + } + } +} +// --------------------------------------------------------------------------- +// DCT-II — matches scipy.fftpack.dct(x, type=2, norm=None) +// Uses Lee's O(N log N) FFT-based reduction. N must be a power of 2. +// +// Algorithm: reorder as v[n]=x[2n], v[N-1-n]=x[2n+1], then +// y[k] = 2 * Re( FFT(v)[k] * exp(-i*pi*k/(2N)) ) +// --------------------------------------------------------------------------- +/** + * Type-II Discrete Cosine Transform. + * + * Used internally by improvedSheatherJones to transform the binned data into + * frequency space, where the ISJ fixed-point equation can be evaluated + * efficiently. Not needed directly in typical usage — call fftkde instead. + * + * @param x - real-valued array whose length must be a power of 2 + * @returns DCT-II coefficients, same length as x + */ +export function dct2(x) { + const N = x.length; + if (N < 2 || (N & (N - 1)) !== 0) + throw new Error(`dct2 requires power-of-2 length, got ${N}`); + const half = N >> 1; + const v = new Float64Array(N); + for (let n = 0; n < half; n++) { + v[n] = x[2 * n]; + v[N - 1 - n] = x[2 * n + 1]; + } + const re = new Float64Array(v); + const im = new Float64Array(N); + fftInPlace(re, im); + const y = new Array(N); + for (let k = 0; k < N; k++) { + const angle = (-Math.PI * k) / (2 * N); + y[k] = 2 * (re[k] * Math.cos(angle) - im[k] * Math.sin(angle)); + } + return y; +} +// --------------------------------------------------------------------------- +// Brent's root-finding — matches scipy.optimize.brentq +// --------------------------------------------------------------------------- +function brentq(f, a, b, xtol = 2e-12, rtol = 4.4e-16, maxIter = 100) { + let fa = f(a); + let fb = f(b); + if (fa === 0) return { x: a, converged: true }; + if (fb === 0) return { x: b, converged: true }; + if (fa * fb > 0) return { x: 0, converged: false }; + let c = b, + fc = fb; + let d = 0, + e = 0; + for (let iter = 0; iter < maxIter; iter++) { + if (fb * fc > 0) { + c = a; + fc = fa; + d = e = b - a; + } + if (Math.abs(fc) < Math.abs(fb)) { + a = b; + fa = fb; + b = c; + fb = fc; + c = a; + fc = fa; + } + const tol1 = 2 * rtol * Math.abs(b) + 0.5 * xtol; + const xm = 0.5 * (c - b); + if (Math.abs(xm) <= tol1 || fb === 0) return { x: b, converged: true }; + if (Math.abs(e) >= tol1 && Math.abs(fa) > Math.abs(fb)) { + let s = fb / fa; + let p, q; + if (a === c) { + p = 2 * xm * s; + q = 1 - s; + } else { + const r = fb / fc; + q = fa / fc; + p = s * (2 * xm * q * (q - r) - (b - a) * (r - 1)); + q = (q - 1) * (r - 1) * (s - 1); + } + if (p > 0) q = -q; + else p = -p; + if (2 * p < Math.min(3 * xm * q - Math.abs(tol1 * q), Math.abs(e * q))) { + e = d; + d = p / q; + } else { + d = xm; + e = xm; + } + } else { + d = xm; + e = xm; + } + a = b; + fa = fb; + b += Math.abs(d) > tol1 ? d : xm > 0 ? tol1 : -tol1; + fb = f(b); + } + return { x: b, converged: false }; +} +// --------------------------------------------------------------------------- +// 1D linear binning — port of KDEpy's linbin_cython / linbin_numpy +// +// Each data point distributes its weight linearly to the two nearest grid +// points (floor and ceil), proportional to the fractional distance. +// Returns a Float64Array of length gridPoints.length that sums to 1. +// --------------------------------------------------------------------------- +/** + * Bin scattered data onto a uniform grid using linear (tent) weighting. + * + * Rather than placing each data point in a single bucket (histogram-style), + * each point splits its weight between its two nearest grid neighbours + * proportionally to how close it is to each. This avoids the sharp edges + * of ordinary histograms and is required before FFT-based convolution. + * + * @param data - raw sample values + * @param gridPoints - uniformly-spaced grid positions (e.g. from autogrid1D) + * @param weights - per-sample weights; if omitted, all samples are equal + * @returns Float64Array of length gridPoints.length that sums to ≈1 + */ +export function linearBinning1D(data, gridPoints, weights) { + const G = gridPoints.length; + const minGrid = gridPoints[0]; + const maxGrid = gridPoints[G - 1]; + const dx = (maxGrid - minGrid) / (G - 1); + // Normalised weights + let w; + if (weights !== undefined) { + const wSum = weights.reduce((a, b) => a + b, 0); + w = weights.map((wi) => wi / wSum); + } else { + const uni = 1 / data.length; + w = new Array(data.length).fill(uni); + } + // Extra element absorbs any data point that lands exactly on the upper edge + const result = new Float64Array(G + 1); + for (let i = 0; i < data.length; i++) { + const t = (data[i] - minGrid) / dx; + const lo = Math.floor(t); + const frac = t - lo; + if (lo >= 0 && lo < G) result[lo] += w[i] * (1 - frac); + if (lo + 1 <= G) result[lo + 1] += w[i] * frac; + } + return result.slice(0, G); +} +// --------------------------------------------------------------------------- +// 1D autogrid — port of KDEpy's autogrid for 1D +// --------------------------------------------------------------------------- +/** + * Build a uniform evaluation grid that spans the data range with padding. + * + * The padding prevents the KDE from dropping to zero too abruptly at the + * edges of the observed data, which would distort bandwidth estimation. + * The grid size should be a power of 2 for efficient FFT convolution. + * + * @param data - raw sample values (used only to find min/max) + * @param boundaryAbs - minimum padding in data units on each side (default 3) + * @param numPoints - number of grid points; use a power of 2 (default 1024) + * @param boundaryRel - padding as a fraction of the data range (default 0.05) + * @returns Float64Array of numPoints evenly-spaced x values + */ +export function autogrid1D( + data, + boundaryAbs = 3, + numPoints = 1024, + boundaryRel = 0.05, +) { + let minData = data[0], + maxData = data[0]; + for (let i = 1; i < data.length; i++) { + if (data[i] < minData) minData = data[i]; + if (data[i] > maxData) maxData = data[i]; + } + const range = maxData - minData; + const outside = Math.max(boundaryRel * range, boundaryAbs); + const lo = minData - outside; + const hi = maxData + outside; + const grid = new Float64Array(numPoints); + for (let i = 0; i < numPoints; i++) { + grid[i] = lo + ((hi - lo) * i) / (numPoints - 1); + } + return grid; +} +// --------------------------------------------------------------------------- +// Gaussian kernel (1D) +// K(x, bw) = exp(-x^2 / (2*bw^2)) / (sqrt(2*pi) * bw) +// This is the standard Gaussian PDF with std = bw, matching KDEpy's +// Kernel(gaussian, var=1).evaluate(x, bw, norm=2) in 1D. +// --------------------------------------------------------------------------- +function gaussianKernel1D(x, bw) { + return Math.exp((-x * x) / (2 * bw * bw)) / (Math.sqrt(2 * Math.PI) * bw); +} +// Find x > 0 where Gaussian kernel drops to atol — matches +// KDEpy's Kernel.practical_support(bw, atol=10e-5) for Gaussian. +// 10e-5 in Python == 1e-4. +function gaussianPracticalSupport(bw, atol = 10e-5) { + const xtol = 1e-3; + const result = brentq((x) => gaussianKernel1D(x, bw) - atol, 0, 8 * bw, xtol); + if (!result.converged) { + throw new Error('Could not find practical support for Gaussian kernel.'); + } + return result.x + xtol; +} +// --------------------------------------------------------------------------- +// ISJ fixed-point function — port of KDEpy's _fixed_point +// +// Implements the fixed-point equation t = ξ γ^5(t) from Botev et al. (2010). +// I_sq = [1², 2², ..., (n-1)²] (length n-1) +// a2 = dct[1:]² (length n-1) +// --------------------------------------------------------------------------- +function fixedPoint(t, N, I_sq, a2) { + const ell = 7; // 5 derivative steps as recommended in the paper + const piSq = Math.PI * Math.PI; + // f = 0.5 * π^(2*ell) * Σ_i I_sq[i]^ell * a2[i] * exp(-I_sq[i] * π² * t) + let f = 0; + for (let i = 0; i < I_sq.length; i++) { + f += Math.pow(I_sq[i], ell) * a2[i] * Math.exp(-I_sq[i] * piSq * t); + } + f *= 0.5 * Math.pow(Math.PI, 2 * ell); + if (f <= 0) return -1; + // Loop s = ell-1 down to 2 (mirrors Python's reversed(range(2, ell))) + for (let s = ell - 1; s >= 2; s--) { + // odd_numbers_prod = 1 * 3 * 5 * ... * (2s-1) == (2s-1)!! + let oddProd = 1; + for (let k = 1; k <= 2 * s - 1; k += 2) oddProd *= k; + const K0 = oddProd / Math.sqrt(2 * Math.PI); + const constVal = (1 + Math.pow(0.5, s + 0.5)) / 3; + const time = Math.pow((2 * constVal * K0) / (N * f), 2 / (3 + 2 * s)); + f = 0; + for (let i = 0; i < I_sq.length; i++) { + f += Math.pow(I_sq[i], s) * a2[i] * Math.exp(-I_sq[i] * piSq * time); + } + f *= 0.5 * Math.pow(Math.PI, 2 * s); + } + const tOpt = Math.pow(2 * N * Math.sqrt(Math.PI) * f, -0.4); + return t - tOpt; +} +// --------------------------------------------------------------------------- +// ISJ root solver — port of KDEpy's _root +// --------------------------------------------------------------------------- +function isjRoot(N, I_sq, a2) { + const Nc = Math.max(Math.min(1050, N), 50); + let tol = 10e-12 + (0.01 * (Nc - 50)) / 1000; + for (;;) { + const res = brentq((t) => fixedPoint(t, N, I_sq, a2), 0, tol); + if (res.converged && res.x > 0) return res.x; + tol *= 2; + if (tol >= 1) + throw new Error('ISJ root finding did not converge. Need more data.'); + } +} +// --------------------------------------------------------------------------- +// ISJ bandwidth selection — port of KDEpy's improved_sheather_jones +// --------------------------------------------------------------------------- +/** + * Data-driven bandwidth selection using the Improved Sheather–Jones (ISJ) + * plug-in estimator. + * + * What is bandwidth? + * ------------------ + * KDE works by placing a small "bump" (kernel) at each data point and summing + * them. The bandwidth controls how wide each bump is — too narrow and the + * curve is spiky and noisy; too wide and it blurs out real features. Choosing + * the right bandwidth automatically is the central problem in KDE. + * + * Why ISJ? + * -------- + * Simpler rules like Silverman's rule assume the data looks roughly Gaussian. + * ISJ makes no such assumption: it finds the bandwidth that minimises the + * mean integrated squared error by solving a fixed-point equation derived + * from the data's own frequency content (via DCT). This makes it reliable + * for multimodal or skewed distributions, which are common in performance data. + * + * @param data - raw sample values (at least a few dozen points recommended) + * @param weights - optional per-sample weights; zero/negative weights are dropped + * @returns optimal bandwidth in the same units as the data + */ +export function improvedSheatherJones(data, weights) { + const n = 1024; // 2^10, matching KDEpy + let d = data; + let w = weights; + // Drop zero/negative weights (KDEpy does: data = data[weights > 0]) + if (w !== undefined) { + const pairs = d.map((v, i) => [v, w[i]]); + const pos = pairs.filter(([, wi]) => wi > 0); + d = pos.map(([v]) => v); + w = pos.map(([, wi]) => wi); + } + let minD = d[0], + maxD = d[0]; + for (let i = 1; i < d.length; i++) { + if (d[i] < minD) minD = d[i]; + if (d[i] > maxD) maxD = d[i]; + } + const R = maxD - minD; + const N = new Set(d).size; // number of unique values + // ISJ uses boundary_abs=6, boundary_rel=0.5 (wider grid for stable estimation) + const xmesh = autogrid1D(d, 6, n, 0.5); + const xmeshArr = Array.from(xmesh); + const initialData = linearBinning1D(d, xmeshArr, w); + // Type-2 DCT of the binned data + const a = dct2(Array.from(initialData)); + // I_sq = [1², 2², ..., (n-1)²] + const I_sq = new Float64Array(n - 1); + for (let i = 0; i < n - 1; i++) I_sq[i] = (i + 1) * (i + 1); + // a2 = a[1:]² (skip DC component) + const a2 = new Float64Array(n - 1); + for (let i = 0; i < n - 1; i++) a2[i] = a[i + 1] * a[i + 1]; + const tStar = isjRoot(N, I_sq, a2); + return Math.sqrt(tStar) * R; +} +// --------------------------------------------------------------------------- +// Silverman's rule — port of KDEpy's silvermans_rule +// --------------------------------------------------------------------------- +/** + * Simple rule-of-thumb bandwidth selection (Silverman 1986). + * + * Estimates bandwidth as a function of sample size and spread (std / IQR). + * Fast and robust, but assumes the data is roughly unimodal and bell-shaped. + * Prefer improvedSheatherJones for multimodal or heavy-tailed distributions. + * + * @param data - raw sample values + * @returns bandwidth estimate in the same units as the data + */ +export function silvermansRule(data) { + const n = data.length; + if (n <= 1) return 1; + const mean = data.reduce((a, b) => a + b, 0) / n; + const variance = data.reduce((s, x) => s + (x - mean) ** 2, 0) / (n - 1); + const std = Math.sqrt(variance); + const sorted = [...data].sort((a, b) => a - b); + // numpy percentile linear interpolation: index = q * (n-1), interpolate floor/ceil + function percentile(q) { + const idx = q * (n - 1); + const lo = Math.floor(idx), + hi = Math.ceil(idx); + return sorted[lo] + (idx - lo) * (sorted[hi] - sorted[lo]); + } + // scipy.stats.norm.ppf(.75) - scipy.stats.norm.ppf(.25) = 1.3489795003921634 + const iqr = (percentile(0.75) - percentile(0.25)) / 1.3489795003921634; + let sigma = Math.min(std, iqr > 0 ? iqr : std); + if (sigma <= 0) return 1; + return sigma * Math.pow((n * 3) / 4, -0.2); +} +// --------------------------------------------------------------------------- +// 1D convolution, mode='same' — matches scipy.signal.convolve(a, b, mode='same') +// +// Full convolution c[m] = Σ_k a[k] * b[m-k]. +// 'same' returns M elements starting at index (N-1)//2 of the full result, +// where M=len(a), N=len(b). +// --------------------------------------------------------------------------- +function convolve1DSame(a, b) { + const M = a.length; + const N = b.length; + const start = (N - 1) >> 1; + const result = new Float64Array(M); + for (let i = 0; i < M; i++) { + const cIdx = i + start; + const kMin = Math.max(0, cIdx - (N - 1)); + const kMax = Math.min(M - 1, cIdx); + let sum = 0; + for (let k = kMin; k <= kMax; k++) { + sum += a[k] * b[cIdx - k]; + } + result[i] = sum; + } + return result; +} +/** + * Kernel Density Estimate using FFT-based convolution (FFTKDE). + * + * What is KDE? + * ------------ + * A KDE turns a set of discrete samples into a smooth continuous curve that + * estimates the underlying probability density — think of it as a smooth + * histogram. Each sample contributes a small Gaussian "bump"; summing all + * bumps gives the density curve. + * + * Why FFT? + * -------- + * Naively evaluating the sum of N kernels at G grid points costs O(N·G). + * FFTKDE instead bins the data onto the grid (linearBinning1D) and convolves + * the binned data with the kernel using FFT, reducing cost to O(G log G) + * regardless of N. + * + * @param data - raw sample values (at least 2 required) + * @param bw - bandwidth: "ISJ" (default, data-driven), "silverman" + * (faster rule of thumb), or a positive number (fixed) + * @param weights - optional per-sample weights + * @param numGridPoints - number of x-axis evaluation points (default 1024) + * @param boundary - "none" (default) or "reflection" for data that cannot + * be negative (e.g. latency values): mirrors data at x=0 + * so the density doesn't leak below zero + * @returns { x, y, bandwidth } where x and y are the KDE curve coordinates + */ +export function fftkde( + data, + bw = 'ISJ', + weights, + numGridPoints = 1024, + boundary = 'none', +) { + if (data.length < 2) + throw new Error('fftkde requires at least 2 data points.'); + if (boundary === 'reflection') { + return fftkdeReflection(data, bw, weights, numGridPoints); + } + // 1. Bandwidth + let bandwidth; + if (bw === 'ISJ') { + bandwidth = improvedSheatherJones(data, weights); + } else if (bw === 'silverman') { + bandwidth = silvermansRule(data); + } else { + bandwidth = bw; + } + return fftkdeCore(data, bandwidth, weights, numGridPoints); +} +function fftkdeCore(data, bandwidth, weights, numGridPoints) { + // 2. Gaussian practical support — used as boundary_abs for the grid + const realBw = gaussianPracticalSupport(bandwidth); + // 3. Evaluation grid — boundary_abs = practical_support(bw), boundary_rel=0.05 + const gridArr = autogrid1D(data, realBw, numGridPoints, 0.05); + const grid = Array.from(gridArr); + // 4. Linear binning + const binnedData = linearBinning1D(data, grid, weights); + // 5. Grid spacing + const minGrid = grid[0]; + const maxGrid = grid[numGridPoints - 1]; + const dx = (maxGrid - minGrid) / (numGridPoints - 1); + // 6. L = number of grid steps for kernel half-width + const L = Math.min(Math.floor(realBw / dx), numGridPoints); + // 7. Evaluate kernel on [-L*dx, ..., 0, ..., L*dx] (2L+1 points) + const kernelSize = 2 * L + 1; + const kernelWeights = new Float64Array(kernelSize); + for (let k = 0; k < kernelSize; k++) { + kernelWeights[k] = gaussianKernel1D((k - L) * dx, bandwidth); + } + // 8. Convolve (mode='same') + const raw = convolve1DSame(binnedData, kernelWeights); + // 9. Clamp negatives (floating-point noise) + for (let i = 0; i < raw.length; i++) { + if (raw[i] < 0) raw[i] = 0; + } + return { x: grid, y: Array.from(raw), bandwidth }; +} +// Reflection method for non-negative data. +// Silverman, B. W. (1986). Density Estimation for Statistics and Data +// Analysis. Chapman and Hall, London. Pages 20–22. +// Mirror data at x=0, run KDE on augmented set, take x≥0 and double density. +function fftkdeReflection(data, bw, weights, numGridPoints) { + // Augment: original + reflection across 0 + const augmented = new Array(data.length * 2); + for (let i = 0; i < data.length; i++) { + augmented[i] = data[i]; + augmented[data.length + i] = -data[i]; + } + // Duplicate weights if provided + let augWeights; + if (weights) { + augWeights = new Array(weights.length * 2); + for (let i = 0; i < weights.length; i++) { + augWeights[i] = weights[i]; + augWeights[weights.length + i] = weights[i]; + } + } + // Bandwidth from original data (not augmented — augmented is symmetric and + // would give a wider bandwidth than appropriate for the one-sided distribution) + let bandwidth; + if (bw === 'ISJ') { + bandwidth = improvedSheatherJones(data, weights); + } else if (bw === 'silverman') { + bandwidth = silvermansRule(data); + } else { + bandwidth = bw; + } + // Run KDE on augmented data with double the grid points (we'll discard the left half) + const result = fftkdeCore( + augmented, + bandwidth, + augWeights, + numGridPoints * 2, + ); + // Keep only x ≥ 0, double density (the reflected half integrates to 0.5) + const x = []; + const y = []; + for (let i = 0; i < result.x.length; i++) { + if (result.x[i] >= 0) { + x.push(result.x[i]); + y.push(result.y[i] * 2); + } + } + return { x, y, bandwidth }; +} +// --------------------------------------------------------------------------- +// argrelmax — port of scipy.signal.argrelmax(y, order=order) +// +// Returns indices i where y[i] is strictly greater than all y[i±j] for +// j = 1..order. Boundary points (i < order or i >= N-order) are excluded. +// --------------------------------------------------------------------------- +/** + * Find indices of local maxima in an array. + * + * A point is a local maximum if it is strictly greater than all neighbours + * within `order` positions on each side. Larger `order` suppresses narrow + * noise spikes at the cost of merging closely-spaced genuine peaks. + * + * @param y - array of values (e.g. KDE density at each grid point) + * @param order - neighbourhood half-width to check (default 1) + * @returns array of indices where local maxima occur + */ +export function argrelmax(y, order = 1) { + const N = y.length; + const peaks = []; + for (let i = order; i < N - order; i++) { + let isMax = true; + for (let j = 1; j <= order; j++) { + if (y[i] <= y[i - j] || y[i] <= y[i + j]) { + isMax = false; + break; + } + } + if (isMax) peaks.push(i); + } + return peaks; +} +// --------------------------------------------------------------------------- +// fitKdeModes — port of perf_compare_stats.fit_kde_modes +// +// Runs FFTKDE with ISJ bandwidth, finds local maxima, applies valley-depth +// and data-fraction filters to produce a list of distinct modes. +// +// Parameters +// ---------- +// data : raw sample values +// valleyThreshold : mode boundary is valid only if valley < threshold * min(peak_heights) +// minPeakFraction : peak must be >= this fraction of the global max to count +// minDataFraction : a mode must contain >= this fraction of data to be kept +// --------------------------------------------------------------------------- +/** + * Detect distinct modes (peaks) in a sample distribution via KDE. + * + * Why does this matter for performance data? + * ------------------------------------------ + * Performance measurements are often multimodal: a benchmark may have a + * "fast path" (cache warm, branch predicted) and a "slow path" (cache miss, + * JIT deoptimisation). A single summary statistic like the mean or median + * conflates these paths and can hide regressions or improvements. Finding + * modes lets us report each code path separately. + * + * How it works + * ------------ + * 1. Fit a KDE to the data (trimmed to 1st–99th percentile for stability). + * 2. Find local maxima (peaks) in the density curve. + * 3. Valley-depth filter: two peaks are only counted as separate modes if + * the valley between them is deep enough (< valleyThreshold × shorter + * peak height). Shallow saddles are KDE smoothing artefacts. + * 4. Data-fraction filter: a mode must contain >= minDataFraction of the + * actual data points to be reported (avoids noise bumps at the tails). + * + * @param data - raw sample values (fewer than 4 → always 1 mode) + * @param valleyThreshold - how deep a valley must be to split two modes; + * 0 = never split, 1 = always split (default 0.5) + * @param minPeakFraction - minimum peak height as fraction of global max, + * filters tiny noise bumps (default 0.05) + * @param minDataFraction - minimum fraction of data a mode must contain + * to be kept (default 0.05) + * @returns { nModes, peakLocs, boundaries, x, y, bandwidth } + */ +export function fitKdeModes( + data, + valleyThreshold = 0.5, + minPeakFraction = 0.05, + minDataFraction = 0.05, +) { + const fallbackX = data.reduce((a, b) => a + b, 0) / data.length; + function fallback(x, y, bw) { + const medianVal = [...data].sort((a, b) => a - b)[ + Math.floor(data.length / 2) + ]; + return { + nModes: 1, + peakLocs: [medianVal], + boundaries: [], + x, + y, + bandwidth: bw, + }; + } + if (data.length < 4) { + return { + nModes: 1, + peakLocs: [fallbackX], + boundaries: [], + x: [], + y: [], + bandwidth: 0, + }; + } + // Trim 1st–99th percentile for fitting (matches Python) + const sorted = [...data].sort((a, b) => a - b); + const n = sorted.length; + function pct(q) { + const idx = q * (n - 1); + const lo = Math.floor(idx), + hi = Math.ceil(idx); + return sorted[lo] + (idx - lo) * (sorted[hi] - sorted[lo]); + } + const p1 = pct(0.01), + p99 = pct(0.99); + let dataFit = data.filter((v) => v >= p1 && v <= p99); + if (dataFit.length < 4) dataFit = data; + let kde; + try { + kde = fftkde(dataFit, 'ISJ'); + } catch { + return fallback([], [], 0); + } + const { x, y, bandwidth } = kde; + // Local maxima with order=3 (matches Python argrelmax order=3) + let peakIdxs = argrelmax(y, 3); + const yMax = Math.max(...y); + peakIdxs = peakIdxs.filter((i) => y[i] >= minPeakFraction * yMax); + if (peakIdxs.length === 0) { + const globalMaxIdx = y.indexOf(yMax); + return { + nModes: 1, + peakLocs: [x[globalMaxIdx]], + boundaries: [], + x, + y, + bandwidth, + }; + } + // Valley-depth filter — build `good` list of surviving peak indices + const good = [peakIdxs[0]]; + for (let k = 1; k < peakIdxs.length; k++) { + const nxt = peakIdxs[k]; + const prev = good[good.length - 1]; + let valleyMin = y[prev]; + for (let j = prev; j <= nxt; j++) if (y[j] < valleyMin) valleyMin = y[j]; + if (valleyMin < valleyThreshold * Math.min(y[prev], y[nxt])) { + good.push(nxt); + } else if (y[nxt] > y[good[good.length - 1]]) { + good[good.length - 1] = nxt; + } + } + // Compute boundaries (x-position of valley minimum between each adjacent pair) + function boundaries(peaks) { + const bs = []; + for (let i = 0; i < peaks.length - 1; i++) { + let minIdx = peaks[i]; + for (let j = peaks[i]; j <= peaks[i + 1]; j++) + if (y[j] < y[minIdx]) minIdx = j; + bs.push(x[minIdx]); + } + return bs; + } + // Data-fraction filter: drop modes with < minDataFraction of points + function assignModes(bounds) { + return data.map((v) => { + let m = 0; + while (m < bounds.length && v > bounds[m]) m++; + return m; + }); + } + const bs0 = boundaries(good); + const assignments0 = assignModes(bs0); + const keep = good + .map((_, i) => i) + .filter( + (i) => + assignments0.filter((a) => a === i).length / data.length >= + minDataFraction, + ); + if (keep.length < 2) { + const bestIdx = good.reduce((a, b) => (y[a] > y[b] ? a : b)); + return { + nModes: 1, + peakLocs: [x[bestIdx]], + boundaries: [], + x, + y, + bandwidth, + }; + } + const finalGood = keep.map((i) => good[i]); + const finalBounds = boundaries(finalGood); + return { + nModes: finalGood.length, + peakLocs: finalGood.map((i) => x[i]), + boundaries: finalBounds, + x, + y, + bandwidth, + }; +}