Icard/angular-clarity-master(working)/node_modules/hdr-histogram-js/dist/Histogram.fc.spec.js

"use strict";
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
    function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
    return new (P || (P = Promise))(function (resolve, reject) {
        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
        function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
        step((generator = generator.apply(thisArg, _arguments || [])).next());
    });
};
Object.defineProperty(exports, "__esModule", { value: true });
/*
 * This is a TypeScript port of the original Java version, which was written by
 * Gil Tene as described in
 * https://github.com/HdrHistogram/HdrHistogram
 * and released to the public domain, as explained at
 * http://creativecommons.org/publicdomain/zero/1.0/
 */
const fc = require("fast-check");
const hdr = require("./index");
const wasm_1 = require("./wasm");
const runFromStryker = __dirname.includes("stryker");
const runnerOptions = {
    numRuns: runFromStryker ? 10 : 1000,
    verbose: true,
};
describe("Histogram percentile computation", () => {
    beforeAll(wasm_1.initWebAssembly);
    const numberOfSignificantValueDigits = 3;
    [true, false].forEach((useWebAssembly) => [16, "packed"].forEach((bitBucketSize) => it(`Histogram ${bitBucketSize} (wasm: ${useWebAssembly}) should be accurate according to its significant figures`, () => __awaiter(void 0, void 0, void 0, function* () {
        yield wasm_1.initWebAssembly();
        fc.assert(fc.property(arbData(2000), (numbers) => {
            const histogram = hdr.build({
                bitBucketSize,
                numberOfSignificantValueDigits,
                useWebAssembly,
            });
            numbers.forEach((n) => histogram.recordValue(n));
            const actual = quantile(numbers, 90);
            const got = histogram.getValueAtPercentile(90);
            const relativeError = Math.abs(1 - got / actual);
            const variation = Math.pow(10, -numberOfSignificantValueDigits);
            histogram.destroy();
            return relativeError < variation;
        }), runnerOptions);
    }))));
});
describe("Histogram percentile computation (packed vs classic)", () => {
    const numberOfSignificantValueDigits = 3;
    const classicHistogram = hdr.build({
        numberOfSignificantValueDigits,
    });
    const histogram = hdr.build({
        numberOfSignificantValueDigits,
        bitBucketSize: "packed",
        useWebAssembly: false,
    });
    it(`should be accurate according to its significant figures`, () => {
        fc.assert(fc.property(arbData(5), (numbers) => {
            histogram.reset();
            classicHistogram.reset();
            numbers.forEach((n) => histogram.recordValue(n));
            numbers.forEach((n) => classicHistogram.recordValue(n));
            const actual = classicHistogram.getValueAtPercentile(90);
            const got = histogram.getValueAtPercentile(90);
            return actual === got;
        }), runnerOptions);
    });
});
describe("Histogram percentile computation with CO correction (wasm vs js)", () => {
    beforeAll(wasm_1.initWebAssembly);
    let jsHistogram;
    let wasmHistogram;
    beforeEach(() => {
        jsHistogram = hdr.build({
            useWebAssembly: false,
        });
        wasmHistogram = hdr.build({
            useWebAssembly: true,
        });
    });
    afterEach(() => {
        jsHistogram.destroy();
        wasmHistogram.destroy();
    });
    it(`should be accurate according to its significant figures`, () => {
        fc.assert(fc.property(arbData(1, 100 * 1000), (numbers) => {
            jsHistogram.reset();
            wasmHistogram.reset();
            numbers.forEach((n) => {
                jsHistogram.recordValueWithExpectedInterval(n, 1000);
            });
            numbers.forEach((n) => {
                wasmHistogram.recordValueWithExpectedInterval(n, 1000);
            });
            const js = jsHistogram.getValueAtPercentile(90);
            const wasm = wasmHistogram.getValueAtPercentile(90);
            const relativeError = Math.abs(1 - js / wasm);
            const variation = Math.pow(10, -3);
            if (relativeError >= variation) {
                console.log({ js, wasm });
            }
            return relativeError < variation;
        }), runnerOptions);
    });
});
describe("Histogram encoding/decoding", () => {
    beforeAll(wasm_1.initWebAssembly);
    const numberOfSignificantValueDigits = 3;
    [true, false].forEach((useWebAssembly) => [8, 16, 32, 64, "packed"].forEach((bitBucketSize) => {
        it(`Histogram ${bitBucketSize} (wasm: ${useWebAssembly}) should keep all data after an encoding/decoding roundtrip`, () => {
            fc.assert(fc.property(arbData(1), fc.double(50, 100), (numbers, percentile) => {
                const histogram = hdr.build({
                    bitBucketSize,
                    numberOfSignificantValueDigits,
                    useWebAssembly,
                });
                numbers.forEach((n) => histogram.recordValue(n));
                const encodedHistogram = hdr.encodeIntoCompressedBase64(histogram);
                const decodedHistogram = hdr.decodeFromCompressedBase64(encodedHistogram);
                const actual = histogram.getValueAtPercentile(percentile);
                const got = decodedHistogram.getValueAtPercentile(percentile);
                histogram.destroy();
                decodedHistogram.destroy();
                return actual === got;
            }), runnerOptions);
        });
    }));
});
const arbData = (size, max = Number.MAX_SAFE_INTEGER) => fc.array(fc.integer(1, max), size, size);
// reference implementation
const quantile = (inputData, percentile) => {
    const data = [...inputData].sort((a, b) => a - b);
    const index = (percentile / 100) * (data.length - 1);
    let result;
    if (Math.floor(index) === index) {
        result = data[index];
    }
    else {
        const i = Math.floor(index);
        const fraction = index - i;
        result = data[i] + (data[i + 1] - data[i]) * fraction;
    }
    return result;
};
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