diff --git a/CHANGELOG.md b/CHANGELOG.md index b2fc93a..f73c16a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,13 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## Unreleased + +### Changed + +* [dev] Drop c8 / pta / zora for tests, instead rely on built-in nodejs/bun test library, this has no dev dependency anymore. +* [jsdoc] improve jsdoc to contains output type definition. + ## [1.0.4] 2022-02-01 ### Fixed diff --git a/README.md b/README.md index 0310735..47beebf 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,9 @@ This is a JavaScript/Node.js library for computing running (or rolling) statistics for one set of values. -Requires node 14 or more recent. +Any up to date nodejs compatible runtime should properly work. (bun / nodejs). + +For testing, node 18.17.0 or more recent is required, or an up to date bun runtime. 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"node": ">=14" + "node": ">=18.17.0", + "bun": ">=1.1.38" } } diff --git a/src/index.test.js b/src/index.test.js index b76533c..0a89fdc 100644 --- a/src/index.test.js +++ b/src/index.test.js @@ -1,5 +1,14 @@ -const test = require("zora").test; -const StatsArray = require("./index"); +let describe, it; +if (typeof Bun !== 'undefined') { + describe = require('bun:test').describe; + it = require('bun:test').test; +} else { + describe = require('node:test').describe; + it = require('node:test').it; +} + +const assert = require('assert'); +const StatsArray = require('./index'); const TEST_PRECISION = 1000000000; // 10^-9 precision (9 can fail on sum/avg) @@ -11,101 +20,102 @@ function applyPrecision(obj, precision = TEST_PRECISION) { return obj; } - -test("statsArray", (t) => { - t.test('should not have stats when empty', function () { +describe('statsArray', () => { + it('should not have stats when empty', () => { const stat = new StatsArray(10); - t.deepEqual(stat.getStats(), null); + assert.deepStrictEqual(stat.getStats(), null); }); - - t.test('should have good stats : 0', function () { + it('should have good stats: 0', () => { const stat = new StatsArray(10); stat.append(0); - t.deepEqual(stat.getStats(), { "n": 1, "min": 0, "max": 0, "sum": 0, "mean": 0, "variance": 0, "standard_deviation": 0 }); + assert.deepStrictEqual(stat.getStats(), { "n": 1, "min": 0, "max": 0, "sum": 0, "mean": 0, "variance": 0, "standard_deviation": 0 }); }); - t.test('should have good stats : 1', function () { + + it('should have good stats: 1', () => { const stat = new StatsArray(10); stat.append(1); - t.deepEqual(stat.getStats(), { "n": 1, "min": 1, "max": 1, "sum": 1, "mean": 1, "variance": 0, "standard_deviation": 0 }); + assert.deepStrictEqual(stat.getStats(), { "n": 1, "min": 1, "max": 1, "sum": 1, "mean": 1, "variance": 0, "standard_deviation": 0 }); }); - t.test('should have good stats : -1', function () { + + it('should have good stats: -1', () => { const stat = new StatsArray(10); stat.append(-1); - t.deepEqual(stat.getStats(), { "n": 1, "min": -1, "max": -1, "sum": -1, "mean": -1, "variance": 0, "standard_deviation": 0 }); + assert.deepStrictEqual(stat.getStats(), { "n": 1, "min": -1, "max": -1, "sum": -1, "mean": -1, "variance": 0, "standard_deviation": 0 }); }); - t.test('should recompute internally', function () { + + it('should recompute internally', () => { const stat = new StatsArray(10); let qt = stat.nextCompute + 1; - while (qt--){ + while (qt--) { stat.append(Math.random()); } const output = stat.getStats(); - t.equal(output.n, 10); + assert.strictEqual(output.n, 10); }); - - t.test('should properly compute min', function () { + it('should properly compute min', () => { const stat = new StatsArray(2); stat.append(1); - t.deepEqual(stat.getStats().min, 1); + assert.strictEqual(stat.getStats().min, 1); stat.append(2); - t.deepEqual(stat.getStats().min, 1); + assert.strictEqual(stat.getStats().min, 1); stat.append(3); - t.deepEqual(stat.getStats().min, 2); + assert.strictEqual(stat.getStats().min, 2); stat.append(1); - t.deepEqual(stat.getStats().min, 1); + assert.strictEqual(stat.getStats().min, 1); stat.append(1); - t.deepEqual(stat.getStats().min, 1); + assert.strictEqual(stat.getStats().min, 1); stat.append(1); - t.deepEqual(stat.getStats().min, 1); + assert.strictEqual(stat.getStats().min, 1); }); - t.test('should properly compute max', function () { + + it('should properly compute max', () => { const stat = new StatsArray(2); stat.append(1); - t.deepEqual(stat.getStats().max, 1); + assert.strictEqual(stat.getStats().max, 1); stat.append(2); - t.deepEqual(stat.getStats().max, 2); + assert.strictEqual(stat.getStats().max, 2); stat.append(3); - t.deepEqual(stat.getStats().max, 3); + assert.strictEqual(stat.getStats().max, 3); stat.append(2); - t.deepEqual(stat.getStats().max, 3); + assert.strictEqual(stat.getStats().max, 3); stat.append(1); - t.deepEqual(stat.getStats().max, 2); + assert.strictEqual(stat.getStats().max, 2); stat.append(1); - t.deepEqual(stat.getStats().max, 1); + assert.strictEqual(stat.getStats().max, 1); stat.append(1); - t.deepEqual(stat.getStats().max, 1); + assert.strictEqual(stat.getStats().max, 1); }); - t.test('Compute data on the go (2 size)', function () { + it('should compute data on the go (2 size)', () => { const stat = new StatsArray(2); stat.append(1); - t.deepEqual(stat.getStats(), { n: 1, min: 1, max: 1, sum: 1, mean: 1, variance: 0, standard_deviation: 0 }); + assert.deepStrictEqual(stat.getStats(), { n: 1, min: 1, max: 1, sum: 1, mean: 1, variance: 0, standard_deviation: 0 }); stat.append(0); - t.deepEqual(stat.getStats(), { n: 2, min: 0, max: 1, sum: 1, mean: 0.5, variance: 0.25, standard_deviation: 0.5 }); - t.deepEqual(stat.recompute(), { n: 2, min: 0, max: 1, sum: 1, mean: 0.5, variance: 0.25, standard_deviation: 0.5 }); + assert.deepStrictEqual(stat.getStats(), { n: 2, min: 0, max: 1, sum: 1, mean: 0.5, variance: 0.25, standard_deviation: 0.5 }); + assert.deepStrictEqual(stat.recompute(), { n: 2, min: 0, max: 1, sum: 1, mean: 0.5, variance: 0.25, standard_deviation: 0.5 }); }); - t.test('Compute data on the go (2 same value)', function () { + it('Compute data on the go (2 same value)', () => { const stat = new StatsArray(2); stat.append(1); - t.deepEqual(stat.getStats(), { n: 1, min: 1, max: 1, sum: 1, mean: 1, variance: 0, standard_deviation: 0 }); + assert.deepEqual(stat.getStats(), { n: 1, min: 1, max: 1, sum: 1, mean: 1, variance: 0, standard_deviation: 0 }); stat.append(1); - t.deepEqual(stat.getStats(), { n: 2, min: 1, max: 1, sum: 2, mean: 1, variance: 0, standard_deviation: 0 }); - t.deepEqual(stat.recompute(), { n: 2, min: 1, max: 1, sum: 2, mean: 1, variance: 0, standard_deviation: 0 }); + assert.deepEqual(stat.getStats(), { n: 2, min: 1, max: 1, sum: 2, mean: 1, variance: 0, standard_deviation: 0 }); + assert.deepEqual(stat.recompute(), { n: 2, min: 1, max: 1, sum: 2, mean: 1, variance: 0, standard_deviation: 0 }); }); - t.test('Compute data on the go', function () { + it('should compute data on the go', () => { const stat = new StatsArray(10); stat.append(1); - t.deepEqual(stat.getStats(), { n: 1, min: 1, max: 1, sum: 1, mean: 1, variance: 0, standard_deviation: 0 }); + assert.deepStrictEqual(stat.getStats(), { n: 1, min: 1, max: 1, sum: 1, mean: 1, variance: 0, standard_deviation: 0 }); stat.append(-1); - t.deepEqual(stat.getStats(), { n: 2, min: -1, max: 1, sum: 0, mean: 0, variance: 1, standard_deviation: 1 }); + assert.deepStrictEqual(stat.getStats(), { n: 2, min: -1, max: 1, sum: 0, mean: 0, variance: 1, standard_deviation: 1 }); stat.append(1); - t.deepEqual(stat.getStats(), { n: 3, min: -1, max: 1, sum: 1, mean: 1 / 3, variance: 0.888888888888889, standard_deviation: 0.9428090415820634 }); + assert.deepStrictEqual(stat.getStats(), { n: 3, min: -1, max: 1, sum: 1, mean: 1 / 3, variance: 0.888888888888889, standard_deviation: 0.9428090415820634 }); stat.append(-1); - t.deepEqual(stat.getStats(), { n: 4, min: -1, max: 1, sum: 0, mean: 0, variance: 1, standard_deviation: 1 }); + assert.deepStrictEqual(stat.getStats(), { n: 4, min: -1, max: 1, sum: 0, mean: 0, variance: 1, standard_deviation: 1 }); stat.append(1); stat.append(-1); @@ -114,10 +124,10 @@ test("statsArray", (t) => { stat.append(1); stat.append(-1); // Final stats - t.deepEqual(stat.getStats(), { n: 10, min: -1, max: 1, sum: 0, mean: 0, variance: 1, standard_deviation: 1 }); + assert.deepStrictEqual(stat.getStats(), { n: 10, min: -1, max: 1, sum: 0, mean: 0, variance: 1, standard_deviation: 1 }); }); - t.test('Compute data on the go : and correctly (no over-wrt)', function () { + it('should compute data on the go and correctly (no overwrite)', () => { const array = []; const SIZE = 42; let qt = SIZE; @@ -131,15 +141,12 @@ test("statsArray", (t) => { stat1.append(element); }); - // Important note : - // t.test is possible that due to how t.test is computed (incrementaly) variance changes a tad - // This means we have to reduce the number of digt.test that matters const res1 = applyPrecision(stat1.getStats()); const res2 = applyPrecision(stat1.recompute()); - t.deepEqual(res1, res2); + assert.deepStrictEqual(res1, res2); }); - t.test('Compute data on the go : and correctly (with over-wrt)', function () { + it('should compute data on the go and correctly (with overwrite)', () => { const array = []; const SIZE = 42; let qt = SIZE; @@ -149,12 +156,10 @@ test("statsArray", (t) => { const stat1 = new StatsArray(SIZE); - // Random data array.forEach(element => { stat1.append(element); }); - // Full 0 - array.forEach(element => { + array.forEach(() => { stat1.append(0); }); @@ -163,10 +168,10 @@ test("statsArray", (t) => { // This means we have to reduce the number of digt.test that matters const res1 = applyPrecision(stat1.getStats()); const res2 = applyPrecision(stat1.recompute()); - t.deepEqual(res1, res2); + assert.deepEqual(res1, res2); - t.equal(res1.mean, 0); - t.equal(res1.min, 0); - t.equal(res1.variance, 0); + assert.equal(res1.mean, 0); + assert.equal(res1.min, 0); + assert.equal(res1.variance, 0); }); -}); \ No newline at end of file +});