Merge branch 'master' into es6

.gitignore

@@ -1,4 +1,6 @@
 .DS_Store
+.vscode
 node_modules
 package-lock.json
+yarn.lock
 tests/coverage

.travis.yml

@@ -1,31 +1,33 @@
 language: node_js
 dist: trusty
 env:
-  - DISPLAY=:99.0 CHROME_BIN=chromium-browser
+- DISPLAY=:99.0 CHROME_BIN=chromium-browser
 node_js:
-  - "7"
+- "7"
 addons:
-  firefox: latest
+- firefox: latest
 before_script:
-  - sh -e /etc/init.d/xvfb start
+- sh -e /etc/init.d/xvfb start
-before_deploy:
+script:
-  - mkdir -p deploy
+- yarn run build
-  - ./build.py -l lib -d deploy
+after_success:
+- yarn run docs
 notifications:
   slack:
     secure: 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
 deploy:
-  - provider: releases
+- provider: releases
   api_key:
     secure: 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
   file: deploy/octo.js
   skip_cleanup: true
   on:
     tags: true
-  - provider: pages
+- provider: pages
-    github_token:
+  keep-history: true
-      secure: HszUovTqcRxuXTmH3/rStEe1ZeZbrZweQah8Bq0POs89P3/GjyEat11WbH9S7tfIT1yVcF736TmsDNRtl34Rmr71P7rczwFlX1yJOIamH4jT9vG8ocpZH4Orc6XA2s7UclD1HwJwStM1k2J4CV34eTTlX36ngjYrp09HertXAQWSK6TZk9RK6ql2HlNzchImz2EY1aQnfqkrFByOtM4o/SJJryJJBJUMD69gWrYu6cv51w/jMEoUtzvV6Nr2o9YOuo97B0scJmbxgfx67FaCQwEOxKtr8xT5nttJ2q6EeaAiErAieG8/o8yRsJRFrHyVm42v9PEfoe8O8Y4g/1VrtbjY3vH8LPpCotMxhshjUCBaLDc8TCC/osik6dxhv/fqjbdPwrGRW2qzsmXIZug7+YCEW+qUFRPwwX+Cr0ENnaPFMSDFxANeLF7KfF7oaVnr8JTlZnu9G+Uliqe6RNCoWfQtpTRM759cDLQp4b5eBQF2G61x2XOdRqcNZepRaRVnsaCo0lNPW9o5HCQnJnMbEB9Gdowt18PwgVucIdIPJeAPodMs40s7sUOP5Xik58b4YBG9lsgLBcfCgTy+n8JPS2LJsclG82pBACtSV+ucc6rVeD+5fbSUxn4ItaK4e0MonCv4Jnp7zGn6hErcpexBmXSdYQSrdsemKDTGHibOF6U=
-    local_dir: deploy
   skip_cleanup: true
+  github_token:
+    secure: PvCrB9ckDfWedl1rdo/3mUvCy/Vg4ksZR2mj4KT3AdEyukbpMiVVDkocMFohuIq+mnRdeyhpvM2KluLHhQrEhWjvqVobiZ9/c0gWWxZHV8doDdqNUPm693MpGojMOxHT4qRih6x9KyjanmWAwy/Bc972dpD5vtaTx1gKOzRUjUI=
   on:
     branch: master
+  local_dir: docs/

Gruntfile.js

@@ -1,31 +0,0 @@
-module.exports = function (grunt) {
-  let pkg = grunt.file.readJSON('package.json')
-
-  grunt.initConfig({
-    pkg: pkg,
-
-    karma: {
-      unit: {
-        configFile: 'karma.conf.js'
-      }
-    },
-    coveralls: {
-      options: {
-        coverageDir: 'tests/coverage/',
-        force: true
-      }
-    },
-    standard: {
-      options: pkg.standard,
-      lib: {
-        src: ['lib/**/*.js']
-      }
-    }
-  })
-
-  grunt.loadNpmTasks('grunt-karma')
-  grunt.loadNpmTasks('grunt-karma-coveralls')
-  grunt.loadNpmTasks('grunt-standard')
-
-  grunt.registerTask('test', ['standard', 'karma', 'coveralls'])
-}

README.md

@@ -18,19 +18,24 @@ Octo.js bundles core functions and generic boilerplate code commonly used in mos
 
 Octo's future aims to be a stable, well-tested and well-documented standard library for fuzzing in a JavaScript environment.
 
-## Note
+## Table of Contents
-The ES6 branch is under active development while we are incorporating it with our existing fuzzers.
+- [Table of Contents](#table-of-contents)
+  - [Playbook](#playbook)
+  - [Usage in Node](#usage-in-node)
+  - [Usage in Browser](#usage-in-browser)
+  - [Develop](#develop)
+  - [Testing](#testing)
+  - [API Documentation](#api-documentation)
 
-
+### Playbook
-## Playbook
 
 https://runkit.com/posidron/octo-js-playbook
 
 
-## Node
+### Usage in Node
 
 ```
-npm i @mozillasecurity/octo
+yarn add @mozillasecurity/octo
 ```
 
 ```
@@ -38,33 +43,35 @@ const {random} = require('@mozillasecurity/octo')
 random.init()
 ```
 
-
+### Usage in Browser
-## Browser
-
-We have not yet merged ES6 to master, hence the browser version which was released on master is not up-to-date.
-Use the `dist/octo.js` version of this branch by running the following command.
 
 ```
-npm run build
+yarn build
 ```
 
-
+### Develop
-## Development
 
 ```bash
-npm install
+yarn install
-npm run build
+yarn lint
-npm run watch
+yarn test
-npm run test:lint
+yarn build
 ```
 
-## Testing
+### Testing
 
-Tests live in the `tests/` subdirectory, and are written for [QUnit](https://qunitjs.com/).
+Octo.js uses Jest for testing. Each directory should contain a `__tests__` folder containing the tests.
-To run tests locally, open `tests/index.html` in a browser.
-The automated tests are run in Firefox or Chrome using [Karma](https://karma-runner.github.io/).
-To run the automated tests:
 
 ```bash
-npm test
+yarn test
+```
+
+### API Documentation
+
+* https://
+
+or
+
+```
+yarn docs
 ```

karma.conf.js

@@ -1,60 +0,0 @@
-module.exports = function (config) {
-  let configuration = {
-    basePath: './tests',
-
-    frameworks: ['qunit'],
-
-    files: [
-      '../lib/utils/init.js',
-      '../lib/utils/*.js',
-      '../lib/logging/*.js',
-      '../lib/make/init.js',
-      '../lib/make/*.js',
-      '../lib/random/*.js',
-      '**/*.js'
-    ],
-
-    exclude: [
-    ],
-
-    preprocessors: {
-      '../lib/**/*.js': ['coverage']
-    },
-
-    reporters: ['progress', 'coverage'],
-
-    port: 9876,
-
-    colors: true,
-
-    logLevel: config.LOG_INFO,
-
-    autoWatch: true,
-
-    browsers: ['Chrome', 'Firefox'],
-
-    singleRun: true,
-
-    browserNoActivityTimeout: 30000,
-
-    customLaunchers: {
-      Chrome_travis_ci: {
-        base: 'Chrome',
-        flags: ['--no-sandbox']
-      }
-    },
-
-    coverageReporter: {
-      reporters: [
-        { type: 'lcov', dir: 'coverage/' },
-        { type: 'text-summary' }
-      ]
-    }
-  }
-
-  if (process.env.TRAVIS) {
-    configuration.browsers = ['Chrome_travis_ci', 'Firefox']
-  }
-
-  config.set(configuration)
-}

lib/random/__tests__/mersennetwister.test.js

@@ -0,0 +1,92 @@
+/* This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
+
+/* eslint-env jest */
+const MersenneTwister = require('../mersennetwister')
+
+describe('MersenneTwister', () => {
+  test('uniform distribution', () => {
+    const N = Math.pow(2, 18)
+    const TRIES = 10
+    const XSQ = 293.25 // quantile of chi-square dist. k=255, p=.05
+
+    let mt = new MersenneTwister()
+    mt.seed(Math.random() * 0x100000000)
+
+    const _test = () => {
+      let tries = []
+      for (let attempt = 0; attempt < TRIES; ++attempt) {
+        let data = new Uint32Array(N)
+        let sh
+        for (let i = 0; i < data.length; ++i) {
+          data[i] = mt.int32()
+        }
+        for (sh = 0; sh <= 24; ++sh) {
+          let bins = new Uint32Array(256)
+          for (let b of data) {
+            ++bins[(b >>> sh) & 0xff]
+          }
+          let xsq = bins.reduce((a, v) => {
+            let e = N / bins.length
+            return a + Math.pow(v - e, 2) / e
+          }, 0)
+          /*
+          * XSQ = scipy.stats.chi2.isf(.05, 255)
+          * if xsq > XSQ, the result is biased at 95% significance
+          */
+          if (xsq < XSQ) {
+            console.log(`Expected x^2 to be < ${XSQ}, got ${xsq} on attempt #${attempt + 1}`)
+            return true
+          }
+          tries.push(xsq)
+        }
+        if (sh === 25) {
+          return
+        }
+      }
+      return false
+    }
+
+    expect(_test()).toBe(true)
+  })
+
+  test('float distribution', () => {
+    const N = Math.pow(2, 18)
+    const TRIES = 3
+    const XSQ = 564.7 // quantile of chi-square dist. k=511, p=.05
+
+    let mt = new MersenneTwister()
+    mt.seed(Math.random() * 0x100000000)
+
+    const _test = () => {
+      let tries = []
+      for (let attempt = 0; attempt < TRIES; ++attempt) {
+        let bins = new Uint32Array(512)
+        for (let i = 0; i < N; ++i) {
+          let tmp = (mt.real2() * bins.length) >>> 0
+          if (tmp >= bins.length) {
+            throw new Error('random.float() >= 1.0')
+          }
+          ++bins[tmp]
+        }
+        let xsq = bins.reduce((a, v) => {
+          let e = N / bins.length
+          return a + Math.pow(v - e, 2) / e
+        }, 0)
+        /*
+        * XSQ = scipy.stats.chi2.isf(.05, 511)
+        * if xsq > XSQ, the result is biased at 95% significance
+        */
+        if (xsq < XSQ) {
+          console.log(`Expected x^2 to be < ${XSQ}, got ${xsq} on attempt #${attempt + 1}`)
+          return true
+        }
+        tries.push(xsq)
+      }
+      // assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": " + tries)
+      return false
+    }
+    expect(_test()).toBe(true)
+  })
+})

lib/random/random.js

@@ -3,7 +3,7 @@
  * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
 
 const MersenneTwister = require('./mersennetwister')
-const {logger} = require('../logging')
+const logger = require('../logging')
 
 class random {
   /**

lib/utils/objects.js

@@ -4,7 +4,7 @@
 
 const random = require('../random')
 const utils = require('../utils')
-const {logger} = require('../logging')
+const logger = require('../logging')
 
 var o = null // eslint-disable-line no-unused-vars
 

package.json

@@ -1,7 +1,7 @@
 {
   "name": "@mozillasecurity/octo",
-  "version": "1.0.14",
+  "version": "1.0.15",
-  "description": "",
+  "description": "A unified shared library which aids in building fuzzers for browsers or as complement for an existing fuzzing framework.",
   "keywords": [
     "fuzzing",
     "browser",
@@ -9,7 +9,7 @@
     "node",
     "library"
   ],
-  "homepage": "https://github.com/mozillasecurity/octo/tree/es6",
+  "homepage": "https://github.com/mozillasecurity/octo",
   "repository": {
     "type": "git",
     "url": "https://github.com/mozillasecurity/octo.git"
@@ -21,18 +21,20 @@
   "author": "Christoph Diehl <cdiehl@mozilla.com>",
   "license": "MPL-2.0",
   "scripts": {
-    "test": "grunt test --verbose",
+    "test": "jest --silent",
-    "test:lint": "cross-env NODE_ENV=test standard --verbose",
+    "coverage": "cross-env NODE_ENV=test jest --silent --coverage --collectCoverageFrom=lib/**/*.js",
-    "test:lint:fix": "cross-env NODE_ENV=test standard --fix --verbose",
+    "coveralls": "yarn coverage && cat ./coverage/lcov.info | coveralls",
+    "lint": "cross-env NODE_ENV=test standard --verbose",
+    "lint:fix": "cross-env NODE_ENV=test standard --fix --verbose",
+    "docs": "esdoc",
     "build": "webpack -p",
     "watch": "webpack --watch",
-    "precommit": "npm run test:lint",
+    "precommit": "yarn lint",
-    "postinstall": "npm run build",
+    "postinstall": "yarn build",
     "release": "np"
   },
   "standard": {
     "ignore": [
-      "tests/**",
       "dist/"
     ],
     "envs": {
@@ -41,26 +43,43 @@
       "es6": true
     }
   },
+  "jest": {
+    "verbose": true
+  },
+  "esdoc": {
+    "source": "./lib",
+    "destination": "./docs",
+    "plugins": [
+      {
+        "name": "esdoc-standard-plugin",
+        "option": {
+          "lint": {
+            "enable": true
+          },
+          "coverage": {
+            "enable": true
+          }
+        }
+      },
+      {
+        "name": "esdoc-node"
+      }
+    ]
+  },
   "devDependencies": {
+    "coveralls": "^3.0.2",
     "cross-env": "^5.1.4",
-    "grunt": "*",
+    "esdoc": "^1.1.0",
-    "grunt-karma": "*",
+    "esdoc-node": "^1.0.3",
-    "grunt-karma-coveralls": "*",
+    "esdoc-standard-plugin": "^1.0.0",
-    "grunt-standard": "*",
     "husky": "^0.14.3",
-    "karma": "*",
+    "jest": "^23.5.0",
-    "karma-chrome-launcher": "*",
-    "karma-coverage": "*",
-    "karma-firefox-launcher": "^1.1.0",
-    "karma-qunit": "^2.0.1",
     "np": "^3.0.4",
-    "qunit": "^2.5.1",
+    "standard": "^11.0.1",
-    "qunitjs": "^2.4.1",
+    "webpack": "^4.1.1",
-    "standard": "^11.0.1"
+    "webpack-cli": "^3.1.0"
   },
   "dependencies": {
-    "jsesc": "^2.5.1",
+    "jsesc": "^2.5.1"
-    "webpack": "^4.1.1",
-    "webpack-cli": "^2.0.12"
   }
 }

tests/index.html

@@ -1,44 +0,0 @@
-<!-- This Source Code Form is subject to the terms of the Mozilla Public
-   - License, v. 2.0. If a copy of the MPL was not distributed with this
-   - file, You can obtain one at http://mozilla.org/MPL/2.0/. -->
-
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="utf-8">
-  <meta name="viewport" content="width=device-width">
-  <title>Octo Unit Tests</title>
-  <link rel="stylesheet" href="https://code.jquery.com/qunit/qunit-2.3.0.css">
-</head>
-<body>
-  <div id="qunit"></div>
-  <div id="qunit-fixture"></div>
-  <script src="https://code.jquery.com/qunit/qunit-2.3.0.js"></script>
-  <!-- Include sources -->
-  <script src="../lib/utils/init.js"></script>
-  <script src="../lib/utils/block.js"></script>
-  <script src="../lib/utils/common.js"></script>
-  <script src="../lib/utils/objects.js"></script>
-  <script src="../lib/utils/platform.js"></script>
-  <script src="../lib/utils/prototypes.js"></script>
-  <script src="../lib/utils/script.js"></script>
-  <script src="../lib/logging/console.js"></script>
-  <script src="../lib/make/init.js"></script>
-  <script src="../lib/make/arrays.js"></script>
-  <script src="../lib/make/colors.js"></script>
-  <script src="../lib/make/files.js"></script>
-  <script src="../lib/make/fonts.js"></script>
-  <script src="../lib/make/mime.js"></script>
-  <script src="../lib/make/network.js"></script>
-  <script src="../lib/make/numbers.js"></script>
-  <script src="../lib/make/shaders.js"></script>
-  <script src="../lib/make/text.js"></script>
-  <script src="../lib/make/types.js"></script>
-  <script src="../lib/make/units.js"></script>
-  <script src="../lib/random/mersennetwister.js"></script>
-  <script src="../lib/random/random.js"></script>
-  <!-- Include tests -->
-  <script src="random/mersennetwister.js"></script>
-  <script src="random/random.js"></script>
-</body>
-</html>

tests/random/mersennetwister.js

@@ -1,58 +0,0 @@
-/* This Source Code Form is subject to the terms of the Mozilla Public
- * License, v. 2.0. If a copy of the MPL was not distributed with this
- * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
-
-QUnit.test("MersenneTwister test uniform distribution", function(assert) {
-  const N = Math.pow(2, 18), TRIES = 10, XSQ = 293.25; // quantile of chi-square dist. k=255, p=.05
-  let mt = new MersenneTwister();
-  mt.seed(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let data = new Uint32Array(N), sh;
-    for (let i = 0; i < data.length; ++i) {
-      data[i] = mt.int32();
-    }
-    for (sh = 0; sh <= 24; ++sh) {
-      let bins = new Uint32Array(256);
-      for (let b of data) {
-        ++bins[(b >>> sh) & 0xFF];
-      }
-      let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-      /*
-       * XSQ = scipy.stats.chi2.isf(.05, 255)
-       * if xsq > XSQ, the result is biased at 95% significance
-       */
-      if (xsq >= XSQ)
-        break;
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-    }
-    if (sh == 25) {
-      return;
-    }
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ);
-});
-
-QUnit.test("MersenneTwister test float distribution", function(assert) {
-  const N = Math.pow(2, 18), TRIES = 3, XSQ = 564.7; // quantile of chi-square dist. k=511, p=.05
-  let tries = [], mt = new MersenneTwister();
-  mt.seed(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(512);
-    for (let i = 0; i < N; ++i) {
-      let tmp = (mt.real2() * bins.length) >>> 0;
-      if (tmp >= bins.length) throw "random.float() >= 1.0";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 511)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});

tests/random/random.js

@@ -1,892 +0,0 @@
-/* This Source Code Form is subject to the terms of the Mozilla Public
- * License, v. 2.0. If a copy of the MPL was not distributed with this
- * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
-
-QUnit.test("random.init() is required", function(assert) {
-  assert.throws(random.number, /undefined/, "twister should be uninitialized before random.init()");
-  random.init(1);
-  random.number();
-});
-
-QUnit.test("random.number() corner cases", function(assert) {
-  random.init(Math.random() * 0x100000000);
-  let sum = 0;
-  for (let i = 0; i < 100; ++i)
-    sum += random.number(0);
-  assert.equal(sum, 0);
-  for (let i = 0; i < 100; ++i)
-    sum += random.number(1);
-  assert.equal(sum, 0);
-  let bins = new Uint32Array(2);
-  for (let i = 0; i < 100; ++i)
-    ++bins[random.number(2)];
-  assert.equal(bins[0] + bins[1], 100);
-  assert.ok(bins[0] > 20);
-  sum = 0;
-  for (let i = 0; i < 15; ++i)
-    sum |= random.number();
-  assert.equal(sum>>>0, 0xFFFFFFFF);
-});
-
-QUnit.test("random.number() uniform distribution", function(assert) {
-  const N = Math.pow(2, 17), TRIES = 3, XSQ = 564.7; // quantile of chi-square dist. k=511, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(512);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.number(bins.length);
-      if (tmp >= bins.length) throw "random.number() >= limit";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 511)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.float() uniform distribution", function(assert) {
-  const N = Math.pow(2, 17), TRIES = 3, XSQ = 564.7; // quantile of chi-square dist. k=511, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(512);
-    for (let i = 0; i < N; ++i) {
-      let tmp = (random.float() * bins.length) >>> 0;
-      if (tmp >= bins.length) throw "random.float() >= 1.0";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 511)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.range() uniform distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 66.34; // quantile of chi-square dist. k=49, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(50);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.range(0, bins.length - 1);
-      if (tmp >= bins.length) throw "random.range() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 49)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.range() uniform distribution with offset", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 66.34; // quantile of chi-square dist. k=49, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(50);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.range(10, 10 + bins.length - 1) - 10;
-      if (tmp < 0) throw "random.range() < lower bound";
-      if (tmp >= bins.length) throw "random.range() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 49)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.range() PRNG reproducibility", function(assert) {
-  let seed, result1, result2;
-  seed = Math.random() * 0x100000000;
-  for (let t = 0; t < 50; ++t) {
-    random.init(seed);
-    result1 = random.range(1, 20);
-    for (let i = 0; i < 5; ++i) {
-      random.init(seed);
-      result2 = random.range(1, 20);
-      assert.equal(result1, result2, "both results are the same")
-    }
-    seed = random.number();
-  }
-});
-
-QUnit.test("random.ludOneTo() distribution", function(assert) {
-  const N = 1e5, TRIES = 3, XSQ = 123.22; // quantile of chi-square dist. k=99, p=.05
-  let dist = new Uint32Array(100), tries = [];
-  random.init(Math.random() * 0x100000000);
-  /* build the ideal distribution for comparison
-   * I thought this would be the PDF of the log-normal distribution, but I couldn't get mu & sigma figured out? */
-  for (let i = 0; i < (100 * dist.length); ++i) {
-    dist[Math.floor(Math.exp(i / (100*dist.length) * Math.log(dist.length)))] += N / (dist.length * 100);
-  }
-  assert.equal(dist[0], 0);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(dist.length), xsq = 0;
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.ludOneTo(bins.length)>>>0;
-      if (tmp >= bins.length) throw "random.ludOneTo() > upper bound"; // this could happen..
-      ++bins[tmp];
-    }
-    assert.equal(bins[0], 0);
-    for (let i = 1; i < bins.length; ++i) {
-      xsq += Math.pow(bins[i] - dist[i], 2) / dist[i];
-    }
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 99)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.item() exception cases", function(assert) {
-  assert.throws(random.item, /received an invalid object/);
-  assert.throws(function(){ return random.item(1); }, /received an invalid object/);
-  assert.throws(function(){ return random.item("1"); }, /received an invalid object/);
-  assert.throws(function(){ return random.item({}); }, /received an invalid object/);
-});
-
-QUnit.test("random.item() distribution with list", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.item([99, 100, 101]) - 99;
-      if (tmp < 0) throw "random.item() < lower bound";
-      if (tmp >= bins.length) throw "random.item() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.key() distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.key({99: 0, 100: 0, 101: 0}) - 99;
-      if (tmp < 0) throw "random.key() < lower bound";
-      if (tmp >= bins.length) throw "random.key() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.bool() distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(2);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.bool();
-      if (tmp === true)
-        tmp = 1;
-      else if (tmp === false)
-        tmp = 0;
-      else
-        assert.ok(false, "unexpected random.bool() result: " + tmp);
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 1)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.pick() cases", function(assert) {
-  random.init(Math.random() * 0x100000000);
-  for (let i = 0; i < 100; ++i) {
-    let tmp = Math.random();
-    assert.equal(tmp, random.pick(tmp));
-  }
-  for (let i = 0; i < 100; ++i) {
-    let tmp = (Math.random() * 100) >>> 0;
-    assert.equal(tmp, random.pick(tmp));
-  }
-  for (let i = 0; i < 100; ++i) {
-    let tmp = Math.random() + "";
-    assert.equal(tmp, random.pick(tmp));
-  }
-  for (let i = 0; i < 100; ++i) {
-    let tmp = Math.random();
-    assert.equal(tmp, random.pick([tmp]));
-  }
-  for (let i = 0; i < 100; ++i) {
-    let tmp = Math.random();
-    assert.equal(tmp, random.pick(function(){ return tmp; }));
-  }
-  for (let i = 0; i < 100; ++i) {
-    let tmp = Math.random();
-    assert.equal(tmp, random.pick(function(){ return [tmp]; }));
-  }
-});
-
-QUnit.test("random.pick() with equal distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.pick([0, [1, 1], function(){ return 2; }]);
-      if (tmp < 0) throw "random.pick() < lower bound";
-      if (tmp >= bins.length) throw "random.pick() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.pick() with unequal distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.pick([[0, 1], [1], function(){ return [2]; }]);
-      if (tmp < 0) throw "random.pick() < lower bound";
-      if (tmp >= bins.length) throw "random.pick() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = Math.pow(bins[0] - N / 6, 2) / (N / 6) + Math.pow(bins[1] - N / 2, 2) / (N / 2) + Math.pow(bins[2] - N / 3, 2) / (N / 3);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.chance(2) distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(2);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.chance(2);
-      if (tmp === true)
-        tmp = 1;
-      else if (tmp === false)
-        tmp = 0;
-      else
-        assert.ok(false, "unexpected random.chance() result: " + tmp);
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 1)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.chance(undefined) distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(2);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.chance();
-      if (tmp === true)
-        tmp = 1;
-      else if (tmp === false)
-        tmp = 0;
-      else
-        assert.ok(false, "unexpected random.chance() result: " + tmp);
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 1)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.chance(3) distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(2);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.chance(3);
-      if (tmp === true)
-        tmp = 0;
-      else if (tmp === false)
-        tmp = 1;
-      else
-        assert.ok(false, "unexpected random.chance() result: " + tmp);
-      ++bins[tmp];
-    }
-    let xsq = Math.pow(bins[0] - (N / 3), 2) / (N / 3) + Math.pow(bins[1] - (2 * N / 3), 2) / (2 * N / 3);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 1)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.chance(1000) distribution", function(assert) {
-  const N = 1e6, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(2);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.chance(1000);
-      if (tmp === true)
-        tmp = 0;
-      else if (tmp === false)
-        tmp = 1;
-      else
-        assert.ok(false, "unexpected random.chance() result: " + tmp);
-      ++bins[tmp];
-    }
-    let xsq = Math.pow(bins[0] - (N / 1000), 2) / (N / 1000) + Math.pow(bins[1] - (999 * N / 1000), 2) / (999 * N / 1000);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 1)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.choose() with equal distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.choose([[1, 0], [1, 1], [1, 2]]);
-      if (tmp >= bins.length) throw "random.choose() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.choose() with unequal distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.choose([[1, 0], [2, 1], [1, 2]]);
-      if (tmp >= bins.length) throw "random.choose() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = Math.pow(bins[0] - N / 4, 2) / (N / 4) + Math.pow(bins[1] - N / 2, 2) / (N / 2) + Math.pow(bins[2] - N / 4, 2) / (N / 4);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.choose() with unequal distribution and pick", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.choose([[1, 0], [2, [1, 2]], [1, function(){ return 2; }]]);
-      if (tmp >= bins.length) throw "random.choose() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = Math.pow(bins[0] - N / 4, 2) / (N / 4) + Math.pow(bins[1] - N / 4, 2) / (N / 4) + Math.pow(bins[2] - N / 2, 2) / (N / 2);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.choose(flat) with unequal distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.choose([[1, 0], [2, 1], [1, 2]], true);
-      if (tmp >= bins.length) throw "random.choose() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = Math.pow(bins[0] - N / 4, 2) / (N / 4) + Math.pow(bins[1] - N / 2, 2) / (N / 2) + Math.pow(bins[2] - N / 4, 2) / (N / 4);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.choose(flat) equal distribution with types not picked", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  const v1 = 1, v2 = [12], v3 = function(){};
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.choose([[1, v1], [1, v2], [1, v3]], true);
-      if (tmp === v1)
-        tmp = 0;
-      else if (tmp === v2)
-        tmp = 1;
-      else if (tmp === v3)
-        tmp = 2;
-      else
-        assert.ok(false, "unexpected random.choose() result: " + tmp);
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.weighted() with equal distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.item(random.weighted([{w: 1, v: 0}, {w: 1, v: 1}, {w: 1, v: 2}]));
-      if (tmp >= bins.length) throw "random.weighted() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.weighted() with unequal distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.item(random.weighted([{w: 1, v: 0}, {w: 2, v: 1}, {w: 1, v: 2}]));
-      if (tmp >= bins.length) throw "random.weighted() > upper bound";
-      ++bins[tmp];
-    }
-    let xsq = Math.pow(bins[0] - N / 4, 2) / (N / 4) + Math.pow(bins[1] - N / 2, 2) / (N / 2) + Math.pow(bins[2] - N / 4, 2) / (N / 4);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.weighted() equal distribution with types not picked", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  const v1 = 1, v2 = [12], v3 = function(){};
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.item(random.weighted([{w: 1, v: v1}, {w: 1, v: v2}, {w: 1, v: v3}]));
-      if (tmp === v1)
-        tmp = 0;
-      else if (tmp === v2)
-        tmp = 1;
-      else if (tmp === v3)
-        tmp = 2;
-      else
-        assert.ok(false, "unexpected random.weighted() result: " + tmp);
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.use() distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 3.84; // quantile of chi-square dist. k=1, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(2);
-    for (let i = 0; i < N; ++i) {
-      let rnd = Math.random(), use = random.use(rnd);
-      if (use === rnd)
-        use = 1;
-      else if (use === "")
-        use = 0;
-      else
-        assert.ok(false, "unexpected random.use() result: " + use);
-      ++bins[use];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 1)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.shuffle() distribution", function(assert) {
-  const N = 1e4, M = 10, TRIES = 3, XSQ = 123.23; // quantile of chi-square dist. k=M*M-1, p=.05
-  // XXX: shouldn't k be M! ?
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(M * M);
-    for (let i = 0; i < N; ++i) {
-      let array = [];
-      for (let j = 0; j < M; ++j)
-        array.push(j);
-      random.shuffle(array);
-      for (let j = 0; j < M; ++j)
-        ++bins[j * M + array[j]];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / M; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 99)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.shuffled() distribution", function(assert) {
-  const N = 1e4, M = 10, TRIES = 3, XSQ = 123.23; // quantile of chi-square dist. k=M*M-1, p=.05
-  // XXX: shouldn't k be M! ?
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(M * M);
-    let array_ref = [];
-    for (let j = 0; j < M; ++j)
-      array_ref.push(j);
-    for (let i = 0; i < N; ++i) {
-      let array = random.shuffled(array_ref);
-      for (let j = 0; j < M; ++j) {
-        ++bins[j * M + array[j]];
-        if (array_ref[j] !== j)
-          throw "array modified";
-      }
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / M; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 99)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});
-
-QUnit.test("random.subset() with equal distribution", function(assert) {
-  /*
-   * this doesn't specify limit, so length distribution should be even, and selections should be even within each length
-   */
-  const N = 1e4, M = 3, TRIES = 3, B0_XSQ = 5.99, B1_XSQ = 15.51, B2_XSQ = 38.89, LEN_XSQ = 7.81; // quantile of chi-square dist. k=[2,8,26,3], p=.05
-  let bin0_xsq, bin1_xsq, bin2_xsq, length_xsq;
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = [new Uint32Array(3), new Uint32Array(9), new Uint32Array(27)], lengths = new Uint32Array(M+1);
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.subset([0, [1, 1], function(){ return 2; }]);
-      if (tmp.length > M) throw "random.subset() result length > input";
-      ++lengths[tmp.length];
-      if (tmp.length)
-        ++bins[tmp.length-1][tmp.reduce(function(a, v){ return a * 3 + v; }, 0)];
-    }
-    bin0_xsq = bins[0].reduce(function(a, v){ let e = N / (M + 1) / Math.pow(M, 1); return a + Math.pow(v - e, 2) / e; }, 0);
-    bin1_xsq = bins[1].reduce(function(a, v){ let e = N / (M + 1) / Math.pow(M, 2); return a + Math.pow(v - e, 2) / e; }, 0);
-    bin2_xsq = bins[2].reduce(function(a, v){ let e = N / (M + 1) / Math.pow(M, 3); return a + Math.pow(v - e, 2) / e; }, 0);
-    length_xsq = lengths.reduce(function(a, v){ let e = N / (M + 1); return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (bin0_xsq < B0_XSQ && bin1_xsq < B1_XSQ && bin2_xsq < B2_XSQ && length_xsq < LEN_XSQ) {
-      assert.ok(true, "Expected lengths x^2 to be < " + LEN_XSQ + ", got " + length_xsq + " on attempt #" + (attempt + 1));
-      assert.ok(true, "Expected length=1 x^2 to be < " + B0_XSQ + ", got " + bin0_xsq + " on attempt #" + (attempt + 1));
-      assert.ok(true, "Expected length=2 x^2 to be < " + B1_XSQ + ", got " + bin1_xsq + " on attempt #" + (attempt + 1));
-      assert.ok(true, "Expected length=3 x^2 to be < " + B2_XSQ + ", got " + bin2_xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-  }
-  console.log("Expected lengths x^2 to be < " + LEN_XSQ + ", got " + length_xsq);
-  console.log("Expected length=1 x^2 to be < " + B0_XSQ + ", got " + bin0_xsq);
-  console.log("Expected length=2 x^2 to be < " + B1_XSQ + ", got " + bin1_xsq);
-  console.log("Expected length=3 x^2 to be < " + B2_XSQ + ", got " + bin2_xsq);
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq low enough");
-});
-
-QUnit.test("random.subset(limit) with equal distribution", function(assert) {
-  /*
-   * limit is specified, so length should always == limit, and selections should be even
-   */
-  const N = 1e4, M = 3, TRIES = 3, B0_XSQ = 5.99, B1_XSQ = 15.51, B2_XSQ = 38.89, B3_XSQ = 101.88; // quantile of chi-square dist. k=[2,8,26,80], p=.05
-  let bin0_xsq, bin1_xsq, bin2_xsq, bin3_xsq;
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < 100; ++attempt) {
-    if (random.subset([1,2,3], 0).length !== 0) throw "random.subset(..., 0) returned non-empty array";
-  }
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = [new Uint32Array(3), new Uint32Array(9), new Uint32Array(27), new Uint32Array(81)];
-    for (let i = 0; i < N; ++i) {
-      let tmp = random.subset([0, 1, 2], 1);
-      if (tmp.length !== 1) throw "random.subset() result length != limit";
-      ++bins[0][tmp.reduce(function(a, v){ return a * 3 + v; }, 0)];
-      tmp = random.subset([0, 1, 2], 2);
-      if (tmp.length !== 2) throw "random.subset() result length != limit";
-      ++bins[1][tmp.reduce(function(a, v){ return a * 3 + v; }, 0)];
-      tmp = random.subset([0, 1, 2], 3);
-      if (tmp.length !== 3) throw "random.subset() result length != limit";
-      ++bins[2][tmp.reduce(function(a, v){ return a * 3 + v; }, 0)];
-      tmp = random.subset([0, 1, 2], 4);
-      if (tmp.length !== 4) throw "random.subset() result length != limit";
-      ++bins[3][tmp.reduce(function(a, v){ return a * 3 + v; }, 0)];
-    }
-    bin0_xsq = bins[0].reduce(function(a, v){ let e = N / Math.pow(M, 1); return a + Math.pow(v - e, 2) / e; }, 0);
-    bin1_xsq = bins[1].reduce(function(a, v){ let e = N / Math.pow(M, 2); return a + Math.pow(v - e, 2) / e; }, 0);
-    bin2_xsq = bins[2].reduce(function(a, v){ let e = N / Math.pow(M, 3); return a + Math.pow(v - e, 2) / e; }, 0);
-    bin3_xsq = bins[3].reduce(function(a, v){ let e = N / Math.pow(M, 4); return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (bin0_xsq < B0_XSQ && bin1_xsq < B1_XSQ && bin2_xsq < B2_XSQ && bin3_xsq < B3_XSQ) {
-      assert.ok(true, "Expected length=1 x^2 to be < " + B0_XSQ + ", got " + bin0_xsq + " on attempt #" + (attempt + 1));
-      assert.ok(true, "Expected length=2 x^2 to be < " + B1_XSQ + ", got " + bin1_xsq);
-      assert.ok(true, "Expected length=3 x^2 to be < " + B2_XSQ + ", got " + bin2_xsq);
-      assert.ok(true, "Expected length=4 x^2 to be < " + B3_XSQ + ", got " + bin3_xsq);
-      return;
-    }
-  }
-  console.log("Expected length=1 x^2 to be < " + B0_XSQ + ", got " + bin0_xsq);
-  console.log("Expected length=2 x^2 to be < " + B1_XSQ + ", got " + bin1_xsq);
-  console.log("Expected length=3 x^2 to be < " + B2_XSQ + ", got " + bin2_xsq);
-  console.log("Expected length=4 x^2 to be < " + B3_XSQ + ", got " + bin3_xsq);
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq low enough");
-});
-
-QUnit.test("random.pop() distribution", function(assert) {
-  const N = 1e4, TRIES = 3, XSQ = 5.99; // quantile of chi-square dist. k=2, p=.05
-  let tries = [];
-  random.init(Math.random() * 0x100000000);
-  for (let attempt = 0; attempt < TRIES; ++attempt) {
-    let bins = new Uint32Array(3);
-    const orig = [99, 100, 101];
-    for (let i = 0; i < N; ++i) {
-      let arr = orig.slice(), tmp = random.pop(arr) - 99;
-      if (tmp < 0) throw "random.pop() < lower bound";
-      if (tmp >= bins.length) throw "random.pop() > upper bound";
-      if (arr.length !== 2) throw "random.pop() did not pop";
-      if (arr.reduce(function(a, v){ return a + v; }, tmp) !== 201) throw "random.pop() sum error";
-      ++bins[tmp];
-    }
-    let xsq = bins.reduce(function(a, v){ let e = N / bins.length; return a + Math.pow(v - e, 2) / e; }, 0);
-    /*
-     * XSQ = scipy.stats.chi2.isf(.05, 2)
-     * if xsq > XSQ, the result is biased at 95% significance
-     */
-    if (xsq < XSQ) {
-      assert.ok(true, "Expected x^2 to be < " + XSQ + ", got " + xsq + " on attempt #" + (attempt + 1));
-      return;
-    }
-    tries.push(xsq);
-  }
-  assert.ok(false, "Failed in " + TRIES + " attempts to get xsq lower than " + XSQ + ": "+ tries);
-});