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radiancejs

1.1.5 • Public • Published

radiancejs

Functional Programming in Javascript

dependencies npm version

This library was built for educational purposes.

Briefly, it does 4 things:

  1. It contains a bunch of helper higher-order functions (eg. compose, pipe, curry) with more than one implementation (Of course, one is exported by default, but you can study the source code to view the others).
  2. It transformes some of the array methods to their curried functional equivalents.
  3. It produces a list data structure. It is a recursively defined data structure (purely functional) that resembles a linked list with cons cells.
  4. It produces a stream data structure. Stream is a lazy equivalent of the list data structure. It utilizes thunks to simulate the laziness (Nothing new here, you can achieve the same behaviour more efficiently through js generators)

Installation

Using it whith Node.js

$ npm install radiancejs

Using it directly in the browser:

Firsty navigate to a newly created folder for your project and run the command

$ npm install radiancejs

You can find the distribution files inside the path ./node_modules/radiancejs/dist

<script src="path/to/distributionFiles/radiancejs.js"></script>

If for some reason you need the unminified version you have to manually build in development (see building instructions)

Building

Navigate to the node_modules folder of your project and find the radiancejs package. Inside it run the command

$ npm install

to install the development dependencies. Now you can manually add functions to the library. Just create the new file in the source folder and add a record to the exporter.js file (view it to see the pattern).

You can build in production mode with the command

$ npm run build

or in development mode (unminified version)

$ npm run build-dev

Development building creates test/radiance.html. It is preloaded with the library so you can expreriment with it in the browser.

Testing

Navigate to the node_modules folder of your project and find the radiancejs package. Inside it run the command

$ npm install jest

to install the testing suite. You can manually add tests inside the test folder (be sure to adhere to the naming pattern)

To run the test suite

$ npm run test

Usage

You can import the package using a require call

const r = require("radiancejs");
 
r.map(x => x + 1)([1, 2, 3, 4]);

or if you are using a bundler with es6 module support

import * as r from "radiancejs";
 
r.map(x => x + 1)([1, 2, 3, 4]);

In the browser the object is available through the letter r but feel free to use whatever symbol or letter you wish.

const _ = r;
 
_.map(x => x + 1)([1, 2, 3, 4]);

Examples

Math operations

The basic math operations are available as curried functions

add, subtract, multiply, divide, binaryOp

const r = require("radiance");
 
r.add(2, 3);
//-> 5
r.add(2)(3);
//-> 5
 
const mulWith10 = r.multiply(10);
 
mulWith10(20);
//-> 200
 
const biggerThan5 = r.binaryOp(">")(5);
 
biggerThan5(6);
//-> true

Fliping and Method decoupling

flip is a simple hof (higher order function) that inverts the arguments of a dual-parameter uncurried function. Unmethodify simple decouples a method from the object context forcing an explicit pass

flip, unmethod

const r = require("radiance");
 
const div = (x, y) => x / y;
 
div(10, 2);
//-> 5
 
const flippedDiv = r.flip(div);
 
flippedDiv(10, 2);
//-> 0.2
 
const obj = {
    age: 109,
    getAge: function () {
        return this.age;
    }
};
 
obj.getAge();
//-> 109
 
const decoupledGetAge = r.unmethod(obj.getAge);
 
decoupledGetAge(obj);
//-> 109
 

Array methods

Some of the array methods are decoupled with the unmethod hof and curried

map, filter, concat, every, some, reduce, reduceRight

const r = require("radiance");
 
r.map(x => x + 5, [1, 2, 3, 4, 5]);
//-> [ 6, 7, 8, 9, 10 ]
 
// even :: Number -> Boolean
const even = x => x % 2 === 0;
 
r.filter(even)(r.map(x => x + 100)([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]));
//-> [ 102, 104, 106, 108, 110 ]

Folding operations on Arrays and Ranging

You can use the native functional folding operatations with Arrays. And you can create arrays of type [Number] with the range function call

foldl, foldr, range

const r = require("radiance");
 
// add (reducer) :: (Number, Number) -> Number
const add = (x, y) => x + y;
 
r.foldr(add)(0)([1, 2, 3, 4, 5]);
//-> add(1, add(2, add(3, add(4, add(5, 0)))))
//-> 15
 
r.foldl(add)(0)([1, 2, 3, 4, 5]);
//-> add(add(add(add(add(0, 1), 2), 3), 4), 5)
//-> 15
 
r.range(1, 11, 2);
//-> [ 1, 3, 5, 7, 9 ]
 
r.range(-15, -5);
//-> [ -15, -14, -13, -12, -11, -10, -9, -8, -7, -6 ]

Monadic operations on Arrays

You can use join function to flatten a (one) layer of a self composite Array structure. And you can use the chain function aka flatMap, to feed a function of type a -> [b] to an Array structure of type [a].

chain function is hooked to the Array.prototype object so it is available as a method call too

join, chain

const r = require("radiance");
 
r.join([[1, 2, 3], [4, 5], [], [6]]);
//-> [ 1, 2, 3, 4, 5, 6 ]
 
r.chain([1, 2, 3])(x => ["m", x]);
//-> [ 'm', 1, 'm', 2, 'm', 3 ]
 
[1, 2, 3].chain(x => ["m", x]);
//-> [ 'm', 1, 'm', 2, 'm', 3 ]

Composition and Pipelining

You can compose functions using the compose function helper. Pipelining is composition in the reverse order. The M alternatives allows multiple arguments to be passed on the inner funtion. The K alternatives implements the Kleisli composition/pipelining between functions of signature a -> M b, where M is a Monadic type (eg. Array).

composeK and pipeK will work only if the monadic type imlements the chain function (as method), You can use it with arrays, lists, and streams (see below)

compose, pipe, composeM, pipeM, composeK, pipeK

const r = require("radiance");
 
// add5 :: Number -> Number
const add5 = r.add(5);
 
// mul15 :: Number -> Number
const mul15 = r.multiply(15);
 
// add5AndMul15 :: Number -> Number
add5AndMul15 = r.compose(mul15, add5);
 
// mul15AndAdd5 :: Number -> Number
mul15AndAdd5 = r.pipe(mul15, add5);
 
add5AndMul15(10);
//-> 225
 
mul15AndAdd5(10);
//-> 155
 
 
 
const a = [10, 11, 12, 13, 14, 15];
 
a.chain(x => [x, 1]).chain(x => [x, 2])
//-> [ 10, 2, 1, 2, 11, 2, 1, 2, 12, 2, 1, 2, 13, 2, 1, 2, 14, 2, 1, 2, 15, 2, 1, 2 ]
 
r.chain(a)(r.composeK(x => [x, 2], x => [x, 1]));
//-> [ 10, 2, 1, 2, 11, 2, 1, 2, 12, 2, 1, 2, 13, 2, 1, 2, 14, 2, 1, 2, 15, 2, 1, 2 ]

Currying and Uncurrying

You can convert a multiparameter function to a stepwise sequence of unary functions with the curry hof. You can also partial curry a function with the pcurry call. Partial currying permits you to pass more than one arguments to the curried function. Uncurrying is the reverse operation.

if you call curry and pcurry with a function of type (...args) => {} you have to declare the number of parameters the function expects in order to use currying. It is always better to pass functions in their point-wise form when you can

curry, uncurry, pcurry

const r = require("radiance");
 
// add :: (Number, Number, Number) -> Number
const add = (x, y, z) => x + y + z;
 
r.curry(add)(1)(2)(3);
//-> 6
r.pcurry(add)(1, 2, 3);
//-> 6
r.pcurry(add)(1, 2)(3);
//-> 6
r.pcurry(add)(1)(2)(3);
//-> 6
r.uncurry(r.curry(add))(1, 2, 3)
//-> 6
 
const addm = (...args) => r.foldr((x, y) => x + y)(0)(args);
 
r.curry(addm, 5)(1)(2)(3)(4)(5);
//-> 15
r.pcurry(addm, 5)(1, 2, 3)(4, 5);
//-> 15
r.uncurry(r.curry(addm, 3))(1, 2, 3)
//-> 6
r.uncurry(r.pcurry(addm, 3))(1, 2, 3)
//-> 6
 

Lists

List constructor acts as an object wrapper to a recursively defined pair structure that acts as a cons cell. Effectively it wraps a function of type Cons(x, Cons(y, Cons(z, ...))) and creates a new context with available methods. The difference between lists and arrays is that the former is a purely functional persistent data structure and cannot be mutated (Technically in JavaScript you always can but you have to try). The implementation and methods are based strictly on recursion to simulate effectively the functional definition, so for a big number of elements you have to avoid using it or you will end with a stack overflow.

The list constructor just simulates a LISP list. Use it only for small number of elements (< 5000)

list.map, list.filter, list.forEach, list.print, list.foldl, list.foldr, list.toArray, list.toStream, list.concat, list.mconcat, list.takeWhile, list.zip, list.reverse, list.join, list.chain, list.getPairContext

You can use the above functions in a functional style or as method calls

const r = require("radiance");
 
const list = r.list;
 
// You can call the list constructor with a multiargument approach
list(1, 2, 3, 4, 5).map(x => x + 5).filter(x => x < 8).reverse().toArray();
//-> [ 7, 6 ]
 
// You can pass an array inside constuctor
list([1, 2, 3, 4, 5]).map(x => x + 5).filter(x => x < 8).reverse().toArray();
//-> [ 7, 6 ]
 
// You can use a functional approach
list.toArray(list.reverse(list.filter(x => x < 8)(list.map(x => x + 5)(list(1, 2, 3, 4, 5)))))
//-> [ 7, 6 ]
 
 
list(r.range(1, 1001)).foldr((x, y) => x + y, 0);
//-> 500500
 
 
list.mconcat([list(1, 2, 3, 4), list(1, 2, 3), list(4, 5, 6)]);
//-> Cons(1, Cons(2, Cons(3, Cons(4, Cons(1, Cons(2, Cons(3, Cons(4, Cons(5, Cons(6, empty))))))))))
 
list.join(list(list(1, 2, 3), list(4, 5, 6)));
//-> Cons(1, Cons(2, Cons(3, Cons(4, Cons(5, Cons(6, empty))))))
 
list(1, 2, 3).chain(x => list(x, 1)).toArray();
//-> [ 1, 1, 2, 1, 3, 1 ]
 
list(r.range(10, 16)).chain(r.composeK(x => list(x, 2), x => list(x, 1))).toArray();
//-> [ 10, 2, 1, 2, 11, 2, 1, 2, 12, 2, 1, 2, 13, 2, 1, 2, 14, 2, 1, 2, 15, 2, 1, 2 ]
 

Pairs

Pair data structure is simply the lambda expression that simulates the cons cell. It is utilized heavily by the list (see above) constructor. In fact every list function call is translated to its pair equivalent with the result of the call, wrapped. The interface of pair functionallity is exported by default so you can play with it but you'd better use the higher level list object. You can transform a list to its pair equivalent with the list.getPairContext function call.

pair.map, pair.filter, pair.forEach, pair.print, pair.foldl, pair.foldr, pair.fromArray, pair.toArray, pair.concat, pair.mconcat, pair.takeWhile, pair.zip, pair.reverse, pair.join, pair.chain

const r = require("radiance");
 
const pair = r.pair;
const list = r.list;
 
// You can manually create a pair like this
const a = pair.prepend(1, pair.prepend(2, pair.prepend(3, pair.empty)));
//-> pair(1, pair(2, pair(3, null)))
 
pair.print(a);
//-> 1, 2, 3
 
pair.print(pair.map(x => x + 1)(a));
//-> 2, 3, 4
 
pair.filter(x => x === 2)(a);
//-> pair(2, null)
 
pair.fromArray([1, 2, 3, 4]);
//-> pair(1, pair(2, pair(3, pair(4, null))))
 
pair.zip(pair.fromArray([1, 2, 3]), pair.fromArray([4, 5, 6]));
//-> pair([1, 4], pair([2, 5], pair([3, 6], empty)))
 
pair.toArray(pair.zip(pair.fromArray([1, 2, 3]), pair.fromArray([4, 5, 6])));
//-> [[1, 4], [2, 5], [3, 6]]
 
pair.chain(pair.fromArray([1, 2, 3]))(x => pair.prepend(x, pair.prepend(1, pair.empty)));
//-> pair(1, pair(1, pair(2, pair(1, pair(3, pair(1, null))))))
 
// Additionally you can take the pair context from a List object
list(1, 2, 3, 4).getPairContext();
//-> pair(1, pair(2, pair(3, null)))
 

Streams

Stream function constructor defines an object wrapper to a recursively defined lazy pair structure. Effectively it wraps a function of type () => Cons(x, () => Cons(y, () => Cons(z, …))) and creates a new context with available methods. By utilizing thunks it can simulate a lazily evaluated List structure. This means that you can define and process infinite streams. The implementation and methods are based strictly on recursion to simulate effectively the functional definition. The lazy pair data structure is the same as the pair data structure with the addition of thunks. Due to its functional approach and the adherence to the lazy pair data structure, some of the operations are inherently lazy (delaying the consuming or the production process), while others are greedy (forcing the consuming or the production process). You can stack multiple lazy streams and then trigger the consuming process through a greedy method. Greedy functions are recursive so you have to be cautious (max stack records). If you want to consume an infinite stream use the tramboline method that utilizes a while loop.

Lazy (safe)

stream.fromArray, stream.fromPair, stream.map, stream.range, stream.takeWhile, stream.repeat, stream.concat, steam.zip, stream.getLazyPairContext

Lazy (unsafe)

stream.mconcat, stream.join, stream.chain, stream.lfilter

Greedy (safe)

stream.tramboline, stream.print

Greedy (unsafe)

stream.take, stream.filter, stream.toPair, stream.toArray, stream.foldl, stream.foldr

const r = require("radiance");
 
const stream = r.stream;
 
stream(1, 2, 3, 4, 5).print();
//-> 1, 2, 3, 4, 5
 
stream([1, 2, 3, 4, 5]).print();
//-> 1, 2, 3, 4, 5
 
stream.range(1, Infinity).map(x => -x).lfilter(x => x % 2 === 0).take(1000).print();
//-> -2, -4, -6, -8, ..., -2000
 
const a = stream.repeat(3);
//-> To be evaluated -> 3, 3, 3, 3, 3, 3, 3, ..., 3, ...
const b = stream.range(1, Infinity);
//-> To be evaluated -> 1, 2, 3, 4, 5, 6, 7, ..., 100, ...
const c = stream.zip(a, b);
//-> To be evaluated -> [3, 1], [3, 2], [3, 3], ..., [3, 100], ...
c.map(x => [x[0] + 1, x[1]]).takeWhile(x => x[1] < 3000).take(100).print();
//-> [4, 1], [4, 2], [4, 3], ..., [4, 100], ...
 
 
// You can safely run tramboline method to consume an infinite stream
stream.range(-1, -Infinity).lfilter(x => x % 2 !== 0 ).tramboline(console.log.bind(console));
//-> -1, -3, -5, -7, ..., -16461, ... -Inifinity
 
const w = [];
stream.tramboline((x) => w.push(x))(stream.repeat(1).map(x => x + 1).take(5000));
 
w;
//-> [2, 2, 2, 2, 2, ..., 2]
 
// Stream defines a Monad, so you can use the chain function
stream(r.range(1, 100)).chain(x => stream([[x, "a"]])).print();
//-> [1, "a"], [2, "a"], ..., [99, "a"]
 
stream.join(stream(stream.range(1, Infinity), stream.range(-1, -Infinity))).print();
//-> 1, 2, 3, 4, ..., Infinity
 
 
stream.range(1, 101).foldr((x, y) => x + y, 0);
//-> 5050
stream.range(1, 101).toList().foldl((x, y) => x + y, 0);
//-> 5050
 

Lazy Pairs

Lazy Pair data structure is simply the lambda expression that simulates the cons cell. It is utilized heavily by the Stream (see above) constructor. In fact every stream function call is translated to its lazy pair equivalent with the result of the call, wrapped. The interface of lazy pair functionallity is exported by default so you can play with it but you'd better use the higher level stream object. You can transform a stream to its lazy pair equivalent with the stream.getLazyPairContext function call.

const r = require("radiance");
 
const lpair = r.lpair;
const stream = r.stream;
 
// You can manually create a lazy pair like this
const a = () => lpair.prepend(1, () => lpair.prepend(2, () => lpair.prepend(3, () => lpair.empty)));
//-> () => pair(1, () => pair(2, () => pair(3, null)))
 
lpair.print(a);
//-> 1, 2, 3
 
lpair.print(lpair.map(x => x + 1)(a));
//-> 2, 3, 4
 
lpair.filter(x => x === 2)(a);
//-> () => pair(2, () => null)
 
lpair.fromArray([1, 2, 3, 4]);
//-> () => pair(1, () => pair(2, () => pair(3, () => pair(4, null))))
 
lpair.zip(lpair.fromArray([1, 2, 3]), lpair.fromArray([4, 5, 6]));
//-> () => pair([1, 4], () => pair([2, 5], () => pair([3, 6], () => empty)))
 
lpair.toArray(lpair.zip(lpair.fromArray([1, 2, 3]), lpair.fromArray([4, 5, 6])));
//-> [[1, 4], [2, 5], [3, 6]]
 
lpair.chain(lpair.fromArray([1, 2, 3]))(x => () => lpair.prepend(x, () => lpair.prepend(1, () => lpair.empty)));
//-> () => pair(1, () => pair(1, () => pair(2, () => pair(1, () => pair(3, () => pair(1, ()=> null))))))
 
lpair.print(lpair.takeWhile(x => x < 3000)(lpair.range(1, Infinity)));
//-> 1, 2, 3, 4, 5, ..., 2999
 
 
// Additionally you can take the lazy pair context from a List object
stream(1, 2, 3, 4).getLazyPairContext();
//-> pair(1, pair(2, pair(3, null)))
 

Final Notes

As i mentioned this library was built for educational purposes. I recommend studying the source code and playing with it if your are familiar with the language and want to gain a small taste on the functional approach of it.

Install

npm i radiancejs

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4

Version

1.1.5

License

MIT

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