Package Exports
- imlazy
This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (imlazy) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.
Readme
imlazy
Functional, declarative, immutable and lazy as you like
What is this?
JS library, for dealing with iterables, iterators and generators
imlazy can be used to create iterables, "transform" them (returning new iterables rather than mutating them) or query them
Iterables returned by imlazy are of the form:
const someIterable = Object.freeze({[Symbol.iterator]: function* () {
// do some stuff
}});
Therefore they are lazy and immutable
If you want to turn them into arrays or feed them into a function etc then just spread them (don't spread anything infinite or circular!):
const someArray = [...someIterable];
const someReturnedValue = someFunction(...someIterable);
All functions exposed by imlazy are curried and data-last which makes them ideal for partial application and functional programming
Installation
npm i -S imlazy
N.B. imlazy is written in ES2015. It runs fine in node 6 but will not run in a non ES2015 environment
Getting Started
Debugging
imlazy implements a custom toString
method for the iterables it returns which is useful for debugging. Just invoke String
on an iterable returned by one of imlazy's functions, for instance:
String(range(1, 8)) // => (1 2 3 4 5 6 7 8)
String(range(1, Infinity)) // => (1 2 3 4 5 6 7 8 9 10...)
The custom toString
method can handle infinite iterables (in which case it lists the first 10 elements followed by ellipsis), nested iterables and uses a LISP-like notation to differentiate iterables from arrays and other JS data structures
Code Examples
const {cycle, filter, range, reduce, sum, take} = require('imlazy')
// all functions are autocurried for partial application
const takeEight = take(8)
const isEven = x => x % 2 === 0
const positiveIntegers = range(1, Infinity) // => (1 2 3 4 5 6 7 8 9 10...)
const positiveEvenIntegers = filter(isEven, positiveIntegers) // => (2 4 6 8 10 12 14 16 18 20...)
const twoFourSix = take(3, positiveEvenIntegers) // => (2 4 6)
sum(twoFourSix) // => 12
// NB twoFourSix is an immutable lazy iterable
// convert to an array like this
[...twoFourSix] // => [2, 4, 6]
const oneTwoThree = range(1, 3) // => (1 2 3)
const circularOneTwoThree = cycle(oneTwoThree) // => (1 2 3 1 2 3 1 2 3 1...)
takeEight(circularOneTwoThree) // => (1 2 3 1 2 3 1 2)
const fibonacciGenerator = function* () {
let [a, b] = [0, 1]
while (true) yield ([a, b] = [b, a + b])[0]
}
takeEight(fibonacciGenerator()) // => (1 1 2 3 5 8 13 21)
Click Here for Documentation
Interoperability
Symbol.iterator
This library works with all native iterable types including the Generator, String, Array, TypedArray, Map and Set types
In fact anything that has a Symbol.iterator
property can be processed by this library and that includes custom data structures like iterables from immutable-js
Static Land
This library implements the following Static Land algebraic types:
Functor
Apply
Applicative
Chain
Monad
Foldable
Semigroup
Monoid
Performance
There is a benchmark in the root of this repo comparing imlazy with Ramda and native array methods. The infiniteIterable
benchmarks map, filter and take over an infinite iterable and the array
benchmarks map and filter over an array
These are the results on my machine when using node v7.4.0:
infiniteIterable - imlazy x 113 ops/sec ±0.20% (80 runs sampled)
infiniteIterable - ramdaTransducer x 555 ops/sec ±0.93% (91 runs sampled)
array - imlazy x 1,263 ops/sec ±1.01% (91 runs sampled)
array - ramdaTransducer x 20,968 ops/sec ±1.10% (93 runs sampled)
array - native x 3,764 ops/sec ±0.46% (95 runs sampled)
array - ramda x 29,460 ops/sec ±0.56% (94 runs sampled)
Ramda's transducers are significantly faster than imlazy over both infinite iterables and arrays
The six-speed test results suggest that performance could be significantly improved in future iterations of the V8 engine
Project Scope
The scope of this project is limited to manipulating iterables using the iteration protocols. It does not expose standard FP functions like curry, compose, identity, flip, tap etc. It also does not prescribe a notion of equality, so functions like includes, has, or contains cannot exist
Influences
- Ramda
- Haskell
- Clojure