ikeru

!npm

The art of arranging data

Usage

Install ikeru
by running:

``sh
yarn add ikeru
`

$3

A sorted dictionary of number keys to arbitrary values.

Note that these trees do not self-balance. See RedBlackTree.

`js
import { get, set, remove, entries } from 'ikeru/binary-search-tree'

let tree
tree = set(tree, 5, { title: 'Five' })
tree = set(tree, 3, { title: 'Three' })
tree = set(tree, 7, { title: 'Seven' })

console.log(get(tree, 1))
// null

for (const node of entries(tree)) {
console.log(node.key, node.title)
// 3, 'Three'
// 5, 'Five'
// 7, 'Seven'
}

console.log(JSON.stringify(tree, null, 2))
// {
// "key": 5,
// "value": {
// "name": "Five"
// },
// "left": {
// "key": 3,
// "value": {
// "name": "Three"
// },
// "left": null
// "right": null
// }
// "right": {
// "key": 7,
// "value": {
// "name": "Seven"
// },
// "left": null
// "right": null
// }
// }

tree = remove(tree, 5)

for (const node of entries(tree)) {
console.log(node.key, node.value)
// 3, { name: 'Three' }
// 7, { name: 'Seven' }
}
`

$3


A BinarySearchTree that self-balances after every update,
maintaining O(log n) reads and writes.

RedBlackTree follows the same interface as BinarySearchTree.

`js
import { get, set, remove, entries } from 'ikeru/red-black-tree'

let tree
tree = set(tree, 5, { title: 'Five' })
tree = set(tree, 3, { title: 'Three' })
tree = set(tree, 7, { title: 'Seven' })

for (const node of entries(tree)) {
console.log(node.key, node.title)
// 3, 'Three'
// 5, 'Five'
// 7, 'Seven'
}
`

$3


ikeru provides various utilities for summarizing and transforming time series
data.

`js
import {
merge, downsample, interpolate, extrapolate, window, join
} from 'ikeru/time-series'
import {
getMonth, startOfMonth, differenceInDays, addDays, addMonths, subMonths
} from 'date-fns'

const series1 = [
{ time: Date.parse('2019-01-01') },
{ time: Date.parse('2019-02-03') },
{ time: Date.parse('2019-03-07') }
]
const series2 = [
{ time: Date.parse('2019-01-02') },
{ time: Date.parse('2019-02-04') },
{ time: Date.parse('2019-03-08') }
]
const series3 = [
{ time: Date.parse('2019-01-05') },
{ time: Date.parse('2019-02-06') },
{ time: Date.parse('2019-03-09') },
{ time: Date.parse('2019-05-03') }
]

const series = merge(series1, series2, series3)

const monthlyMissingApril = downsample(
series,
point => startOfMonth(point.time).getTime(),
(time, points) => ({ time })
)

const monthly = interpolate(
monthlyMissingApril,
(before, after) => {
const newPoints = []
let point = before
while (differenceInDays(after.time, point.time) > 1) {
point = {
time: addDays(point.time, 1).getTime()
}
newPoints.push(point)
}
return newPoints
}
)

const everyMonthInYear = extrapolate(
monthly,
start => {
const newPoints = []
let point = start
while (getMonth(point.time) > 0) {
point = subMonths(point, 1)
newPoints.push(point)
}
newPoints.reverse()
return newPoints
},
end => {
const newPoints = []
let point = end
while (getMonth(point.time) < 11) {
point = addMonths(point.time, 1)
newPoints.push(point)
}
return newPoints
}
)

const firstThreeMonths = window(
everyMonthInYear,
Date.parse('2019-01-01'),
Date.parse('2019-03-31')
)


const parallelSeries1 = [
{ time: Date.parse('2019-01-01'), value: 1 },
{ time: Date.parse('2019-01-02'), value: 2 },
{ time: Date.parse('2019-01-03'), value: 3 }
]
const parallelSeries2 = [
{ time: Date.parse('2019-01-01'), value: 4 },
{ time: Date.parse('2019-01-02'), value: 5 },
{ time: Date.parse('2019-01-03'), value: 6 }
]
const addedUp = join([parallelSeries1, parallelSeries2], (time, [p1, p2]) => ({
time,
value: p1.value + p2.value
}))
``