Press n or j to go to the next uncovered block, b, p or k for the previous block.
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* Copyright (c) Microsoft. All rights reserved.
* Licensed under the MIT license. See LICENSE file in the project.
*/
/**
* The Bin holds an array of values that match the bounding criteria,
* along with an additional pair of properties indicating that bounding.
*/
export interface Bin extends Array<number> {
/**
* Minimum _computed_ value for the bin
*/
x0: number
/**
* Maximum _computed_ value for the bin
*/
x1: number
}
/**
* A Histogram is just an array of Bins.
* This follows the d3 format, so these outputs can be used as drop-ins to
* any code that expects a d3-style histogram.
*/
export type Histogram = Array<Bin>
const extent = (values: number[]) =>
values.reduce(
(acc, cur) => [Math.min(acc[0], cur), Math.max(acc[1], cur)],
[Number.MAX_VALUE, Number.MIN_VALUE],
)
const standardHistogram = (
data: any[],
bins: number,
accessor = (d: any) => d,
): Histogram => {
const values = data.map((d) => accessor(d))
const ext = extent(values)
const [min, max] = ext
const range = max - min
const binSize = range / bins
const binStructure = new Array(bins).fill(1).map((d, i) => {
const bin: any = []
bin.x0 = binSize * i + min
bin.x1 = binSize * (i + 1) + min
return bin
})
const histo = values.reduce((acc, cur) => {
const index = binStructure.findIndex(
(bin) => cur >= bin.x0 && cur <= bin.x1,
)
acc[index < 0 ? acc.length - 1 : index].push(cur)
return acc
}, binStructure)
return histo
}
const findForwardBreak = (data: number[], start: number) => {
const value = data[start - 1]
for (let i = start; i < data.length; i++) {
if (data[i] !== value) {
return i - start
}
}
return data.length - start
}
const findBackwardBreak = (data: number[], start: number) => {
const value = data[start - 1]
for (let i = start - 1; i >= 0; i--) {
if (data[i] !== value) {
return start - i - 1
}
}
return start
}
/**
* Quantize a list of data values into n bins
* This adds a correction to make sure bins don't break on the same value,
* so that values only have one correct bin to lie within.
* Some distributions need this correction because they have such an extreme shape (e.g., zipf)
* @param data
* @param bins
*/
const quantizeHistogram = (
data: any[],
bins: number,
accessor = (d: any) => d,
smooth?: boolean,
): Histogram => {
const values = data
.map((d: any) => accessor(d))
.sort((a: number, b: number) => a - b)
let binLength = Math.ceil(values.length / bins) // starting bin length is always the ideal
const binStructure: any = []
let start = 0
for (let i = 0; i < bins; i++) {
const end = start + binLength
const forward = findForwardBreak(values, end)
const backward = findBackwardBreak(values, end)
const moveBackward = backward < forward && backward < binLength
const newEnd = smooth && moveBackward ? end - backward : end + forward
const bin: any = values.slice(start, newEnd)
if (bin.length > 0) {
bin.x0 = bin[0]
bin.x1 = bin[bin.length - 1]
binStructure.push(bin)
}
start = newEnd
// recalculate bin length with remaining values and bins, in case a really large bin skewed everything
if (newEnd !== end) {
binLength = Math.ceil((values.length - newEnd) / (bins - (i + 1)))
}
}
return binStructure
}
/**
* Creates a histogram from an array of objects.
* The histogram will be populated with numeric values.
* If your input data is not numeric, supply an accessor function.
* By default, a standard equal-interval histogram will be created.
* If the quantize parameter is set, it will instead use quantiles (i.e., equal-length bins).
* If you are quantizing, you can also set a smooth parameter to indicate that breaks in the data should try to minimize variance.
*
* Note on quantiles: the bin count and length are ideals, but not guaranteed.
* The way the quantiles are calculated, a precise number of bins can't be guaranteed unless you will accept empty bins.
* For example, if your dataset is [1,1,1,1,1,2,2], you will always only get back two bins.
* Depending on the data distribution, we also may not be able to assure each bin is the same length as the others.
* Using the same example dataset, here we would have one bin with 5 items, and the other with 2.
* We _could_ split them evenly and put some of the 1s in the 2 bin, but that seems less preferable.
*
*/
export const histogram = (
data: any[],
bins: number,
accessor = (d: any) => d,
quantize?: boolean,
smooth?: boolean,
): Histogram => {
return quantize
? quantizeHistogram(data, bins, accessor, smooth)
: standardHistogram(data, bins, accessor)
}
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