File size: 3,620 Bytes
1e92f2d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import { BoxPlotDatum, BoxPlotCommonProps, BoxPlotSummary } from '../types'
import { defaultProps } from '../props'
/** stratify an array of raw data objects into an array of arrays;
* each array will create one box plot */
export const stratifyData = <RawDatum extends BoxPlotDatum>({
data,
groups = defaultProps.groups,
getGroup,
subGroups = defaultProps.subGroups,
getSubGroup,
}: {
data: RawDatum[]
groups?: BoxPlotCommonProps<RawDatum>['groups']
getGroup: ((datum: RawDatum) => string) | null
subGroups?: BoxPlotCommonProps<RawDatum>['subGroups']
getSubGroup: ((datum: RawDatum) => string) | null
}) => {
const groupsMap = {} as BoxPlotDatum
if (groups) {
groups.map((g, i) => (groupsMap[g] = i))
}
const subGroupsMap = {} as BoxPlotDatum
if (subGroups) {
subGroups.map((sg, i) => (subGroupsMap[sg] = i))
}
const nGroups = Math.max(1, groups ? groups.length : 1)
const nSubGroups = Math.max(1, subGroups ? subGroups.length : 1)
const n = nGroups * nSubGroups
const result = Array(n)
.fill([])
.map(() => Array<RawDatum>())
data.forEach((d: RawDatum) => {
const groupIndex = getGroup ? Number(groupsMap[getGroup(d)]) : 0
const subGroupIndex = getSubGroup ? Number(subGroupsMap[getSubGroup(d)] ?? 0) : 0
const index = groupIndex * nSubGroups + subGroupIndex
if (index >= 0) {
result[index].push(d)
}
})
return result
}
const getQuantile = (values: number[], quantile = 0.5) => {
const realIndex = (values.length - 1) * Math.max(0, Math.min(1, quantile))
const intIndex = Math.floor(realIndex)
if (realIndex === intIndex) return values[intIndex]
const v1 = values[intIndex],
v2 = values[intIndex + 1]
return v1 + (v2 - v1) * (realIndex - intIndex)
}
const getMean = (values: number[]) => {
const sum = values.reduce((acc, x) => acc + x, 0)
return sum / values.length
}
const isPrecomputedDistribution = <RawDatum>(
datum: RawDatum | Omit<BoxPlotSummary, 'groupIndex' | 'subGroupIndex'>
): datum is Omit<BoxPlotSummary, 'groupIndex' | 'subGroupIndex'> => {
const preComputedKeys = ['values', 'extrema', 'mean', 'quantiles', 'group', 'subGroup', 'n']
return preComputedKeys.every(k => k in (datum as object))
}
export const summarizeDistribution = <RawDatum extends BoxPlotDatum>({
data,
getValue,
groups,
subGroups,
groupIndex,
subGroupIndex,
quantiles,
}: {
data: RawDatum[]
getValue: (datum: RawDatum) => unknown
groups: string[] | null
subGroups: string[] | null
groupIndex: number
subGroupIndex: number
quantiles: number[]
}) => {
// accept a precomputed summary representation if it has all the required keys
if (data.length === 1 && isPrecomputedDistribution(data[0])) {
return {
groupIndex: groupIndex,
subGroupIndex: subGroupIndex,
...data[0],
} as BoxPlotSummary
}
// compute the summary representation from raw data using quantiles
const values = data.map(v => Number(getValue(v))) as number[]
values.sort((a, b) => a - b)
return {
group: groups ? groups[groupIndex] : '',
groupIndex: groupIndex,
subGroup: subGroups ? subGroups[subGroupIndex] : '',
subGroupIndex: subGroupIndex,
n: values.length,
extrema: [values[0], values[values.length - 1]],
quantiles: quantiles,
values: quantiles.map(q => getQuantile(values, q)),
mean: getMean(values),
} as BoxPlotSummary
}
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