Implement generic sparklines with tests
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open Jest
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open Expect
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let env: DistributionOperation.env = {
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sampleCount: 100,
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xyPointLength: 100,
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}
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let normalDist: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 5.0, stdev: 2.0}))
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let uniformDist: GenericDist_Types.genericDist = Symbolic(#Uniform({low: 9.0, high: 10.0}))
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let betaDist: GenericDist_Types.genericDist = Symbolic(#Beta({alpha: 2.0, beta: 5.0}))
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let lognormalDist: GenericDist_Types.genericDist = Symbolic(#Lognormal({mu: 0.0, sigma: 1.0}))
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let cauchyDist: GenericDist_Types.genericDist = Symbolic(#Cauchy({local: 1.0, scale: 1.0}))
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let triangularDist: GenericDist_Types.genericDist = Symbolic(#Triangular({low: 1.0, medium: 2.0, high: 3.0}))
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let exponentialDist: GenericDist_Types.genericDist = Symbolic(#Exponential({rate: 2.0}))
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let runTest = (name: string, dist : GenericDist_Types.genericDist, expected: string) => {
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test(name, () => {
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let result = GenericDist.toSparkline(~xyPointLength=100, ~sampleCount=100, ~buckets=20, dist)
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switch result {
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| Ok(sparkline) => expect(sparkline)->toEqual(expected)
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| Error(err) => expect("Error")->toEqual(expected)
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}
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})
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}
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describe("sparkline of generic distribution", () => {
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runTest("normal", normalDist, `▁▁▁▁▂▃▄▆▇██▇▆▄▃▂▁▁▁`)
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runTest("uniform", uniformDist, `████████████████████`)
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runTest("beta", uniformDist, `████████████████████`)
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runTest("lognormal", lognormalDist, `█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁`)
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runTest("cauchy", cauchyDist, `▁▁▁▁▁▁▁▁▁██▁▁▁▁▁▁▁▁▁`)
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runTest("triangular", triangularDist, `▁▂▃▄▄▅▆▇████▇▆▅▄▄▃▂▁`)
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runTest("exponential", exponentialDist, `█▆▄▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁`)
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})
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@ -49,7 +49,7 @@ let toFloatOperation = (
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}
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}
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//Todo: If it's a pointSet, but the xyPointLenght is different from what it has, it should change.
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//Todo: If it's a pointSet, but the xyPointLength is different from what it has, it should change.
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// This is tricky because the case of discrete distributions.
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// Also, change the outputXYPoints/pointSetDistLength details
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let toPointSet = (~xyPointLength, ~sampleCount, t): result<PointSetTypes.pointSetDist, error> => {
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@ -75,6 +75,9 @@ let toPointSet = (~xyPointLength, ~sampleCount, t): result<PointSetTypes.pointSe
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}
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}
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let toSparkline = (~xyPointLength: int, ~sampleCount: int, ~buckets: int = 20, t: t) : result<string, error> =>
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toPointSet(~xyPointLength, ~sampleCount, t) -> E.R2.fmap(PointSetDist.toSparkline(buckets))
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module Truncate = {
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let trySymbolicSimplification = (leftCutoff, rightCutoff, t: t): option<t> =>
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switch (leftCutoff, rightCutoff, t) {
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@ -24,6 +24,12 @@ let toPointSet: (
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~sampleCount: int,
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t,
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) => result<PointSetTypes.pointSetDist, error>
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let toSparkline: (
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~xyPointLength: int,
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~sampleCount: int,
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~buckets: int=?,
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t,
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) => result<string, error>
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let truncate: (
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t,
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@ -168,6 +168,22 @@ let pdf = (f: float, t: t) => {
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let inv = T.Integral.yToX
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let cdf = T.Integral.xToY
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let diff = (arr: array<float>): array<float> =>
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Belt.Array.zipBy(arr, Belt.Array.sliceToEnd(arr, 1), (left, right) => right -. left)
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let rec rangeByFloat = (start : float, end: float, step: float) =>
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start > end ?
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[]
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: Belt.Array.concat([start], rangeByFloat(start +. step, end, step))
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@genType
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let toSparkline = (buckets: int, t: t ): string => {
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let size : float = T.maxX(t) -. T.minX(t)
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let stepSize = size /. Belt.Int.toFloat(buckets)
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let cdf = rangeByFloat(T.minX(t), T.maxX(t), stepSize) -> Belt.Array.map(val => cdf(val,t))
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Sparklines.create(diff(cdf), ())
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}
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let doN = (n, fn) => {
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let items = Belt.Array.make(n, 0.0)
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for x in 0 to n - 1 {
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