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* Refactor boxplot jfx demo. * First step of the geom_boxplot() splitting. * Fixes after merging with master. * Fix outlier positions when there is additional grouping. * Add geometry for the boxplot outlier. * Update Python function for the geom_boxplot(). * Remove Aes.Y from GeomMeta for the boxplot geometry. * Remove the BoxplotOutlierGeom and connected code. * Remove sampling parameter from API of the geom_boxplot() function. * Small improvements of code in the BoxplotStat. * Tiny fix in the BoxplotStat. * Small fixes of boxplot in different places. * Remove extra mentions of an outliers. * Increase default value of the midline fatten for the boxplot geometry. * Add dodge width to default position for the outlier geometry.
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124
...se-portable/src/commonMain/kotlin/jetbrains/datalore/plot/base/stat/BoxplotOutlierStat.kt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,124 @@ | ||
| /* | ||
| * Copyright (c) 2023. JetBrains s.r.o. | ||
| * Use of this source code is governed by the MIT license that can be found in the LICENSE file. | ||
| */ | ||
|
|
||
| package jetbrains.datalore.plot.base.stat | ||
|
|
||
| import jetbrains.datalore.plot.base.Aes | ||
| import jetbrains.datalore.plot.base.DataFrame | ||
| import jetbrains.datalore.plot.base.StatContext | ||
| import jetbrains.datalore.plot.base.data.TransformVar | ||
| import jetbrains.datalore.plot.common.data.SeriesUtil | ||
| import kotlin.math.sqrt | ||
|
|
||
| class BoxplotOutlierStat( | ||
| private val whiskerIQRRatio: Double, // ggplot: 'coef' | ||
| private val computeWidth: Boolean // ggplot: 'varWidth' | ||
| ) : BaseStat(DEF_MAPPING) { | ||
| // Note: outliers will need 'width' value, for the 'dodge' positioning to work correctly for all data-points. | ||
|
|
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| override fun hasDefaultMapping(aes: Aes<*>): Boolean { | ||
| return super.hasDefaultMapping(aes) || | ||
| aes == Aes.WIDTH && computeWidth | ||
| } | ||
|
|
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| override fun getDefaultMapping(aes: Aes<*>): DataFrame.Variable { | ||
| return if (aes == Aes.WIDTH) { | ||
| Stats.WIDTH | ||
| } else { | ||
| super.getDefaultMapping(aes) | ||
| } | ||
| } | ||
|
|
||
| override fun consumes(): List<Aes<*>> { | ||
| return listOf(Aes.X, Aes.Y) | ||
| } | ||
|
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| override fun apply(data: DataFrame, statCtx: StatContext, messageConsumer: (s: String) -> Unit): DataFrame { | ||
| if (!hasRequiredValues(data, Aes.Y)) { | ||
| return withEmptyStatValues() | ||
| } | ||
|
|
||
| val ys = data.getNumeric(TransformVar.Y) | ||
| val xs = if (data.has(TransformVar.X)) { | ||
| data.getNumeric(TransformVar.X) | ||
| } else { | ||
| List(ys.size) { 0.0 } | ||
| } | ||
|
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| val statData = buildStat(xs, ys, whiskerIQRRatio) | ||
| val statCount = statData.remove(Stats.COUNT) | ||
|
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| if (computeWidth) { | ||
| // 'width' is in range 0..1 | ||
| val maxCountPerBin = statCount?.maxOrNull()?.toInt() ?: 0 | ||
| val norm = sqrt(maxCountPerBin.toDouble()) | ||
| val statWidth = statCount!!.map { count -> sqrt(count) / norm } | ||
| statData[Stats.WIDTH] = statWidth | ||
| } | ||
|
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| val builder = DataFrame.Builder() | ||
| for ((variable, series) in statData) { | ||
| builder.putNumeric(variable, series) | ||
| } | ||
| return builder.build() | ||
| } | ||
|
|
||
| companion object { | ||
| private val DEF_MAPPING: Map<Aes<*>, DataFrame.Variable> = mapOf( | ||
| Aes.X to Stats.X, | ||
| Aes.Y to Stats.Y | ||
| ) | ||
|
|
||
| private fun buildStat( | ||
| xs: List<Double?>, | ||
| ys: List<Double?>, | ||
| whiskerIQRRatio: Double | ||
| ): MutableMap<DataFrame.Variable, List<Double>> { | ||
| val xyPairs = SeriesUtil.filterFinite(xs, ys) | ||
| .let { (xs, ys) -> xs zip ys } | ||
| if (xyPairs.isEmpty()) { | ||
| return mutableMapOf() | ||
| } | ||
|
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| val binnedData: MutableMap<Double, MutableList<Double>> = HashMap() | ||
| for ((x, y) in xyPairs) { | ||
| binnedData.getOrPut(x) { ArrayList() }.add(y) | ||
| } | ||
|
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| val statX = ArrayList<Double>() | ||
| val statY = ArrayList<Double>() | ||
| val statCount = ArrayList<Double>() | ||
|
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| for ((x, bin) in binnedData) { | ||
| val count = bin.size.toDouble() | ||
| val summary = FiveNumberSummary(bin) | ||
| val lowerHinge = summary.firstQuartile | ||
| val upperHinge = summary.thirdQuartile | ||
| val IQR = upperHinge - lowerHinge | ||
| val lowerFence = lowerHinge - IQR * whiskerIQRRatio | ||
| val upperFence = upperHinge + IQR * whiskerIQRRatio | ||
| val outliers = bin.filter { y -> y < lowerFence || y > upperFence } | ||
| for (y in outliers) { | ||
| statX.add(x) | ||
| statY.add(y) | ||
| statCount.add(count) | ||
| } | ||
|
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| // If there are no outliers, add a fake one to correct splitting for additional grouping | ||
| if (outliers.isEmpty() && count > 0) { | ||
| statX.add(x) | ||
| statY.add(Double.NaN) | ||
| statCount.add(count) | ||
| } | ||
| } | ||
|
|
||
| return mutableMapOf( | ||
| Stats.X to statX, | ||
| Stats.Y to statY, | ||
| Stats.COUNT to statCount | ||
| ) | ||
| } | ||
| } | ||
| } |
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