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/*
* Copyright (c) 2019. 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.DataFrame.Variable.Source.STAT
import jetbrains.datalore.plot.base.Stat
import jetbrains.datalore.plot.base.StatContext
object Stats {
// stat variables can be referenced by name ..name.. (p 54)
val X = DataFrame.Variable("..x..", STAT, "x")
val Y = DataFrame.Variable("..y..", STAT, "y")
val COUNT = DataFrame.Variable("..count..", STAT, "count")
val DENSITY = DataFrame.Variable("..density..", STAT, "density")
val Y_MIN = DataFrame.Variable("..ymin..", STAT, "y min")
val Y_MAX = DataFrame.Variable("..ymax..", STAT, "y max")
val SE = DataFrame.Variable("..se..", STAT, "standard error")
val LEVEL = DataFrame.Variable("..level..", STAT, "level")
val LOWER = DataFrame.Variable("..lower..", STAT, "lower")
val MIDDLE = DataFrame.Variable("..middle..", STAT, "middle")
val UPPER = DataFrame.Variable("..upper..", STAT, "upper")
val WIDTH = DataFrame.Variable("..width..", STAT, "width")
val CORR = DataFrame.Variable("..corr..", STAT, "corr")
val CORR_ABS = DataFrame.Variable("..corr_abs..", STAT, "corr_abs")
val SCALED = DataFrame.Variable("..scaled..", STAT, "scaled")
val GROUP = DataFrame.Variable("..group..", STAT, "group")
val IDENTITY: Stat = IdentityStat()
private val VARS: Map = run {
val variableList = listOf(
X,
Y,
COUNT,
DENSITY,
Y_MIN,
Y_MAX,
SE,
LEVEL,
LOWER,
MIDDLE,
UPPER,
WIDTH,
SCALED,
GROUP,
CORR,
CORR_ABS
)
val result = HashMap()
for (variable in variableList) {
result[variable.name] = variable
}
result
}
fun isStatVar(varName: String): Boolean {
return VARS.containsKey(varName)
}
fun statVar(varName: String): DataFrame.Variable {
require(VARS.containsKey(varName)) { "Unknown stat variable $varName" }
return VARS[varName]!!
}
fun defaultMapping(stat: Stat): Map, DataFrame.Variable> {
val map = HashMap, DataFrame.Variable>()
for (aes in Aes.values()) {
if (stat.hasDefaultMapping(aes)) {
val variable = stat.getDefaultMapping(aes)
map[aes] = variable
}
}
return map
}
fun count(): Stat {
return CountStat()
}
fun bin(
binCount: Int = BinStat.DEF_BIN_COUNT,
binWidth: Double? = null,
center: Double? = null,
boundary: Double? = null
): BinStat {
var xPosKind = BinStat.XPosKind.NONE
var xPosValue = 0.0
if (boundary != null) {
xPosKind = BinStat.XPosKind.BOUNDARY
xPosValue = boundary
} else if (center != null) {
xPosKind = BinStat.XPosKind.CENTER
xPosValue = center
}
return BinStat(
binCount = binCount,
binWidth = binWidth,
xPosKind = xPosKind,
xPos = xPosValue
)
}
fun smooth(
smootherPointCount: Int = SmoothStat.DEF_EVAL_POINT_COUNT,
smoothingMethod: SmoothStat.Method = SmoothStat.DEF_SMOOTHING_METHOD,
confidenceLevel: Double = SmoothStat.DEF_CONFIDENCE_LEVEL,
displayConfidenceInterval: Boolean = SmoothStat.DEF_DISPLAY_CONFIDENCE_INTERVAL,
span: Double = SmoothStat.DEF_SPAN,
polynomialDegree: Int = SmoothStat.DEF_DEG,
loessCriticalSize: Int = SmoothStat.DEF_LOESS_CRITICAL_SIZE,
samplingSeed: Long = SmoothStat.DEF_SAMPLING_SEED
): SmoothStat {
return SmoothStat(
smootherPointCount = smootherPointCount,
smoothingMethod = smoothingMethod,
confidenceLevel = confidenceLevel,
displayConfidenceInterval = displayConfidenceInterval,
span = span,
polynomialDegree = polynomialDegree,
loessCriticalSize = loessCriticalSize,
samplingSeed = samplingSeed
)
}
fun corr(
correlationMethod: CorrelationStat.Method = CorrelationStat.DEF_CORRELATION_METHOD,
type: CorrelationStat.Type = CorrelationStat.DEF_TYPE,
fillDiagonal: Boolean = CorrelationStat.DEF_FILL_DIAGONAL,
threshold: Double = CorrelationStat.DEF_THRESHOLD
): CorrelationStat {
return CorrelationStat(
correlationMethod = correlationMethod,
type = type,
fillDiagonal = fillDiagonal,
threshold = threshold
)
}
fun contour(
binCount: Int = ContourStat.DEF_BIN_COUNT,
binWidth: Double? = null
): ContourStat {
return ContourStat(
binCount = binCount,
binWidth = binWidth
)
}
fun contourf(
binCount: Int = ContourStat.DEF_BIN_COUNT,
binWidth: Double? = null
): ContourfStat {
return ContourfStat(
binCount = binCount,
binWidth = binWidth
)
}
fun boxplot(
whiskerIQRRatio: Double = BoxplotStat.DEF_WHISKER_IQR_RATIO,
computeWidth: Boolean = BoxplotStat.DEF_COMPUTE_WIDTH
): BoxplotStat {
return BoxplotStat(whiskerIQRRatio, computeWidth)
}
fun density(
bandWidth: Double? = null,
bandWidthMethod: DensityStat.BandWidthMethod = DensityStat.DEF_BW,
adjust: Double = DensityStat.DEF_ADJUST,
kernel: DensityStat.Kernel = DensityStat.DEF_KERNEL,
n: Int = DensityStat.DEF_N,
fullScalMax: Int = DensityStat.DEF_FULL_SCAN_MAX
): DensityStat {
return DensityStat(
bandWidth = bandWidth,
bandWidthMethod = bandWidthMethod,
adjust = adjust,
kernel = kernel,
n = n,
fullScalMax = fullScalMax
)
}
fun density2d(
bandWidthX: Double? = null,
bandWidthY: Double? = null,
bandWidthMethod: DensityStat.BandWidthMethod = AbstractDensity2dStat.DEF_BW, // Used is `bandWidth` is not set.
adjust: Double = AbstractDensity2dStat.DEF_ADJUST,
kernel: DensityStat.Kernel = AbstractDensity2dStat.DEF_KERNEL,
nX: Int = AbstractDensity2dStat.DEF_N,
nY: Int = AbstractDensity2dStat.DEF_N,
isContour: Boolean = AbstractDensity2dStat.DEF_CONTOUR,
binCount: Int = AbstractDensity2dStat.DEF_BIN_COUNT,
binWidth: Double = AbstractDensity2dStat.DEF_BIN_WIDTH
): AbstractDensity2dStat {
return Density2dStat(
bandWidthX = bandWidthX,
bandWidthY = bandWidthY,
bandWidthMethod = bandWidthMethod,
adjust = adjust,
kernel = kernel,
nX = nX,
nY = nY,
isContour = isContour,
binCount = binCount,
binWidth = binWidth
)
}
fun density2df(
bandWidthX: Double? = null,
bandWidthY: Double? = null,
bandWidthMethod: DensityStat.BandWidthMethod, // Used is `bandWidth` is not set.
adjust: Double = AbstractDensity2dStat.DEF_ADJUST,
kernel: DensityStat.Kernel = AbstractDensity2dStat.DEF_KERNEL,
nX: Int = AbstractDensity2dStat.DEF_N,
nY: Int = AbstractDensity2dStat.DEF_N,
isContour: Boolean = AbstractDensity2dStat.DEF_CONTOUR,
binCount: Int = AbstractDensity2dStat.DEF_BIN_COUNT,
binWidth: Double = AbstractDensity2dStat.DEF_BIN_WIDTH
): AbstractDensity2dStat {
return Density2dfStat(
bandWidthX = bandWidthX,
bandWidthY = bandWidthY,
bandWidthMethod = bandWidthMethod,
adjust = adjust,
kernel = kernel,
nX = nX,
nY = nY,
isContour = isContour,
binCount = binCount,
binWidth = binWidth
)
}
private class IdentityStat internal constructor() : BaseStat(emptyMap()) {
override fun apply(data: DataFrame, statCtx: StatContext, messageConsumer: (s: String) -> Unit): DataFrame {
return DataFrame.Builder.emptyFrame()
}
override fun consumes(): List> {
return emptyList()
}
}
}
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