Add new stat variables and use them in the SummaryStat.

plot-base-portable/src/commonMain/kotlin/jetbrains/datalore/plot/base/stat/Stats.kt

@@ -23,7 +23,12 @@ object Stats {
  val THEORETICAL = DataFrame.Variable("..theoretical..", STAT, "theoretical")
  val SE = DataFrame.Variable("..se..", STAT, "standard error")
  val LEVEL = DataFrame.Variable("..level..", STAT, "level")
+ val MEAN = DataFrame.Variable("..mean..", STAT, "mean")
+ val MEDIAN = DataFrame.Variable("..median..", STAT, "median")
  val QUANTILE = DataFrame.Variable("..quantile..", STAT, "quantile")
+ val QUANTILE_A = DataFrame.Variable("..qa..", STAT, "quantile a")
+ val QUANTILE_B = DataFrame.Variable("..qb..", STAT, "quantile b")
+ val QUANTILE_C = DataFrame.Variable("..qc..", STAT, "quantile c")
  val LOWER = DataFrame.Variable("..lower..", STAT, "lower")
  val MIDDLE = DataFrame.Variable("..middle..", STAT, "middle")
  val UPPER = DataFrame.Variable("..upper..", STAT, "upper")
@@ -53,7 +58,12 @@ object Stats {
  THEORETICAL,
  SE,
  LEVEL,
+ MEAN,
+ MEDIAN,
  QUANTILE,
+ QUANTILE_A,
+ QUANTILE_B,
+ QUANTILE_C,
  LOWER,
  MIDDLE,
  UPPER,

plot-base-portable/src/commonMain/kotlin/jetbrains/datalore/plot/base/stat/SummaryStat.kt

@@ -13,7 +13,7 @@ import jetbrains.datalore.plot.base.data.TransformVar
 import jetbrains.datalore.plot.common.data.SeriesUtil
 
 class SummaryStat(
- private val aggFunctionsMap: Map<Aes<*>, (List<Double>) -> Double>
+ private val aggFunctionsMap: Map<DataFrame.Variable, (List<Double>) -> Double>
 ) : BaseStat(DEF_MAPPING) {
 
  override fun consumes(): List<Aes<*>> {
@@ -57,28 +57,25 @@ class SummaryStat(
  }
 
  val statX = ArrayList<Double>()
- val statAggValues: Map<Aes<*>, MutableList<Double>> = AGG_MAPPING.keys.associateWith { mutableListOf() }
+ val statAggValues: Map<DataFrame.Variable, MutableList<Double>> = aggFunctionsMap.keys.associateWith { mutableListOf() }
  for ((x, bin) in binnedData) {
  statX.add(x)
  val sortedBin = Ordering.natural<Double>().sortedCopy(bin)
- for ((aes, aggValues) in statAggValues) {
- val aggFunction = aggFunctionsMap[aes] ?: SummaryUtil::nan
+ for ((statVar, aggValues) in statAggValues) {
+ val aggFunction = aggFunctionsMap[statVar] ?: SummaryUtil::nan
  aggValues.add(aggFunction(sortedBin))
  }
  }
 
- return mapOf(Stats.X to statX) + statAggValues.map { (aes, aggValues) -> Pair(AGG_MAPPING[aes]!!, aggValues) }.toMap()
+ return mapOf(Stats.X to statX) + statAggValues
  }
 
  companion object {
- private val AGG_MAPPING: Map<Aes<*>, DataFrame.Variable> = mapOf(
+ private val DEF_MAPPING: Map<Aes<*>, DataFrame.Variable> = mapOf(
+ Aes.X to Stats.X,
  Aes.Y to Stats.Y,
  Aes.YMIN to Stats.Y_MIN,
- Aes.YMAX to Stats.Y_MAX,
- Aes.MIDDLE to Stats.MIDDLE,
- Aes.LOWER to Stats.LOWER,
- Aes.UPPER to Stats.UPPER,
+ Aes.YMAX to Stats.Y_MAX
  )
- private val DEF_MAPPING: Map<Aes<*>, DataFrame.Variable> = mapOf(Aes.X to Stats.X) + AGG_MAPPING
  }
 }

plot-config-portable/src/commonMain/kotlin/jetbrains/datalore/plot/config/StatProto.kt

@@ -5,11 +5,10 @@
 
 package jetbrains.datalore.plot.config
 
-import jetbrains.datalore.plot.base.Aes
+import jetbrains.datalore.plot.base.DataFrame
 import jetbrains.datalore.plot.base.GeomKind
 import jetbrains.datalore.plot.base.Stat
 import jetbrains.datalore.plot.base.stat.*
-import jetbrains.datalore.plot.config.Option.Mapping
 import jetbrains.datalore.plot.config.Option.Stat.Bin
 import jetbrains.datalore.plot.config.Option.Stat.Bin2d
 import jetbrains.datalore.plot.config.Option.Stat.Boxplot
@@ -409,22 +408,22 @@ object StatProto {
 
  private fun configureSummaryStat(options: OptionsAccessor): SummaryStat {
  val defaultAggFunctions = mapOf(
- Aes.Y to (getAggFunction(options, Summary.FUN) ?: SummaryUtil::mean),
- Aes.YMIN to (getAggFunction(options, Summary.FUN_MIN) ?: SummaryUtil::min),
- Aes.YMAX to (getAggFunction(options, Summary.FUN_MAX) ?: SummaryUtil::max)
+ Stats.Y to (getAggFunction(options, Summary.FUN) ?: SummaryUtil::mean),
+ Stats.Y_MIN to (getAggFunction(options, Summary.FUN_MIN) ?: SummaryUtil::min),
+ Stats.Y_MAX to (getAggFunction(options, Summary.FUN_MAX) ?: SummaryUtil::max)
  )
 
  val additionalAggFunctions = configureAggFunMap(options.getMap(Summary.FUN_MAP))
 
  return SummaryStat(defaultAggFunctions + additionalAggFunctions)
  }
 
- private fun configureAggFunMap(aggFunMap: Map<String, Any>): Map<Aes<*>, (List<Double>) -> Double> {
+ private fun configureAggFunMap(aggFunMap: Map<String, Any>): Map<DataFrame.Variable, (List<Double>) -> Double> {
  val aggFunOptions = OptionsAccessor(aggFunMap)
- return aggFunMap.keys.associate { aesName ->
+ return aggFunMap.keys.associate { statVarName ->
  Pair(
- Mapping.toAes(aesName),
- getAggFunction(aggFunOptions, aesName) ?: SummaryUtil::nan
+ Stats.statVar("..$statVarName.."),
+ getAggFunction(aggFunOptions, statVarName) ?: SummaryUtil::nan
  )
  }
  }

plot-demo-common/src/commonMain/kotlin/jetbrains/datalore/plotDemo/model/plotConfig/Summary.kt

@@ -90,8 +90,9 @@ class Summary {
  'layers': [
  {
  'geom': 'crossbar',
+ 'mapping': {'middle': '..median..'},
  'stat': 'summary',
- 'fun_map': {'middle': 'median'}
+ 'fun_map': {'median': 'median'}
  }
  ]
  }

python-package/lets_plot/plot/stat.py

@@ -1,9 +1,8 @@
 # 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.
-
+from .core import aes
 from .geom import _geom
 
-
 #
 # Stats - functions, drawing attention to the statistical transformation rather than the visual appearance.
 #
@@ -16,15 +15,38 @@ def stat_summary(mapping=None, *, data=None, geom='pointrange',
  fun=None, fun_min=None, fun_max=None, fun_map=None,
  color_by=None, fill_by=None,
  **other_args):
+ mapping_dict = mapping.as_dict() if mapping is not None else {}
+
+ quantile_agg_functions = {"qa": None, "qb": None, "qc": None}
+
+ def get_stat_name(agg_fun):
+ if isinstance(agg_fun, str) and agg_fun not in ["q1", "q2", "q3"]:
+ return agg_fun
+ else:
+ name = next((q for (q, f) in quantile_agg_functions.items() if f is None or f == agg_fun), None)
+ if name is None:
+ raise Exception("No more than three different quantiles can be used in fun_map parameter")
+ quantile_agg_functions[name] = agg_fun
+ return name
+
+ inner_fun_map = {}
+ fun_mapping_dict = {}
+ for aes_name, fun_name in (fun_map or {}).items():
+ stat_name = get_stat_name(fun_name)
+ inner_fun_map[stat_name] = fun_name
+ fun_mapping_dict[aes_name] = "..{0}..".format(stat_name)
+ inner_mapping_dict = {**fun_mapping_dict, **mapping_dict}
+ inner_mapping = aes(**inner_mapping_dict) if len(inner_mapping_dict.keys()) > 0 else None
+
  return _geom(geom,
- mapping=mapping,
+ mapping=inner_mapping,
  data=data,
  stat='summary',
  position=position,
  show_legend=show_legend,
  sampling=sampling,
  tooltips=tooltips,
  orientation=orientation,
- fun=fun, fun_min=fun_min, fun_max=fun_max, fun_map=fun_map,
+ fun=fun, fun_min=fun_min, fun_max=fun_max, fun_map=inner_fun_map,
  color_by=color_by, fill_by=fill_by,
  **other_args)