Fix formatting (it faster than via the review...)

dataset/src/test/scala/frameless/functions/AggregateFunctionsTests.scala

@@ -363,11 +363,10 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
 
  def bivariatePropTemplate[A: TypedEncoder, B: TypedEncoder]
  (
- xs: List[X3[Int,A,B]]
+ xs: List[X3[Int, A, B]]
  )
  (
-
- framelessFun: (TypedColumn[X3[Int,A,B], A], TypedColumn[X3[Int,A,B], B]) => TypedAggregate[X3[Int,A,B], Option[Double]],
+ framelessFun: (TypedColumn[X3[Int, A, B], A], TypedColumn[X3[Int, A, B], B]) => TypedAggregate[X3[Int, A, B], Option[Double]],
  sparkFun: (Column, Column) => Column
  )
  (
@@ -378,18 +377,15 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  evCanBeDoubleB: CatalystCast[B, Double]
  ): Prop = {
 
-
  val tds = TypedDataset.create(xs)
- //typed implementation of bivar stats function
+ // Typed implementation of bivar stats function
  val tdBivar = tds.groupBy(tds('a)).agg(framelessFun(tds('b), tds('c)))
  .map(
  kv => (kv._1, kv._2.flatMap(DoubleBehaviourUtils.nanNullHandler))
  ).collect().run()
 
-
-
  val cDF = session.createDataset(xs.map(x => (x.a, x.b, x.c)))
- //comparison implementation of bivar stats functions
+ // Comparison implementation of bivar stats functions
  val compBivar = cDF
  .groupBy(cDF("_1"))
  .agg(sparkFun(cDF("_2"), cDF("_3")))
@@ -400,17 +396,16 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  }
  )
 
- //should be the same
+ // Should be the same
  tdBivar.toMap ?= compBivar.collect().toMap
  }
 
  def univariatePropTemplate[A: TypedEncoder]
  (
- xs: List[X2[Int,A]]
+ xs: List[X2[Int, A]]
  )
  (
-
- framelessFun: (TypedColumn[X2[Int,A], A]) => TypedAggregate[X2[Int,A], Option[Double]],
+ framelessFun: (TypedColumn[X2[Int, A], A]) => TypedAggregate[X2[Int, A], Option[Double]],
  sparkFun: (Column) => Column
  )
  (
@@ -420,18 +415,15 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  evCanBeDoubleA: CatalystCast[A, Double]
  ): Prop = {
 
-
  val tds = TypedDataset.create(xs)
- //typed implementation of bivar stats function
+ // Typed implementation of bivar stats function
  val tdUnivar = tds.groupBy(tds('a)).agg(framelessFun(tds('b)))
  .map(
  kv => (kv._1, kv._2.flatMap(DoubleBehaviourUtils.nanNullHandler))
  ).collect().run()
 
-
-
  val cDF = session.createDataset(xs.map(x => (x.a, x.b)))
- //comparison implementation of bivar stats functions
+ // Comparison implementation of bivar stats functions
  val compUnivar = cDF
  .groupBy(cDF("_1"))
  .agg(sparkFun(cDF("_2")))
@@ -442,7 +434,7 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  }
  )
 
- //should be the same
+ // Should be the same
  tdUnivar.toMap ?= compUnivar.collect().toMap
  }
 
@@ -473,7 +465,10 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  encEv: Encoder[(Int, A, B)],
  evCanBeDoubleA: CatalystCast[A, Double],
  evCanBeDoubleB: CatalystCast[B, Double]
- ): Prop = bivariatePropTemplate(xs)(covar_pop[A,B,X3[Int, A, B]],org.apache.spark.sql.functions.covar_pop)
+ ): Prop = bivariatePropTemplate(xs)(
+ covar_pop[A, B, X3[Int, A, B]],
+ org.apache.spark.sql.functions.covar_pop
+ )
 
  check(forAll(prop[Double, Double] _))
  check(forAll(prop[Double, Int] _))
@@ -491,7 +486,10 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  encEv: Encoder[(Int, A, B)],
  evCanBeDoubleA: CatalystCast[A, Double],
  evCanBeDoubleB: CatalystCast[B, Double]
- ): Prop = bivariatePropTemplate(xs)(covar_samp[A,B,X3[Int, A, B]],org.apache.spark.sql.functions.covar_samp)
+ ): Prop = bivariatePropTemplate(xs)(
+ covar_samp[A, B, X3[Int, A, B]],
+ org.apache.spark.sql.functions.covar_samp
+ )
 
  check(forAll(prop[Double, Double] _))
  check(forAll(prop[Double, Int] _))
@@ -508,8 +506,10 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  implicit
  encEv: Encoder[(Int, A)],
  evCanBeDoubleA: CatalystCast[A, Double]
- ): Prop = univariatePropTemplate(xs)(kurtosis[A,X2[Int, A]],org.apache.spark.sql.functions.kurtosis)
-
+ ): Prop = univariatePropTemplate(xs)(
+ kurtosis[A, X2[Int, A]],
+ org.apache.spark.sql.functions.kurtosis
+ )
 
  check(forAll(prop[Double] _))
  check(forAll(prop[Int] _))
@@ -526,8 +526,10 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  implicit
  encEv: Encoder[(Int, A)],
  evCanBeDoubleA: CatalystCast[A, Double]
- ): Prop = univariatePropTemplate(xs)(skewness[A,X2[Int, A]],org.apache.spark.sql.functions.skewness)
-
+ ): Prop = univariatePropTemplate(xs)(
+ skewness[A, X2[Int, A]],
+ org.apache.spark.sql.functions.skewness
+ )
 
  check(forAll(prop[Double] _))
  check(forAll(prop[Int] _))
@@ -544,8 +546,10 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  implicit
  encEv: Encoder[(Int, A)],
  evCanBeDoubleA: CatalystCast[A, Double]
- ): Prop = univariatePropTemplate(xs)(stddev_pop[A,X2[Int, A]],org.apache.spark.sql.functions.stddev_pop)
-
+ ): Prop = univariatePropTemplate(xs)(
+ stddev_pop[A, X2[Int, A]],
+ org.apache.spark.sql.functions.stddev_pop
+ )
 
  check(forAll(prop[Double] _))
  check(forAll(prop[Int] _))
@@ -562,8 +566,10 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  implicit
  encEv: Encoder[(Int, A)],
  evCanBeDoubleA: CatalystCast[A, Double]
- ): Prop = univariatePropTemplate(xs)(stddev_samp[A,X2[Int, A]],org.apache.spark.sql.functions.stddev_samp)
-
+ ): Prop = univariatePropTemplate(xs)(
+ stddev_samp[A, X2[Int, A]],
+ org.apache.spark.sql.functions.stddev_samp
+ )
 
  check(forAll(prop[Double] _))
  check(forAll(prop[Int] _))