scala 3 code formatting

.scalafmt.conf

@@ -1,2 +1,2 @@
 version = 3.8.3
-runner.dialect = scala213
+runner.dialect = scala3

build.sbt

@@ -21,7 +21,6 @@ libraryDependencies ++= Seq(
   ("org.ddahl" %% "rscala" % "3.2.19").cross(CrossVersion.for3Use2_13)
 )
 
-
 mdocIn := file("mdoc/")
 
 mdocOut := file("docs/")

examples/src/main/scala/FBm.scala

@@ -7,48 +7,47 @@ Fractional Brownian motion via and inverse Discrete Cosine Transform (DCT)
 
 import scalaglm.Utils.*
 
-  import breeze.linalg.*
-  import breeze.numerics.*
-  import breeze.stats.distributions.*
-  import breeze.stats.distributions.Rand.VariableSeed.randBasis
-  import java.awt.image.BufferedImage
-
-  def dm2bi(m: DenseMatrix[Double]): BufferedImage =
-    val canvas = new BufferedImage(m.cols, m.rows, BufferedImage.TYPE_INT_RGB)
-    val wr = canvas.getRaster
-    val mx = max(m)
-    val mn = min(m)
-    (0 until m.cols).foreach { x =>
-      (0 until m.rows).foreach { y =>
-        val shade = round(255 * (m(y, x) - mn) / (mx - mn)).toInt
-        wr.setSample(x, y, 0, shade) // R
-        wr.setSample(x, y, 1, shade) // G
-        wr.setSample(x, y, 2, 255) // B
-      }
+import breeze.linalg.*
+import breeze.numerics.*
+import breeze.stats.distributions.*
+import breeze.stats.distributions.Rand.VariableSeed.randBasis
+import java.awt.image.BufferedImage
+
+def dm2bi(m: DenseMatrix[Double]): BufferedImage =
+  val canvas = new BufferedImage(m.cols, m.rows, BufferedImage.TYPE_INT_RGB)
+  val wr = canvas.getRaster
+  val mx = max(m)
+  val mn = min(m)
+  (0 until m.cols).foreach { x =>
+    (0 until m.rows).foreach { y =>
+      val shade = round(255 * (m(y, x) - mn) / (mx - mn)).toInt
+      wr.setSample(x, y, 0, shade) // R
+      wr.setSample(x, y, 1, shade) // G
+      wr.setSample(x, y, 2, 255) // B
     }
-    canvas
-
-  def showImage(m: DenseMatrix[Double]): Unit =
-    import breeze.plot.*
-    val fig = Figure("fBm")
-    fig.width = 1200
-    fig.height = 1200
-    val p0 = fig.subplot(0)
-    p0 += image(m)
-    fig.refresh()
-
-  @main def fBmSim() =
-    val N = 1024
-    val H = 0.9
-    val sd = DenseMatrix.tabulate(N, N){(j, k) =>
-      if (j*j + k*k < 9) 0.0 else
-        math.pow(j*j + k*k, -(H + 1)/2) }
-    val M = sd map (s => Gaussian(0.0, s).draw())
-    val m = dct2(M, true)
-    javax.imageio.ImageIO.write(dm2bi(m), "png", new java.io.File("fBm.png"))
-    showImage(m)
-
-
+  }
+  canvas
+
+def showImage(m: DenseMatrix[Double]): Unit =
+  import breeze.plot.*
+  val fig = Figure("fBm")
+  fig.width = 1200
+  fig.height = 1200
+  val p0 = fig.subplot(0)
+  p0 += image(m)
+  fig.refresh()
+
+@main def fBmSim() =
+  val N = 1024
+  val H = 0.9
+  val sd = DenseMatrix.tabulate(N, N) { (j, k) =>
+    if (j * j + k * k < 9) 0.0
+    else
+      math.pow(j * j + k * k, -(H + 1) / 2)
+  }
+  val M = sd map (s => Gaussian(0.0, s).draw())
+  val m = dct2(M, true)
+  javax.imageio.ImageIO.write(dm2bi(m), "png", new java.io.File("fBm.png"))
+  showImage(m)
 
 // eof
-

examples/src/main/scala/IrisPca.scala

@@ -9,64 +9,76 @@ import scalaglm.*
 
 @main def irisPca() =
 
-    val url = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
-    val fileName = "iris.csv"
-    val imap = Map(
-      "Iris-setosa" -> 0,
-      "Iris-versicolor" -> 1,
-      "Iris-virginica" -> 2)
+  val url =
+    "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+  val fileName = "iris.csv"
+  val imap =
+    Map("Iris-setosa" -> 0, "Iris-versicolor" -> 1, "Iris-virginica" -> 2)
 
-    // download the file to disk if it hasn't been already
-    val file = new java.io.File(fileName)
-    if !file.exists then
-      val s = new java.io.PrintWriter(file)
-      val data = scala.io.Source.fromURL(url).getLines()
-      data.foreach(l => s.write(l.trim.split(',').
-        map(x => imap.getOrElse(x, x)).mkString("", ",", "\n")))
-      s.close
+  // download the file to disk if it hasn't been already
+  val file = new java.io.File(fileName)
+  if !file.exists then
+    val s = new java.io.PrintWriter(file)
+    val data = scala.io.Source.fromURL(url).getLines()
+    data.foreach(l =>
+      s.write(
+        l.trim.split(',').map(x => imap.getOrElse(x, x)).mkString("", ",", "\n")
+      )
+    )
+    s.close
 
-    // read the file from disk
-    val mat = csvread(file)
-    println("Mat Dim: " + mat.rows + " " + mat.cols)
-    val x = mat(::, 0 to 3)
-    println("X Dim: " + x.rows + " " + x.cols)
-    val clas = mat(::, 4).toDenseVector
+  // read the file from disk
+  val mat = csvread(file)
+  println("Mat Dim: " + mat.rows + " " + mat.cols)
+  val x = mat(::, 0 to 3)
+  println("X Dim: " + x.rows + " " + x.cols)
+  val clas = mat(::, 4).toDenseVector
 
-    println("PCA with built-in Breeze version (like R princomp):")
-    val pca = new PCA(x, covmat(x))
-    println("Loadings:")
-    println(pca.loadings)
-    println("Stdev:")
-    println(pca.sdev)
-    println(pca.scores(0 to 5, ::))
+  println("PCA with built-in Breeze version (like R princomp):")
+  val pca = new PCA(x, covmat(x))
+  println("Loadings:")
+  println(pca.loadings)
+  println("Stdev:")
+  println(pca.sdev)
+  println(pca.scores(0 to 5, ::))
 
-    println("Now my version (like R prcomp):")
-    val myPca = Pca(x, List("S-L","S-W","P-L","P-W"))
-    println(myPca.loadings) // loadings transposed
-    println(myPca.sdev)
-    myPca.summary
-    println("Scores:")
-    println(myPca.scores(0 to 5, ::))
-    myPca.plots.saveas("IrisPcaDiag.png")
+  println("Now my version (like R prcomp):")
+  val myPca = Pca(x, List("S-L", "S-W", "P-L", "P-W"))
+  println(myPca.loadings) // loadings transposed
+  println(myPca.sdev)
+  myPca.summary
+  println("Scores:")
+  println(myPca.scores(0 to 5, ::))
+  myPca.plots.saveas("IrisPcaDiag.png")
 
-    // scatter plot first 2 principal components
-    import breeze.plot.*
-    val fig = Figure("PCA")
-    val p = fig.subplot(0)
-    val ind0 = (0 until x.rows) filter (i => clas(i) == 0)
-    p += plot(myPca.scores(ind0, 0).toDenseVector,
-      myPca.scores(ind0, 1).toDenseVector, '.', colorcode = "blue")
-    val ind1 = (0 until x.rows) filter (i => clas(i) == 1)
-    p += plot(myPca.scores(ind1, 0).toDenseVector,
-      myPca.scores(ind1, 1).toDenseVector, '.', colorcode = "red")
-    val ind2 = (0 until x.rows) filter (i => clas(i) == 2)
-    p += plot(myPca.scores(ind2, 0).toDenseVector,
-      myPca.scores(ind2, 1).toDenseVector, '.', colorcode = "green")
-    fig.saveas("IrisPca.png")
-
-    // Test without variable names
-    Pca(x).summary
+  // scatter plot first 2 principal components
+  import breeze.plot.*
+  val fig = Figure("PCA")
+  val p = fig.subplot(0)
+  val ind0 = (0 until x.rows) filter (i => clas(i) == 0)
+  p += plot(
+    myPca.scores(ind0, 0).toDenseVector,
+    myPca.scores(ind0, 1).toDenseVector,
+    '.',
+    colorcode = "blue"
+  )
+  val ind1 = (0 until x.rows) filter (i => clas(i) == 1)
+  p += plot(
+    myPca.scores(ind1, 0).toDenseVector,
+    myPca.scores(ind1, 1).toDenseVector,
+    '.',
+    colorcode = "red"
+  )
+  val ind2 = (0 until x.rows) filter (i => clas(i) == 2)
+  p += plot(
+    myPca.scores(ind2, 0).toDenseVector,
+    myPca.scores(ind2, 1).toDenseVector,
+    '.',
+    colorcode = "green"
+  )
+  fig.saveas("IrisPca.png")
 
+  // Test without variable names
+  Pca(x).summary
 
 // eof
-

examples/src/main/scala/LinearRegression.scala

@@ -10,31 +10,33 @@ import breeze.linalg.*
 
 @main def linearRegression() =
 
-    val url = "http://archive.ics.uci.edu/ml/machine-learning-databases/00291/airfoil_self_noise.dat"
-    val fileName = "self-noise.csv"
-
-    // download the file to disk if it hasn't been already
-    val file = new java.io.File(fileName)
-    if !file.exists then
-      val s = new java.io.PrintWriter(file)
-      val data = scala.io.Source.fromURL(url).getLines()
-      data.foreach(l => s.write(l.trim.split('\t').filter(_ != "").mkString("", ",", "\n")))
-      s.close
-
-    // read the file from disk
-    val mat = csvread(file)
-    println("Dim: " + mat.rows + " " + mat.cols)
-    val fig = Utils.pairs(mat, List("Freq", "Angle", "Chord", "Velo", "Thick", "Sound"))
-    val y = mat(::, 5) // response is the final column
-    val X = mat(::, 0 to 4)
-    val mod = Lm(y, X, List("Freq", "Angle", "Chord", "Velo", "Thick"))
-    mod.summary
-    mod.plots.saveas("LinearRegression.png")
-
-    // test without name list
-    Lm(y,X,false).summary
-    Lm(y,X).summary
-
+  val url =
+    "http://archive.ics.uci.edu/ml/machine-learning-databases/00291/airfoil_self_noise.dat"
+  val fileName = "self-noise.csv"
+
+  // download the file to disk if it hasn't been already
+  val file = new java.io.File(fileName)
+  if !file.exists then
+    val s = new java.io.PrintWriter(file)
+    val data = scala.io.Source.fromURL(url).getLines()
+    data.foreach(l =>
+      s.write(l.trim.split('\t').filter(_ != "").mkString("", ",", "\n"))
+    )
+    s.close
+
+  // read the file from disk
+  val mat = csvread(file)
+  println("Dim: " + mat.rows + " " + mat.cols)
+  val fig =
+    Utils.pairs(mat, List("Freq", "Angle", "Chord", "Velo", "Thick", "Sound"))
+  val y = mat(::, 5) // response is the final column
+  val X = mat(::, 0 to 4)
+  val mod = Lm(y, X, List("Freq", "Angle", "Chord", "Velo", "Thick"))
+  mod.summary
+  mod.plots.saveas("LinearRegression.png")
+
+  // test without name list
+  Lm(y, X, false).summary
+  Lm(y, X).summary
 
 // eof
-

examples/src/main/scala/LogisticRegression.scala

@@ -10,15 +10,16 @@ import breeze.linalg.*
 
 @main def logisticRegression() =
 
-  val url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00267/data_banknote_authentication.txt"
+  val url =
+    "https://archive.ics.uci.edu/ml/machine-learning-databases/00267/data_banknote_authentication.txt"
   val fileName = "banknote.csv"
 
   // download the file to disk if it hasn't been already
   val file = new java.io.File(fileName)
   if !file.exists then
     val s = new java.io.PrintWriter(file)
     val data = scala.io.Source.fromURL(url).getLines()
-    data.foreach(l => s.write(l+"\n"))
+    data.foreach(l => s.write(l + "\n"))
     s.close
 
   // read the file from disk
@@ -27,10 +28,8 @@ import breeze.linalg.*
   val y = mat(::, 4) // response is the final column
   val X = mat(::, 0 to 3)
   // println(y)
-  val mod = Glm(y, X, List("Var","Skew","Kurt","Entropy"), LogisticGlm)
+  val mod = Glm(y, X, List("Var", "Skew", "Kurt", "Entropy"), LogisticGlm)
   // println(mod.coefficients)
   mod.summary
 
-
 // eof
-

examples/src/main/scala/PolyFit.scala

@@ -3,37 +3,33 @@ PolyFit.scala
 
  */
 
-
 import breeze.linalg.*
 import breeze.numerics.*
 import breeze.stats.distributions.Gaussian
 import breeze.stats.distributions.Rand.VariableSeed.randBasis
 import scalaglm.*
 
-
 @main def polyFit() =
 
   // simulate some synthetic data
   val n = 500
   val x = linspace(2.0, 5.0, n)
-  val yt = 0.5*x + sin(x*x)
+  val yt = 0.5 * x + sin(x * x)
   val y = yt + DenseVector(Gaussian(0.0, 1.0).sample(n).toArray)
 
   // scatter plot
   import breeze.plot.*
   val fig = Figure("Synthetic data")
   val p = fig.subplot(0)
-  p += plot(x, y, '+', name="Data")
-  p += plot(x, yt, name="Truth")
+  p += plot(x, y, '+', name = "Data")
+  p += plot(x, yt, name = "Truth")
 
   // some polynomial fits
   (1 to 17 by 4).foreach(i =>
     val lm = Lm(y, Basis.poly(x, i))
-      p += plot(x, lm.fitted, name="P"+i)
+    p += plot(x, lm.fitted, name = "P" + i)
   )
   p.legend = true
   p.title = "Polynomial fits"
 
-
 // eof
-