minor fixes

Author: qnt-dmitry

src/main/scala/ai/quantnet/bz/DataFrame.scala

@@ -160,13 +160,13 @@ class DataFrame[R, C, @specialized(Double, Int, Float, Long) V]
     : DataFrame[R, C, V] = iloc(colIdx.locRange(start, end, step, keepStart, keepEnd, round).slices)
 
 
-    def iget(ri: Int): Series[R, V] = {
+    def iget(ci: Int): Series[R, V] = {
       var d = data match {
-        case d: DenseMatrix[V] => d.apply(ri, ::)
-        case d: SliceMatrix[Int, Int, V] => d.apply(ri, ::)
+        case d: DenseMatrix[V] => d.apply(::, ci)
+        case d: SliceMatrix[Int, Int, V] => d.apply(::, ci)
         case _ => ???
       }
-      Series(rowIdx, d.t)
+      Series(rowIdx, d)
     }
 
     def get(c: C): Series[R, V] = {

src/main/scala/ai/quantnet/netcdf.scala

@@ -125,7 +125,7 @@ object netcdf {
     val assetArray = assetRawArray.map(i => new String(i.filter(c => c != '\u0000')))
 
     // C-order of dimensions: time,asset
-    val varName = vars.keys.filter(_!="time").filter(_!="field")/*.filter(_!="asset")*/.toArray.apply(0)
+    val varName = vars.keys.filter(vars(_).getDataType == DataType.DOUBLE).toArray.apply(0)
     val values = dataNetcdf.readSection(varName).copyTo1DJavaArray().asInstanceOf[Array[Double]]
 
     val timeIdx = DataIndexVector[LocalDate](timeArray)
@@ -164,7 +164,7 @@ object netcdf {
     val assetArray = assetRawArray.map(i => intern(new String(i.filter(c => c != '\u0000'))))
 
     // C-order of dimensions: field,time,asset
-    val varName = vars.keys.filter(_!="time").filter(_!="field").filter(_!="asset").toArray.apply(0)
+    val varName = vars.keys.filter(vars(_).getDataType == DataType.DOUBLE).filter(_!="time").toArray.apply(0)
     val values = dataNetcdf.readSection(varName).copyTo1DJavaArray().asInstanceOf[Array[Double]]
 
     val timeIdx = DataIndexVector.apply[LocalDate](timeArray)

src/test/scala/ai/quantnet/StatTest.scala

@@ -30,6 +30,8 @@ class StatTest extends FunSuite {
 
     val s = stats.calcStats(dataSet, output)
 
+    val srr = s.colOps.get(stats.fields.relativeReturn)
+
     println(s.toString(headRows = 20))
 
     //println(stats.calcCorrelation(rr))