Update tests to prefer use of rf_agg_approx_quantiles implementation

Author: vpipkt

core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala

@@ -89,7 +89,7 @@ case class CellHistogram(bins: Seq[CellHistogram.Bin]) {
 
   // derived from locationtech/geotrellis/.../StreamingHistogram.scala
 
-  def percentileBreaks(qs: Seq[Double]): Seq[Double] = {
+  private def percentileBreaks(qs: Seq[Double]): Seq[Double] = {
     if(bins.size == 1) {
       qs.map(z => bins.head.value)
     } else {

core/src/test/scala/org/locationtech/rasterframes/RasterFramesStatsSpec.scala

@@ -0,0 +1,76 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2018 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
+
+import org.apache.spark.sql.functions.{col, explode}
+
+class RasterFramesStatsSpec extends TestEnvironment with TestData {
+
+  import spark.implicits._
+
+  val df = TestData.sampleGeoTiff
+    .toDF()
+    .withColumn("tilePlus2", rf_local_add(col("tile"), 2))
+
+
+  describe("Tile quantiles through built-in functions") {
+
+    it("should compute approx percentiles for a single tile col") {
+      // Use "explode"
+      val result = df
+        .select(rf_explode_tiles($"tile"))
+        .stat
+        .approxQuantile("tile", Array(0.10, 0.50, 0.90), 0.00001)
+
+      result.length should be(3)
+
+      // computing externally with numpy we arrive at 7963, 10068, 12160 for these quantiles
+      result should contain inOrderOnly(7963.0, 10068.0, 12160.0)
+
+      // Use "to_array" and built-in explode
+      val result2 = df
+        .select(explode(rf_tile_to_array_double($"tile")) as "tile")
+        .stat
+        .approxQuantile("tile", Array(0.10, 0.50, 0.90), 0.00001)
+
+      result2.length should be(3)
+
+      // computing externally with numpy we arrive at 7963, 10068, 12160 for these quantiles
+      result2 should contain inOrderOnly(7963.0, 10068.0, 12160.0)
+
+    }
+  }
+
+  describe("Tile quantiles through custom aggregate") {
+    it("should compute approx percentiles for a single tile col") {
+      val result = df
+        .select(rf_agg_approx_quantiles($"tile", Seq(0.1, 0.5, 0.9)))
+        .first()
+
+      result.length should be(3)
+
+      // computing externally with numpy we arrive at 7963, 10068, 12160 for these quantiles
+      result should contain inOrderOnly(7963.0, 10068.0, 12160.0)
+    }
+  }
+}
+