fix pr comments

Unidata · Jun 25, 2024 · ece1779 · ece1779
1 parent a47ebc5
commit ece1779
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Showing 3 changed files with 27 additions and 30 deletions.
diff --git a/cdm/core/src/main/java/ucar/nc2/filter/Classifier.java b/cdm/core/src/main/java/ucar/nc2/filter/Classifier.java
@@ -1,47 +1,45 @@
 package ucar.nc2.filter;
 
-import java.io.IOException;
 import ucar.ma2.Array;
 import ucar.ma2.IndexIterator;
-import ucar.nc2.NetcdfFile;
 import ucar.nc2.Variable;
-import ucar.nc2.dataset.NetcdfDatasets;
-import ucar.nc2.dataset.VariableDS;
+import ucar.nc2.constants.CDM;
 import ucar.nc2.Attribute;
 import java.util.ArrayList;
 import java.util.List;
-import java.util.Arrays;
+
 
 public class Classifier implements Enhancement {
-  private Classifier classifier = null;
-  private static Classifier emptyClassifier;
   private int classifiedVal;
   private List<Attribute> AttCat;
   private List<int[]> rules = new ArrayList<>();
 
-  public static Classifier createFromVariable(VariableDS var) {
-    try {
-      Array arr = var.read();
-      return emptyClassifier();
-    } catch (IOException e) {
-      return emptyClassifier();
-    }
-  }
-
-  public static Classifier emptyClassifier() {
-    emptyClassifier = new Classifier();
-    return emptyClassifier;
+  // Constructor with no arguments
+  public Classifier() {
+    this.AttCat = new ArrayList<>();
+    this.rules = new ArrayList<>();
   }
 
-  /** Enough of a constructor */
-  public Classifier() {}
-
+  // Constructor with attributes
   public Classifier(List<Attribute> AttCat) {
     this.AttCat = AttCat;
     this.rules = loadClassificationRules();
+  }
+
+  // Factory method to create a Classifier from a Variable
+  public static Classifier createFromVariable(Variable var) {
+
+    List<Attribute> attributes = var.getAttributes();
+
+    if (var.attributes().hasAttribute(CDM.RANGE_CAT)) {
+      return new Classifier(attributes);
+    } else {
+      return new Classifier();
+    }
 
   }
 
+
   public int[] classifyWithAttributes(Array arr) {
     int[] classifiedArray = new int[(int) arr.getSize()];
     IndexIterator iterArr = arr.getIndexIterator();
@@ -95,7 +93,7 @@ public int classifyArrayAttribute(double val) {
   }
 
   // Method to load classification rules from the attributes
-  public List<int[]> loadClassificationRules() {
+  private List<int[]> loadClassificationRules() {
     for (Attribute attribute : this.AttCat) {
       int[] rule = stringToIntArray(attribute.getStringValue());
       this.rules.add(rule);
@@ -105,7 +103,7 @@ public List<int[]> loadClassificationRules() {
 
   @Override
   public double convert(double val) {
-    return emptyClassifier.classifyArray(val);
+    return classifyArray(val);
   }
 
   public static int[] stringToIntArray(String str) {

diff --git a/cdm/core/src/test/data/ncml/enhance/testAddToClassifier.ncml b/cdm/core/src/test/data/ncml/enhance/testAddToClassifier.ncml
@@ -8,10 +8,10 @@
 <netcdf xmlns="http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2" enhance="all">
 
     <variable name="class_specs" shape="17" type="double">
-        <attribute name="Cat1" type="string"  value="NaN 0 0" />
-        <attribute name="Cat2" type="string"  value="0 10 10" />
-        <attribute name="Cat3" type="string"  value="10 100 100" />
-        <attribute name="Cat4" type="string"  value="100 NaN 1000" />
+        <attribute name="range_cat" type="string"  value="NaN 0 0" />
+        <attribute name="range_cat1" type="string"  value="0 10 10" />
+        <attribute name="range_cat2" type="string"  value="10 100 100" />
+        <attribute name="range_cat3" type="string"  value="100 NaN 1000" />
         <values>-500000 NaN -10 0 1 2 3 11 25 29 NaN 100 150 121 102 199999 12211</values>
     </variable>
 

diff --git a/cdm/core/src/test/java/ucar/nc2/ncml/TestEnhanceClassifier.java b/cdm/core/src/test/java/ucar/nc2/ncml/TestEnhanceClassifier.java
@@ -127,8 +127,7 @@ public void testEnhanceClassifier_classification() throws IOException {
       assertThat((Object) Classify_Specsx).isNotNull();
       assertThat(!Classify_Specsx.attributes().isEmpty()).isTrue();
       Array Data = Classify_Specsx.read();
-      List<Attribute> Whatever = Classify_Specsx.getAttributes();
-      Classifier classifier = new Classifier(Whatever);
+      Classifier classifier = Classifier.createFromVariable(Classify_Specsx);
       int[] ClassifiedArray = classifier.classifyWithAttributes(Data);
       assertThat(nearlyEquals(Array.makeFromJavaArray(ClassifiedArray), CLASSIFICATION_TEST)).isTrue();