Classifier

cdm/core/src/main/java/ucar/nc2/constants/CDM.java

@@ -58,6 +58,7 @@ public class CDM {
   public static final String RUNTIME_COORDINATE = "runtimeCoordinate";
   public static final String STANDARDIZE = "standardize";
   public static final String NORMALIZE = "normalize";
+  public static final String CLASSIFY = "classify";
 
   // Special attributes
 

cdm/core/src/main/java/ucar/nc2/dataset/NetcdfDataset.java

@@ -125,11 +125,16 @@ public enum Enhance {
      * If the enhanced data type is not {@code FLOAT} or {@code DOUBLE}, this has no effect.
      */
     ApplyNormalizer,
+    /**
+     * Classify doubles or floats based on positive/negative into 1 or 0 {@code}
+     * x<0 --> 0 x>0 --> 1
+     */
+    ApplyClassifier,
   }
 
-  private static Set<Enhance> EnhanceAll =
-      Collections.unmodifiableSet(EnumSet.of(Enhance.ConvertEnums, Enhance.ConvertUnsigned, Enhance.ApplyScaleOffset,
-          Enhance.ConvertMissing, Enhance.CoordSystems, Enhance.ApplyStandardizer, Enhance.ApplyNormalizer));
+  private static Set<Enhance> EnhanceAll = Collections.unmodifiableSet(
+      EnumSet.of(Enhance.ConvertEnums, Enhance.ConvertUnsigned, Enhance.ApplyScaleOffset, Enhance.ConvertMissing,
+          Enhance.CoordSystems, Enhance.ApplyStandardizer, Enhance.ApplyNormalizer, Enhance.ApplyClassifier));
   private static Set<Enhance> EnhanceNone = Collections.unmodifiableSet(EnumSet.noneOf(Enhance.class));
   private static Set<Enhance> defaultEnhanceMode = EnhanceAll;
 

cdm/core/src/main/java/ucar/nc2/dataset/VariableDS.java

@@ -289,6 +289,9 @@ Array convert(Array data, Set<NetcdfDataset.Enhance> enhancements) {
       if (enhancements.contains(Enhance.ApplyNormalizer) && normalizer != null) {
         toApply.add(normalizer);
       }
+      if (enhancements.contains(Enhance.ApplyClassifier) && classifier != null) {
+        toApply.add(classifier);
+      }
 
       double[] dataArray = (double[]) data.get1DJavaArray(DataType.DOUBLE);
 
@@ -865,6 +868,7 @@ public Array convert(Array in, boolean convertUnsigned, boolean applyScaleOffset
   private ScaleOffset scaleOffset;
   private Standardizer standardizer;
   private Normalizer normalizer;
+  private Classifier classifier;
   private ConvertMissing convertMissing;
   private Set<Enhance> enhanceMode = EnumSet.noneOf(Enhance.class); // The set of enhancements that were made.
 
@@ -939,6 +943,10 @@ private void createEnhancements() {
     if (normalizerAtt != null && this.enhanceMode.contains(Enhance.ApplyNormalizer) && dataType.isFloatingPoint()) {
       this.normalizer = Normalizer.createFromVariable(this);
     }
+    Attribute classifierAtt = findAttribute(CDM.CLASSIFY);
+    if (classifierAtt != null && this.enhanceMode.contains(Enhance.ApplyClassifier) && dataType.isNumeric()) {
+      this.classifier = Classifier.createFromVariable(this);
+    }
   }
 
   public Builder<?> toBuilder() {

cdm/core/src/main/java/ucar/nc2/filter/Classifier.java

@@ -0,0 +1,70 @@
+package ucar.nc2.filter;
+
+import java.io.IOException;
+import ucar.ma2.Array;
+import ucar.ma2.DataType;
+import ucar.ma2.IndexIterator;
+import ucar.nc2.dataset.VariableDS;
+
+public class Classifier implements Enhancement {
+  private Classifier classifier = null;
+  private static Classifier emptyClassifier;
+  private int classifiedVal;
+  private int[] classifiedArray;
+
+  public static Classifier createFromVariable(VariableDS var) {
+    try {
+      Array arr = var.read();
+      // DataType type = var.getDataType();
+      return emptyClassifier();
+    } catch (IOException e) {
+      return emptyClassifier();
+    }
+  }
+
+  public static Classifier emptyClassifier() {
+    emptyClassifier = new Classifier();
+    return emptyClassifier;
+  }
+
+  /** Enough of a constructor */
+  public Classifier() {}
+
+  /** Classify double array */
+  public int[] classifyDoubleArray(Array arr) {
+    int[] classifiedArray = new int[(int) arr.getSize()];
+    int i = 0;
+    IndexIterator iterArr = arr.getIndexIterator();
+    while (iterArr.hasNext()) {
+      Number value = (Number) iterArr.getObjectNext();
+      if (!Double.isNaN(value.doubleValue())) {
+
+        classifiedArray[i] = classifyArray(value.doubleValue());
+      }
+      i++;
+    }
+    return classifiedArray;
+  }
+
+
+
+  /** for a single double */
+  public int classifyArray(double val) {
+    if (val >= 0) {
+      classifiedVal = 1;
+    } else {
+      classifiedVal = 0;
+    }
+
+    return classifiedVal;
+  }
+
+  @Override
+  public double convert(double val) {
+    return emptyClassifier.classifyArray(val);
+  }
+
+
+}
+
+

cdm/core/src/test/data/ncml/enhance/testClassifier.ncml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Copyright (c) 1998-2023 University Corporation for Atmospheric Research/Unidata
+  ~  See LICENSE for license information.
+  -->
+
+<netcdf xmlns="http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2" enhance="all">
+
+    <variable name="doublePositives" shape="5" type="double">
+        <attribute name="classify"/>
+        <values>1.0 2.0 3.0 4.0 5.0</values>
+    </variable>
+
+    <variable name="doubleNegatives" shape="5" type="double">
+            <attribute name="classify"/>
+            <values>-1.0 -2.0 -3.0 -4.0 -5.0</values>
+    </variable>
+
+    <variable name="doubleMix" shape="5" type="double">
+        <attribute name="classify"/>
+        <values>1.0 -2.0 0.0 4.0 -5.0</values>
+    </variable>
+
+    <variable name="floatPositives" shape="5" type="float">
+        <attribute name="classify"/>
+        <values>1.0 2.0 3.0 4.0 5.0</values>
+    </variable>
+
+     <variable name="floatNegatives" shape="5" type="float">
+         <attribute name="classify"/>
+         <values>-1.0 -2.0 -3.0 -4.0 -5.0</values>
+     </variable>
+         <variable name="floatMix" shape="5" type="float">
+             <attribute name="classify"/>
+             <values>1.0 -2.0 0.0 4.0 -5.0</values>
+         </variable>
+
+    <variable name="intPositives" shape="5" type="int">
+        <attribute name="classify"/>
+        <values>1 2 3 4 5</values>
+    </variable>
+
+         <variable name="intNegatives" shape="5" type="int">
+             <attribute name="classify"/>
+             <values>-1.0 -2.0 -3.0 -4.0 -5.0</values>
+         </variable>
+             <variable name="intMix" shape="5" type="int">
+                 <attribute name="classify"/>
+                 <values>1.0 -2.0 0.0 4.0 -5.0</values>
+             </variable>
+
+
+
+</netcdf>

cdm/core/src/test/java/ucar/nc2/filter/TestClassifier.java

@@ -0,0 +1,51 @@
+package ucar.nc2.filter;
+
+import static org.junit.Assert.*;
+import org.junit.Test;
+import ucar.ma2.Array;
+
+
+public class TestClassifier {
+
+
+
+  /** test doubles */
+  @Test
+  public void testClassifyDoubleArray_AllPositive() {
+    Classifier classifier = new Classifier();
+    double[] input = {1.1, 2.2, 3.3};
+    int[] expected = {1, 1, 1};
+    Array DATA = Array.makeFromJavaArray(input);
+    assertArrayEquals(expected, classifier.classifyDoubleArray(DATA));
+  }
+
+  @Test
+  public void testClassifyDoubleArray_AllNegative() {
+    Classifier classifier = new Classifier();
+    double[] input = {-1.1, -2.2, -3.3};
+    int[] expected = {0, 0, 0};
+    Array DATA = Array.makeFromJavaArray(input);
+    assertArrayEquals(expected, classifier.classifyDoubleArray(DATA));
+  }
+
+  @Test
+  public void testClassifyDoubleArray_Mixed() {
+    Classifier classifier = new Classifier();
+    double[] input = {-1.1, 2.2, -3.3, 4.4};
+    int[] expected = {0, 1, 0, 1};
+    Array DATA = Array.makeFromJavaArray(input);
+    assertArrayEquals(expected, classifier.classifyDoubleArray(DATA));
+  }
+
+  @Test
+  public void testClassifyDoubleArray_WithZero() {
+    Classifier classifier = new Classifier();
+    double[] input = {0.0, -1.1, 1.1, 0.0, 0.0, 0.0};
+    int[] expected = {1, 0, 1, 1, 1, 1};
+    Array DATA = Array.makeFromJavaArray(input);
+    assertArrayEquals(expected, classifier.classifyDoubleArray(DATA));
+  }
+
+
+
+}

cdm/core/src/test/java/ucar/nc2/ncml/TestEnhanceClassifier.java

@@ -0,0 +1,115 @@
+package ucar.nc2.ncml;
+
+import static com.google.common.truth.Truth.assertThat;
+import static ucar.ma2.MAMath.nearlyEquals;
+
+import java.io.IOException;
+import org.junit.Test;
+import ucar.ma2.Array;
+import ucar.ma2.DataType;
+import ucar.nc2.NetcdfFile;
+import ucar.nc2.Variable;
+import ucar.nc2.dataset.NetcdfDatasets;
+import ucar.unidata.util.test.TestDir;
+
+public class TestEnhanceClassifier {
+
+  private static String dataDir = TestDir.cdmLocalTestDataDir + "ncml/enhance/";
+
+  public static final int[] all_ones = {1, 1, 1, 1, 1};
+  public static final Array DATA_all_ones = Array.makeFromJavaArray(all_ones);
+  public static final int[] all_zeroes = {0, 0, 0, 0, 0};
+  public static final Array DATA_all_zeroes = Array.makeFromJavaArray(all_zeroes);
+  public static final int[] mixNumbers = {1, 0, 1, 1, 0};
+  public static final Array DATA_mixNumbers = Array.makeFromJavaArray(mixNumbers);
+
+
+  /** test on doubles, all positives, all negatives and a mixed array */
+  @Test
+  public void testEnhanceClassifier_doubles() throws IOException {
+    try (NetcdfFile ncfile = NetcdfDatasets.openDataset(dataDir + "testClassifier.ncml", true, null)) {
+      Variable doublePositives = ncfile.findVariable("doublePositives");
+      assertThat((Object) doublePositives).isNotNull();
+      assertThat(doublePositives.getDataType()).isEqualTo(DataType.DOUBLE);
+      assertThat(doublePositives.attributes().hasAttribute("classify")).isTrue();
+      Array dataDoubles = doublePositives.read();
+      assertThat(nearlyEquals(dataDoubles, DATA_all_ones)).isTrue();
+
+      Variable doubleNegatives = ncfile.findVariable("doubleNegatives");
+      assertThat((Object) doubleNegatives).isNotNull();
+      assertThat(doubleNegatives.getDataType()).isEqualTo(DataType.DOUBLE);
+      assertThat(doubleNegatives.attributes().hasAttribute("classify")).isTrue();
+      Array datadoubleNegatives = doubleNegatives.read();
+      assertThat(nearlyEquals(datadoubleNegatives, DATA_all_zeroes)).isTrue();
+
+      Variable doubleMix = ncfile.findVariable("doubleMix");
+      assertThat((Object) doubleMix).isNotNull();
+      assertThat(doubleMix.getDataType()).isEqualTo(DataType.DOUBLE);
+      assertThat(doubleMix.attributes().hasAttribute("classify")).isTrue();
+      Array datadoubleMix = doubleMix.read();
+      assertThat(nearlyEquals(datadoubleMix, DATA_mixNumbers)).isTrue();
+
+    }
+
+
+  }
+
+  /** test on floats, all positives, all negatives and a mixed array */
+  @Test
+  public void testEnhanceClassifier_floats() throws IOException {
+    try (NetcdfFile ncfile = NetcdfDatasets.openDataset(dataDir + "testClassifier.ncml", true, null)) {
+
+      Variable floatPositives = ncfile.findVariable("floatPositives");
+      assertThat((Object) floatPositives).isNotNull();
+      assertThat(floatPositives.getDataType()).isEqualTo(DataType.FLOAT);
+      assertThat(floatPositives.attributes().hasAttribute("classify")).isTrue();
+      Array datafloats = floatPositives.read();
+      assertThat(nearlyEquals(datafloats, DATA_all_ones)).isTrue();
+
+      Variable floatNegatives = ncfile.findVariable("floatNegatives");
+      assertThat((Object) floatNegatives).isNotNull();
+      assertThat(floatNegatives.getDataType()).isEqualTo(DataType.FLOAT);
+      assertThat(floatNegatives.attributes().hasAttribute("classify")).isTrue();
+      Array datafloatNegatives = floatNegatives.read();
+      assertThat(nearlyEquals(datafloatNegatives, DATA_all_zeroes)).isTrue();
+
+      Variable floatMix = ncfile.findVariable("floatMix");
+      assertThat((Object) floatMix).isNotNull();
+      assertThat(floatMix.getDataType()).isEqualTo(DataType.FLOAT);
+      assertThat(floatMix.attributes().hasAttribute("classify")).isTrue();
+      Array datafloatsMix = floatMix.read();
+      assertThat(nearlyEquals(datafloatsMix, DATA_mixNumbers)).isTrue();
+
+    }
+
+  }
+
+  /** enhance is not applied to Integers, so we expect the same values after application */
+  @Test
+  public void testEnhanceClassifier_integers() throws IOException {
+
+    try (NetcdfFile ncfile = NetcdfDatasets.openDataset(dataDir + "testClassifier.ncml", true, null)) {
+      Variable IntegerPositives = ncfile.findVariable("intPositives");
+      assertThat((Object) IntegerPositives).isNotNull();
+      assertThat(IntegerPositives.getDataType()).isEqualTo(DataType.INT);
+      assertThat(IntegerPositives.attributes().hasAttribute("classify")).isTrue();
+      Array dataIntegers = IntegerPositives.read();
+      assertThat(nearlyEquals(dataIntegers, DATA_all_ones)).isTrue();
+
+      Variable intNegatives = ncfile.findVariable("intNegatives");
+      assertThat((Object) intNegatives).isNotNull();
+      assertThat(intNegatives.getDataType()).isEqualTo(DataType.INT);
+      assertThat(intNegatives.attributes().hasAttribute("classify")).isTrue();
+      Array dataintNegatives = intNegatives.read();
+      assertThat(nearlyEquals(dataintNegatives, DATA_all_zeroes)).isTrue();
+
+      Variable intMix = ncfile.findVariable("intMix");
+      assertThat((Object) intMix).isNotNull();
+      assertThat(intMix.getDataType()).isEqualTo(DataType.INT);
+      assertThat(intMix.attributes().hasAttribute("classify")).isTrue();
+      Array dataintMix = intMix.read();
+      assertThat(nearlyEquals(dataintMix, DATA_mixNumbers)).isTrue();
+    }
+
+  }
+}