Merge branch 'develop' into features/mchmmer

[jalview.git] / test / jalview / analysis / AAFrequencyTest.java

diff --git a/test/jalview/analysis/AAFrequencyTest.java b/test/jalview/analysis/AAFrequencyTest.java
index 86a2a0c..2b6c512 100644 (file)
--- a/test/jalview/analysis/AAFrequencyTest.java
+++ b/test/jalview/analysis/AAFrequencyTest.java
@@ -25,17 +25,27 @@ import static org.testng.AssertJUnit.assertNull;
 
 import jalview.datamodel.AlignmentAnnotation;
 import jalview.datamodel.Annotation;
+import jalview.datamodel.HiddenMarkovModel;
+import jalview.datamodel.Profile;
 import jalview.datamodel.ProfileI;
+import jalview.datamodel.Profiles;
 import jalview.datamodel.ProfilesI;
 import jalview.datamodel.Sequence;
 import jalview.datamodel.SequenceI;
 import jalview.gui.JvOptionPane;
+import jalview.io.DataSourceType;
+import jalview.io.FileParse;
+import jalview.io.HMMFile;
+
+import java.io.IOException;
+import java.net.MalformedURLException;
 
 import org.testng.annotations.BeforeClass;
 import org.testng.annotations.Test;
 
 public class AAFrequencyTest
 {
+  HiddenMarkovModel hmm;
 
   @BeforeClass(alwaysRun = true)
   public void setUpJvOptionPane()
@@ -44,6 +54,17 @@ public class AAFrequencyTest
     JvOptionPane.setMockResponse(JvOptionPane.CANCEL_OPTION);
   }
 
+  @BeforeClass(alwaysRun = true)
+  public void setUp() throws IOException, MalformedURLException
+  {
+    /*
+     * load a dna (ACGT) HMM file to a HiddenMarkovModel
+     */
+    HMMFile hmmFile = new HMMFile(new FileParse(
+            "test/jalview/io/test_MADE1_hmm.txt", DataSourceType.FILE));
+    hmm = hmmFile.getHMM();
+  }
+
   @Test(groups = { "Functional" })
   public void testCalculate_noProfile()
   {
@@ -233,43 +254,93 @@ public class AAFrequencyTest
     assertEquals("T", ann.displayCharacter);
   }
 
-  /*
   @Test(groups = { "Functional" })
-  public void testGetHMMProfileFor()
+  public void testExtractHMMProfile()
           throws MalformedURLException, IOException
   {
-  
-    HMMFile hmmFile = new HMMFile(new FileParse(
-            "test/jalview/io/test_MADE1_hmm.txt", DataSourceType.FILE));
-    hmmFile.parse();
-    HiddenMarkovModel hmm = hmmFile.getHMM();
     int[] expected = { 0, 4, 100, 'T', 71, 'C', 12, 'G', 9, 'A', 9 };
-    int[] actual = AAFrequency.getHMMProfileFor(hmm, 17, false);
+    int[] actual = AAFrequency.extractHMMProfile(hmm, 17, false, false);
     for (int i = 0; i < actual.length; i++)
     {
       if (i == 2)
       {
-        assertEquals(actual[i], expected[i], 5);
+        assertEquals(actual[i], expected[i]);
       }
       else
       {
-        assertEquals(actual[i], expected[i], 1);
+        assertEquals(actual[i], expected[i]);
       }
     }
-  
-    int[] expected2 = { 0, 4, 85, 'A', 85, 'C', 0, 'G', 0, 'T', 0 };
-    int[] actual2 = AAFrequency.getHMMProfileFor(hmm, 2, true);
-    for (int i = 0; i < actual.length; i++)
+
+    int[] expected2 = { 0, 4, 100, 'A', 85, 'C', 0, 'G', 0, 'T', 0 };
+    int[] actual2 = AAFrequency.extractHMMProfile(hmm, 2, true, false);
+    for (int i = 0; i < actual2.length; i++)
     {
       if (i == 2)
       {
-        assertEquals(actual[i], expected[i], 5);
+        assertEquals(actual[i], expected[i]);
       }
       else
       {
-        assertEquals(actual[i], expected[i], 1);
+        assertEquals(actual[i], expected[i]);
       }
     }
+
+    assertNull(AAFrequency.extractHMMProfile(null, 98978867, true, false));
   }
-  */
+
+  @Test(groups = { "Functional" })
+  public void testGetAnalogueCount()
+  {
+    /*
+     * 'T' in column 0 has emission probability 0.7859, scales to 7859
+     */
+    int count = AAFrequency.getAnalogueCount(hmm, 0, 'T', false, false);
+    assertEquals(7859, count);
+
+    /*
+     * same with 'use info height': value is multiplied by log ratio
+     * log(value / background) / log(2) = log(0.7859/0.25)/0.693
+     * = log(3.1)/0.693 = 1.145/0.693 = 1.66
+     * so value becomes 1.2987 and scales up to 12987
+     */
+    count = AAFrequency.getAnalogueCount(hmm, 0, 'T', false, true);
+    assertEquals(12987, count);
+
+    /*
+     * 'G' in column 20 has emission probability 0.75457, scales to 7546
+     */
+    count = AAFrequency.getAnalogueCount(hmm, 20, 'G', false, false);
+    assertEquals(7546, count);
+
+    /*
+     * 'G' in column 1077 has emission probability 0.0533, here
+     * ignored (set to 0) since below background of 0.25
+     */
+    count = AAFrequency.getAnalogueCount(hmm, 1077, 'G', true, false);
+    assertEquals(0, count);
+  }
+
+  @Test(groups = { "Functional" })
+  public void testCompleteInformation()
+  {
+    ProfileI prof1 = new Profile(1, 0, 100, "A");
+    ProfileI prof2 = new Profile(1, 0, 100, "-");
+
+    ProfilesI profs = new Profiles(new ProfileI[] { prof1, prof2 });
+    Annotation ann1 = new Annotation(6.5f);
+    Annotation ann2 = new Annotation(0f);
+    Annotation[] annots = new Annotation[] { ann1, ann2 };
+    SequenceI seq = new Sequence("", "AA", 0, 0);
+    seq.setHMM(hmm);
+    AlignmentAnnotation annot = new AlignmentAnnotation("", "", annots);
+    annot.setSequenceRef(seq);
+    AAFrequency.completeInformation(annot, profs, 0, 1);
+    float ic = annot.annotations[0].value;
+    assertEquals(0.91532f, ic, 0.0001f);
+    ic = annot.annotations[1].value;
+    assertEquals(0f, ic, 0.0001f);
+    int i = 0;
+  }
+
 }