X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fgui%2Fstructurechooser%2FTDBResultAnalyser.java;h=e817b26bc8d18cfc4bc8934f1b60a66a949b2270;hb=a6f0678764ea06034460236c7a811bbfcad682aa;hp=2408d7a2c8c248641896ca62447b20885b91d9c7;hpb=5a352aa2f3330ae269d9b70c4a7374c2518bfb2e;p=jalview.git

diff --git a/src/jalview/gui/structurechooser/TDBResultAnalyser.java b/src/jalview/gui/structurechooser/TDBResultAnalyser.java
index 2408d7a..e817b26 100644
--- a/src/jalview/gui/structurechooser/TDBResultAnalyser.java
+++ b/src/jalview/gui/structurechooser/TDBResultAnalyser.java
@@ -20,8 +20,6 @@
  */
 package jalview.gui.structurechooser;
 
-import java.util.Locale;
-
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.BitSet;
@@ -29,6 +27,7 @@ import java.util.Collection;
 import java.util.Collections;
 import java.util.Comparator;
 import java.util.List;
+import java.util.Locale;
 
 import jalview.datamodel.SequenceI;
 import jalview.fts.api.FTSData;
@@ -45,8 +44,8 @@ public class TDBResultAnalyser
    */
   private static List<String> EXP_CATEGORIES = Arrays
           .asList(new String[]
-          { "EXPERIMENTALLY DETERMINED", "DEEP-LEARNING", "TEMPLATE-BASED",
-              "AB-INITIO", "CONFORMATIONAL ENSEMBLE" });
+          { "EXPERIMENTALLY DETERMINED", "DEEP-LEARNING", "AB-INITIO",
+              "TEMPLATE-BASED", "CONFORMATIONAL ENSEMBLE" });
 
   private SequenceI seq;
 
@@ -62,6 +61,8 @@ public class TDBResultAnalyser
 
   private int idx_mqual;
 
+  private int idx_mqualtype;
+
   private int idx_resol;
 
   /**
@@ -92,6 +93,7 @@ public class TDBResultAnalyser
     idx_mprov = lastTdbRequest.getFieldIndex("Provider");
     idx_mqual = lastTdbRequest.getFieldIndex("Confidence");
     idx_resol = lastTdbRequest.getFieldIndex("Resolution");
+    idx_mqualtype = lastTdbRequest.getFieldIndex("Confidence Score Type");
   }
 
   /**
@@ -169,6 +171,8 @@ public class TDBResultAnalyser
         int o2_cat = scoreCategory((String) o2data[idx_mcat]);
         String o2_prov = ((String) o2data[idx_mprov])
                 .toUpperCase(Locale.ROOT);
+        String o1_qualtype = (String) o1data[idx_mqualtype],
+                o2_qualtype = (String) o2data[idx_mqualtype];
 
         if (o1_cat == o2_cat)
         {
@@ -178,6 +182,7 @@ public class TDBResultAnalyser
             int o2_xtent = o2_e - o2_s;
             if (o1_xtent == o2_xtent)
             {
+              // EXPERIMENTAL DATA ALWAYS TRUMPS MODELS
               if (o1_cat == scoreCategory(EXP_CATEGORIES.get(0)))
               {
                 if (o1_prov.equals(o2_prov))
@@ -209,6 +214,19 @@ public class TDBResultAnalyser
               }
               else
               {
+                // RANK ON QUALITY - DOWNRANK THOSE WITH NO QUALITY MEASURE
+                if (eitherNull(idx_mqualtype, o1data, o2data))
+                {
+                  return nonNullFirst(idx_mqualtype, o1data, o2data);
+                }
+                // ONLY COMPARE LIKE QUALITY SCORES
+                if (!o1_qualtype.equals(o2_qualtype))
+                {
+                  // prefer LDDT measure over others
+                  return "pLDDT".equals(o1_prov) ? -1
+                          : "pLDDT".equals(o2_prov) ? 1 : 0;
+                }
+                // OR NO VALUE FOR THE QUALITY
                 if (eitherNull(idx_mqual, o1data, o2data))
                 {
                   return nonNullFirst(idx_mqual, o1data, o2data);
@@ -231,6 +249,12 @@ public class TDBResultAnalyser
         }
         else
         {
+          // if both are not experimental, then favour alphafold
+          if (o2_cat > 0 && o1_cat > 0)
+          {
+            return "ALPHAFOLD DB".equals(o1_prov) ? -1
+                    : "ALPHAFOLD DB".equals(o2_prov) ? 1 : 0;
+          }
           return o2_cat - o1_cat;
         }
       }