Merge pull request #101 from NeuroML/feat/99-treat-all-normal

Feat/99 treat all normal - test in experimental branch
NeuroML · May 26, 2023 · b5bf3d6 · b5bf3d6
2 parents 179d6dd + 8a36b63
commit b5bf3d6
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Showing 3 changed files with 77 additions and 72 deletions.
diff --git a/pom.xml b/pom.xml
@@ -4,7 +4,7 @@
     <groupId>org.neuroml.export</groupId>
     <artifactId>org.neuroml.export</artifactId>
     <packaging>bundle</packaging>
-    <version>1.9.0</version>
+    <version>1.9.1</version>
     <name>org.neuroml.export</name>
     <url>http://maven.apache.org</url>
 

diff --git a/src/main/java/org/neuroml/export/neuron/JSONCellSerializer.java b/src/main/java/org/neuroml/export/neuron/JSONCellSerializer.java
@@ -297,92 +297,97 @@ else if(fract == 0 && segsPar.get(0) == parentId)
             g.writeEndArray();
 
             g.writeArrayFieldStart("groups");
-            boolean foundAll = false;
+            /* Convert all segment groups.
+             * Note that the "nrn_all" segment group/section list id is reserved.
+             * We use it to keep a list of all segments/sections
+             */
             for(SegmentGroup grp : morph.getSegmentGroup())
             {
-                if(!grp.getId().equals("all"))    // I'll calculate this here...
+                /* Ask the user not to use nrn_all as a segment group id */
+                if(grp.getId().equals("nrn_all"))
                 {
-                    if(!(foundNeuroLexFlags && CellUtils.isUnbranchedNonOverlapping(grp)))
-                    {
-                        g.writeStartObject();
-                        g.writeStringField("name", grp.getId());
+                    throw new NeuroMLException("A segment group named \"nrn_all\" was found. Please use another id for the segment group. The \"nrn_all\" group is reserved");
+                }
+                if(!(foundNeuroLexFlags && CellUtils.isUnbranchedNonOverlapping(grp)))
+                {
+                    g.writeStartObject();
+                    g.writeStringField("name", grp.getId());
 
-                            if(!grp.getMember().isEmpty())
+                    if(!grp.getMember().isEmpty())
+                    {
+                        g.writeArrayFieldStart("segments");
+                        for(Member m : grp.getMember())
+                        {
+                            g.writeString(idsVsNames.get(m.getSegment()));
+                        }
+                        g.writeEndArray();
+                    }
+                    if(!grp.getInclude().isEmpty())
+                    {
+                        g.writeArrayFieldStart("groups");
+                        for(org.neuroml.model.Include inc : grp.getInclude())
+                        {
+                            boolean isSection = CellUtils.isUnbranchedNonOverlapping(namesVsSegmentGroups.get(inc.getSegmentGroup()));
+                            if(!isSection)
                             {
-                                g.writeArrayFieldStart("segments");
-                                for(Member m : grp.getMember())
-                                {
-                                    g.writeString(idsVsNames.get(m.getSegment()));
-                                }
-                                g.writeEndArray();
+                                g.writeString(inc.getSegmentGroup());
                             }
-                            if(!grp.getInclude().isEmpty())
+                        }
+                        g.writeEndArray();
+                        g.writeArrayFieldStart("sections");
+                        for(org.neuroml.model.Include inc : grp.getInclude())
+                        {
+                            boolean isSection = CellUtils.isUnbranchedNonOverlapping(namesVsSegmentGroups.get(inc.getSegmentGroup()));
+                            if(isSection)
                             {
-                                g.writeArrayFieldStart("groups");
-                                for(org.neuroml.model.Include inc : grp.getInclude())
-                                {
-                                    boolean isSection = CellUtils.isUnbranchedNonOverlapping(namesVsSegmentGroups.get(inc.getSegmentGroup()));
-                                    if(!isSection)
-                                    {
-                                        g.writeString(inc.getSegmentGroup());
-                                    }
-                                }
-                                g.writeEndArray();
-                                g.writeArrayFieldStart("sections");
-                                for(org.neuroml.model.Include inc : grp.getInclude())
-                                {
-                                    boolean isSection = CellUtils.isUnbranchedNonOverlapping(namesVsSegmentGroups.get(inc.getSegmentGroup()));
-                                    if(isSection)
-                                    {
-                                        g.writeString(inc.getSegmentGroup());
-                                    }
-                                }
-                                g.writeEndArray();
+                                g.writeString(inc.getSegmentGroup());
+                            }
+                        }
+                        g.writeEndArray();
+                    }
+                    // System.out.println("+++ " +grp.getInhomogeneousParameter());
+                    // System.out.println("--  " +ChannelDensity.class.getSimpleName());
+                    if(!grp.getInhomogeneousParameter().isEmpty())
+                    {
+                        g.writeArrayFieldStart("inhomogeneousParameters");
+                        for(InhomogeneousParameter ih : grp.getInhomogeneousParameter())
+                        {
+                            g.writeStartObject();
+                            g.writeStringField("id", ih.getId());
+                            g.writeStringField("variable", ih.getVariable());
+                            inhomogeneousParametersVsVariables.put(ih.getId(), ih.getVariable());
+                            g.writeStringField("metric", ih.getMetric().value());
+                            if(ih.getProximal() != null)
+                            {
+                                g.writeStringField("proximalTranslationStart", ih.getProximal().getTranslationStart() + "");
                             }
-                            // System.out.println("+++ " +grp.getInhomogeneousParameter());
-                            // System.out.println("--  " +ChannelDensity.class.getSimpleName());
-                            if(!grp.getInhomogeneousParameter().isEmpty())
+                            if(ih.getDistal() != null)
                             {
-                                g.writeArrayFieldStart("inhomogeneousParameters");
-                                for(InhomogeneousParameter ih : grp.getInhomogeneousParameter())
-                                {
-                                    g.writeStartObject();
-                                    g.writeStringField("id", ih.getId());
-                                    g.writeStringField("variable", ih.getVariable());
-                                    inhomogeneousParametersVsVariables.put(ih.getId(), ih.getVariable());
-                                    g.writeStringField("metric", ih.getMetric().value());
-                                    if(ih.getProximal() != null)
-                                    {
-                                        g.writeStringField("proximalTranslationStart", ih.getProximal().getTranslationStart() + "");
-                                    }
-                                    if(ih.getDistal() != null)
-                                    {
-                                        g.writeStringField("distalNormalizationEnd", ih.getDistal().getNormalizationEnd() + "");
-                                    }
-                                    g.writeEndObject();
-                                }
-                                g.writeEndArray();
+                                g.writeStringField("distalNormalizationEnd", ih.getDistal().getNormalizationEnd() + "");
                             }
-
-                        g.writeEndObject();
+                            g.writeEndObject();
+                        }
+                        g.writeEndArray();
                     }
+
+                    g.writeEndObject();
                 }
             }
-            if(!foundAll)
-            {
-                g.writeStartObject();
-                g.writeStringField("name", "all");
-                g.writeArrayFieldStart("sections");
-                for(Segment seg : morph.getSegment())
-                {
-                    String name = nh.getNrnSectionName(seg);
-                    g.writeString(name);
-                }
-                g.writeEndArray();
-                g.writeEndObject();
 
+            /* Create the nrn_all section list that contains all sections */
+            g.writeStartObject();
+            g.writeStringField("name", "nrn_all");
+            g.writeArrayFieldStart("sections");
+            for(Segment seg : morph.getSegment())
+            {
+                String name = nh.getNrnSectionName(seg);
+                g.writeString(name);
             }
             g.writeEndArray();
+            g.writeEndObject();
+
+            /* groups */
+            g.writeEndArray();
 
             IntracellularProperties ip = null;
             MembraneProperties mp = null;

diff --git a/src/main/java/org/neuroml/export/utils/Utils.java b/src/main/java/org/neuroml/export/utils/Utils.java
@@ -42,7 +42,7 @@ public class Utils
 
 	private static Lems lemsWithNML2CompTypes;
 
-	public static String ORG_NEUROML_EXPORT_VERSION = "1.9.0";
+	public static String ORG_NEUROML_EXPORT_VERSION = "1.9.1";
 
 	public static final String ARCH_I686 = "i686";
 	public static final String ARCH_I386 = "i386";