Separated build process to support NetLogo version 6. Added separate demo models and test models for NetLogo 5 & 6. Updated documentation.

Author: chrfrantz

README.md

@@ -25,7 +25,13 @@ extensions [dbscan]
 
 ; Cluster agents by variable "wealth", with at least 3 members to constitute a cluster, and a maximum value difference of 3
 let clusters dbscan:cluster-by-variable agents "wealth" 3 3
+```
+
+You can then use the cluster information to modify the output, e.g. by colouring and labelling the turtles.
 
+Example (NetLogo 5):
+
+```
 ; Colour and label the agents by cluster
 let ctr 1
 (foreach clusters (n-of (length clusters) base-colors)
@@ -38,6 +44,21 @@ let ctr 1
     set ctr (ctr + 1) ])
 ```
 
+The same example in NetLogo 6:
+
+```
+; Colour and label the agents by cluster
+let ctr 1
+  (foreach clusters (n-of (length clusters) base-colors)
+    [ [ x y ] -> let aset turtles with [ member? self x ]
+      ask aset
+        [ set color y
+          set label (word "ID: " who ", Cluster: " ctr ", Wealth: " wealth) ]
+      ; Print agent sets
+      output-print (word "Cluster " ctr ": " aset)
+      set ctr (ctr + 1) ])
+```
+
 ### Clustering by location
 
 Syntax: `cluster-by-location` *agentset-to-be-clustered* *minimum-members* *maximum-distance*
@@ -56,35 +77,36 @@ extensions [dbscan]
 
 ; Cluster agents by location, with at least 3 members to constitute a cluster, and a maximum distance of 3
 let clusters dbscan:cluster-by-location agents 3 3
-
-; Colour and label individual agents by cluster
-let ctr 1
-(foreach clusters (n-of (length clusters) base-colors)
-  [ let aset turtles with [member? self ?1 ]
-    ask aset
-      [ set color ?2
-        set label (word "ID: " who ", Cluster: " ctr) ]
-    ; Print agent sets
-    output-print (word "Cluster " ctr ": " aset)
-    set ctr (ctr + 1) ])
 ```
 
+The clusters can then be used as shown in the previous section.
+
 ## Demo
 
-For more comprehensive examples for both reporters, try out the demo under `demo/dbscan-clustering-demo.nlogo`
+For more comprehensive examples for both reporters, try out the demo that corresponds to your NetLogo version.
+
+* For NetLogo 5, use `demo/dbscan-clustering-demo-v5.nlogo`
+
+* For NetLogo 6, use `demo/dbscan-clustering-demo-v6.nlogo`
 
-Clustering agents by location should produce the following output.
+Clustering agents by location should produce the following output (irrespective of NetLogo version).
 
 ![Location-based clustering demo output](https://github.com/chrfrantz/NetLogo-Extension-DBSCAN/raw/master/doc/ExampleLocationBasedClusteringOutput.png)
 
 ## Deployment
 
 ### Variant 1: Downloading jar files
 
-To install the extension, download the zip file containing the latest version from the releases page. It contains a folder `dbscan` that contains all relevant jar files. Unzip it to the `NetLogo/extensions` folder of your NetLogo installation (the final structure should be `NetLogo/extensions/dbscan/<jar files>`).
+To install the extension, download the zip file containing the latest version from the releases page for your NetLogo version (5 or 6). It contains a folder `dbscan` that contains all relevant jar files. Unzip it to the `NetLogo/extensions` folder of your NetLogo installation (the final structure should correspond to `NetLogo x.x.x/app/extensions/dbscan/<jar files>`, with `x.x.x` being the installed NetLogo version).
 
 ### Variant 2: Building from source
 
-You can build the extension from scratch using maven by running `mvn package` after cloning the repository. In addition, you will need to do the same for the [DBSCAN repository](https://github.com/chrfrantz/DBSCAN.git) which contains the underlying DBSCAN algorithm. Place both jar files in the extensions subfolder `dbscan` (following the structure described under Variant 1).
+You can build the extension from scratch using maven after cloning the repository. 
+
+* For NetLogo version 5, run `mvn package -f pom-v5.xml`
+
+* For NetLogo version 6, run `mvn package -f pom-v6.xml`
+
+In addition, you will need to build the [DBSCAN repository](https://github.com/chrfrantz/DBSCAN.git) (Command: `mvn package`) which contains the underlying DBSCAN algorithm. Place both jar files in the extensions subfolder `dbscan` (following the structure described under Variant 1).
 
 

demo/dbscan-clustering-demo.nlogo demo/dbscan-clustering-demo-v5.nlogodemo/dbscan-clustering-demo.nlogo renamed to demo/dbscan-clustering-demo-v5.nlogo

@@ -6,7 +6,7 @@ globals [
   max-wealth
 ]
 
-breed [ agents agent ]
+breed [ agents agt ]
 
 agents-own [
   wealth