Topic's layer distribution
Topic's layer distribution is an ordered set of numbers representing topic's distribution between layers. Layer distribution is used to give a rapid outlook of the topic and how the topic is merged between layers.
Topic's layer distribution is shown at the footer, near bottom right corner of the Wandora window. Layer distribution contains one number for each layer. First number represents the first layer, second number the second layer etc. Numbers build up a vector or tree structure if layer stacks are nested.
If the number in the layer distribution is 0 (zero), current topic doesn't exist in equivalent layer. If the number is 1 (one), current topic has one merged topic in the equivalent layer. If the number is 2, topic has two merged topics in the equivalent layer etc. Layer distribution vector contains always positive integer numbers including zero.
For example, if we have a topic with layer distribution [ 1:1:0 ], we can conclude the topic is a merged one and contains topics in first and second layer but the third layer is intact. However, the vector doesn't express the cause of the merge. Topics in Wandora do merge when they share base name, subject locator or subject identifier.
If we have a layer distribution [ 1:2 ], we can conclude the topic is a merged one but also that the topic in first layer causes two different topics to merge in second layer. This is possible for example when a topic in the first layer shares a base name and a subject identifier with two distinct topics in layer 2.
Some distributions are impossible. For example situation where single layer contains two or more merged topics can never occur as the virtual merge collapses into a real one as soon as topics share identifier. [ 2:0:0 ] is an example of such impossible layer distribution.
If layer structure contains nested layer stacks (i.e. layer groups within layers) layer distribution is a tree structure. For example [ 1:[ 0:1 ] ] is an example of such layer distribution. Now Wandora contains a layer stack with one topic map layer and one nested layer stack. Nested layer stack contains two layers and the inspected topic is distributed along first topic map layer and second layer in nested stack.
Math with Layer distribution?
This is very interesting question. Can one calculate anything reasonable with the layer distribution? We are open for your suggestions and welcome all ideas.
