= Description of Gaphors data model =

Gaphor is an UML tool. In order to keep as close as possible to the UML specification the data model is based on the UML Metamodel. Since the OMG has an XMI (XML) specification of the metamodel, the easiest way to do that is to generate the code directly from the model. Doing this raises two issues:

   1. There are more attributes defined in the data model than we will use.
   2. How do we check if the model is consistent?

The first point is not such a problem: attributes we don't use don't consume memory.

There are no consistency rules in the XML definition, we have to get them from the UML syntax description. It is probably best to create a special consistency module that checks the model and reports errors.

In the UML metamodel all classes are derived from {{{Element}}}. So all we have to do is create a substitute for {{{Element}}} that gives some behaviour to the data objects (see {{{gaphor/UML/element.py}}}).

The data model is described in Python. Since the Python language doesn't make a difference between classes and objects, we can define the possible attributes that an object of a particular kind can have in a dictionary (name-value map) at class level. If a value is set, the object checks if an attribute exists in the class' dictionary (and the parents dictionary). If it does, the value is assigned, if it doesn't an exception is raised.

== Bidirectional references ==

But how, you might wonder, do you handle bidirectional references (object one references object two and visa versa)? Well, this is basically the same as the uni-directional reference. Only now we need to add some extra information to the dictionary at class level. We just define and extra field that gives us the name of the opposite reference and viola, we can create bi-directional references. You should check out the code in {{{gaphor/UML/element.py}}} for more details.

== Implementation ==

This will allow the user to assign a value to an instance of {{{Element}}} with name '{{{name}}}'. If no value is assigned before the value is requested, it returns and empty string ''.

{{{
m = Class()
print m.name                 # Returns ''
m.name = 'MyName'
print m.name	             # Returns 'MyName'
}}}


{{{
m = Element()
c = Comment()
print m.comment              # Returns an empty list '[]'
print c.annotatedElement     # Returns an empty list '[]'
m.comment = c                # Add 'c' to 'm.comment' and
                             # add 'm' to 'c.annotatedElement'
print m.comment              # Returns a list '[c]'
print c.annotatedElement     # Returns a list '[m]'
}}}

All this wisdom is defined in the data-models base class: {{{Element}}}. The datamodel itself code is generated.
Extensions to the data model

A few changes have been made to Gaphors implementation of the metamodel. First of all some relationships have to be modified since the same name is used for different relationships. Some n:m relationships have been made 1:n. These are all small changed and should not restrict the usability of Gaphors model.

The biggest change is the addition of a whole new class: Diagram. Diagram is inherited from Namespace and is used to hold a diagram. It contains a {{{gaphas.canvas.Canvas}}} object which can be displayed on screen by a {{{DiagramView}}} class.

