|
| |
Visualisation How-To
Concept
All visualisation in Gato happens automatically based
upon edges or vertices of the graph changing their status
with respect to data structures used in an algorithm.
Files
Algorithms are supplied in two files: The prolog (*.pro)
and the actual algorithm implementation visible to the
user (*.alg).
The prolog contains declaration which allow for 'nicer'
code in the algorithm, subroutines specific to the algorithm
which should not be visible to the user, variables,
and a proper setup of the animation engine.
Before running
Making sure we have the right graph
If you have a statement like
self.NeededProperties({propName1:value1, propName2:value2,...]})
Gato will check that the graph the user selected has the
specified values for all the properties in the dictionary.
Note, this is currently not supported by Gato's
file format.
Setting breakpoints
With
self.SetBreakpoints([7,9,2])
you can switch on breakpoints at educationally sensible points
of the algorithm.
Using Animated Data Structures
Typical data structures like stacks and queues etc. have
animated counter parts. If you say
Q = AnimatedVertexQueue()
putting a vertex v on the queue
Q.Append(v)
will color it in the color cColorOnQueue (defined in GatoGlobals.py)
and removing it with the command
v = Q.Top()
will color it in the color cRemovedFromQueue.
So an ordinary breadth-first-search will give you a reasonable
visualisation just by replacing the Queue by an AnimatedVertexQueue.
Interacting with the user
You can the user allow to select a vertex or an edge by calling
v = self.PickVertex()
e = self.PickEdge()
These functions take an optional argument for a callback function.
|
