Important API changes

Since Tulip 4.8.1

New methods for getting / setting graph properties values for nodes and edges added

Convenient methods that relies on the use of a dictionnary for setting and getting properties values for nodes and edges have been added to the tlp.Graph class :

• tlp.Graph.getNodePropertiesValues() : returns a dictionnary filled with all graph properties values for a given node
• tlp.Graph.getEdgePropertiesValues() : returns a dictionnary filled with all graph properties values for a given edge
• tlp.Graph.setNodePropertiesValues() : sets multiple properties values stored in a dictionnary for a given node
• tlp.Graph.setEdgePropertiesValues() : sets multiple properties values stored in a dictionnary for a given edge

For instance, the sample code below sets multiple graph view properties values for each node of a graph:

def getRandomFontAwesomeIcon():
  iconKeys = vars(tlp.TulipFontAwesome).keys()
  while 1:
    attName = random.choice(list(iconKeys))
    attr = getattr(tlp.TulipFontAwesome, attName)
    if not attName.startswith('_') and type(attr) == str:
      return attr

def getRandomColor():
  r = int(random.random()*255)
  g = int(random.random()*255)
  b = int(random.random()*255)
  return tlp.Color(r, g, b)

def getRandomSize(minSize, maxSize):
  return minSize + random.random() * (maxSize - minSize)

for n in graph.getNodes():
    values = {'viewShape': tlp.NodeShape.FontAwesomeIcon,
              'viewColor' : getRandomColor(),
              'viewSize' : getRandomSize(tlp.Size(0.1), tlp.Size(1)),
              'viewFontAwesomeIcon' : getRandomFontAwesomeIcon()}
    graph.setNodePropertiesValues(n, values)

Since Tulip 4.8

Deprecation of the direct use of the tlp.DataSet class

Formerly, the class tlp.DataSet was used to transmit parameters to the algorithms that can be executed on an instance of a tlp.Graph class (see Applying an algorithm on a graph).

For commodity of use in the Python world, that class is now internally mapped to a dictionnary indexed by string keys (parameters names). To get a dictionnary filled with default parameters for an algorithm, you can use the tlp.getDefaultPluginParameters() function.

Nevertheless for backward compatibilty, it is still possible to create instance of that class.

Deprecation of the direct use of the tlp.StringCollection class

The tlp.StringCollection class represents a list of selectable string entries that can be used as plugin parameter. Formerly, to select the string to transmit to a plugin, the following code have to be used:

# get defaut parameters for the 'FM^3 (OGDF)' layout plugin
params = tlp.getDefaultPluginParameters('FM^3 (OGDF)')
# set 'Page Format' as 'Landscape'
params['Page Format'].setCurrent('Landscape')

For syntactic sugar, the tlp.StringCollection class does not need to be instantiated anymore to transmit the string to the algorithm. The creation of the string collection is handled internally and you can now simply write:

# get defaut parameters for the 'FM^3 (OGDF)' layout plugin
params = tlp.getDefaultPluginParameters('FM^3 (OGDF)')
# set 'Page Format' as 'Landscape'
params['Page Format'] = 'Landscape'

If the provided string is not contained in the string collection associated to a plugin parameter, an exception will be thrown when trying to execute the plugin trough dedicated methods/functions.

Nevertheless for backward compatibilty, it is still possible to create instance of that class.