Creating and manipulating Tulip visualizations with the tulipgui module

Starting the 3.8 release of Tulip, a new module called tulipgui is available allowing to create and manipulate Tulip views (typically Node Link diagrams). The module can be used inside the Python Script editor integrated in the Tulip software GUI but also through the classical Python interpreter.

The main features offered by that module are :

  • creation of interactive Tulip visualizations
  • the ability to change the data source on opened visualizations
  • the possibilty to modify the rendering parameters for node link diagram visualizations
  • the ability to save visualization snapshots to image files on disk

Using the module from the main Tulip GUI

When the tulipgui module is used through the Python script editor integrated in the Tulip GUI, it enables to add and manipulate views in the Tulip workspace.

For instance, the following script works on a geographical network. We suppose that we already have an opened Node Link Diagram view (plus one Python Script view) in Tulip for visualizing the whole network. The script will extract the induced sub-graph of the european nodes, create a new Node Link Diagram view for visualizing it and set some rendering parameters:

from tulip import *
from tulipgui import *

def main(graph):

    # extraction and creation of the european sub-graph
    continent =  graph.getStringProperty("continent")
    europeNodes = []
    for n in graph.getNodes():
        if continent[n] == "Europe":
    europeSg = graph.inducedSubGraph(europeNodes)

    # get a reference to the already opened Node Link Diagram view
    nlvWorld = tlp.getOpenedViews()[0]

    # create a new Node Link Diagram view for visualizing the Europe sub-graph
    nlvEurope = tlp.addView("Node Link Diagram view", europeSg)

    # set labels scaled to node sizes mode
    renderingParameters = nlvEurope.getRenderingParameters()

    # tiles the opened views in the Tulip workspace

    # recenter the node link views after the tile operation

Using the module with the classical Python interpreter

The tulipgui module can also be used with the classical Python interpreter and shell. Tulip interactive visualizations will be displayed in separate windows once they have been created.

Interactive mode

When working through the Python shell, Tulip views can be created interactively. The opened views will be updated each time the graph or its properties are modified.

For instance, the following session imports a grid graph, creates a Node Link Diagram view of it and then changes the nodes colors. The Node Link Diagram view will be updated each time a node color is modified:

>>> from tulip import *
>>> from tulipgui import *
>>> grid = tlp.importGraph("Grid")
>>> view = tlp.addNodeLinkDiagramView(grid)
>>> viewColor = graph.getColorProperty("viewColor")
>>> for n in graph.getNodes():
...     viewColor[n] = tlp.Color(0, 255, 0)


When working on Windows platforms, you have to use the Python command line utility (not the IDLE one) if you want to use the tulipgui module interactively. In a same maneer, if you intend to launch python through a terminal, you have to used the basic windows console cmd.exe. Other shells like mintty or rxvt do not allow to process the GUI events, required for interactive use.

Script execution mode

When executing a script from a command line through the classical python interpreter, if Tulip views had been created during its execution, the script will terminate once all view windows had been closed.

Below are some samples scripts illustrating the features of the tulipgui module. The first script imports a grid approximation graph, computes some visual attributes on it and creates a Node Link Diagram visualization (which will remain displayed at the end of the script execution). Figure 1 introduces a screenshot of the created view.:

from tulip import *
from tulipogl import *
from tulipgui import *

# Import a grid approximation (with default parameters)
graph = tlp.importGraph("Grid Approximation")

viewLayout = graph.getLayoutProperty("viewLayout")
viewSize = graph.getSizeProperty("viewSize")
viewBorderWidth = graph.getDoubleProperty("viewBorderWidth")
viewColor = graph.getColorProperty("viewColor")
viewLabel = graph.getStringProperty("viewLabel")

# Apply an FM^3 layout on it
fm3pParams = tlp.getDefaultPluginParameters("FM^3 (OGDF)", graph)
fm3pParams["Unit edge length"] = 100
graph.applyLayoutAlgorithm("FM^3 (OGDF)", viewLayout, fm3pParams)

# Compute an anonymous degree property
degree = tlp.DoubleProperty(graph)
degreeParams = tlp.getDefaultPluginParameters("Degree")
graph.applyDoubleAlgorithm("Degree", degree, degreeParams)

# Map the node sizes to their degree
sizeMappingParams = tlp.getDefaultPluginParameters("Metric Mapping", graph)
sizeMappingParams["property"] = degree
sizeMappingParams["min size"] = 1
sizeMappingParams["max size"] = 30
graph.applySizeAlgorithm("Metric Mapping", viewSize, sizeMappingParams)

# Create a heat map color scale
heatMap = tlp.ColorScale([tlp.Color(0, 255, 0), tlp.Color(0,0,0), tlp.Color(255, 0, 0)])

# Map the node colors to their degree using the heat map color scale
# Also set the nodes labels to their id
for n in graph.getNodes():
    viewColor[n] = heatMap.getColorAtPos((degree[n] - degree.getNodeMin()) / (degree.getNodeMax() - degree.getNodeMin()))
    viewLabel[n] = str(

# Add a border to edges

# Create a Node Link Diagram view and set some rendering parameters
nodeLinkView = tlp.addNodeLinkDiagramView(graph)
renderingParameters = nodeLinkView.getRenderingParameters()

Figure 1: Screenshot of the view created by the above script.

The second script aims to generate a snapshot of a file system directory visualization. It begins by calling the “File System Directory” import plugin, then it sets some visual attributes on graph elements and finally it creates a node link diagram view (that will then be hided) with particular rendering parameters for taking the snapshot. Figure 2 introduces the resulting snaphot.:

from tulip import *
from tulipogl import *
from tulipgui import *

# Create an empty graph
graph = tlp.newGraph()

# Set the parameters for the "File System Directory" Import module
fsImportParams = tlp.getDefaultPluginParameters("File System Directory", graph)
fsImportParams["dir::directory"] = "/home/antoine/tulip_3_8_install"

# Import a file system directory content as a tree
tlp.importGraph("File System Directory", fsImportParams, graph)

# Get some visual attributes properties
viewLabel =  graph.getStringProperty("viewLabel")
viewLabelColor =  graph.getColorProperty("viewLabelColor")
viewLayout =  graph.getLayoutProperty("viewLayout")
viewBorderWidth = graph.getDoubleProperty("viewBorderWidth")

# Apply the "Bubble Tree" layout on the imported graph
bubbleTreeParams = tlp.getDefaultPluginParameters("Bubble Tree", graph)
graph.applyLayoutAlgorithm("Bubble Tree", viewLayout, bubbleTreeParams)

# Creates a property that will be used to order the rendering of graph elements
# as we want to be sure that the directory nodes labels will be visible
renderingOrderingProp = graph.getDoubleProperty("rendering ordering")

for n in graph.getNodes():
    # the "File System Directory" import plugin adds a name property containg the file name
    viewLabel[n] = graph["name"][n]
    # if the node represents a directory, ensure that its label will be visible (as we will activate the "no labels overlaps" mode)
    # also change its label color to blue
    if graph.deg(n) > 1:
        renderingOrderingProp[n] = 1
        viewLabelColor[n] = tlp.Color(0, 0, 255)
        renderingOrderingProp[n] = 0


# Create a Node Link Diagram view
nodeLinkView = tlp.addNodeLinkDiagramView(graph)

# Hides the view after creating it. That way, the script will terminate at the end of its execution.
# The view window will quickly appear however.
renderingParams = nodeLinkView.getRenderingParameters()

# Activate the ordered rendering mode

# Activate the "no labels overlaps" mode

# Save a snapshot of the view to an image file on disk
nodeLinkView.savePicture("/home/antoine/tulip_3_8_install.png", 1920, 1080, True)

Figure 2: Snapshot obtained with the above script.

Managing the visualizations updates

If you are working through the Python Script editor in the main Tulip GUI, the updates of visualizations are blocked until the script execution terminates. You can however trigger the redraw of visualizations through the updateVisualization(center=True) function of by calling the tlp.View.draw() method.

If you are working through the classical Python interpreter (shell or script mode), the opened visualizations will be updated each time the visualized graphs and their properties will be modified. You can however block those updates by the function call below:


This will put the Tulip observation mecanism on hold and stop the triggers of visualizations updates. The visualizations can still be updated by calling the tlp.View.draw() method.

To reactivate the automatic views updates, issue the following function call: