skip to navigation
skip to content

tulipgui-python 5.1.0

Tulip GUI Python bindings

Module description

Graphs play an important role in many research areas, such as biology, microelectronics, social sciences, data mining, and computer science. Tulip ( [1] [2] is an Information Visualization framework dedicated to the analysis and visualization of such relational data. Written in C++ the framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations.

The Tulip GUI library is available to the Python community through the Tulip-Python bindings [3] allowing to create and manipulate Tulip views (typically Node Link diagrams) trough the tulipgui module. It has to be used with the tulip module dedicated to the creation, storage and manipulation of the graphs to visualize. The bindings have been developed using the SIP tool [4] from Riverbank Computed Limited, allowing to easily create quality Python bindings for any C/C++ library.

The main features provided by the bindings are the following ones:

  • creation of interactive Tulip visualizations (Adjacency Matrix, Geographic, Histogram, Node Link Diagram, Parallel Coordinates, Pixel Oriented, Scatter Plot, Self Organizing Map, Spreadsheet)
  • 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

Release notes

Some information regarding the Tulip-Python releases pushed on the Python Packaging Index:

  • 5.1.0: based on Tulip 5.1.0 released on 07/11/2017
  • 5.0.0: based on Tulip 5.0.0 released on 27/06/2017
  • 4.10.0: based on Tulip 4.10.0 released on 08/12/2016
  • 4.9.0 : based on Tulip 4.9.0 released on 08/07/2016
  • 4.8.1 : based on Tulip 4.8.1 released on 16/02/2016
  • 4.8.0 : Initial release based on Tulip 4.8


The following script imports the tree structure of the file system directory of the Python standard library, applies colors to nodes according to degrees, computes a tree layout and quadratic Bézier shapes for edges. The imported graph and its visual encoding are then visualized by creating an interactive Node Link Diagram view. A window containing an OpenGL visualization of the graph will be created and displayed.

from tulip import tlp
from tulipgui import tlpgui

import os

# get the root directory of the Python Standard Libraries
pythonStdLibPath = os.path.dirname(os.__file__)

# call the 'File System Directory' import plugin from Tulip
# importing the tree structure of a file system
params = tlp.getDefaultPluginParameters('File System Directory')
params['directory color'] = tlp.Color.Blue
params['other color'] = tlp.Color.Red
params['directory'] = pythonStdLibPath
graph = tlp.importGraph('File System Directory', params)

# compute an anonymous graph double property that will store node degrees
degree = tlp.DoubleProperty(graph)
degreeParams = tlp.getDefaultPluginParameters('Degree')
graph.applyDoubleAlgorithm('Degree', degree, degreeParams)

# create a heat map color scale
heatMap = tlp.ColorScale([tlp.Color.Green, tlp.Color.Black, tlp.Color.Red])

# linearly map node degrees to colors using the 'Color Mapping' plugin from Tulip
# using the heat map color scale
colorMappingParams = tlp.getDefaultPluginParameters('Color Mapping', graph)
colorMappingParams['input property'] = degree
colorMappingParams['color scale'] = heatMap
graph.applyColorAlgorithm('Color Mapping', colorMappingParams)

# apply the 'Bubble Tree' graph layout plugin from Tulip
graph.applyLayoutAlgorithm('Bubble Tree')

# compute quadratic bezier shapes for edges
curveEdgeParams = tlp.getDefaultPluginParameters('Curve edges', graph)
curveEdgeParams['curve type'] = 'QuadraticDiscrete'
graph.applyAlgorithm('Curve edges', curveEdgeParams)

# create a node link diagram view of the graph,
# a window containing the Tulip OpenGL visualization
# will be created and displayed
nodeLinkView = tlpgui.createNodeLinkDiagramView(graph)
# set some rendering parameters for the graph
renderingParameters = nodeLinkView.getRenderingParameters()


[1]David Auber, Romain Bourqui, Maylis Delest, Antoine Lambert, Patrick Mary, Guy Mélançon, Bruno Pinaud, Benjamin Renoust and Jason Vallet. TULIP 4. Research report. LaBRI - Laboratoire Bordelais de Recherche en Informatique. 2016.
[2]David Auber, Daniel Archambault, Romain Bourqui, Antoine Lambert, Morgan Mathiaut, Patrick Mary, Maylis Delest, Jonathan Dubois, and Guy Mélançon. The Tulip 3 Framework: A Scalable Software Library for Information Visualization Applications Based on Relational Data. Technical report RR-7860, INRIA, January 2012
[3]Antoine Lambert and David Auber. Graph analysis and visualization with Tulip-Python. EuroSciPy 2012 - 5th European meeting on Python in Science, Bruxelles
[4]Riverbank Computing Limited. SIP - a tool for automatically generating Python bindings for C and C++ libraries.
File Type Py Version Uploaded on Size
tulipgui_python-5.1.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (md5) Python Wheel cp27 2017-11-08 44MB
tulipgui_python-5.1.0-cp27-cp27m-manylinux1_i686.whl (md5) Python Wheel cp27 2017-11-09 24MB
tulipgui_python-5.1.0-cp27-cp27m-manylinux1_x86_64.whl (md5) Python Wheel cp27 2017-11-08 24MB
tulipgui_python-5.1.0-cp27-cp27m-win_amd64.whl (md5) Python Wheel cp27 2017-11-09 28MB
tulipgui_python-5.1.0-cp27-cp27mu-manylinux1_i686.whl (md5) Python Wheel cp27 2017-11-09 24MB
tulipgui_python-5.1.0-cp27-cp27mu-manylinux1_x86_64.whl (md5) Python Wheel cp27 2017-11-08 24MB
tulipgui_python-5.1.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (md5) Python Wheel cp34 2017-11-08 44MB
tulipgui_python-5.1.0-cp34-cp34m-manylinux1_i686.whl (md5) Python Wheel cp34 2017-11-09 24MB
tulipgui_python-5.1.0-cp34-cp34m-manylinux1_x86_64.whl (md5) Python Wheel cp34 2017-11-08 24MB
tulipgui_python-5.1.0-cp34-cp34m-win_amd64.whl (md5) Python Wheel cp34 2017-11-09 28MB
tulipgui_python-5.1.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (md5) Python Wheel cp35 2017-11-08 44MB
tulipgui_python-5.1.0-cp35-cp35m-manylinux1_i686.whl (md5) Python Wheel cp35 2017-11-09 24MB
tulipgui_python-5.1.0-cp35-cp35m-manylinux1_x86_64.whl (md5) Python Wheel cp35 2017-11-08 24MB
tulipgui_python-5.1.0-cp35-cp35m-win_amd64.whl (md5) Python Wheel cp35 2017-11-09 28MB
tulipgui_python-5.1.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (md5) Python Wheel cp36 2017-11-08 44MB
tulipgui_python-5.1.0-cp36-cp36m-manylinux1_i686.whl (md5) Python Wheel cp36 2017-11-09 24MB
tulipgui_python-5.1.0-cp36-cp36m-manylinux1_x86_64.whl (md5) Python Wheel cp36 2017-11-08 24MB
tulipgui_python-5.1.0-cp36-cp36m-win_amd64.whl (md5) Python Wheel cp36 2017-11-09 28MB