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Project Description

Module description

Graphs play an important role in many research areas, such as biology, microelectronics, social sciences, data mining, and computer science. Tulip (http://tulip.labri.fr) [1] 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 [2] 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 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 informations regarding the Tulip-Python releases pushed on the Python Packaging Index:

  • 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.post1 :

    • Fix crash on MacOS when calling OGDF layout algorithms
    • minor fixes
  • 4.8.0 : Initial release based on Tulip 4.8

Example

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 *
from tulipgui import *

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['colorScale'] = 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()
renderingParameters.setViewArrow(True)
renderingParameters.setMinSizeOfLabel(8)
renderingParameters.setEdgeColorInterpolate(True)
nodeLinkView.setRenderingParameters(renderingParameters)

References

[1]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
[2]Antoine Lambert and David Auber. Graph analysis and visualization with Tulip-Python. EuroSciPy 2012 - 5th European meeting on Python in Science, Bruxelles
[3]Riverbank Computing Limited. SIP - a tool for automatically generating Python bindings for C and C++ libraries. http://www.riverbankcomputing.co.uk/software/sip
Release History

Release History

4.9.0

This version

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4.8.1

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4.8.0.post1

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4.8.0

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
tulipgui_python-4.9.0-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 (45.7 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp27-cp27m-manylinux1_i686.whl (26.6 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp27-cp27m-manylinux1_x86_64.whl (26.8 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp27-cp27mu-manylinux1_i686.whl (26.6 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp27-cp27mu-manylinux1_x86_64.whl (26.8 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp27-cp27m-win32.whl (18.1 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 10, 2016
tulipgui_python-4.9.0-cp27-cp27m-win_amd64.whl (18.2 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 10, 2016
tulipgui_python-4.9.0-cp33-cp33m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (45.7 MB) Copy SHA256 Checksum SHA256 cp33 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp33-cp33m-manylinux1_i686.whl (26.6 MB) Copy SHA256 Checksum SHA256 cp33 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp33-cp33m-manylinux1_x86_64.whl (26.8 MB) Copy SHA256 Checksum SHA256 cp33 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp33-cp33m-win32.whl (18.1 MB) Copy SHA256 Checksum SHA256 cp33 Wheel Jul 10, 2016
tulipgui_python-4.9.0-cp33-cp33m-win_amd64.whl (18.2 MB) Copy SHA256 Checksum SHA256 cp33 Wheel Jul 10, 2016
tulipgui_python-4.9.0-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 (45.7 MB) Copy SHA256 Checksum SHA256 cp34 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp34-cp34m-manylinux1_i686.whl (26.6 MB) Copy SHA256 Checksum SHA256 cp34 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp34-cp34m-manylinux1_x86_64.whl (26.8 MB) Copy SHA256 Checksum SHA256 cp34 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp34-cp34m-win32.whl (18.1 MB) Copy SHA256 Checksum SHA256 cp34 Wheel Jul 10, 2016
tulipgui_python-4.9.0-cp34-cp34m-win_amd64.whl (18.2 MB) Copy SHA256 Checksum SHA256 cp34 Wheel Jul 10, 2016
tulipgui_python-4.9.0-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 (45.7 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp35-cp35m-manylinux1_i686.whl (26.6 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp35-cp35m-manylinux1_x86_64.whl (26.8 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Jul 9, 2016
tulipgui_python-4.9.0-cp35-cp35m-win32.whl (18.1 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Jul 10, 2016
tulipgui_python-4.9.0-cp35-cp35m-win_amd64.whl (18.2 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Jul 10, 2016

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