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TreeD

Visual representation of the branch-and-cut tree of SCIP using spatial dissimilarities of LP solutions -- Interactive Example

Example

Usage:

  • run TreeD.py to get usage information

Dependencies:

  • PySCIPOpt to solve the instance and generate the necessary tree data
  • Plot.ly to draw the 3D visualization
  • pandas to organize the collected data
  • sklearn for multi-dimensional scaling
  • pysal to compute statistics based on spatial (dis)similarity

Export to Amira:

  • run AmiraTreeD.py to get usage information.

AmiraTreeD.py generates the '.am' data files to be loaded by Amira software to draw the tree using LineRaycast.

Settings

Project View

  • DataTree.am: SpatialGraph data file with tree nodes and edges.
  • LineRaycast: Module to display the SpatialGraph. Note that is needed to set the colormap according to py code output (For instance 'Color map from 1 to 70' in this picture).
  • DataOpt.am: SpatialGraph data file with optimun value.
  • Opt Plane: Display the optimal value as a plane.

Preview

Amira preview

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