TreeD

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

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

• 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