Pyhthon Notebook examples

Perhaps the easiest to get starting using MPSlib is by running and adjusting a number of jupyter notebooks.

• MPSlib: Getting started with MPSlib/scikit-mps in Python
  • Setup MPSlib
  • Choose training image
  • Run MPSlib
  • Plot some realizations using matplotlib
• MPSlib: Hard and soft data
  • Define hard data
  • Define soft/uncertain data
  • Preferential path
  • Setup
  • Hard data
  • Soft/uncertain data
• MPSlib: Using masks
  • Load training images
  • Setup MPS object
  • Define masks
  • Simulate in region 1 (mask 1)
  • Extract hard data from simulation in region 1
  • Simulate in region 2 (mask 2), conditioned on region 1
• MPSlib: Training images in scikit-mps
  • List available training images
  • Plot training images
  • Coarsen a 2D training image to a 3D training image
• MPSlib: Estimation
  • Setup
  • Estimation
  • Simulation
• MPSlib: Computation of entropy and self-information
  • Setup MPSlib
  • Plot entropy
  • Entropy as a function of number of conditional data
• MPSlib: Variable template size in mps_snesim_tree and mps_snesim_list
  • Setup
  • Sweep over template sizes at the finest grid
  • Plot realizations and computation time
  • Speedup relative to full template
• GENESIM with distance weighting
  • Setup the MPS object and training image
  • Run simulations for varying distance parameters
  • Computation time vs. distance_max
  • Inspect final parameter set
• Example: Mapping buried valleys in Kasted, Denmark
  • Get the training image and conditional data
  • Set up the simulation grid geometry
  • Plot the training image and the conditional data
• MPSlib applied to Kasted
  • Unconditional simulation
• Conditional simulation - hard data
  • Conditional simulation - soft data
  • Conditional simulation - hard and soft data
• Conditional estimation - hard and soft data