MPSlib: Getting started with MPSlib/scikit-mps in Python

This a small example getting started with MPSlib through an iPython notebook

import numpy as np
import matplotlib.pyplot as plt
import mpslib as mps

Setup MPSLib

First one need to initialize an instance of the mpslib object.

# Initialize MPSlib using default algortihm, and seetings
O = mps.mpslib();

# Initialize MPSlib using the mps_snesim_tree algorthm, and a simulation grid of size [80,70,1]
O = mps.mpslib(method='mps_snesim_tree', simulation_grid_size=[80,70,1])

# specific parameters can be parsed directly when calling mps.mpslib (as abobve), or set by updating the O and O.par structure as
#O.parameter_filename = 'mps_snesim.txt'
O.par['debug_level']=-1
O.par['n_cond']=25
O.par['n_real']=16
O.par['n_threads']=5
O.par['do_entropy']=1
O.par['simulation_grid_size']=np.array([80,50,1])

Using mps_genesim installed in /mnt/f/PROGRAMMING/mpslib/scikit-mps/mpslib/bin (scikit-mps in /mnt/f/PROGRAMMING/mpslib/scikit-mps/mpslib/mpslib.py)
Using mps_snesim_tree installed in /mnt/f/PROGRAMMING/mpslib/scikit-mps/mpslib/bin (scikit-mps in /mnt/f/PROGRAMMING/mpslib/scikit-mps/mpslib/mpslib.py)

# All adjustable parameters for the specifric chosen MPSlib algorithm are
O.par

{'n_real': 16,
 'rseed': 1,
 'n_max_cpdf_count': 1,
 'out_folder': '.',
 'ti_fnam': 'ti.dat',
 'simulation_grid_size': array([80, 50,  1]),
 'origin': array([0., 0., 0.]),
 'grid_cell_size': array([1, 1, 1]),
 'mask_fnam': 'mask.dat',
 'hard_data_fnam': 'hard.dat',
 'shuffle_simulation_grid': 2,
 'entropyfactor_simulation_grid': 4,
 'shuffle_ti_grid': 1,
 'hard_data_search_radius': 1,
 'soft_data_categories': array([0, 1]),
 'soft_data_fnam': 'soft.dat',
 'n_threads': 5,
 'debug_level': -1,
 'do_estimation': 0,
 'do_entropy': 1,
 'template_size': array([8, 7, 1]),
 'n_multiple_grids': 3,
 'n_min_node_count': 0,
 'n_cond': 25}

Choose training image

TI, TI_filename = mps.trainingimages.strebelle(di=2, coarse3d=1)
#TI, TI_filename = mps.trainingimages.rot90()
O.par['ti_fnam']=TI_filename
plt.imshow(TI[:,:,0].T)

Beginning download of https://github.com/GAIA-UNIL/trainingimages/raw/master/MPS_book_data/Part2/ti_strebelle.sgems to ti_strebelle.dat

<matplotlib.image.AxesImage at 0x7f8ce3029bb0>

Run MPSlib

The chosen MPSlib algorithm is run using a single thread by executing

O.run()

and using multiple threads by executing

O.run_parallel()

#O.run()
O.run_parallel()

parallel: Using 4 of max 10 threads

[<mpslib.mpslib.mpslib at 0x7f8ce0c1aeb0>,
 <mpslib.mpslib.mpslib at 0x7f8ce319f580>,
 <mpslib.mpslib.mpslib at 0x7f8ce0c1ae80>,
 <mpslib.mpslib.mpslib at 0x7f8ce319f6a0>]

Plot some realizations using matplotlib

O.plot_reals()