'''
import mpslib as mps
import time
import numpy as np
import os
import copy
#%% Initialize the MPS object, using a specific algorithm (def='mps_snesim_tree')
# for windows users :
# IT IS SUPER IMPOERTANT TO ADD THE MEXT LINE IN ORDER TO PREVENT
# PYTHON ENTERING AN INFINITE LOOP DOING PRALLEL SIMULATION!
if __name__ == '__main__':
#%% Setup MPSlib
O = mps.mpslib(method='mps_snesim_tree',
simulation_grid_size=(80, 70, 1),
origin=(0, 0, 0))
#O=mps.mpslib(method='mps_genesim', simulation_grid_size=(80,70,1), origin=(0,0,0))
O.par['n_real'] = 50
O.par['n_cond'] = 4
# Set training image
O.ti = mps.trainingimages.strebelle()[0]
#O.plot_ti()
# Set hard and soft data
O.delete_local_files() # Make sure no hard/soft data are conditioned to
d_hard = np.array([[25, 10, 0, 0], [28, 13, 0, 1]])
d_soft = np.array([[10, 10, 0, 0.1, 0.9], [10, 13, 0, .1, 0.9],
[30, 53, 0, .01, 0.99]])
O.d_hard = d_hard
#%% import matplotlib.pyplot as plt import mpslib as mps import numpy as np #%% MPS_SNESIM_TREE O1=mps.mpslib(method='mps_snesim_tree') O1=mps.mpslib(method='mps_genesim') O1.delete_local_files() O1.par['debug_level']=-1 # when debug_level>-1 and the text output from mpslib increase, the mpslib exe file is detached from # python before it has finished running!!! #TI, TI_filename=mps.trainingimages.lines() #TI, TI_filename=mps.trainingimages.bangladesh() #TI, TI_filename=mps.trainingimages.checkerboard() #TI, TI_filename=mps.trainingimages.checkerboard2() #TI, TI_filename=mps.trainingimages.checkerboard2(nx=40, ny=50, cell_x=8, cell_y=4, cell_2=10) TI, TI_filename=mps.trainingimages.checkerboard2(nx=100, ny=100, cell_x=4, cell_y=4, cell_2=3) #TI, TI_filename=mps.trainingimages.checkerboard2(nx=100, ny=100, cell_x=14, cell_y=8, cell_2=5) #TI, TI_filename=mps.trainingimages.checkerboard2(nx=40, ny=40, cell_x=4, cell_y=4, cell_2=9) #TI, TI_filename=mps.trainingimages.maze() O1.par['ti_fnam']=TI_filename O1.par['simulation_grid_size'][0]=35 O1.par['simulation_grid_size'][1]=35 O1.par['simulation_grid_size'][2]=1 O1.par['soft_data_fnam']='soft_case2.dat'
# mpslib_example.py
# example of using mpslib.py
#%%
import mpslib as mps;
import matplotlib.pyplot as plt;
import numpy as np;
from scipy import squeeze
O1 = mps.mpslib(method='mps_snesim_tree', n_real = 1)
O2 = mps.mpslib(method='mps_snesim_tree', n_real = 1)
#%% Load and set training image
ti = mps.trainingimages.strebelle()[0]
O1.ti=ti
O2.ti=np.swapaxes(ti,0,1)
O1.par['ti_fnam']='ti1.dat'
O2.par['ti_fnam']='ti2.dat'
#%% SIMULATE
O1.run()
O2.run()
#%% plot the TI's and simulation results
plt.set_cmap('hot')
fig1 = plt.figure(1)
plt.subplot(2,2,1)
plt.title('TI')
import sys import numpy as np import time from random import randint # import mpslib import mpslib as mps; # import the RGBMatrix library from # https://github.com/hzeller/rpi-rgb-led-matrix/tree/master/bindings from rgbmatrix import RGBMatrix, RGBMatrixOptions from PIL import Image #%% Setup MPSlib O1 = mps.mpslib(method='mps_genesim', n_real = 1, verbose_level=-1) #O1.par['simulation_grid_size'][0]=matrix.width; #O1.par['simulation_grid_size'][1]=matrix.height; O1.par['simulation_grid_size'][0]=32; O1.par['simulation_grid_size'][1]=32; O1.par['debug_level']=-1; O1.par['n_cond']=25; O1.ti= mps.trainingimages.maze()[0] O1.ti = O1.ti[10:300:1][:,10:300:1] #O1.ti = O1.ti[10:300:2][:,10:300:2] #O1.ti= mps.trainingimages.checkerboard()[0] O1.par['rseed']=0; O1.par['shuffle_simulation_grid']=1; O1.run() #%% Configuration for the RGB matrix
TI2, TI_filename2 = mps.trainingimages.strebelle(di=2)
mps.eas.write_mat(TI2,TI_filename2)
plt.figure(1)
plt.subplot(121)
plt.imshow(np.transpose(squeeze(TI1)))
plt.title(TI_filename1)
plt.subplot(122)
plt.imshow(np.transpose(squeeze(TI2)))
plt.title(TI_filename2)
plt.show()
#%% MPS_SNESIM_TREE
grid_size = np.array([350, 200, 1])
O = mps.mpslib(method='mps_snesim_tree',
n_real = 1, verbose_level=-1)
#O = mps.mpslib(method='mps_genesim',
# n_real = 1, verbose_level=-1)
O.par['debug_level']=-1
O.par['n_cond']=49
O.par['simulation_grid_size']=grid_size
#%% USE MASK
d_mask1=np.zeros([grid_size[0],grid_size[1],grid_size[2]])
d_mask1[80:150,100:180]=1;
d_mask1[0:40,80:150,]=1;
d_mask2=1-d_mask1;
mask_fnam1='mask_01.dat'
mask_fnam2='mask_02.dat'
ax = plt.Subplot(fig, inner[j])
fig.add_subplot(ax)
plt.imshow(np.transpose(TI2[:, :, j]))
plt.axis('off')
#%% RUN THE SIMULATIONS
alg='mps_snesim_tree'
#alg='mps_genesim'
nc=8*8
rseed=1;
#nc=8*8
#Coarsen channel TI, using only coarsened TI
# Single coarsened TI
O1=mps.mpslib(method=alg)
O1.par['debug_level']=-1
O1.par['ti_fnam']='ti1.dat'
O1.par['simulation_grid_size'][0]=85
O1.par['simulation_grid_size'][1]=45
O1.ti = TI1
O1.par['simulation_grid_size'][2]=1
O1.par['shuffle_simulation_grid']=0
O1.par['n_cond']=nc
O1.par['rseed']=rseed
O1.par['n_real']=1
O1.parameter_filename='sim1.par'
O1.delete_local_files()
O1.run()
# Detailed Coarsened TI
# import import sys import numpy as np import time from random import randint # import mpslib import mpslib as mps # import the RGBMatrix library from # https://github.com/hzeller/rpi-rgb-led-matrix/tree/master/bindings from rgbmatrix import RGBMatrix, RGBMatrixOptions from PIL import Image #%% Setup MPSlib O1 = mps.mpslib(method='mps_genesim', n_real=1, verbose_level=-1) #O1.par['simulation_grid_size'][0]=matrix.width; #O1.par['simulation_grid_size'][1]=matrix.height; O1.par['simulation_grid_size'][0] = 32 O1.par['simulation_grid_size'][1] = 32 O1.par['debug_level'] = -1 O1.par['n_cond'] = 9 #O1.ti= mps.trainingimages.maze()[0] O1.ti = mps.trainingimages.checkerboard()[0] O1.ti = O1.ti[10:300:2][:, 10:300:2] O1.par['rseed'] = 0 #%% Configuration for the RGB matrix options = RGBMatrixOptions() options.rows = 32 options.chain_length = 1
'''Python Example of using sequential Gibbs simualtion to randoly pertub a realization ''' import mpslib as mps import matplotlib.pyplot as plt import numpy as np import random O1=mps.mpslib(method='mps_snesim_tree') TI1, TI_filename1 = mps.trainingimages.strebelle(3, coarse3d=1) O1.ti=TI1 O1.par['n_multiple_grids']=2; O1.par['n_cond']=49 O1.par['n_real']=1 O1.par['debug_level']=-1 O1.par['simulation_grid_size'][0]=35 O1.par['simulation_grid_size'][1]=30 O1.par['shuffle_simulation_grid']=1 O1.run() step=0.75; N_run = 3; for i in range(0,N_run):
'''
import mpslib as mps
import time
import numpy as np
import os
import copy
#%% Initialize the MPS object, using a specific algorithm (def='mps_snesim_tree')
# for windows users :
# IT IS SUPER IMPOERTANT TO ADD THE MEXT LINE IN ORDER TO PREVENT
# PYTHON ENTERING AN INFINITE LOOP DOING PRALLEL SIMULATION!
if __name__ == '__main__':
#%% Setup MPSlib
O=mps.mpslib(method='mps_snesim_tree', simulation_grid_size=(80,70,1), origin=(0,0,0))
#O=mps.mpslib(method='mps_genesim', simulation_grid_size=(80,70,1), origin=(0,0,0))
O.par['n_real']=50
O.par['n_cond']=4
# Set training image
O.ti = mps.trainingimages.strebelle()[0]
#O.plot_ti()
# Set hard and soft data
O.delete_local_files() # Make sure no hard/soft data are conditioned to
d_hard = np.array([[25,10,0,0],[28,13,0,1]])
d_soft = np.array([[10,10,0,0.1, 0.9],[10,13,0,.1, 0.9],[30,53,0,.01, 0.99]])
O.d_hard = d_hard
O.d_soft = d_soft
# mpslib_example.py
# example of using mpslib.py
#%%
import mpslib as mps;
import matplotlib.pyplot as plt;
import numpy as np;
from scipy import squeeze
O1 = mps.mpslib(method='mps_snesim_tree', n_real = 1)
O2 = mps.mpslib(method='mps_snesim_tree', n_real = 1)
#%% Load and set training image
ti = mps.trainingimages.strebelle()[0]
ti2=np.transpose(ti)
O1.ti=ti
O2.ti=np.swapaxes(ti,2,1)
#%% SIMULATE
O1.run()
O2.run()
#%% plot the TI's and simulation results
plt.set_cmap('hot')
fig1 = plt.figure(1)
plt.subplot(2,2,1)
plt.title('TI')
plt.imshow(squeeze(O1.ti))
plt.subplot(2,2,2)
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 1 20:44:17 2018
@author: Thomas Mejer Hansen
"""
##%load_ext autoreload
##%autoreload 2
##%%
#import sys
#sys.path.append('../mpslib')
import mpslib as mps
import numpy as np
O1=mps.mpslib(method='mps_genesim')
O1.par['debug_level']=-1
# when debug_level>-1 and the text output from mpslib increase, the mpslib exe file is detached from
# python before it has finished running!!!
TI, TI_filename=mps.trainingimages.checkerboard2()
O1.ti = TI
O1.par['ti_fnam']=TI_filename
O1.par['simulation_grid_size'][0]=35
O1.par['simulation_grid_size'][1]=15
O1.par['simulation_grid_size'][2]=1
O1.par['soft_data_fnam']='soft_case2.dat'
O1.par['shuffle_simulation_grid']=2
O1.par['n_cond']=49
O1.par['rseed']=0
O1.par['n_real']=36
O1.d_hard=np.array([[10,10,0,2],[13,10,0,2]])
# mpslib_example_1.py
# example of using mpslib
#%%
import mpslib as mps
import matplotlib.pyplot as plt
plt.set_cmap('hot')
#%% MPS_SNESIM_TREE
O1 = mps.mpslib(method='mps_snesim_tree',
n_real = 4, verbose_level=-1)
O1.parameter_filename = 'mps_snesim.txt'
O1.par['debug_level']=-1
O1.par['n_cond']=49
TI, TI_fname = mps.trainingimages.checkerboard()
TI, TI_fname = mps.trainingimages.bangladesh()
#O1.ti=TI
O1.par['ti_fnam']=TI_fname
O1.run()
#%%
plt.figure(1)
O1.plot_reals()
plt.savefig(O1.method+'.png', dpi=600)
plt.show()
#%%
import mpslib as mps
import matplotlib.pyplot as plt
import numpy as np
#%% LOAD TI
#%% LOAD TI
ti = mps.trainingimages.bangladesh(di=2)[0]
ti_file1 = 'ti_1.dat'
ti_file2 = 'ti_2.dat'
#%% SIMULATE
O1 = mps.mpslib(method='mps_snesim_tree',
n_real=1,
verbose_level=0,
ti_fnam=ti_file1)
O1.ti = ti
O1.run()
O1.blank_grid = np.zeros(
(O1.par['simulation_grid_size'][1], O1.par['simulation_grid_size'][0]),
dtype=np.int)
O1.blank_grid[0:20, :] = 1
O1.blank_sim()
O2 = mps.mpslib(method='mps_snesim_tree',
n_real=1,
verbose_level=0,
ti_fnam=ti_file2)
O2.ti = np.transpose(ti)
'''
mpslib_hard_and_soft_data.py
Example of parsing hard and soft data to MPSLIB algorithms
'''
import mpslib as mps
import numpy as np
import matplotlib as plt
plt.ion()
if __name__ == '__main__':
#%%
#O1=mps.mpslib(method='mps_snesim_tree', parameter_filename='mps_snesim.txt')
O1 = mps.mpslib(method='mps_genesim', parameter_filename='mps_genesim.txt')
TI1, TI_filename1 = mps.trainingimages.strebelle(3, coarse3d=1)
O1.par['soft_data_categories'] = np.array([0, 1])
#TI1, TI_filename1 = mps.trainingimages.checkerboard2()
#O1.par['soft_data_categories']=np.array([0,1,2])
O1.ti = TI1
#%%
O1.par['rseed'] = 1
O1.par['n_multiple_grids'] = 0
O1.par['n_cond'] = 25
O1.par['n_cond_soft'] = 3
O1.par['n_real'] = 50
''' mpslib_simple: The simplest example of running MPSlib from python ''' import mpslib as mps #%% Initialize the MPS object, using a specific algorithm (def='mps_snesim_tree') O=mps.mpslib(method='mps_snesim_tree') #%% Select number of iterations [def=1] O.par['n_real']=4 # Set training image O.ti = mps.trainingimages.strebelle()[0] O.plot_ti() #%% Run MPSlib O.run() #%% Plot the results O.plot_reals(hardcopy=1, hardcopy_filename='mpslib_simple_reals') O.plot_etype(hardcopy=1, hardcopy_filename='mpslib_simple_etype') # O.plot_reals_vtk()
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 1 20:44:17 2018
@author: Thomas Mejer Hansen
"""
##%load_ext autoreload
##%autoreload 2
##%%
#import sys
#sys.path.append('../mpslib')
import matplotlib.pyplot as plt
import mpslib as mps
O1 = mps.mpslib(method='mps_genesim')
O1.par['debug_level'] = -1
# when debug_level>-1 and the text output from mpslib increase, the mpslib exe file is detached from
# python before it has finished running!!!
TI, TI_filename = mps.trainingimages.checkerboard2()
O1.ti = TI
O1.par['ti_fnam'] = TI_filename
O1.par['simulation_grid_size'][0] = 35
O1.par['simulation_grid_size'][1] = 35
O1.par['simulation_grid_size'][2] = 1
O1.par['soft_data_fnam'] = 'soft_case2.dat'
O1.par['shuffle_simulation_grid'] = 2
O1.par['n_cond'] = 25
O1.par['rseed'] = 0
O1.par['n_real'] = 16
O1.parameter_filename = 'plot.par'
'''
mpslib_hard_and_soft_data.py
Example of parsing hard and soft data to MPSLIB algorithms
'''
import mpslib as mps
import numpy as np
if __name__ == '__main__':
#%%
#O1=mps.mpslib(method='mps_snesim_tree', parameter_filename='mps_snesim.txt')
O1=mps.mpslib(method='mps_genesim', parameter_filename='mps_genesim.txt')
TI1, TI_filename1 = mps.trainingimages.strebelle(3, coarse3d=1)
O1.par['soft_data_categories']=np.array([0,1])
#TI1, TI_filename1 = mps.trainingimages.checkerboard2()
#O1.par['soft_data_categories']=np.array([0,1,2])
O1.ti=TI1
#%%
O1.par['rseed']=1
O1.par['n_multiple_grids']=0;
O1.par['n_cond']=25
O1.par['n_cond_soft']=3
O1.par['n_real']=50
# mpslib_example_3.py
# Example of 'blank_simulation' --> Currently not working!!
#%%
import mpslib as mps
import mpslib.eas as eas
import matplotlib.pyplot as plt
import numpy as np
#%% LOAD TI
ti = mps.trainingimages.strebelle(3, 1)[0]
#%% SIMULATE
O1 = mps.mpslib(method='mps_snesim_tree', n_real=1, verbose_level=-1)
O1.ti = ti
O1.run()
O1.blank_grid = np.zeros(
(O1.par['simulation_grid_size'][1], O1.par['simulation_grid_size'][0]),
dtype=np.int)
O1.blank_grid[0:20, :] = 1
O1.blank_sim()
O2 = mps.mpslib(method='mps_snesim_tree', n_real=1, verbose_level=0)
O2.ti = np.transpose(ti)
O2.run()
O2.blank_grid = np.zeros(
(O1.par['simulation_grid_size'][1], O1.par['simulation_grid_size'][0]),
dtype=np.int)
O2.blank_grid[20:, :] = 1
[13., 16., 0., 0.14375, 0.85625],
[3., 14., 0., 0.14375, 0.85625]])
# The soft data, but coded as 'hard' data
d_soft_hard = np.array([[6., 14., 0., 0., 1.], [13., 16., 0., 0., 1.],
[3., 14., 0., 0., 1.]])
#%% Initialize the MPS object, using a specific algorithm (def='mps_snesim_tree')
nx = 20
ny = 30
nz = 1
x0 = 0
y0 = 0
z0 = 0
O = mps.mpslib(method='mps_genesim',
simulation_grid_size=np.array([nx, ny, nz]),
origin=np.array([x0, y0, z0]))
O.delete_local_files() # Make sure no hard/soft data are conditioned to
# Use soft data
O.par['n_real'] = 100
O.par[
'shuffle_simulation_grid'] = 2 # '2' indicates using a preferential simulation path
O.par['n_max_ite'] = 10000000
O.par['n_max_cpd_count'] = 20
# Direct sampling style
O.par['n_cond'] = 16
O.par['n_cond_soft'] = 0
O.par['max_search_radius'] = 100
