make a start on grey

grey.py

@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Oct 8 17:07:13 2019
+
+@author: Tom
+"""
+
+import porespy as ps
+import pytrax as pt
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.ndimage as spim
+if __name__ == '__main__':
+ im = ps.generators.blobs(shape=[1000, 1000], porosity=0.7).astype(np.int)
+ dt = spim.distance_transform_edt(im)
+ grey = dt.copy()/dt.max()
+ # Number of time steps and walkers
+ num_t = 10000
+ num_w = 800
+ stride = 1
+
+ rw = pt.RandomWalk(grey, seed=False)
+ rw.run(num_t, num_w, same_start=False, stride=stride, num_proc=8)
+ # Plot mean square displacement
+ rw.plot_msd()
+ rw.plot_walk_2d()

pytrax/__RandomWalk__.py

@@ -210,6 +210,24 @@ def _get_starts(self, same_start=False):
  walkers = np.tile(w, (self.nw, 1))
  return walkers
 
+ def get_probable_cancels(self, walkers):
+ r'''
+ If the image is grey scale and has been normalized with values ranging
+ between 0.0 and 1.0 where the fractional value in each pixel represents
+ a fraction of space that is void we can moderate the walker movements
+ so that walkers in higher void space fractions have a higher chance of
+ moving on.
+ '''
+ random_cancel = np.random.random(len(walkers))
+ if self.dim == 2:
+ greys = self.im[walkers[:, 0],
+ walkers[:, 1]]
+ elif self.dim == 3:
+ greys = self.im[walkers[:, 0],
+ walkers[:, 1],
+ walkers[:, 2]]
+ return greys < random_cancel
+
  def _run_walk(self, walkers):
  r'''
  Run the walk in self contained way to enable parallel processing for
@@ -245,6 +263,12 @@ def _run_walk(self, walkers):
  if np.any(wall_hit):
  m[wall_hit] = 0
  mr[wall_hit] = 0
+ # Check that the move out of the current voxel is ok based on the
+ # Grey scale of the image
+ cancels = self.get_probable_cancels(walkers)
+ if np.any(cancels):
+ m[cancels] = 0
+ mr[cancels] = 0
  # Reflected velocity in real direction
  wr += mr*real
  walkers += m
@@ -435,9 +459,9 @@ def _build_big_image(self, num_copies=0):
  num_copies: int
  the number of times to copy the image along each axis
  '''
- big_im = self.im.copy()
+ big_im = self.im != self.solid_value
  func = [np.vstack, np.hstack, np.dstack]
- temp_im = self.im.copy()
+ temp_im = big_im.copy()
  for ax in tqdm(range(self.dim), desc='building big image'):
  flip_im = np.flip(temp_im, ax)
  for c in range(num_copies):
@@ -541,7 +565,8 @@ def plot_walk_2d(self, w_id=None, data='t', check_solid=False):
  solid = big_im == self.solid_value-2
  solid = solid.astype(float)
  solid[np.where(solid == 0)] = np.nan
- porous = big_im == self.solid_value-1
+# porous = big_im == self.solid_value-1
+ porous = big_im == -1
  porous = porous.astype(float)
  porous[np.where(porous == 0)] = np.nan
  plt.imshow(big_im, cmap=cmap)