added options for selective seed picking based on Mitchell’s best candidate algorithm for more uniformly distributed (spatially) seeds points

processing/pre/seeds_fromRandom.py

@@ -3,8 +3,10 @@
 
 import os,sys,string,math,random
 import numpy as np
-from optparse import OptionParser
 import damask
+from optparse import OptionParser,OptionGroup
+from scipy import spatial
+
 
 scriptID   = string.replace('$Id$','\n','\\n')
 scriptName = os.path.splitext(scriptID.split()[1])[0]
@@ -33,6 +35,17 @@ parser.add_option('--sigma', dest='sigma', type='float', metavar='float', \
                   help='standard deviation of Gaussian Distribution for weights [%default]')
 parser.add_option('-m', '--microstructure', dest='microstructure', type='int',
                   help='first microstructure index [%default]', metavar='int')
+parser.add_option('-s','--selective', dest='selective', action='store_true',
+                  help = 'selective picking of seed points from random seed points [%default]')
+group = OptionGroup(parser, "Selective Seeding Options",
+                    "More uniform distribution of seed points using Mitchells Best Candidate Algorithm"
+                   )
+group.add_option('--distance', dest='bestDistance', type='float', metavar='float', \
+                  help='minimum distance to the next neighbor  [%default]')
+group.add_option('--numCandidates', dest='numCandidates', type='int', metavar='int', \
+                  help='maximum number of point to consider for initial random points generation  [%default]')    
+parser.add_option_group(group)
+
 parser.set_defaults(randomSeed = None)
 parser.set_defaults(grid = (16,16,16))
 parser.set_defaults(N = 20)
@@ -40,6 +53,10 @@ parser.set_defaults(weights=False)
 parser.set_defaults(mean = 0.0)
 parser.set_defaults(sigma = 1.0)
 parser.set_defaults(microstructure = 1)
+parser.set_defaults(selective = False)
+parser.set_defaults(bestDistance = 0.2)
+parser.set_defaults(numCandidates = 10)
+
 
 
 (options,filename) = parser.parse_args()
@@ -47,6 +64,18 @@ options.grid = np.array(options.grid)
 
 labels = "1_coords\t2_coords\t3_coords\tphi1\tPhi\tphi2\tmicrostructure"
 
+# ------------------------------------------ Functions Definitions ---------------------------------
+
+def kdtree_search(xyz, point) :
+    dist, index = spatial.cKDTree(xyz).query(np.array(point))
+    return dist
+    
+def generatePoint() :
+    return np.array([random.uniform(0,options.grid[0]/max(options.grid)), \
+                     random.uniform(0,options.grid[1]/max(options.grid)), \
+                     random.uniform(0,options.grid[2]/max(options.grid))])
+
+
 # ------------------------------------------ setup file handle -------------------------------------
 if filename == []:
   file = {'output':sys.stdout, 'croak':sys.stderr}
@@ -69,6 +98,9 @@ grainEuler[0,:] *= 360.0
 grainEuler[1,:] = np.arccos(2*grainEuler[1,:]-1)*180.0/math.pi                          # cos(Phi) is uniformly distributed
 grainEuler[2,:] *= 360.0                                                                # phi_2    is uniformly distributed
 
+microstructure=np.arange(options.microstructure,options.microstructure+options.N).reshape(1,options.N)
+
+if options.selective == False :
   seedpoints = -np.ones(options.N,dtype='int')                                            # init grid positions of seed points
 
   if options.N * 1024 < gridSize:                                                         # heuristic limit for random search
@@ -88,9 +120,23 @@ seeds[1,:] = (np.mod(seedpoints//                 options.grid[0] ,options.grid[
                +np.random.random())/options.grid[1]
   seeds[2,:] = (np.mod(seedpoints//(options.grid[1]*options.grid[0]),options.grid[2])\
                +np.random.random())/options.grid[2]
-microstructure=np.arange(options.microstructure,options.microstructure+options.N).reshape(1,options.N)
-
   table = np.transpose(np.concatenate((seeds,grainEuler,microstructure),axis = 0))
+else :
+  samples = generatePoint().reshape(1,3)
+
+  while  samples.shape[0] < options.N :
+     bestDistance  = options.bestDistance
+     for i in xrange(options.numCandidates) :
+       c = generatePoint()
+       d = kdtree_search(samples, c)
+       if (d > bestDistance) :
+        bestDistance = d
+        bestCandidate = c
+     if kdtree_search(samples,bestCandidate) != 0.0 :
+        samples = np.append(samples,bestCandidate.reshape(1,3),axis=0)
+     else :
+        continue
+  table = np.transpose(np.concatenate((samples.T,grainEuler,microstructure),axis = 0))
 
 if options.weights :
    weight = np.random.normal(loc=options.mean, scale=options.sigma, size=options.N)
@@ -98,7 +144,7 @@ if options.weights :
    table = np.append(table, weight.reshape(options.N,1), axis=1)
    labels += "\tweight"
 
-
+# -------------------------------------- Write Data --------------------------------------------------
 
 header = ["5\theader",
           scriptID + " " + " ".join(sys.argv[1:]),