diff --git a/processing/post/addEuclideanDistance.py b/processing/post/addEuclideanDistance.py
new file mode 100755
index 000000000..04ee8ea6b
--- /dev/null
+++ b/processing/post/addEuclideanDistance.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python
+
+import os,re,sys,math,numpy,skfmm,string,damask
+from scipy import ndimage
+from optparse import OptionParser, Option
+
+# -----------------------------
+class extendableOption(Option):
+# -----------------------------
+# used for definition of new option parser action 'extend', which enables to take multiple option arguments
+# taken from online tutorial http://docs.python.org/library/optparse.html
+  
+  ACTIONS = Option.ACTIONS + ("extend",)
+  STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
+  TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
+  ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
+
+  def take_action(self, action, dest, opt, value, values, parser):
+    if action == "extend":
+      lvalue = value.split(",")
+      values.ensure_value(dest, []).extend(lvalue)
+    else:
+      Option.take_action(self, action, dest, opt, value, values, parser)
+
+
+def periodic_3Dpad(array, rimdim=(1,1,1)):
+
+  rimdim = numpy.array(rimdim,'i')
+  size = numpy.array(array.shape,'i')
+  padded = numpy.empty(size+2*rimdim,array.dtype)
+  padded[rimdim[0]:rimdim[0]+size[0],
+         rimdim[1]:rimdim[1]+size[1],
+         rimdim[2]:rimdim[2]+size[2]] = array
+
+  p = numpy.zeros(3,'i')
+  for side in xrange(3):
+    for p[(side+2)%3] in xrange(padded.shape[(side+2)%3]):
+      for p[(side+1)%3] in xrange(padded.shape[(side+1)%3]):
+        for p[side%3] in xrange(rimdim[side%3]):
+          spot = (p-rimdim)%size
+          padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
+        for p[side%3] in xrange(rimdim[side%3]+size[side%3],size[side%3]+2*rimdim[side%3]):
+          spot = (p-rimdim)%size
+          padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
+  return padded
+
+# --------------------------------------------------------------------
+#                                MAIN
+# --------------------------------------------------------------------
+
+features = [ \
+            {'aliens': 1, 'names': ['boundary','biplane'],},
+            {'aliens': 2, 'names': ['tripleline',],},
+            {'aliens': 3, 'names': ['quadruplepoint',],}
+           ]
+
+neighborhoods = {
+                  'neumann':numpy.array([
+                                        [-1, 0, 0],
+                                        [ 1, 0, 0],
+                                        [ 0,-1, 0],
+                                        [ 0, 1, 0],
+                                        [ 0, 0,-1],
+                                        [ 0, 0, 1],
+                                      ]),
+                  'moore':numpy.array([
+                                        [-1,-1,-1],
+                                        [ 0,-1,-1],
+                                        [ 1,-1,-1],
+                                        [-1, 0,-1],
+                                        [ 0, 0,-1],
+                                        [ 1, 0,-1],
+                                        [-1, 1,-1],
+                                        [ 0, 1,-1],
+                                        [ 1, 1,-1],
+                                        [-1,-1, 0],
+                                        [ 0,-1, 0],
+                                        [ 1,-1, 0],
+                                        [-1, 0, 0],
+#
+                                        [ 1, 0, 0],
+                                        [-1, 1, 0],
+                                        [ 0, 1, 0],
+                                        [ 1, 1, 0],
+                                        [-1,-1, 1],
+                                        [ 0,-1, 1],
+                                        [ 1,-1, 1],
+                                        [-1, 0, 1],
+                                        [ 0, 0, 1],
+                                        [ 1, 0, 1],
+                                        [-1, 1, 1],
+                                        [ 0, 1, 1],
+                                        [ 1, 1, 1],
+                                      ])
+                }
+
+parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
+Add column containing Euclidean distance to grain structural features:
+boundaries, triple lines, and quadruple points.
+
+""" + string.replace('$Id$','\n','\\n')
+)
+
+parser.add_option('-g','--geom',        dest='geom', action='store', type='string', \
+                                        help='optional geometry file', \
+                                        metavar='<file>')
+parser.add_option('-i','--identifier',  dest='id', action='store', type='string', \
+                                        help='heading of column containing grain identifier [%default]', \
+                                        metavar='<label>')
+parser.add_option('-t','--type',        dest='type', action='extend', type='string', \
+                                        help='feature type (%s)'%(', '.join(map(lambda x:', '.join(x['names']),features))))
+parser.add_option('-n','--neighborhood',  dest='neigborhood', action='store', type='string', \
+                                        help='type of neighborhood (%s)'%(', '.join(neighborhoods.keys())), \
+                                        metavar='<int>')
+
+parser.set_defaults(type = [])
+parser.set_defaults(id = 'texture')
+parser.set_defaults(neighborhood = 'neumann')
+
+(options,filenames) = parser.parse_args()
+
+options.neighborhood = options.neighborhood.lower()
+if options.neighborhood not in neighborhoods:
+  parser.error('unknown neighborhood %s!'%options.neighborhood)
+  
+feature_list = []
+for i,feature in enumerate(features):
+  for name in feature['names']:
+    for type in options.type:
+      if name.startswith(type):
+        feature_list.append(i)                            # remember valid features
+        break
+
+# ------------------------------------------ setup file handles ---------------------------------------  
+
+files = []
+if filenames == []:
+  files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
+else:
+  for name in filenames:
+    if os.path.exists(name):
+      files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
+
+# ------------------------------------------ loop over input files ---------------------------------------  
+
+for file in files:
+  if file['name'] != 'STDIN': file['croak'].write(file['name']+'\n')
+
+  table = damask.ASCIItable(file['input'],file['output'],False)             # make unbuffered ASCII_table
+  table.head_read()                                                         # read ASCII header info
+  table.info_append(string.replace('$Id$','\n','\\n') + \
+                    '\t' + ' '.join(sys.argv[1:]))
+
+# ------------------------------------------ assemble header ---------------------------------------  
+
+  if options.id not in table.labels:
+    file['croak'].write('column %s not found...\n'%options.id)
+    continue
+
+  for feature in feature_list:
+    table.labels_append('ED_%s(%s)'%(features[feature]['names'][0],options.id))     # extend ASCII header with new labels
+
+  table.head_write()
+
+# ------------------------------------------ process data ---------------------------------------  
+
+  structure = table.data_asArray(['ip.x','ip.y','ip.z',options.id])
+
+  grid = [{},{},{}]
+  for i in xrange(len(structure)):
+    for j in xrange(3):
+      grid[j][str(structure[i,j])] = True
+
+  resolution = numpy.array(map(len,grid),'i')
+  unitlength = 0.0
+  for i,r in enumerate(resolution):
+    if r > 1: unitlength = max(unitlength,(max(map(float,grid[i].keys()))-min(map(float,grid[i].keys())))/(r-1.0))
+
+  neighborhood = neighborhoods[options.neighborhood]
+  convoluted = numpy.empty([len(neighborhood)]+list(resolution+2),'i')
+  microstructure = periodic_3Dpad(numpy.array(structure[:,3].reshape(resolution),'i'))
+
+  for i,p in enumerate(neighborhood):
+    stencil = numpy.zeros((3,3,3),'i')
+    stencil[1,1,1] = -1
+    stencil[p[0]+1,
+            p[1]+1,
+            p[2]+1] = 1
+
+    convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil)
+
+  distance = numpy.ones((len(feature_list),resolution[0],resolution[1],resolution[2]),'d')
+  for x in xrange(resolution[0]):
+    for y in xrange(resolution[1]):
+      for z in xrange(resolution[2]):
+        uniques = len(numpy.unique(convoluted[:,x+1,y+1,z+1]))
+        for i,feature_id in enumerate(feature_list):
+          if uniques > features[feature_id]['aliens']:
+            distance[i,x,y,z] = 0.0  # found a starting point
+
+  for i in xrange(len(feature_list)):
+    distance[i,:,:,:] = skfmm.distance(distance[i,:,:,:], dx=[unitlength]*3)
+  distance.shape = (len(feature_list),resolution.prod())
+
+  table.data_rewind()
+  l = 0
+  while table.data_read():
+    for i in xrange(len(feature_list)):
+      table.data_append(distance[i,l])                                      # add all distance fields
+    table.data_write()                                                      # output processed line
+    l += 1
+    
+# ------------------------------------------ output result ---------------------------------------  
+
+  table.output_flush()                                                      # just in case of buffered ASCII table
+
+  if file['name'] != 'STDIN':
+    file['input'].close()                                                   # close input ASCII table
+    file['output'].close()                                                  # close output ASCII table
+    os.rename(file['name']+'_tmp',file['name'])                             # overwrite old one with tmp new