diff --git a/processing/pre/geom_fromVoronoiTessellation.py b/processing/pre/geom_fromVoronoiTessellation.py
index fbf30ae00..3ecf7c099 100755
--- a/processing/pre/geom_fromVoronoiTessellation.py
+++ b/processing/pre/geom_fromVoronoiTessellation.py
@@ -13,43 +13,44 @@ from scipy import spatial
 import damask
 
 
-def findClosestSeed(seeds, weights, point):
-  return np.argmin(np.sum((np.broadcast_to(point,(len(seeds),3))-seeds)**2,axis=1) - weights)
-
 scriptName = os.path.splitext(os.path.basename(__file__))[0]
 scriptID   = ' '.join([scriptName,damask.version])
 
 
+def findClosestSeed(seeds, weights, point):
+    return np.argmin(np.sum((np.broadcast_to(point,(len(seeds),3))-seeds)**2,axis=1) - weights)
+
+
 def Laguerre_tessellation(grid, seeds, grains, size, periodic, weights, cpus):
 
-  if periodic:
-    weights_p = np.tile(weights,27).flatten(order='F')                                              # Laguerre weights (1,2,3,1,2,3,...,1,2,3)
-    seeds_p = np.vstack((seeds  -np.array([size[0],0.,0.]),seeds,  seeds  +np.array([size[0],0.,0.])))
-    seeds_p = np.vstack((seeds_p-np.array([0.,size[1],0.]),seeds_p,seeds_p+np.array([0.,size[1],0.])))
-    seeds_p = np.vstack((seeds_p-np.array([0.,0.,size[2]]),seeds_p,seeds_p+np.array([0.,0.,size[2]])))
-  else:
-    weights_p = weights.flatten()
-    seeds_p   = seeds
+    if periodic:
+        weights_p = np.tile(weights,27).flatten(order='F')                                          # Laguerre weights (1,2,3,1,2,3,...,1,2,3)
+        seeds_p = np.vstack((seeds  -np.array([size[0],0.,0.]),seeds,  seeds  +np.array([size[0],0.,0.])))
+        seeds_p = np.vstack((seeds_p-np.array([0.,size[1],0.]),seeds_p,seeds_p+np.array([0.,size[1],0.])))
+        seeds_p = np.vstack((seeds_p-np.array([0.,0.,size[2]]),seeds_p,seeds_p+np.array([0.,0.,size[2]])))
+    else:
+        weights_p = weights.flatten()
+        seeds_p   = seeds
 
-  if cpus > 1:
-    default_args = partial(findClosestSeed,seeds_p,weights_p)
-    pool = multiprocessing.Pool(processes = cpus)                                                   # initialize workers
-    result = pool.map_async(default_args, [point for point in grid])                                # evaluate function in parallel
-    pool.close()
-    pool.join()
-    closestSeeds = np.array(result.get()).flatten()
-  else:
-    closestSeeds= np.array([findClosestSeed(seeds_p,weights_p,point) for point in grid])
+    if cpus > 1:
+        default_args = partial(findClosestSeed,seeds_p,weights_p)
+        pool = multiprocessing.Pool(processes = cpus)                                               # initialize workers
+        result = pool.map_async(default_args, [point for point in grid])                            # evaluate function in parallel
+        pool.close()
+        pool.join()
+        closestSeeds = np.array(result.get()).flatten()
+    else:
+        closestSeeds= np.array([findClosestSeed(seeds_p,weights_p,point) for point in grid])
 
-  return grains[closestSeeds%seeds.shape[0]]
+    return grains[closestSeeds%seeds.shape[0]]
 
 
 def Voronoi_tessellation(grid, seeds, grains, size, periodic = True):
 
-  KDTree = spatial.cKDTree(seeds,boxsize=size) if periodic else spatial.cKDTree(seeds)
-  devNull,closestSeeds = KDTree.query(grid)
+    KDTree = spatial.cKDTree(seeds,boxsize=size) if periodic else spatial.cKDTree(seeds)
+    devNull,closestSeeds = KDTree.query(grid)
 
-  return grains[closestSeeds]
+    return grains[closestSeeds]
 
 
 # --------------------------------------------------------------------
diff --git a/processing/pre/seeds_fromRandom.py b/processing/pre/seeds_fromRandom.py
index f497518dc..bb5b5e88e 100755
--- a/processing/pre/seeds_fromRandom.py
+++ b/processing/pre/seeds_fromRandom.py
@@ -2,7 +2,6 @@
 
 import os
 import sys
-import random
 from optparse import OptionParser,OptionGroup
 
 import numpy as np
@@ -139,8 +138,8 @@ for name in filenames:
         i = 1
         progress = damask.util._ProgressBar(options.N,'',50)
         while i < options.N:
-            candidates = np.random.choice(unpicked[unpicked.mask==False],replace=False,
-                                          size=min(len(unpicked[unpicked.mask==False]),options.numCandidates))
+            candidates = np.random.choice(unpicked[not unpicked.mask],replace=False,
+                                          size=min(len(unpicked[not unpicked.mask]),options.numCandidates))
             tree = spatial.cKDTree(seeds[:i])
             distances, dev_null = tree.query(coords[candidates])
             best = distances.argmax()
diff --git a/python/damask/_config/__init__.py b/python/damask/_config/__init__.py
index f3635d2a9..f5c57a879 100644
--- a/python/damask/_config/__init__.py
+++ b/python/damask/_config/__init__.py
@@ -1,5 +1,3 @@
-# -*- coding: UTF-8 no BOM -*-
-
-"""Aggregator for configuration file handling"""
+"""Aggregator for configuration file handling."""
 
 from .material import Material    # noqa