diff --git a/python/damask/seeds.py b/python/damask/seeds.py
index 10d247859..76c0bbb26 100644
--- a/python/damask/seeds.py
+++ b/python/damask/seeds.py
@@ -8,7 +8,7 @@ from . import grid_filters
 def from_random(size,N_seeds,grid=None,seed=None):
     """
     Random seeding in space.
-    
+
     Parameters
     ----------
     size : numpy.ndarray of shape (3)
@@ -21,7 +21,7 @@ def from_random(size,N_seeds,grid=None,seed=None):
     seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
         A seed to initialize the BitGenerator. Defaults to None.
         If None, then fresh, unpredictable entropy will be pulled from the OS.
-    
+
     """
     rng = _np.random.default_rng(seed)
     if grid is None:
@@ -31,13 +31,13 @@ def from_random(size,N_seeds,grid=None,seed=None):
         coords = grid_coords[rng.choice(_np.prod(grid),N_seeds, replace=False)] \
                + _np.broadcast_to(size/grid,(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25)           # wobble without leaving grid
 
-    coords
+    return coords
 
 
-def from_Poisson_disc(size,N_seeds,N_candidates,distance,seed=None):
+def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,seed=None):
     """
     Seeding in space according to a Poisson disc distribution.
-    
+
     Parameters
     ----------
     size : numpy.ndarray of shape (3)
@@ -48,10 +48,12 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,seed=None):
         Number of candidates to consider for finding best candidate.
     distance : float
         Minimum acceptable distance to other seeds.
+    periodic : boolean, optional
+        Calculate minimum distance for periodically repeated grid.
     seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
         A seed to initialize the BitGenerator. Defaults to None.
         If None, then fresh, unpredictable entropy will be pulled from the OS.
-    
+
     """
     rng = _np.random.default_rng(seed)
     coords = _np.empty((N_seeds,3))
@@ -61,7 +63,8 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,seed=None):
     progress = util._ProgressBar(N_seeds+1,'',50)
     while i < N_seeds:
         candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
-        tree = _spatial.cKDTree(coords[:i])
+        tree = _spatial.cKDTree(coords[:i],boxsize=size) if periodic else \
+               _spatial.cKDTree(coords[:i])
         distances, dev_null = tree.query(candidates)
         best = distances.argmax()
         if distances[best] > distance:                                                              # require minimum separation
@@ -75,12 +78,12 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,seed=None):
 def from_geom(geom,selection=None,invert=False):
     """
     Create seed from existing geometry description.
-    
+
     Parameters
     ----------
     geom : damask.Geom
         ...
-    
+
     """
     material = geom.materials.reshape((-1,1),order='F')
     mask = _np.full(geom.grid.prod(),True,dtype=bool) if selection is None else \
diff --git a/python/tests/test_seeds.py b/python/tests/test_seeds.py
index 57631ccba..55b659ad1 100644
--- a/python/tests/test_seeds.py
+++ b/python/tests/test_seeds.py
@@ -13,11 +13,13 @@ class TestSeeds:
         coords = seeds.from_random(size,N_seeds,grid)
         assert (0<=coords).all() and (coords<size).all()
 
-    def test_from_Poisson_disc(self):
+    @pytest.mark.parametrize('periodic',[True,False])
+    def test_from_Poisson_disc(self,periodic):
         N_seeds = np.random.randint(30,300)
         N_candidates = N_seeds//15
         distance = np.random.random()
         size = np.ones(3)*distance*N_seeds
-        coords = seeds.from_Poisson_disc(size,N_seeds,N_candidates,distance)
-        min_dists, _ = cKDTree(coords).query(coords, 2)
+        coords = seeds.from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=periodic)
+        min_dists, _ = cKDTree(coords,boxsize=size).query(coords, 2) if periodic else \
+                       cKDTree(coords).query(coords, 2)
         assert (0<= coords).all() and (coords<size).all() and np.min(min_dists[:,1])>=distance