Merge branch 'development' into env-reporting

PRIVATE

@@ -1 +1 @@
-Subproject commit 7f0594060779d9a8a4e774d558134309ab77b96e
+Subproject commit ab64793bb04c506d815ebc850672ed0f2d013e67

VERSION

@@ -1 +1 @@
-v3.0.0-alpha3-37-ge9cfb2f96
+v3.0.0-alpha3-40-ga1c46b445

python/damask/seeds.py

@@ -71,18 +71,25 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=
     coords = _np.empty((N_seeds,3))
     coords[0] = rng.random(3) * size
 
-    i = 1
+    s = 1
+    i = 0
     progress = util._ProgressBar(N_seeds+1,'',50)
-    while i < N_seeds:
+    while s < N_seeds:
         candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
-        tree = _spatial.cKDTree(coords[:i],boxsize=size) if periodic else \
-               _spatial.cKDTree(coords[:i])
+        tree = _spatial.cKDTree(coords[:s],boxsize=size) if periodic else \
+               _spatial.cKDTree(coords[:s])
         distances, dev_null = tree.query(candidates)
         best = distances.argmax()
         if distances[best] > distance:                                                              # require minimum separation
-            coords[i] = candidates[best]                                                            # maximum separation to existing point cloud
+            coords[s] = candidates[best]                                                            # maximum separation to existing point cloud
+            s += 1
+            progress.update(s)
+            i = 0
+        else:
             i += 1
-            progress.update(i)
+
+        if i == 100:
+            raise ValueError('Seeding not possible')
 
     return coords
 

python/tests/test_seeds.py

@@ -26,6 +26,15 @@ class TestSeeds:
                        cKDTree(coords).query(coords, 2)
         assert (0<= coords).all() and (coords<size).all() and np.min(min_dists[:,1])>=distance
 
+    @pytest.mark.parametrize('periodic',[True,False])
+    def test_from_Poisson_disc_invalid(self,periodic):
+        N_seeds = np.random.randint(30,300)
+        N_candidates = N_seeds//15
+        distance = np.random.random()
+        size = np.ones(3)*distance
+        with pytest.raises(ValueError):
+            seeds.from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=periodic)
+
     def test_from_grid_reconstruct(self):
         cells = np.random.randint(10,20,3)
         N_seeds = np.random.randint(30,300)