compatible with older scipy versions

python/damask/_grid.py

@@ -436,8 +436,12 @@ class Grid:
 
         """
         coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3)
-        KDTree = spatial.cKDTree(seeds,boxsize=size) if periodic else spatial.cKDTree(seeds)
-        devNull,material_ = KDTree.query(coords, workers = int(os.environ.get('OMP_NUM_THREADS',4)))
+        tree = spatial.cKDTree(seeds,boxsize=size) if periodic else \
+               spatial.cKDTree(seeds)
+        try:
+            material_ = tree.query(coords, workers = int(os.environ.get('OMP_NUM_THREADS',4)))[1]
+        except TypeError:
+            material_ = tree.query(coords, n_jobs = int(os.environ.get('OMP_NUM_THREADS',4)))[1]    # scipy <1.6
 
         return Grid(material = (material_ if material is None else material[material_]).reshape(cells),
                     size     = size,

python/damask/seeds.py

@@ -78,7 +78,7 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=
         candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
         tree = _spatial.cKDTree(coords[:s],boxsize=size) if periodic else \
                _spatial.cKDTree(coords[:s])
-        distances, dev_null = tree.query(candidates)
+        distances = tree.query(candidates)[0]
         best = distances.argmax()
         if distances[best] > distance:                                                              # require minimum separation
             coords[s] = candidates[best]                                                            # maximum separation to existing point cloud