Bug fixes in voronoi generators

openpnm/_skgraph/generators/_voronoi_delaunay_dual.py

@@ -65,7 +65,7 @@ def voronoi_delaunay_dual(
         points=points,
         shape=shape,
         reflect=reflect,
-        f=1,
+        f=f,
     )
 
     # Generate mask to remove any dims with all 0's

openpnm/_skgraph/generators/tools/_funcs.py

@@ -150,7 +150,7 @@ def parse_points(shape, points, reflect=False, f=1):
 
     """
     # Deal with input arguments
-    shape = np.array(shape, dtype=int)
+    shape = np.array(shape, dtype=float)
     if isinstance(points, int):
         points = generate_base_points(num_points=points,
                                       domain_size=shape,

openpnm/models/misc/_neighbor_lookups.py

@@ -37,6 +37,8 @@
                          neighboring throats
             'mean'       Returns the value of the mean property of the
                          neighboring throats
+            'sum'        Returns the sum of the property of the neighboring
+                         throats
             ===========  =====================================================
 
     Returns
@@ -69,6 +71,11 @@
         if ignore_nans:
             np.subtract.at(counts, im.row, nans[im.col])
         values = values/counts
+    if mode == 'sum':
+        if ignore_nans:
+            data[nans] = 0
+        values = np.zeros((network.Np, ))
+        np.add.at(values, im.row, data[im.col])
     return values
 
 
@@ -98,6 +105,8 @@
                          neighboring pores
             'mean'       Returns the value of the mean property of the
                          neighboring pores
+            'sum'        Returns the sum of the property of the neighrboring
+                         pores
             ===========  =====================================================
 
     ignore_nans : bool (default is ``True``)
@@ -122,6 +131,8 @@
             value = np.amax(pvalues, axis=1)
         if mode == 'mean':
             value = np.mean(pvalues, axis=1)
+        if mode == 'sum':
+            value = np.sum(pvalues, axis=1)
     except np.AxisError:  # Handle case of empty pvalues
         value = []
     return np.array(value)