COMPAT: tackle numpy row_stack deprecation (#773)

Author: martinfleis

docs/user-guide/graph/matching-graph.ipynb

@@ -46,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "points = np.row_stack([(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3), (7, 4)])\n",
+    "points = np.vstack([(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3), (7, 4)])\n",
     "gdf = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(*points.T))"
    ]
   },

libpysal/graph/_triangulation.py

@@ -262,7 +262,7 @@ def _gabriel(coordinates, coplanar):
         edges,
         points,
     )
-    edges = numpy.row_stack(list(set(map(tuple, edges)).difference(set(droplist))))
+    edges = numpy.vstack(list(set(map(tuple, edges)).difference(set(droplist))))
     heads_ix, tails_ix = edges.T
     order = numpy.lexsort((tails_ix, heads_ix))
     sorted_heads_ix = heads_ix[order]

libpysal/graph/tests/test_matching.py

@@ -40,14 +40,14 @@ def stores():
 
 def test_correctness_k1():
     # manual solution for simple k=1 by hungarian method
-    known = np.row_stack([(0, 3), (1, 4), (2, 3), (3, 0), (3, 2), (4, 1)])
+    known = np.vstack([(0, 3), (1, 4), (2, 3), (3, 0), (3, 2), (4, 1)])
     computed = _spatial_matching(simple, n_matches=1, solver=default_solver)
     np.testing.assert_array_equal(known, np.column_stack((computed[0], computed[1])))
     computed_partial = _spatial_matching(
         simple, n_matches=1, allow_partial_match=True, solver=default_solver
     )
     # manual solution by relaxing the above
-    known = np.row_stack(
+    known = np.vstack(
         [
             (0, 2, 0.5),
             (0, 3, 0.5),

libpysal/weights/gabriel.py

@@ -191,7 +191,7 @@ def __init__(self, coordinates, **kwargs):
             edges,
             dt.points,
         )
-        output = numpy.row_stack(list(set(map(tuple, edges)).difference(set(droplist))))
+        output = numpy.vstack(list(set(map(tuple, edges)).difference(set(droplist))))
         ids = kwargs.get("ids")
         if ids is not None:
             ids = numpy.asarray(ids)