perf: a vertex makes its coordinate with np.array, not np.r_

np.r_[coord] is a class that parses its index and cost 1.4 us, 88% of
creating a vertex.  np.array(coord, ndmin=1) costs 0.16 us and gives the same
array for every coordinate checked (a list, a tuple, a scalar, a numpy array of
either type, a range, a 2-D list): same shape, type and values, and a copy.

Vertex(coord) 1.59 -> 0.35 us; add_vertex(coord) 3.12 -> 1.72 us, where 0.6.5
took 2.18 us.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

Author: petercorke

src/pgraph/PGraph.py

@@ -4518,7 +4518,8 @@ def __init__(self, coord: ArrayLike | None = None, name: str | None = None):
         # invalidate; writing the attributes directly avoids the cost of
         # ``__setattr__`` on each of them.
         self.__dict__.update(
-            coord=None if coord is None else np.r_[coord],
+            # np.array is a tenth of the cost of np.r_ for the same result here
+            coord=None if coord is None else np.array(coord, ndmin=1),
             _name=name,
             _label=None,
             _connectivitychange=True,

tests/test_vertex_init.py

@@ -39,6 +39,37 @@ def test_the_coordinate_is_a_float_array_copy(self):
         self.assertEqual(v.coord[0], 1.0)  # not the caller's array
         self.assertIsInstance(v.coord, np.ndarray)
 
+    def test_the_coordinate_is_what_np_r_made(self):
+        # the coordinate used to be made with np.r_[coord]; for everything a
+        # coordinate can be it is the same array, in shape, type and values
+        for coord in (
+            [1, 2],
+            [1.5, 2],
+            (1, 2, 3),
+            5,
+            2.5,
+            np.array([1.0, 2.0]),
+            np.array([1, 2], dtype=np.int32),
+            range(3),
+            [[1, 2], [3, 4]],
+        ):
+            with self.subTest(coord=repr(coord)):
+                v = Vertex(coord=coord)
+                expected = np.r_[coord]
+                self.assertEqual(v.coord.shape, expected.shape)
+                self.assertEqual(v.coord.dtype, expected.dtype)
+                np.testing.assert_array_equal(v.coord, expected)
+
+    def test_a_scalar_is_a_vector_of_one(self):
+        self.assertEqual(Vertex(coord=7).coord.shape, (1,))
+
+    def test_an_array_given_is_copied(self):
+        c = np.array([1, 2])
+        v = Vertex(coord=c)
+        c[0] = 99
+        self.assertEqual(v.coord[0], 1)
+        self.assertFalse(np.shares_memory(v.coord, c))
+
     def test_the_lists_are_not_shared_between_vertices(self):
         a, b = Vertex(), Vertex()
         self.assertIsNot(a._edgelist, b._edgelist)