Fix indent issues in retworkx bipartition docstrings

src/tree.rs

@@ -166,8 +166,8 @@ fn _minimum_spanning_tree<'a>(
 /// allowed while still being a valid balanced cut edge.
 ///
 /// :returns: A list of tuples, with each tuple representing a distinct
-/// balanced edge that can be cut. The tuple contains the root of one of the
-/// two partitioned subtrees and the set of nodes making up that subtree.
+///  balanced edge that can be cut. The tuple contains the root of one of the
+///  two partitioned subtrees and the set of nodes making up that subtree.
 #[pyfunction]
 #[pyo3(text_signature = "(spanning_tree, pop, target_pop, epsilon)")]
 pub fn bipartition_tree(
@@ -242,14 +242,14 @@ pub fn bipartition_tree(
 /// will be passed the edge object and expected to return a ``float``. See
 /// :func:`~minimum_spanning_tree` for details.
 /// :param pops: The populations assigned to each node in the graph.
-/// :param float pop_target: The population target to reach when partitioning the
-/// graph.
+/// :param float pop_target: The population target to reach when partitioning
+/// the graph.
 /// :param float epsilon: The maximum percent deviation from the pop_target
 /// allowed while still being a valid balanced cut edge.
 ///
 /// :returns: A list of tuples, with each tuple representing a distinct
-/// balanced edge that can be cut. The tuple contains the root of one of the
-/// two partitioned subtrees and the set of nodes making up that subtree.
+///  balanced edge that can be cut. The tuple contains the root of one of the
+///  two partitioned subtrees and the set of nodes making up that subtree.
 #[pyfunction]
 #[pyo3(text_signature = "(graph, weight_fn, pop, target_pop, epsilon)")]
 pub fn bipartition_graph(

tests/graph/test_bipartition.py

@@ -56,17 +56,17 @@ def setUp(self):
 
  def test_one_balanced_edge_tree(self):
  balanced_edges = retworkx.bipartition_tree(
- self.tree, 
+ self.tree,
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,
  0.2,
  )
  self.assertEqual(len(balanced_edges), 1)
 
- # Since this is already a spanning tree, bipartition_graph should 
+ # Since this is already a spanning tree, bipartition_graph should
  # behave identically. That is, it should be invariant to weight_fn
  graph_balanced_edges = retworkx.bipartition_graph(
- self.tree, 
+ self.tree,
  lambda x: random.random(),
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,
@@ -76,15 +76,15 @@ def test_one_balanced_edge_tree(self):
 
  def test_two_balanced_edges_tree(self):
  balanced_edges = retworkx.bipartition_tree(
- self.tree, 
+ self.tree,
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,
  0.5,
  )
  self.assertEqual(len(balanced_edges), 1)
 
  graph_balanced_edges = retworkx.bipartition_graph(
- self.tree, 
+ self.tree,
  lambda x: random.random(),
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,
@@ -94,15 +94,15 @@ def test_two_balanced_edges_tree(self):
 
  def test_three_balanced_edges_line(self):
  balanced_edges = retworkx.bipartition_tree(
- self.line, 
+ self.line,
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,
  0.5,
  )
  self.assertEqual(len(balanced_edges), 3)
 
  graph_balanced_edges = retworkx.bipartition_graph(
- self.line, 
+ self.line,
  lambda x: random.random(),
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,
@@ -112,15 +112,15 @@ def test_three_balanced_edges_line(self):
 
  def test_one_balanced_edges_line(self):
  balanced_edges = retworkx.bipartition_tree(
- self.line, 
+ self.line,
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,
  0.01,
  )
  self.assertEqual(len(balanced_edges), 1)
 
  graph_balanced_edges = retworkx.bipartition_graph(
- self.line, 
+ self.line,
  lambda x: random.random(),
  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
  3.0,