MAINT: use ruff format instead of black

.pre-commit-config.yaml

@@ -2,26 +2,23 @@
 # pre-commit install
 
 repos:
-  - repo: https://github.com/psf/black
-    rev: 23.9.1
-    hooks:
-      - id: black
   - repo: https://github.com/adamchainz/blacken-docs
     rev: 1.16.0
     hooks:
       - id: blacken-docs
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.3
+    rev: v3.1.0
     hooks:
       - id: prettier
         files: \.(html|md|toml|yml|yaml)
         args: [--prose-wrap=preserve]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.291
+    rev: v0.1.8
     hooks:
       - id: ruff
         args:
           - --fix
+      - id: ruff-format
   - repo: local
     hooks:
       - id: generate_requirements.py

networkx/algorithms/assortativity/correlation.py

@@ -94,7 +94,7 @@ def degree_assortativity_coefficient(G, x="out", y="in", weight=None, nodes=None
     else:
         degrees = {d for _, d in G.degree(nodes, weight=weight)}
 
-    mapping = {d: i for i, d, in enumerate(degrees)}
+    mapping = {d: i for i, d in enumerate(degrees)}
     M = degree_mixing_matrix(G, x=x, y=y, nodes=nodes, weight=weight, mapping=mapping)
 
     return _numeric_ac(M, mapping=mapping)
@@ -251,7 +251,7 @@ def numeric_assortativity_coefficient(G, attribute, nodes=None):
     if nodes is None:
         nodes = G.nodes
     vals = {G.nodes[n][attribute] for n in nodes}
-    mapping = {d: i for i, d, in enumerate(vals)}
+    mapping = {d: i for i, d in enumerate(vals)}
     M = attribute_mixing_matrix(G, attribute, nodes, mapping)
     return _numeric_ac(M, mapping)
 

networkx/algorithms/isomorphism/ismags.py

@@ -882,10 +882,7 @@ def _map_nodes(self, sgn, candidates, constraints, mapping=None, to_be_mapped=No
 
             # The next node is the one that is unmapped and has fewest
             # candidates
-            # Pylint disables because it's a one-shot function.
-            next_sgn = min(
-                left_to_map, key=lambda n: min(new_candidates[n], key=len)
-            )  # pylint: disable=cell-var-from-loop
+            next_sgn = min(left_to_map, key=lambda n: min(new_candidates[n], key=len))
             yield from self._map_nodes(
                 next_sgn,
                 new_candidates,
@@ -909,10 +906,7 @@ def _largest_common_subgraph(self, candidates, constraints, to_be_mapped=None):
         # "part of" the subgraph in to_be_mapped, and we make it a little
         # smaller every iteration.
 
-        # pylint disable because it's guarded against by default value
-        current_size = len(
-            next(iter(to_be_mapped), [])
-        )  # pylint: disable=stop-iteration-return
+        current_size = len(next(iter(to_be_mapped), []))
 
         found_iso = False
         if current_size <= len(self.graph):

networkx/algorithms/isomorphism/tests/test_vf2pp_helpers.py

@@ -57,8 +57,9 @@ def test_single_node(self):
             dict(zip(G2, it.cycle(labels_many))),
             "label",
         )
-        l1, l2 = nx.get_node_attributes(G1, "label"), nx.get_node_attributes(
-            G2, "label"
+        l1, l2 = (
+            nx.get_node_attributes(G1, "label"),
+            nx.get_node_attributes(G2, "label"),
         )
 
         gparams = _GraphParameters(
@@ -118,8 +119,9 @@ def test_matching_order(self):
             dict(zip(G2, it.cycle(labels))),
             "label",
         )
-        l1, l2 = nx.get_node_attributes(G1, "label"), nx.get_node_attributes(
-            G2, "label"
+        l1, l2 = (
+            nx.get_node_attributes(G1, "label"),
+            nx.get_node_attributes(G2, "label"),
         )
         gparams = _GraphParameters(
             G1,
@@ -155,8 +157,9 @@ def test_matching_order_all_branches(self):
         G2.nodes[4]["label"] = "red"
         G2.nodes[5]["label"] = "blue"
 
-        l1, l2 = nx.get_node_attributes(G1, "label"), nx.get_node_attributes(
-            G2, "label"
+        l1, l2 = (
+            nx.get_node_attributes(G1, "label"),
+            nx.get_node_attributes(G2, "label"),
         )
         gparams = _GraphParameters(
             G1,

networkx/algorithms/shortest_paths/generic.py

@@ -647,8 +647,9 @@ def all_pairs_all_shortest_paths(G, weight=None, method="dijkstra"):
     single_source_all_shortest_paths
     """
     for n in G:
-        yield n, dict(
-            single_source_all_shortest_paths(G, n, weight=weight, method=method)
+        yield (
+            n,
+            dict(single_source_all_shortest_paths(G, n, weight=weight, method=method)),
         )
 
 

networkx/algorithms/tests/test_bridges.py

@@ -127,7 +127,7 @@ def test_no_weight(self):
         inf = float("inf")
         expected = {(3, 4, inf), (4, 3, inf)}
         assert next(nx.local_bridges(self.BB)) in expected
-        expected = {(u, v, 3) for u, v, in self.square.edges}
+        expected = {(u, v, 3) for u, v in self.square.edges}
         assert set(nx.local_bridges(self.square)) == expected
         assert list(nx.local_bridges(self.tri)) == []
 

networkx/utils/tests/test_misc.py

@@ -247,7 +247,8 @@ def test_arbitrary_element(iterable_type, expected):
 
 
 @pytest.mark.parametrize(
-    "iterator", ((i for i in range(3)), iter([1, 2, 3]))  # generator
+    "iterator",
+    ((i for i in range(3)), iter([1, 2, 3])),  # generator
 )
 def test_arbitrary_element_raises(iterator):
     """Value error is raised when input is an iterator."""