Format code with black and isort

Author: dongwonmoon

axelrod/strategies/momentum.py

@@ -37,7 +37,7 @@ class Momentum(Player):
     def __init__(
         self,
         alpha=0.9914655399877477,  # Optimized by Genetic Algorithm. You can try to adapt it to any Env.
-        threshold=0.9676595613724907, # This one too
+        threshold=0.9676595613724907,  # This one too
     ) -> None:
         super().__init__()
         self.alpha = alpha

axelrod/tests/strategies/test_momentum.py

@@ -1,10 +1,11 @@
 import axelrod as axl
 from axelrod import Action
-from axelrod.tests.strategies.test_player import TestPlayer
 from axelrod.strategies.momentum import Momentum
+from axelrod.tests.strategies.test_player import TestPlayer
 
 C, D = Action.C, Action.D
 
+
 class TestMomentum(TestPlayer):
     name = "Momentum"
     player = Momentum
@@ -32,68 +33,56 @@ def test_strategy(self):
         self.versus_test(
             axl.MockPlayer(actions=[C]),
             expected_actions=actions,
-            init_kwargs={"alpha": 0.5,
-                         "threshold": 0.5
-                         },
-            attrs={"momentum": 1.0}
+            init_kwargs={"alpha": 0.5, "threshold": 0.5},
+            attrs={"momentum": 1.0},
         )
-        
+
         actions = [(C, D), (C, D), (D, D)]
         self.versus_test(
             axl.MockPlayer(actions=[D]),
             expected_actions=actions,
-            init_kwargs={"alpha": 0.5,
-                         "threshold": 0.5
-                         },
-            attrs={"momentum": 0.25}
+            init_kwargs={"alpha": 0.5, "threshold": 0.5},
+            attrs={"momentum": 0.25},
         )
-    
+
     def test_vs_alternator(self):
         actions = [(C, C), (C, D), (C, C), (C, D), (D, C)]
-        self.versus_test(axl.Alternator(), 
-                         expected_actions=actions, 
-                         init_kwargs={"alpha": 0.5,
-                                "threshold": 0.5
-            },
+        self.versus_test(
+            axl.Alternator(),
+            expected_actions=actions,
+            init_kwargs={"alpha": 0.5, "threshold": 0.5},
         )
 
     def test_vs_cooperator(self):
         actions = [(C, C), (C, C), (C, C), (C, C), (C, C)]
-        self.versus_test(axl.Cooperator(), 
-                         expected_actions=actions, 
-                         init_kwargs={"alpha": 0.5,
-                                "threshold": 0.5
-            },
+        self.versus_test(
+            axl.Cooperator(),
+            expected_actions=actions,
+            init_kwargs={"alpha": 0.5, "threshold": 0.5},
         )
 
     def test_vs_defector(self):
         actions = [(C, D), (C, D), (D, D), (D, D), (D, D)]
-        self.versus_test(axl.Defector(), 
-                         expected_actions=actions, 
-                         init_kwargs={"alpha": 0.5,
-                                "threshold": 0.5
-            },
+        self.versus_test(
+            axl.Defector(),
+            expected_actions=actions,
+            init_kwargs={"alpha": 0.5, "threshold": 0.5},
         )
 
     def test_vs_random(self):
-        # We can also test against random strategies
         actions = [(C, D), (C, C), (C, C), (C, D), (D, D)]
-        self.versus_test(axl.Random(), 
-                         expected_actions=actions, 
-                         seed=17,
-                         init_kwargs={"alpha": 0.5,
-                                "threshold": 0.5
-            },
+        self.versus_test(
+            axl.Random(),
+            expected_actions=actions,
+            seed=17,
+            init_kwargs={"alpha": 0.5, "threshold": 0.5},
         )
 
     def test_vs_random2(self):
         actions = [(C, C), (C, C), (C, C), (C, C)]
-        self.versus_test(axl.Random(), 
-                         expected_actions=actions, 
-                         seed=3,
-                         init_kwargs={"alpha": 0.5,
-                                "threshold": 0.5
-            },
+        self.versus_test(
+            axl.Random(),
+            expected_actions=actions,
+            seed=3,
+            init_kwargs={"alpha": 0.5, "threshold": 0.5},
         )
-
-