removed flip_action, now use C.flip() and D.flip()

axelrod/__init__.py

@@ -7,7 +7,7 @@
 from .version import __version__
 from .load_data_ import load_pso_tables, load_weights
 from . import graph
-from .actions import Actions, flip_action
+from .actions import Actions
 from .random_ import random_choice, seed, Pdf
 from .plot import Plot
 from .game import DefaultGame, Game

axelrod/actions.py

@@ -55,12 +55,6 @@ def from_char(cls, character):
 Action = Actions
 
 
-def flip_action(action: Action) -> Action:
-    if not isinstance(action, Action):
-        raise UnknownActionError('Not an Action')
-    return action.flip()
-
-
 def str_to_actions(actions: str) -> tuple:
     """Takes a string like 'CCDD' and returns a tuple of the appropriate
     actions."""

axelrod/player.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 
-from axelrod.actions import Actions, flip_action
+from axelrod.actions import Actions
 from .game import DefaultGame
 
 import types
@@ -208,10 +208,10 @@ def __repr__(self):
     def _add_noise(noise, s1, s2):
         r = random.random()
         if r < noise:
-            s1 = flip_action(s1)
+            s1 = s1.flip()
         r = random.random()
         if r < noise:
-            s2 = flip_action(s2)
+            s2 = s2.flip()
         return s1, s2
 
     def strategy(self, opponent):

axelrod/strategies/axelrod_first.py

@@ -4,7 +4,7 @@
 
 import random
 
-from axelrod.actions import Actions, flip_action, Action
+from axelrod.actions import Actions, Action
 from axelrod.player import Player
 from axelrod.random_ import random_choice
 from axelrod.strategy_transformers import FinalTransformer
@@ -124,7 +124,7 @@ def strategy(self, opponent: Player) -> Action:
         if (c >= 0 and c >= alt):
             move = C
         elif (c >= 0 and c < alt) or (alt >= 0):
-            move = flip_action(self.history[-1])
+            move = self.history[-1].flip()
         else:
             move = D
         return move

axelrod/strategies/axelrod_second.py

@@ -4,7 +4,7 @@
 
 import random
 
-from axelrod.actions import Actions, Action, flip_action
+from axelrod.actions import Actions, Action
 from axelrod.player import Player
 from axelrod.random_ import random_choice
 
@@ -139,7 +139,7 @@ def strategy(self, opponent: Player) -> Action:
             if len(self.history) in [1, 2]:
                 return C
             # Alternate C and D
-            return flip_action(self.history[-1])
+            return self.history[-1].flip()
 
     def reset(self):
         super().reset()

axelrod/strategies/negation.py

@@ -1,4 +1,4 @@
-from axelrod.actions import Actions, flip_action, Action
+from axelrod.actions import Actions, Action
 from axelrod.player import Player
 from axelrod.random_ import random_choice
 
@@ -31,4 +31,4 @@ def strategy(self, opponent: Player) -> Action:
         if not self.history:
             return random_choice()
         # Act opposite of opponent otherwise
-        return flip_action(opponent.history[-1])
+        return opponent.history[-1].flip()

axelrod/strategy_transformers.py

@@ -10,7 +10,7 @@
 import inspect
 import random
 from numpy.random import choice
-from .actions import Actions, flip_action
+from .actions import Actions
 from .random_ import random_choice
 
 
@@ -200,8 +200,8 @@ def generic_strategy_wrapper(player, opponent, proposed_action, *args,
 
 
 def flip_wrapper(player, opponent, action):
-    """Applies flip_action at the class level."""
-    return flip_action(action)
+    """Flips the player's original actions."""
+    return action.flip()
 
 
 FlipTransformer = StrategyTransformerFactory(
@@ -233,17 +233,17 @@ def dual_wrapper(player, opponent, proposed_action):
 
     action = player.original_player.strategy(opponent)
     player.original_player.history.append(action)
-    return flip_action(action)
+    return action.flip()
 
 
 DualTransformer = StrategyTransformerFactory(dual_wrapper, name_prefix="Dual")
 
 
 def noisy_wrapper(player, opponent, action, noise=0.05):
-    """Applies flip_action at the class level."""
+    """Flips the player's actions with probability: `noise`."""
     r = random.random()
     if r < noise:
-        return flip_action(action)
+        return action.flip()
     return action
 
 def noisy_reclassifier(original_classifier, noise):

axelrod/tests/strategies/test_calculator.py

@@ -2,7 +2,6 @@
 
 import axelrod
 from .test_player import TestPlayer
-from axelrod.actions import flip_action
 from axelrod._strategy_utils import detect_cycle
 C, D = axelrod.Actions.C, axelrod.Actions.D
 
@@ -104,7 +103,7 @@ def get_joss_strategy_actions(opponent_moves: list, indices_to_flip: list) -> li
         if index == 0:
             out.append((C, action))
         elif index in indices_to_flip:
-            out.append((flip_action(previous_action), action))
+            out.append((previous_action.flip(), action))
         else:
             out.append((previous_action, action))
     return out

axelrod/tests/unit/test_actions.py

@@ -1,5 +1,5 @@
 import unittest
-from axelrod import Actions, flip_action
+from axelrod import Actions
 from axelrod.actions import str_to_actions, UnknownActionError,  actions_to_str
 
 C, D = Actions.C, Actions.D
@@ -39,13 +39,6 @@ def test_from_char_error(self):
         self.assertRaises(UnknownActionError, Actions.from_char, 'd')
         self.assertRaises(UnknownActionError, Actions.from_char, 'A')
 
-    def test_flip_action(self):
-        self.assertEqual(flip_action(D), C)
-        self.assertEqual(flip_action(C), D)
-
-    def test_flip_action_error(self):
-        self.assertRaises(UnknownActionError, flip_action, 'R')
-
     def test_str_to_actions(self):
         self.assertEqual(str_to_actions(''), ())
         self.assertEqual(str_to_actions('C'), (C, ))

axelrod/tests/unit/test_strategy_transformers.py

@@ -2,7 +2,6 @@
 
 import axelrod
 from axelrod.strategy_transformers import *
-from axelrod.actions import flip_action
 from axelrod.tests.strategies.test_titfortat import TestTitForTat
 from axelrod.tests.strategies.test_cooperator import TestCooperator
 
@@ -102,7 +101,7 @@ def test_dual_wsls_transformer(self):
         for _ in range(10):
             p2.play(p3)
 
-        self.assertEqual(p1.history, [flip_action(x) for x in p2.history])
+        self.assertEqual(p1.history, [x.flip() for x in p2.history])
 
     def test_dual_tft_transformer(self):
         """Tests that DualTransformer produces the opposite results when faced
@@ -119,7 +118,7 @@ def test_dual_tft_transformer(self):
         for _ in range(10):
             p2.play(p3)
 
-        self.assertEqual(p1.history, [flip_action(x) for x in p2.history])
+        self.assertEqual(p1.history, [x.flip() for x in p2.history])
 
     def test_dual_majority_transformer(self):
         """Tests that DualTransformer produces the opposite results when faced
@@ -136,7 +135,7 @@ def test_dual_majority_transformer(self):
         for _ in range(10):
             p2.play(p3)
 
-        self.assertEqual(p1.history, [flip_action(x) for x in p2.history])
+        self.assertEqual(p1.history, [x.flip() for x in p2.history])
 
     def test_jossann_transformer(self):
         """Tests the JossAnn transformer.

docs/tutorials/further_topics/noisy_tournaments.rst

@@ -3,7 +3,7 @@ Noisy tournaments
 
 A common variation on iterated prisoner’s dilemma tournaments is to add
 stochasticity in the choice of actions, simply called noise. This noise is
-introduced by flipping plays between ‘C’ and ‘D’ with some probability that is
+introduced by flipping plays between C and D with some probability that is
 applied to all plays after they are delivered by the player [Bendor1993]_.
 
 The presence of this persistent background noise causes some strategies to