Added docstrings to strategy methods

.github/workflows/config.yml

@@ -58,6 +58,10 @@ jobs:
       run: |
         python -m pip install pylint
         python -m pylint --disable=all --enable=unused-import axelrod/strategies/_strategies.py
+    - name: Check format
+      run: |
+        python -m pip install black
+        python -m black -l 80 . --check
     - name: Check that installs
       run: |
         python setup.py install

.isort.cfg

@@ -4,4 +4,4 @@ multi_line_output = 3
 include_trailing_comma = True
 force_grid_wrap = 0
 combine_as_imports = True
-line_length = 88
+line_length = 80

.prepare-commit-msg.py

@@ -41,7 +41,9 @@
 
 if branch.startswith(issue_prefix):
     issue_number = re.match("%s(.*)" % issue_prefix, branch).group(1)
-    print("prepare-commit-msg: Prepending [#%s] to commit message" % issue_number)
+    print(
+        "prepare-commit-msg: Prepending [#%s] to commit message" % issue_number
+    )
 
     with open(commit_msg_filepath, "r+") as f:
         content = f.read()

axelrod/_strategy_utils.py

@@ -113,7 +113,7 @@ def simulate_match(player_1, player_2, strategy, rounds=10):
 
 
 def _calculate_scores(p1, p2, game):
-    """Calculates the scores for two players based their history.
+    """Calculates the scores for two players based on their history.
 
     Parameters
     ----------
@@ -142,9 +142,9 @@ def look_ahead(player1, player2, game, rounds=10):
 
     Parameters
     ----------
-    player_1: Player
+    player1: Player
         The player that will look ahead.
-    player_2: Player
+    player2: Player
         The opponent that will be inspected.
     game: Game
         The Game object used to score rounds.

axelrod/compute_finite_state_machine_memory.py

@@ -113,7 +113,10 @@ def get_accessible_transitions(
         if trans.state in accessible_states:
             accessible_transitions[
                 (trans.state, trans.last_opponent_action)
-            ] = (trans.next_state, trans.next_action)
+            ] = (
+                trans.next_state,
+                trans.next_action,
+            )
 
     return accessible_transitions
 

axelrod/ecosystem.py

@@ -33,7 +33,7 @@ def __init__(
         population: List[int] = None,
     ) -> None:
         """Create a new ecosystem.
-        
+
         Parameters
         ----------
         results: ResultSet
@@ -83,7 +83,7 @@ def __init__(
 
     def reproduce(self, turns: int):
         """Reproduce populations according to the payoff matrix.
-        
+
         Parameters
         ----------
         turns: int

axelrod/evolvable_player.py

@@ -7,6 +7,7 @@
 
 class InsufficientParametersError(Exception):
     """Error indicating that insufficient parameters were specified to initialize an Evolvable Player."""
+
     def __init__(self, *args):
         super().__init__(*args)
 
@@ -49,7 +50,7 @@ def create_new(self, **kwargs):
     def serialize_parameters(self):
         """Serialize parameters."""
         pickled = dumps(self.init_kwargs)  # bytes
-        s = base64.b64encode(pickled).decode('utf8')  # string
+        s = base64.b64encode(pickled).decode("utf8")  # string
         return s
 
     @classmethod

axelrod/fingerprint.py

@@ -42,7 +42,7 @@ def _create_points(step: float, progress_bar: bool = True) -> List[Point]:
     num = int((1 / step) // 1) + 1
 
     if progress_bar:
-        p_bar = tqdm.tqdm(total=num**2, desc="Generating points")
+        p_bar = tqdm.tqdm(total=num ** 2, desc="Generating points")
 
     points = []
     for x in np.linspace(0, 1, num):
@@ -88,8 +88,8 @@ def _create_jossann(point: Point, probe: Any) -> Player:
 
     if x + y >= 1:
         joss_ann = DualTransformer()(
-            JossAnnTransformer((1 - x, 1 - y))(
-                probe_class))(**init_kwargs)
+            JossAnnTransformer((1 - x, 1 - y))(probe_class)
+        )(**init_kwargs)
     else:
         joss_ann = JossAnnTransformer((x, y))(probe_class)(**init_kwargs)
     return joss_ann
@@ -177,7 +177,10 @@ def _generate_data(interactions: dict, points: list, edges: list) -> dict:
     """
     edge_scores = [
         np.mean(
-            [compute_final_score_per_turn(scores)[0] for scores in interactions[edge]]
+            [
+                compute_final_score_per_turn(scores)[0]
+                for scores in interactions[edge]
+            ]
         )
         for edge in edges
     ]
@@ -215,7 +218,9 @@ def _reshape_data(data: dict, points: list, size: int) -> np.ndarray:
 
 class AshlockFingerprint(object):
     def __init__(
-        self, strategy: Union[type, Player], probe: Union[type, Player] = axl.TitForTat
+        self,
+        strategy: Union[type, Player],
+        probe: Union[type, Player] = axl.TitForTat,
     ) -> None:
         """
         Parameters
@@ -277,7 +282,7 @@ def fingerprint(
         processes: int = None,
         filename: str = None,
         progress_bar: bool = True,
-        seed: int = None
+        seed: int = None,
     ) -> dict:
         """Build and play the spatial tournament.
 
@@ -323,8 +328,11 @@ def fingerprint(
 
         self.step = step
         self.spatial_tournament = axl.Tournament(
-            tourn_players, turns=turns, repetitions=repetitions, edges=edges,
-            seed=seed
+            tourn_players,
+            turns=turns,
+            repetitions=repetitions,
+            edges=edges,
+            seed=seed,
         )
         self.spatial_tournament.play(
             build_results=False,
@@ -432,7 +440,7 @@ def fingerprint(
         processes: int = None,
         filename: str = None,
         progress_bar: bool = True,
-        seed: int = None
+        seed: int = None,
     ) -> np.array:
         """Creates a spatial tournament to run the necessary matches to obtain
         fingerprint data.
@@ -479,7 +487,7 @@ def fingerprint(
             turns=turns,
             noise=noise,
             repetitions=repetitions,
-            seed=seed
+            seed=seed,
         )
         tournament.play(
             filename=filename,
@@ -516,7 +524,9 @@ def analyse_cooperation_ratio(filename):
             opponent in each turn. The ith row corresponds to the ith opponent
             and the jth column the jth turn.
         """
-        did_c = np.vectorize(lambda actions: [int(action == "C") for action in actions])
+        did_c = np.vectorize(
+            lambda actions: [int(action == "C") for action in actions]
+        )
 
         cooperation_rates = {}
         df = dd.read_csv(filename)
@@ -525,7 +535,10 @@ def analyse_cooperation_ratio(filename):
         df = df[df["Player index"] == 0][["Opponent index", "Actions"]]
 
         for _, row in df.iterrows():
-            opponent_index, player_history = row["Opponent index"], row["Actions"]
+            opponent_index, player_history = (
+                row["Opponent index"],
+                row["Actions"],
+            )
             if opponent_index in cooperation_rates:
                 cooperation_rates[opponent_index].append(did_c(player_history))
             else:
@@ -590,7 +603,8 @@ def plot(
 
         if display_names:
             plt.yticks(
-                range(len(self.opponents)), [str(player) for player in self.opponents]
+                range(len(self.opponents)),
+                [str(player) for player in self.opponents],
             )
         else:
             plt.yticks([0, len(self.opponents) - 1], [0, 1])

axelrod/game.py

@@ -16,9 +16,11 @@ class Game(object):
         The numerical score attribute to all combinations of action pairs.
     """
 
-    def __init__(self, r: Score = 3, s: Score = 0, t: Score = 5, p: Score = 1) -> None:
+    def __init__(
+        self, r: Score = 3, s: Score = 0, t: Score = 5, p: Score = 1
+    ) -> None:
         """Create a new game object.
-        
+
         Parameters
         ----------
         r: int or float
@@ -30,7 +32,12 @@ def __init__(self, r: Score = 3, s: Score = 0, t: Score = 5, p: Score = 1) -> No
         p: int or float
             Score obtained by both player for mutual defection.
         """
-        self.scores = {(C, C): (r, r), (D, D): (p, p), (C, D): (s, t), (D, C): (t, s)}
+        self.scores = {
+            (C, C): (r, r),
+            (D, D): (p, p),
+            (C, D): (s, t),
+            (D, C): (t, s),
+        }
 
     def RPST(self) -> Tuple[Score, Score, Score, Score]:
         """Returns game matrix values in Press and Dyson notation."""

axelrod/graph.py

@@ -9,20 +9,20 @@
 
 class Graph(object):
     """Weighted and directed graph class.
-    
+
     This class is intended for the graph associated to a Markov process,
     since it gives easy access to the neighbors of a particular state.
 
     Vertices can be any hashable Python object.
-    
+
     Initialize with a list of edges:
         [[node1, node2, weights], ...]
     Weights can be omitted for an undirected graph.
 
     For efficiency, neighbors are cached in dictionaries. Undirected
     graphs are implemented as directed graphs in which every edge (s, t)
     has the opposite edge (t, s).
-    
+
     Attributes
     ----------
     directed: Boolean indicating whether the graph is directed
@@ -31,7 +31,7 @@ class Graph(object):
         all tails to their edge weights (None means no weight)
     in_mapping: a dictionary mapping all tails to dictionaries that map
         all heads to their edge weights (none means to weight)
-    
+
     Properties
     ----------
     vertices: the set of vertices in the graph
@@ -155,8 +155,9 @@ def attached_complete_graphs(length, loops=True, directed=False):
     for cluster in range(2):
         for i in range(length):
             for j in range(i + 1, length):
-                edges.append(("{}:{}".format(cluster, i),
-                              "{}:{}".format(cluster, j)))
+                edges.append(
+                    ("{}:{}".format(cluster, i), "{}:{}".format(cluster, j))
+                )
     # Attach at one node
     edges.append(("0:0", "1:0"))
     graph = Graph(directed=directed, edges=edges)

axelrod/history.py

@@ -82,7 +82,9 @@ def __eq__(self, other):
         if isinstance(other, list):
             return self._plays == other
         elif isinstance(other, History):
-            return self._plays == other._plays and self._coplays == other._coplays
+            return (
+                self._plays == other._plays and self._coplays == other._coplays
+            )
         raise TypeError("Cannot compare types.")
 
     def __getitem__(self, key):
@@ -121,7 +123,9 @@ def __init__(self, memory_depth, plays=None, coplays=None):
     def flip_plays(self):
         """Creates a flipped plays history for use with DualTransformer."""
         flipped_plays = [action.flip() for action in self._plays]
-        return self.__class__(self.memory_depth, plays=flipped_plays, coplays=self._coplays)
+        return self.__class__(
+            self.memory_depth, plays=flipped_plays, coplays=self._coplays
+        )
 
     def append(self, play, coplay):
         """Appends a new (play, coplay) pair an updates metadata for

axelrod/interaction_utils.py

@@ -32,7 +32,8 @@ def compute_final_score(interactions, game=None):
         return None
 
     final_score = tuple(
-        sum([score[player_index] for score in scores]) for player_index in [0, 1]
+        sum([score[player_index] for score in scores])
+        for player_index in [0, 1]
     )
     return final_score
 

axelrod/load_data_.py

@@ -56,7 +56,12 @@ def load_pso_tables(filename="pso_gambler.csv", directory="data"):
     rows = load_file(filename, directory)
     d = dict()
     for row in rows:
-        name, a, b, c, = str(row[0]), int(row[1]), int(row[2]), int(row[3])
+        name, a, b, c, = (
+            str(row[0]),
+            int(row[1]),
+            int(row[2]),
+            int(row[3]),
+        )
         values = list(map(float, row[4:]))
         d[(name, int(a), int(b), int(c))] = values
     return d

axelrod/match.py

@@ -29,7 +29,7 @@ def __init__(
         noise=0,
         match_attributes=None,
         reset=True,
-        seed=None
+        seed=None,
     ):
         """
         Parameters
@@ -193,7 +193,8 @@ def play(self):
             result = []
             for _ in range(turns):
                 plays = self.simultaneous_play(
-                    self.players[0], self.players[1], self.noise)
+                    self.players[0], self.players[1], self.noise
+                )
                 result.append(plays)
 
             if self._cache_update_required:

axelrod/match_generator.py

@@ -12,7 +12,7 @@ def __init__(
         prob_end=None,
         edges=None,
         match_attributes=None,
-        seed=None
+        seed=None,
     ):
         """
         A class to generate matches. This is used by the Tournament class which