Merge pull request #1037 from Axelrod-Python/pep8

Various PEP8 and style fixes

Author: meatballs

axelrod/deterministic_cache.py

@@ -1,10 +1,10 @@
 from collections import UserDict
 import pickle
-from typing import List, Tuple
 
 from .actions import Action
 from .player import Player
 
+from typing import List, Tuple
 
 CachePlayerKey = Tuple[Player, Player, int]
 CacheKey = Tuple[str, str, int]

axelrod/ecosystem.py

@@ -1,6 +1,8 @@
-from axelrod.result_set import ResultSet
 import random
+from axelrod.result_set import ResultSet
 from typing import List, Callable
+
+
 class Ecosystem(object):
     """Create an ecosystem based on the payoff matrix from an Axelrod
     tournament."""
@@ -21,14 +23,17 @@ def __init__(self, results: ResultSet, fitness: Callable[[float], float] =None,
         # values.
         if population:
             if min(population) < 0:
-                raise TypeError("Minimum value of population vector must be non-negative")
+                raise TypeError(
+                    "Minimum value of population vector must be non-negative")
             elif len(population) != self.nplayers:
-                raise TypeError("Population vector must be same size as number of players")
+                raise TypeError(
+                    "Population vector must be same size as number of players")
             else:
                 norm = sum(population)
                 self.population_sizes = [[p / norm for p in population]]
         else:
-            self.population_sizes = [[1 / self.nplayers for i in range(self.nplayers)]]
+            self.population_sizes = [
+                [1 / self.nplayers for _ in range(self.nplayers)]]
 
         # This function is quite arbitrary and probably only influences the
         # kinetics for the current code.

axelrod/fingerprint.py

@@ -1,16 +1,16 @@
 from collections import namedtuple
 from tempfile import NamedTemporaryFile
 import matplotlib.pyplot as plt
-from typing import List, Any, Union
 import numpy as np
 import tqdm
-import axelrod as axl
 
+import axelrod as axl
 from axelrod import on_windows, Player
 from axelrod.strategy_transformers import JossAnnTransformer, DualTransformer
 from axelrod.interaction_utils import (
     compute_final_score_per_turn, read_interactions_from_file)
 
+from typing import List, Any, Union
 
 Point = namedtuple('Point', 'x y')
 

axelrod/interaction_utils.py

@@ -9,11 +9,11 @@
 """
 from collections import Counter
 import csv
+import tqdm
 
 from axelrod.actions import Actions
 from .game import Game
 
-import tqdm
 
 C, D = Actions.C, Actions.D
 

axelrod/load_data_.py

@@ -1,5 +1,6 @@
 import pkg_resources
-from typing import List, Dict, Tuple, Union
+from typing import List, Dict, Tuple
+
 
 def load_file(filename: str, directory: str) -> List[List[str]]:
     """Loads a data file stored in the Axelrod library's data subdirectory,
@@ -28,6 +29,7 @@ def load_weights(filename: str ="ann_weights.csv", directory: str ="data") -> Di
         d[name] = (num_features, num_hidden, weights)
     return d
 
+
 def load_pso_tables(filename="pso_gambler.csv", directory="data"):
     """Load lookup tables."""
     rows = load_file(filename, directory)

axelrod/mock_player.py

@@ -1,8 +1,9 @@
+from collections import defaultdict
+from itertools import cycle
 import warnings
+
 from axelrod.actions import Actions, Action
 from axelrod.player import Player, update_history, update_state_distribution
-from collections import defaultdict
-from itertools import cycle
 
 from typing import List, Tuple
 

axelrod/player.py

@@ -1,15 +1,15 @@
 from collections import defaultdict
-from functools import wraps
-import random
 import copy
 import inspect
-import types
-import numpy as np
 import itertools
+import random
+
+import numpy as np
 
-from axelrod.actions import Actions, flip_action, Action
+from axelrod.actions import Actions, flip_action
 from .game import DefaultGame
 
+import types
 from typing import Dict, Any
 
 C, D = Actions.C, Actions.D
@@ -97,12 +97,14 @@ def __new__(cls, *args, **kwargs):
     @classmethod
     def init_params(cls, *args, **kwargs):
         """
-        Return a dictionary containing the init parameters of a strategy (without 'self').
+        Return a dictionary containing the init parameters of a strategy
+        (without 'self').
         Use *args and *kwargs as value if specified
         and complete the rest with the default values.
         """
         sig = inspect.signature(cls.__init__)
-        # the 'self' parameter needs to be removed or the first *args will be assigned to it
+        # The 'self' parameter needs to be removed or the first *args will be
+        # assigned to it
         self_param = sig.parameters.get('self')
         new_params = list(sig.parameters.values())
         new_params.remove(self_param)
@@ -125,7 +127,6 @@ def __init__(self):
         self.state_distribution = defaultdict(int)
         self.set_match_attributes()
 
-
     def __eq__(self, other):
         """
         Test if two players are equal.
@@ -149,7 +150,6 @@ def __eq__(self, other):
                 generator, original_value = itertools.tee(value)
                 other_generator, original_other_value = itertools.tee(other_value)
 
-
                 if isinstance(value, types.GeneratorType):
                     setattr(self, attribute,
                             (ele for ele in original_value))
@@ -171,14 +171,13 @@ def __eq__(self, other):
             # Code for a strange edge case where each strategy points at each
             # other
             elif (value is other and other_value is self):
-                    pass
+                pass
 
             else:
                 if value != other_value:
                     return False
         return True
 
-
     def receive_match_attributes(self):
         # Overwrite this function if your strategy needs
         # to make use of match_attributes such as

axelrod/strategies/_filters.py

@@ -71,7 +71,7 @@ class ExampleStrategy(Player):
 
     Parameters
     ----------
-        strategy : a descendant class of axelrod.Player
+        player: a descendant class of axelrod.Player
         classifier_key: string
             Defining which entry from the strategy's classifier dict is to be
             tested (e.g. 'makes_use_of').

axelrod/strategies/adaptive.py

@@ -1,6 +1,5 @@
 from axelrod.actions import Actions, Action
 from axelrod.player import Player
-import axelrod
 
 from typing import List
 

axelrod/strategies/ann.py

@@ -6,12 +6,12 @@
 
 import numpy as np
 
-from typing import List, Tuple
-
 from axelrod.actions import Actions, Action
 from axelrod.player import Player
 from axelrod.load_data_ import load_weights
 
+from typing import List, Tuple
+
 C, D = Actions.C, Actions.D
 nn_weights = load_weights()
 
@@ -69,8 +69,6 @@ def compute_features(player: Player, opponent: Player) -> List[int]:
         opponent_previous2_c = 0
         opponent_previous2_d = 0
 
-
-
     else:
         opponent_first_c = 1 if opponent.history[0] == C else 0
         opponent_first_d = 1 if opponent.history[0] == D else 0
@@ -179,7 +177,8 @@ class ANN(Player):
         'long_run_time': False
     }
 
-    def __init__(self, weights: List[float], num_features: int, num_hidden: int) -> None:
+    def __init__(self, weights: List[float], num_features: int,
+                 num_hidden: int) -> None:
         super().__init__()
         (i2h, h2o, bias) = split_weights(weights, num_features, num_hidden)
         self.input_to_hidden_layer_weights = np.matrix(i2h)