Run black and isort.

Author: drvinceknight

axelrod/classifier.py

@@ -14,6 +14,7 @@
 )
 
 import yaml
+
 from axelrod.makes_use_of import makes_use_of
 from axelrod.player import Player
 

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/fingerprint.py

@@ -3,18 +3,19 @@
 from tempfile import mkstemp
 from typing import Any, List, Union
 
-import axelrod as axl
 import dask.dataframe as dd
 import matplotlib.pyplot as plt
 import numpy as np
 import tqdm
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+import axelrod as axl
 from axelrod import Player
 from axelrod.interaction_utils import (
     compute_final_score_per_turn,
     read_interactions_from_file,
 )
 from axelrod.strategy_transformers import DualTransformer, JossAnnTransformer
-from mpl_toolkits.axes_grid1 import make_axes_locatable
 
 Point = namedtuple("Point", "x y")
 

axelrod/game.py

@@ -20,7 +20,7 @@ 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

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

axelrod/interaction_utils.py

@@ -11,6 +11,7 @@
 
 import pandas as pd
 import tqdm
+
 from axelrod.action import Action, str_to_actions
 
 from .game import Game

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/moran.py

@@ -5,6 +5,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+
 from axelrod import DEFAULT_TURNS, EvolvablePlayer, Game, Player
 from axelrod.deterministic_cache import DeterministicCache
 from axelrod.graph import Graph, complete_graph

axelrod/player.py

@@ -6,6 +6,7 @@
 from typing import Any, Dict
 
 import numpy as np
+
 from axelrod import _module_random
 from axelrod.action import Action
 from axelrod.game import DefaultGame

axelrod/random_.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
 import numpy as np
-from axelrod.action import Action
 from numpy.random import RandomState
 
+from axelrod.action import Action
+
 C, D = Action.C, Action.D