From 8aa17a0579ed1b4559f36b6135036482b089ced5 Mon Sep 17 00:00:00 2001
From: Ben Elliston <bje@air.net.au>
Date: Mon, 24 Apr 2023 16:04:40 +1000
Subject: [PATCH] Round one of flake8 warnings.

---
 deap/benchmarks/__init__.py     | 12 +++++++++++-
 deap/benchmarks/gp.py           | 10 +++++++++-
 deap/benchmarks/movingpeaks.py  |  6 +++++-
 deap/benchmarks/tools.py        |  7 +++++++
 deap/creator.py                 |  2 ++
 deap/tests/test_algorithms.py   |  5 +++++
 deap/tests/test_creator.py      |  4 ++++
 deap/tests/test_logbook.py      |  4 +---
 deap/tests/test_mutation.py     |  1 +
 deap/tests/test_pickle.py       |  3 ++-
 deap/tools/_hypervolume/pyhv.py | 15 ++-------------
 deap/tools/constraint.py        |  4 ++++
 deap/tools/emo.py               | 17 +++++++++++++++++
 deap/tools/indicator.py         |  1 +
 deap/tools/init.py              |  4 +++-
 deap/tools/migration.py         |  1 +
 deap/tools/selection.py         |  6 +++++-
 deap/tools/support.py           |  8 ++++++--
 18 files changed, 86 insertions(+), 24 deletions(-)

diff --git a/deap/benchmarks/__init__.py b/deap/benchmarks/__init__.py
index ca2acd89b..a371ddc02 100644
--- a/deap/benchmarks/__init__.py
+++ b/deap/benchmarks/__init__.py
@@ -22,6 +22,7 @@
 from operator import mul
 from functools import reduce
 
+
 # Unimodal
 def rand(individual):
     r"""Random test objective function.
@@ -41,6 +42,7 @@ def rand(individual):
     """
     return random.random(),
 
+
 def plane(individual):
     r"""Plane test objective function.
 
@@ -59,6 +61,7 @@ def plane(individual):
     """
     return individual[0],
 
+
 def sphere(individual):
     r"""Sphere test objective function.
 
@@ -77,6 +80,7 @@ def sphere(individual):
     """
     return sum(gene * gene for gene in individual),
 
+
 def cigar(individual):
     r"""Cigar test objective function.
 
@@ -95,6 +99,7 @@ def cigar(individual):
     """
     return individual[0]**2 + 1e6 * sum(gene * gene for gene in individual[1:]),
 
+
 def rosenbrock(individual):
     r"""Rosenbrock test objective function.
 
@@ -117,6 +122,7 @@ def rosenbrock(individual):
     return sum(100 * (x * x - y)**2 + (1. - x)**2 \
                    for x, y in zip(individual[:-1], individual[1:])),
 
+
 def h1(individual):
     r""" Simple two-dimensional function containing several local maxima.
     From: The Merits of a Parallel Genetic Algorithm in Solving Hard
@@ -145,7 +151,7 @@ def h1(individual):
     denum = ((individual[0] - 8.6998)**2 + (individual[1] - 6.7665)**2)**0.5 + 1
     return num / denum,
 
- #
+
 # Multimodal
 def ackley(individual):
     r"""Ackley test objective function.
@@ -596,6 +602,7 @@ def dtlz5(ind, n_objs):
                        reduce(lambda x,y: x*y, [cos(theta(a)) for a in ind[1:m-1]], 1) * sin(theta(ind[m-1])))
     return fit
 
+
 def dtlz6(ind, n_objs):
     r"""DTLZ6 multiobjective function. It returns a tuple of *obj* values. The
     individual must have at least *obj* elements.
@@ -616,6 +623,7 @@ def dtlz6(ind, n_objs):
                        reduce(lambda x,y: x*y, [cos(theta(a)) for a in ind[1:m-1]], 1) * sin(theta(ind[m-1])))
     return fit
 
+
 def dtlz7(ind, n_objs):
     r"""DTLZ7 multiobjective function. It returns a tuple of *obj* values. The
     individual must have at least *obj* elements.
@@ -627,6 +635,7 @@ def dtlz7(ind, n_objs):
     fit.append((1 + gval) * (n_objs - sum([a / (1.0 + gval) * (1 + sin(3 * pi * a)) for a in ind[:n_objs-1]])))
     return fit
 
+
 def fonseca(individual):
     r"""Fonseca and Fleming's multiobjective function.
     From: C. M. Fonseca and P. J. Fleming, "Multiobjective optimization and
@@ -642,6 +651,7 @@ def fonseca(individual):
     f_2 = 1 - exp(-sum((xi + 1/sqrt(3))**2 for xi in individual[:3]))
     return f_1, f_2
 
+
 def poloni(individual):
     r"""Poloni's multiobjective function on a two attribute *individual*. From:
     C. Poloni, "Hybrid GA for multi objective aerodynamic shape optimization",
diff --git a/deap/benchmarks/gp.py b/deap/benchmarks/gp.py
index 94423bc00..a196d2615 100644
--- a/deap/benchmarks/gp.py
+++ b/deap/benchmarks/gp.py
@@ -15,6 +15,7 @@
 
 from math import exp, sin, cos
 
+
 def kotanchek(data):
     """Kotanchek benchmark function.
 
@@ -29,6 +30,7 @@ def kotanchek(data):
     """
     return exp(-(data[0] - 1)**2) / (3.2 + (data[1] - 2.5)**2)
 
+
 def salustowicz_1d(data):
     """Salustowicz benchmark function.
 
@@ -43,8 +45,9 @@ def salustowicz_1d(data):
     """
     return exp(-data[0]) * data[0]**3 * cos(data[0]) * sin(data[0]) * (cos(data[0]) * sin(data[0])**2 - 1)
 
+
 def salustowicz_2d(data):
-    """Salustowicz benchmark function.
+    r"""Salustowicz benchmark function.
 
     .. list-table:: 
        :widths: 10 50
@@ -57,6 +60,7 @@ def salustowicz_2d(data):
     """
     return exp(-data[0]) * data[0]**3 * cos(data[0]) * sin(data[0]) * (cos(data[0]) * sin(data[0])**2 - 1) * (data[1] - 5)
 
+
 def unwrapped_ball(data):
     """Unwrapped ball benchmark function.
 
@@ -71,6 +75,7 @@ def unwrapped_ball(data):
     """
     return 10. / (5. + sum((d - 3)**2 for d in data))
 
+
 def rational_polynomial(data):
     """Rational polynomial ball benchmark function.
 
@@ -85,6 +90,7 @@ def rational_polynomial(data):
     """
     return 30. * (data[0] - 1) * (data[2] - 1) / (data[1]**2 * (data[0] - 10))
 
+
 def sin_cos(data):
     """Sine cosine benchmark function.
 
@@ -99,6 +105,7 @@ def sin_cos(data):
     """
     6 * sin(data[0]) * cos(data[1])
 
+
 def ripple(data):
     """Ripple benchmark function.
 
@@ -113,6 +120,7 @@ def ripple(data):
     """
     return (data[0] - 3) * (data[1] - 3) + 2 * sin((data[0] - 4) * (data[1] - 4))
 
+
 def rational_polynomial2(data):
     """Rational polynomial benchmark function.
 
diff --git a/deap/benchmarks/movingpeaks.py b/deap/benchmarks/movingpeaks.py
index b90a558ed..c0ca45218 100644
--- a/deap/benchmarks/movingpeaks.py
+++ b/deap/benchmarks/movingpeaks.py
@@ -30,6 +30,7 @@
 except:
     from collections import Sequence
 
+
 def cone(individual, position, height, width):
     """The cone peak function to be used with scenario 2 and 3.
 
@@ -41,6 +42,7 @@ def cone(individual, position, height, width):
         value += (x - p)**2
     return height - width * math.sqrt(value)
 
+
 def sphere(individual, position, height, width):
     value = 0.0
     for x, p in zip(individual, position):
@@ -58,6 +60,7 @@ def function1(individual, position, height, width):
         value += (x - p)**2
     return height / (1 + width * value)
 
+
 class MovingPeaks:
     """The Moving Peaks Benchmark is a fitness function changing over time. It
     consists of a number of peaks, changing in height, width and location. The
@@ -161,7 +164,6 @@ def __init__(self, dim, random=random, **kargs):
         else:
             self.peaks_height = [self.random.uniform(self.min_height, self.max_height) for _ in range(npeaks)]
 
-
         if uniform_width != 0:
             self.peaks_width = [uniform_width for _ in range(npeaks)]
         else:
@@ -382,6 +384,7 @@ def changePeaks(self):
               "width_severity": 0.5,
               "period": 1000}
 
+
 def diversity(population):
     nind = len(population)
     ndim = len(population[0])
@@ -391,6 +394,7 @@ def diversity(population):
     d = [di / nind for di in d]
     return math.sqrt(sum((di - xi)**2 for x in population for di, xi in zip(d, x)))
 
+
 if __name__ == "__main__":
     mpb = MovingPeaks(dim=2, npeaks=[1,1,10], number_severity=0.1)
     print(mpb.maximums())
diff --git a/deap/benchmarks/tools.py b/deap/benchmarks/tools.py
index bc3043e2f..bce1d2680 100644
--- a/deap/benchmarks/tools.py
+++ b/deap/benchmarks/tools.py
@@ -22,6 +22,7 @@
     # fallback on python version
     from ..tools._hypervolume import pyhv as hv
 
+
 class translate(object):
     """Decorator for evaluation functions, it translates the objective
     function by *vector* which should be the same length as the individual
@@ -61,6 +62,7 @@ def evaluate(individual):
         """
         self.vector = vector
 
+
 class rotate(object):
     """Decorator for evaluation functions, it rotates the objective function
     by *matrix* which should be a valid orthogonal NxN rotation matrix, with N
@@ -114,6 +116,7 @@ def evaluate(individual):
         """
         self.matrix = numpy.linalg.inv(matrix)
 
+
 class noise(object):
     """Decorator for evaluation functions, it evaluates the objective function
     and adds noise by calling the function(s) provided in the *noise*
@@ -168,6 +171,7 @@ def evaluate(individual):
         except TypeError:
             self.rand_funcs = repeat(noise)
 
+
 class scale(object):
     """Decorator for evaluation functions, it scales the objective function by
     *factor* which should be the same length as the individual size. When
@@ -209,6 +213,7 @@ def evaluate(individual):
         # objective function
         self.factor = tuple(1.0/f for f in factor)
 
+
 class bound(object):
     """Decorator for crossover and mutation functions, it changes the
     individuals after the modification is done to bring it back in the allowed
@@ -253,6 +258,7 @@ def __init__(self, bounds, type):
         elif type == "clip":
             self.bound = self._clip
 
+
 def diversity(first_front, first, last):
     """Given a Pareto front `first_front` and the two extreme points of the
     optimal Pareto front, this function returns a metric of the diversity
@@ -275,6 +281,7 @@ def diversity(first_front, first, last):
     delta = (df + dl + di)/(df + dl + len(dt) * dm )
     return delta
 
+
 def convergence(first_front, optimal_front):
     """Given a Pareto front `first_front` and the optimal Pareto front,
     this function returns a metric of convergence
diff --git a/deap/creator.py b/deap/creator.py
index 06e00e8a7..3976797f9 100644
--- a/deap/creator.py
+++ b/deap/creator.py
@@ -90,6 +90,8 @@ def __deepcopy__(self, memo):
 
     def __reduce__(self):
         return (self.__class__, (list(self),), self.__dict__)
+
+
 class_replacers[array.array] = _array
 
 
diff --git a/deap/tests/test_algorithms.py b/deap/tests/test_algorithms.py
index 62e17a757..dbece852c 100644
--- a/deap/tests/test_algorithms.py
+++ b/deap/tests/test_algorithms.py
@@ -36,19 +36,23 @@ def setup_func_single_obj():
     creator.create(FITCLSNAME, base.Fitness, weights=(-1.0,))
     creator.create(INDCLSNAME, list, fitness=creator.__dict__[FITCLSNAME])
 
+
 def setup_func_multi_obj():
     creator.create(FITCLSNAME, base.Fitness, weights=(-1.0, -1.0))
     creator.create(INDCLSNAME, list, fitness=creator.__dict__[FITCLSNAME])
 
+
 def setup_func_multi_obj_numpy():
     creator.create(FITCLSNAME, base.Fitness, weights=(-1.0, -1.0))
     creator.create(INDCLSNAME, numpy.ndarray, fitness=creator.__dict__[FITCLSNAME])
 
+
 def teardown_func():
     # Messy way to remove a class from the creator
     del creator.__dict__[FITCLSNAME]
     del creator.__dict__[INDCLSNAME]
 
+
 @with_setup(setup_func_single_obj, teardown_func)
 def test_cma():
     NDIM = 5
@@ -65,6 +69,7 @@ def test_cma():
 
     assert best.fitness.values < (1e-8,), "CMA algorithm did not converged properly."
 
+
 @with_setup(setup_func_multi_obj, teardown_func)
 def test_nsga2():
     NDIM = 5
diff --git a/deap/tests/test_creator.py b/deap/tests/test_creator.py
index a7e3178cd..016ffff29 100644
--- a/deap/tests/test_creator.py
+++ b/deap/tests/test_creator.py
@@ -27,9 +27,11 @@
 
 CNAME = "CLASS_NAME"
 
+
 def teardown_func():
     creator.__dict__.pop(CNAME)
 
+
 @with_setup(None, teardown_func)
 def test_create():
     creator.create(CNAME, list)
@@ -44,6 +46,7 @@ def test_attribute():
 
     assert l.a == 1, "%s, expected %i" % (l.a, 1)
 
+
 @with_setup(None, teardown_func)
 def test_array():
     creator.create(CNAME, array.array, typecode="i")
@@ -56,6 +59,7 @@ def test_array():
     assert a == ta, "%s, expected %s" % (a, ta)
     assert b == tb, "%s, expected %s" % (b, tb)
 
+
 @skipIf(not numpy, "Cannot import Numpy numerical library")
 @with_setup(None, teardown_func)
 def test_numpy_nocopy():
diff --git a/deap/tests/test_logbook.py b/deap/tests/test_logbook.py
index 3194adebc..369eaad90 100644
--- a/deap/tests/test_logbook.py
+++ b/deap/tests/test_logbook.py
@@ -1,8 +1,8 @@
-import sys
 import unittest
 
 from deap import tools
 
+
 class LogbookTest(unittest.TestCase):
 
     def setUp(self):
@@ -37,8 +37,6 @@ def test_no_chapters(self):
         print(self.logbook.stream)
 
 
-
 if __name__ == "__main__":
     suite = unittest.TestLoader().loadTestsFromTestCase(LogbookTest)
     unittest.TextTestRunner(verbosity=2).run(suite)
-
diff --git a/deap/tests/test_mutation.py b/deap/tests/test_mutation.py
index 2822f8a83..666809ed3 100644
--- a/deap/tests/test_mutation.py
+++ b/deap/tests/test_mutation.py
@@ -3,6 +3,7 @@
 
 from deap.tools.mutation import mutInversion
 
+
 class MutationTest(unittest.TestCase):
 
     def test_mutInverstion_size_zero_chromosome_returns_unchanged_chromosome_in_tuple(self):
diff --git a/deap/tests/test_pickle.py b/deap/tests/test_pickle.py
index 873db8f46..7bf1eb1de 100644
--- a/deap/tests/test_pickle.py
+++ b/deap/tests/test_pickle.py
@@ -13,9 +13,11 @@
 from deap import gp
 from deap import tools
 
+
 def func():
     return "True"
 
+
 class Pickling(unittest.TestCase):
 
     def setUp(self):
@@ -131,7 +133,6 @@ def test_pickle_population(self):
         self.assertEqual(pop[2], pop_l[2], "Unpickled individual list != pickled individual list")
         self.assertEqual(pop[2].fitness, pop_l[2].fitness, "Unpickled individual fitness != pickled individual fitness")
 
-
     # @unittest.skipIf(platform.python_implementation() == "PyPy", "PyPy support for pickling ndarrays (thus stats) is very unstable.")
     def test_pickle_logbook(self):
         stats = tools.Statistics()
diff --git a/deap/tools/_hypervolume/pyhv.py b/deap/tools/_hypervolume/pyhv.py
index 2a9fb3439..c6059a29b 100644
--- a/deap/tools/_hypervolume/pyhv.py
+++ b/deap/tools/_hypervolume/pyhv.py
@@ -28,6 +28,7 @@
 
 import numpy
 
+
 def hypervolume(pointset, ref):
     """Compute the absolute hypervolume of a *pointset* according to the
     reference point *ref*.
@@ -54,7 +55,6 @@ def __init__(self, referencePoint):
         self.referencePoint = referencePoint
         self.list = []
 
-
     def compute(self, front):
         """Returns the hypervolume that is dominated by a non-dominated front.
 
@@ -99,7 +99,6 @@ def weaklyDominates(point, other):
         hyperVolume = self.hvRecursive(dimensions - 1, len(relevantPoints), bounds)
         return hyperVolume
 
-
     def hvRecursive(self, dimIndex, length, bounds):
         """Recursive call to hypervolume calculation.
 
@@ -178,7 +177,6 @@ def hvRecursive(self, dimIndex, length, bounds):
             hvol -= q.area[dimIndex] * q.cargo[dimIndex]
             return hvol
 
-
     def preProcess(self, front):
         """Sets up the list data structure needed for calculation."""
         dimensions = len(self.referencePoint)
@@ -189,7 +187,6 @@ def preProcess(self, front):
             nodeList.extend(nodes, i)
         self.list = nodeList
 
-
     def sortByDimension(self, nodes, i):
         """Sorts the list of nodes by the i-th value of the contained points."""
         # build a list of tuples of (point[i], node)
@@ -200,7 +197,6 @@ def sortByDimension(self, nodes, i):
         nodes[:] = [node for (_, node) in decorated]
 
 
-
 class _MultiList: 
     """A special data structure needed by FonsecaHyperVolume. 
 
@@ -236,7 +232,6 @@ def __init__(self, numberLists):
         self.sentinel.next = [self.sentinel] * numberLists
         self.sentinel.prev = [self.sentinel] * numberLists  
 
-
     def __str__(self):
         strings = []
         for i in range(self.numberLists):
@@ -251,12 +246,10 @@ def __str__(self):
             stringRepr += string + "\n"
         return stringRepr
 
-
     def __len__(self):
         """Returns the number of lists that are included in this _MultiList."""
         return self.numberLists
 
-
     def getLength(self, i):
         """Returns the length of the i-th list."""
         length = 0
@@ -267,7 +260,6 @@ def getLength(self, i):
             node = node.next[i]
         return length
 
-
     def append(self, node, index):
         """Appends a node to the end of the list at the given index."""
         lastButOne = self.sentinel.prev[index]
@@ -277,7 +269,6 @@ def append(self, node, index):
         self.sentinel.prev[index] = node
         lastButOne.next[index] = node
 
-
     def extend(self, nodes, index):
         """Extends the list at the given index with the nodes."""
         sentinel = self.sentinel
@@ -289,7 +280,6 @@ def extend(self, nodes, index):
             sentinel.prev[index] = node
             lastButOne.next[index] = node
 
-
     def remove(self, node, index, bounds): 
         """Removes and returns 'node' from all lists in [0, 'index'[."""
         for i in range(index): 
@@ -301,7 +291,6 @@ def remove(self, node, index, bounds):
                 bounds[i] = node.cargo[i]
         return node
 
-
     def reinsert(self, node, index, bounds):
         """
         Inserts 'node' at the position it had in all lists in [0, 'index'[
@@ -315,6 +304,7 @@ def reinsert(self, node, index, bounds):
             if bounds[i] > node.cargo[i]:
                 bounds[i] = node.cargo[i]
 
+
 __all__ = ["hypervolume_kmax", "hypervolume"]
 
 if __name__ == "__main__":
@@ -333,4 +323,3 @@ def reinsert(self, node, index, bounds):
     if hv:
         print("C version: %f" % hv.hypervolume(pointset, ref))
     print("Approximated: %f" % hypervolume_approximation(pointset, ref))
-
diff --git a/deap/tools/constraint.py b/deap/tools/constraint.py
index 5617aff35..673331ac8 100644
--- a/deap/tools/constraint.py
+++ b/deap/tools/constraint.py
@@ -7,6 +7,7 @@
 except ImportError:
     from collections import Sequence
 
+
 class DeltaPenalty(object):
     r"""This decorator returns penalized fitness for invalid individuals and the
     original fitness value for valid individuals. The penalized fitness is made
@@ -63,8 +64,10 @@ def wrapper(individual, *args, **kwargs):
 
         return wrapper
 
+
 DeltaPenality = DeltaPenalty
 
+
 class ClosestValidPenalty(object):
     r"""This decorator returns penalized fitness for invalid individuals and the
     original fitness value for valid individuals. The penalized fitness is made
@@ -131,6 +134,7 @@ def wrapper(individual, *args, **kwargs):
 
         return wrapper
 
+
 ClosestValidPenality = ClosestValidPenalty
 
 # List of exported function names.
diff --git a/deap/tools/emo.py b/deap/tools/emo.py
index 19fb796a2..ab273c90c 100644
--- a/deap/tools/emo.py
+++ b/deap/tools/emo.py
@@ -11,6 +11,7 @@
 # Non-Dominated Sorting   (NSGA-II)  #
 ######################################
 
+
 def selNSGA2(individuals, k, nd='standard'):
     """Apply NSGA-II selection operator on the *individuals*. Usually, the
     size of *individuals* will be larger than *k* because any individual
@@ -115,6 +116,7 @@ def sortNondominated(individuals, k, first_front_only=False):
 
     return fronts
 
+
 def assignCrowdingDist(individuals):
     """Assign a crowding distance to each individual's fitness. The
     crowding distance can be retrieve via the :attr:`crowding_dist`
@@ -141,6 +143,7 @@ def assignCrowdingDist(individuals):
     for i, dist in enumerate(distances):
         individuals[i].fitness.crowding_dist = dist
 
+
 def selTournamentDCD(individuals, k):
     """Tournament selection based on dominance (D) between two individuals, if
     the two individuals do not interdominate the selection is made
@@ -197,11 +200,13 @@ def tourn(ind1, ind2):
 # Generalized Reduced runtime ND sort #
 #######################################
 
+
 def identity(obj):
     """Returns directly the argument *obj*.
     """
     return obj
 
+
 def isDominated(wvalues1, wvalues2):
     """Returns whether or not *wvalues2* dominates *wvalues1*.
 
@@ -218,6 +223,7 @@ def isDominated(wvalues1, wvalues2):
             not_equal = True
     return not_equal
 
+
 def median(seq, key=identity):
     """Returns the median of *seq* - the numeric value separating the higher
     half of a sample from the lower half. If there is an even number of
@@ -230,6 +236,7 @@ def median(seq, key=identity):
     else:
         return (key(sseq[(length - 1) // 2]) + key(sseq[length // 2])) / 2.0
 
+
 def sortLogNondominated(individuals, k, first_front_only=False):
     """Sort *individuals* in pareto non-dominated fronts using the Generalized
     Reduced Run-Time Complexity Non-Dominated Sorting Algorithm presented by
@@ -274,6 +281,7 @@ def sortLogNondominated(individuals, k, first_front_only=False):
     else:
         return pareto_fronts[0]
 
+
 def sortNDHelperA(fitnesses, obj, front):
     """Create a non-dominated sorting of S on the first M objectives"""
     if len(fitnesses) < 2:
@@ -295,6 +303,7 @@ def sortNDHelperA(fitnesses, obj, front):
         sortNDHelperB(best, worst, obj-1, front)
         sortNDHelperA(worst, obj, front)
 
+
 def splitA(fitnesses, obj):
     """Partition the set of fitnesses in two according to the median of
     the objective index *obj*. The values equal to the median are put in
@@ -323,6 +332,7 @@ def splitA(fitnesses, obj):
     else:
         return best_b, worst_b
 
+
 def sweepA(fitnesses, front):
     """Update rank number associated to the fitnesses according
     to the first two objectives using a geometric sweep procedure.
@@ -342,6 +352,7 @@ def sweepA(fitnesses, front):
         stairs.insert(idx, -fit[1])
         fstairs.insert(idx, fit)
 
+
 def sortNDHelperB(best, worst, obj, front):
     """Assign front numbers to the solutions in H according to the solutions
     in L. The solutions in L are assumed to have correct front numbers and the
@@ -371,6 +382,7 @@ def sortNDHelperB(best, worst, obj, front):
         sortNDHelperB(best1, worst2, obj-1, front)
         sortNDHelperB(best2, worst2, obj, front)
 
+
 def splitB(best, worst, obj):
     """Split both best individual and worst sets of fitnesses according
     to the median of objective *obj* computed on the set containing the
@@ -410,6 +422,7 @@ def splitB(best, worst, obj):
     else:
         return best1_b, best2_b, worst1_b, worst2_b
 
+
 def sweepB(best, worst, front):
     """Adjust the rank number of the worst fitnesses according to
     the best fitnesses on the first two objectives using a sweep
@@ -443,6 +456,7 @@ def sweepB(best, worst, front):
 # Non-Dominated Sorting  (NSGA-III)  #
 ######################################
 
+
 NSGA3Memory = namedtuple("NSGA3Memory", ["best_point", "worst_point", "extreme_points"])
 
 
@@ -806,6 +820,7 @@ def selSPEA2(individuals, k):
 
     return [individuals[i] for i in chosen_indices]
 
+
 def _randomizedSelect(array, begin, end, i):
     """Allows to select the ith smallest element from array without sorting it.
     Runtime is expected to be O(n).
@@ -819,11 +834,13 @@ def _randomizedSelect(array, begin, end, i):
     else:
         return _randomizedSelect(array, q + 1, end, i - k)
 
+
 def _randomizedPartition(array, begin, end):
     i = random.randint(begin, end)
     array[begin], array[i] = array[i], array[begin]
     return _partition(array, begin, end)
 
+
 def _partition(array, begin, end):
     x = array[begin]
     i = begin - 1
diff --git a/deap/tools/indicator.py b/deap/tools/indicator.py
index b34c13571..e51729529 100644
--- a/deap/tools/indicator.py
+++ b/deap/tools/indicator.py
@@ -7,6 +7,7 @@
     # fallback on python version
     from ._hypervolume import pyhv as hv
 
+
 def hypervolume(front, **kargs):
     """Returns the index of the individual with the least the hypervolume
     contribution. The provided *front* should be a set of non-dominated
diff --git a/deap/tools/init.py b/deap/tools/init.py
index ac4ad56a8..d79429850 100644
--- a/deap/tools/init.py
+++ b/deap/tools/init.py
@@ -22,6 +22,7 @@ def initRepeat(container, func, n):
     """
     return container(func() for _ in range(n))
 
+
 def initIterate(container, generator):
     """Call the function *container* with an iterable as
     its only argument. The iterable must be returned by
@@ -49,6 +50,7 @@ def initIterate(container, generator):
     """
     return container(generator())
 
+
 def initCycle(container, seq_func, n=1):
     """Call the function *container* with a generator function corresponding
     to the calling *n* times the functions present in *seq_func*.
@@ -72,6 +74,7 @@ def initCycle(container, seq_func, n=1):
     """
     return container(func() for _ in range(n) for func in seq_func)
 
+
 __all__ = ['initRepeat', 'initIterate', 'initCycle']
 
 
@@ -84,4 +87,3 @@ def initCycle(container, seq_func, n=1):
     random.seed(64)
     doctest.run_docstring_examples(initIterate, globals())
     doctest.run_docstring_examples(initCycle, globals())
-
diff --git a/deap/tools/migration.py b/deap/tools/migration.py
index 421410e37..3e1bf914a 100644
--- a/deap/tools/migration.py
+++ b/deap/tools/migration.py
@@ -47,4 +47,5 @@ def migRing(populations, k, selection, replacement=None, migarray=None):
             indx = populations[to_deme].index(immigrant)
             populations[to_deme][indx] = emigrants[from_deme][i]
 
+
 __all__ = ['migRing']
diff --git a/deap/tools/selection.py b/deap/tools/selection.py
index e6fdcaf5e..185b9e958 100644
--- a/deap/tools/selection.py
+++ b/deap/tools/selection.py
@@ -8,6 +8,7 @@
 # Selections                         #
 ######################################
 
+
 def selRandom(individuals, k):
     """Select *k* individuals at random from the input *individuals* with
     replacement. The list returned contains references to the input
@@ -67,6 +68,7 @@ def selTournament(individuals, k, tournsize, fit_attr="fitness"):
         chosen.append(max(aspirants, key=attrgetter(fit_attr)))
     return chosen
 
+
 def selRoulette(individuals, k, fit_attr="fitness"):
     """Select *k* individuals from the input *individuals* using *k*
     spins of a roulette. The selection is made by looking only at the first
@@ -178,6 +180,7 @@ def _fitTournament(individuals, k, select):
         tsize = partial(_sizeTournament, select=selRandom)
         return _fitTournament(individuals, k, tsize)
 
+
 def selStochasticUniversalSampling(individuals, k, fit_attr="fitness"):
     """Select the *k* individuals among the input *individuals*.
     The selection is made by using a single random value to sample all of the
@@ -210,6 +213,7 @@ def selStochasticUniversalSampling(individuals, k, fit_attr="fitness"):
 
     return chosen
 
+
 def selLexicase(individuals, k):
     """Returns an individual that does the best on the fitness cases when
     considered one at a time in random order.
@@ -276,6 +280,7 @@ def selEpsilonLexicase(individuals, k, epsilon):
 
     return selected_individuals
 
+
 def selAutomaticEpsilonLexicase(individuals, k):
     """
     Returns an individual that does the best on the fitness cases when considered one at a
@@ -319,4 +324,3 @@ def selAutomaticEpsilonLexicase(individuals, k):
 __all__ = ['selRandom', 'selBest', 'selWorst', 'selRoulette',
            'selTournament', 'selDoubleTournament', 'selStochasticUniversalSampling',
            'selLexicase', 'selEpsilonLexicase', 'selAutomaticEpsilonLexicase']
-
diff --git a/deap/tools/support.py b/deap/tools/support.py
index 475e7199e..0f13920be 100644
--- a/deap/tools/support.py
+++ b/deap/tools/support.py
@@ -13,6 +13,7 @@ def identity(obj):
     """
     return obj
 
+
 class History(object):
     """The :class:`History` class helps to build a genealogy of all the
     individuals produced in the evolution. It contains two attributes,
@@ -131,6 +132,7 @@ def getGenealogy(self, individual, max_depth=float("inf")):
         """
         gtree = {}
         visited = set()     # Adds memory to the breadth first search
+
         def genealogy(index, depth):
             if index not in self.genealogy_tree:
                 return
@@ -146,6 +148,7 @@ def genealogy(index, depth):
         genealogy(individual.history_index, 0)
         return gtree
 
+
 class Statistics(object):
     """Object that compiles statistics on a list of arbitrary objects.
     When created the statistics object receives a *key* argument that
@@ -204,6 +207,7 @@ def compile(self, data):
             entry[key] = func(values)
         return entry
 
+
 class MultiStatistics(dict):
     """Dictionary of :class:`Statistics` object allowing to compute
     statistics on multiple keys using a single call to :meth:`compile`. It
@@ -253,6 +257,7 @@ def register(self, name, function, *args, **kargs):
         for stats in self.values():
             stats.register(name, function, *args, **kargs)
 
+
 class Logbook(list):
     """Evolution records as a chronological list of dictionaries.
 
@@ -634,13 +639,12 @@ def update(self, population):
             if not is_dominated and not has_twin:
                 self.insert(ind)
 
+
 __all__ = ['HallOfFame', 'ParetoFront', 'History', 'Statistics', 'MultiStatistics', 'Logbook']
 
 if __name__ == "__main__":
     import doctest
-    from operator import itemgetter
 
-    import numpy
     doctest.run_docstring_examples(Statistics, globals())
     doctest.run_docstring_examples(Statistics.register, globals())
     doctest.run_docstring_examples(Statistics.compile, globals())