Round one of flake8 warnings.

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",

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.
 

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())

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

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
 
 

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

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():

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)
-

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):

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()