MAINT: sort imports

odl/diagnostics/examples.py

@@ -8,7 +8,8 @@
 
 """Functions for generating standardized examples in spaces."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 from itertools import product
 
 __all__ = ('samples',)

odl/diagnostics/operator.py

@@ -8,15 +8,16 @@
 
 """Standardized tests for `Operator`'s."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 from builtins import object
+
 import numpy as np
 
 from odl.diagnostics.examples import samples
 from odl.operator import power_method_opnorm
 from odl.util.testutils import FailCounter
 
-
 __all__ = ('OperatorTest',)
 
 

odl/diagnostics/space.py

@@ -8,15 +8,15 @@
 
 """Standardized tests for `LinearSpace`'s."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 from builtins import object
 from copy import copy, deepcopy
 
-from odl.set import Field
 from odl.diagnostics.examples import samples
+from odl.set import Field
 from odl.util.testutils import FailCounter
 
-
 __all__ = ('SpaceTest',)
 
 

odl/operator/oputils.py

@@ -9,9 +9,9 @@
 """Convenience functions for operators."""
 
 from __future__ import absolute_import, division, print_function
-from future.utils import native
 
 import numpy as np
+from future.utils import native
 
 from odl.space.base_tensors import TensorSpace
 from odl.space.pspace import ProductSpace

odl/phantom/emission.py

@@ -8,12 +8,11 @@
 
 """Phantoms used in emission tomography."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
 
 from odl.phantom.geometric import ellipsoid_phantom
 from odl.phantom.phantom_utils import cylinders_from_ellipses
 
-
 __all__ = ('derenzo_sources',)
 
 

odl/phantom/geometric.py

@@ -8,7 +8,8 @@
 
 """Phantoms given by simple geometric objects such as cubes or spheres."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
 from odl.discr.lp_discr import uniform_discr_fromdiscr

odl/phantom/misc_phantoms.py

@@ -8,10 +8,11 @@
 
 """Miscellaneous phantoms that do not fit in other categories."""
 
-from __future__ import print_function, division, absolute_import
-import numpy as np
+from __future__ import absolute_import, division, print_function
+
 import sys
 
+import numpy as np
 
 __all__ = ('submarine', 'text')
 

odl/phantom/noise.py

@@ -8,12 +8,12 @@
 
 """Functions to create noise samples of different distributions."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
 from odl.util import NumpyRandomSeed
 
-
 __all__ = ('white_noise', 'poisson_noise', 'salt_pepper_noise',
            'uniform_noise')
 

odl/phantom/phantom_utils.py

@@ -8,9 +8,9 @@
 
 """Utilities for creating phantoms."""
 
-from __future__ import print_function, division, absolute_import
-import numpy as np
+from __future__ import absolute_import, division, print_function
 
+import numpy as np
 
 __all__ = ('cylinders_from_ellipses',)
 

odl/phantom/transmission.py

@@ -8,13 +8,13 @@
 
 """Phantoms typically used in transmission tomography."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
 from odl.discr import DiscreteLp
 from odl.phantom.geometric import ellipsoid_phantom
 
-
 __all__ = ('shepp_logan_ellipsoids', 'shepp_logan', 'forbild')
 
 

odl/set/domain.py

@@ -8,14 +8,14 @@
 
 """Domains for continuous functions. """
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
 from odl.set.sets import Set
 from odl.util import (
-    array_str, is_valid_input_array, is_valid_input_meshgrid, safe_int_conv,
-    signature_string_parts, repr_string, npy_printoptions, REPR_PRECISION)
-
+    REPR_PRECISION, array_str, is_valid_input_array, is_valid_input_meshgrid,
+    npy_printoptions, repr_string, safe_int_conv, signature_string_parts)
 
 __all__ = ('IntervalProd',)
 

odl/set/sets.py

@@ -8,16 +8,17 @@
 
 """Basic abstract and concrete sets."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 from builtins import int, object
-from numbers import Integral, Real, Complex
-from past.types.basestring import basestring
+from numbers import Complex, Integral, Real
+
 import numpy as np
+from past.types.basestring import basestring
 
 from odl.util import (
-    is_int_dtype, is_real_dtype, is_numeric_dtype, unique,
-    signature_string_parts, repr_string)
-
+    is_int_dtype, is_numeric_dtype, is_real_dtype, repr_string,
+    signature_string_parts, unique)
 
 __all__ = ('Set', 'EmptySet', 'UniversalSet', 'Field', 'Integers',
            'RealNumbers', 'ComplexNumbers', 'Strings', 'CartesianProduct',

odl/set/space.py

@@ -8,13 +8,14 @@
 
 """Abstract linear vector spaces."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 from builtins import object
+
 import numpy as np
 
 from odl.set.sets import Field, Set, UniversalSet
 
-
 __all__ = ('LinearSpace', 'UniversalSpace')
 
 

odl/solvers/functional/default_functionals.py

@@ -8,28 +8,28 @@
 
 """Default functionals defined on any space similar to R^n or L^2."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 from numbers import Integral
+
 import numpy as np
 
-from odl.solvers.functional.functional import (Functional,
-                                               FunctionalQuadraticPerturb)
-from odl.space import ProductSpace
-from odl.operator import (Operator, ConstantOperator, ZeroOperator,
-                          ScalingOperator, DiagonalOperator, PointwiseNorm)
+from odl.operator import (
+    ConstantOperator, DiagonalOperator, Operator, PointwiseNorm,
+    ScalingOperator, ZeroOperator)
+from odl.solvers.functional.functional import (
+    Functional, FunctionalQuadraticPerturb)
 from odl.solvers.nonsmooth.proximal_operators import (
-    proximal_l1, proximal_convex_conj_l1,
-    proximal_l1_l2, proximal_convex_conj_l1_l2,
-    proximal_l2, proximal_convex_conj_l2, proximal_l2_squared,
-    proximal_huber,
-    proximal_const_func, proximal_box_constraint,
-    proximal_convex_conj_kl, proximal_convex_conj_kl_cross_entropy,
-    combine_proximals, proximal_convex_conj)
+    combine_proximals, proximal_box_constraint, proximal_const_func,
+    proximal_convex_conj, proximal_convex_conj_kl,
+    proximal_convex_conj_kl_cross_entropy, proximal_convex_conj_l1,
+    proximal_convex_conj_l1_l2, proximal_convex_conj_l2, proximal_huber,
+    proximal_l1, proximal_l1_l2, proximal_l2, proximal_l2_squared)
+from odl.space import ProductSpace
 from odl.ufunc_ops import exp
 from odl.util import (
-    REPR_PRECISION, npy_printoptions, repr_string, signature_string_parts,
-    conj_exponent, moveaxis)
-
+    REPR_PRECISION, conj_exponent, moveaxis, npy_printoptions, repr_string,
+    signature_string_parts)
 
 __all__ = ('ZeroFunctional', 'ConstantFunctional', 'ScalingFunctional',
            'IdentityFunctional',

odl/solvers/functional/derivatives.py

@@ -8,14 +8,14 @@
 
 """Utilities for computing the gradient and Hessian of functionals."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
-from odl.solvers.functional.functional import Functional
 from odl.operator import Operator
+from odl.solvers.functional.functional import Functional
 from odl.space.base_tensors import TensorSpace
 
-
 __all__ = ('NumericalDerivative', 'NumericalGradient',)
 
 

odl/solvers/functional/example_funcs.py

@@ -8,14 +8,14 @@
 
 """Example functionals used in optimization."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
+from odl.operator import MatrixOperator, Operator
 from odl.solvers.functional.functional import Functional
-from odl.operator import Operator, MatrixOperator
 from odl.space.base_tensors import TensorSpace
 
-
 __all__ = ('RosenbrockFunctional',)
 
 

odl/solvers/functional/functional.py

@@ -8,18 +8,18 @@
 # v. 2.0. If a copy of the MPL was not distributed with this file, You can
 # obtain one at https://mozilla.org/MPL/2.0/.
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
+from odl.operator.default_ops import ConstantOperator, IdentityOperator
 from odl.operator.operator import (
-    Operator, OperatorComp, OperatorLeftScalarMult, OperatorRightScalarMult,
-    OperatorRightVectorMult, OperatorSum, OperatorPointwiseProduct)
-from odl.operator.default_ops import (IdentityOperator, ConstantOperator)
-from odl.solvers.nonsmooth import (proximal_arg_scaling, proximal_translation,
-                                   proximal_quadratic_perturbation,
-                                   proximal_const_func, proximal_convex_conj)
-from odl.util import signature_string, indent
-
+    Operator, OperatorComp, OperatorLeftScalarMult, OperatorPointwiseProduct,
+    OperatorRightScalarMult, OperatorRightVectorMult, OperatorSum)
+from odl.solvers.nonsmooth import (
+    proximal_arg_scaling, proximal_const_func, proximal_convex_conj,
+    proximal_quadratic_perturbation, proximal_translation)
+from odl.util import indent, signature_string
 
 __all__ = ('Functional', 'FunctionalLeftScalarMult',
            'FunctionalRightScalarMult', 'FunctionalComp',

odl/solvers/iterative/iterative.py

@@ -8,14 +8,15 @@
 
 """Simple iterative type optimization schemes."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 from builtins import next
+
 import numpy as np
 
 from odl.operator import IdentityOperator, OperatorComp, OperatorSum
 from odl.util import normalized_scalar_param_list
 
-
 __all__ = ('landweber', 'conjugate_gradient', 'conjugate_gradient_normal',
            'gauss_newton', 'kaczmarz')
 

odl/solvers/iterative/statistical.py

@@ -8,7 +8,8 @@
 
 """Maximum Likelihood Expectation Maximization algorithm."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
 __all__ = ('mlem', 'osmlem', 'loglikelihood')

odl/solvers/nonsmooth/admm.py

@@ -1,10 +1,10 @@
 """Alternating Direction method of Multipliers (ADMM) method variants."""
 
 from __future__ import division
-from builtins import range
 
-from odl.operator import Operator, OpDomainError
+from builtins import range
 
+from odl.operator import OpDomainError, Operator
 
 __all__ = ('admm_linearized',)
 

odl/solvers/nonsmooth/alternating_dual_updates.py

@@ -13,7 +13,7 @@
 each of the components.
 """
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
 
 import numpy as np
 

odl/solvers/nonsmooth/douglas_rachford.py

@@ -8,11 +8,10 @@
 
 """Douglas-Rachford splitting algorithm for convex optimization."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
 
 from odl.operator import Operator
 
-
 __all__ = ('douglas_rachford_pd',)
 
 

odl/solvers/nonsmooth/forward_backward.py

@@ -8,11 +8,10 @@
 
 """Optimization methods based on a forward-backward splitting scheme."""
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
 
 from odl.operator import Operator
 
-
 __all__ = ('forward_backward_pd',)
 
 

odl/solvers/nonsmooth/primal_dual_hybrid_gradient.py

@@ -12,12 +12,12 @@
 non-smooth convex optimization problems in imaging.
 """
 
-from __future__ import print_function, division, absolute_import
+from __future__ import absolute_import, division, print_function
+
 import numpy as np
 
 from odl.operator import Operator
 
-
 __all__ = ('pdhg',)