No whitespace after """ and further cosmetics

nifty5/field.py

@@ -25,7 +25,7 @@ from .domain_tuple import DomainTuple
 class Field(object):
     _scalar_dom = DomainTuple.scalar_domain()
 
-    """ The discrete representation of a continuous field over multiple spaces.
+    """The discrete representation of a continuous field over multiple spaces.
 
     In NIFTy, Fields are used to store data arrays and carry all the needed
     metainformation (i.e. the domain) for operators to be able to work on them.
@@ -165,7 +165,7 @@ class Field(object):
 
     @staticmethod
     def from_random(random_type, domain, dtype=np.float64, **kwargs):
-        """ Draws a random field with the given parameters.
+        """Draws a random field with the given parameters.
 
         Parameters
         ----------
@@ -284,7 +284,7 @@ class Field(object):
         return res
 
     def weight(self, power=1, spaces=None):
-        """ Weights the pixels of `self` with their invidual pixel-volume.
+        """Weights the pixels of `self` with their invidual pixel-volume.
 
         Parameters
         ----------
@@ -325,7 +325,7 @@ class Field(object):
         return Field.from_local_data(self._domain, aout)
 
     def outer(self, x):
-        """ Computes the outer product of 'self' with x.
+        """Computes the outer product of 'self' with x.
 
         Parameters
         ----------
@@ -342,7 +342,7 @@ class Field(object):
         return OuterProduct(self, x.domain)(x)
 
     def vdot(self, x=None, spaces=None):
-        """ Computes the dot product of 'self' with x.
+        """Computes the dot product of 'self' with x.
 
         Parameters
         ----------
@@ -375,7 +375,7 @@ class Field(object):
         return (self.conjugate()*x).sum(spaces=spaces)
 
     def norm(self, ord=2):
-        """ Computes the L2-norm of the field values.
+        """Computes the L2-norm of the field values.
 
         Parameters
         ----------
@@ -390,7 +390,7 @@ class Field(object):
         return dobj.norm(self._val, ord)
 
     def conjugate(self):
-        """ Returns the complex conjugate of the field.
+        """Returns the complex conjugate of the field.
 
         Returns
         -------

nifty5/library/adjust_variances.py

@@ -30,7 +30,7 @@ def make_adjust_variances(a,
                           samples=[],
                           scaling=None,
                           ic_samp=None):
-    """ Creates a Hamiltonian for constant likelihood optimizations.
+    """Creates a Hamiltonian for constant likelihood optimizations.
 
     Constructs a Hamiltonian to solve constant likelihood optimizations of the
     form phi = a * xi under the constraint that phi remains constant.

nifty5/minimization/conjugate_gradient.py

@@ -20,7 +20,7 @@ from .minimizer import Minimizer
 
 
 class ConjugateGradient(Minimizer):
-    """ Implementation of the Conjugate Gradient scheme.
+    """Implementation of the Conjugate Gradient scheme.
 
     It is an iterative method for solving a linear system of equations:
                                     Ax = b
@@ -44,7 +44,7 @@ class ConjugateGradient(Minimizer):
         self._nreset = nreset
 
     def __call__(self, energy, preconditioner=None):
-        """ Runs the conjugate gradient minimization.
+        """Runs the conjugate gradient minimization.
 
         Parameters
         ----------

nifty5/minimization/descent_minimizers.py

@@ -23,7 +23,7 @@ from .minimizer import Minimizer
 
 
 class DescentMinimizer(Minimizer):
-    """ A base class used by gradient methods to find a local minimum.
+    """A base class used by gradient methods to find a local minimum.
 
     Descent minimization methods are used to find a local minimum of a scalar
     function by following a descent direction. This class implements the
@@ -44,7 +44,7 @@ class DescentMinimizer(Minimizer):
         self.line_searcher = line_searcher
 
     def __call__(self, energy):
-        """ Performs the minimization of the provided Energy functional.
+        """Performs the minimization of the provided Energy functional.
 
         Parameters
         ----------
@@ -104,7 +104,7 @@ class DescentMinimizer(Minimizer):
         pass
 
     def get_descent_direction(self, energy):
-        """ Calculates the next descent direction.
+        """Calculates the next descent direction.
 
         Parameters
         ----------
@@ -121,7 +121,7 @@ class DescentMinimizer(Minimizer):
 
 
 class SteepestDescent(DescentMinimizer):
-    """ Implementation of the steepest descent minimization scheme.
+    """Implementation of the steepest descent minimization scheme.
 
     Also known as 'gradient descent'. This algorithm simply follows the
     functional's gradient for minimization.
@@ -132,7 +132,7 @@ class SteepestDescent(DescentMinimizer):
 
 
 class RelaxedNewton(DescentMinimizer):
-    """ Calculates the descent direction according to a Newton scheme.
+    """Calculates the descent direction according to a Newton scheme.
 
     The descent direction is determined by weighting the gradient at the
     current parameter position with the inverse local metric.
@@ -149,7 +149,7 @@ class RelaxedNewton(DescentMinimizer):
 
 
 class NewtonCG(DescentMinimizer):
-    """ Calculates the descent direction according to a Newton-CG scheme.
+    """Calculates the descent direction according to a Newton-CG scheme.
 
     Algorithm derived from SciPy sources.
     """

nifty5/minimization/energy.py

@@ -19,7 +19,7 @@ from ..utilities import NiftyMetaBase
 
 
 class Energy(NiftyMetaBase()):
-    """ Provides the functional used by minimization schemes.
+    """Provides the functional used by minimization schemes.
 
     The Energy object is an implementation of a scalar function including its
     gradient and metric at some position.
@@ -47,7 +47,7 @@ class Energy(NiftyMetaBase()):
         self._gradnorm = None
 
     def at(self, position):
-        """ Returns a new Energy object, initialized at `position`.
+        """Returns a new Energy object, initialized at `position`.
 
         Parameters
         ----------

nifty5/minimization/line_search.py

@@ -21,7 +21,7 @@ import numpy as np
 
 
 class LineEnergy(object):
-    """ Evaluates an underlying Energy along a certain line direction.
+    """Evaluates an underlying Energy along a certain line direction.
 
     Given an Energy class and a line direction, its position is parametrized by
     a scalar step size along the descent direction relative to a zero point.
@@ -60,7 +60,7 @@ class LineEnergy(object):
             self._energy = energy.at(position=pos)
 
     def at(self, line_position):
-        """ Returns LineEnergy at new position, memorizing the zero point.
+        """Returns LineEnergy at new position, memorizing the zero point.
 
         Parameters
         ----------

nifty5/minimization/minimizer.py

@@ -19,11 +19,11 @@ from ..utilities import NiftyMetaBase
 
 
 class Minimizer(NiftyMetaBase()):
-    """ A base class used by all minimizers."""
+    """A base class used by all minimizers."""
 
     # MR FIXME: the docstring is partially ignored by Sphinx. Why?
     def __call__(self, energy, preconditioner=None):
-        """ Performs the minimization of the provided Energy functional.
+        """Performs the minimization of the provided Energy functional.
 
         Parameters
         ----------

nifty5/minimization/nonlinear_cg.py

@@ -20,7 +20,7 @@ from .minimizer import Minimizer
 
 
 class NonlinearCG(Minimizer):
-    """ Nonlinear Conjugate Gradient scheme according to Polak-Ribiere.
+    """Nonlinear Conjugate Gradient scheme according to Polak-Ribiere.
 
     Algorithm 5.4 from Nocedal & Wright.
 

nifty5/minimization/quadratic_energy.py

@@ -42,7 +42,7 @@ class QuadraticEnergy(Energy):
         return QuadraticEnergy(position, self._A, self._b)
 
     def at_with_grad(self, position, grad):
-        """ Specialized version of `at`, taking also a gradient.
+        """Specialized version of `at`, taking also a gradient.
 
         This custom method is meant for use within :class:ConjugateGradient`
         minimizers, which already have the gradient available. It saves time

nifty5/multi_field.py

@@ -142,7 +142,7 @@ class MultiField(object):
                           for key in domain.keys()))
 
     def norm(self, ord=2):
-        """ Computes the norm of the field values.
+        """Computes the norm of the field values.
 
         Parameters
         ----------
@@ -161,7 +161,7 @@ class MultiField(object):
 #        return np.sqrt(np.abs(self.vdot(x=self)))
 
     def sum(self):
-        """ Computes the sum all field values.
+        """Computes the sum all field values.
 
         Returns
         -------
@@ -172,7 +172,7 @@ class MultiField(object):
 
     @property
     def size(self):
-        """ Computes the overall degrees of freedom.
+        """Computes the overall degrees of freedom.
 
         Returns
         -------
@@ -222,7 +222,7 @@ class MultiField(object):
 
     @staticmethod
     def union(fields, domain=None):
-        """ Returns the union of its input fields.
+        """Returns the union of its input fields.
 
         Parameters
         ----------

nifty5/operators/block_diagonal_operator.py

@@ -21,14 +21,14 @@ from .endomorphic_operator import EndomorphicOperator
 
 
 class BlockDiagonalOperator(EndomorphicOperator):
-    def __init__(self, domain, operators):
     """
     Parameters
     ----------
     operators : dict
-            dictionary with operators domain names as keys and
-            LinearOperators as items
+        Dictionary with operators domain names as keys and LinearOperators as
+        items.
     """
+    def __init__(self, domain, operators):
         if not isinstance(domain, MultiDomain):
             raise TypeError("MultiDomain expected")
         if not isinstance(operators, tuple):

nifty5/operators/contraction_operator.py

@@ -35,7 +35,7 @@ class ContractionOperator(LinearOperator):
     spaces : int or tuple of int
         The elements of "domain" which are contracted.
     weight : int, default=0
-        if nonzero, the fields defined on self.domain are weighted with the
+        If nonzero, the fields defined on self.domain are weighted with the
         specified power.
     """
 

nifty5/operators/diagonal_operator.py

@@ -24,7 +24,7 @@ from .endomorphic_operator import EndomorphicOperator
 
 
 class DiagonalOperator(EndomorphicOperator):
-    """ NIFTy class for diagonal operators.
+    """NIFTy class for diagonal operators.
 
     The NIFTy DiagonalOperator class is a subclass derived from the
     EndomorphicOperator. It multiplies an input field pixel-wise with its

nifty5/operators/endomorphic_operator.py

@@ -21,7 +21,7 @@ from .linear_operator import LinearOperator
 
 
 class EndomorphicOperator(LinearOperator):
-    """ NIFTy class for endomorphic operators.
+    """NIFTy class for endomorphic operators.
 
     The  NIFTy EndomorphicOperator class is a class derived from the
     LinearOperator. By definition, domain and target are the same in

nifty5/operators/exp_transform.py

@@ -27,8 +27,7 @@ from .linear_operator import LinearOperator
 
 
 class ExpTransform(LinearOperator):
-    """
-    Transforms log-space to target.
+    """Transforms log-space to target
 
     This operator creates a log-space subject to the degrees of freedom and
     and its target-domain.

nifty5/operators/harmonic_operators.py

@@ -385,9 +385,8 @@ class HarmonicTransformOperator(LinearOperator):
 
 
 def HarmonicSmoothingOperator(domain, sigma, space=None):
-    """ This function returns an operator that carries out a smoothing with
-    a Gaussian kernel of width `sigma` on the part of `domain` given by
-    `space`.
+    """Returns an operator that carries out a smoothing with a Gaussian kernel
+    of width `sigma` on the part of `domain` given by `space`
 
     Parameters
     ----------

nifty5/operators/linear_interpolation.py

@@ -30,8 +30,7 @@ from .linear_operator import LinearOperator
 
 
 class LinearInterpolator(LinearOperator):
-    """
-    Multilinear interpolation for points in an RGSpace
+    """Multilinear interpolation for points in an RGSpace
 
     Parameters
     ----------

nifty5/operators/linear_operator.py

@@ -143,7 +143,7 @@ class LinearOperator(Operator):
         return self.apply(x.extract(self.domain), self.TIMES)
 
     def apply(self, x, mode):
-        """ Applies the Operator to a given `x`, in a specified `mode`.
+        """Applies the Operator to a given `x`, in a specified `mode`.
 
         Parameters
         ----------
@@ -178,7 +178,7 @@ class LinearOperator(Operator):
         return self.__matmul__(x)
 
     def times(self, x):
-        """ Applies the Operator to a given Field.
+        """Applies the Operator to a given Field.
 
         Parameters
         ----------
@@ -223,7 +223,7 @@ class LinearOperator(Operator):
         return self.apply(x, self.ADJOINT_TIMES)
 
     def adjoint_inverse_times(self, x):
-        """ Applies the adjoint-inverse Operator to a given Field.
+        """Applies the adjoint-inverse Operator to a given Field.
 
         Parameters
         ----------

nifty5/operators/mask_operator.py

@@ -27,16 +27,16 @@ from .linear_operator import LinearOperator
 # Possible approach: keep everything defined on `domain` distributed and only
 # collect the unstructured Fields.
 class MaskOperator(LinearOperator):
-    def __init__(self, mask):
     """Implementation of a mask response
 
-        This operator takes a field, applies a mask and returns the values of
-        the field in a UnstructuredDomain. It can be used as response operator.
+    Takes a field, applies a mask and returns the values of the field in a
+    UnstructuredDomain. It can be used as response operator.
 
     Parameters
     ----------
     mask : Field
     """
+    def __init__(self, mask):
         if not isinstance(mask, Field):
             raise TypeError
 

nifty5/operators/outer_product_operator.py

@@ -15,17 +15,11 @@
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
 
-import itertools
-
 import numpy as np
 
-from .. import dobj, utilities
 from ..domain_tuple import DomainTuple
-from ..domains.rg_space import RGSpace
-from ..multi_field import MultiField, MultiDomain
 from ..field import Field
 from .linear_operator import LinearOperator
-import operator
 
 
 class OuterProduct(LinearOperator):
@@ -37,14 +31,11 @@ class OuterProduct(LinearOperator):
     domain: DomainTuple, the domain of the input field
     ---------
     """
-
     def __init__(self, field, domain):
-
         self._domain = domain
         self._field = field
         self._target = DomainTuple.make(
             tuple(sub_d for sub_d in field.domain._dom + domain._dom))
-
         self._capability = self.TIMES | self.ADJOINT_TIMES
 
     def apply(self, x, mode):