code style fixes

demos/polynomial_fit.py

@@ -44,7 +44,8 @@ def polynomial(coefficients, sampling_points):
 
 
 class PolynomialResponse(ift.LinearOperator):
-    """Calculates values of a polynomial parameterized by input at sampling points.
+    """Calculates values of a polynomial parameterized by input at sampling
+    points.
 
     Parameters
     ----------

nifty5/extra.py

@@ -171,4 +171,3 @@ def check_jacobian_consistency(op, loc, tol=1e-8, ntries=100):
         else:
             raise ValueError("gradient and value seem inconsistent")
         loc = locnext
-

nifty5/library/light_cone_operator.py

@@ -97,24 +97,24 @@ def _cone_arrays(c, domain, sigx, want_gradient):
 
 class LightConeOperator(Operator):
     '''Constructs a Light cone from a set of lightspeed parameters.
-    
+
     The resulting cone is defined as follows
-    
+
     .. math::
         \\exp \\left(- \\frac{1}{2} \\Re \\left( \\Delta \\right)^2 \\right)
-    
+
     with
-    
+
     .. math::
         \\Delta = \\sqrt{- \\left(t^2 - \\frac{x^\\dagger C^{-1} x}
         {\\sigma_x^2} \\right)}
-        
+
     where t and x are the coordinates of the target space. Note that axis zero
     of the space is interpreted as the time axis. C denotes the input
     paramters of the operator and parametrizes the shape of the cone.
     sigx is the width of the asymptotic Gaussian in x necessary for
     discretization.
-    
+
     Parameters
     ----------
     domain : Domain, tuple of Domain or DomainTuple

nifty5/operators/exp_transform.py

@@ -31,8 +31,8 @@ class ExpTransform(LinearOperator):
 
     This operator creates a log-space subject to the degrees of freedom and
     and its target-domain.
-    Then it transforms between this log-space and its target, which is defined in
-    normal units.
+    Then it transforms between this log-space and its target, which is defined
+    in normal units.
 
     FIXME Write something on t_0 of domain space