cosmetics and fix in SamplingEnabler

nifty5/library/nonlinear_wiener_filter_energy.py

@@ -24,7 +24,8 @@ from ..sugar import makeOp
 
 class NonlinearWienerFilterEnergy(Energy):
     def __init__(self, position, d, Instrument, nonlinearity, ht, power, N, S,
-                 iteration_controller=None, iteration_controller_sampling=None):
+                 iteration_controller=None,
+                 iteration_controller_sampling=None):
         super(NonlinearWienerFilterEnergy, self).__init__(position=position)
         self.d = d.lock()
         self.Instrument = Instrument

nifty5/multi/multi_field.py

@@ -180,13 +180,14 @@ class MultiField(object):
         if not isinstance(other, MultiField):
             return False
         for key, val in self._domain.items():
-            if not key in other._domain or other._domain[key] != val:
+            if key not in other._domain or other._domain[key] != val:
                 return False
         for key, val in self._val.items():
             if not val.isSubsetOf(other[key]):
                 return False
         return True
 
+
 for op in ["__add__", "__radd__", "__iadd__",
            "__sub__", "__rsub__", "__isub__",
            "__mul__", "__rmul__", "__imul__",

nifty5/operators/sampling_enabler.py

@@ -66,8 +66,11 @@ class SamplingEnabler(EndomorphicOperator):
             energy = QuadraticEnergy(s, self._op, sp + nj,
                                      _grad=self._likelihood(s) - nj)
             inverter = ConjugateGradient(self._ic)
-            energy, convergence = inverter(energy,
-                                           preconditioner=self._approximation.inverse)
+            if self._approximation is not None:
+                energy, convergence = inverter(
+                    energy, preconditioner=self._approximation.inverse)
+            else:
+                energy, convergence = inverter(energy)
             return energy.position
 
     @property