minor cleanups

nifty5/energies/hamiltonian.py

@@ -31,10 +31,10 @@ class Hamiltonian(Energy):
         lh: Likelihood (energy object)
         prior:
         """
-        super(Hamiltonian, self).__init__(lh.position)
+        super(Hamiltonian, self).__init__(lh._position)
         self._lh = lh
         self._ic_samp = iteration_controller_sampling
-        self._prior = GaussianEnergy(Variable(self.position))
+        self._prior = GaussianEnergy(Variable(self._position))
 
     def at(self, position):
         return self.__class__(self._lh.at(position), self._ic_samp)

nifty5/library/gaussian_energy.py

@@ -32,7 +32,7 @@ class GaussianEnergy(Energy):
         value = 0.5 * s.vdot(s), i.e. a log-Gauss distribution with unit
         covariance
         """
-        super(GaussianEnergy, self).__init__(inp.position)
+        super(GaussianEnergy, self).__init__(inp._position)
         self._inp = inp
         self._mean = mean
         self._cov = covariance

nifty5/minimization/quadratic_energy.py

@@ -35,14 +35,14 @@ class QuadraticEnergy(Energy):
             self._grad = _grad
             Ax = _grad if b is None else _grad + b
         else:
-            Ax = self._A(self.position)
+            Ax = self._A(self._position)
             self._grad = Ax if b is None else Ax - b
-        self._value = 0.5*self.position.vdot(Ax)
+        self._value = 0.5*self._position.vdot(Ax)
         if b is not None:
-            self._value -= b.vdot(self.position)
+            self._value -= b.vdot(self._position)
 
     def at(self, position):
-        return QuadraticEnergy(position=position, A=self._A, b=self._b)
+        return QuadraticEnergy(position, self._A, self._b)
 
     def at_with_grad(self, position, grad):
         """ Specialized version of `at`, taking also a gradient.
@@ -63,8 +63,7 @@ class QuadraticEnergy(Energy):
         Energy
             Energy object at new position.
         """
-        return QuadraticEnergy(position=position, A=self._A, b=self._b,
-                               _grad=grad)
+        return QuadraticEnergy(position, self._A, self._b, grad)
 
     @property
     def value(self):

nifty5/models/multi_model.py

@@ -35,7 +35,7 @@ class MultiModel(Model):
         val = self._model.value
         if not isinstance(val.domain, DomainTuple):
             raise TypeError
-        self._value = MultiField({key: val})
+        self._value = MultiField.from_dict({key: val})
         self._jacobian = (MultiAdaptor(self.value.domain) *
                           self._model.jacobian)