merge NIFTy_5

README.md

 NIFTy - Numerical Information Field Theory
 ==========================================
-[![build status](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_5/build.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_5)
-[![coverage report](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_5/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_5)
+[![build status](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/badges/NIFTy_5/build.svg)](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/commits/NIFTy_5)
+[![coverage report](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/badges/NIFTy_5/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/commits/NIFTy_5)
 
 **NIFTy** project homepage:
 [http://ift.pages.mpcdf.de/NIFTy](http://ift.pages.mpcdf.de/NIFTy)
@@ -108,7 +108,7 @@ For a quick start, you can browse through the [informal
 introduction](http://ift.pages.mpcdf.de/NIFTy/code.html) or
 dive into NIFTy by running one of the demonstrations, e.g.:
 
-    python demos/wiener_filter_via_curvature.py
+    python demos/getting_started_1.py
 
 
 ### Acknowledgement

demos/getting_started_3.py

@@ -74,7 +74,7 @@ if __name__ == '__main__':
     N_samples = 20
     for i in range(5):
         H = H.at(position)
-        samples = [H.curvature.draw_sample(from_inverse=True)
+        samples = [H.metric.draw_sample(from_inverse=True)
                    for _ in range(N_samples)]
 
         KL = ift.SampledKullbachLeiblerDivergence(H, samples)

docs/source/code.rst

@@ -306,16 +306,16 @@ Energy functionals
 In NIFTy5 such functions are represented by objects of type :class:`Energy`.
 These hold the prescription how to calculate the function's
 :attr:`~Energy.value`, :attr:`~Energy.gradient` and
-(optionally) :attr:`~Energy.curvature` at any given :attr:`~Energy.position`
+(optionally) :attr:`~Energy.metric` at any given :attr:`~Energy.position`
 in parameter space.
 Function values are floating-point scalars, gradients have the form of fields
-living on the energy's position domain, and curvatures are represented by
+living on the energy's position domain, and metrics are represented by
 linear operator objects.
 
 Energies are classes that typically have to be provided by the user when
 tackling new IFT problems.
 Some examples of concrete energy classes delivered with NIFTy5 are
-:class:`QuadraticEnergy` (with position-independent curvature, mainly used with
+:class:`QuadraticEnergy` (with position-independent metric, mainly used with
 conjugate gradient minimization) and :class:`~nifty5.library.WienerFilterEnergy`.
 
 
@@ -367,7 +367,7 @@ This family of algorithms is encapsulated in NIFTy's :class:`DescentMinimizer`
 class, which currently has three concrete implementations:
 :class:`SteepestDescent`, :class:`VL_BFGS`, and :class:`RelaxedNewton`.
 Of these algorithms, only :class:`RelaxedNewton` requires the energy object to
-provide a :attr:`~Energy.curvature` property, the others only need energy
+provide a :attr:`~Energy.metric` property, the others only need energy
 values and gradients.
 
 The flexibility of NIFTy's design allows using externally provided

nifty5/__init__.py

@@ -21,7 +21,7 @@ from .nonlinearities import Exponential, Linear, PositiveTanh, Tanh
 from .models.constant import Constant
 from .models.linear_model import LinearModel
 from .models.local_nonlinearity import (LocalModel, PointwiseExponential,
-                                 PointwisePositiveTanh, PointwiseTanh)
+                                        PointwisePositiveTanh, PointwiseTanh)
 from .models.model import Model
 from .models.multi_model import MultiModel
 from .models.variable import Variable
@@ -65,7 +65,8 @@ from .minimization.steepest_descent import SteepestDescent
 from .minimization.vl_bfgs import VL_BFGS
 from .minimization.l_bfgs import L_BFGS
 from .minimization.relaxed_newton import RelaxedNewton
-from .minimization.scipy_minimizer import ScipyMinimizer, NewtonCG, L_BFGS_B, ScipyCG
+from .minimization.scipy_minimizer import (ScipyMinimizer, NewtonCG, L_BFGS_B,
+                                           ScipyCG)
 from .minimization.energy import Energy
 from .minimization.quadratic_energy import QuadraticEnergy
 from .minimization.line_energy import LineEnergy
@@ -82,7 +83,8 @@ from .library.point_sources import PointSources
 from .library.poissonian_energy import PoissonianEnergy
 from .library.wiener_filter_curvature import WienerFilterCurvature
 from .library.wiener_filter_energy import WienerFilterEnergy
-from .library.correlated_fields import make_correlated_field, make_mf_correlated_field
+from .library.correlated_fields import (make_correlated_field,
+                                        make_mf_correlated_field)
 from .library.bernoulli_energy import BernoulliEnergy
 
 from . import extra

nifty5/energies/hamiltonian.py

@@ -49,13 +49,13 @@ class Hamiltonian(Energy):
 
     @property
     @memo
-    def curvature(self):
-        prior_curv = self._prior.curvature
+    def metric(self):
+        prior_mtr = self._prior.metric
         if self._ic_samp is None:
-            return self._lh.curvature + prior_curv
+            return self._lh.metric + prior_mtr
         else:
-            return SamplingEnabler(self._lh.curvature, prior_curv.inverse,
-                                   self._ic_samp, prior_curv.inverse)
+            return SamplingEnabler(self._lh.metric, prior_mtr.inverse,
+                                   self._ic_samp, prior_mtr.inverse)
 
     def __str__(self):
         res = 'Likelihood:\t{:.2E}\n'.format(self._lh.value)

nifty5/energies/kl.py

@@ -33,6 +33,6 @@ class SampledKullbachLeiblerDivergence(Energy):
 
     @property
     @memo
-    def curvature(self):
-       return (my_sum(map(lambda v: v.curvature, self._energy_list)) *
-               (1./len(self._energy_list)))
+    def metric(self):
+        return (my_sum(map(lambda v: v.metric, self._energy_list)) *
+                (1./len(self._energy_list)))

nifty5/extra/energy_and_model_tests.py

@@ -22,7 +22,7 @@ from ..minimization.energy import Energy
 from ..models.model import Model
 
 __all__ = ["check_value_gradient_consistency",
-           "check_value_gradient_curvature_consistency"]
+           "check_value_gradient_metric_consistency"]
 
 
 def _get_acceptable_model(M):
@@ -30,7 +30,7 @@ def _get_acceptable_model(M):
     if not np.isfinite(val.sum()):
         raise ValueError('Initial Model value must be finite')
     dir = from_random("normal", M.position.domain)
-    dirder = M.gradient(dir)
+    dirder = M.jacobian(dir)
     dir *= val/(dirder).norm()*1e-5
     # Find a step length that leads to a "reasonable" Model
     for i in range(50):
@@ -82,7 +82,7 @@ def check_value_gradient_consistency(E, tol=1e-8, ntries=100):
             if isinstance(E, Energy):
                 dirder = Emid.gradient.vdot(dir)/dirnorm
             else:
-                dirder = Emid.gradient(dir)/dirnorm
+                dirder = Emid.jacobian(dir)/dirnorm
             numgrad = (E2.value-val)/dirnorm
             if isinstance(E, Model):
                 xtol = tol * dirder.norm() / np.sqrt(dirder.size)
@@ -100,9 +100,9 @@ def check_value_gradient_consistency(E, tol=1e-8, ntries=100):
         E = Enext
 
 
-def check_value_gradient_curvature_consistency(E, tol=1e-8, ntries=100):
+def check_value_gradient_metric_consistency(E, tol=1e-8, ntries=100):
     if isinstance(E, Model):
-        raise ValueError('Models have no curvature, thus it cannot be tested.')
+        raise ValueError('Models have no metric, thus it cannot be tested.')
     for _ in range(ntries):
         E2 = _get_acceptable_energy(E)
         val = E.value
@@ -112,7 +112,7 @@ def check_value_gradient_curvature_consistency(E, tol=1e-8, ntries=100):
         for i in range(50):
             Emid = E.at(E.position + 0.5*dir)
             dirder = Emid.gradient.vdot(dir)/dirnorm
-            dgrad = Emid.curvature(dir)/dirnorm
+            dgrad = Emid.metric(dir)/dirnorm
             xtol = tol*Emid.gradient_norm
             if abs((E2.value-val)/dirnorm - dirder) < xtol and \
                (abs((E2.gradient-E.gradient)/dirnorm-dgrad) < xtol).all():
@@ -121,5 +121,5 @@ def check_value_gradient_curvature_consistency(E, tol=1e-8, ntries=100):
             dirnorm *= 0.5
             E2 = Emid
         else:
-            raise ValueError("gradient, value and curvature seem inconsistent")
+            raise ValueError("gradient, value and metric seem inconsistent")
         E = Enext

nifty5/library/bernoulli_energy.py

@@ -39,9 +39,9 @@ class BernoulliEnergy(Energy):
         if isnan(self._value):
             self._value = inf
         metric = makeOp(1./((p_val) * (1.-p_val)))
-        self._gradient = self._p.gradient.adjoint_times(metric(p_val-d))
+        self._gradient = self._p.jacobian.adjoint_times(metric(p_val-d))
 
-        self._curvature = SandwichOperator.make(self._p.gradient, metric)
+        self._metric = SandwichOperator.make(self._p.jacobian, metric)
 
     def at(self, position):
         return self.__class__(self._p.at(position), self._d)
@@ -55,5 +55,5 @@ class BernoulliEnergy(Energy):
         return self._gradient
 
     @property
-    def curvature(self):
-        return self._curvature
+    def metric(self):
+        return self._metric

nifty5/library/correlated_fields.py

@@ -47,7 +47,8 @@ def make_mf_correlated_field(s_space_spatial, s_space_energy,
     from ..models.variable import Variable
     from ..domain_tuple import DomainTuple
     from ..operators.domain_distributor import DomainDistributor
-    from ..operators.harmonic_transform_operator import HarmonicTransformOperator
+    from ..operators.harmonic_transform_operator \
+        import HarmonicTransformOperator
     h_space_spatial = s_space_spatial.get_default_codomain()
     h_space_energy = s_space_energy.get_default_codomain()
     h_space = DomainTuple.make((h_space_spatial, h_space_energy))

nifty5/library/gaussian_energy.py

@@ -49,18 +49,20 @@ class GaussianEnergy(Energy):
     def value(self):
         if self._cov is None:
             return .5 * self.residual.vdot(self.residual).real
-        return .5 * self.residual.vdot(self._cov.inverse(self.residual)).real
+        return .5 * self.residual.vdot(
+            self._cov.inverse_times(self.residual)).real
 
     @property
     @memo
     def gradient(self):
         if self._cov is None:
-            return self._inp.gradient.adjoint(self.residual)
-        return self._inp.gradient.adjoint(self._cov.inverse(self.residual))
+            return self._inp.jacobian.adjoint_times(self.residual)
+        return self._inp.jacobian.adjoint_times(
+            self._cov.inverse_times(self.residual))
 
     @property
     @memo
-    def curvature(self):
+    def metric(self):
         if self._cov is None:
-            return SandwichOperator.make(self._inp.gradient, None)
-        return SandwichOperator.make(self._inp.gradient, self._cov.inverse)
+            return SandwichOperator.make(self._inp.jacobian, None)
+        return SandwichOperator.make(self._inp.jacobian, self._cov.inverse)

nifty5/library/point_sources.py

@@ -28,7 +28,7 @@ class PointSources(Model):
 
     @property
     @memo
-    def gradient(self):
+    def jacobian(self):
         u = self.position['points'].local_data
         inner = norm.pdf(u)
         outer_inv = invgamma.pdf(invgamma.ppf(norm.cdf(u),
@@ -51,7 +51,8 @@ class PointSources(Model):
     @staticmethod
     def IG_prime(field, alpha, q):
         inner = norm.pdf(field.local_data)
-        outer = invgamma.pdf(invgamma.ppf(norm.cdf(field.local_data), alpha, scale=q), alpha, scale=q)
+        outer = invgamma.pdf(invgamma.ppf(norm.cdf(field.local_data), alpha,
+                                          scale=q), alpha, scale=q)
         # # FIXME
         # outer = np.clip(outer, 1e-20, None)
         outer = 1/outer

nifty5/library/poissonian_energy.py

@@ -40,11 +40,11 @@ class PoissonianEnergy(Energy):
         self._value = lamb_val.sum() - d.vdot(log(lamb_val))
         if isnan(self._value):
             self._value = inf
-        self._gradient = self._lamb.gradient.adjoint_times(1 - d/lamb_val)
+        self._gradient = self._lamb.jacobian.adjoint_times(1 - d/lamb_val)
 
         # metric = makeOp(d/lamb_val/lamb_val)
         metric = makeOp(1./lamb_val)
-        self._curvature = SandwichOperator.make(self._lamb.gradient, metric)
+        self._metric = SandwichOperator.make(self._lamb.jacobian, metric)
 
     def at(self, position):
         return self.__class__(self._lamb.at(position), self._d)
@@ -58,5 +58,5 @@ class PoissonianEnergy(Energy):
         return self._gradient
 
     @property
-    def curvature(self):
-        return self._curvature
+    def metric(self):
+        return self._metric

nifty5/minimization/conjugate_gradient.py

@@ -47,7 +47,7 @@ class ConjugateGradient(Minimizer):
         Parameters
         ----------
         energy : Energy object at the starting point of the iteration.
-            Its curvature operator must be independent of position, otherwise
+            Its metric operator must be independent of position, otherwise
             linear conjugate gradient minimization will fail.
         preconditioner : Operator *optional*
             This operator can be provided which transforms the variables of the
@@ -73,7 +73,7 @@ class ConjugateGradient(Minimizer):
             return energy, controller.CONVERGED
 
         while True:
-            q = energy.curvature(d)
+            q = energy.metric(d)
             ddotq = d.vdot(q).real
             if ddotq == 0.:
                 logger.error("Error: ConjugateGradient: ddotq==0.")

nifty5/minimization/descent_minimizer.py

@@ -51,7 +51,7 @@ class DescentMinimizer(Minimizer):
         Parameters
         ----------
         energy : Energy
-           Energy object which provides value, gradient and curvature at a
+           Energy object which provides value, gradient and metric at a
            specific position in parameter space.
 
         Returns

nifty5/minimization/energy.py

@@ -25,7 +25,7 @@ class Energy(NiftyMetaBase()):
     """ Provides the functional used by minimization schemes.
 
    The Energy object is an implementation of a scalar function including its
-   gradient and curvature at some position.
+   gradient and metric at some position.
 
     Parameters
     ----------
@@ -35,11 +35,11 @@ class Energy(NiftyMetaBase()):
     Notes
     -----
     An instance of the Energy class is defined at a certain location. If one
-    is interested in the value, gradient or curvature of the abstract energy
+    is interested in the value, gradient or metric of the abstract energy
     functional one has to 'jump' to the new position using the `at` method.
     This method returns a new energy instance residing at the new position. By
     this approach, intermediate results from computing e.g. the gradient can
-    safely be reused for e.g. the value or the curvature.
+    safely be reused for e.g. the value or the metric.
 
     Memorizing the evaluations of some quantities (using the memo decorator)
     minimizes the computational effort for multiple calls.
@@ -75,7 +75,7 @@ class Energy(NiftyMetaBase()):
         Field : selected location in parameter space.
 
         The Field location in parameter space where value, gradient and
-        curvature are evaluated.
+        metric are evaluated.
         """
         return self._position
 
@@ -104,11 +104,11 @@ class Energy(NiftyMetaBase()):
         return self.gradient.norm()
 
     @property
-    def curvature(self):
+    def metric(self):
         """
-        LinearOperator : implicitly defined curvature.
+        LinearOperator : implicitly defined metric.
             A positive semi-definite operator or function describing the
-            curvature of the potential at the given `position`.
+            metric of the potential at the given `position`.
         """
         raise NotImplementedError
 
@@ -132,7 +132,7 @@ class Energy(NiftyMetaBase()):
         from .iteration_controller import IterationController
         if not isinstance(controller, IterationController):
             raise TypeError
-        return CurvatureInversionEnabler(self, controller, preconditioner)
+        return MetricInversionEnabler(self, controller, preconditioner)
 
     def __mul__(self, factor):
         from .energy_sum import EnergySum
@@ -160,9 +160,9 @@ class Energy(NiftyMetaBase()):
         return EnergySum.make([self], [-1.])
 
 
-class CurvatureInversionEnabler(Energy):
+class MetricInversionEnabler(Energy):
     def __init__(self, ene, controller, preconditioner):
-        super(CurvatureInversionEnabler, self).__init__(ene.position)
+        super(MetricInversionEnabler, self).__init__(ene.position)
         self._energy = ene
         self._controller = controller
         self._preconditioner = preconditioner
@@ -170,7 +170,7 @@ class CurvatureInversionEnabler(Energy):
     def at(self, position):
         if self._position.isSubsetOf(position):
             return self
-        return CurvatureInversionEnabler(
+        return MetricInversionEnabler(
             self._energy.at(position), self._controller, self._preconditioner)
 
     @property
@@ -186,16 +186,16 @@ class CurvatureInversionEnabler(Energy):
         return self._energy.gradient
 
     @property
-    def curvature(self):
+    def metric(self):
         from ..operators.linear_operator import LinearOperator
         from ..operators.inversion_enabler import InversionEnabler
-        curv = self._energy.curvature
+        curv = self._energy.metric
         if self._preconditioner is None:
             precond = None
         elif isinstance(self._preconditioner, LinearOperator):
             precond = self._preconditioner
         elif isinstance(self._preconditioner, Energy):
-            precond = self._preconditioner.at(self.position).curvature
+            precond = self._preconditioner.at(self.position).metric
         return InversionEnabler(curv, self._controller, precond)
 
     def longest_step(self, dir):

nifty5/minimization/energy_sum.py

@@ -67,6 +67,6 @@ class EnergySum(Energy):
 
     @property
     @memo
-    def curvature(self):
-        return my_lincomb(map(lambda v: v.curvature, self._energies),
+    def metric(self):
+        return my_lincomb(map(lambda v: v.metric, self._energies),
                           self._factors)

nifty5/minimization/quadratic_energy.py

@@ -21,7 +21,7 @@ from .energy import Energy
 
 class QuadraticEnergy(Energy):
     """The Energy for a quadratic form.
-    The most important aspect of this energy is that its curvature must be
+    The most important aspect of this energy is that its metric must be
     position-independent.
     """
 
@@ -74,5 +74,5 @@ class QuadraticEnergy(Energy):
         return self._grad
 
     @property
-    def curvature(self):
+    def metric(self):
         return self._A

nifty5/minimization/relaxed_newton.py

@@ -24,7 +24,7 @@ class RelaxedNewton(DescentMinimizer):
     """ 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 curvature.
+    current parameter position with the inverse local metric.
     """
     def __init__(self, controller, line_searcher=None):
         if line_searcher is None:
@@ -34,4 +34,4 @@ class RelaxedNewton(DescentMinimizer):
                                             line_searcher=line_searcher)
 
     def get_descent_direction(self, energy):
-        return -energy.curvature.inverse_times(energy.gradient)
+        return -energy.metric.inverse_times(energy.gradient)

nifty5/minimization/scipy_minimizer.py

@@ -56,7 +56,7 @@ class _MinHelper(object):
 
     def hessp(self, x, p):
         self._update(x)
-        res = self._energy.curvature(_toField(p, self._domain))
+        res = self._energy.metric(_toField(p, self._domain))
         return _toFlatNdarray(res)
 
 

nifty5/models/binary_helpers.py

@@ -42,7 +42,7 @@ class ScalarMul(Model):
         self._factor = factor
 
         self._value = self._factor * self._model.value
-        self._gradient = self._factor * self._model.gradient
+        self._jacobian = self._factor * self._model.jacobian
 
     def at(self, position):
         return self.__class__(self._factor, self._model.at(position))
@@ -57,7 +57,7 @@ class Add(Model):
         self._model2 = model2.at(position)
 
         self._value = self._model1.value + self._model2.value
-        self._gradient = self._model1.gradient + self._model2.gradient
+        self._jacobian = self._model1.jacobian + self._model2.jacobian
 
     @staticmethod
     def make(model1, model2):
@@ -87,8 +87,8 @@ class Mul(Model):
         self._model2 = model2.at(position)
 
         self._value = self._model1.value * self._model2.value
-        self._gradient = (makeOp(self._model1.value) * self._model2.gradient +
-                          makeOp(self._model2.value) * self._model1.gradient)
+        self._jacobian = (makeOp(self._model1.value) * self._model2.jacobian +
+                          makeOp(self._model2.value) * self._model1.jacobian)
 
     @staticmethod
     def make(model1, model2):

nifty5/models/constant.py

@@ -32,7 +32,7 @@ class Constant(Model):
     -----
     Since there is no model-function associated:
         - Position has no influence on value.
-        - There is no gradient.
+        - There is no Jacobian.
     """
     # TODO Remove position
     def __init__(self, position, constant):
@@ -40,7 +40,7 @@ class Constant(Model):
         self._constant = constant
 
         self._value = self._constant
-        self._gradient = 0.
+        self._jacobian = 0.
 
     def at(self, position):
         return self.__class__(position, self._constant)

nifty5/models/linear_model.py

@@ -34,7 +34,7 @@ class LinearModel(Model):
         -------
         Model with linear Operator applied:
             - Model.value = LinOp (inp.value) [key-wise]
-            - Gradient = LinOp * inp.gradient
+            - Jacobian = LinOp * inp.jacobian
         """
         from ..operators.linear_operator import LinearOperator
         super(LinearModel, self).__init__(inp.position)
@@ -49,7 +49,7 @@ class LinearModel(Model):
                                              self._lin_op._key)
 
         self._value = self._lin_op(self._inp.value)
-        self._gradient = self._lin_op*self._inp.gradient
+        self._jacobian = self._lin_op*self._inp.jacobian
 
     def at(self, position):
         return self.__class__(self._inp.at(position), self._lin_op)

nifty5/models/local_nonlinearity.py

@@ -27,7 +27,7 @@ class LocalModel(Model):
         """
         Computes nonlinearity(inp)
             - LocalModel.value = nonlinearity(value) (pointwise)
-            - LocalModel.gradient = Outer Product of gradients
+            - LocalModel.jacobian = Outer Product of Jacobians
 
         Parameters
         ----------
@@ -40,9 +40,9 @@ class LocalModel(Model):
         self._inp = inp
         self._nonlinearity = nonlinearity
         self._value = nonlinearity(self._inp.value)
-        d_inner = self._inp.gradient
+        d_inner = self._inp.jacobian
         d_outer = makeOp(self._nonlinearity.derivative(self._inp.value))
-        self._gradient = d_outer * d_inner
+        self._jacobian = d_outer * d_inner
 
     def at(self, position):
         return self.__class__(self._inp.at(position), self._nonlinearity)

nifty5/models/model.py

@@ -28,7 +28,7 @@ class Model(NiftyMetaBase()):
     The Model object is an implementation of a * which knows:
         - position in parameterspace.  (Field, MulitField)
         - value according to its modelfunction A.  A(position)
-        - gradient of the modelfunction at the current position.
+        - Jacobian of the model function at the current position.
 
     Parameters
     ----------
@@ -37,9 +37,9 @@ class Model(NiftyMetaBase()):
 
     Notes
     -----
-    An instance of the model class knows its position, value and gradient.
+    An instance of the model class knows its position, value and Jacobian.
     One can 'jump' to a new position, with the help of the 'at' method, whereby
-    one automatically gets the value and gradient of the model. The 'at' method
+    one automatically gets the value and Jacobian of the model. The 'at' method
     creates a new instance of the class.
     """
     def __init__(self, position):
@@ -65,7 +65,7 @@ class Model(NiftyMetaBase()):
         """
         Field or MultiField: selected location in parameter space.
 
-        The location in parameter space where value and gradient are
+        The location in parameter space where value and Jacobian are
         evaluated.
         """
         return self._position
@@ -80,11 +80,11 @@ class Model(NiftyMetaBase()):
         return self._value
 
     @property
-    def gradient(self):
+    def jacobian(self):
         """
         LinearOperator : The derivative of the model at given `position`.
         """
-        return self._gradient
+        return self._jacobian
 
     def __getitem__(self, key):
         sel = SelectionOperator(self.value.domain, key)

nifty5/models/multi_model.py

@@ -34,7 +34,8 @@ class MultiModel(Model):
         if not isinstance(val.domain, DomainTuple):
             raise TypeError
         self._value = MultiField({key: val})
-        self._gradient = MultiAdaptor(self.value.domain) * self._model.gradient
+        self._jacobian = (MultiAdaptor(self.value.domain) *
+                          self._model.jacobian)
 
     def at(self, position):
         return self.__class__(self._model.at(position), self._key)

nifty5/models/variable.py

@@ -32,7 +32,7 @@ class Variable(Model):
         super(Variable, self).__init__(position)
 
         self._value = position
-        self._gradient = ScalingOperator(1., position.domain)
+        self._jacobian = ScalingOperator(1., position.domain)
 
     def at(self, position):
         return self.__class__(position)

nifty5/operators/chain_operator.py

@@ -94,9 +94,8 @@ class ChainOperator(LinearOperator):
 
     @staticmethod
     def make(ops):
-        """Build a ChainOperator (or something simpler if possible), 
+        """Build a ChainOperator (or something simpler if possible),
         a sequence of concatenated LinearOperators.
-        
 
         Parameters