no more chains

demos/Wiener_Filter.ipynb

@@ -429,7 +429,7 @@
     "mask[l:h] = 0\n",
     "mask = ift.Field.from_global_data(s_space, mask)\n",
     "\n",
-    "R = ift.DiagonalOperator(mask).chain(HT)\n",
+    "R = ift.DiagonalOperator(mask)(HT)\n",
     "n = n.to_global_data_rw()\n",
     "n[l:h] = 0\n",
     "n = ift.Field.from_global_data(s_space, n)\n",
@@ -585,7 +585,7 @@
     "mask[l:h,l:h] = 0.\n",
     "mask = ift.Field.from_global_data(s_space, mask)\n",
     "\n",
-    "R = ift.DiagonalOperator(mask).chain(HT)\n",
+    "R = ift.DiagonalOperator(mask)(HT)\n",
     "n = n.to_global_data_rw()\n",
     "n[l:h, l:h] = 0\n",
     "n = ift.Field.from_global_data(s_space, n)\n",

demos/bernoulli_demo.py

@@ -53,7 +53,7 @@ if __name__ == '__main__':
     A = pd(a)
 
     # Set up a sky model
-    sky = HT.chain(ift.makeOp(A)).positive_tanh()
+    sky = HT(ift.makeOp(A)).positive_tanh()
 
     GR = ift.GeometryRemover(position_space)
     # Set up instrumental response
@@ -61,7 +61,7 @@ if __name__ == '__main__':
 
     # Generate mock data
     d_space = R.target[0]
-    p = R.chain(sky)
+    p = R(sky)
     mock_position = ift.from_random('normal', harmonic_space)
     pp = p(mock_position)
     data = np.random.binomial(1, pp.to_global_data().astype(np.float64))

demos/getting_started_1.py

@@ -78,7 +78,7 @@ if __name__ == '__main__':
     GR = ift.GeometryRemover(position_space)
     mask = ift.Field.from_global_data(position_space, mask)
     Mask = ift.DiagonalOperator(mask)
-    R = GR.chain(Mask).chain(HT)
+    R = GR(Mask(HT))
 
     data_space = GR.target
 
@@ -93,7 +93,7 @@ if __name__ == '__main__':
 
     # Build propagator D and information source j
     j = R.adjoint_times(N.inverse_times(data))
-    D_inv = R.adjoint.chain(N.inverse).chain(R) + S.inverse
+    D_inv = R.adjoint(N.inverse(R)) + S.inverse
     # Make it invertible
     IC = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=1e-3)
     D = ift.InversionEnabler(D_inv, IC, approximation=S.inverse).inverse
@@ -112,7 +112,7 @@ if __name__ == '__main__':
                         title="getting_started_1")
     else:
         ift.plot(HT(MOCK_SIGNAL), title='Mock Signal')
-        ift.plot(mask_to_nan(mask, (GR.chain(Mask)).adjoint(data)),
+        ift.plot(mask_to_nan(mask, (GR(Mask)).adjoint(data)),
                  title='Data')
         ift.plot(HT(m), title='Reconstruction')
         ift.plot(mask_to_nan(mask, HT(m-MOCK_SIGNAL)), title='Residuals')

demos/getting_started_2.py

@@ -70,16 +70,16 @@ if __name__ == '__main__':
     A = pd(a)
 
     # Set up a sky model
-    sky = ift.exp(HT.chain(ift.makeOp(A)))
+    sky = ift.exp(HT(ift.makeOp(A)))
 
     M = ift.DiagonalOperator(exposure)
     GR = ift.GeometryRemover(position_space)
     # Set up instrumental response
-    R = GR.chain(M)
+    R = GR(M)
 
     # Generate mock data
     d_space = R.target[0]
-    lamb = R.chain(sky)
+    lamb = R(sky)
     mock_position = ift.from_random('normal', domain)
     data = lamb(mock_position)
     data = np.random.poisson(data.to_global_data().astype(np.float64))

demos/getting_started_3.py

@@ -44,8 +44,8 @@ if __name__ == '__main__':
     domain = ift.MultiDomain.union(
         (A.domain, ift.MultiDomain.make({'xi': harmonic_space})))
 
-    correlated_field = ht.chain(
-        power_distributor.chain(A)*ift.FieldAdapter(domain, "xi"))
+    correlated_field = ht(
+        power_distributor(A)*ift.FieldAdapter(domain, "xi"))
     # alternatively to the block above one can do:
     # correlated_field = ift.CorrelatedField(position_space, A)
 
@@ -57,7 +57,7 @@ if __name__ == '__main__':
     R = ift.LOSResponse(position_space, starts=LOS_starts,
                         ends=LOS_ends)
     # build signal response model and model likelihood
-    signal_response = R.chain(signal)
+    signal_response = R(signal)
     # specify noise
     data_space = R.target
     noise = .001
@@ -69,7 +69,7 @@ if __name__ == '__main__':
 
     # set up model likelihood
     likelihood = ift.GaussianEnergy(
-        mean=data, covariance=N).chain(signal_response)
+        mean=data, covariance=N)(signal_response)
 
     # set up minimization and inversion schemes
     ic_cg = ift.GradientNormController(iteration_limit=10)

demos/polynomial_fit.py

@@ -97,7 +97,7 @@ d = ift.from_global_data(d_space, y)
 N = ift.DiagonalOperator(ift.from_global_data(d_space, var))
 
 IC = ift.GradientNormController(tol_abs_gradnorm=1e-8)
-likelihood = ift.GaussianEnergy(d, N).chain(R)
+likelihood = ift.GaussianEnergy(d, N)(R)
 H = ift.Hamiltonian(likelihood, IC)
 H = ift.EnergyAdapter(params, H)
 H = H.make_invertible(IC)

nifty5/energies/hamiltonian.py

@@ -40,7 +40,7 @@ class Hamiltonian(Operator):
     def target(self):
         return DomainTuple.scalar_domain()
 
-    def __call__(self, x):
+    def apply(self, x):
         if self._ic_samp is None or not isinstance(x, Linearization):
             return self._lh(x) + self._prior(x)
         else:

nifty5/energies/kl.py

@@ -42,6 +42,6 @@ class SampledKullbachLeiblerDivergence(Operator):
     def target(self):
         return DomainTuple.scalar_domain()
 
-    def __call__(self, x):
+    def apply(self, x):
         return (my_sum(map(lambda v: self._h(x+v), self._res_samples)) *
                 (1./len(self._res_samples)))

nifty5/library/amplitude_model.py

@@ -130,7 +130,7 @@ class AmplitudeModel(Operator):
         cepstrum = create_cepstrum_amplitude_field(dof_space, kern)
 
         ceps = makeOp(sqrt(cepstrum))
-        self._smooth_op = sym.chain(qht).chain(ceps)
+        self._smooth_op = sym(qht(ceps))
         self._keys = tuple(keys)
 
     @property
@@ -141,7 +141,7 @@ class AmplitudeModel(Operator):
     def target(self):
         return self._target
 
-    def __call__(self, x):
+    def apply(self, x):
         smooth_spec = self._smooth_op(x[self._keys[0]])
         phi = x[self._keys[1]] + self._norm_phi_mean
         linear_spec = self._slope(phi)

nifty5/library/bernoulli_energy.py

@@ -39,7 +39,7 @@ class BernoulliEnergy(Operator):
     def target(self):
         return DomainTuple.scalar_domain()
 
-    def __call__(self, x):
+    def apply(self, x):
         x = self._p(x)
         v = x.log().vdot(-self._d) - (1.-x).log().vdot(1.-self._d)
         if not isinstance(x, Linearization):

nifty5/library/correlated_fields.py

@@ -58,7 +58,7 @@ class CorrelatedField(Operator):
     def target(self):
         return self._ht.target
 
-    def __call__(self, x):
+    def apply(self, x):
         A = self._power_distributor(self._amplitude_model(x))
         correlated_field_h = A * x["xi"]
         correlated_field = self._ht(correlated_field_h)

nifty5/library/gaussian_energy.py

@@ -55,7 +55,7 @@ class GaussianEnergy(Operator):
     def target(self):
         return DomainTuple.scalar_domain()
 
-    def __call__(self, x):
+    def apply(self, x):
         residual = x if self._mean is None else x-self._mean
         icovres = residual if self._icov is None else self._icov(residual)
         res = .5*residual.vdot(icovres)

nifty5/library/poissonian_energy.py

@@ -41,7 +41,7 @@ class PoissonianEnergy(Operator):
     def target(self):
         return DomainTuple.scalar_domain()
 
-    def __call__(self, x):
+    def apply(self, x):
         x = self._op(x)
         res = x.sum() - x.log().vdot(self._d)
         if not isinstance(x, Linearization):

nifty5/linearization.py

@@ -46,8 +46,8 @@ class Linearization(object):
 
     def __neg__(self):
         return Linearization(
-            -self._val, self._jac.chain(-1),
-            None if self._metric is None else self._metric.chain(-1))
+            -self._val, self._jac*(-1),
+            None if self._metric is None else self._metric*(-1))
 
     def __add__(self, other):
         if isinstance(other, Linearization):
@@ -77,24 +77,24 @@ class Linearization(object):
             d2 = makeOp(other._val)
             return Linearization(
                 self._val*other._val,
-                d2.chain(self._jac) + d1.chain(other._jac))
+                d2(self._jac) + d1(other._jac))
         if isinstance(other, (int, float, complex)):
             # if other == 0:
             #     return ...
-            met = None if self._metric is None else self._metric.chain(other)
-            return Linearization(self._val*other, self._jac.chain(other), met)
+            met = None if self._metric is None else self._metric(other)
+            return Linearization(self._val*other, self._jac(other), met)
         if isinstance(other, (Field, MultiField)):
             d2 = makeOp(other)
-            return Linearization(self._val*other, d2.chain(self._jac))
+            return Linearization(self._val*other, d2(self._jac))
         raise TypeError
 
     def __rmul__(self, other):
         from .sugar import makeOp
         if isinstance(other, (int, float, complex)):
-            return Linearization(self._val*other, self._jac.chain(other))
+            return Linearization(self._val*other, self._jac(other))
         if isinstance(other, (Field, MultiField)):
             d1 = makeOp(other)
-            return Linearization(self._val*other, d1.chain(self._jac))
+            return Linearization(self._val*other, d1(self._jac))
 
     def vdot(self, other):
         from .domain_tuple import DomainTuple
@@ -102,11 +102,11 @@ class Linearization(object):
         if isinstance(other, (Field, MultiField)):
             return Linearization(
                 Field(DomainTuple.scalar_domain(),self._val.vdot(other)),
-                VdotOperator(other).chain(self._jac))
+                VdotOperator(other)(self._jac))
         return Linearization(
             Field(DomainTuple.scalar_domain(),self._val.vdot(other._val)),
-            VdotOperator(self._val).chain(other._jac) +
-            VdotOperator(other._val).chain(self._jac))
+            VdotOperator(self._val)(other._jac) +
+            VdotOperator(other._val)(self._jac))
 
     def sum(self):
         from .domain_tuple import DomainTuple
@@ -114,24 +114,24 @@ class Linearization(object):
         from .sugar import full
         return Linearization(
             Field(DomainTuple.scalar_domain(), self._val.sum()),
-            SumReductionOperator(self._jac.target).chain(self._jac))
+            SumReductionOperator(self._jac.target)(self._jac))
 
     def exp(self):
         tmp = self._val.exp()
-        return Linearization(tmp, makeOp(tmp).chain(self._jac))
+        return Linearization(tmp, makeOp(tmp)(self._jac))
 
     def log(self):
         tmp = self._val.log()
-        return Linearization(tmp, makeOp(1./self._val).chain(self._jac))
+        return Linearization(tmp, makeOp(1./self._val)(self._jac))
 
     def tanh(self):
         tmp = self._val.tanh()
-        return Linearization(tmp, makeOp(1.-tmp**2).chain(self._jac))
+        return Linearization(tmp, makeOp(1.-tmp**2)(self._jac))
 
     def positive_tanh(self):
         tmp = self._val.tanh()
         tmp2 = 0.5*(1.+tmp)
-        return Linearization(tmp2, makeOp(0.5*(1.-tmp**2)).chain(self._jac))
+        return Linearization(tmp2, makeOp(0.5*(1.-tmp**2))(self._jac))
 
     def add_metric(self, metric):
         return Linearization(self._val, self._jac, metric)

nifty5/multi/block_diagonal_operator.py

@@ -68,7 +68,7 @@ class BlockDiagonalOperator(EndomorphicOperator):
     def _combine_chain(self, op):
         if self._domain is not op._domain:
             raise ValueError("domain mismatch")
-        res = tuple(v1.chain(v2) for v1, v2 in zip(self._ops, op._ops))
+        res = tuple(v1(v2) for v1, v2 in zip(self._ops, op._ops))
         return BlockDiagonalOperator(self._domain, res)
 
     def _combine_sum(self, op, selfneg, opneg):

nifty5/operators/harmonic_smoothing_operator.py

@@ -67,4 +67,4 @@ def HarmonicSmoothingOperator(domain, sigma, space=None):
     ddom = list(domain)
     ddom[space] = codomain
     diag = DiagonalOperator(kernel, ddom, space)
-    return Hartley.inverse.chain(diag).chain(Hartley)
+    return Hartley.inverse(diag(Hartley))

nifty5/operators/linear_operator.py

@@ -142,9 +142,6 @@ class LinearOperator(Operator):
         from .chain_operator import ChainOperator
         return ChainOperator.make([self, other2])
 
-    def chain(self, other):
-        return self.__matmul__(other)
-
     def __rmatmul__(self, other):
         if np.isscalar(other) and other == 1.:
             return self
@@ -213,10 +210,14 @@ class LinearOperator(Operator):
 
     def __call__(self, x):
         """Same as :meth:`times`"""
+        from ..field import Field
+        from ..multi.multi_field import MultiField
+        if isinstance(x, (Field, MultiField)):
+            return self.apply(x, self.TIMES)
         from ..linearization import Linearization
         if isinstance(x, Linearization):
-            return Linearization(self(x._val), self.chain(x._jac))
-        return self.apply(x, self.TIMES)
+            return Linearization(self(x._val), self(x._jac))
+        return self.__matmul__(x)
 
     def times(self, x):
         """ Applies the Operator to a given Field.

nifty5/operators/operator.py

@@ -33,26 +33,13 @@ class Operator(NiftyMetaBase()):
             return NotImplemented
         return _OpProd.make((self, x))
 
-    def chain(self, x):
-        res = self.__matmul__(x)
-        if res == NotImplemented:
-            raise TypeError("operator expected")
-        return res
+    def apply(self, x):
+        raise NotImplementedError
 
     def __call__(self, x):
-        """Returns transformed x
-
-        Parameters
-        ----------
-        x : Linearization
-            input
-
-        Returns
-        -------
-        Linearization
-            output
-        """
-        raise NotImplementedError
+       if isinstance(x, Operator):
+           return _OpChain.make((self, x))
+       return self.apply(x)
 
 
 for f in ["sqrt", "exp", "log", "tanh", "positive_tanh"]:
@@ -78,7 +65,7 @@ class _FunctionApplier(Operator):
     def target(self):
         return self._domain
 
-    def __call__(self, x):
+    def apply(self, x):
         return getattr(x, self._funcname)()
 
 
@@ -117,7 +104,7 @@ class _OpChain(_CombinedOperator):
     def target(self):
         return self._ops[0].target
 
-    def __call__(self, x):
+    def apply(self, x):
         for op in reversed(self._ops):
             x = op(x)
         return x
@@ -135,7 +122,7 @@ class _OpProd(_CombinedOperator):
     def target(self):
         return self._ops[0].target
 
-    def __call__(self, x):
+    def apply(self, x):
         from ..utilities import my_product
         return my_product(map(lambda op: op(x), self._ops))
 
@@ -154,7 +141,7 @@ class _OpSum(_CombinedOperator):
     def target(self):
         return self._target
 
-    def __call__(self, x):
+    def apply(self, x):
         raise NotImplementedError
 
 
@@ -193,7 +180,7 @@ class QuadraticFormOperator(Operator):
     def target(self):
         return self._target
 
-    def __call__(self, x):
+    def apply(self, x):
         if isinstance(x, Linearization):
             jac = self._op(x)
             val = Field(self._target, 0.5 * x.vdot(jac))

nifty5/operators/sandwich_operator.py

@@ -56,9 +56,9 @@ class SandwichOperator(EndomorphicOperator):
             raise TypeError("cheese must be a linear operator")
         if cheese is None:
             cheese = ScalingOperator(1., bun.target)
-            op = bun.adjoint.chain(bun)
+            op = bun.adjoint(bun)
         else:
-            op = bun.adjoint.chain(cheese).chain(bun)
+            op = bun.adjoint(cheese(bun))
 
         # if our sandwich is diagonal, we can return immediately
         if isinstance(op, (ScalingOperator, DiagonalOperator)):

nifty5/operators/smoothness_operator.py

@@ -54,4 +54,4 @@ def SmoothnessOperator(domain, strength=1., logarithmic=True, space=None):
     if strength == 0.:
         return ScalingOperator(0., domain)
     laplace = LaplaceOperator(domain, logarithmic=logarithmic, space=space)
-    return (strength**2)*laplace.adjoint.chain(laplace)
+    return (strength**2)*laplace.adjoint(laplace)