Performance fixups 4/n

nifty6/operators/adder.py

@@ -15,9 +15,12 @@
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
 
+import numpy as np
+
 from ..field import Field
 from ..multi_field import MultiField
 from .operator import Operator
+from ..sugar import makeDomain
 
 
 class Adder(Operator):
@@ -25,18 +28,22 @@ class Adder(Operator):
 
     Parameters
     ----------
-    field : Field or MultiField
+    a : Field or MultiField or Scalar
         The field by which the input is shifted.
     """
-    def __init__(self, field, neg=False):
-        if not isinstance(field, (Field, MultiField)):
+    def __init__(self, a, neg=False, domain=None):
+        self._a = a
+        if isinstance(a, (Field, MultiField)):
+            dom = a.domain
+        elif np.isscalar(a):
+            dom = makeDomain(domain)
+        else:
             raise TypeError
-        self._field = field
-        self._domain = self._target = field.domain
+        self._domain = self._target = dom
         self._neg = bool(neg)
 
     def apply(self, x):
         self._check_input(x)
         if self._neg:
-            return x - self._field
-        return x + self._field
+            return x - self._a
+        return x + self._a

nifty6/operators/energy_operators.py

@@ -136,9 +136,7 @@ class VariableCovarianceGaussianEnergy(EnergyOperator):
         res0 = r.vdot(r*icov).real
         res1 = icov.log().sum()
         res = (res0-res1).scale(0.5)(x)
-        if not lin:
-            return Field.scalar(res)
-        if not x.want_metric:
+        if not lin or not x.want_metric:
             return res
         mf = {self._r: x.val[self._icov], self._icov: .5*x.val[self._icov]**(-2)}
         metric = makeOp(MultiField.from_dict(mf))
@@ -242,9 +240,7 @@ class PoissonianEnergy(EnergyOperator):
         self._check_input(x)
         fa = FieldAdapter(self._domain, 'foo')
         res = (fa.sum() - fa.log().vdot(self._d))(fa.adjoint(x))
-        if not isinstance(x, Linearization):
-            return Field.scalar(res)
-        if not x.want_metric:
+        if not isinstance(x, Linearization) or not x.want_metric:
             return res
         metric = SandwichOperator.make(x.jac, makeOp(1./x.val))
         return res.add_metric(metric)
@@ -287,9 +283,7 @@ class InverseGammaLikelihood(EnergyOperator):
         fa = FieldAdapter(self._domain, 'foo')
         x = fa.adjoint(x)
         res = (fa.log().vdot(self._alphap1) + fa.one_over().vdot(self._beta))(x)
-        if not isinstance(x, Linearization):
-            return Field.scalar(res)
-        if not x.want_metric:
+        if not isinstance(x, Linearization) or not x.want_metric:
             return res
         metric = SandwichOperator.make(x.jac, makeOp(self._alphap1/(x.val**2)))
         return res.add_metric(metric)
@@ -357,11 +351,9 @@ class BernoulliEnergy(EnergyOperator):
         self._check_input(x)
         iden = FieldAdapter(self._domain, 'foo')
         from .adder import Adder
-        v = -iden.log().vdot(self._d) + (Adder(Field.full(self._domain, 1.)) @ iden.scale(-1)).log().vdot(self._d-1.)
+        v = -iden.log().vdot(self._d) + (Adder(1, domain=self._domain) @ iden.scale(-1)).log().vdot(self._d-1.)
         v = v(iden.adjoint(x))
-        if not isinstance(x, Linearization):
-            return Field.scalar(v)
-        if not x.want_metric:
+        if not isinstance(x, Linearization) or not x.want_metric:
             return v
         met = makeOp(1./(x.val*(1. - x.val)))
         met = SandwichOperator.make(x.jac, met)