fix partial inference with Newton minimizers

nifty5/minimization/energy_adapter.py

@@ -4,6 +4,8 @@ from ..compat import *
 from ..minimization.energy import Energy
 from ..linearization import Linearization
 from ..multi_field import MultiField
+from ..operators.scaling_operator import ScalingOperator
+from ..operators.block_diagonal_operator import BlockDiagonalOperator
 import numpy as np
 
 
@@ -25,14 +27,10 @@ class EnergyAdapter(Energy):
         if len(self._constants) == 0:
             tmp = self._op(Linearization.make_var(self._position))
         else:
-            ctmp = MultiField.from_dict({key: val
-                                        for key, val in self._position.items()
-                                        if key in self._constants})
-            vtmp = MultiField.from_dict({key: val
-                                        for key, val in self._position.items()
-                                        if key not in self._constants})
-            lin = Linearization.make_var(vtmp) + Linearization.make_const(ctmp)
-            tmp = self._op(lin)
+            ops = [ScalingOperator(0. if key in self._constants else 1., dom)
+                   for key, dom in self._position.domain.items()]
+            bdop = BlockDiagonalOperator(self._position.domain, tuple(ops))
+            tmp = self._op(Linearization(self._position, bdop))
         self._val = tmp.val.local_data[()]
         self._grad = tmp.gradient
         if self._controller is not None: