Merge branch 'constantfixups' into 'NIFTy_7'

Fixups regarding constant minimization, nanisinf for EnergyAdapter

See merge request !523

src/minimization/energy_adapter.py

@@ -15,6 +15,8 @@
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
 
+import numpy as np
+
 from ..linearization import Linearization
 from ..minimization.energy import Energy
 from ..sugar import makeDomain
@@ -39,33 +41,34 @@ class EnergyAdapter(Energy):
         If True, the class will provide a `metric` property. This should only
         be enabled if it is required, because it will most likely consume
         additional resources. Default: False.
+    nanisinf : bool
+        If true, nan energies which can happen due to overflows in the forward
+        model are interpreted as inf. Thereby, the code does not crash on
+        these occaisions but rather the minimizer is told that the position it
+        has tried is not sensible.
     """
 
     def __init__(self, position, op, constants=[], want_metric=False,
-                 _op4eval=None):
+                 nanisinf=False):
         super(EnergyAdapter, self).__init__(position)
         self._op = op
-        self._constants = constants
-        self._op4eval = _op4eval
-        if self._op4eval is None:
-            if len(constants) > 0:
-                dom = {kk: vv for kk, vv in position.domain.items()
-                       if kk in constants}
-                dom = makeDomain(dom)
-                cstpos = position.extract(dom)
-                _, self._op4eval = op.simplify_for_constant_input(cstpos)
-            else:
-                self._op4eval = op
+        if len(constants) > 0:
+            dom = makeDomain({kk: vv for kk, vv in position.domain.items()
+                              if kk in constants})
+            _, self._op = op.simplify_for_constant_input(position.extract(dom))
         self._want_metric = want_metric
         lin = Linearization.make_var(position, want_metric)
         tmp = self._op(lin)
         self._val = tmp.val.val[()]
         self._grad = tmp.gradient
         self._metric = tmp._metric
+        self._nanisinf = bool(nanisinf)
+        if self._nanisinf and np.isnan(self._val):
+            self._val = np.inf
 
     def at(self, position):
-        return EnergyAdapter(position, self._op, self._constants,
-                             self._want_metric, self._op4eval)
+        return EnergyAdapter(position, self._op, want_metric=self._want_metric,
+                             nanisinf=self._nanisinf)
 
     @property
     def value(self):