Merge branch 'NIFTy_4' into draw_sample2

needed newest features from master

nifty4/library/nonlinear_power_energy.py

@@ -16,7 +16,7 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from ..sugar import exp
+from ..sugar import exp, makeOp
 from ..minimization.energy import Energy
 from ..operators.smoothness_operator import SmoothnessOperator
 from ..operators.inversion_enabler import InversionEnabler
@@ -27,8 +27,9 @@ def _LinearizedPowerResponse(Instrument, nonlinearity, ht, Distributor, tau,
                              xi):
     power = exp(0.5*tau)
     position = ht(Distributor(power)*xi)
-    linearization = nonlinearity.derivative(position)
-    return 0.5*Instrument*linearization*ht*xi*Distributor*power
+    linearization = makeOp(nonlinearity.derivative(position))
+    return (0.5 * Instrument * linearization * ht * makeOp(xi) * Distributor *
+            makeOp(power))
 
 
 class NonlinearPowerEnergy(Energy):

nifty4/library/nonlinear_wiener_filter_energy.py

@@ -19,6 +19,7 @@
 from .wiener_filter_curvature import WienerFilterCurvature
 from ..utilities import memo
 from ..minimization.energy import Energy
+from ..sugar import makeOp
 
 
 class NonlinearWienerFilterEnergy(Energy):
@@ -39,7 +40,8 @@ class NonlinearWienerFilterEnergy(Energy):
         t1 = S.inverse_times(position)
         t2 = N.inverse_times(residual)
         self._value = 0.5 * (position.vdot(t1) + residual.vdot(t2)).real
-        self.R = Instrument * nonlinearity.derivative(m) * ht * power
+        self.R = (Instrument * makeOp(nonlinearity.derivative(m)) *
+                  ht * makeOp(power))
         self._gradient = (t1 - self.R.adjoint_times(t2)).lock()
 
     def at(self, position):

nifty4/operators/linear_operator.py

@@ -118,12 +118,9 @@ class LinearOperator(NiftyMetaBase()):
 
     @staticmethod
     def _toOperator(thing, dom):
-        from .diagonal_operator import DiagonalOperator
         from .scaling_operator import ScalingOperator
         if isinstance(thing, LinearOperator):
             return thing
-        if isinstance(thing, Field):
-            return DiagonalOperator(thing)
         if np.isscalar(thing):
             return ScalingOperator(thing, dom)
         return NotImplemented

nifty4/sugar.py

@@ -21,6 +21,7 @@ import numpy as np
 from .domains.power_space import PowerSpace
 from .field import Field
 from .multi.multi_field import MultiField
+from .multi.block_diagonal_operator import BlockDiagonalOperator
 from .multi.multi_domain import MultiDomain
 from .operators.diagonal_operator import DiagonalOperator
 from .operators.power_distributor import PowerDistributor
@@ -32,7 +33,7 @@ __all__ = ['PS_field', 'power_analyze', 'create_power_operator',
            'create_harmonic_smoothing_operator', 'from_random',
            'full', 'empty', 'from_global_data', 'from_local_data',
            'makeDomain', 'sqrt', 'exp', 'log', 'tanh', 'conjugate',
-           'get_signal_variance']
+           'get_signal_variance', 'makeOp']
 
 
 def PS_field(pspace, func):
@@ -232,6 +233,14 @@ def makeDomain(domain):
     return DomainTuple.make(domain)
 
 
+def makeOp(input):
+    if isinstance(input, Field):
+        return DiagonalOperator(input)
+    if isinstance(input, MultiField):
+        return BlockDiagonalOperator({key: makeOp(val)
+                                      for key, val in input.items()})
+    raise NotImplementedError
+
 # Arithmetic functions working on Fields
 
 _current_module = sys.modules[__name__]