workaround for complex lh sampling

nifty6/minimization/metric_gaussian_kl.py

@@ -14,6 +14,7 @@
 # Copyright(C) 2013-2019 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
+import numpy as np
 
 from .. import utilities
 from ..linearization import Linearization
@@ -78,7 +79,7 @@ class MetricGaussianKL(Energy):
 
     def __init__(self, mean, hamiltonian, n_samples, constants=[],
                  point_estimates=[], mirror_samples=False,
-                 napprox=0, _samples=None):
+                 napprox=0, _samples=None, lh_sampling_dtype = np.float):
         super(MetricGaussianKL, self).__init__(mean)
 
         if not isinstance(hamiltonian, StandardHamiltonian):
@@ -99,7 +100,8 @@ class MetricGaussianKL(Energy):
                 mean, point_estimates, True)).metric
             if napprox > 1:
                 met._approximation = makeOp(approximation2endo(met, napprox))
-            _samples = tuple(met.draw_sample(from_inverse=True)
+            _samples = tuple(met.draw_sample(from_inverse=True,
+                                             dtype = lh_sampling_dtype)
                              for _ in range(n_samples))
             if mirror_samples:
                 _samples += tuple(-s for s in _samples)

nifty6/operators/sampling_enabler.py

@@ -71,7 +71,7 @@ class SamplingEnabler(EndomorphicOperator):
             else:
                 s = self._prior.draw_sample(from_inverse=True)
                 sp = self._prior(s)
-                nj = self._likelihood.draw_sample()
+                nj = self._likelihood.draw_sample(dtype = dtype)
                 energy = QuadraticEnergy(s, self._op, sp + nj,
                                          _grad=self._likelihood(s) - nj)
             inverter = ConjugateGradient(self._ic)