likely to work on sphere

demos/wiener_filter_harmonic.py

@@ -91,7 +91,7 @@ if __name__ == "__main__":
 
     x1 = HPSpace(64)
     k1 = HPLMTransformation.get_codomain(x1)
-    p1 = PowerSpace(harmonic_domain=k1, log=False)
+    p1 = PowerSpace(harmonic_partner=k1, logarithmic=False)
 
     # creating Power Operator with given spectrum
     spec = (lambda k: a_s / (1 + (k / k_0) ** 2) ** 2)
@@ -107,7 +107,7 @@ if __name__ == "__main__":
                             exposure=[exposure])
 
     # drawing a random field
-    sk = p_field.power_synthesize(decompose_power=True, mean=0.)
+    sk = p_field.power_synthesize(real_power=True, mean=0.)
     s = Fft_op.adjoint_times(sk)
 
     signal_to_noise = 1

nifty/operators/propagator_operator/harmonic_propagator_operator.py

@@ -136,7 +136,7 @@ class HarmonicPropagatorOperator(InvertibleOperatorMixin, EndomorphicOperator):
         return result
 
     def _inverse_times(self, x, spaces):
-        pre_result = self._S.times(x, spaces)
+        pre_result = self._S.inverse_times(x, spaces)
         pre_result += self._likelihood_times(x)
         result = x.copy_empty()
         result.set_val(pre_result, copy=False)

nifty/sugar.py

@@ -37,7 +37,7 @@ def create_power_operator(domain, power_spectrum, dtype=None,
                val=power_spectrum, dtype=dtype,
                distribution_strategy=distribution_strategy)
 
-    f = fp.power_synthesize(mean=1, std=0, decompose_power=False)
+    f = fp.power_synthesize(mean=1, std=0, real_signal=False)
 
     power_operator = DiagonalOperator(domain, diagonal=f, bare=True)