remove inverse_draw_sample() for good

nifty4/library/nonlinear_power_energy.py

@@ -70,7 +70,7 @@ class NonlinearPowerEnergy(Energy):
             if samples is None or samples == 0:
                 xi_sample_list = [xi]
             else:
-                xi_sample_list = [D.inverse_draw_sample() + xi
+                xi_sample_list = [D.draw_sample(from_inverse=True) + xi
                                   for _ in range(samples)]
         self.xi_sample_list = xi_sample_list
         self.inverter = inverter

nifty4/operators/endomorphic_operator.py

@@ -55,15 +55,3 @@ class EndomorphicOperator(LinearOperator):
             A sample from the Gaussian of given covariance.
         """
         raise NotImplementedError
-
-    def inverse_draw_sample(self, dtype=np.float64):
-        """Generates a zero-mean sample
-
-        Generates a sample from a Gaussian distribution with zero mean and
-        covariance given by the inverse of the operator.
-
-        Returns
-        -------
-            A sample from the Gaussian of given covariance
-        """
-        return self.draw_sample(True, dtype)

nifty4/probing/utils.py

@@ -51,7 +51,7 @@ class StatCalculator(object):
 def probe_with_posterior_samples(op, post_op, nprobes):
     sc = StatCalculator()
     for i in range(nprobes):
-        sample = post_op(op.inverse_draw_sample())
+        sample = post_op(op.draw_sample(from_inverse=True))
         sc.add(sample)
 
     if nprobes == 1:

test/test_energies/test_noise.py

@@ -84,7 +84,7 @@ class Noise_Energy_Tests(unittest.TestCase):
             S=S,
             inverter=inverter).curvature
 
-        res_sample_list = [d - R(f(ht(C.inverse_draw_sample() + xi)))
+        res_sample_list = [d - R(f(ht(C.draw_sample(from_inverse=True) + xi)))
                            for _ in range(10)]
 
         energy = ift.library.NoiseEnergy(eta0, alpha, q, res_sample_list)