Add changelog entry

ChangeLog

@@ -10,9 +10,34 @@ convention is consistent with almost all other numerical FFT libraries.
 Interface change in EndomorphicOperator.draw_sample()
 =====================================================
 
-This method now requires a `dtype` argument to be passed.
-As a consequence, `dtype` moves to the first place of the argument list.
-(This of course applies to all derived classes as well.)
+Both complex-valued and real-valued Gaussian probability distributions have
+hermitian and positive endomorphisms as covariance. Just by looking at an
+endomorphic operator itself it is not clear whether it is viewed as covariance
+for real or complex Gaussians when a sample of the respective distribution shall
+be drawn. Therefore, we introduce the method `draw_sample_with_dtype()` which
+needs to be given the data type of the probability distribution. This function
+is implemented for all operators which actually draw random numbers
+(`DiagonalOperator` and `ScalingOperator`). The class `SamplingDtypeSetter` acts
+as a wrapper for these kind of operators in order to fix the data type of the
+distribution. Samples from these operators can be drawn with `.draw_sample()`.
+In order to dive into those subtleties I suggest running the following code and
+playing around with the dtypes.
+
+```
+import nifty6 as ift
+import numpy as np
+
+dom = ift.UnstructuredDomain(5)
+dtype = [np.float64, np.complex128][1]
+invcov = ift.ScalingOperator(dom, 3)
+e = ift.GaussianEnergy(mean=ift.from_random('normal', dom, dtype=dtype),
+                       inverse_covariance=invcov)
+pos = ift.from_random('normal', dom, dtype=np.complex128)
+lin = e(ift.Linearization.make_var(pos, want_metric=True))
+met = lin.metric
+print(met)
+print(met.draw_sample())
+```
 
 MPI parallelisation over samples in MetricGaussianKL
 ====================================================

nifty6/operators/block_diagonal_operator.py

@@ -46,6 +46,10 @@ class BlockDiagonalOperator(EndomorphicOperator):
                     for op, v in zip(self._ops, x.values()))
         return MultiField(self._domain, val)
 
+    def draw_sample(self, from_inverse=False):
+        val = tuple(op.draw_sample(from_inverse) for op in self._ops)
+        return MultiField(self._domain, val)
+
     def draw_sample_with_dtype(self, dtype, from_inverse=False):
         from ..sugar import from_random
         val = tuple(

nifty6/operators/sampling_enabler.py

@@ -146,3 +146,9 @@ class SamplingDtypeSetter(EndomorphicOperator):
     def draw_sample(self, from_inverse=False):
         return self._op.draw_sample_with_dtype(self._dtype,
                                                from_inverse=from_inverse)
+
+    def __repr__(self):
+        from ..utilities import indent
+        return "\n".join((
+            f"SamplingDtypeSetter {self._dtype}:",
+            indent(self._op.__repr__())))