Updated docstrings and changelog to reflect new behaviour

ChangeLog

@@ -58,6 +58,19 @@ print(met)
 print(met.draw_sample())
 ```
 
+New approach for sampling complex numbers
+=========================================
+
+When calling draw_sample_with_dtype with a complex dtype,
+the variance is now used for the imaginary part and real part separately.
+This is done in order to be consistent with the Hamiltonian.
+Note that by this, 
+```
+np.std(ift.from_random(domain, 'normal', dtype=np.complex128).val)
+```` 
+does not give 1, but sqrt(2) as a result.
+
+
 MPI parallelisation over samples in MetricGaussianKL
 ====================================================
 
@@ -73,6 +86,7 @@ the generation of reproducible random numbers in the presence of MPI parallelism
 and leads to cleaner code overall. Please see the documentation of
 `nifty6.random` for details.
 
+
 Interface Change for from_random and OuterProduct
 =================================================
 

nifty6/field.py

@@ -135,6 +135,8 @@ class Field(Operator):
             The domain of the output random Field.
         dtype : type
             The datatype of the output random Field.
+            If the datatype is complex, each real and imaginary part
+            have variance 1
 
         Returns
         -------

nifty6/multi_field.py

@@ -112,6 +112,8 @@ class MultiField(Operator):
             The domain of the output random Field.
         dtype : type
             The datatype of the output random Field.
+            If the datatype is complex, each real an imaginary part have
+            variance 1.
 
         Returns
         -------

nifty6/sugar.py

@@ -266,6 +266,8 @@ def from_random(domain, random_type='normal', dtype=np.float64, **kwargs):
             The random distribution to use.
     dtype : type
         data type of the output field (e.g. numpy.float64)
+        If the datatype is complex, each real an imaginary part have
+        variance 1.
     **kwargs : additional parameters for the random distribution
         ('mean' and 'std' for 'normal', 'low' and 'high' for 'uniform')