diff --git a/nifty6/operator_tree_optimiser.py b/nifty6/operator_tree_optimiser.py
index d45023650be677a7b97c8f7ef8bc11529ec09a00..61a5fef6bc7bc05eea5a8b5c9d623f85c13c0a82 100644
--- a/nifty6/operator_tree_optimiser.py
+++ b/nifty6/operator_tree_optimiser.py
@@ -266,24 +266,29 @@ from .multi_field import MultiField
 from numpy import allclose
 
 
+
 def optimise_operator(op):
     """
     Merges redundant operations in the tree structure of an operator.
-    For example it is ensured that for ``(f@x + x)`` ``x`` is only computed once.
-    Currently works only on ``_OpChain``, ``_OpSum`` and ``_OpProd`` and does not optimise their linear pendants
+    For example it is ensured that for ``f@x + x`` the operator ``x`` is only computed once.
+    It is supposed to be used on the whole operator chain before doing minimisation.
+
+    Currently optimises only ``_OpChain``, ``_OpSum`` and ``_OpProd`` and not their linear pendants
     ``ChainOp`` and ``SumOperator``.
 
     Parameters
     ----------
-    op: Operator
+    op : Operator
+        Operator with a tree structure.
 
     Returns
     -------
     op_optimised : Operator
+        Operator with same input/output, but optimised tree structure.
 
     Notes
     -----
-    Since operators are compared by id best results are achieved when the following code
+    Operators are compared only by id, so best results are achieved when the following code
 
     >>> from nifty6 import UniformOperator, DomainTuple
     >>> uni1 = UniformOperator(DomainTuple.scalar_domain()
@@ -291,16 +296,18 @@ def optimise_operator(op):
     >>> op = (uni1 + uni2)*(uni1 + uni2)
 
     is replaced by something comparable to
+
     >>> uni = UniformOperator(DomainTuple.scalar_domain())
     >>> uni_add = uni + uni
     >>> op = uni_add * uni_add
 
-    After optimisation the operator is comparable in speed to
+    After optimisation the operator is as fast as
+
     >>> op = (2*uni)**2
     """
     op_optimised = deepcopy(op)
     op_optimised = _optimise_operator(op_optimised)
-    test_field = from_random('normal', op.domain)
+    test_field = from_random(op.domain)
     if isinstance(op(test_field), MultiField):
         for key in op(test_field).keys():
             assert allclose(op(test_field).val[key], op_optimised(test_field).val[key], 1e-10)