diff --git a/nifty5/library/adjust_variances.py b/nifty5/library/adjust_variances.py
index 7ef27c586fde0381f4e7797f4028db07f338f304..4a1802bb2fe099c57e5141895972957c72ccb0f7 100644
--- a/nifty5/library/adjust_variances.py
+++ b/nifty5/library/adjust_variances.py
@@ -1,13 +1,34 @@
-from ..operators.energy_operators import InverseGammaLikelihood,Hamiltonian
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+#
+# Copyright(C) 2013-2018 Max-Planck-Society
+#
+# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
+# and financially supported by the Studienstiftung des deutschen Volkes.
+
+from __future__ import absolute_import, division, print_function
+
+from ..compat import *
+from ..operators.energy_operators import Hamiltonian, InverseGammaLikelihood
 from ..operators.scaling_operator import ScalingOperator
 
-def make_adjust_variances(a,xi,position,samples=[],scaling=None,ic_samp=None):
+
+def make_adjust_variances(a, xi, position, samples=[], scaling=None, ic_samp=None):
     """ Creates a Hamiltonian for constant likelihood optimizations.
-    
-    Constructs a Hamiltonian to solve constant likelihood optimizations of the form
-        phi = a * xi
-    under the constraint that phi remains constant.
-    
+
+    Constructs a Hamiltonian to solve constant likelihood optimizations of the
+    form phi = a * xi under the constraint that phi remains constant.
+
     Parameters
     ----------
     a : Operator
@@ -29,19 +50,19 @@ def make_adjust_variances(a,xi,position,samples=[],scaling=None,ic_samp=None):
         A Hamiltonian that can be used for further minimization
     """
 
-    d = a * xi
+    d = a*xi
     d = (d.conjugate()*d).real
     n = len(samples)
-    if n>0:
+    if n > 0:
         d_eval = 0.
         for i in range(n):
-            d_eval = d_eval + d(position+samples[i])
-        d_eval = d_eval / n
+            d_eval = d_eval + d(position + samples[i])
+        d_eval = d_eval/n
     else:
         d_eval = d(position)
 
     x = (a.conjugate()*a).real
     if scaling is not None:
-        x = ScalingOperator(scaling,x.target)(x)
+        x = ScalingOperator(scaling, x.target)(x)
 
-    return Hamiltonian(InverseGammaLikelihood(x,d_eval),ic_samp=ic_samp)
\ No newline at end of file
+    return Hamiltonian(InverseGammaLikelihood(x, d_eval), ic_samp=ic_samp)
diff --git a/test/test_energies/test_consistency.py b/test/test_energies/test_consistency.py
index d5f38ebffde894da77bdcd2fe6027633098e9363..91a090c75e1941fb605d9cae0ca3260e6414f7ff 100644
--- a/test/test_energies/test_consistency.py
+++ b/test/test_energies/test_consistency.py
@@ -56,15 +56,14 @@ class Energy_Tests(unittest.TestCase):
 #         energy = ift.QuadraticEnergy(s[0], ift.makeOp(s[1]), s[2])
 #         ift.extra.check_value_gradient_consistency(energy)
 
-    @expand(product(
-        [ift.GLSpace(15),
-         ift.RGSpace(64, distances=.789),
-         ift.RGSpace([32, 32], distances=.789)],
-        [4, 78, 23]
-        ))
+    @expand(
+        product([
+            ift.GLSpace(15),
+            ift.RGSpace(64, distances=.789),
+            ift.RGSpace([32, 32], distances=.789)
+        ], [4, 78, 23]))
     def testInverseGammaLikelihood(self, space, seed):
-        model = self.make_model(
-            space_key='s1', space=space, seed=seed)['s1']
+        model = self.make_model(space_key='s1', space=space, seed=seed)['s1']
         d = np.random.normal(10, size=space.shape)**2
         d = ift.Field.from_global_data(space, d)
         energy = ift.InverseGammaLikelihood(ift.exp, d)