diff --git a/nifty_core.py b/nifty_core.py
index cc27ec3862ae9c4abbeadc1601f1e45b8023c8b3..88bc0882772478c0d71d427397b86b90521e36e8 100644
--- a/nifty_core.py
+++ b/nifty_core.py
@@ -5897,17 +5897,27 @@ class field(object):
                 x = self.domain.calc_weight(x,power=1)
             return self.domain.calc_dot(self.val,x)
 
-    def norm(self): ## TODO: extend to L^q norm
+    def norm(self,q=None):
         """
-            Computes the L2-norm of the field values.
+            Computes the Lq-norm of the field values.
+
+            Parameters
+            ----------
+            q : scalar
+                Parameter q of the Lq-norm (default: 2).
 
             Returns
             -------
             norm : scalar
-              The L2-norm of the field values.
+                The Lq-norm of the field values.
 
         """
-        return np.sqrt(self.dot(x=self.val))
+        if(q is None):
+            return np.sqrt(self.dot(x=self.val))
+        else:
+            return self.dot(x=self.val**(q-1))**(1/q)
+
+    ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
     def pseudo_dot(self,x=1,**kwargs):
         """
diff --git a/nifty_power.py b/nifty_power.py
index 58b3c3bffb82c7791b66a91773d329fb2614279f..6ef88ae498dd715ab74447ec67979ba49e7c81cb 100644
--- a/nifty_power.py
+++ b/nifty_power.py
@@ -36,11 +36,8 @@
     homogeneity and isotropy. Fields which are only statistically homogeneous
     can also be created using the diagonal operator routine.
 
-    At the moment, NIFTy offers one additional routine for power spectrum
-    manipulation, the smooth_power function to smooth a power spectrum with a
-    Gaussian convolution kernel. This can be necessary in cases where power
-    spectra are reconstructed and reused in an iterative algorithm, where
-    too much statistical variation might severely effect the results.
+    At the moment, NIFTY offers several additional routines for power spectrum
+    manipulation.
 
 """