diff --git a/tests/packages/pyeeg/entropy.py b/tests/packages/pyeeg/entropy.py
index ebd4467168..bfe7c8803c 100644
--- a/tests/packages/pyeeg/entropy.py
+++ b/tests/packages/pyeeg/entropy.py
@@ -1,6 +1,6 @@
 import numpy
 from .embedded_sequence import embed_seq
-
+from .spectrum import bin_power
 
 def ap_entropy(X, M, R):
     """Computer approximate entropy (ApEN) of series X, specified by M and R.
diff --git a/tests/packages/pyrem/univariate.py b/tests/packages/pyrem/univariate.py
index 7492f95ea5..d74f0f5d4a 100644
--- a/tests/packages/pyrem/univariate.py
+++ b/tests/packages/pyrem/univariate.py
@@ -427,9 +427,9 @@ def hfd(a, k_max):
     # km_idxs[:,1] -= 1
     #
 
-    for k in xrange(1,k_max):
+    for k in range(1,k_max):
         Lk = 0
-        for m in xrange(0,k):
+        for m in range(0,k):
             #we pregenerate all idxs
             idxs = np.arange(1,int(np.floor((N-m)/k)),dtype=np.int32)
 
@@ -465,7 +465,7 @@ def dfa(X, Ave = None, L = None, sampling= 1):
 
     for i,n in enumerate(L):
         sampled = 0
-        for j in xrange(0,len(X) -n ,n):
+        for j in range(0,len(X) -n ,n):
 
             if np.random.rand() < sampling:
                 F[i] += np.polyfit(np.arange(j,j+n), Y[j:j+n],1, full=True)[1]
diff --git a/tests/tests_complexity.py b/tests/tests_complexity.py
index 36e116628b..eb41fb5d2f 100644
--- a/tests/tests_complexity.py
+++ b/tests/tests_complexity.py
@@ -28,8 +28,9 @@ def test_complexity():
 #    assert nk.entropy_approximate(signal, 2, 0.2) == pyeeg.ap_entropy(signal, 2, 0.2)
 
     # Sample
-    assert np.allclose(nk.entropy_sample(signal, order=2, r=0.2*np.std(signal)), nolds.sampen(signal, emb_dim=2, tolerance=0.2*np.std(signal)), atol=0.000001)
-#    assert nk.entropy_shannon(signal, 2, 0.2) == pyrem.samp_entropy(signal, 2, 0.2, relative_r=False)
+    assert np.allclose(nk.entropy_sample(signal, order=2, r=0.2*np.std(signal)),
+                       nolds.sampen(signal, emb_dim=2, tolerance=0.2*np.std(signal)), atol=0.000001)
+#    assert nk.entropy_sample(signal, 2, 0.2) == pyeeg.samp_entropy(signal, 2, 0.2)
 #    pyentrp.sample_entropy(signal, 2, 0.2)  # Gives something different
 
     # Fuzzy