py3: xrange() -> range()

Signed-off-by: Igor Gnatenko <i.gnatenko.brain@gmail.com>

7 files changed, 12 insertions, 12 deletions

diff --git a/examples/multivariate.py b/examples/multivariate.py
index 9a44758..93f8516 100644
--- a/examples/multivariate.py
+++ b/examples/multivariate.py
@@ -24,7 +24,7 @@ t_loading = time.clock()
 
 print("Number of spike trains: %d" % len(spike_trains))
 num_of_spikes = sum([len(spike_trains[i])
-                     for i in xrange(len(spike_trains))])
+                     for i in range(len(spike_trains))])
 print("Number of spikes: %d" % num_of_spikes)
 
 # calculate the multivariate spike distance
diff --git a/examples/performance.py b/examples/performance.py
index d0c3b91..ec6c830 100644
--- a/examples/performance.py
+++ b/examples/performance.py
@@ -26,7 +26,7 @@ print("%d spike trains with %d spikes" % (M, int(r*T)))
 spike_trains = []
 
 t_start = datetime.now()
-for i in xrange(M):
+for i in range(M):
     spike_trains.append(spk.generate_poisson_spikes(r, T))
 t_end = datetime.now()
 runtime = (t_end-t_start).total_seconds()
diff --git a/pyspike/DiscreteFunc.py b/pyspike/DiscreteFunc.py
index 55c0bc8..fe97bc2 100644
--- a/pyspike/DiscreteFunc.py
+++ b/pyspike/DiscreteFunc.py
@@ -80,7 +80,7 @@ class DiscreteFunc(object):
             expected_mp = (averaging_window_size+1) * int(self.mp[0])
             y_plot = np.zeros_like(self.y)
             # compute the values in a loop, could be done in cython if required
-            for i in xrange(len(y_plot)):
+            for i in range(len(y_plot)):
 
                 if self.mp[i] >= expected_mp:
                     # the current value contains already all the wanted
@@ -244,7 +244,7 @@ def average_profile(profiles):
     assert len(profiles) > 1
 
     avrg_profile = profiles[0].copy()
-    for i in xrange(1, len(profiles)):
+    for i in range(1, len(profiles)):
         avrg_profile.add(profiles[i])
     avrg_profile.mul_scalar(1.0/len(profiles))  # normalize
 
diff --git a/pyspike/directionality/spike_train_order.py b/pyspike/directionality/spike_train_order.py
index 44d931d..e6c9830 100644
--- a/pyspike/directionality/spike_train_order.py
+++ b/pyspike/directionality/spike_train_order.py
@@ -260,7 +260,7 @@ def optimal_spike_train_order(spike_trains,  indices=None, interval=None,
 def permutate_matrix(D, p):
     N = len(D)
     D_p = np.empty_like(D)
-    for n in xrange(N):
-        for m in xrange(N):
+    for n in range(N):
+        for m in range(N):
             D_p[n, m] = D[p[n], p[m]]
     return D_p
diff --git a/pyspike/generic.py b/pyspike/generic.py
index 904c3c2..81ae660 100644
--- a/pyspike/generic.py
+++ b/pyspike/generic.py
@@ -137,8 +137,8 @@ def _generic_distance_matrix(spike_trains, dist_function,
     assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \
         "Invalid index list."
     # generate a list of possible index pairs
-    pairs = [(i, j) for i in xrange(len(indices))
-             for j in xrange(i+1, len(indices))]
+    pairs = [(i, j) for i in range(len(indices))
+             for j in range(i+1, len(indices))]
 
     distance_matrix = np.zeros((len(indices), len(indices)))
     for i, j in pairs:
diff --git a/pyspike/psth.py b/pyspike/psth.py
index 4027215..7cf1140 100644
--- a/pyspike/psth.py
+++ b/pyspike/psth.py
@@ -24,7 +24,7 @@ def psth(spike_trains, bin_size):
 
     # N = len(spike_trains)
     combined_spike_train = spike_trains[0].spikes
-    for i in xrange(1, len(spike_trains)):
+    for i in range(1, len(spike_trains)):
         combined_spike_train = np.append(combined_spike_train,
                                          spike_trains[i].spikes)
 
diff --git a/test/test_distance.py b/test/test_distance.py
index e45ac16..d5bce30 100644
--- a/test/test_distance.py
+++ b/test/test_distance.py
@@ -309,10 +309,10 @@ def check_dist_matrix(dist_func, dist_matrix_func):
 
     f_matrix = dist_matrix_func(spike_trains)
     # check zero diagonal
-    for i in xrange(4):
+    for i in range(4):
         assert_equal(0.0, f_matrix[i, i])
-    for i in xrange(4):
-        for j in xrange(i+1, 4):
+    for i in range(4):
+        for j in range(i+1, 4):
             assert_equal(f_matrix[i, j], f_matrix[j, i])
     assert_equal(f12, f_matrix[1, 0])
     assert_equal(f13, f_matrix[2, 0])