diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 590aa2d..b49393a 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -9,6 +9,7 @@
 
 
 def test_statistics():
+    eps = 1e-8
     ts = np.array([
         [[0, 2], [11, -11], [1, 2]],
         [[10, 21], [1, -1], [6, 8]]])
@@ -23,6 +24,12 @@ def test_statistics():
 
     assert (tsgm.metrics.statistics.axis_mode_s(ts, axis=None) == [1]).all()
 
+    assert (tsgm.metrics.statistics.axis_mean_s(ts, axis=None) - np.asarray([4.16666667]) < eps).all()
+    assert (tsgm.metrics.statistics.axis_mean_s(ts, axis=1) - np.asarray([4.83333333, 3.5]) < eps).all()
+    assert (tsgm.metrics.statistics.axis_mean_s(ts, axis=2) - np.asarray([8.25, 0., 4.25]) < eps).all()
+
+    assert (tsgm.metrics.statistics.axis_percentile_s(ts, axis=None, percentile=50) - np.asarray([2]) < eps).all()
+
     # Now, checking with tf.Tensor
     ts_tf = tf.convert_to_tensor(ts)
 
@@ -36,6 +43,11 @@ def test_statistics():
     assert (tsgm.metrics.statistics.axis_min_s(ts_tf, axis=2) == [0, -11, 1]).all()
 
     assert (tsgm.metrics.statistics.axis_mode_s(ts_tf, axis=None) == [1]).all()
+    assert (tsgm.metrics.statistics.axis_mean_s(ts_tf, axis=None) - np.asarray([4.16666667]) < eps).all()
+    assert (tsgm.metrics.statistics.axis_mean_s(ts_tf, axis=1) - np.asarray([4.83333333, 3.5]) < eps).all()
+    assert (tsgm.metrics.statistics.axis_mean_s(ts, axis=2) - np.asarray([8.25, 0., 4.25]) < eps).all()
+
+    assert (tsgm.metrics.statistics.axis_percentile_s(ts_tf, axis=None, percentile=50) - np.asarray([2]) < eps).all()
 
 
 def test_distance_metric():
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 2c2afcb..d8f605e 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,5 +1,7 @@
 import pytest
 
+import os
+import uuid
 import functools
 import numpy as np
 import random
@@ -64,6 +66,10 @@ def test_ucr_manager():
     X_train, y_train, X_test, y_test = ucr_data_manager.get()
     assert X_train.shape == (50, 150) and X_test.shape == (150, 150)
 
+    # test y_all is None
+    ucr_data_manager.y_all = None
+    assert ucr_data_manager.get_classes_distribution() == {}
+
 
 def test_sine_vs_const_dataset():
     Xs, ys = tsgm.utils.gen_sine_vs_const_dataset(10, 100, 20, max_value=2, const=1)
@@ -261,3 +267,19 @@ def test_get_physionet2012():
 
     assert val_X.shape == (1765303, 4)
     assert val_y.shape == (4000, 6)
+
+
+def test_download(mocker, caplog):
+    file_download_mock = mocker.patch("urllib.request.urlretrieve")
+    resource_name = f"resource_{uuid.uuid4()}"
+    resource_folder = "./tmp/test_download/"
+    os.makedirs(resource_folder, exist_ok=True)
+    resource_path = os.path.join(resource_folder, resource_name)
+    open(resource_path, 'w')
+    try:
+        with pytest.raises(ValueError) as excinfo:
+            tsgm.utils.download(f"https://pseudourl/{resource_name}", resource_folder, md5=123, max_attempt=1)
+        assert "Reference md5 value (123) is not equal to the downloaded" in caplog.text
+        assert "Cannot download dataset" in str(excinfo.value)
+    finally:
+        os.remove(resource_path)
diff --git a/tests/test_visualizations.py b/tests/test_visualizations.py
index 36033c2..b686a13 100644
--- a/tests/test_visualizations.py
+++ b/tests/test_visualizations.py
@@ -4,9 +4,11 @@
 import tsgm
 
 
-def test_visualize_dataset():
-    Xs = np.array([[[1, 2, 3], [3, 4, 5]]])
-    tsgm.utils.visualize_dataset(Xs)
+@pytest.mark.parametrize("ds", [
+    np.array([[[1, 2, 3], [3, 4, 5]]]), tsgm.dataset.Dataset(np.array([[[1, 2, 3], [3, 4, 5]]]), y=None)
+])
+def test_visualize_dataset(ds):
+    tsgm.utils.visualize_dataset(ds)
 
 
 @pytest.mark.parametrize("feature_averaging", [
@@ -25,7 +27,10 @@ def test_visualize_tsne_unlabeled(feature_averaging):
     tsgm.utils.visualize_tsne_unlabeled(Xs, Xgen, perplexity=2, feature_averaging=feature_averaging)
 
 
-def test_visualize_tsne():
+@pytest.mark.parametrize("feature_averaging", [
+    True, False
+])
+def test_visualize_tsne(feature_averaging):
     Xs = np.array([
         [[1, 2, 3], [3, 4, 5]],
         [[1, 2, 3], [3, 4, 5]],
@@ -36,7 +41,7 @@ def test_visualize_tsne():
     X_gen = Xs
     ys = np.ones((Xs.shape[0], 1))
     y_gen = ys
-    tsgm.utils.visualize_tsne(X=Xs, y=ys, X_gen=X_gen, y_gen=y_gen, perplexity=2)
+    tsgm.utils.visualize_tsne(X=Xs, y=ys, X_gen=X_gen, y_gen=y_gen, perplexity=2, feature_averaging=feature_averaging)
 
 
 def test_visualize_ts():