1 files changed, 102 insertions, 0 deletions
diff --git a/test/test_backend.py b/test/test_backend.py
index cbfaf94..859da5a 100644
--- a/test/test_backend.py
+++ b/test/test_backend.py
@@ -1,6 +1,7 @@
 """Tests for backend module """
 
 # Author: Remi Flamary <remi.flamary@polytechnique.edu>
+#         Nicolas Courty <ncourty@irisa.fr>
 #
 # License: MIT License
 
@@ -156,6 +157,8 @@ def test_empty_backend():
     with pytest.raises(NotImplementedError):
         nx.sqrt(M)
     with pytest.raises(NotImplementedError):
+        nx.power(v, 2)
+    with pytest.raises(NotImplementedError):
         nx.dot(v, v)
     with pytest.raises(NotImplementedError):
         nx.norm(M)
@@ -174,7 +177,37 @@ def test_empty_backend():
     with pytest.raises(NotImplementedError):
         nx.argsort(M)
     with pytest.raises(NotImplementedError):
+        nx.searchsorted(v, v)
+    with pytest.raises(NotImplementedError):
         nx.flip(M)
+    with pytest.raises(NotImplementedError):
+        nx.clip(M, -1, 1)
+    with pytest.raises(NotImplementedError):
+        nx.repeat(M, 0, 1)
+    with pytest.raises(NotImplementedError):
+        nx.take_along_axis(M, v, 0)
+    with pytest.raises(NotImplementedError):
+        nx.concatenate([v, v])
+    with pytest.raises(NotImplementedError):
+        nx.zero_pad(M, v)
+    with pytest.raises(NotImplementedError):
+        nx.argmax(M)
+    with pytest.raises(NotImplementedError):
+        nx.mean(M)
+    with pytest.raises(NotImplementedError):
+        nx.std(M)
+    with pytest.raises(NotImplementedError):
+        nx.linspace(0, 1, 50)
+    with pytest.raises(NotImplementedError):
+        nx.meshgrid(v, v)
+    with pytest.raises(NotImplementedError):
+        nx.diag(M)
+    with pytest.raises(NotImplementedError):
+        nx.unique([M, M])
+    with pytest.raises(NotImplementedError):
+        nx.logsumexp(M)
+    with pytest.raises(NotImplementedError):
+        nx.stack([M, M])
 
 
 @pytest.mark.parametrize('backend', backend_list)
@@ -278,6 +311,10 @@ def test_func_backends(backend):
         lst_b.append(nx.to_numpy(A))
         lst_name.append('sqrt')
 
+        A = nx.power(Mb, 2)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('power')
+
         A = nx.dot(vb, vb)
         lst_b.append(nx.to_numpy(A))
         lst_name.append('dot(v,v)')
@@ -326,10 +363,75 @@ def test_func_backends(backend):
         lst_b.append(nx.to_numpy(A))
         lst_name.append('argsort')
 
+        A = nx.searchsorted(Mb, Mb, 'right')
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('searchsorted')
+
         A = nx.flip(Mb)
         lst_b.append(nx.to_numpy(A))
         lst_name.append('flip')
 
+        A = nx.clip(vb, 0, 1)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('clip')
+
+        A = nx.repeat(Mb, 0)
+        A = nx.repeat(Mb, 2, -1)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('repeat')
+
+        A = nx.take_along_axis(vb, nx.arange(3), -1)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('take_along_axis')
+
+        A = nx.concatenate((Mb, Mb), -1)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('concatenate')
+
+        A = nx.zero_pad(Mb, len(Mb.shape) * [(3, 3)])
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('zero_pad')
+
+        A = nx.argmax(Mb)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('argmax')
+
+        A = nx.mean(Mb)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('mean')
+
+        A = nx.std(Mb)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('std')
+
+        A = nx.linspace(0, 1, 50)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('linspace')
+
+        X, Y = nx.meshgrid(vb, vb)
+        lst_b.append(np.stack([nx.to_numpy(X), nx.to_numpy(Y)]))
+        lst_name.append('meshgrid')
+
+        A = nx.diag(Mb)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('diag2D')
+
+        A = nx.diag(vb, 1)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('diag1D')
+
+        A = nx.unique(nx.from_numpy(np.stack([M, M])))
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('unique')
+
+        A = nx.logsumexp(Mb)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('logsumexp')
+
+        A = nx.stack([Mb, Mb])
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('stack')
+
         lst_tot.append(lst_b)
 
     lst_np = lst_tot[0]