TST: un-xfail TestClip

Author: ev-br

torch_np/_ndarray.py

@@ -376,7 +376,7 @@ def nonzero(self):
         tensor = self._tensor
         return tuple(asarray(_) for _ in tensor.nonzero(as_tuple=True))
 
-    def clip(self, min, max, out=None):
+    def clip(self, min=None, max=None, out=None):
         tensor, t_min, t_max = _helpers.to_tensors_or_none(self, min, max)
         result = _impl.clip(tensor, t_min, t_max)
         return _helpers.result_or_out(result, out)

torch_np/_wrapper.py

@@ -678,7 +678,9 @@ def round_(a, decimals=0, out=None):
 round = round_
 
 
-def clip(a, a_min, a_max, out=None):
+def clip(a, a_min=None, a_max=None, out=None):
+    # np.clip requires both a_min and a_max not None, while ndarray.clip
+    # allows of then be None. Allow both here.
     arr = asarray(a)
     return arr.clip(a_min, a_max, out=out)
 

torch_np/tests/numpy_tests/core/test_multiarray.py

@@ -4037,14 +4037,14 @@ def test_datetime(self):
             assert_equal(np.amin(a), a[3])
             assert_equal(np.amax(a), a[3])
 
-@pytest.mark.xfail(reason='TODO')
+
 class TestNewaxis:
     def test_basic(self):
         sk = np.array([0, -0.1, 0.1])
         res = 250*sk[:, np.newaxis]
         assert_almost_equal(res.ravel(), 250*sk)
 
-@pytest.mark.xfail(reason='TODO')
+
 class TestClip:
     def _check_range(self, x, cmin, cmax):
         assert_(np.all(x >= cmin))
@@ -4084,6 +4084,7 @@ def _clip_type(self, type_group, array_max,
                 self._check_range(x, expected_min, expected_max)
         return x
 
+    @pytest.mark.skip(reason="endianness")
     def test_basic(self):
         for inplace in [False, True]:
             self._clip_type(
@@ -4101,12 +4102,6 @@ def test_basic(self):
             self._clip_type(
                 'uint', 1024, -120, 100, inplace=inplace, expected_min=0)
 
-    def test_record_array(self):
-        rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
-                       dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8')])
-        y = rec['x'].clip(-0.3, 0.5)
-        self._check_range(y, -0.3, 0.5)
-
     def test_max_or_min(self):
         val = np.array([0, 1, 2, 3, 4, 5, 6, 7])
         x = val.clip(3)