lint

Author: lucascolley

scipy/cluster/tests/test_vq.py

@@ -20,6 +20,7 @@
     SCIPY_ARRAY_API, xp_copy, xp_assert_close, xp_assert_equal
 )
 
+xfail_xp_backends = pytest.mark.xfail_xp_backends
 skip_xp_backends = pytest.mark.skip_xp_backends
 
 TESTDATA_2D = np.array([

scipy/fft/tests/test_basic.py

@@ -331,9 +331,8 @@ def test_dtypes_real(self, dtype, xp):
 
     @pytest.mark.parametrize("dtype", ["complex64", "complex128"])
     def test_dtypes_complex(self, dtype, xp):
-        xp_test = array_namespace(xp.asarray(1))
         rng = np.random.default_rng(1234)
-        x = xp.asarray(rng.random(30), dtype=getattr(xp_test, dtype))
+        x = xp.asarray(rng.random(30), dtype=getattr(xp, dtype))
 
         res_fft = fft.ifft(fft.fft(x))
         # Check both numerical results and exact dtype matches

scipy/ndimage/tests/test_measurements.py

@@ -1608,8 +1608,7 @@ def test_watershed_ift08(self, xp):
     @skip_xp_backends("cupy", reason="no watershed_ift on CuPy"	)
     def test_watershed_ift09(self, xp):
         # Test large cost. See gh-19575
-        xp_test = array_namespace(xp.empty(0))  # dask.array needs iinfo
-        data = xp.asarray([[xp_test.iinfo(xp.uint16).max, 0],
+        data = xp.asarray([[xp.iinfo(xp.uint16).max, 0],
                            [0, 0]], dtype=xp.uint16)
         markers = xp.asarray([[1, 0],
                               [0, 0]], dtype=xp.int8)

scipy/signal/tests/test_signaltools.py

@@ -2454,8 +2454,7 @@ def decimal(self, dt, xp):
 
         # emulate np.finfo(dt).precision for complex64 and complex128
         # note: unwrapped dask has no finfo
-        xp_compat = array_namespace(xp.asarray(1))
-        prec = {64: 15, 32: 6}[xp_compat.finfo(dt).bits]
+        prec = {64: 15, 32: 6}[xp.finfo(dt).bits]
         return int(2 * prec / 3)
 
     def _setup_rank1(self, dt, mode, xp):

scipy/stats/tests/test_stats.py

@@ -6357,17 +6357,17 @@ def test_describe_numbers(self, xp):
         assert n == nc
         xp_assert_equal(mm[0], mmc[0])
         xp_assert_equal(mm[1], mmc[1])
-        xp_assert_close(m, mc, rtol=4 * xp_test.finfo(m.dtype).eps)
-        xp_assert_close(v, vc, rtol=4 * xp_test.finfo(m.dtype).eps)
+        xp_assert_close(m, mc, rtol=4 * xp.finfo(m.dtype).eps)
+        xp_assert_close(v, vc, rtol=4 * xp.finfo(m.dtype).eps)
         xp_assert_close(sk, skc)
         xp_assert_close(kurt, kurtc)
 
         n, mm, m, v, sk, kurt = stats.describe(x.T, axis=1)
         assert n == nc
         xp_assert_equal(mm[0], mmc[0])
         xp_assert_equal(mm[1], mmc[1])
-        xp_assert_close(m, mc, rtol=4 * xp_test.finfo(m.dtype).eps)
-        xp_assert_close(v, vc, rtol=4 * xp_test.finfo(m.dtype).eps)
+        xp_assert_close(m, mc, rtol=4 * xp.finfo(m.dtype).eps)
+        xp_assert_close(v, vc, rtol=4 * xp.finfo(m.dtype).eps)
         xp_assert_close(sk, skc)
         xp_assert_close(kurt, kurtc)
 
@@ -9471,8 +9471,7 @@ def test_empty(self, xp):
 
     @pytest.mark.filterwarnings("ignore:overflow encountered in reduce") # for dask
     def test_dtype(self, xp):
-        xp_test = array_namespace(xp.asarray(1))
-        max = xp_test.finfo(xp.float32).max
+        max = xp.finfo(xp.float32).max
         x_np = np.asarray([max, max], dtype=np.float32)
         x_xp = xp.asarray(x_np)
 
@@ -9577,8 +9576,7 @@ def test_empty(self, xp):
         xp_assert_equal(res, ref)
 
     def test_dtype(self, xp):
-        xp_test = array_namespace(xp.asarray(1)) # dask.array needs finfo
-        max = xp_test.finfo(xp.float32).max
+        max = xp.finfo(xp.float32).max
         x_np = np.asarray([max, max/2], dtype=np.float32)
         x_xp = xp.asarray(x_np)