Update tests due to new generate_random_numpy_array()

Author: vlad-perevezentsev

dpnp/tests/test_fft.py

@@ -380,8 +380,7 @@ def setup_method(self):
     @pytest.mark.parametrize("norm", [None, "forward", "backward", "ortho"])
     @pytest.mark.parametrize("order", ["C", "F"])
     def test_fft2(self, dtype, axes, norm, order):
-        x = generate_random_numpy_array((2, 3, 4), dtype)
-        a_np = numpy.array(x, order=order)
+        a_np = generate_random_numpy_array((2, 3, 4), dtype, order)
         a = dpnp.array(a_np)
 
         result = dpnp.fft.fft2(a, axes=axes, norm=norm)
@@ -443,8 +442,7 @@ def setup_method(self):
     @pytest.mark.parametrize("norm", [None, "backward", "forward", "ortho"])
     @pytest.mark.parametrize("order", ["C", "F"])
     def test_fftn(self, dtype, axes, norm, order):
-        x = generate_random_numpy_array((2, 3, 4, 5), dtype)
-        a_np = numpy.array(x, order=order)
+        a_np = generate_random_numpy_array((2, 3, 4, 5), dtype, order)
         a = dpnp.array(a_np)
 
         result = dpnp.fft.fftn(a, axes=axes, norm=norm)
@@ -698,8 +696,7 @@ def test_irfft_1D_on_2D_array(self, dtype, n, axis, norm, order):
     @pytest.mark.parametrize("norm", [None, "backward", "forward", "ortho"])
     @pytest.mark.parametrize("order", ["C", "F"])
     def test_irfft_1D_on_3D_array(self, dtype, n, axis, norm, order):
-        x = generate_random_numpy_array((4, 5, 6), dtype)
-        a_np = numpy.array(x, order=order)
+        a_np = generate_random_numpy_array((4, 5, 6), dtype, order)
         # each 1-D array of input should be Hermitian
         if axis == 0:
             a_np[0].imag = 0
@@ -936,8 +933,7 @@ def setup_method(self):
     @pytest.mark.parametrize("norm", [None, "backward", "forward", "ortho"])
     @pytest.mark.parametrize("order", ["C", "F"])
     def test_rfft2(self, dtype, axes, norm, order):
-        x = generate_random_numpy_array((2, 3, 4), dtype)
-        a_np = numpy.array(x, order=order)
+        a_np = generate_random_numpy_array((2, 3, 4), dtype, order)
         a = dpnp.asarray(a_np)
 
         result = dpnp.fft.rfft2(a, axes=axes, norm=norm)
@@ -1001,8 +997,7 @@ def setup_method(self):
     @pytest.mark.parametrize("norm", [None, "backward", "forward", "ortho"])
     @pytest.mark.parametrize("order", ["C", "F"])
     def test_rfftn(self, dtype, axes, norm, order):
-        x = generate_random_numpy_array((2, 3, 4, 5), dtype)
-        a_np = numpy.array(x, order=order)
+        a_np = generate_random_numpy_array((2, 3, 4, 5), dtype, order)
         a = dpnp.asarray(a_np)
 
         result = dpnp.fft.rfftn(a, axes=axes, norm=norm)

dpnp/tests/test_linalg.py

@@ -503,24 +503,23 @@ def test_eigenvalues(self, func, shape, dtype, order):
         # non-symmetric for eig() and eigvals()
         is_hermitian = func in ("eigh, eigvalsh")
         a = generate_random_numpy_array(
-            shape, dtype, hermitian=is_hermitian, low=-4, high=4
+            shape, dtype, order, hermitian=is_hermitian, low=-4, high=4
         )
-        a_order = numpy.array(a, order=order)
-        a_dp = dpnp.array(a, order=order)
+        a_dp = dpnp.array(a)
 
         # NumPy with OneMKL and with rocSOLVER sorts in ascending order,
         # so w's should be directly comparable.
         # However, both OneMKL and rocSOLVER pick a different convention for
         # constructing eigenvectors, so v's are not directly comparable and
         # we verify them through the eigen equation A*v=w*v.
         if func in ("eig", "eigh"):
-            w, _ = getattr(numpy.linalg, func)(a_order)
+            w, _ = getattr(numpy.linalg, func)(a)
             w_dp, v_dp = getattr(dpnp.linalg, func)(a_dp)
 
             self.assert_eigen_decomposition(a_dp, w_dp, v_dp)
 
         else:  # eighvals or eigvalsh
-            w = getattr(numpy.linalg, func)(a_order)
+            w = getattr(numpy.linalg, func)(a)
             w_dp = getattr(dpnp.linalg, func)(a_dp)
 
         assert_dtype_allclose(w_dp, w, factor=24)