TST tests passing for 1D. CAVEAT: we may need to check cases for 1-d …

…arrays and add a channel axis somewhere

tests/test_1d/test_forward_1d.py

@@ -106,6 +106,41 @@ def test_1d_t2_forward_CPU(targets: torch.Tensor):
     )
 
 
+@pytest.mark.parametrize("N", Ns)
+def test_t1_forward_CPU(N: int) -> None:
+    """
+    Tests against implementations of the FFT by setting up a uniform grid
+    over which to call FINUFFT through the API.
+    """
+    g = np.mgrid[:N] * 2 * np.pi / N
+    g.shape = 1, -1
+    points = torch.from_numpy(g.reshape(1, -1))
+
+    values = torch.randn(*points[0].shape, dtype=torch.complex128)
+
+    print("N is " + str(N))
+    print("shape of points is " + str(points.shape))
+    print("shape of values is " + str(values.shape))
+
+    finufft_out = pytorch_finufft.functional.finufft_type1.apply(
+        points,
+        values,
+        (N,),
+    )
+
+    against_torch = torch.fft.fft(values.reshape(g[0].shape))
+
+    abs_errors = torch.abs(finufft_out - against_torch)
+    l_inf_error = abs_errors.max()
+    l_2_error = torch.sqrt(torch.sum(abs_errors**2))
+    l_1_error = torch.sum(abs_errors)
+
+    assert l_inf_error < 4.5e-5 * N
+    assert l_2_error < 1e-5 * N ** 2
+    assert l_1_error < 1e-5 * N ** 3
+
+
+
 # @pytest.mark.parametrize("values", cases)
 # def test_1d_t3_forward_CPU(values: torch.Tensor) -> None:
 #     """