tests: test non-contiguous arrays

python/cufinufft/tests/test_basic.py

@@ -14,6 +14,7 @@
 MS = [256, 1024, 4096]
 TOLS = [1e-2, 1e-3]
 OUTPUT_ARGS = [False, True]
+CONTIGUOUS = [False, True]
 
 def _transfer_funcs(module_name):
     if module_name == "pycuda":
@@ -83,25 +84,39 @@ def test_type1(framework, dtype, shape, M, tol, output_arg):
 @pytest.mark.parametrize("M", MS)
 @pytest.mark.parametrize("tol", TOLS)
 @pytest.mark.parametrize("output_arg", OUTPUT_ARGS)
-def test_type2(framework, dtype, shape, M, tol, output_arg):
+@pytest.mark.parametrize("contiguous", CONTIGUOUS)
+def test_type2(framework, dtype, shape, M, tol, output_arg, contiguous):
+    if framework == "pycuda" and not contiguous:
+        pytest.skip("Pycuda does not support copy to contiguous")
+
     to_gpu, to_cpu = _transfer_funcs(framework)
 
     complex_dtype = utils._complex_dtype(dtype)
 
     k, fk = utils.type2_problem(dtype, shape, M)
 
+    plan = Plan(2, shape, eps=tol, dtype=complex_dtype)
+
+    if not contiguous and len(shape) > 1:
+        fk = fk.copy(order="F")
+
+        def _execute(*args, **kwargs):
+            with pytest.warns(UserWarning, match="requirement: C. Copying"):
+                return plan.execute(*args, **kwargs)
+    else:
+        def _execute(*args, **kwargs):
+            return plan.execute(*args, **kwargs)
+
     k_gpu = to_gpu(k)
     fk_gpu = to_gpu(fk)
 
-    plan = Plan(2, shape, eps=tol, dtype=complex_dtype)
-
     plan.setpts(*k_gpu)
 
     if output_arg:
         c_gpu = _compat.array_empty_like(fk_gpu, (M,), dtype=complex_dtype)
-        plan.execute(fk_gpu, out=c_gpu)
+        _execute(fk_gpu, out=c_gpu)
     else:
-        c_gpu = plan.execute(fk_gpu)
+        c_gpu = _execute(fk_gpu)
 
     c = to_cpu(c_gpu)