Fix CartesianSamplingOp

src/mrpro/operators/CartesianSamplingOp.py

@@ -47,26 +47,28 @@ def __init__(self, encoding_matrix: SpatialDimension[int], traj: KTrajectory) ->
             kx_idx = ktraj_tensor[-1, ...].round().to(dtype=torch.int64) + sorted_grid_shape.x // 2
         else:
             sorted_grid_shape.x = ktraj_tensor.shape[-1]
-            kx_idx = repeat(torch.arange(ktraj_tensor.shape[-1]), 'k0->other k1 k2 k0', other=1, k2=1, k1=1)
+            kx_idx = repeat(torch.arange(ktraj_tensor.shape[-1]), 'k0->other k2 k1 k0', other=1, k2=1, k1=1)
 
         if traj_type_kzyx[-2] == TrajType.ONGRID:  # ky
             ky_idx = ktraj_tensor[-2, ...].round().to(dtype=torch.int64) + sorted_grid_shape.y // 2
         else:
             sorted_grid_shape.y = ktraj_tensor.shape[-2]
-            ky_idx = repeat(torch.arange(ktraj_tensor.shape[-2]), 'k1->other k1 k2 k0', other=1, k2=1, k0=1)
+            ky_idx = repeat(torch.arange(ktraj_tensor.shape[-2]), 'k1->other k2 k1 k0', other=1, k2=1, k0=1)
 
         if traj_type_kzyx[-3] == TrajType.ONGRID:  # kz
             kz_idx = ktraj_tensor[-3, ...].round().to(dtype=torch.int64) + sorted_grid_shape.z // 2
         else:
             sorted_grid_shape.z = ktraj_tensor.shape[-3]
-            kz_idx = repeat(torch.arange(ktraj_tensor.shape[-3]), 'k2->other k1 k2 k0', other=1, k1=1, k0=1)
+            kz_idx = repeat(torch.arange(ktraj_tensor.shape[-3]), 'k2->other k2 k1 k0', other=1, k1=1, k0=1)
 
         # 1D indices into a flattened tensor.
         kidx = kz_idx * sorted_grid_shape.y * sorted_grid_shape.x + ky_idx * sorted_grid_shape.x + kx_idx
         kidx = rearrange(kidx, '... kz ky kx -> ... 1 (kz ky kx)')
         self.register_buffer('_fft_idx', kidx)
         # we can skip the indexing if the data is already sorted
-        self._needs_indexing = not torch.all(torch.diff(kidx) == 1)
+        self._needs_indexing = (
+            not torch.all(torch.diff(kidx) == 1) or traj.broadcasted_shape[-3:] != sorted_grid_shape.zyx
+        )
 
         self._trajectory_shape = traj.broadcasted_shape
         self._sorted_grid_shape = sorted_grid_shape

tests/operators/test_cartesian_sampling_op.py

@@ -2,6 +2,7 @@
 
 import pytest
 import torch
+from einops import rearrange
 from mrpro.data import KTrajectory, SpatialDimension
 from mrpro.operators import CartesianSamplingOp
 
@@ -59,6 +60,9 @@ def test_cart_sampling_op_data_match():
         'regular_undersampling',
         'random_undersampling',
         'different_random_undersampling',
+        'cartesian_and_non_cartesian',
+        'kx_ky_along_k0',
+        'kx_ky_along_k0_undersampling',
     ],
 )
 def test_cart_sampling_op_fwd_adj(sampling):
@@ -70,8 +74,8 @@ def test_cart_sampling_op_fwd_adj(sampling):
     nky = (2, 1, 40, 1)
     nkz = (2, 20, 1, 1)
     sx = 'uf'
-    sy = 'uf'
-    sz = 'uf'
+    sy = 'nuf' if sampling == 'cartesian_and_non_cartesian' else 'uf'
+    sz = 'nuf' if sampling == 'cartesian_and_non_cartesian' else 'uf'
     trajectory_tensor = create_traj(k_shape, nkx, nky, nkz, sx, sy, sz).as_tensor()
 
     # Subsample data and trajectory
@@ -94,6 +98,15 @@ def test_cart_sampling_op_fwd_adj(sampling):
                 for traj_one_other in trajectory_tensor.unbind(1)
             ]
             trajectory = KTrajectory.from_tensor(torch.stack(traj_list, dim=1))
+        case 'cartesian_and_non_cartesian':
+            trajectory = KTrajectory.from_tensor(trajectory_tensor)
+        case 'kx_ky_along_k0':
+            trajectory_tensor = rearrange(trajectory_tensor, '... k1 k0->... 1 (k1 k0)')
+            trajectory = KTrajectory.from_tensor(trajectory_tensor)
+        case 'kx_ky_along_k0_undersampling':
+            trajectory_tensor = rearrange(trajectory_tensor, '... k1 k0->... 1 (k1 k0)')
+            random_idx = torch.randperm(trajectory_tensor.shape[-1])
+            trajectory = KTrajectory.from_tensor(trajectory_tensor[..., random_idx[: trajectory_tensor.shape[-1] // 2]])
         case _:
             raise NotImplementedError(f'Test {sampling} not implemented.')