refactor!: s/num_iterations/max_iter

Author: paquiteau

src/mrinufft/operators/interfaces/cufinufft.py

@@ -860,7 +860,7 @@ def pipe(
         cls,
         kspace_loc,
         volume_shape,
-        num_iterations=10,
+        max_iter=10,
         osf=2,
         normalize=True,
         **kwargs,
@@ -873,7 +873,7 @@ def pipe(
             the kspace locations
         volume_shape: np.ndarray
             the volume shape
-        num_iterations: int default 10
+        max_iter: int default 10
             the number of iterations for density estimation
         osf: float or int
             The oversampling factor the volume shape
@@ -894,7 +894,7 @@ def pipe(
             **kwargs,
         )
         density_comp = cp.ones(kspace_loc.shape[0], dtype=grid_op.cpx_dtype)
-        for _ in range(num_iterations):
+        for _ in range(max_iter):
             density_comp /= cp.abs(
                 grid_op.op(
                     grid_op.adj_op(density_comp.astype(grid_op.cpx_dtype))

src/mrinufft/operators/interfaces/finufft.py

@@ -174,7 +174,7 @@ def pipe(
         cls,
         kspace_loc,
         volume_shape,
-        num_iterations=10,
+        max_iter=10,
         osf=2,
         normalize=True,
         **kwargs,
@@ -187,7 +187,7 @@ def pipe(
             the kspace locations
         volume_shape: np.ndarray
             the volume shape
-        num_iterations: int default 10
+        max_iter: int default 10
             the number of iterations for density estimation
         osf: float or int
             The oversampling factor the volume shape
@@ -208,7 +208,7 @@ def pipe(
             **kwargs,
         )
         density_comp = np.ones(kspace_loc.shape[0], dtype=grid_op.cpx_dtype)
-        for _ in range(num_iterations):
+        for _ in range(max_iter):
             density_comp /= np.abs(
                 grid_op.op(
                     grid_op.adj_op(density_comp.astype(grid_op.cpx_dtype))

src/mrinufft/operators/interfaces/gpunufft.py

@@ -584,7 +584,7 @@ def pipe(
         cls,
         kspace_loc,
         volume_shape,
-        num_iterations=10,
+        max_iter=10,
         osf=2,
         normalize=True,
         **kwargs,
@@ -597,7 +597,7 @@ def pipe(
             the kspace locations
         volume_shape: np.ndarray
             the volume shape
-        num_iterations: int default 10
+        max_iter: int default 10
             the number of iterations for density estimation
         osf: float or int
             The oversampling factor the volume shape
@@ -616,9 +616,7 @@ def pipe(
             osf=1,
             **kwargs,
         )
-        density_comp = grid_op.raw_op.operator.estimate_density_comp(
-            max_iter=num_iterations
-        )
+        density_comp = grid_op.raw_op.operator.estimate_density_comp(max_iter=max_iter)
         if normalize:
             test_op = cls(samples=kspace_loc, shape=original_shape, **kwargs)
             test_im = np.ones(original_shape, dtype=np.complex64)

src/mrinufft/operators/interfaces/tfnufft.py

@@ -156,7 +156,7 @@ def pipe(
         cls,
         samples,
         shape,
-        num_iterations=10,
+        max_iter=10,
         osf=2,
         normalize=True,
     ):
@@ -169,7 +169,7 @@ def pipe(
             It should be C-contiguous.
         shape: tuple
             Shape of the image space.
-        n_iter: int
+        max_iter: int
             Number of iterations.
         osf: int, default 2
             Currently, we support only OSF=2 and this value cannot be changed.
@@ -192,7 +192,7 @@ def pipe(
                 samples.astype(np.float32),
                 shape,
                 method="pipe",
-                max_iter=num_iterations,
+                max_iter=max_iter,
             )
         )
 

src/mrinufft/operators/interfaces/torchkbnufft.py

@@ -192,7 +192,7 @@ def pipe(
         cls,
         kspace_loc,
         volume_shape,
-        num_iterations=10,
+        max_iter=10,
         osf=2,
         normalize=True,
         use_gpu=False,
@@ -206,7 +206,7 @@ def pipe(
             the kspace locations
         volume_shape: tuple
             the volume shape
-        num_iterations: int default 10
+        max_iter: int default 10
             the number of iterations for density estimation
         osf: float or int
             The oversampling factor the volume shape
@@ -225,7 +225,7 @@ def pipe(
             **kwargs,
         )
         density_comp = tkbn.calc_density_compensation_function(
-            ktraj=kspace_loc, im_size=volume_shape, num_iterations=num_iterations
+            ktraj=kspace_loc, im_size=volume_shape, max_iter=max_iter
         )
         if normalize:
             spike = torch.zeros(volume_shape, dtype=torch.float32).to(grid_op.device)