refactor!: s/n_iter/max_iter

Author: paquiteau

examples/GPU/example_pinv.py

@@ -98,7 +98,7 @@ def process_cb_results(cb_results):
 for optim in OPTIM:
     image, iter_cb = nufft.pinv_solver(
         kspace_data=kspace_data_gpu,
-        n_iter=1000,
+        max_iter=1000,
         callback=mixed_cb,
         optim=optim,
     )
@@ -180,7 +180,7 @@ def process_cb_results(cb_results):
 for optim in OPTIM:
     image, iter_cb = nufft.pinv_solver(
         kspace_data=kspace_data_gpu,
-        n_iter=1000,
+        max_iter=1000,
         callback=mixed_cb,
         damp=0.1,
         optim=optim,

src/mrinufft/extras/optim.py

@@ -27,7 +27,7 @@
     If provided, a callback function will be called at the end of each
     iteration with the current estimate. It should have the following signature
     ``callback(operator, kspace_data, damp, x0)``
-n_iter: int, optional
+max_iter: int, optional
     Maximum number of iterations. Default is 100.
 progressbar: bool, optional
     If True (default) display a progress bar to track iterations.
@@ -230,7 +230,7 @@ def lsqr(
     atol: float = 1e-6,
     btol: float = 1e-6,
     conlim: float = 1e8,
-    n_iter: int = 100,
+    max_iter: int = 100,
     x0: NDArray | None = None,
     x_init: NDArray | None = None,
     callback: Callable | None = None,
@@ -332,7 +332,7 @@ def lsqr(
     sn2 = 0.0
     istop = 0
     callback_returns = []
-    for _ in tqdm(range(n_iter), disable=not progressbar):
+    for _ in tqdm(range(max_iter), disable=not progressbar):
         u *= -_bc_left(alpha, u)
         u += operator.op(v).reshape(operator.ksp_full_shape)
         beta = norm_batched(u)
@@ -463,7 +463,7 @@ def lsmr(
     atol: float = 1e-6,
     btol: float = 1e-6,
     conlim: float = 1e8,
-    n_iter: int = 100,
+    max_iter: int = 100,
     x0: NDArray | None = None,
     x_init: NDArray | None = None,
     callback: Callable | None = None,
@@ -612,7 +612,7 @@ def A(x):
     normr = beta
 
     callback_returns = []
-    for _ in tqdm(range(n_iter), disable=not progressbar):
+    for _ in tqdm(range(max_iter), disable=not progressbar):
 
         u *= -_bc_left(alpha, u)
         u += A(v)
@@ -751,7 +751,7 @@ def cg(
     damp: float = 0.0,
     x0: NDArray | None = None,
     x_init: NDArray | None = None,
-    n_iter: int = 10,
+    max_iter: int = 10,
     tol: float = 1e-4,
     progressbar: bool = True,
     callback: Callable | None = None,
@@ -796,7 +796,7 @@ def cg(
     image = image - velocity
 
     callbacks_results = []
-    for _ in tqdm(range(n_iter), disable=not progressbar):
+    for _ in tqdm(range(max_iter), disable=not progressbar):
         grad_new = operator.data_consistency(image, kspace_data).reshape(
             operator.img_full_shape
         )

tests/operators/test_batch.py

@@ -235,7 +235,7 @@ def test_pinv_solver(operator, array_interface, image_data, kspace_data, optim):
     from mrinufft.extras.optim import loss_l2_reg
 
     img, res = operator.pinv_solver(
-        kspace_data, optim=optim, callback=loss_l2_reg, n_iter=5
+        kspace_data, optim=optim, callback=loss_l2_reg, max_iter=5
     )
 
     assert img.shape == operator.img_full_shape

tests/operators/test_density_for_op.py

@@ -33,7 +33,7 @@ def test_pipe(backend, traj, shape, osf):
         pytest.skip("OSF < 2 not supported for tensorflow.")
     if osf == 1 and "finufft" in backend:
         pytest.skip("cufinufft and finufft dont support OSF=1")
-    result = pipe(traj, shape, backend=backend, osf=osf, num_iterations=10)
+    result = pipe(traj, shape, backend=backend, osf=osf, max_iter=10)
     if backend == "cufinufft":
         result = result.get()
     result = result / np.mean(result)

tests/operators/test_optim.py

@@ -62,6 +62,6 @@ def test_pinv(operator, image_data, optim):
     kspace_nufft = operator.op(image_data).squeeze()
 
     _, residuals = operator.pinv_solver(
-        kspace_nufft, optim=optim, n_iter=10, callback=loss_l2_reg
+        kspace_nufft, optim=optim, max_iter=10, callback=loss_l2_reg
     )
     assert residuals[-1] <= residuals[0]