Update packages to latest versions (#101)

* Update packages to latest versions

* Run black (macos)

* Fix other black files

* Update readthedocs yaml

* Move conf.py

* Fix conf.py conf

* Resolve deprecation warning

.github/workflows/python-app.yml

@@ -17,21 +17,21 @@ jobs:
       max-parallel: 4
       matrix:
         platform: [ubuntu-latest]
-        python-version: ["3.8", "3.10"]
+        python-version: ["3.10", "3.11"]
 
     runs-on: ${{ matrix.platform }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install
         run: |
           python -m pip install --upgrade pip
-          pip install --upgrade wheel build setuptools
-          python -m build .
+          pip install --upgrade wheel
+          python setup.py bdist_wheel
           pip install dist/*.whl
       - name: Test Import
         run: |
@@ -43,27 +43,17 @@ jobs:
       max-parallel: 4
       matrix:
         platform: [ubuntu-latest]
-        python-version: ["3.8", "3.10"]
+        python-version: ["3.10", "3.11"]
 
     runs-on: ${{ matrix.platform }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Get pip cache dir
-        id: pip-cache
-        run: |
-          echo "::set-output name=dir::$(pip cache dir)"
-      - name: pip cache
-        uses: actions/cache@v3
-        with:
-          path: ${{ steps.pip-cache.outputs.dir }}
-          key: ${{ runner.os }}-pip-py${{ matrix.python-version }}-${{ hashFiles('**/setup.cfg') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-py${{ matrix.python-version }}-
+          cache: 'pip'
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

.readthedocs.yml

@@ -1,8 +1,23 @@
+# Read the Docs configuration file for Sphinx projects
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+
 python:
-   version: 3.8
    install:
+      - requirements: docs/requirements.txt
       - method: pip
         path: .
         extra_requirements:
             - docs
-   system_packages: true
+
+sphinx:
+  builder: html
+  configuration: docs/source/conf.py

docs/requirements.txt

@@ -1,5 +1 @@
-sphinx>=3.2.0
-sphinx-rtd-theme>=0.5.1
-sphinxcontrib-katex
-sphinx-autodoc-typehints
 torch --extra-index-url https://download.pytorch.org/whl/cpu

setup.cfg

@@ -43,24 +43,29 @@ ignore_missing_imports=True
 install_requires =
     numpy>=1.22.4
     scipy>=1.8.1
-    torch>=1.12
+    torch>=2.0
 packages = find:
-python_requires = >=3.8
+python_requires = >=3.10
 
 [options.extras_require]
 dev = 
-    black==22.10.0
-    flake8==5.0.4
-    mypy==0.991
-    pytest==7.2.0
+    black==24.10.0
+    flake8==6.1.0
+    mypy==1.13.0
+    pytest==8.3.3
 tests =
-    black==22.10.0
-    flake8==5.0.4
-    mypy==0.991
-    numpy==1.23.5
-    pytest==7.2.0
-    scipy==1.9.3
-    torch==1.13.0
+    black==24.10.0
+    flake8==6.1.0
+    mypy==1.13.0
+    numpy==2.1.3
+    pytest==8.3.3
+    scipy==1.14.1
+    torch==2.5.1
+docs =
+    sphinx>=3.2.0
+    sphinx-rtd-theme>=0.5.1
+    sphinxcontrib-katex
+    sphinx-autodoc-typehints
 
 [options.packages.find]
 exclude = 

tests/data/create_old_data.py

@@ -20,7 +20,7 @@ def create_interp_data():
     ]
 
     outputs = []
-    for (shape, klength, is_complex) in test_params:
+    for shape, klength, is_complex in test_params:
         torch.manual_seed(123)
         im_size = shape[2:-1]
 
@@ -74,7 +74,7 @@ def create_nufft_data():
     ]
 
     outputs = []
-    for (shape, klength, is_complex) in test_params:
+    for shape, klength, is_complex in test_params:
         torch.manual_seed(123)
         im_size = shape[2:-1]
 
@@ -128,7 +128,7 @@ def create_sense_nufft_data():
     ]
 
     outputs = []
-    for (shape, klength, is_complex) in test_params:
+    for shape, klength, is_complex in test_params:
         torch.manual_seed(123)
         im_size = shape[2:-1]
 

tests/test_interp.py

@@ -19,7 +19,7 @@ def test_interp_accuracy():
     with open("tests/data/interp_data.pkl", "rb") as f:
         old_data = pickle.load(f)
 
-    for (image, ktraj, old_kdata) in old_data:
+    for image, ktraj, old_kdata in old_data:
         im_size = image.shape[2:-1]
 
         forw_ob = tkbn.KbInterp(im_size=im_size, grid_size=im_size)

tests/test_nufft.py

@@ -18,7 +18,7 @@ def test_nufft_accuracy():
     with open("tests/data/nufft_data.pkl", "rb") as f:
         old_data = pickle.load(f)
 
-    for (image, ktraj, old_kdata) in old_data:
+    for image, ktraj, old_kdata in old_data:
         im_size = image.shape[2:-1]
 
         forw_ob = tkbn.KbNufft(im_size=im_size)

tests/test_sense_nufft.py

@@ -13,7 +13,7 @@ def test_sense_nufft_accuracy():
     with open("tests/data/sense_nufft_data.pkl", "rb") as f:
         old_data = pickle.load(f)
 
-    for (image, ktraj, smaps, old_kdata) in old_data:
+    for image, ktraj, smaps, old_kdata in old_data:
         im_size = image.shape[2:-1]
 
         forw_ob = tkbn.KbNufft(im_size=im_size)

torchkbnufft/_nufft/fft.py

@@ -26,7 +26,7 @@ def crop_dims(image: Tensor, dim_list: Tensor, end_list: Tensor) -> Tensor:
     """Crops an n-dimensional Tensor."""
     image = torch.view_as_real(image)  # index select only works for real
 
-    for (dim, end) in zip(dim_list, end_list):
+    for dim, end in zip(dim_list, end_list):
         image = torch.index_select(image, dim, torch.arange(end, device=image.device))
 
     return torch.view_as_complex(image)
@@ -64,7 +64,7 @@ def fft_and_scale(
 
     # zero pad for oversampled nufft
     pad_sizes: List[int] = []
-    for (gd, im) in zip(grid_size.flip((0,)), im_size.flip((0,))):
+    for gd, im in zip(grid_size.flip((0,)), im_size.flip((0,))):
         pad_sizes.append(0)
         pad_sizes.append(int(gd - im))
 
@@ -153,7 +153,7 @@ def fft_filter(image: Tensor, kernel: Tensor, norm: Optional[str] = "ortho") ->
     # set up n-dimensional zero pad
     # zero pad for oversampled nufft
     pad_sizes: List[int] = []
-    for (gd, im) in zip(grid_size.flip((0,)), im_size.flip((0,))):
+    for gd, im in zip(grid_size.flip((0,)), im_size.flip((0,))):
         pad_sizes.append(0)
         pad_sizes.append(int(gd - im))
 

torchkbnufft/_nufft/interp.py

@@ -215,7 +215,7 @@ def table_interp_multiple_batches(
 ) -> Tensor:
     """Table interpolation with for loop over batch dimension."""
     kdat = []
-    for (it_image, it_omega) in zip(image, omega):
+    for it_image, it_omega in zip(image, omega):
         kdat.append(
             table_interp_one_batch(
                 it_image.unsqueeze(0),
@@ -245,7 +245,7 @@ def table_interp_fork_over_batchdim(
     """Table interpolation with forking over k-space."""
     # initialize the fork processes
     futures: List[torch.jit.Future[torch.Tensor]] = []
-    for (image_chunk, omega_chunk) in zip(
+    for image_chunk, omega_chunk in zip(
         image.tensor_split(num_forks), omega.tensor_split(num_forks)
     ):
         futures.append(
@@ -409,11 +409,11 @@ def accum_tensor_index_add(
 ) -> Tensor:
     """We fork this function for the adjoint accumulation."""
     if batched_nufft:
-        for (image_batch, arr_ind_batch, data_batch) in zip(image, arr_ind, data):
-            for (image_coil, data_coil) in zip(image_batch, data_batch):
+        for image_batch, arr_ind_batch, data_batch in zip(image, arr_ind, data):
+            for image_coil, data_coil in zip(image_batch, data_batch):
                 image_coil.index_add_(0, arr_ind_batch, data_coil)
     else:
-        for (image_it, data_it) in zip(image, data):
+        for image_it, data_it in zip(image, data):
             image_it.index_add_(0, arr_ind, data_it)
 
     return image
@@ -427,7 +427,7 @@ def fork_and_accum(
     # initialize the fork processes
     futures: List[torch.jit.Future[torch.Tensor]] = []
     if batched_nufft:
-        for (image_chunk, arr_ind_chunk, data_chunk) in zip(
+        for image_chunk, arr_ind_chunk, data_chunk in zip(
             image.tensor_split(num_forks),
             arr_ind.tensor_split(num_forks),
             data.tensor_split(num_forks),
@@ -442,7 +442,7 @@ def fork_and_accum(
                 )
             )
     else:
-        for (image_chunk, data_chunk) in zip(
+        for image_chunk, data_chunk in zip(
             image.tensor_split(num_forks), data.tensor_split(num_forks)
         ):
             futures.append(
@@ -476,7 +476,7 @@ def calc_coef_and_indices_batch(
     """For loop coef calculation over batch dim."""
     coef = []
     arr_ind = []
-    for (tm_it, base_offset_it) in zip(tm, base_offset):
+    for tm_it, base_offset_it in zip(tm, base_offset):
         coef_it, arr_ind_it = calc_coef_and_indices(
             tm_it,
             base_offset_it,
@@ -511,7 +511,7 @@ def calc_coef_and_indices_fork_over_batches(
     if batched_nufft:
         # initialize the fork processes
         futures: List[torch.jit.Future[Tuple[Tensor, Tensor]]] = []
-        for (tm_chunk, base_offset_chunk) in zip(
+        for tm_chunk, base_offset_chunk in zip(
             tm.tensor_split(num_forks),
             base_offset.tensor_split(num_forks),
         ):
@@ -570,7 +570,7 @@ def sort_data(
     if batched_nufft:
         # loop over batch dimension to get sorted k-space
         results: List[Tuple[Tensor, Tensor, Tensor]] = []
-        for (tm_it, omega_it, data_it) in zip(tm, omega, data):
+        for tm_it, omega_it, data_it in zip(tm, omega, data):
             results.append(
                 sort_one_batch(tm_it, omega_it, data_it.unsqueeze(0), grid_size)
             )

torchkbnufft/_nufft/spmat.py

@@ -98,8 +98,8 @@ def calc_tensor_spmatrix(
     shape = coo.shape
 
     interp_mats = (
-        torch.sparse.FloatTensor(inds, real_vals, torch.Size(shape)),  # type: ignore
-        torch.sparse.FloatTensor(inds, imag_vals, torch.Size(shape)),  # type: ignore
+        torch.sparse_coo_tensor(inds, real_vals, torch.Size(shape)),  # type: ignore
+        torch.sparse_coo_tensor(inds, imag_vals, torch.Size(shape)),  # type: ignore
     )
 
     return interp_mats

torchkbnufft/modules/kbnufft.py

@@ -445,7 +445,7 @@ def toep_batch_loop(
         if len(kernel.shape) > len(image.shape[2:]):
             # run with batching for kernel
             if smaps.shape[0] == 1:
-                for (mini_image, mini_kernel) in zip(image, kernel):
+                for mini_image, mini_kernel in zip(image, kernel):
                     mini_image = mini_image.unsqueeze(0) * smaps
                     mini_image = tkbnF.fft_filter(
                         image=mini_image, kernel=mini_kernel, norm=norm
@@ -457,7 +457,7 @@ def toep_batch_loop(
                     )
                     output.append(mini_image.squeeze(0))
             else:
-                for (mini_image, smap, mini_kernel) in zip(image, smaps, kernel):
+                for mini_image, smap, mini_kernel in zip(image, smaps, kernel):
                     mini_image = mini_image.unsqueeze(0) * smap.unsqueeze(0)
                     mini_image = tkbnF.fft_filter(
                         image=mini_image, kernel=mini_kernel, norm=norm
@@ -469,7 +469,7 @@ def toep_batch_loop(
                     )
                     output.append(mini_image.squeeze(0))
         else:
-            for (mini_image, smap) in zip(image, smaps):
+            for mini_image, smap in zip(image, smaps):
                 mini_image = mini_image.unsqueeze(0) * smap.unsqueeze(0)
                 mini_image = tkbnF.fft_filter(
                     image=mini_image, kernel=kernel, norm=norm