updates including tests

.github/workflows/ci.yml

@@ -32,7 +32,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12"]
         platform: [ubuntu-latest, ]  # macos-latest, windows-latest]
 
     steps:

pyproject.toml

@@ -19,16 +19,14 @@ name = "torch-fourier-slice"
 dynamic = ["version"]
 description = "Fourier slice extraction/insertion in PyTorch."
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 license = { text = "BSD-3-Clause" }
 authors = [{ name = "Alister Burt", email = "[email protected]" }]
 # https://pypi.org/classifiers/
 classifiers = [
     "Development Status :: 3 - Alpha",
     "License :: OSI Approved :: BSD License",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -40,13 +38,19 @@ dependencies = [
     "numpy",
     "einops",
     "torch_image_lerp",
+    "torch_grid_utils",
 ]
 
 # https://peps.python.org/pep-0621/#dependencies-optional-dependencies
 # "extras" (e.g. for `pip install .[test]`)
 [project.optional-dependencies]
 # add dependencies used for testing here
-test = ["pytest", "pytest-cov"]
+test = [
+    "pytest",
+    "pytest-cov",
+    "torch-fourier-shell-correlation",
+    "scipy"
+]
 # add anything else you like to have in your dev environment here
 dev = [
     "ipython",

src/torch_fourier_slice/backproject.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn.functional as F
+from torch_grid_utils import fftfreq_grid
 
-from .grids import fftfreq_grid
 from .slice_insertion import insert_central_slices_rfft_3d
 
 

src/torch_fourier_slice/grids/__init__.py

@@ -1 +1 @@
-from .fftfreq_grid import fftfreq_grid
+from .central_slice_fftfreq_grid import central_slice_fftfreq_grid

src/torch_fourier_slice/grids/central_slice_grid.py → src/torch_fourier_slice/grids/central_slice_fftfreq_grid.py

@@ -1,7 +1,7 @@
 import einops
 import torch
 
-from .fftfreq_grid import _construct_fftfreq_grid_2d
+from torch_grid_utils import fftfreq_grid
 from ..dft_utils import rfft_shape, fftshift_2d
 
 
@@ -13,7 +13,7 @@ def central_slice_fftfreq_grid(
 ) -> torch.Tensor:
     # generate 2d grid of DFT sample frequencies, shape (h, w, 2)
     h, w = volume_shape[-2:]
-    grid = _construct_fftfreq_grid_2d(
+    grid = fftfreq_grid(
         image_shape=(h, w),
         rfft=rfft,
         device=device

src/torch_fourier_slice/project.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn.functional as F
+from torch_grid_utils import fftfreq_grid
 
-from .grids import fftfreq_grid
 from .slice_extraction import extract_central_slices_rfft_3d
 
 
@@ -18,7 +18,7 @@ def project_3d_to_2d(
     volume: torch.Tensor
         `(d, d, d)` volume.
     rotation_matrices: torch.Tensor
-        `(..., 3, 3)` array of rotation matrices for insert of `images`.
+        `(..., 3, 3)` array of rotation matrices for insertion of `images`.
         Rotation matrices left-multiply column vectors containing xyz coordinates.
     pad: bool
         Whether to pad the volume 2x with zeros to increase sampling rate in the DFT.

src/torch_fourier_slice/slice_extraction/_extract_central_slices_rfft_3d.py

@@ -3,7 +3,7 @@
 from torch_image_lerp import sample_image_3d
 
 from ..dft_utils import fftfreq_to_dft_coordinates
-from ..grids.central_slice_grid import central_slice_fftfreq_grid
+from ..grids.central_slice_fftfreq_grid import central_slice_fftfreq_grid
 
 
 def extract_central_slices_rfft_3d(

src/torch_fourier_slice/slice_insertion/_insert_central_slices_rfft_3d.py

@@ -3,7 +3,7 @@
 from torch_image_lerp import insert_into_image_3d
 
 from ..dft_utils import fftfreq_to_dft_coordinates, rfft_shape
-from ..grids.central_slice_grid import central_slice_fftfreq_grid
+from ..grids.central_slice_fftfreq_grid import central_slice_fftfreq_grid
 
 
 def insert_central_slices_rfft_3d(

tests/conftest.py

@@ -0,0 +1,10 @@
+import torch
+from pytest import fixture
+
+
+@fixture
+def cube() -> torch.Tensor:
+    volume = torch.zeros((32, 32, 32))
+    volume[8:24, 8:24, 8:24] = 1
+    volume[16, 16, 16] = 32
+    return volume

tests/test_torch_fourier_slice.py

@@ -1,4 +1,59 @@
-# temporary stub
+import pytest
+import torch
 
-def test_something():
-    pass
+from torch_fourier_slice import project_3d_to_2d, backproject_2d_to_3d
+from torch_fourier_shell_correlation import fsc
+from scipy.stats import special_ortho_group
+
+
+def test_project_3d_to_2d_rotation_center():
+    # rotation center should be at position of DC in DFT
+    volume = torch.zeros((32, 32, 32))
+    volume[16, 16, 16] = 1
+
+    # make projections
+    rotation_matrices = torch.tensor(special_ortho_group.rvs(dim=3, size=100)).float()
+    projections = project_3d_to_2d(
+        volume=volume,
+        rotation_matrices=rotation_matrices,
+    )
+
+    # check max is always at (16, 16), implying point (16, 16) never moves
+    for image in projections:
+        max = torch.argmax(image)
+        i, j = divmod(max.item(), 32)
+        assert (i, j) == (16, 16)
+
+
+def test_3d_2d_projection_backprojection_cycle(cube):
+    # make projections
+    rotation_matrices = torch.tensor(special_ortho_group.rvs(dim=3, size=1500)).float()
+    projections = project_3d_to_2d(
+        volume=cube,
+        rotation_matrices=rotation_matrices,
+    )
+
+    # reconstruct
+    volume = backproject_2d_to_3d(
+        images=projections,
+        rotation_matrices=rotation_matrices,
+    )
+
+    # calculate FSC between the projections and the reconstructions
+    _fsc = fsc(cube, volume.float())
+
+    assert torch.all(_fsc[-5:] > 0.99)  # few low res shells at 0.98...
+
+
+@pytest.mark.parametrize(
+    "images, rotation_matrices",
+    [
+        (
+            torch.rand((10, 28, 28)).float(),
+            torch.tensor(special_ortho_group.rvs(dim=3, size=10)).float()
+        ),
+    ]
+)
+def test_dtypes_slice_insertion(images, rotation_matrices):
+    result = backproject_2d_to_3d(images, rotation_matrices)
+    assert result.dtype == torch.float64