refactor: remove old common lines code

src/tttsa/optimizers.py

@@ -6,7 +6,6 @@
 from torch_fourier_slice import project_2d_to_1d
 
 from .affine import affine_transform_2d
-from .projection import common_lines_projection
 from .transformations import R_2d, T_2d, stretch_matrix
 
 
@@ -70,10 +69,10 @@ def optimize_tilt_axis_angle(
     coarse_aligned_masked = aligned_ts * coarse_alignment_mask
 
     # generate a weighting for the common line ROI by projecting the mask
-    mask_weights = common_lines_projection(
-        einops.rearrange(coarse_alignment_mask, "h w -> 1 h w"),
-        0.0,  # angle does not matter
-    )
+    mask_weights = project_2d_to_1d(
+        coarse_alignment_mask,
+        torch.eye(2).to(coarse_alignment_mask.device),  # angle does not matter
+    ).squeeze()  # remove starting empty dimension
     mask_weights /= mask_weights.max()  # normalise to 0 and 1
 
     # optimize tilt axis angle

src/tttsa/projection/__init__.py

@@ -1,8 +1,7 @@
 """Projection of images and volumes."""
 
-from .project_real import common_lines_projection, tomogram_reprojection
+from .project_real import tomogram_reprojection
 
 __all__ = [
-    "common_lines_projection",
     "tomogram_reprojection",
 ]

src/tttsa/projection/project_real.py

@@ -9,50 +9,15 @@
 from torch_grid_utils import coordinate_grid
 from torch_image_lerp import insert_into_image_2d
 
-from tttsa.affine import affine_transform_2d
 from tttsa.transformations import (
-    R_2d,
-    T_2d,
     projection_model_to_projection_matrix,
 )
-from tttsa.utils import dft_center, homogenise_coordinates
+from tttsa.utils import homogenise_coordinates
 
 # update shift
 PMDL.SHIFT = [PMDL.SHIFT_Y, PMDL.SHIFT_X]
 
 
-def common_lines_projection(
-    images: torch.Tensor,
-    tilt_axis_angles: torch.Tensor,
-    # this might as well takes shifts
-) -> torch.Tensor:
-    """Predict a projection from an intermediate reconstruction.
-
-    For now only assumes to project with a single matrix, but should also work for
-    sets of matrices.
-    """
-    device = images.device
-    image_dimensions = images.shape[-2:]
-
-    # TODO pad image if not square
-
-    image_center = dft_center(image_dimensions, rfft=False, fftshifted=True)
-
-    # time for real space projection
-    s0 = T_2d(-image_center)
-    r0 = R_2d(tilt_axis_angles, yx=True)
-    s1 = T_2d(image_center)
-    # invert because the tilt axis angle is forward in the sample projection model
-    M = torch.linalg.inv(s1 @ r0 @ s0).to(device)
-
-    rotated = affine_transform_2d(
-        images,
-        M,
-    )
-    projections = rotated.mean(axis=-1).squeeze()
-    return projections
-
-
 def tomogram_reprojection(
     tomogram: torch.Tensor,
     tilt_image_dimensions: Tuple[int, int],