Merge pull request #106 from PolarizedLightFieldMicroscopy/two_var_op…

…tic_axis

Two variable optic axis

.github/workflows/pytest-action.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}

README.md

@@ -1,4 +1,4 @@
-![python versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)
+![python versions](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)
 [![Run Pytest](https://github.com/PolarizedLightFieldMicroscopy/forward-model/actions/workflows/pytest-action.yml/badge.svg)](https://github.com/PolarizedLightFieldMicroscopy/forward-model/actions/workflows/pytest-action.yml)
 # GeoBirT
 Polarized light field microscopy forward model and inverse model using geometrical optics and Jones Calculus.

src/VolumeRaytraceLFM/birefringence_implementations.py

@@ -1972,7 +1972,9 @@ def calc_cummulative_JM_of_ray_torch(
         )
         return material_jones
 
-    def retrieve_properties_from_vox_idx(self, volume, vox, active_props_only=False):
+    def retrieve_properties_from_vox_idx(
+        self, volume: BirefringentVolume, vox: torch.Tensor, active_props_only=False
+    ):
         """Retrieves the birefringence and optic axis from the volume based on the
         provided voxel indices. This function is used to retrieve the properties
         of the voxels that each ray segment interacts with."""
@@ -1983,21 +1985,30 @@ def retrieve_properties_from_vox_idx(self, volume, vox, active_props_only=False)
             safe_indices = torch.clamp(indices, min=0)
             mask = indices >= 0
             Delta_n = torch.where(
-                mask, volume.birefringence_active[safe_indices], torch.tensor(0.0)
+                mask,
+                volume.birefringence_active[safe_indices],
+                torch.tensor(0.0, device=device),
             )
+
             if volume.optic_axis_planar is not None:
                 opticAxis = torch.empty(
-                    (3, len(indices)), dtype=torch.get_default_dtype(), device=device
+                    (3, *indices.shape), dtype=torch.get_default_dtype(), device=device
                 )
-                opticAxis[0, :] = torch.where(
-                    mask, volume.optic_axis_active[0, safe_indices], torch.tensor(0.0)
+                opticAxis[0, :, :] = torch.where(
+                    mask,
+                    volume.optic_axis_active[0, safe_indices],
+                    torch.tensor(0.0, device=device),
                 )
-                opticAxis[1:, :] = torch.where(
-                    mask, volume.optic_axis_planar[:, safe_indices], torch.tensor(0.0)
+                opticAxis[1:, :, :] = torch.where(
+                    mask.unsqueeze(0),
+                    volume.optic_axis_planar[:, safe_indices],
+                    torch.tensor(0.0, device=device),
                 )
             else:
                 opticAxis = torch.where(
-                    mask, volume.optic_axis_active[:, safe_indices], torch.tensor(0.0)
+                    mask.unsqueeze(0),
+                    volume.optic_axis_active[:, safe_indices],
+                    torch.tensor(0.0, device=device),
                 )
         else:
             Delta_n = volume.Delta_n[vox]

src/VolumeRaytraceLFM/jones/jones_calculus.py

@@ -78,7 +78,7 @@ def linear_retarter_azim90(ret, backend=BackEnds.NUMPY):
             return np.array([[np.exp(1j * ret / 2), 0], [0, np.exp(-1j * ret / 2)]])
         else:
             return torch.tensor(
-                [torch.exp(1j * ret / 2), 0], [0, torch.exp(-1j * ret / 2)]
+                [[torch.exp(1j * ret / 2), 0], [0, torch.exp(-1j * ret / 2)]]
             )
 
     @staticmethod

src/streamlit_app/pages/2_Reconstruction.py

@@ -398,8 +398,8 @@ def create_optimizer(volume):
         st.error("Ground truth volume is unknown.")
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    ret_image_measured = torch.tensor(output_ret_image, device=device)
-    azim_image_measured = torch.tensor(output_azim_image, device=device)
+    ret_image_measured = torch.as_tensor(output_ret_image, device=device)
+    azim_image_measured = torch.as_tensor(output_azim_image, device=device)
     optical_info["volume_shape"] = list(est_vol_shape)
     # Compute ray geometry
     rays_est = BirefringentRaytraceLFM(backend=backend, optical_info=optical_info)