cleaning up

mehta-lab · Oct 2, 2024 · 1cb7d53 · 1cb7d53
1 parent 902c690
commit 1cb7d53
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Showing 4 changed files with 7 additions and 8 deletions.
diff --git a/examples/models/inplane_oriented_thick_pol3d_vector.py b/examples/models/inplane_oriented_thick_pol3d_vector.py
@@ -15,7 +15,7 @@
 wavelength_illumination = 0.532
 z_padding = 0
 index_of_refraction_media = 1.3
-numerical_aperture_illumination = 0.01  # 0.5
+numerical_aperture_illumination = 0.5
 numerical_aperture_detection = 1.2
 fourier_oversample_factor = 1
 

diff --git a/waveorder/models/inplane_oriented_thick_pol3d_vector.py b/waveorder/models/inplane_oriented_thick_pol3d_vector.py
@@ -195,10 +195,6 @@ def _calculate_wrap_unsafe_transfer_function(
     G_3D = torch.abs(torch.fft.ifft(G, dim=-3)) * (-1j)
     S_3D = torch.fft.ifft(S, dim=-3)
 
-    # # Normalize
-    # P_3D /= torch.amax(torch.abs(P_3D))
-    # G_3D /= torch.amax(torch.abs(G_3D))
-    # S_3D /= torch.amax(torch.abs(S_3D))
 
     # Main part
     PG_3D = torch.einsum("zyx,ipzyx->ipzyx", P_3D, G_3D)
@@ -256,6 +252,7 @@ def visualize_transfer_function(viewer, sfZYX_transfer_function, zyx_scale):
         zyx_scale=zyx_scale,
         layer_name="Transfer Function",
         complex_rgb=True,
+        clim_factor=0.5,
     )
 
 

diff --git a/waveorder/visuals/napari_visuals.py b/waveorder/visuals/napari_visuals.py
@@ -25,7 +25,8 @@ def add_transfer_function_to_viewer(
 
     if complex_rgb:
         rgb_transfer_function = complex_tensor_to_rgb(
-            torch.fft.ifftshift(transfer_function, dim=shift_dims)
+            torch.fft.ifftshift(transfer_function, dim=shift_dims),
+            saturate_clim_fraction=clim_factor,
         )
         viewer.add_image(
             rgb_transfer_function,

diff --git a/waveorder/visuals/utils.py b/waveorder/visuals/utils.py
@@ -4,7 +4,7 @@
 
 # Main function to convert a complex-valued torch tensor to RGB numpy array
 # with red at +1, green at +i, blue at -1, and purple at -i
-def complex_tensor_to_rgb(tensor):
+def complex_tensor_to_rgb(tensor, saturate_clim_fraction=1.0):
     # Convert the torch tensor to a numpy array
     tensor_np = tensor.numpy()
 
@@ -17,7 +17,8 @@ def complex_tensor_to_rgb(tensor):
     hue = np.mod(hue + 0.5, 1)
 
     # Normalize magnitude to [0, 1] for saturation
-    max_abs_val = np.amax(magnitude)
+    max_abs_val = np.amax(magnitude) * saturate_clim_fraction
+
     sat = magnitude / max_abs_val if max_abs_val != 0 else magnitude
 
     # Create HSV array: hue, saturation, value (value is set to 1)