Merge branch 'vector-prototype' of github.com:mehta-lab/waveorder int…

…o vector-prototype

pyproject.toml

@@ -45,12 +45,12 @@ classifiers = [
   "Operating System :: MacOS",
 ]
 dependencies = [
-  "numpy>=1.21, <2",
+  "numpy>=1.24",
   "matplotlib>=3.1.1",
   "scipy>=1.3.0",
   "pywavelets>=1.1.1",
   "ipywidgets>=7.5.1",
-  "torch>=2.2.1",
+  "torch>=2.4.1",
 ]
 dynamic = ["version"]
 

waveorder/models/isotropic_fluorescent_thick_3d.py

@@ -110,7 +110,10 @@ def _calculate_wrap_unsafe_transfer_function(
 
 def visualize_transfer_function(viewer, optical_transfer_function, zyx_scale):
     add_transfer_function_to_viewer(
-        viewer, torch.real(optical_transfer_function), zyx_scale, clim_factor=0.05
+        viewer,
+        torch.real(optical_transfer_function),
+        zyx_scale,
+        clim_factor=0.05,
     )
 
 

waveorder/models/isotropic_thin_3d.py

@@ -5,7 +5,6 @@
 from torch import Tensor
 
 from waveorder import optics, sampling, util
-from waveorder.visuals.napari_visuals import add_transfer_function_to_viewer
 
 
 def generate_test_phantom(

waveorder/scripts/visualize-greens.py

@@ -0,0 +1,139 @@
+from skimage import measure
+import napari
+from napari.experimental import link_layers
+import numpy as np
+import torch
+import os
+from waveorder import util, optics
+from scipy.ndimage import gaussian_filter
+
+# Parameters
+# all lengths must use consistent units e.g. um
+output_dirpath = "./greens_plots"
+os.makedirs(output_dirpath, exist_ok=True)
+grid_size = 300
+blur_width = grid_size // 35  # blurring to smooth sharp corners
+zyx_shape = 3 * (grid_size,)
+yx_pixel_size = 6.5 / 63
+z_pixel_size = 6.5 / 63
+wavelength_illumination = 0.532
+index_of_refraction_media = 1.3
+threshold = 0.5  # for marching cubes
+
+
+# Calculate coordinate grids
+zyx_pixel_size = (z_pixel_size, yx_pixel_size, yx_pixel_size)
+y_frequencies, x_frequencies = util.generate_frequencies(
+    zyx_shape[1:], yx_pixel_size
+)
+radial_frequencies = torch.sqrt(x_frequencies**2 + y_frequencies**2)
+z_position_list = torch.fft.ifftshift(
+    (torch.arange(zyx_shape[0]) - zyx_shape[0] // 2) * z_pixel_size
+)
+z_frequencies = torch.fft.fftfreq(zyx_shape[0], d=z_pixel_size)
+
+freq_shape = z_position_list.shape + x_frequencies.shape
+z_broadcast = torch.broadcast_to(z_frequencies[:, None, None], freq_shape)
+y_broadcast = torch.broadcast_to(y_frequencies[None, :, :], freq_shape)
+x_broadcast = torch.broadcast_to(x_frequencies[None, :, :], freq_shape)
+nu_rr = torch.sqrt(z_broadcast**2 + y_broadcast**2 + x_broadcast**2)
+
+freq_voxel_size = [1 / (d * n) for d, n in zip(zyx_pixel_size, zyx_shape)]
+
+# Calculate Greens tensor spectrum
+G_3D = optics.generate_greens_tensor_spectrum(
+    zyx_shape=zyx_shape,
+    zyx_pixel_size=zyx_pixel_size,
+    wavelength=wavelength_illumination / index_of_refraction_media,
+)
+
+# Mask to zero outside of a spherical shell
+wavelength = wavelength_illumination / index_of_refraction_media
+nu_max = (33 / 32) / (wavelength)
+nu_min = (31 / 32) / (wavelength)
+mask = torch.logical_and(nu_rr < nu_max, nu_rr > nu_min)
+G_3D *= mask
+
+# Split into positve and negative imaginary parts
+G3D_imag = torch.imag(torch.fft.fftshift(G_3D, dim=(-3, -2, -1)))
+G_pos = G3D_imag * (G3D_imag > 0)
+G_neg = G3D_imag * (G3D_imag < 0)
+
+# Blur to reduce aliasing
+sigma = (
+    0,
+    0,
+) + 3 * (blur_width,)
+G_pos = gaussian_filter(np.array(G_pos), sigma=sigma)
+G_neg = gaussian_filter(np.array(G_neg), sigma=sigma)
+
+# Add to napari
+viewer = napari.Viewer()
+
+viewer.theme = "light"
+viewer.dims.ndisplay = 3
+viewer.camera.zoom = 100
+
+for i in range(3):
+    for j in range(3):
+        name = f"{i}_{j}"
+
+        volume = G_pos[i, j]
+        verts, faces, normals, _ = measure.marching_cubes(
+            volume, level=threshold * np.max(volume)
+        )
+        viewer.add_surface(
+            (verts, faces),
+            name=name + "-positive-surface",
+            colormap="greens",
+            scale=freq_voxel_size,
+            shading="smooth",
+        )
+
+        volume = -G_neg[i, j]
+        if i != j:
+            verts, faces, normals, _ = measure.marching_cubes(
+                volume, level=threshold * np.max(volume)
+            )
+            viewer.add_surface(
+                (verts, faces),
+                opacity=1.0,
+                name=name + "-negative-surface",
+                colormap="I Purple",
+                scale=freq_voxel_size,
+                blending="translucent",
+                shading="smooth",
+            )
+        else:
+            viewer.add_surface(
+                (
+                    np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0]]),
+                    np.array([[0, 1, 2]]),
+                ),
+                opacity=1.0,
+                name=name + "-dummy-surface",
+                colormap="gray",
+                scale=freq_voxel_size,
+                blending="translucent",
+                shading="smooth",
+            )
+        link_layers(viewer.layers[-2:])
+
+        print(f"Screenshotting {i}_{j}")
+        viewer.camera.set_view_direction(
+            view_direction=[-1, -1, -1], up_direction=[0, 0, 1]
+        )
+        viewer.screenshot(
+            os.path.join(output_dirpath, f"{i}_{j}.png"), scale=2
+        )
+        viewer.layers[-1].visible = False
+        viewer.layers[-2].visible = False
+
+# Show in complete grid
+for layer in viewer.layers:
+    layer.visible = True
+viewer.grid.enabled = True
+viewer.grid.stride = 2
+viewer.grid.shape = (-1, 3)
+viewer.theme = "dark"
+napari.run()