Vector-optics birefringence reconstruction prototype

.gitignore

@@ -150,3 +150,4 @@ recOrder/_version.py
 *.npz
 *.png
 *.tif[f]
+*.pdf

examples/documentation/PTI_experiment/PTI_Experiment_Recon3D_anisotropic_target_small.py

@@ -6,11 +6,12 @@
 from numpy.fft import fftshift
 
 import waveorder as wo
-from waveorder import optics, waveorder_reconstructor, util, visual
+from waveorder import optics, waveorder_reconstructor, util
 
 import zarr
 from pathlib import Path
 from iohub import open_ome_zarr
+from waveorder.visuals import jupyter_visuals
 
 # %%
 # Initialization
@@ -110,7 +111,7 @@
     Source_PolState[i, 1] = E_in[1]
 
 
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     fftshift(Source, axes=(1, 2)), origin="lower", num_col=5
 )
 
@@ -162,7 +163,7 @@
 
 # %%
 # browse raw intensity stacks (stack_idx_1: z index, stack_idx2: pattern index)
-visual.parallel_5D_viewer(
+jupyter_visuals.parallel_5D_viewer(
     np.transpose(I_meas[:, :, :, :, ::-1], (4, 1, 0, 2, 3)),
     num_col=4,
     size=10,
@@ -171,7 +172,7 @@
 
 # %%
 # browse uncorrected Stokes parameters (stack_idx_1: z index, stack_idx2: pattern index)
-visual.parallel_5D_viewer(
+jupyter_visuals.parallel_5D_viewer(
     np.transpose(S_image_recon, (4, 1, 0, 2, 3)),
     num_col=3,
     size=8,
@@ -182,7 +183,7 @@
 
 # %%
 # browse corrected Stokes parameters (stack_idx_1: z index, stack_idx2: pattern index)
-visual.parallel_5D_viewer(
+jupyter_visuals.parallel_5D_viewer(
     np.transpose(S_image_tm, (4, 1, 0, 2, 3)),
     num_col=3,
     size=8,
@@ -213,7 +214,7 @@
 
 # %%
 # browse the z-stack of components of scattering potential tensor
-visual.parallel_4D_viewer(
+jupyter_visuals.parallel_4D_viewer(
     np.transpose(f_tensor, (3, 0, 1, 2)),
     num_col=4,
     origin="lower",
@@ -278,7 +279,7 @@
 
 # %%
 # browse the reconstructed physical properties
-visual.parallel_4D_viewer(
+jupyter_visuals.parallel_4D_viewer(
     np.transpose(
         np.stack(
             [
@@ -546,7 +547,7 @@
 
 # %%
 # browse XY planes of the phase and differential permittivity
-visual.parallel_4D_viewer(
+jupyter_visuals.parallel_4D_viewer(
     np.transpose(
         [
             np.clip(phase_PT, phase_min, phase_max),
@@ -585,7 +586,7 @@
     ),
     (3, 1, 2, 0),
 )
-orientation_3D_image_RGB = visual.orientation_3D_to_rgb(
+orientation_3D_image_RGB = jupyter_visuals.orientation_3D_to_rgb(
     orientation_3D_image, interp_belt=20 / 180 * np.pi, sat_factor=1
 )
 
@@ -600,7 +601,7 @@
 
 # plot the top view of 3D orientation colorsphere
 plt.figure(figsize=(3, 3))
-visual.orientation_3D_colorwheel(
+jupyter_visuals.orientation_3D_colorwheel(
     wheelsize=256, circ_size=50, interp_belt=20 / 180 * np.pi, sat_factor=1
 )
 
@@ -639,7 +640,7 @@
     in_plane_orientation[:, y_layer], origin="lower", aspect=z_step / ps
 )
 plt.figure(figsize=(3, 3))
-visual.orientation_2D_colorwheel()
+jupyter_visuals.orientation_2D_colorwheel()
 
 # %%
 # out-of-plane tilt
@@ -686,7 +687,7 @@
 
 fig, ax = plt.subplots(1, 1, figsize=(15, 15))
 
-visual.plot3DVectorField(
+jupyter_visuals.plot3DVectorField(
     np.abs(differential_permittivity_PT[1, :, :, z_layer]),
     azimuth[1, :, :, z_layer],
     theta[1, :, :, z_layer],
@@ -722,7 +723,7 @@
 # %%
 # Angular histogram of 3D orientation
 
-visual.orientation_3D_hist(
+jupyter_visuals.orientation_3D_hist(
     azimuth[1].flatten(),
     theta[1].flatten(),
     ret_mask.flatten(),

examples/maintenance/PTI_simulation/PTI_Simulation_Forward_2D3D.py

@@ -15,9 +15,9 @@
 from waveorder import (
     optics,
     waveorder_simulator,
-    visual,
     util,
 )
+from waveorder.visuals import jupyter_visuals
 
 #####################################################################
 # Initialization - imaging system and sample                        #
@@ -145,7 +145,7 @@
 ### Visualize sample properties
 
 #### XY sections
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     [
         target[:, :, z_layer],
         azimuth[:, :, z_layer] % (2 * np.pi),
@@ -158,7 +158,7 @@
     set_title=True,
 )
 #### XZ sections
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     [
         np.transpose(target[y_layer, :, :]),
         np.transpose(azimuth[y_layer, :, :]) % (2 * np.pi),
@@ -197,7 +197,7 @@
     ),
     (3, 1, 2, 0),
 )
-orientation_3D_image_RGB = visual.orientation_3D_to_rgb(
+orientation_3D_image_RGB = jupyter_visuals.orientation_3D_to_rgb(
     orientation_3D_image, interp_belt=20 / 180 * np.pi, sat_factor=1
 )
 
@@ -206,7 +206,7 @@
 plt.figure(figsize=(10, 10))
 plt.imshow(orientation_3D_image_RGB[:, y_layer], origin="lower")
 plt.figure(figsize=(3, 3))
-visual.orientation_3D_colorwheel(
+jupyter_visuals.orientation_3D_colorwheel(
     wheelsize=128,
     circ_size=50,
     interp_belt=20 / 180 * np.pi,
@@ -216,7 +216,7 @@
 plt.show()
 
 #### Angular histogram of 3D orientation
-visual.orientation_3D_hist(
+jupyter_visuals.orientation_3D_hist(
     azimuth.flatten(),
     inclination.flatten(),
     np.abs(target).flatten(),
@@ -258,7 +258,7 @@
 epsilon_tensor[2, 2] = epsilon_mean + epsilon_del * np.cos(2 * inclination)
 
 
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     [
         epsilon_tensor[0, 0, :, :, z_layer],
         epsilon_tensor[0, 1, :, :, z_layer],
@@ -334,7 +334,7 @@
 )
 
 
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     [
         del_f_component[0, :, :, z_layer],
         del_f_component[1, :, :, z_layer],
@@ -425,11 +425,11 @@
 
 #### Circularly polarized illumination patterns
 
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     fftshift(Source_cont, axes=(1, 2)), origin="lower", num_col=5, size=5
 )
 # discretized illumination patterns used in simulation (faster forward model)
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     fftshift(Source, axes=(1, 2)), origin="lower", num_col=5, size=5
 )
 print(Source_PolState)

examples/maintenance/PTI_simulation/PTI_Simulation_Recon2D.py

@@ -16,8 +16,8 @@
 from waveorder import (
     optics,
     waveorder_reconstructor,
-    visual,
 )
+from waveorder.visuals import jupyter_visuals
 
 ## Initialization
 ## Load simulated images and parameters
@@ -76,7 +76,7 @@
 ## Visualize 2  D transfer functions as a function of illumination pattern
 
 # illumination patterns used
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     fftshift(Source_cont, axes=(1, 2)), origin="lower", num_col=5, size=5
 )
 plt.show()
@@ -118,7 +118,7 @@
     S_image_tm, reg_inc=reg_inc, cupy_det=True
 )
 
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     f_tensor,
     num_col=4,
     origin="lower",
@@ -255,14 +255,14 @@
     ),
     (1, 2, 0),
 )
-orientation_3D_image_RGB = visual.orientation_3D_to_rgb(
+orientation_3D_image_RGB = jupyter_visuals.orientation_3D_to_rgb(
     orientation_3D_image, interp_belt=20 / 180 * np.pi, sat_factor=1
 )
 
 plt.figure(figsize=(5, 5))
 plt.imshow(orientation_3D_image_RGB, origin="lower")
 plt.figure(figsize=(3, 3))
-visual.orientation_3D_colorwheel(
+jupyter_visuals.orientation_3D_colorwheel(
     wheelsize=256, circ_size=50, interp_belt=20 / 180 * np.pi, sat_factor=1
 )
 plt.show()
@@ -297,7 +297,7 @@
 plt.figure(figsize=(5, 5))
 plt.imshow(in_plane_orientation, origin="lower")
 plt.figure(figsize=(3, 3))
-visual.orientation_2D_colorwheel()
+jupyter_visuals.orientation_2D_colorwheel()
 plt.show()
 
 # out-of-plane tilt
@@ -339,7 +339,7 @@
 plt.figure(figsize=(10, 10))
 
 fig, ax = plt.subplots(1, 1, figsize=(20, 10))
-visual.plot3DVectorField(
+jupyter_visuals.plot3DVectorField(
     np.abs(retardance_pr_nm[0]),
     azimuth[0],
     theta[0],
@@ -362,7 +362,7 @@
 
 plt.figure(figsize=(10, 10))
 plt.imshow(ret_mask, cmap="gray", origin="lower")
-visual.orientation_3D_hist(
+jupyter_visuals.orientation_3D_hist(
     azimuth[0].flatten(),
     theta[0].flatten(),
     ret_mask.flatten(),

examples/maintenance/PTI_simulation/PTI_Simulation_Recon3D.py

@@ -14,8 +14,8 @@
 from waveorder import (
     optics,
     waveorder_reconstructor,
-    visual,
 )
+from waveorder.visuals import jupyter_visuals
 
 ## Initialization
 ## Load simulated images and parameters
@@ -65,7 +65,7 @@
 
 
 ### Illumination patterns used
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     fftshift(Source_cont, axes=(1, 2)), origin="lower", num_col=5, size=5
 )
 plt.show()
@@ -113,7 +113,7 @@
     S_image_tm, reg_inc=reg_inc, cupy_det=True
 )
 
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     f_tensor[..., L // 2],
     num_col=4,
     origin="lower",
@@ -183,7 +183,7 @@
 ### Reconstructed phase, absorption, principal retardance, azimuth, and inclination assuming (+) and (-) optic sign
 
 # browse the reconstructed physical properties
-visual.plot_multicolumn(
+jupyter_visuals.plot_multicolumn(
     np.stack(
         [
             phase_PT[..., L // 2],
@@ -389,7 +389,7 @@
     ),
     (3, 1, 2, 0),
 )
-orientation_3D_image_RGB = visual.orientation_3D_to_rgb(
+orientation_3D_image_RGB = jupyter_visuals.orientation_3D_to_rgb(
     orientation_3D_image, interp_belt=20 / 180 * np.pi, sat_factor=1
 )
 
@@ -402,7 +402,7 @@
 )
 # plot the top view of 3D orientation colorsphere
 plt.figure(figsize=(3, 3))
-visual.orientation_3D_colorwheel(
+jupyter_visuals.orientation_3D_colorwheel(
     wheelsize=128,
     circ_size=50,
     interp_belt=20 / 180 * np.pi,
@@ -440,7 +440,7 @@
 plt.figure(figsize=(10, 10))
 plt.imshow(in_plane_orientation[:, y_layer], origin="lower", aspect=psz / ps)
 plt.figure(figsize=(3, 3))
-visual.orientation_2D_colorwheel()
+jupyter_visuals.orientation_2D_colorwheel()
 plt.show()
 
 
@@ -511,7 +511,7 @@
 
 
 fig, ax = plt.subplots(2, 2, figsize=(10, 10))
-visual.plot3DVectorField(
+jupyter_visuals.plot3DVectorField(
     np.abs(retardance_pr_PT[0, :, :, z_layer]),
     azimuth[0, :, :, z_layer],
     theta[0, :, :, z_layer],
@@ -529,7 +529,7 @@
 )
 ax[0, 0].set_title(f"XY section (z= {z_layer})")
 
-visual.plot3DVectorField(
+jupyter_visuals.plot3DVectorField(
     np.transpose(np.abs(retardance_pr_PT[0, :, x_layer, :])),
     np.transpose(azimuth_x[0, :, x_layer, :]),
     np.transpose(theta_x[0, :, x_layer, :]),
@@ -547,7 +547,7 @@
 )
 ax[0, 1].set_title(f"YZ section (x = {x_layer})")
 
-visual.plot3DVectorField(
+jupyter_visuals.plot3DVectorField(
     np.transpose(np.abs(retardance_pr_PT[0, y_layer, :, :])),
     np.transpose(azimuth_y[0, y_layer, :, :]),
     np.transpose(theta_y[0, y_layer, :, :]),
@@ -584,7 +584,7 @@
 
 plt.figure(figsize=(10, 10))
 plt.imshow(ret_mask[:, :, z_layer], cmap="gray", origin="lower")
-visual.orientation_3D_hist(
+jupyter_visuals.orientation_3D_hist(
     azimuth[0].flatten(),
     theta[0].flatten(),
     ret_mask.flatten(),