From e465119c0a3c7a194a8405d909560ed20c1b5c4f Mon Sep 17 00:00:00 2001 From: Talon Chandler Date: Thu, 19 Dec 2024 15:36:58 -0800 Subject: [PATCH] remove matplotlib plot example --- .../visuals/plot_vector_transfer_function.py | 239 ------------------ 1 file changed, 239 deletions(-) delete mode 100644 examples/visuals/plot_vector_transfer_function.py diff --git a/examples/visuals/plot_vector_transfer_function.py b/examples/visuals/plot_vector_transfer_function.py deleted file mode 100644 index 92d7418..0000000 --- a/examples/visuals/plot_vector_transfer_function.py +++ /dev/null @@ -1,239 +0,0 @@ -import torch -import os -import numpy as np -from waveorder import util, optics -from waveorder.visuals.matplotlib_visuals import plot_5d_ortho - -output_folder = "2024-10-15" -os.makedirs(output_folder, exist_ok=True) - -# Parameters -# all lengths must use consistent units e.g. um -zyx_shape = (101, 128, 128) # (101, 256, 256) -swing = 0.1 -scheme = "5-State" -yx_pixel_size = 6.5 / 63 -z_pixel_size = 0.15 -wavelength_illumination = 0.532 -z_padding = 0 -index_of_refraction_media = 1.3 -numerical_aperture_detection = 1.0 - -for i, numerical_aperture_illumination in enumerate([0.01, 0.75]): - file_suffix = str(i) - - input_jones = torch.tensor([-1j, 1.0 + 0j]) / torch.sqrt( - torch.tensor([2]) - ) # circular - - # input_jones = torch.tensor([0, 1.0 + 0j]) - - # Calculate frequencies - y_frequencies, x_frequencies = util.generate_frequencies( - zyx_shape[1:], yx_pixel_size - ) - radial_frequencies = torch.sqrt(x_frequencies**2 + y_frequencies**2) - - z_total = zyx_shape[0] + 2 * z_padding - z_position_list = torch.fft.ifftshift( - (torch.arange(z_total) - z_total // 2) * z_pixel_size - ) - z_frequencies = torch.fft.fftfreq(z_total, d=z_pixel_size) - - # 2D pupils - ill_pupil = optics.generate_pupil( - radial_frequencies, - numerical_aperture_illumination, - wavelength_illumination, - ) - det_pupil = optics.generate_pupil( - radial_frequencies, - numerical_aperture_detection, - wavelength_illumination, - ) - pupil = optics.generate_pupil( - radial_frequencies, - index_of_refraction_media, # largest possible NA - wavelength_illumination, - ) - - # Defocus pupils - defocus_pupil = optics.generate_propagation_kernel( - radial_frequencies, - pupil, - wavelength_illumination / index_of_refraction_media, - z_position_list, - ) - - # Calculate vector defocus pupils - S = optics.generate_vector_source_defocus_pupil( - x_frequencies, - y_frequencies, - z_position_list, - defocus_pupil, - input_jones, - ill_pupil, - wavelength_illumination / index_of_refraction_media, - ) - - # Simplified scalar pupil - P = optics.generate_propagation_kernel( - radial_frequencies, - det_pupil, - wavelength_illumination / index_of_refraction_media, - z_position_list, - ) - - - P_3D = torch.abs(torch.fft.ifft(P, dim=-3)).type(torch.complex64) - S_3D = torch.fft.ifft(S, dim=-3) - G_3D = optics.generate_greens_tensor_spectrum( - zyx_shape=(z_total, zyx_shape[1], zyx_shape[2]), - zyx_pixel_size=(z_pixel_size, yx_pixel_size, yx_pixel_size), - wavelength=wavelength_illumination / index_of_refraction_media - ) - - ## CANDIDATE FOR REMOVAL - # cleanup some ringing - 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) - 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) - - P_3D *= mask - S_3D *= mask - - # CANDIDATE FOR REMOVAL - - # Main transfer function calculation - PG_3D = torch.einsum("zyx,ipzyx->ipzyx", P_3D, G_3D) - PS_3D = torch.einsum("zyx,jzyx,kzyx->jkzyx", P_3D, S_3D, torch.conj(S_3D)) - - pg = torch.fft.fftn(PG_3D, dim=(-3, -2, -1)) - ps = torch.fft.fftn(PS_3D, dim=(-3, -2, -1)) - - H1 = torch.fft.ifftn( - torch.einsum("ipzyx,jkzyx->ijpkzyx", pg, torch.conj(ps)), - dim=(-3, -2, -1), - ) - - H2 = torch.fft.ifftn( - torch.einsum("ikzyx,jpzyx->ijpkzyx", ps, torch.conj(pg)), - dim=(-3, -2, -1), - ) - - # Not necessary yet - illum_intensity = torch.sum(S_3D * torch.conj(S_3D), dim=0) - direct_intensity = torch.real( - torch.sum(illum_intensity * P_3D) * torch.sum(P_3D) - ) - - H_re = H1[1:, 1:] + H2[1:, 1:] # drop data-side z components - # H_im = 1j * (H1[1:, 1:] - H2[1:,1:]) # ignore absorptive terms - - # H_re /= torch.amax(torch.abs(H_re)) - - s_labels = [0, 1, 2, 3] - f_labels = [0, 4, 8] - s = util.pauli()[s_labels] # select s0, s1, and s2 (drop s3) - Y = util.gellmann()[f_labels] - # select phase f00 and transverse linear isotropic terms 2-2, and f22 - sfZYX_transfer_function = torch.einsum( - "sik,ikpjzyx,lpj->slzyx", s, H_re, Y - ) - - sfzyx_point_response_function = torch.fft.fftn( - sfZYX_transfer_function, dim=(-3, -2, -1) - ) - - # # Use this to visualize Green's tensor - # from waveorder.visuals.napari_visuals import ( - # add_transfer_function_to_viewer, - # ) - # import napari - - # v = napari.Viewer() - # add_transfer_function_to_viewer( - # v, - # sfZYX_transfer_function, - # zyx_scale=(z_pixel_size, yx_pixel_size, yx_pixel_size), - # complex_rgb=True, - # ) - # import pdb - # pdb.set_trace() - - g_3d = torch.fft.ifftn(G_3D, dim=(-3, -2, -1)) - plot_5d_ortho( - g_3d, - filename=os.path.join(output_folder, f"greens_{file_suffix}.pdf"), - zyx_scale=(z_pixel_size, yx_pixel_size, yx_pixel_size), - z_slice=0, - row_labels=["Z", "Y", "X"], - column_labels=["Z", "Y", "X"], - rose_path=None, - inches_per_column=1, - saturate_clim_fraction=1.0, - trim_edges=50, - fourier_space=False, - ) - - plot_5d_ortho( - G_3D, - filename=os.path.join(output_folder, f"G_{file_suffix}.pdf"), - zyx_scale=(z_pixel_size, yx_pixel_size, yx_pixel_size), - z_slice=0, - row_labels=["Z", "Y", "X"], - column_labels=["Z", "Y", "X"], - rose_path=None, - inches_per_column=1, - saturate_clim_fraction=0.7, - trim_edges=0, - ) - - plot_5d_ortho( - S_3D[1:][None], - filename=os.path.join(output_folder, f"source_{file_suffix}.pdf"), - zyx_scale=(z_pixel_size, yx_pixel_size, yx_pixel_size), - z_slice=-35, - row_labels=[""], - column_labels=["Y", "X"], - rose_path=None, - inches_per_column=1, - saturate_clim_fraction=1.0, - trim_edges=0, - fourier_space=True, - ) - - plot_5d_ortho( - sfZYX_transfer_function, - filename=os.path.join(output_folder, f"tf_{file_suffix}.pdf"), - zyx_scale=(z_pixel_size, yx_pixel_size, yx_pixel_size), - z_slice=-5, - row_labels=s_labels, - column_labels=f_labels, - rose_path=None, - inches_per_column=1, - saturate_clim_fraction=0.2, - trim_edges=40, - ) - - plot_5d_ortho( - sfzyx_point_response_function, - filename=os.path.join(output_folder, f"psf_{file_suffix}.pdf"), - zyx_scale=(z_pixel_size, yx_pixel_size, yx_pixel_size), - z_slice=-10, - row_labels=s_labels, - column_labels=f_labels, - rose_path=None, - inches_per_column=1, - saturate_clim_fraction=0.2, - trim_edges=60, - )