Adaptive Photothermal Phase Engineering Using a Thermo-Optic Metamaterial

vortex_simulations.pkl This file contains simulated iSCAT contrast data. The file is stored as a Python dictionary serialized with pickle. The top-level dictionary is indexed by phase-mask configuration, including no_mask and multiple rotated vortex masks, such as vortex_000, vortex_045, ..., vortex_360. For each mask, the following fields are stored: angle_deg: vortex mask rotation angle in degrees elapsed_s: computation time for the corresponding simulation contrast_glass: simulated iSCAT contrast for the glass background only contrast_mt: simulated iSCAT contrast for the microtubule only contrast_glass_mt: simulated iSCAT contrast for the microtubule on glass background Each contrast dataset is stored as a 3D NumPy array of shape (nx, ny, n_focus), where nx and ny are the lateral image dimensions and n_focus is the number of simulated focus positions. The data can be loaded in Python using: import pickle with open("vortex_contrast_optimized_results.pkl", "rb") as f: results = pickle.load(f) exp_data_MT.npz This file contains experimentally acquired iSCAT image data of microtubules for multiple focus positions and vortex mask rotations. The data is stored as a compressed NumPy archive (.npz). The main array: data: a 4D NumPy array of shape (n_focus, n_angles, nx, ny) where: n_focus is the number of focus positions (z-stack) n_angles is the number of phase-mask configurations nx, ny are the lateral image dimensions after cropping The second dimension (n_angles) corresponds to: index 0 → iSCAT reference (no mask) index 1 → vortex mask, 0° index 2 → vortex mask, 45° index 3 → vortex mask, 90° ... index 8 → vortex mask, 315° The corresponding angles are stored in: angles_deg: array of length n_angles, containing rotation angles in degrees(with -1 indicating the iSCAT reference image) Additional metadata: pixel_size_um: pixel size in micrometers The data can be loaded in Python using: import numpy as np d = np.load("exp_data_MT.npz") data = d["data"] angles = d["angles_deg"] pixel_size = d["pixel_size_um"] exp_data_NP.npz This file contains experimentally acquired iSCAT image stacks of a nanoparticle for multiple focus positions. The data is stored as a compressed NumPy archive (.npz). The file contains four image stacks: iscat_stack: iSCAT images recorded without a phase mask glass_vortex_stack: iSCAT images recorded with a glass vortex mask pt_slm_vortex_stack: iSCAT images recorded with a single PT-SLM vortex mask position reconstructed_stack: reconstructed iSCAT images obtained from the amplitude extracted using all PT-SLM vortex positions Each stack is stored as a 3D NumPy array of shape: (n_focus, nx, ny) where: n_focus is the number of retained focus positions nx, ny are the lateral image dimensions Each stack is centered around the focus plane used for visualization, and includes a window of ±half_window focus positions around that center. Additional metadata: pixel_size_um: pixel size in micrometers The data can be loaded in Python using: import numpy as np d = np.load("exp_data_NP.npz") iscat = d["iscat_stack"] glass = d["glass_vortex_stack"] ptslm = d["pt_slm_vortex_stack"] recon = d["reconstructed_stack"] pixel_size = d["pixel_size_um"] fit_vortex_reconstruction.py This script reconstructs amplitude and phase maps from rotational vortex iSCAT data using a pixel-wise sinusoidal fit of the normalized contrast as a function of vortex angle. For each pixel, the measured contrast is fitted as: C(theta_v, x, y) = C0(x, y) + A(x, y) cos(theta_v + phi(x, y)) The fitted amplitude A(x,y) and phase phi(x,y) are saved as reconstructed amplitude and phase maps. Example using exp_data_MT.npz: import numpy as np from fit_vortex_reconstruction import fit_cos_vortex, save_reconstruction_npz d = np.load("exp_data_MT.npz") data = d["data"] angles = d["angles_deg"] focus_index = 0 contrast_stack = data[focus_index, 1:, :, :] vortex_angles = angles[1:] result = fit_cos_vortex(contrast_stack, vortex_angles) save_reconstruction_npz("MT_reconstruction_focus0.npz", result) Here, index 0 in exp_data_MT.npz corresponds to the iSCAT reference without a vortex mask and is therefore excluded from the angular fit. The vortex-mask images at 0°, 45°, ..., 315° are used for the reconstruction.