XLuminA

✨ XLuminA ✨

XLuminA, a highly-efficient, auto-differentiating discovery framework for super-resolution microscopy

📄 Read our paper here:
Automated discovery of experimental designs in super-resolution microscopy with XLuminA
Carla Rodríguez, Sören Arlt, Leonhard Möckl and Mario Krenn

📰 Read the press release:
10,000 times faster than traditional methods: new computational framework automatically discovers experimental designs in microscopy

📚 Related works featuring XLuminA:

Machine Learning and the Physical Sciences Workshop - Poster

AI4Science Workshop - Oral contribution

AI4Science Workshop - Oral contribution

💻 Installation:

Using PyPI:

Create a new conda environment and install xluminafrom PyPI. We recommend using python=3.11:

conda create -n xlumina_env python=3.11

conda activate xlumina_env

pip install xlumina

It should be installed in about 10 seconds. The package automatically installs:

1. JAX (CPU only) and jaxlib (the version of JAX used in this project is v0.4.33),

2. Optax (the version of Optax used in this project is v0.2.3),

3. SciPy (the version of SciPy used in this project is v1.14.1).

Clone repository:

git clone https://github.com/artificial-scientist-lab/XLuminA.git

GPU compatibility:

To install JAX with NVIDIA GPU support (Note: wheels only available on linux), use CUDA 12 installation:

pip install --upgrade "jax[cuda12]"

XLuminA has been tested on the following operating systems:

Linux Enterprise Server 15 SP4 15.4,

and it has been successfully installed in Windows 10 (64-bit) and MacOS Monterey 12.6.2

👾 Features:

XLuminA allows for the simulation, in a (very) fast and efficient way, of classical light propagation through optics hardware configurations,and enables the optimization and automated discovery of new setup designs.

The simulator contains many features:

✦ Light sources (of any wavelength and power) using both scalar or vectorial optical fields.

✦ Phase masks (e.g., spatial light modulators (SLMs), polarizers and general variable retarders (LCDs)).

✦ Amplitude masks (e.g., spatial light modulators (SLMs) and pre-defined circles, triangles and squares).

✦ Beam splitters, fluorescence model for STED, and more!

✦ The light propagation methods available in XLuminA are:

• Fast-Fourier-transform (FFT) based numerical integration of the Rayleigh-Sommerfeld diffraction integral.

• Chirped z-transform. This algorithm is an accelerated version of the Rayleigh-Sommerfeld method, which allows for arbitrary selection and sampling of the region of interest.

• Propagation through high NA objective lenses is available to replicate strong focusing conditions using polarized light.

📝 Examples of usage:

Examples of some experiments that can be reproduced with XLuminA are:

• Optical telescope (or 4f-correlator),
• Polarization-based beam shaping as used in STED (stimulated emission depletion) microscopy,
• The sharp focus of a radially polarized light beam.

The code for each of these optical setups is provided in the Jupyter notebook of examples.ipynb.

➤ A step-by-step guide on how to add noise to the optical elements can be found in noisy_4f_system.ipynb.

🚀 Testing XLuminA’s efficiency:

We evaluated our framework by conducting several tests - see Figure 1. The experiments were run on an Intel CPU Xeon Gold 6130 and Nvidia GPU Quadro RTX 6000.

(1) Average execution time (in seconds) over 100 runs, within a computational window size of $2048\times 2048$, for scalar and vectorial field propagation using Rayleigh-Sommerfeld (RS, VRS) and Chirped z-transform (CZT, VCZT) in Diffractio and XLuminA. Times for XLuminA reflect the run with pre-compiled jitted functions. The Python files corresponding to light propagation algorithms testing are scalar_diffractio.py and vectorial_diffractio.py for Diffractio, and scalar_xlumina.py and vectorial_xlumina.py for XLuminA.

(2) We compare the gradient evaluation time of numerical methods (using Diffractio’s optical simulator and SciPy’s BFGS optimizer) vs autodiff (analytical) differentiation (using XLuminA’s optical simulator with JAX’s ADAM optimizer) across various resolutions:

(3) We compare the convergence time of numerical methods (using Diffractio’s optical simulator and SciPy’s BFGS optimizer) vs autodiff (analytical) differentiation (using XLuminA’s optical simulator with JAX’s ADAM optimizer) across various resolutions:

➤ The Jupyter notebook used for running these simulations is provided as test_diffractio_vs_xlumina.ipynb.

➤ The Python files corresponding to numerical/autodiff evaluations are numerical_methods_evaluation_diffractio.py, and autodiff_evaluation_xlumina.py

If you want to run the comparison test of the propagation functions, you need to install Diffractio - The version of Diffractio used in this project is v0.1.1.

🤖🔎 Discovery of new optical setups:

With XLuminA we were able to rediscover three foundational optics experiments. In particular, we discover new, superior topologies together with their parameter settings using purely continuous optimization.

➤ Optical telescope (or 4f-correlator),

➤ Polarization-based beam shaping as used in STED (stimulated emission depletion) microscopy,

➤ The sharp focus of a radially polarized light beam.

The Python files used for the discovery of these optical setups, as detailed in our paper, are organized in pairs of optical_table and optimizer as follows:

Experiment name	🔬 Optical table	🤖 Optimizer	📄 File for data
Optical telescope	four_f_optical_table.py	four_f_optimizer.py	Generate_synthetic_data.py
Pure topological discovery: large-scale sharp focus (Dorn, Quabis and Leuchs, 2004)	hybrid_with_fixed_PM.py	hybrid_optimizer.py	N/A
Pure topological discovery: STED microscopy	hybrid_with_fixed_PM.py	hybrid_optimizer.py	N/A
6x6 grid: pure topological discovery	six_times_six_ansatz_with_fixed_PM.py	hybrid_optimizer.py	N/A
Large-scale polarization-based STED	hybrid_sted_optical_table.py	hybrid_optimizer.py	N/A
Large-scale sharp focus (Dorn, Quabis and Leuchs, 2004)	hybrid_sharp_optical_table.py	hybrid_optimizer.py	N/A

🦾🤖 Robustness and parallelized optimization of multiple optical tables with our noise-aware scheme:

✦ Importantly, to ensure simulations which approximate real-world experimental conditions we have included imperfections, misalignment, and noise sources in all optical components (during post-processing and/or during optimization). All the results presented in the paper are computed considering a wide variety of experimental errors.

➤ A step-by-step guide on how to setup the optimization using this scheme can be found in noisy_optimization.ipynb.

➤ A step-by-step guide on how to add noise to the optical elements can be found in noisy_4f_system.ipynb.

✦ The optimization procedure is as follows: for each optimization step, we execute $N$ parallel optical tables using vmap. Then, we sample random noise and apply it to all available physical variables across each of the $N$ optical tables. The random noise is uniformly distributed and includes:

• Phase values for spatial light modulators (SLMs) and wave plates (WP) in the range of $\pm$ (0.01 to 0.1) radians, covering all qualities available in current experimental devices.
• Misalignment ranging from $\pm$ (0.01 to 0.1) millimeters, covering both expert-level precision ($\pm$ 0.01 mm) and beginner-level accuracy ($\pm$ 0.1 mm).
• 1% imperfection on the transmissivity/reflectivity of beam splitters (BS), which is a realistic approach given the high quality of the currently available hardware.

We then simulate the optical setup for each of the $N$ tables simultaneously, incorporating the sampled noise. The loss function is computed independently for each of the setups. Afterwards, we calculate the mean loss value across all optical tables, which provides an average performance metric that accounts for the introduced experimental variability (noise). The gradients are computed based on this mean loss value and so the update of the system parameters’.

Importantly, before applying the updated parameters and proceeding to the next iteration, we resample new random noise for each optical table. This ensures that each optimization step encounters different noise values, further enhancing the robustness of the solution. This procedure is repeated iteratively until convergence.

👀 Overview:

In this section we list the available functions in different files and a brief description:

1. In wave_optics.py: module for scalar optical fields.

   Class	Functions	Description
   ScalarLight		Class for scalar optical fields defined in the XY plane: complex amplitude $U® = A®*e^{-ikz}$.
   	.draw	Plots intensity and phase.
   	.apply_circular_mask	Apply a circular mask of variable radius.
   	.apply_triangular_mask	Apply a triangular mask of variable size.
   	.apply_rectangular_mask	Apply a rectangular mask of variable size.
   	.apply_annular_aperture	Apply annular aperture of variable size.
   	.RS_propagation	Rayleigh-Sommerfeld diffraction integral in z-direction (z>0 and z<0).
   	.get_RS_minimum_z	Given a quality factor, determines the minimum (trustworthy) distance for RS_propagation.
   	.CZT	Chirped z-transform - efficient diffraction using the Bluestein method.
   LightSource		Class for scalar optical fields defined in the XY plane - defines light source beams.
   	.gaussian_beam	Gaussian beam.
   	.plane_wave	Plane wave.

2. In vectorized_optics.py: module for vectorized optical fields.

   Class	Functions	Description
   VectorizedLight		Class for vectorized optical fields defined in the XY plane: $\vec{E} = (E_x, E_y, E_z)$
   	.draw	Plots intensity, phase and amplitude.
   	.draw_intensity_profile	Plots intensity profile.
   	.VRS_propagation	Vectorial Rayleigh-Sommerfeld diffraction integral in z-direction (z>0 and z<0).
   	.get_VRS_minimum_z	Given a quality factor, determines the minimum (trustworthy) distance for VRS_propagation.
   	.VCZT	Vectorized Chirped z-transform - efficient diffraction using the Bluestein method.
   PolarizedLightSource		Class for polarized optical fields defined in the XY plane - defines light source beams.
   	.gaussian_beam	Gaussian beam.
   	.plane_wave	Plane wave.

3. In optical_elements.py: shelf with all the optical elements available.

   Function	Description
   Scalar light devices	-
   phase_scalar_SLM	Phase mask for the spatial light modulator available for scalar fields.
   SLM	Spatial light modulator: applies a phase mask to incident scalar field.
   Jones matrices	-
   jones_LP	Jones matrix of a linear polarizer
   jones_general_retarder	Jones matrix of a general retarder.
   jones_sSLM	Jones matrix of the superSLM.
   jones_sSLM_with_amplitude	Jones matrix of the superSLM that modulates phase & amplitude.
   jones_LCD	Jones matrix of liquid crystal display (LCD).
   Polarization-based devices	-
   sSLM	super-Spatial Light Modulator: adds phase mask (pixel-wise) to $E_x$ and $E_y$ independently.
   sSLM_with_amplitude	super-Spatial Light Modulator: adds phase mask and amplitude mask (pixel-wise) to $E_x$ and $E_y$ independently.
   LCD	Liquid crystal device: builds any linear wave-plate.
   linear_polarizer	Linear polarizer.
   BS_symmetric	Symmetric beam splitter.
   BS_symmetric_SI	Symmetric beam splitter with single input.
   BS	Single-side coated dielectric beam splitter.
   high_NA_objective_lens	High NA objective lens (only for VectorizedLight).
   VCZT_objective_lens	Propagation through high NA objective lens (only for VectorizedLight).
   General elements	-
   lens	Transparent lens of variable size and focal length.
   cylindrical_lens	Transparent plano-convex cylindrical lens of variable focal length.
   axicon_lens	Axicon lens function that produces a Bessel beam.
   circular_mask	Circular mask of variable size.
   triangular_mask	Triangular mask of variable size and orientation.
   rectangular_mask	Rectangular mask of variable size and orientation.
   annular_aperture	Annular aperture of variable size.
   forked_grating	Forked grating of variable size, orientation, and topological charge.
   👷‍♀️ Pre-built optical setups	-
   bb_amplitude_and_phase_mod	Basic building unit. Consists of a sSLM (amp & phase modulation), and LCD linked via VRS_propagation.
   building_block	Basic building unit. Consists of a sSLM, and LCD linked via VRS_propagation.
   fluorescence	Fluorescence model.
   vectorized_fluorophores	Vectorized version of fluorescence: Allows to compute effective intensity across an array of detectors.
   robust_discovery	3x3 setup for hybrid (topology + optical settings) discovery with single wavelength. Longitudinal intensity (Iz) is measured across all detectors. Includes noise for robustness.
   hybrid_setup_fixed_slms_fluorophores	3x3 optical table with SLMs randomly positioned displaying fixed phase masks; to be used for pure topological discovery; contains the fluorescence model in all detectors. (Fig. 4a of our paper)
   hybrid_setup_fixed_slms	3x3 optical table with SLMs randomly positioned displaying fixed phase masks; to be used for pure topological discovery. (Fig. 4b of our paper)
   hybrid_setup_fluorophores	3x3 optical table to be used for hybrid (topological + optical parameter) discovery; contains the fluorescence model in all detectors . (Fig. 5a and Fig. 6 of our paper)
   hybrid_setup_sharp_focus	3x3 optical table to be used for hybrid (topological + optical parameter) discovery. (Fig. 5b of our paper)
   six_times_six_ansatz	6x6 optical table to be used for pure topological discovery. (Extended Data Fig. 6 of our paper)
   🫨 Add noise to the optical elements	-
   shake_setup	Literally shakes the setup: creates noise and misalignment for the optical elements. Accepts noise settings (dictionary) as argument. Can’t be used with jit across parallel optical tables.
   shake_setup_jit	Same as shake_setup. Doesn’t accept noise settings as argument. Intended to be pasted in the optimizer file to enable jit compilation across parallel optical tables.

4. In toolbox.py: file with useful functions.

   Function	Description
   Basic operations	-
   space	Builds the space where light is placed.
   wrap_phase	Wraps any phase mask into $[-\pi, \pi]$ range.
   is_conserving_energy	Computes the total intensity from the light source and compares is with the propagated light - Ref.
   softmin	Differentiable version for min() function.
   delta_kronecker	Kronecker delta.
   build_LCD_cell	Builds the cell for LCD.
   draw_sSLM	Plots the two phase masks of sSLM.
   draw_sSLM_amplitude	Plots the two amplitude masks of sSLM.
   moving_avg	Compute the moving average of a dataset.
   image_to_binary_mask	Converts image (png, jpeg) to binary mask.
   rotate_mask	Rotates the (X, Y) frame w.r.t. given point.
   gaussian	Defines a 1D Gaussian distribution.
   gaussian_2d	Defines a 2D Gaussian distribution.
   lorentzian	Defines a 1D Lorentzian distribution.
   lorentzian_2d	Defines a 2D Lorentzian distribution.
   fwhm_1d_fit	Computes FWHM in 1D using fit for gaussian or lorentzian.
   fwhm_2d_fit	Computes FWHM in 2D using fit for gaussian_2d or lorentzian_2d.
   profile	Determines the profile of a given input without using interpolation.
   spot_size	Computes the spot size as $\pi (\text{FWHM}_x \cdot \text{FWHM}_y) /\lambda^2$.
   compute_fwhm	Computes FWHM in 1D or 2D using fit: gaussian, gaussian_2d, lorentzian, lorentzian_2.
   📑 Data loader	-
   MultiHDF5DataLoader	Data loader class for 4f system training.

5. In loss_functions.py: file with loss functions.

   Function	Description
   small_area_hybrid	Small area loss function valid for hybrid (topology + optical parameters) optimization
   vMSE_Intensity	Parallel computation of Mean Squared Error (Intensity) for a given electric field component $E_x$, $E_y$ or $E_z$.
   MSE_Intensity	Mean Squared Error (Intensity) for a given electric field component $E_x$, $E_y$ or $E_z$.
   vMSE_Phase	Parallel computation of Mean Squared Error (Phase) for a given electric field component $E_x$, $E_y$ or $E_z$.
   MSE_Phase	Mean Squared Error (Phase) for a given electric field component $E_x$, $E_y$ or $E_z$.
   vMSE_Amplitude	Parallel computation of Mean Squared Error (Amplitude) for a given electric field component $E_x$, $E_y$ or $E_z$.
   MSE_Amplitude	Mean Squared Error (Amplitude) for a given electric field component $E_x$, $E_y$ or $E_z$.
   mean_batch_MSE_Intensity	Batch-based MSE_Intensity.

⚠️ Considerations when using XLuminA:

1. By default, JAX uses float32 precision. If necessary, enable jax.config.update("jax_enable_x64", True) at the beginning of the file.

2. Basic units are microns (um) and radians. Other units (centimeters, millimeters, nanometers, and degrees) are available at __init.py__.

3. IMPORTANT - RAYLEIGH-SOMMERFELD PROPAGATION: FFT-based diffraction calculation algorithms can be innacurate depending on the computational window size (sampling).
   Before propagating light, one should check which is the minimum distance available for the simulation to be accurate.
   You can use the following functions:

   get_RS_minimum_z, for ScalarLight class, and get_VRS_minimum_z, for VectorizedLight class.

💻 Development:

Some functionalities of XLuminA’s optics simulator (e.g., optical propagation algorithms, lens or amplitude masks) are inspired in an open-source NumPy-based Python module for diffraction and interferometry simulation, Diffractio. We have rewritten and modified these approaches to combine them with JAX just-in-time (jit) functionality. We labeled these functions as such in the docstrings. On top of that, we developed completely new functions (e.g., sSLMs, beam splitters, LCDs or propagation through high NA objective lens with CZT methods, to name a few) which significantly expand the software capabilities.

📝 How to cite XLuminA:

If you use this software, please cite as:

Rodríguez, C., Arlt, S., Möckl, L. and Krenn, M. Automated discovery of experimental designs in super-resolution microscopy with XLuminA. Nat Commun 15, 10658 (2024). https://doi.org/10.1038/s41467-024-54696-y

BibTeX format:

@article{NatCommun.15.10658,
  title={Automated discovery of experimental designs in super-resolution microscopy with {XLuminA}},
  author={Rodríguez, Carla and Arlt, Sören and Möckl, Leonhard and Krenn, Mario},
  journal={Nature Communications},
  volume={15},
  pages={10658},
  year={2024},
  publisher={Nature Publishing Group},
  doi={10.1038/s41467-024-54696-y}
}