• This repository contains the stable version of Adorym. For the most updated version, see https://github.com/mdw771/adorym_dev.
• For a more comprehensive (and more detailed) documentation, please visit the official documentation.

1. Installation
2. Quick start guide
3. Running a demo script
4. Running your own jobs
5. Data format
6. Customization
7. Publications

Get this repository to your hard drive using

git clone https://github.com/mdw771/adorym

and then use PIP to build and install:

pip install ./adorym

If you will modify internal functions of Adorym, e.g., add new forward models or refinable parameters, it is suggested to use the -e flag to enable editable mode so that you don't need to rebuild Adorym each time you make changes to the source code:

pip install -e ./adorym

After installation, type python to open a Python console, and check the installation status using import adorym. If an ImportError occurs, you may need to manually install the missing dependencies. Most dependencies are available on PIP and can be acquired with

pip install <package_name>

or through Conda if you use the Anaconda or Miniconda distribution of Python:

conda install <package_name>

In order to run Adorym using PyTorch backend with GPU support, please make sure the right version of PyTorch that matches your CUDA version is installed. The latter can be checked through nvidia-smi.

Adorym does 2D/3D ptychography, CDI, holography, and tomography all using the reconstruct_ptychography function in ptychography.py. You can make use of the template scripts in demos or tests to start your reconstruction job.

Adorym comes with a few datasets and scripts for demonstration and testing, but the raw data files of some of them are stored elsewhere due to the size limit on GitHub. If the folder in demos or tests corresponding to a certain demo dataset contains only a text file named raw_data_url.txt, please download the dataset at the URL indicated in the file.

On your workstation, open a terminal in the demos folder in Adorym's root directory, and run the desired script -- say, multislice_ptycho_256_theta.py, which will start a multislice ptychotomography reconstruction job that solves for the 256x256x256 "cone" object demonstrated in the paper (see Publications), with

python multislice_ptycho_256_theta.py

To run the script with multiple processes, use

mpirun -n <num_procs> python multislice_ptycho_256_theta.py

You can use the scripts in demos and tests as templates to create the scripts for your own jobs. While the major API is the function reconstruct_ptychography itself, you may also explicitly declare optimizers to be used for the object, the probe, and any other refinable parameters. Below is an example script used for 2D fly-scan ptychography reconstruction with probe position refinement:

import adorym
from adorym.ptychography import reconstruct_ptychography

output_folder = "recon"
distribution_mode = None
optimizer_obj = adorym.AdamOptimizer("obj", output_folder=output_folder,
                                     distribution_mode=distribution_mode,
                                     options_dict={"step_size": 1e-3})
optimizer_probe = adorym.AdamOptimizer("probe", output_folder=output_folder,
                                       distribution_mode=distribution_mode,
                                       options_dict={"step_size": 1e-3, "eps": 1e-7})
optimizer_all_probe_pos = adorym.AdamOptimizer("probe_pos_correction",
                                               output_folder=output_folder,
                                               distribution_mode=distribution_mode,
                                               options_dict={"step_size": 1e-2})

params_ptych = {"fname": "data.h5",
                "theta_st": 0,
                "theta_end": 0,
                "n_epochs": 1000,
                "obj_size": (618, 606, 1),
                "two_d_mode": True,
                "energy_ev": 8801.121930115722,
                "psize_cm": 1.32789376566526e-06,
                "minibatch_size": 35,
                "output_folder": output_folder,
                "cpu_only": False,
                "save_path": ".",
                "initial_guess": None,
                "random_guess_means_sigmas": (1., 0., 0.001, 0.002),
                "probe_type": "aperture_defocus",
                "forward_model": adorym.PtychographyModel,
                "n_probe_modes": 5,
                "aperture_radius": 10,
                "beamstop_radius": 5,
                "probe_defocus_cm": 0.0069,
                "rescale_probe_intensity": True,
                "free_prop_cm": "inf",
                "backend": "pytorch",
                "raw_data_type": "intensity",
                "optimizer": optimizer_obj,
                "optimize_probe": True,
                "optimizer_probe": optimizer_probe,
                "optimize_all_probe_pos": True,
                "optimizer_all_probe_pos": optimizer_all_probe_pos,
                "save_history": True,
                "unknown_type": "real_imag",
                "loss_function_type": "lsq",
                }

reconstruct_ptychography(**params_ptych)

To learn the settings of the reconstruct_ptychography function, please visit the documentation.

Adorym reads raw data contained an HDF5 file. The diffraction images should be stored in the exchange/data dataset as a 4D array, with a shape of [n_rotation_angles, n_diffraction_spots, image_size_y, image_size_x]. In a large part, Adorym is blind to the type of experiment, which means there no need to explicitly tell it the imaging technique used to generate the dataset. For imaging data collected from only one angle, n_rotation_angles = 1. For full-field imaging without scanning, n_diffraction_spots = 1. For 2D imaging, set the last dimension of the object size to 1 (this will be introduced further below).

Experimental metadata including beam energy, probe position, and pixel size, may also be stored in the HDF5, but they can also be provided individually as arguments to the function reconstruct_ptychography. When these arguments are provided, Adorym uses the arguments rather than reads the metadata from the HDF5.

The following is the full structure of the HDf5:

data.h5
  |___ exchange
  |       |___ data: float, 4D array
  |                  [n_rotation_angles, n_diffraction_spots, image_size_y, image_size_x]
  |
  |___ metadata
          |___ energy_ev: scalar, float. Beam energy in eV
          |___ probe_pos_px: float, [n_diffraction_spots, 2].
          |                  Probe positions (y, x) in pixel.
          |___ psize_cm: scalar, float. Sample-plane pixel size in cm.
          |___ free_prop_cm: (optional) scalar or array
          |                  Distance between sample exiting plane and detector.
          |                  For far-field propagation, do not include this item.
          |___ slice_pos_cm: (optional) float, 1D array
                             Position of each slice in sparse multislice ptychography. Starts from 0.

You can create additional forward models beyond the existing ones. To begin with, in adorym/forward_model.py, create a class inheriting ForwardModel (i.e., class MyNovelModel(ForwardModel)). Each forward model class should contain 4 essential methods: predict, get_data, loss, and get_loss_function. predict maps input variables to predicted quantities (usually the real-numbered magnitude of the detected wavefield). get_data reads from the HDF5 file the raw data corresponding to the minibatch currently being processed. loss is the last-layer loss node that computes the (regularized) loss values from the predicted data and the experimental measurement for the current minibatch. get_loss_function concatenates the above methods and return the end-to-end loss function. If your predict returns the real-numbered magnitude of the detected wavefield, you can use loss inherented from the parent class, although you still need to make a copy of get_loss_function and explicitly change its arguments to match those of predict (do not use implicit argument tuples or dictionaries like *args and **kwargs, as that won't work with Autograd!). If your predict returns something else, you may also need to override loss. Also make sure your new forward model class contains a self.argument_ls attribute, which should be a list of argument strings that exactly matches the signature of predict.

To use your forward model, pass your forward model class to the forward_model argument of reconstruct_ptychography. For example, in the script that you execute with Python, do the following:

import adorym
from adorym.ptychography import reconstruct_ptychography

params = {'fname': 'data.h5',
          ...
          'forward_model': adorym.MyNovelModel,
          ...}

Whenever possible, users who want to create new forward models with new refinable parameters are always recommended to make use of parameter variables existing in the program, because they all have optimizers already linked to them. These include the following:

Adding new refinable parameters (at the current stage) involves some hard coding. To do that, take the following steps:

1. in ptychography.py, find the code block labeled by "Create variables and optimizers for other parameters (probe, probe defocus, probe positions, etc.)." In this block, declare the variable use adorym.wrapper.create_variable, and add it to the dictionary optimizable_params. The name of the variable must match the name of the argument defined in your ForwardModel class.
2. In the argument list of ptychography.reconstruct_ptychography, add an optimization switch for the new variable. Optionally, also add an variable to hold pre-declared optimizer for this variable, and set the default to None.
3. In function create_and_initialize_parameter_optimizers within adorym/optimizers.py, define how the optimizer of the parameter variable should be defined. You can use the existing optimizer declaration codes for other parameters as a template.
4. If the parameter requires a special rule when it is defined, updated, or outputted, you will also need to explicitly modify create_and_initialize_parameter_optimizers, update_parameters, create_parameter_output_folders, and output_intermediate_parameters.

• M. Du, S. Kandel, J. Deng, X. Huang, A. Demortiere, T. T. Nguyen, R. Tucoulou, V. D. Andrade, Q. Jin, C. Jacobsen, Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation. Arxiv, arXiv:2012.12686 (2020).

The early version of Adorym, which was used to demonstrate 3D reconstruction of continuous object beyond the depth of focus, is published as

• M. Du, Y. S. G. Nashed, S. Kandel, D. Gürsoy, C. Jacobsen, Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit. Sci Adv. 6, eaay3700 (2020).