Iterative Ptychography from Virtual Detectors

PyPty can deal not only with raw 4D-STEM like data, but also with lossy-compression. If you want to perform a reconstruction from data compressed by virtual detectors, you first have to ensure that the array of detectors has shape [N_detectors, k_Y, k_X] and your data has shape [Scan_Y, Scan_X, N_detectors]. Then you should attach these two arrays to experimental parameters.

experimental_params={
    "dataset": compressed_data,
    "masks": virtual_detector_array,
    
    
    ###  In this case you must specify one of the following two parameters
    'rez_pixel_size_A': None,
    'rez_pixel_size_mrad': 1,
    
   # ... Rest of your parameters.
}

Also, you may want to update your reconstruction settings. For Compressed data PyPty has following objective function types:

Objective_name	Description
lsq_compressed	Least-squared fit
gauss_compressed	Gaussian noise model
poisson_compressed	Poissonian noise model

Then you must specify algorithm as one of these three options in pypty_params:

pypty_params={
        "algorithm": "gauss_compressed", 
        #... Rest of your parameters
        }

Then you can launch ptychography with the same pypty.iterative.run function.