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Convert 4D-STEM data to a single powder diffraction pattern.

Project description

STEMDIFF :: simple processing of 4D-STEM data

  • The STEMDIFF package converts...
    ... a 4D-STEM dataset from a SEM microscope (huge and complex)
    ... to a powder-like 1D diffraction pattern (simple and easy to work with).
  • The STEMDIFF package is a key part of our 4D-STEM/PNBD method,
    which was described (together with the package) in open-access publications:
  • Gentle introduction to 4D-STEM/PNBD and STEMDIFF
    • Now: See the above-mentioned publication (namely the not-yet-existing #2).
    • Future: Better documentation = next version of the package (coming soon).

Typical usage of STEMDIFF package

  1. STEMDIFF empoyes Spyder IDE as UI (user interface).
    • This may be slightly non-standard, but it is very efficient.
    • Spyder is easy-to-use and easy-to-install (pip install spyder).
    • Spyder shows program run, text and graphical outputs simultaneously.
  2. In a typical STEMDIFF session, you do just three things:
    • In Spyder, open STEMDIFF master script.
    • In the opened master script, modify a few parameters and run it.
    • See program outputs in Spyder and final outputs in active directory
  3. Obtaining master script & simple testing of STEMDIFF package:
    • open the directory where stemdiff is installed
    • open subdirectory demo + unzip the files into testing directory
    • follow the instructions in the unzipped file 00_readme.txt

Brief history of STEMDIFF package

  • Version 1.0 = Matlab: just simple summation of 4D-dataset
  • Version 2.0 = like v1.0 + post-processing in Jupyter
  • Version 3.0 = Python scripts: summation + S-filtering
  • Version 4.0 = Python package: summation + S-filtering + deconvolution
    • summation = summation of all 2D-diffractograms
    • S-filtering = sum only diffractograms with strong diffractions = high S
    • deconvolution = reduce the effect of primary beam spread → better resolution
  • Version 4.2 = like v4.0 + a few important improvements, such as:
    • sum just the central region with the strongest diffractions - higher speed
    • 3 centering types: (0) geometry, (1) weight of 1st, (2) individual weights
    • better definition of summation and centering parameters
    • improved master script/teplate + better documentation strings

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