stemdiff 4.2.4

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

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:
  • Publication #1: Nanomaterials 11 (2021) 962. https://doi.org/10.3390/nano11040962
  • Publication #2: Nanomaterials, submitted.
  • If you use STEMDIFF in your research, please cite publication #2
    (publication #1 describes older version of STEMDIFF package).
• Gentle introduction to 4D-STEM/PNBD and STEMDIFF
  • Now: See the above-mentioned publications (namely the not-yet-existing #2 :-).
  • Future: Better documentation = next version of the package (coming soon).

Typical usage of STEMDIFF

1. STEMDIFF employs Spyder IDE as UI (user interface).
   • This may be slightly non-standard, but it is very efficient.
   • Spyder is stable, user-friendly and easy-to-install (pip install spyder).
   • Spyder can show 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

• 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
  • better documentation strings + demo data + improved master script