stemdiff 5.2.6

Convert 4D-STEM data to 2D-powder diffraction pattern.

STEMDIFF :: 4D-STEM dataset to 2D-diffractogram

• The STEMDIFF package converts...
  ... a 4D-STEM dataset from a SEM microscope (huge and complex)
  ... to a 2D-powder 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:
  1. Nanomaterials 11 (2021) 962. https://doi.org/10.3390/nano11040962
  2. Materials 14 (2021) 7550. https://doi.org/10.3390/ma14247550
• If you use STEMDIFF package, please cite the 2nd publication (or both :-).

Principle

Installation

• Requirement: Python with sci-modules: numpy, matplotlib, scipy, pandas
• pip install scikit-image = 3rd party package for advanced image processing
• pip install tqdm = to show progress meter during long summations
• pip install idiff = to improve diffractograms (remove noise, background ...)
• pip install stemdiff = STEMDIFF package itself (uses all packages above)

Quick start

• See how it works:
  • Look at worked example in Jupyter.
• Try it yourself:
  • Download complete examples with data.
  • After downloading, unzip and follow the instructions in readme file.

Documentation, help and examples

• PyPI repository.
• GitHub repository.
• GitHub Pages with documentation.

Versions of STEMDIFF

• Version 1.0 = Matlab: just a simple summation of 4D-dataset
• Version 2.0 = like v.1.0 + 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 primary beam spread effect ⇒ better resolution
• Version 4.2 = like v.4.0 + a few important improvements, such as:
  • sum just the central region with the strongest diffractions ⇒ higher speed
  • 3 centering types: (0) geometry, (1) center of 1st, (2) individual centers
  • better definition of summation and centering parameters
  • better documentation strings + demo data + improved master script
• Version 5.0 = complete rewrite of v.4.2
  • all key features of v.4.2 (summation, filtering, deconvolution)
  • conversion 2D-diffractogram → 1D-profile moved to package EDIFF
  • several generalizations and improvements, namely:
    • possibility to define and use more detectors/datafile formats
    • better filtering (including estimated number of diffractions)
    • more types of deconvolution (experimental; to be finished in v.6.0)
• Version 5.1 = (beta) support for parallel processing
• Version 5.2 = (beta) improvement of diff.patterns in sister package idiff