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pyTEMlib

Python framework for model based analysis of TEM/STEM data

What?

  • The pyTEMlib package:

    • is a part of the pycroscopy ecosystem based on python
    • enables quantitative analysis through model based approach
    • provides routines for the analysis of diffraction, image and spectroscopic datasets
    • handles one, two, three, and four dimensional datasets
    • works in jupyter notebooks and in python programs.
    • provides dialog windows for metadata and analysis input in jupyter notebooks and in python programs.

  • The pyTEMlib covers:

    • Diffraction: Single and poly crystalline diffraction data and analysis in parallel and convergent illumination
    • Imaging: Image analysis, atom detection and image stack registration.
    • EELS: It provides a framework for quantification of EELS spectra and spectrum images.

  • Just as scipy uses numpy underneath, scientific packages like pyTEMlib use

    • sidpy format for dataset representation and
    • pyNSID for all file-handling.

  • Dialogs are based on ipython widgets

  • The packages sidpy and pyNSID use popular packages such as numpy, h5py, dask, matplotlib, etc. for most of the storage, computation, and visualization.

Why?

pyTEMlib originates in the need for teaching and the development of new techniques for TEM/STEM data analysis. Please, see my lecture note(-books) for information on the background of analysis.

1. Growing data sizes

  • Cannot use desktop computers for analysis
  • Need: High performance computing, storage resources and compatible, scalable file structures

2. Increasing data complexity

  • Sophisticated imaging and spectroscopy modes resulting in 5,6,7... dimensional data
  • Need: Robust software and generalized data formatting

3. Multiple file formats

  • Different formats from each instrument. Proprietary in most cases
  • Incompatible for correlation
  • Need: Open, instrument-independent data format

4. Expensive analysis software

  • Software supplied with instruments often insufficient / incapable of custom analysis routines
  • Commercial software (Eg: Matlab, Origin..) are often prohibitively expensive.
  • Need: Free, powerful, open source, user-friendly software

5. Closed science

  • Analysis software and data not shared
  • No guarantees of reproducibility or traceability
  • Need: open source data structures, file formats, centralized code and data repositories

Who?

  • We envision pyTEMlib to be a convenient package that facilitates all scientists to analyse data and develop new methods of anlysis, without being burdened with basic code functionality.
  • This project is being led by staff members at Oak Ridge National Laboratory (ORNL), and professors at University of Tennessee, Knoxville
  • We invite anyone interested to join our team to build better, free software for the scientific community
  • Please visit our credits and acknowledgements_ page for more information.
  • If you are interested in integrating our in your existing package, please get in touch with us.

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