LiberTEM - Open Pixelated STEM platform¶
LiberTEM is an open source platform for high-throughput distributed processing of large-scale binary data sets using a simplified MapReduce programming model. The current focus is pixelated scanning transmission electron microscopy (STEM) [MNR+16][Oph19] and scanning electron beam diffraction data.
It is designed for high throughput and scalability on PCs, single server nodes, clusters and cloud services. On clusters it can use fast distributed local storage on high-performance SSDs. That way it achieves very high aggregate IO performance on a compact and cost-efficient system built from stock components. All CPU cores and CUDA devices in a system can be used in parallel.
LiberTEM is supported on Linux, Mac OS X and Windows. Other platforms that allow installation of Python 3 and the required packages will likely work as well. The GUI is running in a web browser.
The short version:
$ virtualenv -p python3 ~/libertem-venv/ $ source ~/libertem-venv/bin/activate (libertem) $ pip install "libertem[torch]" # optional for GPU support (libertem) $ pip install cupy
Please see our documentation for details!
Deployment as a single-node system for a local user is thoroughly tested and can be considered stable. Deployment on a cluster is experimental and still requires some additional work, see Issue #105.
Virtual detectors (virtual bright field, virtual HAADF, center of mass [KMM+16], custom shapes via masks)
Please see the applications section of our documentation for details!
The Python API and user-defined functions (UDFs) can be used for more complex operations with arbitrary masks and other features like data export. There are example Jupyter notebooks available in the examples directory. If you are having trouble running the examples, please let us know, either by filing an issue or by joining our Gitter chat.
LiberTEM is suitable as a high-performance processing backend for other applications, including live data streams. Contact us if you are interested!
LiberTEM is evolving rapidly and prioritizes features following user demand and contributions. In the future we’d like to implement live acquisition, and more analysis methods for all applications of pixelated STEM and other large-scale detector data. If you like to influence the direction this project is taking, or if you’d like to contribute, please join our gitter chat and our general mailing list.
Raw binary files
Thermo Fisher EMPAD detector [TPC+16] files
Nanomegas .blo block files
Direct Electron DE5 files (HDF5-based) and Norpix SEQ files for DE-Series detectors
Gatan K2 IS raw format
Stacks of Gatan DM3 and DM4 files (via openNCEM)
FRMS6 from PNDetector pnCCD cameras [SRB+15] (currently alpha, gain correction still needs UI changes)
FEI SER files (via openNCEM)
MRC (via openNCEM)
HDF5-based formats such as Hyperspy files, NeXus and EMD
Please contact us if you are interested in support for an additional format!
LiberTEM is licensed under GPLv3. The I/O parts are also available under the MIT license, please see LICENSE files in the subdirectories for details.
- GUI usage
- Python API
- Loading data
- User-defined functions
- Package overview
- Sample Datasets
- Tips and tricks
- Why Python?
- GSoC 2020 ideas
- Authorship policy
- How does I/O work in LiberTEM?
J. M. Gibson and M. M. J. Treacy. Diminished medium-range order observed in annealed amorphous germanium. Physical Review Letters, 78(6):1074–1077, feb 1997. doi:10.1103/physrevlett.78.1074.
Giulio Guzzinati, Wannes Ghielens, Christoph Mahr, Armand Béché, Andreas Rosenauer, Toon Calders, and Jo Verbeeck. Electron bessel beam diffraction for precise and accurate nanoscale strain mapping. Applied Physics Letters, 2019. arXiv:1902.06979v3, doi:10.1063/1.5096245.
Giulio Guzzinati, Wannes Ghielens, Christoph Mahr, Armand Béché, Andreas Rosenauer, Toon Calders, and Jo Verbeeck. Electron Bessel beam diffraction patterns, line scan of Si/SiGe multilayer. February 2019. URL: https://doi.org/10.5281/zenodo.2566137, doi:10.5281/zenodo.2566137.
Matus Krajnak, Damien McGrouther, Dzmitry Maneuski, Val O\textquotesingle Shea, and Stephen McVitie. Pixelated detectors and improved efficiency for magnetic imaging in STEM differential phase contrast. Ultramicroscopy, 165:42–50, jun 2016. doi:10.1016/j.ultramic.2016.03.006.
H. Lichte and M. Lehmann. Electron holography–basics and applications. Rep. Prog. Phys., 71:016102, 2008. doi:10.1088/0034-4885/71/1/016102.
Ian MacLaren, Magnus Nord, Andrew Ross, Matus Krajnak, Martin Hart, Alastair Doye, Damien McGrouther, Rantej Bali, Archan Banerjee, and Robert Hadfield. Pixelated STEM detectors: opportunities and challenges, pages 663–664. American Cancer Society, 2016. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/9783527808465.EMC2016.6284, doi:10.1002/9783527808465.EMC2016.6284.
Colin Ophus. Four-dimensional scanning transmission electron microscopy (4d-STEM): from scanning nanodiffraction to ptychography and beyond. Microscopy and Microanalysis, 25(3):563–582, may 2019. doi:10.1017/s1431927619000497.
Colin Ophus and Benjamin Savitzky. Simulated calibration dataset for 4D scanning transmission electron microscopy. December 2019. URL: https://doi.org/10.5281/zenodo.3592520, doi:10.5281/zenodo.3592520.
Ouliana Panova, Colin Ophus, Christopher J. Takacs, Karen C. Bustillo, Luke Balhorn, Alberto Salleo, Nitash Balsara, and Andrew M. Minor. Diffraction imaging of nanocrystalline structures in organic semiconductor molecular thin films. Nature Materials, jun 2019. doi:10.1038/s41563-019-0387-3.
Erich Schubert and Michael Gertz. Numerically stable parallel computation of (co-)variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management - SSDBM 18. ACM Press, 2018. doi:10.1145/3221269.3223036.
M. Simson, H. Ryll, H. Banba, R. Hartmann, M. Huth, S. Ihle, L. Jones, Y. Kondo, K. Muller, P.D. Nellist, R. Sagawa, J. Schmidt, H. Soltau, L. Striider, and H. Yang. 4d-STEM imaging with the pnCCD (s)TEM-camera. Microscopy and Microanalysis, 21(S3):2211–2212, aug 2015. doi:10.1017/s1431927615011836.
Mark W. Tate, Prafull Purohit, Darol Chamberlain, Kayla X. Nguyen, Robert Hovden, Celesta S. Chang, Pratiti Deb, Emrah Turgut, John T. Heron, Darrell G. Schlom, Daniel C. Ralph, Gregory D. Fuchs, Katherine S. Shanks, Hugh T. Philipp, David A. Muller, and Sol M. Gruner. High dynamic range pixel array detector for scanning transmission electron microscopy. Microscopy and Microanalysis, 22(01):237–249, jan 2016. doi:10.1017/s1431927615015664.
Steven E Zeltmann, Alexander Müller, Karen C Bustillo, Benjamin Savitzky, Lauren Hughes, Andrew M Minor, and Colin Ophus. Patterned probes for high precision 4D-STEM bragg measurements. Ultramicroscopy, 2019. arXiv:1907.05504v2, doi:10.1016/j.ultramic.2019.112890.
S. C. F. Lin, C. Y. Wong, G. Jiang, M. A. Rahman, and N. M. Kwok. Radial fourier analysis (RFA) image descriptor. In 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 814–819. Aug 2014. doi:10.1109/FSKD.2014.6980942.