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| Hauptseite > Publikationsdatenbank > LiberTEM/LiberTEM: 0.4.1 > print |
| Hauptseite > Publikationsdatenbank > LiberTEM/LiberTEM: 0.4.1 > print |
| 001 | 888560 | ||
| 005 | 20210130011024.0 | ||
| 024 | 7 | _ | |a 10.5281/ZENODO.3674003 |2 doi |
| 037 | _ | _ | |a FZJ-2020-05024 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Clausen, Alexander |0 P:(DE-Juel1)174151 |b 0 |e Corresponding author |
| 245 | _ | _ | |a LiberTEM/LiberTEM: 0.4.1 |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Software |2 DCMI |
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| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Computer Program |0 6 |2 EndNote |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Software |2 DataCite |
| 490 | 0 | _ | |a LiberTEM: 0.4.1 |
| 520 | _ | _ | |a Homepage: https://libertem.github.io/LiberTEM/ GitHub repository: https://github.com/LiberTEM/LiberTEM/ PyPI: https://pypi.org/project/libertem/ 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) 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. 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. Installation The short version: $ virtualenv -p python3.6 ~/libertem-venv/ $ source ~/libertem-venv/bin/activate (libertem) $ pip install libertem[torch] 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. Applications
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. File formats LiberTEM currently opens most file formats used for pixelated STEM. See our general information on loading data and format-specific documentation for more information!
License 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. |
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| 588 | _ | _ | |a Dataset connected to DataCite |
| 700 | 1 | _ | |a Weber, Dieter |0 P:(DE-Juel1)171370 |b 1 |
| 700 | 1 | _ | |a Ruzaeva, Karina |0 P:(DE-Juel1)180323 |b 2 |
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| 700 | 1 | _ | |a Schyndel van, Jay |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Shin, Jaeweon |0 P:(DE-HGF)0 |b 11 |
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| 773 | _ | _ | |a 10.5281/ZENODO.3674003 |
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