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References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Mehdi Bahrami, N.C. Shrikanth, Shade Ruangwan, Lei Liu, Yuji Mizobuchi, Masahiro Fukuyori, Wei-Peng Chen, Kazuki Munakata, Tim Menzies: PyTorrent: A Python Library Corpus for Large-scale Language Models (2021) arXiv
  2. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python 3.6 (2020)
  3. Matsypura, Dmytro; Veremyev, Alexander; Prokopyev, Oleg A.; Pasiliao, Eduardo L.: On exact solution approaches for the longest induced path problem (2019)
  4. Benjamin J. Fulton; Erik A. Petigura; Sarah Blunt; Evan Sinukoff: RadVel: The Radial Velocity Modeling Toolkit (2018) arXiv
  5. Henley, A. J.; Wolf, Dave: Learn data analysis with Python. Lessons in coding (2018)
  6. Hongteng Xu: PoPPy: A Point Process Toolbox Based on PyTorch (2018) arXiv
  7. Juan Rafael Orozco-Arroyave, Juan Camilo Vásquez-Correa, Jesús Francisco Vargas-Bonilla, R. Arora, N. Dehak, P.S. Nidadavolu, H. Christensen, F. Rudzicz, M. Yancheva, H. Chinaei, A. Vann, N. Vogler, T. Bocklet, M. Cernak, J. Hannink, Elmar Nöth: NeuroSpeech (2018) not zbMATH
  8. Lynch, Stephen: Dynamical systems with applications using Python (2018)
  9. Matsypura, Dmytro; Prokopyev, Oleg A.; Zahar, Aizat: Wildfire fuel management: network-based models and optimization of prescribed burning (2018)
  10. Šibalić, N.; Pritchard, J. D.; Adams, C. S.; Weatherill, K. J.: ARC: an open-source library for calculating properties of alkali Rydberg atoms (2017)
  11. Berk Ekmekci, Charles E. McAnany, Cameron Mura: An Introduction to Programming for Bioscientists: A Python-based Primer (2016) arXiv
  12. Korosov, A.A., Hansen, M.W., Dagestad, K.-F., Yamakawa, A., Vines, A., Riechert, M.: Nansat: a Scientist-Orientated Python Package for Geospatial Data Processing (2016) not zbMATH