IPython

IPython: a system for interactive scientific computing. IPython provides a rich architecture for interactive computing with: A powerful interactive shell. A kernel for Jupyter. Support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing.


References in zbMATH (referenced in 59 articles )

Showing results 1 to 20 of 59.
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  1. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python 3.6 (2020)
  2. Leah Wasser, Maxwell B. Joseph, Joe McGlinchy, Jenny Palomino, Korinek, Nathan, Chris Holdgraf, Tim Head: EarthPy: A Python package that makes it easier toexplore and plot raster and vector data using opensource Python tools (2019) not zbMATH
  3. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  4. Rising Odegua: DataSist: A Python-based library for easy data analysis, visualization and modeling (2019) arXiv
  5. Wielemaker, Jan; Riguzzi, Fabrizio; Kowalski, Robert A.; Lager, Torbjörn; Sadri, Fariba; Calejo, Miguel: Using SWISH to realize interactive web-based tutorials for logic-based languages (2019)
  6. Yadu Babuji, Anna Woodard, Zhuozhao Li, Daniel S. Katz, Ben Clifford, Rohan Kumar, Lukasz Lacinski, Ryan Chard, Justin M. Wozniak, Ian Foster, Michael Wilde, Kyle Chard: Parsl: Pervasive Parallel Programming in Python (2019) arXiv
  7. Zhukov, Oleg A.; Kazakova, Tatiana A.; Maksimov, Georgy V.; Brazhe, Alexey R.: Cost of auditory sharpness: model-based estimate of energy use by auditory brainstem “octopus” neurons (2019)
  8. Abreu, Rafael; Su, Zeming; Kamm, Jochen; Gao, Jinghuai: On the accuracy of the complex-step-finite-difference method (2018)
  9. D.M. Straub: flavio: a Python package for flavour and precision phenomenology in the Standard Model and beyond (2018) arXiv
  10. Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
  11. Hauseux, Paul; Hale, Jack S.; Cotin, Stéphane; Bordas, Stéphane P. A.: Quantifying the uncertainty in a hyperelastic soft tissue model with stochastic parameters (2018)
  12. Henley, A. J.; Wolf, Dave: Learn data analysis with Python. Lessons in coding (2018)
  13. Jason Laura; Kelvin Rodriguez; Adam C. Paquette; Evin Dunn: AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data (2018) not zbMATH
  14. Lionel Roubeyrie; Sébastien Celles: Windrose: A Python Matplotlib, Numpy library to manage wind and pollution data, draw windrose (2018) not zbMATH
  15. Lynch, Stephen: Dynamical systems with applications using Python (2018)
  16. Minimair, Manfred: MathChat: computational mathematics via a social machine (2018)
  17. Robert Gieseke; Sven N Willner; Matthias Mengel: Pymagicc: A Python wrapper for the simple climate model MAGICC (2018) not zbMATH
  18. Sven N Willner; Corinne Hartin; Robert Gieseke: pyhector: A Python interface for the simple climate model Hector (2018) not zbMATH
  19. Amen, Saeed: Using Python to analyse financial markets (2017)
  20. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv

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