SciPy

SciPy (pronounced ”Sigh Pie”) is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!


References in zbMATH (referenced in 262 articles )

Showing results 1 to 20 of 262.
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  1. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  2. Andreas F. Haselsteiner; Jannik Lehmkuhl; Tobias Pape; Kai-Lukas Windmeier; Klaus-Dieter Thoben: ViroCon: A software to compute multivariate extremes using the environmental contour method (2019) not zbMATH
  3. Andrew Abi-Mansour: PyGran: An object-oriented library for DEM simulation and analysis (2019) not zbMATH
  4. Benjamin Bengfort; Rebecca Bilbro: Yellowbrick: Visualizing the Scikit-Learn Model Selection Process (2019) not zbMATH
  5. Budanur, Nazmi Burak; Fleury, Marc: State space geometry of the chaotic pilot-wave hydrodynamics (2019)
  6. Campillo-Funollet, Eduard; Venkataraman, Chandrasekhar; Madzvamuse, Anotida: Bayesian parameter identification for Turing systems on stationary and evolving domains (2019)
  7. Carrillo, José Antonio; Craig, Katy; Patacchini, Francesco S.: A blob method for diffusion (2019)
  8. Cole, S., Donoghue, T., Gao, R., Voytek, B.: NeuroDSP: A package for neural digital signal processing (2019) not zbMATH
  9. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  10. Gevorkyan, Migran N.; Korolkova, Anna V.; Kulyabov, Dmitry S.; Lovetskiy, Konstantin P.: Statistically significant comparative performance testing of Julia and Fortran languages in case of Runge-Kutta methods (2019)
  11. Ghosh, Souvik; Loiseau, Jean-Christophe; Breugem, Wim-Paul; Brandt, Luca: Modal and non-modal linear stability of Poiseuille flow through a channel with a porous substrate (2019)
  12. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  13. Matthieu Ancellin; Frédéric Dias: Capytaine: a Python-based linear potential flow solver (2019) not zbMATH
  14. McClarren, Ryan G.: Uncertainty quantification and predictive computational science. A foundation for physical scientists and engineers (2019)
  15. Michael Slugocki , Allison B. Sekuler, Patrick Bennett: BayesFit: A tool for modeling psychophysical data using Bayesian inference (2019) not zbMATH
  16. Miyaji, Tomoyuki; Okamoto, Hisashi: Existence proof of unimodal solutions of the Proudman-Johnson equation via interval analysis (2019)
  17. Paul Walker, Ulrich Krohn, David Carty: ARBTools: A Tricubic Spline Interpolator for Three-Dimensional Scalar or Vector Fields (2019) not zbMATH
  18. Philipp S. Sommer; Dilan Rech; Manuel Chevalier; Basil A. S.Davis: straditize: Digitizing stratigraphic diagrams (2019) not zbMATH
  19. R.D. Martin, Q. Cai, T. Garrow, C. Kapahi: QExpy: A python-3 module to support undergraduate physics laboratories (2019) not zbMATH
  20. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)

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