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 271 articles )

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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. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  9. Cole, S., Donoghue, T., Gao, R., Voytek, B.: NeuroDSP: A package for neural digital signal processing (2019) not zbMATH
  10. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH
  11. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  12. 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)
  13. 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)
  14. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  15. Leo C. Stein: qnm: A Python package for calculating Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients (2019) arXiv
  16. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python 3.6 (2019)
  17. Matthieu Ancellin; Frédéric Dias: Capytaine: a Python-based linear potential flow solver (2019) not zbMATH
  18. McClarren, Ryan G.: Uncertainty quantification and predictive computational science. A foundation for physical scientists and engineers (2019)
  19. Michael E.Rose; John R.Kitchin: pybliometrics: Scriptable bibliometrics using a Python interface to Scopus (2019) not zbMATH
  20. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv

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