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 385 articles )

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  1. Hart, William E.; Watson, Jean-Paul; Woodruff, David L.: Pyomo: modeling and solving mathematical programs in python (2011) ioport
  2. J. R. Johansson, P. D. Nation, Franco Nori: QuTiP: An open-source Python framework for the dynamics of open quantum systems (2011) arXiv
  3. Laxalde, Denis; Legrand, Mathias: Nonlinear modal analysis of mechanical systems with frictionless contact interfaces (2011)
  4. Lonie, David C.; Zurek, Eva: \textttXtalOpt: an open-source evolutionary algorithm for crystal structure prediction (2011)
  5. Lysenko, Mikola; Shapiro, Vadim; Nelaturi, Saigopal: Non-commutative morphology: Shapes, filters, and convolutions (2011)
  6. Macdonald, Colin B.; Brandman, Jeremy; Ruuth, Steven J.: Solving eigenvalue problems on curved surfaces using the closest point method (2011)
  7. Pawela, Łukasz: Control parameters for the quantum Deutsch algorithm (2011) ioport
  8. Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu; Duchesnay, Édouard: Scikit-learn: machine learning in Python (2011)
  9. Anand Patil; David Huard; Christopher Fonnesbeck: PyMC: Bayesian Stochastic Modelling in Python (2010) not zbMATH
  10. Eckstein, Jonathan; Silva, Paulo J. S.: Proximal methods for nonlinear programming: Double regularization and inexact subproblems (2010)
  11. Huard, David; Mailhot, Alain; Duchesne, Sophie: Bayesian estimation of intensity-duration-frequency curves and of the return period associated to a given rainfall event (2010)
  12. Marzuola, Jeremy L.; Raynor, Sarah; Simpson, Gideon: A system of ODEs for a perturbation of a minimal mass soliton (2010)
  13. Deepak Chandran, Frank T. Bergmann, Herbert M. Sauro: TinkerCell: Modular CAD Tool for Synthetic Biology (2009) arXiv
  14. Fischbacher, Thomas: The many vacua of gauged extended supergravities (2009)
  15. Ilison, Lauri; Salupere, Andrus: Propagation of (sech^2)-type solitary waves in hierarchical KdV-type systems (2009)
  16. MacDonald, Colin B.; Ruuth, Steven J.: The implicit closest point method for the numerical solution of partial differential equations on surfaces (2009)
  17. Montani, Fernando; Ince, Robin A. A.; Senatore, Riccardo; Arabzadeh, Ehsan; Diamond, Mathew E.; Panzeri, Stefano: The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex (2009)
  18. Ramachandran, Prabhu; Ramakrishna, M.: An object-oriented design for two-dimensional vortex particle methods (2009)
  19. Tierney, Luke; Rossini, A. J.; Li, Na: \textttSnow: A parallel computing framework for the R system (2009)
  20. Dalcín, Lisandro; Paz, Rodrigo; Storti, Mario; D’elía, Jorge: MPI for python: performance improvements and MPI-2 extensions (2008) ioport

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